﻿<?xml version="1.0" encoding="utf-8"?><doi_batch xmlns="http://www.crossref.org/schema/4.3.7" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.3.7 http://www.crossref.org/schema/deposit/crossref4.3.7.xsd"><head><doi_batch_id>jict-2026052721</doi_batch_id><timestamp>20260527214848</timestamp><depositor><depositor_name>CMV Verlag</depositor_name><email_address>khoffmann@cmv-verlag.com</email_address></depositor><registrant>CMV Verlag</registrant></head><body><journal><journal_metadata language="fa"><full_title>Journal of Information and Communication Technology</full_title><abbrev_title>jict</abbrev_title><issn media_type="electronic">2717-0411</issn></journal_metadata><journal_issue><publication_date media_type="online"><month>12</month><day>7</day><year>2024</year></publication_date><journal_volume><volume>16</volume></journal_volume><issue>61</issue></journal_issue><journal_article publication_type="full_text"><titles><title>Dynamic Load Balancing Improvement in Software-Defined Networks Using Fuzzy Multi-Objective Programming Algorithms</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Mohammadreza</given_name><surname>Forghani</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Mohammadreza</given_name><surname>Soltanaghaei koupaei</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Farsad</given_name><surname>Zamani Boroujeni</surname></person_name></contributors><publication_date media_type="online"><month>12</month><day>7</day><year>2024</year></publication_date><pages><first_page>55</first_page><last_page>71</last_page></pages><doi_data><doi>10.66224/jict.38177.16.61.55</doi><resource>http://jour.aicti.ir/en/Article/38177</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/en/Article/Download/38177</resource></item><item crawler="google"><resource>http://jour.aicti.ir/en/Article/Download/38177</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/en/Article/Download/38177</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/en/Article/Download/38177</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/en/Article/Download/38177</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/en/Article/Download/38177</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/en/Article/Download/38177</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	R. A. Ammal, P. Sajimon, and S. Vinodchandra, "Termite inspired algorithm for traffic engineering in hybrid software defined networks," PeerJ Computer Science, vol. 6, p. e283, 2020.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>
[2]	L. Nkenyereye, L. Nkenyereye, B. Adhi Tama, A. G. Reddy, and J. Song, "Software-defined vehicular cloud networks: architecture, applications and virtual machine migration," Sensors, vol. 20, no. 4, p. 1092, 2020.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>
[3]	K. Rui, H. Pan, and S. Shu, "Secure routing in the Internet of Things (IoT) with intrusion detection capability based on software-defined networking (SDN) and Machine Learning techniques," Scientific Reports, vol. 13, no. 1, p. 18003, 2023/10/21 2023, doi: 10.1038/s41598-023-44764-6.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>
[4]	K. Luo, "A distributed SDN-based intrusion detection system for IoT using optimized forests," (in eng), PLoS One, vol. 18, no. 8, p. e0290694, 2023, doi: 10.1371/journal.pone.0290694.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>
[5]	I. Smołka and J. Stój, "Utilization of SDN Technology for Flexible EtherCAT Networks Applications," Sensors, vol. 22, no. 5, p. 1944, 2022. [Online]. Available: https://www.mdpi.com/1424-8220/22/5/1944.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>
[6]	J. Gong and A. Rezaeipanah, "A fuzzy delay-bandwidth guaranteed routing algorithm for video conferencing services over SDN networks," (in eng), Multimed Tools Appl, pp. 1-30, Jan 23 2023, doi: 10.1007/s11042-023-14349-6.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>
[7]	Z. Liu, X. Dong, L. Wang, J. Feng, C. Pan, and Y. Li, "Satellite Network Task Deployment Method Based on SDN and ICN," (in eng), Sensors (Basel), vol. 22, no. 14, Jul 21 2022, doi: 10.3390/s22145439.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>
[8]	C. Urrea and D. Benitez, "Software-Defined Networking Solutions, Architecture and Controllers for the Industrial Internet of Things: A Review," (in eng), Sensors (Basel), vol. 21, no. 19, Oct 1 2021, doi: 10.3390/s21196585.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>
[9]	A. Savaliya, R. H. Jhaveri, Q. Xin, S. Alqithami, S. Ramani, and T. A. Ahanger, "Securing industrial communication with software-defined networking," (in eng), Math Biosci Eng, vol. 18, no. 6, pp. 8298-8313, Sep 22 2021, doi: 10.3934/mbe.2021411.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>
[10]	D. Wang et al., "DoSDefender: A Kernel-Mode TCP DoS Prevention in Software-Defined Networking," (in eng), Sensors (Basel), vol. 23, no. 12, Jun 8 2023, doi: 10.3390/s23125426.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>
[11]	Z. B. Zuo, R. Y. He, X. W. Zhu, and C. W. Chang, "A novel software-defined network packet security tunnel forwarding mechanism," (in eng), Math Biosci Eng, vol. 16, no. 5, pp. 4359-4381, May 17 2019, doi: 10.3934/mbe.2019217.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>
[12]	Y. Guo et al., "Traffic Management in IoT Backbone Networks Using GNN and MAB with SDN Orchestration," (in eng), Sensors (Basel), vol. 23, no. 16, Aug 10 2023, doi: 10.3390/s23167091.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>
[13]	L. Li, K. Li, X. Meng, Y. Wang, and X. Wang, "Dynamic weight routing and optical-code algorithm based on SDN," (in eng), Heliyon, vol. 9, no. 1, p. e12407, Jan 2023, doi: 10.1016/j.heliyon.2022.e12407.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>
[14]	M. Hussain, N. Shah, R. Amin, S. S. Alshamrani, A. Alotaibi, and S. M. Raza, "Software-Defined Networking: Categories, Analysis, and Future Directions," (in eng), Sensors (Basel), vol. 22, no. 15, Jul 25 2022, doi: 10.3390/s22155551.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>
[15]	M. Hamzei, S. Khandagh, and N. Jafari Navimipour, "A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm," (in eng), Sensors (Basel), vol. 23, no. 16, Aug 17 2023, doi: 10.3390/s23167233.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>
[16]	G. Yuan, Y. Yang, G. Tian, and A. M. Fathollahi-Fard, "Capacitated multi-objective disassembly scheduling with fuzzy processing time via a fruit fly optimization algorithm," (in eng), Environ Sci Pollut Res Int, Jan 31 2022, doi: 10.1007/s11356-022-18883-y.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>
[17]	H. Li, D. Ou, and Y. Ji, "An Environmentally Sustainable Software-Defined Networking Data Dissemination Method for Mixed Traffic Flows in RSU Clouds with Energy Restriction," (in eng), Int J Environ Res Public Health, vol. 19, no. 22, Nov 16 2022, doi: 10.3390/ijerph192215112.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>
[18]	H. Xue, K. T. Kim, and H. Y. Youn, "Dynamic Load Balancing of Software-Defined Networking Based on Genetic-Ant Colony Optimization," (in eng), Sensors (Basel), vol. 19, no. 2, Jan 14 2019, doi: 10.3390/s19020311.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>
[19]	X. Xu, W. K. Jia, Y. Wu, and X. Wang, "On the Optimal Lawful Intercept Access Points Placement Problem in Hybrid Software-Defined Networks," (in eng), Sensors (Basel), vol. 21, no. 2, Jan 9 2021, doi: 10.3390/s21020428.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>
[20]	Z. Kabiri, B. Barekatain, and A. Avokh, "GOP-SDN: an enhanced load balancing method based on genetic and optimized particle swarm optimization algorithm in distributed SDNs," Wireless Networks, vol. 28, no. 6, pp. 2533-2552, 2022.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>
[21]	R. Sharma, I. Sharma, and A. Sharma, "Load Balancing and Resource Utilization Approach in Cloud Computing Using Honey Bee-Inspired Algorithm," in International Conference on Mobile Computing and Sustainable Informatics: ICMCSI 2020, 2021: Springer, pp. 811-820. </unstructured_citation></citation><citation key="ref22"><unstructured_citation>
[22]	S. Ejaz, Z. Iqbal, P. A. Shah, B. H. Bukhari, A. Ali, and F. Aadil, "Traffic load balancing using software defined networking (SDN) controller as virtualized network function," IEEE Access, vol. 7, pp. 46646-46658, 2019.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>
[23]	O. Adekoya, A. Aneiba, and M. Patwary, "An improved switch migration decision algorithm for SDN load balancing," IEEE Open Journal of the Communications Society, vol. 1, pp. 1602-1613, 2020.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>
[24]	Y. Zhao, X. Wang, Q. He, C. Zhang, and M. Huang, "PLOFR: An online flow route framework for power saving and load balance in SDN," IEEE Systems Journal, vol. 15, no. 1, pp. 526-537, 2020.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>
[25]	X. Shi et al., "An openflow-based load balancing strategy in SDN," Comput. Mater. Contin, vol. 62, no. 1, pp. 385-398, 2020.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>
[26]	H. Babbar, S. Rani, D. Gupta, H. M. Aljahdali, A. Singh, and F. Al-Turjman, "Load balancing algorithm on the immense scale of internet of things in SDN for smart cities," Sustainability, vol. 13, no. 17, p. 9587, 2021.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>
[27]	A. El Kamel and H. Youssef, "Improving switch-to-controller assignment with load balancing in multi-controller software defined WAN (SD-WAN)," Journal of Network and Systems Management, vol. 28, pp. 553-575, 2020.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>
[28]	K. S. Sahoo, M. Tiwary, B. Sahoo, B. K. Mishra, S. RamaSubbaReddy, and A. K. Luhach, "RTSM: Response time optimisation during switch migration in software‐defined wide area network," IET wireless sensor systems, vol. 10, no. 3, pp. 105-111, 2020.</unstructured_citation></citation><citation key="ref29"><unstructured_citation>
[29]	G. S. Begam, M. Sangeetha, and N. Shanker, "Load balancing in dcn servers through sdn machine learning algorithm," Arabian Journal for Science and Engineering, pp. 1-12, 2022.</unstructured_citation></citation><citation key="ref30"><unstructured_citation>
[30]	K. A. Jadhav, M. M. Mulla, and D. Narayan, "An efficient load balancing mechanism in software defined networks," in 2020 12th international conference on computational intelligence and communication networks (CICN), 2020: IEEE, pp. 116-122. </unstructured_citation></citation><citation key="ref31"><unstructured_citation>
[31]	G. Li, X. Wang, and Z. J. I. A. Zhang, "SDN-based load balancing scheme for multi-controller deployment," vol. 7, pp. 39612-39622, 2019.</unstructured_citation></citation><citation key="ref32"><unstructured_citation>
[32]	Z. Li and E. Peng, "Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking," (in eng), Sensors (Basel), vol. 21, no. 3, Feb 1 2021, doi: 10.3390/s21030955.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Fault Diagnosis and Detection in Photovoltaic Systems Using Neural Network VGG16</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>ُSamaneh</given_name><surname>Azimi</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Mohammad</given_name><surname>Manthouri</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Mehdi</given_name><surname>Akhbari</surname></person_name></contributors><publication_date media_type="online"><month>12</month><day>7</day><year>2024</year></publication_date><pages><first_page>247</first_page><last_page>260</last_page></pages><doi_data><doi>10.66224/jict.41551.16.61.247</doi><resource>http://jour.aicti.ir/en/Article/41551</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/en/Article/Download/41551</resource></item><item crawler="google"><resource>http://jour.aicti.ir/en/Article/Download/41551</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/en/Article/Download/41551</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/en/Article/Download/41551</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/en/Article/Download/41551</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/en/Article/Download/41551</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/en/Article/Download/41551</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>A. Dhoke, R. Sharma, and T. K. Saha, "A technique for fault detection, identification and location in solar photovoltaic systems," Solar Energy, vol. 206, pp. 864-874, 2020.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>
A. Jäger-Waldau, "Snapshot of photovoltaics—February 2020," Energies, vol. 13, no. 4, p. 930, 2020.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>
C. Buerhop, D. Schlegel, M. Niess, C. Vodermayer, R. Weißmann, and C. Brabec, "Reliability of IR-imaging of PV-plants under operating conditions," Solar Energy Materials and Solar Cells, vol. 107, pp. 154-164, 2012.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>
P. B. Quater, F. Grimaccia, S. Leva, M. Mussetta, and M. Aghaei, "Light Unmanned Aerial Vehicles (UAVs) for cooperative inspection of PV plants," IEEE Journal of Photovoltaics, vol. 4, no. 4, pp. 1107-1113, 2014.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>
J. Tsanakas and P. Botsaris, "An infrared thermographic approach as a hot-spot detection tool for photovoltaic modules using image histogram and line profile analysis," International Journal of Condition Monitoring, vol. 2, no. 1, pp. 22-30, 2012.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>
D. S. Pillai and N. Rajasekar, "An MPPT-based sensorless line–line and line–ground fault detection technique for PV systems," IEEE Transactions on Power Electronics, vol. 34, no. 9, pp. 8646-8659, 2018.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>
D. S. Pillai and N. Rajasekar, "A comprehensive review on protection challenges and fault diagnosis in PV systems," Renewable and Sustainable Energy Reviews, vol. 91, pp. 18-40, 2018.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>
Y. Zhao, J.-F. De Palma, J. Mosesian, R. Lyons, and B. Lehman, "Line–line fault analysis and protection challenges in solar photovoltaic arrays," IEEE transactions on Industrial Electronics, vol. 60, no. 9, pp. 3784-3795, 2012.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>
B. P. Kumar, G. S. Ilango, M. J. B. Reddy, and N. Chilakapati, "Online fault detection and diagnosis in photovoltaic systems using wavelet packets," IEEE Journal of Photovoltaics, vol. 8, no. 1, pp. 257-265, 2017.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>
F. Aziz, A. U. Haq, S. Ahmad, Y. Mahmoud, M. Jalal, and U. Ali, "A novel convolutional neural network-based approach for fault classification in photovoltaic arrays," IEEE Access, vol. 8, pp. 41889-41904, 2020.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>
K. AbdulMawjood, S. S. Refaat, and W. G. Morsi, "Detection and prediction of faults in photovoltaic arrays: A review," in 2018 IEEE 12th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG 2018), 2018, pp. 1-8: IEEE.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>
R. Hariharan, M. Chakkarapani, G. S. Ilango, and C. Nagamani, "A method to detect photovoltaic array faults and partial shading in PV systems," IEEE Journal of Photovoltaics, vol. 6, no. 5, pp. 1278-1285, 2016.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>
M. Catelani, L. Ciani, D. Galar, and G. Patrizi, "Optimizing maintenance policies for a yaw system using reliability-centered maintenance and data-driven condition monitoring," IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 9, pp. 6241-6249, 2020.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>
S. Voutsinas, D. Karolidis, I. Voyiatzis, and M. Samarakou, "Development of a multi-output feed-forward neural network for fault detection in Photovoltaic Systems," Energy Reports, vol. 8, pp. 33-42, 2022.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>
J. Van Gompel, D. Spina, and C. Develder, "Satellite based fault diagnosis of photovoltaic systems using recurrent neural networks," Applied Energy, vol. 305, p. 117874, 2022.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>
M. Dhimish, V. Holmes, B. Mehrdadi, and M. Dales, "Multi‐layer photovoltaic fault detection algorithm," High voltage, vol. 2, no. 4, pp. 244-252, 2017.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>
E. Garoudja, F. Harrou, Y. Sun, K. Kara, A. Chouder, and S. Silvestre, "Statistical fault detection in photovoltaic systems," Solar Energy, vol. 150, pp. 485-499, 2017.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>
A. Dhoke, R. Sharma, and T. K. Saha, "PV module degradation analysis and impact on settings of overcurrent protection devices," Solar Energy, vol. 160, pp. 360-367, 2018.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>
A. V. de Oliveira, M. Aghaei, and R. Rüther, "Automatic fault detection of photovoltaic array by convolutional neural networks during aerial infrared thermography," in Proceedings of the 36th European Photovoltaic Solar Energy Conference and Exhibition, Marseille, France, 2019, pp. 9-13.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>
S. A. Zaki, H. Zhu, M. A. Fakih, A. R. Sayed, and J. Yao, "Deep‐learning–based method for faults classification of PV system," IET Renewable Power Generation, vol. 15, no. 1, pp. 193-205, 2021.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>S.-D. Lu, M.-H. Wang, S.-E. Wei, H.-D. Liu, and C.-C. Wu, "Photovoltaic module fault detection based on a convolutional neural network," Processes, vol. 9, no. 9, p. 1635, 2021.
I. S. Ramírez, J. R. P. Chaparro, and F. P. G. Márquez, "Machine Learning techniques implemented in IoT platform for fault detection in photovoltaic panels," in 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), 2021, pp. 429-434: IEEE.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>
Z. Yi and A. H. Etemadi, "Line-to-line fault detection for photovoltaic arrays based on multiresolution signal decomposition and two-stage support vector machine," IEEE Transactions on Industrial Electronics, vol. 64, no. 11, pp. 8546-8556, 2017.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>
A. Y. Appiah, X. Zhang, B. B. K. Ayawli, and F. Kyeremeh, "Long short-term memory networks based automatic feature extraction for photovoltaic array fault diagnosis," IEEE Access, vol. 7, pp. 30089-30101, 2019.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>
M. N. Akram and S. Lotfifard, "Modeling and health monitoring of DC side of photovoltaic array," IEEE Transactions on Sustainable Energy, vol. 6, no. 4, pp. 1245-1253, 2015.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>
S. R. Madeti and S. Singh, "A comprehensive study on different types of faults and detection techniques for solar photovoltaic system," Solar Energy, vol. 158, pp. 161-185, 2017.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>
V. Kongphet, A. Migan-Dubois, C. Delpha, D. Diallo, and J.-Y. Lechenadec, "Photovoltaic Fault Detection and Diagnosis: Which Level of Granularity for PV Modeling?," in 2020 Prognostics and Health Management Conference (PHM-Besançon), 2020, pp. 180-186: IEEE.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>
M. Köntges et al., "Review of failures of photovoltaic modules," 2014</unstructured_citation></citation><citation key="ref28"><unstructured_citation>
S. R. Madeti and S. Singh, "Online modular level fault detection algorithm for grid-tied and off-grid PV systems," Solar Energy, vol. 157, pp. 349-364, 2017.</unstructured_citation></citation><citation key="ref29"><unstructured_citation>
A. Triki-Lahiani, A. B.-B. Abdelghani, and I. Slama-Belkhodja, "Fault detection and monitoring systems for photovoltaic installations: A review," Renewable and Sustainable Energy Reviews, vol. 82, pp. 2680-2692, 2018.</unstructured_citation></citation><citation key="ref30"><unstructured_citation>
Y.-Y. Hong and R. A. Pula, "Methods of photovoltaic fault detection and classification: A review," Energy Reports, vol. 8, pp. 5898-5929, 2022.</unstructured_citation></citation><citation key="ref31"><unstructured_citation>
[1]	A. Dhoke, R. Sharma, and T. K. Saha, "A technique for fault detection, identification and location in solar photovoltaic systems," Solar Energy, vol. 206, pp. 864-874, 2020.
[2]	A. Jäger-Waldau, "Snapshot of photovoltaics—February 2020," Energies, vol. 13, no. 4, p. 930, 2020.
[3]	C. Buerhop, D. Schlegel, M. Niess, C. Vodermayer, R. Weißmann, and C. Brabec, "Reliability of IR-imaging of PV-plants under operating conditions," Solar Energy Materials and Solar Cells, vol. 107, pp. 154-164, 2012.
[4]	P. B. Quater, F. Grimaccia, S. Leva, M. Mussetta, and M. Aghaei, "Light Unmanned Aerial Vehicles (UAVs) for cooperative inspection of PV plants," IEEE Journal of Photovoltaics, vol. 4, no. 4, pp. 1107-1113, 2014.
[5]	J. Tsanakas and P. Botsaris, "An infrared thermographic approach as a hot-spot detection tool for photovoltaic modules using image histogram and line profile analysis," International Journal of Condition Monitoring, vol. 2, no. 1, pp. 22-30, 2012.
[6]	D. S. Pillai and N. Rajasekar, "An MPPT-based sensorless line–line and line–ground fault detection technique for PV systems," IEEE Transactions on Power Electronics, vol. 34, no. 9, pp. 8646-8659, 2018.
[7]	D. S. Pillai and N. Rajasekar, "A comprehensive review on protection challenges and fault diagnosis in PV systems," Renewable and Sustainable Energy Reviews, vol. 91, pp. 18-40, 2018.
[8]	Y. Zhao, J.-F. De Palma, J. Mosesian, R. Lyons, and B. Lehman, "Line–line fault analysis and protection challenges in solar photovoltaic arrays," IEEE transactions on Industrial Electronics, vol. 60, no. 9, pp. 3784-3795, 2012.
[9]	B. P. Kumar, G. S. Ilango, M. J. B. Reddy, and N. Chilakapati, "Online fault detection and diagnosis in photovoltaic systems using wavelet packets," IEEE Journal of Photovoltaics, vol. 8, no. 1, pp. 257-265, 2017.
[10]	F. Aziz, A. U. Haq, S. Ahmad, Y. Mahmoud, M. Jalal, and U. Ali, "A novel convolutional neural network-based approach for fault classification in photovoltaic arrays," IEEE Access, vol. 8, pp. 41889-41904, 2020.
[11]	K. AbdulMawjood, S. S. Refaat, and W. G. Morsi, "Detection and prediction of faults in photovoltaic arrays: A review," in 2018 IEEE 12th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG 2018), 2018, pp. 1-8: IEEE.
[12]	R. Hariharan, M. Chakkarapani, G. S. Ilango, and C. Nagamani, "A method to detect photovoltaic array faults and partial shading in PV systems," IEEE Journal of Photovoltaics, vol. 6, no. 5, pp. 1278-1285, 2016.
[13]	M. Catelani, L. Ciani, D. Galar, and G. Patrizi, "Optimizing maintenance policies for a yaw system using reliability-centered maintenance and data-driven condition monitoring," IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 9, pp. 6241-6249, 2020.
[14]	S. Voutsinas, D. Karolidis, I. Voyiatzis, and M. Samarakou, "Development of a multi-output feed-forward neural network for fault detection in Photovoltaic Systems," Energy Reports, vol. 8, pp. 33-42, 2022.
[15]	J. Van Gompel, D. Spina, and C. Develder, "Satellite based fault diagnosis of photovoltaic systems using recurrent neural networks," Applied Energy, vol. 305, p. 117874, 2022.
[16]	M. Dhimish, V. Holmes, B. Mehrdadi, and M. Dales, "Multi‐layer photovoltaic fault detection algorithm," High voltage, vol. 2, no. 4, pp. 244-252, 2017.
[17]	E. Garoudja, F. Harrou, Y. Sun, K. Kara, A. Chouder, and S. Silvestre, "Statistical fault detection in photovoltaic systems," Solar Energy, vol. 150, pp. 485-499, 2017.
[18]	A. Dhoke, R. Sharma, and T. K. Saha, "PV module degradation analysis and impact on settings of overcurrent protection devices," Solar Energy, vol. 160, pp. 360-367, 2018.
[19]	A. V. de Oliveira, M. Aghaei, and R. Rüther, "Automatic fault detection of photovoltaic array by convolutional neural networks during aerial infrared thermography," in Proceedings of the 36th European Photovoltaic Solar Energy Conference and Exhibition, Marseille, France, 2019, pp. 9-13.
[20]	S. A. Zaki, H. Zhu, M. A. Fakih, A. R. Sayed, and J. Yao, "Deep‐learning–based method for faults classification of PV system," IET Renewable Power Generation, vol. 15, no. 1, pp. 193-205, 2021.
[21]	S.-D. Lu, M.-H. Wang, S.-E. Wei, H.-D. Liu, and C.-C. Wu, "Photovoltaic module fault detection based on a convolutional neural network," Processes, vol. 9, no. 9, p. 1635, 2021.
[22]	I. S. Ramírez, J. R. P. Chaparro, and F. P. G. Márquez, "Machine Learning techniques implemented in IoT platform for fault detection in photovoltaic panels," in 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), 2021, pp. 429-434: IEEE.
[23]	Z. Yi and A. H. Etemadi, "Line-to-line fault detection for photovoltaic arrays based on multiresolution signal decomposition and two-stage support vector machine," IEEE Transactions on Industrial Electronics, vol. 64, no. 11, pp. 8546-8556, 2017.
[24]	A. Y. Appiah, X. Zhang, B. B. K. Ayawli, and F. Kyeremeh, "Long short-term memory networks based automatic feature extraction for photovoltaic array fault diagnosis," IEEE Access, vol. 7, pp. 30089-30101, 2019.
[25]	M. N. Akram and S. Lotfifard, "Modeling and health monitoring of DC side of photovoltaic array," IEEE Transactions on Sustainable Energy, vol. 6, no. 4, pp. 1245-1253, 2015.
[26]	S. R. Madeti and S. Singh, "A comprehensive study on different types of faults and detection techniques for solar photovoltaic system," Solar Energy, vol. 158, pp. 161-185, 2017.
[27]	V. Kongphet, A. Migan-Dubois, C. Delpha, D. Diallo, and J.-Y. Lechenadec, "Photovoltaic Fault Detection and Diagnosis: Which Level of Granularity for PV Modeling?," in 2020 Prognostics and Health Management Conference (PHM-Besançon), 2020, pp. 180-186: IEEE.
[28]	M. Köntges et al., "Review of failures of photovoltaic modules," 2014.
[29]	S. R. Madeti and S. Singh, "Online modular level fault detection algorithm for grid-tied and off-grid PV systems," Solar Energy, vol. 157, pp. 349-364, 2017.
[30]	A. Triki-Lahiani, A. B.-B. Abdelghani, and I. Slama-Belkhodja, "Fault detection and monitoring systems for photovoltaic installations: A review," Renewable and Sustainable Energy Reviews, vol. 82, pp. 2680-2692, 2018.
[31]	Y.-Y. Hong and R. A. Pula, "Methods of photovoltaic fault detection and classification: A review," Energy Reports, vol. 8, pp. 5898-5929, 2022.
[32]	D. Revati and E. Natarajan, "IV and PV characteristics analysis of a photovoltaic module by different methods using Matlab software," Materials Today: Proceedings, vol. 33, pp. 261-269, 2020.
</unstructured_citation></citation><citation key="ref32"><unstructured_citation>
A. Narin, "Detection of focal and non-focal epileptic seizure using continuous wavelet transform-based scalogram images and pre-trained deep neural networks," Irbm, vol. 43, no. 1, pp. 22-31, 2022.</unstructured_citation></citation><citation key="ref33"><unstructured_citation>
J. Garcia, S. Muller, E. Caicedo, T. Bastos Filho, and A. Souza, "Non-fatigating brain computer interface based on SSVEP and ERD to command an autonomous car," Advances in Data Science and Adaptive Analysis, vol. 1, pp. 1-11, 2018.</unstructured_citation></citation><citation key="ref34"><unstructured_citation>
S. Tammina, "Transfer learning using vgg-16 with deep convolutional neural network for classifying images," International Journal of Scientific and Research Publications (IJSRP), vol. 9, no. 10, pp. 143-150, 2019.
[36]	F. Chollet, Deep learning with Python. Simon and Schuster, 2021
</unstructured_citation></citation><citation key="ref35"><unstructured_citation>
C. Szegedy et al., "Going deeper with convolutions," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1-9.</unstructured_citation></citation><citation key="ref36"><unstructured_citation>
X. Ning, P. Duan, W. Li, and S. Zhang, "Real-time 3D face alignment using an encoder-decoder network with an efficient deconvolution layer," IEEE Signal Processing Letters, vol. 27, pp. 1944-1948, 2020.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Presenting a Method for Agile Enterprise Architecture Modeling</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Ali</given_name><surname>Razi</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Reza</given_name><surname>Rezaei</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Ahmad Ali</given_name><surname>Yazdanpanah</surname></person_name></contributors><publication_date media_type="online"><month>12</month><day>7</day><year>2024</year></publication_date><pages><first_page>85</first_page><last_page>108</last_page></pages><doi_data><doi>10.66224/jict.42147.16.61.85</doi><resource>http://jour.aicti.ir/en/Article/42147</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/en/Article/Download/42147</resource></item><item crawler="google"><resource>http://jour.aicti.ir/en/Article/Download/42147</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/en/Article/Download/42147</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/en/Article/Download/42147</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/en/Article/Download/42147</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/en/Article/Download/42147</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/en/Article/Download/42147</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]. A. Rüping, Agile Documentation: A Pattern Guide to Producing Lightweight Documents for Software Projects. Wiley; 1st edition, 2003. </unstructured_citation></citation><citation key="ref2"><unstructured_citation>
[2]. J. M.Bass, “Artefacts and agile method tailoring in large-scale offshore software development programmes,” Information and Software Technology., vol. 75, pp. 1-16, July 2016. </unstructured_citation></citation><citation key="ref3"><unstructured_citation>
[3]. A. Sadovykh, P. Desfray, B. Elvesæter, A. Berre, E. Landre, “Enterprise architecture modeling with SoaML using BMM and BPMN - MDA approach in practice,” Computer Science, 6th Central and Eastern European Software Engineering Conference, Oct 13-15,  2010, Moscow, Russia. </unstructured_citation></citation><citation key="ref4"><unstructured_citation>
[4]. A. Zrnec, M. Bajec, M. Krisper, "Enterprise modelling with UML," Elektrotehni ski vestnik University of  Ljubljana., vol. 68, pp. 109–114, 2001. </unstructured_citation></citation><citation key="ref5"><unstructured_citation>
[5].  F. Armour, S. H. Kaisler, J. Getter, D. Pippin, “A UML-driven Enterprise Architecture Case Study,” Proceedings of the 36th Annual Hawaii International Conference, February, 2003. </unstructured_citation></citation><citation key="ref6"><unstructured_citation>
[6]. A.Q. Gill, "agile enterprise architecture modelling: Evaluating the applicability and integration of six modelling standards," Information and Software Technology, vol. 67, pp. 196-206, November 2015. </unstructured_citation></citation><citation key="ref7"><unstructured_citation>
]7[. راضی، علی، رضایی، رضا، یزدان پناه، احمد علی، "مدل سازی معماری سازمانی چابک: ارزیابی کاربردپذیری شش استاندارد مدل سازی بر مبنای چارچوب ملی معماری سازمانی ایران"، دو فصلنامه علمی فناوری اطلاعات و ارتباطات ایران، شماره های 47 و 48، 105_ 135، تهران، بهار و تابستان 1400. </unstructured_citation></citation><citation key="ref8"><unstructured_citation>

[8].  Scott W. Ambler. Agile Modeling: Effective Practices for extreme Programming and the Unified Process , Published by John Wiley &amp; Sons, Inc., New York, 2002, &lt;http://msoo.pbworks.com/f/Scott+W.+Ambler+-+Agile+Modeling.pdf&gt;.
[9]. J. Humble, D. Farley, Continuous Delivery. New York: Addison-Wesley Professional, 2010. </unstructured_citation></citation><citation key="ref9"><unstructured_citation>
[10]. S. Yamamoto, Q. Zhia, Z. Zhoua, “Aspect Analysis towards ArchiMate Diagrams,” Procedia Computer Science., vol. 159, pp. 973-980, 2019. </unstructured_citation></citation><citation key="ref10"><unstructured_citation>
[11]. F. Hasić, J. Vanthienen, “Complexity metrics for DMN decision models,” Computer Standards &amp; Interfaces., vol. 65, pp. 15-37, July, 2019. </unstructured_citation></citation><citation key="ref11"><unstructured_citation>
[12]. Scott W. Ambler. Agile Enterprise Architecture, 2021, &lt;http://agiledata.org/essays/enterpriseArchitecture.html&gt;.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>
[13]. R. Pichler, Agile Product Management with Scrum: Creating Products that Customers Love. New York: Addison-Wesley Professional; 1st edition, 2010.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>
[14]. F. Gampfer, “Managing Enterprise Architecture in Agile Environments,” Lecture Notes in Informatics (LNI), Gesellschaft für Informatik, 2018, Bonn. </unstructured_citation></citation><citation key="ref14"><unstructured_citation>
[15]. C. Finkelstein, Enterprise Architecture for Integration: Rapid Delivery Methods and Technologies (Artech House Mobile Communications Library). Artech House Print on Demand; 1st edition, 2006. </unstructured_citation></citation><citation key="ref15"><unstructured_citation>
[16]. P. Clements et al, 2th Ed., Documenting Software Architectures Views and Beyond. United States of America: Pearson, 2011. </unstructured_citation></citation><citation key="ref16"><unstructured_citation>
[17]. J. Patton, P. Economy, User Story Mapping: Discover the Whole Story, Build the Right Product. O'Reilly Media; 1st edition, 2014. </unstructured_citation></citation><citation key="ref17"><unstructured_citation>
[18] M.Z., Muehlen, M. Indulska, G. Kamp, "Business Process and Business Rule Modeling Languages for Compliance Management: A Representational Analysis," in Research and Practice in Information Technology: The Twenty-Sixth International Conference on Conceptual Modeling, ER 2007, Auckland, New Zealand, November 5-9, 2007. </unstructured_citation></citation><citation key="ref18"><unstructured_citation>
[19]. W., Wei, M., Indulska, S., Sadiq, “Guidelines for Business Rule Modeling Decisions,” Journal of Computer Information Systems., vol. 58, Issue 4, pp. 363-373, 2018. </unstructured_citation></citation><citation key="ref19"><unstructured_citation>
[20]. M.z, Muehlen, M., Indulska, “Modeling languages for business processes and business rules: A representational analysis,” Information Systems., vol. 35, Issue 4, pp. 379–390, June, 2010. </unstructured_citation></citation><citation key="ref20"><unstructured_citation>
[21]. H. Herbst, G. Knolmayer, T. Myrach, M. Schlesinger, “THE SPECIFICATION OF BUSINESS RULES: A COMPARISON OF SELECTED METHODOLOGIES,” Methods and Associated Tools for the Information Systems Life Cycle., pp. 29-46, September, 1994. </unstructured_citation></citation><citation key="ref21"><unstructured_citation>
[22]. OMG, Decision Model and Notation 1.4 (DMN), 2022, &lt;http://www.omg.org/spec/DMN/&gt;. </unstructured_citation></citation><citation key="ref22"><unstructured_citation>
[23]. E. Bazhenova, F. Zerbato, B. Oliboni, M. Weske, “From BPMN process models to DMN decision models,” Information Systems., vol. 83, pp. 69-88, July, 2019. </unstructured_citation></citation><citation key="ref23"><unstructured_citation>
[24]. M. Häußler, S. Esser, A. Borrmann, “Code compliance checking of railway designs by integrating BIM, BPMN and DMN,” Automation in Construction., vol. 121, January, 2021, 103427. </unstructured_citation></citation><citation key="ref24"><unstructured_citation>
[25]. I. Essefi, H. Rahmouni, M. Ladeb, “Integrated privacy decision in BPMN clinical care pathways models using DMN,” Procedia Computer Science., vol. 196, pp. 509-516, 2022. </unstructured_citation></citation><citation key="ref25"><unstructured_citation>
[26]. M.D. Leoni, P. Felli, M. Montali, “Integrating BPMN and DMN: Modeling and Analysis,” Journal on Data Semantics., vol. 10, pp. 165-188, June, 2021. </unstructured_citation></citation><citation key="ref26"><unstructured_citation>

[27]. OMG, ArchiMate 3.2, 2022, &lt;https://pubs.opengroup.org/architecture/archimate3-doc/&gt;. </unstructured_citation></citation><citation key="ref27"><unstructured_citation>
[28]. OMG, Unified Modeling Language 2.5 (UML), 2015, &lt;http://www.omg.org/spec/UML/&gt;.  </unstructured_citation></citation><citation key="ref28"><unstructured_citation>
[29]. OMG, Business Process Model and Notation 2.0.2 (BPMN), 2013, &lt;http://www.omg.org/spec/BPMN/index.htm&gt;. </unstructured_citation></citation><citation key="ref29"><unstructured_citation>
[30]. G. Beydoun, G. Low, B. Henderson-Sellers, H. Mouratids, J.J. Gomez-Snaz, J. Pavon, C. Gonzalez-Perez, “FAML: a generic metamodel for MAS development,” IEEE Trans. Softw. Eng., vol. 35, Issue 6, pp. 841–863, Nov-Dec 2009. </unstructured_citation></citation><citation key="ref30"><unstructured_citation>
[31]. OMG, Service Oriented Architecture Modeling Language 1.0.1 (SoaML), 2012, &lt;http://www.omg.org/spec/SoaML/&gt;. </unstructured_citation></citation><citation key="ref31"><unstructured_citation>
[32]. OMG, Business Motivation Model 1.3(BMM), 2015, &lt;http://www.omg.org/spec/BMM/&gt;. </unstructured_citation></citation><citation key="ref32"><unstructured_citation>
]33[. صمدی اوانسر، عسگر، مقدمه ای بر معماری سازمانی (ویژه مدیران)، دبیرخانه شورای عالی اطلاع رسانی، تهران، ایران، 1384.  </unstructured_citation></citation><citation key="ref33"><unstructured_citation>
[34]. I. Band, H. Jonkers, E. Proper, D. Quartel, M. Lankhorst and M. Turner, "Using the TOGAF® 9.1 Framework with the ArchiMate® 3.0 Modeling Language," The Open Group,  AUGUST 25, 2017. </unstructured_citation></citation><citation key="ref34"><unstructured_citation>
[35]. M. Pankowska, “Business Models in CMMN, DMN and ArchiMate language,” Procedia Computer Science., vol. 164, pp. 11-18, 2019. </unstructured_citation></citation><citation key="ref35"><unstructured_citation>
[36]. IBM Rational Unified Process (RUP), Business Modeling Discipline: Artifact Overview:  Business Rules, 2001, &lt;https://sceweb.uhcl.edu/helm/RationalUnifiedProcess/process/artifact/ar_brules.htm&gt;.  </unstructured_citation></citation><citation key="ref36"><unstructured_citation>
[37]. P. Desfray, G. Raymond, Modeling Enterprise Architecture with TOGAF® A Practical Guide Using UML and BPMN.  Morgan Kaufmann; 1st edition, 2014. </unstructured_citation></citation><citation key="ref37"><unstructured_citation>
[38]. K. Figl, J. Mendling, G. Tokdemir, J. Vanthienend, “What we know and what we do not know about DMN,” Enterprise Modelling and Information Systems Architectures., vol. 13, No. 2, 2018. </unstructured_citation></citation><citation key="ref38"><unstructured_citation>
[39]. A. Valencia-Parra, L. Parody, A.J. Varela-Vaca, I. Caballero, M.T. G´omez-L´opez, “DMN4DQ: When data quality meets DMN,” Decision Support Systems., vol. 141, February, 2021, 113450. </unstructured_citation></citation><citation key="ref39"><unstructured_citation>
]40[. شمس علیئی، فریدون. مهجوریان، امیر و همکاران. چارچوب و روش شناسی معماری سازمانی ایران، نسخه 1، شورای اجرایی)عالی(فناوری اطلاعات کشور، کمیسیون توسعه دولت الکترونیکی، تهران، 1395، https://www.ieaf.ir/.  </unstructured_citation></citation><citation key="ref40"><unstructured_citation>
]41[. محمدی لرد، عبدالمحمود، فرآیندهای تحلیل شبکه‌ای (ANP) و سلسله مراتبی (AHP)به همراه معرفی نرم‌افزار Super Decision، تهران، انتشارات البرز فر دانش، 1388. </unstructured_citation></citation><citation key="ref41"><unstructured_citation>
]42[. سرافرازی، اعظم، ایزدیار، صدیقه، حبیبی، آرش، تصمیم‌گیری چند معیاره فازی، تهران، انتشارات کتیبه گیل، 1393. </unstructured_citation></citation><citation key="ref42"><unstructured_citation>
]43[. عطائی، محمد، تصمیم‌گیری چند معیاره، شاهرود، انتشارات دانشگاه صنعتی شاهرود، چاپ اول، 1389. </unstructured_citation></citation><citation key="ref43"><unstructured_citation>
[44]. R., Attri, N., Dev, V., Sharma, “Interpretive Structural Modelling (ISM) approach: An Overview,” Research Journal of Management Sciences., vol. 2, Issue 2, pp. 3-8, February 2013. </unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>An Overview on Replica Consistency Methods in Distributed Systems and Future Works</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Mahsa</given_name><surname>Beigrezaei</surname></person_name></contributors><publication_date media_type="online"><month>12</month><day>7</day><year>2024</year></publication_date><pages><first_page>1</first_page><last_page>19</last_page></pages><doi_data><doi>10.66224/jict.42156.16.61.1</doi><resource>http://jour.aicti.ir/en/Article/42156</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/en/Article/Download/42156</resource></item><item crawler="google"><resource>http://jour.aicti.ir/en/Article/Download/42156</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/en/Article/Download/42156</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/en/Article/Download/42156</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/en/Article/Download/42156</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/en/Article/Download/42156</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/en/Article/Download/42156</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>]	R. Moore, C. Baru, A. Rajasekar, R. Marciano, and M. Wan, “Data Intensive Computing, In``The Grid: Blueprint for a New Computing Infrastructure’’, eds. I. Foster and C. Kesselman.” Morgan Kaufmann, San Francisco, 1999.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>[2]	M. Beigrezaei, A. Toroghi Haghighat, and S. Leili Mirtaheri, “Minimizing data access latency in data grids by neighborhood‐based data replication and job scheduling,” Int. J. Commun. Syst., p. e4552.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>[3]	A. M. Rahmani, Z. Fadaie, and A. T. Chronopoulos, “Data placement using Dewey Encoding in a hierarchical data grid,” J. Netw. Comput. Appl., vol. 49, pp. 88–98, 2015.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>[4]	U. Tos, R. Mokadem, A. Hameurlain, and T. Ayav, “Achieving query performance in the cloud via a cost-effective data replication strategy,” Soft Comput., vol. 25, no. 7, pp. 5437–5454, 2021.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>[5]	X. Dong, J. Li, Z. Wu, D. Zhang, and J. Xu, “On dynamic replication strategies in data service grids,” in 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC), 2008, pp. 155–161.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>[6]	M.-C. Lee, F.-Y. Leu, and Y. Chen, “PFRF: An adaptive data replication algorithm based on star-topology data grids,” Futur. Gener. Comput. Syst., vol. 28, no. 7, pp. 1045–1057, 2012.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>[7]	C. Li, M. Song, M. Zhang, and Y. Luo, “Effective replica management for improving reliability and availability in edge-cloud computing environment,” J. Parallel Distrib. Comput., 2020.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>[8]	M. I. Naas, L. Lemarchand, P. Raipin, and J. Boukhobza, “IoT data replication and consistency management in fog computing,” J. Grid Comput., vol. 19, no. 3, pp. 1–25, 2021.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>[9]	S. Sun, W. Yao, B. Qiao, M. Zong, X. He, and X. Li, “RRSD: A file replication method for ensuring data reliability and reducing storage consumption in a dynamic Cloud-P2P environment,” Futur. Gener. Comput. Syst., vol. 100, pp. 844–858, 2019.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>[10]	M. Beigrezaei, A. T. Haghighat, and H. R. Kanan, “A new fuzzy based dynamic data replication algorithm in data grids,” in 2013 13th Iranian Conference on Fuzzy Systems (IFSC), 2013, pp. 1–5, doi: 10.1109/IFSC.2013.6675676.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>[11]	K. Rajaretnam, M. Rajkumar, and R. Venkatesan, “Rplb: A replica placement algorithm in data grid with load balancing,” Int. Arab J. Inf. Technol., vol. 13, no. 6, 2016.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>[12]	H. K. H. So and R. Brodersen, “A unified hardware/software runtime environment for FPGA-based reconfigurable computers using BORPH,” Trans. Embed. Comput. Syst., vol. 7, no. 2, pp. 1–28, 2008, doi: 10.1145/1331331.1331338.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>[13]	A. M. Rahmani, L. Azari, and H. A. Daniel, “A file group data replication algorithm for data grids,” J. Grid Comput., vol. 15, no. 3, pp. 379–393, 2017.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>[14]	G. Hager and G. Wellein, Introduction to high performance computing for scientists and engineers. CRC Press, 2010.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>[15]	M. Beigrezaei, A. T. Haghighat, M. R. Meybodi, and M. Runiassy, “PPRA: A new pre-fetching and prediction based replication algorithm in data grid,” in 2016 6th International Conference on Computer and Knowledge Engineering (ICCKE), 2016, pp. 257–262.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>[16]	B. L. Chamberlain, D. Callahan, and H. P. Zima, “Parallel programmability and the chapel language,” Int. J. High Perform. Comput. Appl., vol. 21, no. 3, pp. 291–312, 2007.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>[17]	L. Azari, A. M. Rahmani, H. A. Daniel, and N. N. Qader, “A data replication algorithm for groups of files in data grids,” J. Parallel Distrib. Comput., vol. 113, pp. 115–126, 2018.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>[18]	Y. Saito, “Consistency management in optimistic replication algorithms,” INTERNET, AOnlineU, vol. 15, pp. 1–18, 2001.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>[19]	R. A. Campêlo, M. A. Casanova, D. O. Guedes, and A. H. F. Laender, “A brief survey on replica consistency in cloud environments,” J. Internet Serv. Appl., vol. 11, no. 1, pp. 1–13, 2020.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>[20]	J. Curtis, “Consistency of data replication protocols in database systems: a review,” 2014.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>[21]	P. Vashisht, A. Sharma, and R. Kumar, “Strategies for replica consistency in data grid–a comprehensive survey,” Concurr. Comput. Pract. Exp., vol. 29, no. 4, p. e3907, 2017.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>[22]	K. S. Maabreh, “An Enhanced University Registration Model Using Distributed Database Schema.,” KSII Trans. Internet Inf. Syst., vol. 13, no. 7, 2019.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>[23]	I. Foster, C. Kesselman, and S. Tuecke, “The anatomy of the grid: Enabling scalable virtual organizations,” Int. J. High Perform. Comput. Appl., vol. 15, no. 3, pp. 200–222, 2001.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>[24]	H. Yu and A. Vahdat, “Design and evaluation of a conit-based continuous consistency model for replicated services,” ACM Trans. Comput. Syst., vol. 20, no. 3, pp. 239–282, 2002.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>[25]	T. Kraska, M. Hentschel, G. Alonso, and D. Kossmann, “Consistency rationing in the cloud: pay only when it matters,” Proc. VLDB Endow., vol. 2, no. 1, pp. 253–264, 2009.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>[26]	A. S. Tanenbaum and M. Van Steen, Distributed systems: principles and paradigms. Prentice-Hall, 2007.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>[27]	J. Du, S. Elnikety, and W. Zwaenepoel, “Clock-SI: Snapshot isolation for partitioned data stores using loosely synchronized clocks,” in 2013 IEEE 32nd International Symposium on Reliable Distributed Systems, 2013, pp. 173–184.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>[28]	P. Keleher, A. L. Cox, and W. Zwaenepoel, “Lazy release consistency for software distributed shared memory,” ACM SIGARCH Comput. Archit. News, vol. 20, no. 2, pp. 13–21, 1992.</unstructured_citation></citation><citation key="ref29"><unstructured_citation>[29]	L. Iftode, J. P. Singh, and K. Li, “Scope consistency: A bridge between release consistency and entry consistency,” in Proceedings of the eighth annual ACM symposium on Parallel algorithms and architectures, 1996, pp. 277–287.</unstructured_citation></citation><citation key="ref30"><unstructured_citation>[30]	C. Cachin, I. Keidar, and A. Shraer, “Fork sequential consistency is blocking,” Inf. Process. Lett., vol. 109, no. 7, pp. 360–364, 2009.</unstructured_citation></citation><citation key="ref31"><unstructured_citation>[31]	C. Cachin, A. Shelat, and A. Shraer, “Efficient fork-linearizable access to untrusted shared memory,” in Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing, 2007, pp. 129–138.</unstructured_citation></citation><citation key="ref32"><unstructured_citation>[32]	C. Li, D. Porto, A. Clement, J. Gehrke, N. Preguiça, and R. Rodrigues, “Making geo-replicated systems fast as possible, consistent when necessary,” in Presented as part of the 10th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 12), 2012, pp. 265–278.</unstructured_citation></citation><citation key="ref33"><unstructured_citation>[33]	F. J. Torres-Rojas and E. Meneses, “Convergence through a weak consistency model: Timed causal consistency,” CLEI Electron. J, vol. 8, no. 2, pp. 1–2, 2005.</unstructured_citation></citation><citation key="ref34"><unstructured_citation>[34]	J. Li, M. N. Krohn, D. Mazieres, and D. E. Shasha, “Secure Untrusted Data Repository (SUNDR).,” in Osdi, 2004, vol. 4, p. 9.</unstructured_citation></citation><citation key="ref35"><unstructured_citation>[35]	A. J. Feldman, W. P. Zeller, M. J. Freedman, and E. W. Felten, “SPORC: Group collaboration using untrusted cloud resources,” 2010.</unstructured_citation></citation><citation key="ref36"><unstructured_citation>[36]	Y. Saito and M. Shapiro, “Optimistic replication,” ACM Comput. Surv., vol. 37, no. 1, pp. 42–81, 2005.</unstructured_citation></citation><citation key="ref37"><unstructured_citation>[37]	M. Senftleben and K. Schneider, “Operational characterization of weak memory consistency models,” in International Conference on Architecture of Computing Systems, 2018, pp. 195–208.</unstructured_citation></citation><citation key="ref38"><unstructured_citation>[38]	D. Terry, V. Prabhakaran, R. Kotla, M. Balakrishnan, and M. K. Aguilera, “Transactions with Consistency Choices on Geo-Replicated Cloud Storage.”</unstructured_citation></citation><citation key="ref39"><unstructured_citation>[39]	X. Wang, S. Yang, S. Wang, X. Niu, and J. Xu, “An application-based adaptive replica consistency for cloud storage,” in 2010 Ninth International Conference on Grid and Cloud Computing, 2010, pp. 13–17.</unstructured_citation></citation><citation key="ref40"><unstructured_citation>[40]	Y. N. Aye, “Data Consistency on Private Cloud Storage System,” Int. J. Emerg. Trends Technol. Comput. Sci. Vol., vol. 1, 2012.</unstructured_citation></citation><citation key="ref41"><unstructured_citation>[41]	H.-E. Chihoub, S. Ibrahim, G. Antoniu, and M. S. Perez, “Harmony: Towards automated self-adaptive consistency in cloud storage,” in 2012 IEEE International Conference on Cluster Computing, 2012, pp. 293–301.</unstructured_citation></citation><citation key="ref42"><unstructured_citation>[42]	S. Esteves, J. Silva, and L. Veiga, “Quality-of-service for consistency of data geo-replication in cloud computing,” in European Conference on Parallel Processing, 2012, pp. 285–297.</unstructured_citation></citation><citation key="ref43"><unstructured_citation>[43]	H.-E. Chihoub, S. Ibrahim, G. Antoniu, and M. S. Perez, “Consistency in the cloud: When money does matter!,” in 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, 2013, pp. 352–359.</unstructured_citation></citation><citation key="ref44"><unstructured_citation>[44]	Q. Liu, G. Wang, and J. Wu, “Consistency as a service: Auditing cloud consistency,” IEEE Trans. Netw. Serv. Manag., vol. 11, no. 1, pp. 25–35, 2014.</unstructured_citation></citation><citation key="ref45"><unstructured_citation>[45]	X. Ren, W. Ru-chuan, and K. Qiang, “Efficient model for replica consistency maintenance in data grids,” in International Symposium on Computer Science and its Applications, 2008, pp. 159–162.</unstructured_citation></citation><citation key="ref46"><unstructured_citation>[46]	A. Stiemer, I. Fetai, and H. Schuldt, “Analyzing the performance of data replication and data partitioning in the cloud: the BEOWULF approach,” in 2016 IEEE international conference on big data (Big Data), 2016, pp. 2837–2846.</unstructured_citation></citation><citation key="ref47"><unstructured_citation>[47]	T. Kraska, M. Hentschel, G. Alonso, and D. Kossmann, “Consistency rationing in the cloud: Pay only when it matters,” Proc. VLDB Endow., vol. 2, no. 1, pp. 253–264, 2009, doi: 10.14778/1687627.1687657.</unstructured_citation></citation><citation key="ref48"><unstructured_citation>[48]	H. N. S. Aldin, H. Deldari, M. H. Moattar, and M. R. Ghods, “Strict timed causal consistency as a hybrid consistency model in the cloud environment,” Futur. Gener. Comput. Syst., vol. 105, pp. 259–274, 2020.</unstructured_citation></citation><citation key="ref49"><unstructured_citation>[49]	O. Kozina and M. Kozin, “Simulation Model of Data Consistency Protocol for Multicloud Systems,” in 2022 IEEE 3rd KhPI Week on Advanced Technology (KhPIWeek), 2022, pp. 1–4.</unstructured_citation></citation><citation key="ref50"><unstructured_citation>[50]	N. Mostafa, “A dynamic approach for consistency service in cloud and fog environment,” in 2020 fifth international conference on fog and mobile edge computing (FMEC), 2020, pp. 28–33.</unstructured_citation></citation><citation key="ref51"><unstructured_citation>[51]	S. Sun, X. Wang, and F. Zuo, “RPCC: A Replica Placement Method to Alleviate the Replica Consistency under Dynamic Cloud,” in 2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics), 2020, pp. 729–734.</unstructured_citation></citation><citation key="ref52"><unstructured_citation>[52]	A. Lakshman and P. Malik, “Cassandra: a decentralized structured storage system,” ACM SIGOPS Oper. Syst. Rev., vol. 44, no. 2, pp. 35–40, 2010.</unstructured_citation></citation><citation key="ref53"><unstructured_citation>[53]	OpenACC Working Group, “The OpenACC Application Programming Interface 3.0,” Openacc.Org Doc., pp. 1–118, 2019.</unstructured_citation></citation><citation key="ref54"><unstructured_citation>[54]	C.-T. Yang, W.-C. Tsai, T.-T. Chen, and C.-H. Hsu, “A one-way file replica consistency model in data Grids,” in The 2nd IEEE Asia-Pacific Service Computing Conference (APSCC 2007), 2007, pp. 364–373.</unstructured_citation></citation><citation key="ref55"><unstructured_citation>[55]	M. N. Vora, “Hadoop-HBase for large-scale data,” in Proceedings of 2011 International Conference on Computer Science and Network Technology, 2011, vol. 1, pp. 601–605.</unstructured_citation></citation><citation key="ref56"><unstructured_citation>[56]	R. Klophaus, “Riak core: Building distributed applications without shared state,” in ACM SIGPLAN Commercial Users of Functional Programming, 2010, p. 1.</unstructured_citation></citation><citation key="ref57"><unstructured_citation>[57]	L. Xiong, L. Yang, Y. Tao, J. Xu, and L. Zhao, “Replication strategy for spatiotemporal data based on distributed caching system,” Sensors, vol. 18, no. 1, p. 222, 2018.</unstructured_citation></citation><citation key="ref58"><unstructured_citation>[58]	M. I. Naas, J. Boukhobza, P. R. Parvedy, and L. Lemarchand, “An extension to ifogsim to enable the design of data placement strategies,” in 2018 IEEE 2nd International Conference on Fog and Edge Computing (ICFEC), 2018, pp. 1–8.</unstructured_citation></citation><citation key="ref59"><unstructured_citation>[59]	R. Oma, S. Nakamura, D. Duolikun, T. Enokido, and M. Takizawa, “Fault-tolerant fog computing models in the IoT,” in International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 2018, pp. 14–25.</unstructured_citation></citation><citation key="ref60"><unstructured_citation>[60]	A. V. Dastjerdi, H. Gupta, R. N. Calheiros, S. K. Ghosh, and R. Buyya, “Fog computing: Principles, architectures, and applications,” in Internet of things, Elsevier, 2016, pp. 61–75.</unstructured_citation></citation><citation key="ref61"><unstructured_citation>[61]	M. Verma, N. Bhardwaj, and A. K. Yadav, “Real time efficient scheduling algorithm for load balancing in fog computing environment,” Int. J. Inf. Technol. Comput. Sci, vol. 8, no. 4, pp. 1–10, 2016.</unstructured_citation></citation><citation key="ref62"><unstructured_citation>[62]	H. F. Atlam, R. J. Walters, and G. B. Wills, “Fog computing and the internet of things: a review,” big data Cogn. Comput., vol. 2, no. 2, p. 10, 2018.</unstructured_citation></citation><citation key="ref63"><unstructured_citation>[63]	B. Feng, A. Tian, S. Yu, J. Li, H. Zhou, and H. Zhang, “Efficient Cache Consistency Management for Transient IoT Data in Content-Centric Networking,” IEEE Internet Things J., 2022.</unstructured_citation></citation><citation key="ref64"><unstructured_citation>[64]	T. Junfeng, B. Wenqing, and J. Haoyi, “PGCE: A distributed storage causal consistency model based on partial geo-replication and cloud-edge collaboration architecture,” Comput. Networks, vol. 212, p. 109065, 2022.</unstructured_citation></citation><citation key="ref65"><unstructured_citation>[65]	J. Tian, H. Jia, and W. Bai, “CCECGP: causal consistency model of edge–cloud collaborative based on grouping protocol,” J. Supercomput., pp. 1–24, 2022.</unstructured_citation></citation><citation key="ref66"><unstructured_citation>[66]	C. Li, J. Bai, Y. Chen, and Y. Luo, “Resource and replica management strategy for optimizing financial cost and user experience in edge cloud computing system,” Inf. Sci. (Ny)., vol. 516, pp. 33–55, 2020.</unstructured_citation></citation><citation key="ref67"><unstructured_citation>[67]	J. Guo, C. Li, and Y. Luo, “Fast replica recovery and adaptive consistency preservation for edge cloud system,” Soft Comput., vol. 24, pp. 14943–14964, 2020.</unstructured_citation></citation><citation key="ref68"><unstructured_citation>[68]	J. Lan, X. Liu, P. Shenoy, and K. Ramamritham, “Consistency maintenance in peer-to-peer file sharing networks,” in Proceedings the Third IEEE Workshop on Internet Applications. WIAPP 2003, 2003, pp. 90–94.</unstructured_citation></citation><citation key="ref69"><unstructured_citation>[69]	H. Shen, “IRM: Integrated file replication and consistency maintenance in P2P systems,” IEEE Trans. Parallel Distrib. Syst., vol. 21, no. 1, pp. 100–113, 2009.</unstructured_citation></citation><citation key="ref70"><unstructured_citation>[70]	R.-S. Chang and J.-S. Chang, “Adaptable replica consistency service for data grids,” in Third International Conference on Information Technology: New Generations (ITNG’06), 2006, pp. 646–651.</unstructured_citation></citation><citation key="ref71"><unstructured_citation>[71]	X. Meng and C. Zhang, “An ant colony model based replica consistency maintenance strategy in unstructured P2P networks,” Comput. Networks, vol. 62, pp. 1–11, 2014.</unstructured_citation></citation><citation key="ref72"><unstructured_citation>[72]	S. C. Choi and H. Y. Youn, “Dynamic hybrid replication effectively combining tree and grid topology,” J. Supercomput., vol. 59, pp. 1289–1311, 2012.</unstructured_citation></citation><citation key="ref73"><unstructured_citation>[73]	E. Anderson, X. Li, M. A. Shah, J. Tucek, and J. J. Wylie, “What Consistency Does Your {Key-Value} Store Actually Provide?,” 2010.</unstructured_citation></citation><citation key="ref74"><unstructured_citation>[74]	G. Belalem and B. Yagoubi, Collaborative Negotiation to Resolve Conflicts among Replicas in Data Grids. INTECH Open Access Publisher, 2010.</unstructured_citation></citation><citation key="ref75"><unstructured_citation>[75]	G. Belalem, C. Haddad, and Y. Slimani, “An effective approach for consistency management of replicas in Data Grid,” in 2008 IEEE International Symposium on Parallel and Distributed Processing with Applications, 2008, pp. 11–18.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Designing A New Genetic-Fuzzy Type 2 Approach to Evaluate Self-Adaptive Systems by Software Quality Indicators</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Majid</given_name><surname>Abdolrazzagh-Nezhad</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Eshrat </given_name><surname> Zargari</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Mehdi</given_name><surname>Kherad</surname></person_name></contributors><publication_date media_type="online"><month>12</month><day>7</day><year>2024</year></publication_date><pages><first_page>123</first_page><last_page>146</last_page></pages><doi_data><doi>10.66224/jict.42386.16.61.123</doi><resource>http://jour.aicti.ir/en/Article/42386</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/en/Article/Download/42386</resource></item><item crawler="google"><resource>http://jour.aicti.ir/en/Article/Download/42386</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/en/Article/Download/42386</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/en/Article/Download/42386</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/en/Article/Download/42386</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/en/Article/Download/42386</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/en/Article/Download/42386</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	F. D. Macías-Escrivá, R. Haber, R. Del Toro, and V. Hernandez, "Self-adaptive systems: A survey of current approaches, research challenges and applications," Expert Systems with Applications, vol. 40, no. 18, pp. 7267-7279, 2013.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>[2]	F. Kneer and E. Kamsties, "A Framework for Prototyping and Evaluating Self-adaptive Systems - A Research Preview," 2016.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>[3]	S. Sucipto and R. S. Wahono, "A Systematic Literature Review of Requirements Engineering for Self-Adaptive Systems," Software Engineering &amp; Applications, vol. 1, no. 1, 2015.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>[4]	M. Abufouda, "A Framework for Enhancing Performance And Handling Run-Time Uncertainty in Self-Adaptive Systems," Software Engineering &amp; Applications, vol. 5, no. 1, 2014.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>[5]	M. Salehie and L. Tahvildari, "Towards a goal‐driven approach to action selection in self‐adaptive software," Software: Practice and Experience, vol. 42, no. 2, pp. 211-233, 2012.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>[6]	C. Raibulet, F. Arcelli Fontana, R. Capilla, and C. Carrillo, "An Overview on Quality Evaluation of Self-Adaptive Systems," 2016.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>[7]	J. D. Paraiba and L. E. G. Martins, "PERSA: a requirements specification process for self-adaptive systems based on fuzzy logic and NFR-framework," International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 25, no. 01, pp. 145-178, 2017.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>[8]	J. A. McCall, P. K. Richards, and G. F. Walters, "Factors in software quality. volume i. concepts and definitions of software quality," DTIC Document, 1977. </unstructured_citation></citation><citation key="ref9"><unstructured_citation>[9]	S. Valenti, A. Cucchiarelli, and M. Panti, "Computer based assessment systems evaluation via the ISO9126 quality model," Journal of Information Technology Education, vol. 1, no. 3, pp. 157-175, 2002.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>[10]	 B. W. Boehm, J. R. Brown, and M. Lipow, "Quantitative evaluation of software quality," in Proceedings of the 2nd international conference on Software engineering, 1976: IEEE Computer Society Press, pp. 592-605. </unstructured_citation></citation><citation key="ref11"><unstructured_citation>[11]	P. Berander et al., "Software quality attributes and trade-offs," Blekinge Institute of Technology, 2005.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>[12]	C.-W. Chang, C.-R. Wu, and H.-L. Lin, "Integrating fuzzy theory and hierarchy concepts to evaluate software quality," Software Quality Journal, vol. 16, no. 2, pp. 263-276, 2008.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>[13]	P. HOLECEK and J. TALAŠOVÁ, "FuzzME: a new software for multiple-criteria fuzzy evaluation," Acta Universitatis Matthiae Belii ser. Mathematics, vol. 16, pp. 35-51, 2010.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>[14]	J. S. Challa, A. Paul, Y. Dada, V. Nerella, P. R. Srivastava, and A. P. Singh, "Integrated Software Quality Evaluation: A Fuzzy Multi-Criteria Approach," JIPS, vol. 7, no. 3, pp. 473-518, 2011.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>[15]	 E. Letier, D. Stefan, and E. T. Barr, "Uncertainty, risk, and information value in software requirements and architecture," in Proceedings of the 36th International Conference on Software Engineering, 2014, pp. 883-894. </unstructured_citation></citation><citation key="ref16"><unstructured_citation>[16]	F. Haryana, "Software Quality Evaluation using Fuzzy Multi Criteria Decision Method," 2015.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>[17]	A. Mansoor, D. Streitferdt, and F.-F. Füßl, "Fuzzy Based Evaluation of Software Quality Using Quality Models and Goal Models."</unstructured_citation></citation><citation key="ref18"><unstructured_citation>[18]	M. Kara, O. Lamouchi, and A. Ramdane-Cherif, "Ontology software quality model for fuzzy logic evaluation approach," Procedia Computer Science, vol. 83, pp. 637-641, 2016.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>[19]	A. S. Abdygalievich, A. Barlybayev, and K. B. Amanzholovich, "Quality evaluation fuzzy method of automated control systems on the LMS example," IEEE Access, vol. 7, pp. 138000-138010, 2019.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>[20]	U. Dayanandan and V. Kalimuthu, "A fuzzy analytical hierarchy process (FAHP) based software quality assessment model: maintainability analysis," International Journal of Intelligent Engineering and Systems, vol. 11, no. 4, pp. 88-96, 2018.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>[21]	D. Manikavelan and R. Ponnusamy, "Software quality analysis based on cost and error using fuzzy combined COCOMO model," Journal of Ambient Intelligence and Humanized Computing, pp. 1-11, 2020.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>[22]	N. X. Thao and S.-Y. Chou, "Novel similarity measures, entropy of intuitionistic fuzzy sets and their application in software quality evaluation," Soft Computing, pp. 1-12, 2022.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>[23]	A. Barzegar, "Measuring software quality Product based on Fuzzy Inference System techniques in ISO standard," 2021.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>[24]	V. Singh, V. Kumar, and V. Singh, "A hybrid novel fuzzy AHP-TOPSIS technique for selecting parameter-influencing testing in software development," Decision Analytics Journal, vol. 6, p. 100159, 2023.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>[25]	 A. O. de Sousa, C. I. Bezerra, R. M. Andrade, and J. M. Filho, "Quality evaluation of self-adaptive systems: Challenges and opportunities," in Proceedings of the XXXIII Brazilian Symposium on Software Engineering, 2019, pp. 213-218. </unstructured_citation></citation><citation key="ref26"><unstructured_citation>[26]	L. E. Sanchez, J. A. Diaz-Pace, A. Zunino, S. Moisan, and J.-P. Rigault, "An approach based on feature models and quality criteria for adapting component-based systems," Journal of Software Engineering Research and Development, vol. 3, pp. 1-30, 2015.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>[27]	E. Serral, P. Sernani, and F. Dalpiaz, "Personalized adaptation in pervasive systems via non-functional requirements," Journal of Ambient Intelligence and Humanized Computing, vol. 9, no. 6, pp. 1729-1743, 2018.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>[28]	 R. MoeinFar and A. A. Barforoush, "Using models at run-time to measure quality of SAS in the large-scale software systems," in 2017 9th International Conference on Information and Knowledge Technology (IKT), 2017: IEEE, pp. 99-103. </unstructured_citation></citation><citation key="ref29"><unstructured_citation>[29]	 R. Edwards and N. Bencomo, "DeSiRE: further understanding nuances of degrees of satisfaction of non-functional requirements trade-off," in Proceedings of the 13th International Conference on Software Engineering for Adaptive and Self-Managing Systems, 2018, pp. 12-18. </unstructured_citation></citation><citation key="ref30"><unstructured_citation>[30]	 C. Raibulet, "Hints on quality evaluation of self-systems," in 2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems, 2014: IEEE, pp. 185-186. </unstructured_citation></citation><citation key="ref31"><unstructured_citation>[31]	 E. Kaddoum, C. Raibulet, J.-P. Georgé, G. Picard, and M.-P. Gleizes, "Criteria for the evaluation of self-* systems," in Proceedings of the 2010 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems, 2010, pp. 29-38. </unstructured_citation></citation><citation key="ref32"><unstructured_citation>[32]	 C. I. Bezerra, R. M. Andrade, J. M. Monteiro, and D. Cedraz, "Aggregating measures using fuzzy logic for evaluating feature models," in Proceedings of the 12th International Workshop on Variability Modelling of Software-Intensive Systems, 2018, pp. 35-42. </unstructured_citation></citation><citation key="ref33"><unstructured_citation>[33]	R. Wohlrab, J. Cámara, D. Garlan, and B. Schmerl, "Explaining quality attribute tradeoffs in automated planning for self-adaptive systems," Journal of Systems and Software, vol. 198, p. 111538, 2023.</unstructured_citation></citation><citation key="ref34"><unstructured_citation>[34]	S. Malik, M. A. Naqvi, and L. Moonen, "CHESS: A Framework for Evaluation of Self-adaptive Systems based on Chaos Engineering," arXiv preprint arXiv:2303.07283, 2023.</unstructured_citation></citation><citation key="ref35"><unstructured_citation>[35]	 A. Parvizi-Mosaed, S. Moaven, J. Habibi, and A. Heydarnoori, "Towards a Tactic-Based Evaluation of Self-Adaptive Software Architecture Availability," in SEKE, 2014, pp. 168-173. </unstructured_citation></citation><citation key="ref36"><unstructured_citation>[36]	 Q. Yang, J. Lü, J. Li, X. Ma, W. Song, and Y. Zou, "Toward a fuzzy control-based approach to design of self-adaptive software," in Proceedings of the Second Asia-Pacific Symposium on Internetware, 2010: ACM, p. 15. </unstructured_citation></citation><citation key="ref37"><unstructured_citation>[37]	 W. Min, Z. Jun, and Z. Wei, "The application of fuzzy comprehensive evaluation method in the software project risk assessment," in Proceedings of the 2017 International Conference on Management Engineering, Software Engineering and Service Sciences, 2017: ACM, pp. 76-79. </unstructured_citation></citation><citation key="ref38"><unstructured_citation>[38]	Y. Wang, J. Corey, Y. Lao, K. Henrickson, and X. Xin, "Criteria for the Selection and Application of Advanced Traffic Signal Systems," 2013. </unstructured_citation></citation><citation key="ref39"><unstructured_citation>[39]	A. Stevanovic and P. M. Zlatkovic, "Comparative Evaluation of InSync and Time-of-Day Signal Timing Plans under Normal and Varied Traffic Conditions," ed: February, 2013.</unstructured_citation></citation><citation key="ref40"><unstructured_citation>[40]	M. KETABDARI, "Analysis of adaptive traffic control systems and design of a decision support system for better choice," 2013.</unstructured_citation></citation><citation key="ref41"><unstructured_citation>[41]	M. Selinger and L. Schmidt, "Adaptive traffic control systems in the united states: Updated summary and comparison," HDR Engineering, 2010.</unstructured_citation></citation><citation key="ref42"><unstructured_citation>[42]	M. Salehie and L. Tahvildari, "Self-Adaptive Software: Landscape and Research Challenges," ACM Transactions on Autonomous and Adaptive Systems, 2009.</unstructured_citation></citation><citation key="ref43"><unstructured_citation>[43]	S. Elkins and G. Niehus, "Insync adaptive traffic control system for the veterans memorial hwy corridor on long island, ny," 2012.</unstructured_citation></citation><citation key="ref44"><unstructured_citation>[44]	Y. Brun et al., "Engineering Self-Adaptive Systems through Feedback Loops," Software engineering for self-adaptive systems, vol. 5525, pp. 48-70, 2009.</unstructured_citation></citation><citation key="ref45"><unstructured_citation>[45]	M. Ravindranathan and R. Leitch, "Heterogeneous intelligent control systems," IEE Proceedings-Control Theory and Applications, vol. 145, no. 6, pp. 551-558, 1998.</unstructured_citation></citation><citation key="ref46"><unstructured_citation>[46]	M. Naqvi, "Claims and supporting evidence for self-adaptive systems–A literature review," ed, 2012.</unstructured_citation></citation><citation key="ref47"><unstructured_citation>[47]	 R. Almeida and M. Vieira, "Changeloads for resilience benchmarking of self-adaptive systems: a risk-based approach," in Dependable Computing Conference (EDCC), 2012 Ninth European, 2012: IEEE, pp. 173-184. </unstructured_citation></citation><citation key="ref48"><unstructured_citation>[48]	 S. M. Abuelenin, "Decomposed interval Type-2 fuzzy systems with application to inverted pendulum," in Engineering and Technology (ICET), 2014 International Conference on, 2014: IEEE, pp. 1-5. </unstructured_citation></citation><citation key="ref49"><unstructured_citation>[49]	N. N. Karnik, J. M. Mendel, and Q. Liang, "Type-2 fuzzy logic systems," IEEE transactions on Fuzzy Systems, vol. 7, no. 6, pp. 643-658, 1999.</unstructured_citation></citation><citation key="ref50"><unstructured_citation>[50]	ح. م. فراهانی, ج. عسگري, and م. ذکري, "مروري بر منطق فازي نوع- 2: از پیدایش تا کاربرد," محاسبات نرم, vol. 3, 1392.</unstructured_citation></citation><citation key="ref51"><unstructured_citation>[51]	 Q. Liang and J. M. Mendel, "Interval type-2 fuzzy logic systems," in Fuzzy Systems, 2000. FUZZ IEEE 2000. The Ninth IEEE International Conference on, 2000, vol. 1: IEEE, pp. 328-333. </unstructured_citation></citation><citation key="ref52"><unstructured_citation>[52]	 A. Farahani, E. Nazemi, G. Cabri, and A. Rafizadeh, "An evaluation method for Self-Adaptive systems," in Systems, Man, and Cybernetics (SMC), 2016 IEEE International Conference on, 2016: IEEE, pp. 002814-002820. </unstructured_citation></citation><citation key="ref53"><unstructured_citation>[53]	 J. A. McCann and M. C. Huebscher, "Evaluation issues in autonomic computing," in International Conference on Grid and Cooperative Computing, 2004: Springer, pp. 597-608. </unstructured_citation></citation><citation key="ref54"><unstructured_citation>[54]	Danny Weyns, M. Usman Iftikhar, Didac Gil de la Iglesia, and T. Ahmad, "A Survey of Formal Methods in Self-Adaptive Systems," 2012.</unstructured_citation></citation><citation key="ref55"><unstructured_citation>[55]	N. M. Villegas, H. A. Müller, G. Tamura, L. Duchien, and R. Casallas, "A Framework for Evaluating Quality-Driven Self-Adaptive Software Systems," 2011.</unstructured_citation></citation><citation key="ref56"><unstructured_citation>[56]	S. Weibelzahl and G. Weber, "Advantages, opportunities and limits of empirical evaluations: Evaluating adaptive systems," KI, vol. 16, no. 3, pp. 17-20, 2002.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>The Relevance, Importance and Dependence of Critical Infrastructures of The Islamic Republic of Iran from a Cyber-Perspective</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Abouzar</given_name><surname>Solat Rafiee</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Hossein</given_name><surname>Gharaee Garakani</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Fatemeh</given_name><surname>Saghafi</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Mohammad</given_name><surname>Malekinia</surname></person_name></contributors><publication_date media_type="online"><month>12</month><day>7</day><year>2024</year></publication_date><pages><first_page>261</first_page><last_page>281</last_page></pages><doi_data><doi>10.66224/jict.42956.16.61.261</doi><resource>http://jour.aicti.ir/en/Article/42956</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/en/Article/Download/42956</resource></item><item crawler="google"><resource>http://jour.aicti.ir/en/Article/Download/42956</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/en/Article/Download/42956</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/en/Article/Download/42956</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/en/Article/Download/42956</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/en/Article/Download/42956</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/en/Article/Download/42956</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>1.	Neal Ziring, NATIONAL CYBER RESILIENCE AND ROLES FOR PUBLIC AND PRIVATE SECTOR STAKEHOLDERS © IFIP International Federation for Information Processing 2022</unstructured_citation></citation><citation key="ref2"><unstructured_citation>Published by Springer Nature Switzerland AG 2022	</unstructured_citation></citation><citation key="ref3"><unstructured_citation>2.	J. Staggs and S. Shenoi (Eds.): Critical Infrastructure Protection XVI, IFIP AICT 666, pp. 3–46, 2022.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>https://doi.org/10.1007/978-3-031-20137-0_1	</unstructured_citation></citation><citation key="ref5"><unstructured_citation>3.	Cyber Security and Infrastructure Security Agency, Critical Infrastructure Sectors, Arlington, Virginia (www.dhs.gov/CISsa/criti cal-infrastructure-sectors), 2020	</unstructured_citation></citation><citation key="ref6"><unstructured_citation>4.	S. Rinaldi, J. Peerenboom and T. Kelly, Identifying, understanding and analyzing critical infrastructure interdependenCISes, IEEE Control Systems, vol. 21(6), pp. 11–25, 2001.	</unstructured_citation></citation><citation key="ref7"><unstructured_citation>5.	European CounCISl. (2004). Communication from the commission to the counCISl and the European Parliament: Critical infrastructure protection in the fight against terrorism (pp. 1–11).</unstructured_citation></citation><citation key="ref8"><unstructured_citation>6.	Brussels, Belgium: Commission of the European Communities. Retrieved from http://eur-lex.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>7.	europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:52004DC0702:EN:NOT.	</unstructured_citation></citation><citation key="ref10"><unstructured_citation>8.	GAO. (2004). Critical infrastructure protection: Challenges and efforts to secure control systems (pp. 1–47). Washington, DC: US Government Accountability Office.	</unstructured_citation></citation><citation key="ref11"><unstructured_citation>9.	Thissen, W. A., &amp; Herder, P. M. (2003b). Critical infrastructures: State of the art in research and</unstructured_citation></citation><citation key="ref12"><unstructured_citation>10.	application. Boston, MA: Kluwer Academic Publishers.	</unstructured_citation></citation><citation key="ref13"><unstructured_citation>11.	Clinton, W. J. (1996). Executive order 13010: Critical infrastructure protection. Federal Register, 61(138), 37345–37350.	</unstructured_citation></citation><citation key="ref14"><unstructured_citation>12.	US Congress. (2001). Uniting and strengthening America by providing appropriate tools required to intercept and obstruct terrorism (USA PATRIOT ACT) Act of 2001 (No. 147) (p. 115 Stat.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>13.	271–402). Washington, DC: 107th Congress. Retrieved from http://www.gpo.gov/fdsys/pkg/ PLAW 107publ56/content-detail.html.	</unstructured_citation></citation><citation key="ref16"><unstructured_citation>14.	European CounCISl. (2004). Communication from the commission to the counCISl and the European Parliament: Critical infrastructure protection in the fight against terrorism (pp. 1–11). Brussels, Belgium: Commission of the European Communities. Retrieved from http://eur-lex. europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:52004DC0702:EN:NOT.	</unstructured_citation></citation><citation key="ref17"><unstructured_citation>15.	Gheorghe, A. V., Masera, M., Weijnen, M. P. C., &amp; De Vries, J. L. (Eds.). (2006). Critical infrastructures at risk: Securing the European electric power system (Vol. 9). Dordrecht: Springer.	</unstructured_citation></citation><citation key="ref18"><unstructured_citation>16.	ITU Study Group Q.22/1 Report on Best Practices for a National Approach to Cybersecurity: A Management Framework for Organizing National Cybersecurity Efforts, ITU-D Secretariat, Geneva (2008)	</unstructured_citation></citation><citation key="ref19"><unstructured_citation>17.	NATO: Tallinn Manual on the International Law Applicable to Cyber Warfare (2013)	</unstructured_citation></citation><citation key="ref20"><unstructured_citation>18.	National Cybersecurity Strategy - Towards A Secure Cyberspace 2020-2023 (2020)	</unstructured_citation></citation><citation key="ref21"><unstructured_citation>19.	Danish Cyber and Information Security Strategy (2022-2024)</unstructured_citation></citation><citation key="ref22"><unstructured_citation>20.	https://fm.dk/media/25359/national-strategi-for-cyber-og-informationssikkerhed_web-a.pdf	</unstructured_citation></citation><citation key="ref23"><unstructured_citation>21.	NIST Glossary/ NIST SP 800-30 / CNSSI 4009-2015</unstructured_citation></citation><citation key="ref24"><unstructured_citation>22.	https://csrc.nist.gov/glossary/term/Operational-technology	</unstructured_citation></citation><citation key="ref25"><unstructured_citation>23.	ISO/IEC TR 27019:2013 Information technology -- Security techniques -- Information security management guidelines based on ISO/IEC 27002 for process control systems speCISfic to the energy utility industry.	</unstructured_citation></citation><citation key="ref26"><unstructured_citation>24.	IETF RFC449 Internet Security Glossary 2: https://tools.ietf.org/html/rfc4949	</unstructured_citation></citation><citation key="ref27"><unstructured_citation>25.	CounCISl Directive 2008/114/EC of 8 December 2008 on the identification and designation of European critical infrastructures and the assessment of the need to improve their protection.	</unstructured_citation></citation><citation key="ref28"><unstructured_citation>26.	UK.: Centre for the Protection of National Infrastructure (CPNI) </unstructured_citation></citation><citation key="ref29"><unstructured_citation>27.	https://www.cpni.gov.uk/about/cni/	</unstructured_citation></citation><citation key="ref30"><unstructured_citation>28.	RUSSIA: NATIONAL SECURITY OF RUSSIA - Information security (February 3, 2012, № 803)</unstructured_citation></citation><citation key="ref31"><unstructured_citation>29.	http://www.scrf.gov.ru/documents/6/113.html	</unstructured_citation></citation><citation key="ref32"><unstructured_citation>30.	QATAR National Cyber Security Strategy (May 2014)</unstructured_citation></citation><citation key="ref33"><unstructured_citation>31.	http://www.ictqatar.qa/ar/cyber-security/national-cyber-security-strategy	</unstructured_citation></citation><citation key="ref34"><unstructured_citation>32.	Australian :Critical Infrastructure Resilience Strategy, 2010</unstructured_citation></citation><citation key="ref35"><unstructured_citation>33.	https://www.CISsc.gov.au/Documents/Australian+Government+s+Critical+Infrastructure+Resilience+Strategy.pdf	</unstructured_citation></citation><citation key="ref36"><unstructured_citation>34.	Service Public Fédéral Intérieur/Federale Overheidsdienst Binnenlandse Zaken F./N. 2011-1799; C-2011/00399 (2011)</unstructured_citation></citation><citation key="ref37"><unstructured_citation>35.	Germany FRG. (2009). National strategy for critical infrastructure protection (pp. 1–18). Berlin, Germany: </unstructured_citation></citation><citation key="ref38"><unstructured_citation>36.	Federal Ministry of the Interior. Retrieved from http://www.bmi.bund.de/cae/servlet/contentblob/598732/publicationFile/34423/kritis_englisch.pdf.	</unstructured_citation></citation><citation key="ref39"><unstructured_citation>37.	Canada: An Emergency Management Framework for Canada (Second Edition)</unstructured_citation></citation><citation key="ref40"><unstructured_citation>38.	https://www.publicsafety.gc.ca/cnt/rsrcs/pblctns/mrgnc-mngmnt-frmwrk/index-en.aspx (2022)</unstructured_citation></citation><citation key="ref41"><unstructured_citation>39.	Cuba : Glossary of Cyber terms/Glosario de términos, Centro de Seguridad del CISberespaCISo</unstructured_citation></citation><citation key="ref42"><unstructured_citation>40.	http://www.cscuba.cu/es/glosario-de-terminos/A (2018)	</unstructured_citation></citation><citation key="ref43"><unstructured_citation>41.	India : workshop presentation by the NATIONAL CRITICAL INFORMATION INFRASTRUCTURE PROTECTION CENTRE (NCISIPC), 2015</unstructured_citation></citation><citation key="ref44"><unstructured_citation>42.	http://workshop.nkn.in/2015/sources/speakers/sessions/NKN_NCISIPC.pdf	</unstructured_citation></citation><citation key="ref45"><unstructured_citation>43.	Israel : https://ironscales.com/blog-how-machine-learning-can-stop-phishing-attacks-critical-infrastructure/(2023)	</unstructured_citation></citation><citation key="ref46"><unstructured_citation>44.	Japan:  The Information Security Policy CounCISl, The Second Action Plan on Information Security Measures for Critical Infrastructures, Japan (2009)	</unstructured_citation></citation><citation key="ref47"><unstructured_citation>45.	Kingdom of Saudi Arabia : Developing National Information Security Strategy for the Kingdom of Saudi Arabia NISS draft 7(2022)</unstructured_citation></citation><citation key="ref48"><unstructured_citation>46.	http://www.mCISt.gov.sa/Ar/MediaCenter/PubReqDocuments/NISS_Draft_7_EN.pdf	</unstructured_citation></citation><citation key="ref49"><unstructured_citation>47.	Calida, B. Y., &amp; Katina, P. F. (2012). Regional industries as critical infrastructures: A tale of two modern CISties. International Journal of Critical Infrastructures, 8(1), 74–90	</unstructured_citation></citation><citation key="ref50"><unstructured_citation>48.	Krimgold F, Bigger J, Willingham M, Mili L. Power systems, water, transportation and communications lifeline interdependencies, prepared for American lifeline alliance, March. 〈www.americanlifelinesalliance.org〉; 2006.	</unstructured_citation></citation><citation key="ref51"><unstructured_citation>49.	McDaniels T, Chang S, Peterson K, Mikawoz J, Reed D. Empirical framework for characterizing infrastructure failure interdependencies. Journal of Infrastructure Systems 2007;13(3):175–84.	</unstructured_citation></citation><citation key="ref52"><unstructured_citation>50.	McDaniels T, Chang S, Reed DA. Characterizing infrastructure failure interdependencies to inform systemic risk. Wiley Handbook of Science and Technology for Homeland Security 2008:1–16.	</unstructured_citation></citation><citation key="ref53"><unstructured_citation>51.	Conrad SH, LeClaire RJ, O′Reilly GP, Uzunalioglu H. Critical national infrastructure reliability modeling and analysis. Bell Labs Technical Journal 2006;11(3):57–71	</unstructured_citation></citation><citation key="ref54"><unstructured_citation>52.	Zimmerman R. Decision-making and the vulnerability of interdependent critical infrastructure. In: Proceedings of the 2004 IEEE international conference on systems, man and cybernetics; 2004, p. 4059–63	</unstructured_citation></citation><citation key="ref55"><unstructured_citation>53.	Utne IB, Hokstad P, Vatn J. A method for risk modeling of interdependenciesin critical infrastructures. Reliability Engineering and System Safety2011;96:671–8.	</unstructured_citation></citation><citation key="ref56"><unstructured_citation>54.	Kjølle GH, Utne IB, Gjerde O. Risk analysis of critical infrastructures emphasizing electricity supply and terdependencies. Reliability Engineering and System Safety 2012;105:80–9.	</unstructured_citation></citation><citation key="ref57"><unstructured_citation>55.	Basu N, Pryor R, Quint T, Arnold T. ASPEN: a micro-simulation model of the economy. Sandia report. SAND96-2459; 1996	</unstructured_citation></citation><citation key="ref58"><unstructured_citation>56.	Tolone WJ, Wilson D, Raja A, Xiang W, Hao H, Phelps S, et al. Critical infrastructure integration modeling and simulation. Intelligence and Security Informatics Lecture Notes in Computer Science 2004;3073:214–25, http://dx. doi.org/10.1007/978-3-540-25952-7_16	</unstructured_citation></citation><citation key="ref59"><unstructured_citation>57.	Ehlen MA, Scholand AJ. Modeling interdependencies between power and economic sectors using the N-ABLE agent based model. In: Proceedings of the IEEE conference on power systems. San Francisco; July 2005	</unstructured_citation></citation><citation key="ref60"><unstructured_citation>58.	Kelic A, Warren DE, Phillips LR. Cyber and physical infrastructure interdependencies. Sandia report, SAND2008-6192; 2008.	</unstructured_citation></citation><citation key="ref61"><unstructured_citation>59.	Barrett C, Beckman R, Channakeshava K, Huang F, Kumar VSA, Marathe A, et al.. Cascading failures in multiple infrastructures: From transportation to communication network. In: Proceedings of the fifth international CRIS conference on critical infrastructures. Beijing; 2010.	</unstructured_citation></citation><citation key="ref62"><unstructured_citation>60.	Fair JM, LeClaire RJ, Wilson ML, Turk AL, DeLand SM, Powell DR, et  l.. An integrated simulation of pandemic influenza evolution, mitigation and infrastructure response. In: Proceedings of the IEEE conference on technologies for homeland security; May 16–17, 2007.	</unstructured_citation></citation><citation key="ref63"><unstructured_citation>61.	Bush B, Dauelsberg L, LeClaire R, Powell D, DeLand S and Samsa M. Critical infrastructure protection decision support system (CIP/DSS) overview. Los Alamos National Laboratory Report LA-UR-05-1870, Los Alamos, NM 87544; 2005	</unstructured_citation></citation><citation key="ref64"><unstructured_citation>62.	Min HJ, Beyeler W, Brown T, Son YJ, Jones AT. Toward modeling and simulation of critical national infrastructure interdependencies. IEEE Transactions 2007;39:57–71.	</unstructured_citation></citation><citation key="ref65"><unstructured_citation>63.	Santella N, Steinberg LJ, Parks K. Decision making for extreme events: modeling critical infrastructure nterdependencies to aid mitigation and response planning. Review of Policy Research 2009;26(4):409–22.	</unstructured_citation></citation><citation key="ref66"><unstructured_citation>64.	Santos JR, Haimes YY. Modeling the demand reduction input–output (I–O) inoperability due to terrorism of interconnected infrastructures. Risk Analysis 2004	</unstructured_citation></citation><citation key="ref67"><unstructured_citation>65.	Pant R, Barker K, Grant FH, Landers TL. Interdependent impacts of interoperability at multi-modal transportation container terminals. Transportation Research Part E 2011;47:722–37.	</unstructured_citation></citation><citation key="ref68"><unstructured_citation>66.	Jung J, Santos JR, Haimes YY. International trade inoperability input–output model (IT-IIM): theory and application. Risk Analysis 2009;29(1):137–53.	</unstructured_citation></citation><citation key="ref69"><unstructured_citation>67.	Crowther KG, Haimes YY. Development of the multiregional inoperability input–output model (MRIIM) for spatial explicitness in preparedness of interdependent regions. Systems Engineering 2010;13(1):28–46.	</unstructured_citation></citation><citation key="ref70"><unstructured_citation>68.	Cagno E, Ambroggi MD, Grande O, Trucco P. Risk analysis of underground infrastructures in urban areas. Reliability Engineering and System Safety 2011;96:139–48.	</unstructured_citation></citation><citation key="ref71"><unstructured_citation>69.	Cavdaroglu B, Mitchell JE, Sharkey TC, Wallace WA. Integrating restoration and scheduling decisions for disrupted interdependent infrastructure systems. Annals of Operations Research 2013;203:279–94.	</unstructured_citation></citation><citation key="ref72"><unstructured_citation>70.	Patterson SA, Apostolakis GE. Identification of critical locations across multiple infrastructures for terrorist actions. Reliability Engineering and System Safety 2007;92:1183–203	</unstructured_citation></citation><citation key="ref73"><unstructured_citation>71.	Eusgeld I, Nan C. Creating a simulation environment for critical infrastructure interdependencies study. In: .Proceedings of the IEEE international.conference on industrial engineering and engineering management (IEEM); 2009, p. 2104–8.	</unstructured_citation></citation><citation key="ref74"><unstructured_citation>72.	Eusgeld I, Nan C, Dietz S. System-of systems approach for interdependent critical infrastructures. Reliability Engineering and System Safety 2011;96:679–86.	</unstructured_citation></citation><citation key="ref75"><unstructured_citation>73.	Cooper, D.R., &amp; Schindler, P.S. (2006). Business Research Methods. McGraw- Hill/Irwin, New York	</unstructured_citation></citation><citation key="ref76"><unstructured_citation>74.	Fisher, E. (2011). What practitioners consider to be the skills and behaviors of an effective people project manager. Int. J. Proj. Manag. 29, 994–1002.	</unstructured_citation></citation><citation key="ref77"><unstructured_citation>75.	Nan Li a,b, , Fei Wang a, Joseph Jonathan Magoua a, Dongping Fang (2022) .Interdependent effects of critical infrastructure systems under different types of disruptions.</unstructured_citation></citation><citation key="ref78"><unstructured_citation>76.	2212-4209/© 2022 Elsevier Ltd. All rights reserved. https://doi.org/10.1016/j.ijdrr.2022.103266	</unstructured_citation></citation><citation key="ref79"><unstructured_citation>77.	Fei Wang a, Joseph Jonathan Magoua a, Nan L .(2022) Modeling cascading failure of interdependent critical infrastructure systems using HLA-based co-simulation, 0926-5805/© 2021 Elsevier B.V. All rights reserved.</unstructured_citation></citation><citation key="ref80"><unstructured_citation>78.	https://doi.org/10.1016/j.autcon.2021.104008	</unstructured_citation></citation><citation key="ref81"><unstructured_citation>79.	Deniz Berfin Karakoc , Kash Barker *, Andr ́es D. Gonz ́alez. (2023). Analyzing the tradeoff between vulnerability and recoverability investments for interdependent infrastructure networks, 0038-0121/© 2023 Elsevier Ltd. All rights reserved. https://doi.org/10.1016/j.seps.2023.101508	</unstructured_citation></citation><citation key="ref82"><unstructured_citation>80.	May Haggag, Mohamed Ezzeldin, Wael El-Dakhakhni and Elkafi Hassini, Resilient cities critical infrastructure interdependence: a meta-research , © 2020 Informa UK Limited, trading as Taylor &amp; Francis Group https://doi.org/10.1080/23789689.2020.179	</unstructured_citation></citation><citation key="ref83"><unstructured_citation>81.	Lee, E., Mitchell, J., &amp; Wallace, W. (2007). Restoration of services in interdependent infrastructure systems: A network flows approach. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 37 (6), 1303–1317. doi:10.1109/TSMCC.2007.905859</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>The Belief of Persian Text Mining Based on Deep Learning with Emotion-Word Separation</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Hossein</given_name><surname>Alikarami</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>AmirMasoud</given_name><surname>Bidgoli</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Hamid</given_name><surname>Haj Seyyed Javadi</surname></person_name></contributors><publication_date media_type="online"><month>12</month><day>7</day><year>2024</year></publication_date><pages><first_page>20</first_page><last_page>36</last_page></pages><doi_data><doi>10.66224/jict.43027.16.61.20</doi><resource>http://jour.aicti.ir/en/Article/43027</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/en/Article/Download/43027</resource></item><item crawler="google"><resource>http://jour.aicti.ir/en/Article/Download/43027</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/en/Article/Download/43027</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/en/Article/Download/43027</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/en/Article/Download/43027</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/en/Article/Download/43027</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/en/Article/Download/43027</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>1.	Abid, F.; Alam, M.; Yasir, M.; Li, C.J. Sentiment analysis through recurrent variants latterly on convolutional neural network of Twitter. Future Gener. Comput. Syst. 2019, 95, 292–308.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>2.	 Alharbi, A.S.M.; de Doncker, E. Twitter sentiment analysis with a deep neural network: An enhanced approach using user behavioral information. Cogn. Syst. Res. 2019, 54, 50–61. </unstructured_citation></citation><citation key="ref3"><unstructured_citation>3.	H. Alikarami, A. M. Bidgoli and H. H. S. Javadi, (2023), "Belief Mining in Persian Texts Based on Deep Learning and Users' Opinions (revised December 2022)," in IEEE Transactions on Affective Computing, doi: 10.1109/TAFFC.2023.3288407.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>4.	Alikarami, H. and Khadem, F., (2016), Data Mining Using Genetic Algorithms and Cellular Learning Automata Based on Factor Analysis and Cluster Analysis, 1stInternational Conference on New Research Achievements in Electrical and Computer Engineering, Tehran, Iran.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>5.	Available online: http://alt.qcri.org/semeval2017/ (accessed on 12 March 2020).</unstructured_citation></citation><citation key="ref6"><unstructured_citation>6.	Available online: http://help.sentiment140.com/site-functionality (accessed on 12 March 2020).</unstructured_citation></citation><citation key="ref7"><unstructured_citation>7.	Available online: http://www.cs.cornell.edu/people/pabo/movie-review-data/ (accessed on 12 March 2020).</unstructured_citation></citation><citation key="ref8"><unstructured_citation>8.	Available online: https://www.kaggle.com/c/word2vec-nlp-tutorial/data (accessed on 12 March 2020).</unstructured_citation></citation><citation key="ref9"><unstructured_citation>9.	Available online: https://www.kaggle.com/crowdflower/twitter-airline-sentiment (accessed on 12March 2020).</unstructured_citation></citation><citation key="ref10"><unstructured_citation>10.	Barushka, A., Hajek, P.: Spam detection on social networks using cost-sensitive feature selection and ensemble-based regularized deep neural networks. Neural Comput. Appl. 1–19 (2020) </unstructured_citation></citation><citation key="ref11"><unstructured_citation>11.	Basiri, M. E., Nilchi, A. R. N. &amp; Ghassem-aghaee, N., (2014). A Framework for Sentiment Analysis in Persian.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>12.	Basiri, M.E. and kabiri, A., (2018), Words Are Important: Improving Sentiment Analysis in the Persian Language by Lexicon Refining, ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), Vol 17(4), pp. 1145-1154. </unstructured_citation></citation><citation key="ref13"><unstructured_citation>13.	Cach Dang, N. , Moreno-García, M.N. and De la Prieta, F., (2020), Sentiment Analysis Based on Deep Learning: A Comparative Study, Electronics 2020, 9, 483; doi:10.3390/electronics9030483.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>14.	Catal, C., Nangir, M.: A sentiment classification model based on multiple classifiers. Appl. Soft Comput. 50, 135–141 (2017) </unstructured_citation></citation><citation key="ref15"><unstructured_citation>15.	Chen, X., Xue, Y., Zhao, H., Lu, X., Hu, X., Ma, Z.: A novel feature extraction methodology for sentiment analysis of product reviews. Neural Comput. Appl. 31(10), 6625–6642 (2019) </unstructured_citation></citation><citation key="ref16"><unstructured_citation>16.	Chen, Z.; Liu, B. Lifelong machine learning. Synth. Lect. Artif. Intell. Mach. Learn. 2018, 12, 1–207. [CrossRef]</unstructured_citation></citation><citation key="ref17"><unstructured_citation>17.	Dashtipour, K. et al., (2018). Exploiting Deep Learning for Persian Sentiment Analysis. s.l., s.n.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>18.	Dastgheib, M.B. and Koleini, S., (2019), Persian Text Classification Enhancement by Latent Semantic Space,  International Journal of Information Science and Management, Vol 17(1), pp. 33-46.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>19.	Do, H.H., Prasad, P.W.C., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: a comparative review. Expert Syst. Appl. 118, 272–299 (2019)</unstructured_citation></citation><citation key="ref20"><unstructured_citation>20.	Do, H.H.; Prasad, P.; Maag, A.; Alsadoon, A.J. Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review. Expert Syst. Appl. 2019, 118, 272–299. [CrossRef]</unstructured_citation></citation><citation key="ref21"><unstructured_citation>21.	Du, C. and Huang, L., (2018), Text Classification Research with Attention-based Recurrent Neural Networks, International Journal of Computers Communications &amp; Control, ISSN 1841-9836, 13(1),pp. 50-61.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>22.	Fang, Y., Tan, H. and Zhang, J., (2018), Multi-Strategy Sentiment Analysis of Consumer Reviews Based on Semantic Fuzziness, IEEE. Translations and content mining are permitted for academic research only, Vol 6, pp.20625-20631.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>23.	Ferrara, E., Varol, O., Davis, C., Menczer, F., and Flammini, A., (2016), ‘‘The rise of social bots,’’ Commun. ACM, vol. 59, no. 7, pp. 96–104.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>24.	Hajek P., Barushka A., Munk M. (2020) Opinion Mining of Consumer Reviews Using Deep Neural Networks with Word-Sentiment Associations. In: Maglogiannis I., Iliadis L., Pimenidis E. (eds) Artificial Intelligence Applications and Innovations. AIAI 2020. IFIP Advances in Information and Communication Technology, vol 583. Springer, Cham. https://doi.org/10.1007/978-3-030-49161-1_35.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>25.	Hassan, A. and Mahmood, A., (2018), Convolutional Recurrent Deep Learning Model for Sentence Classification, IEEE, Vol 6, pp. 13949 – 13957.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>26.	Hosseini, P. et al., 2018. SentiPers: A Sentiment Analysis Corpus for Persian. arXiv.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>27.	Jason Wei and Kai Zou. 2019. EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks. arXiv e-prints, page arXiv:1901.11196.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>28.	Jeong, B.; Yoon, J.; Lee, J.-M. Social media mining for product planning: A product opportunity mining approach based on topic modeling and sentiment analysis. Int. J. Inf. Manag. 2019, 48, 280–290. [CrossRef] </unstructured_citation></citation><citation key="ref29"><unstructured_citation>29.	Johnson, R., Zhang, T.: Effective use of word order for text categorization with convolutional neural networks. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 103–112 (2015)</unstructured_citation></citation><citation key="ref30"><unstructured_citation>30.	Joseph Turian, Lev-Arie Ratinov, and Yoshua Bengio. 2010. Word representations: A simple and general method for semi-supervised learning. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 384–394, Uppsala, Sweden. Association for Computational Linguistics.</unstructured_citation></citation><citation key="ref31"><unstructured_citation>31.	Kausar, S., Huahu, X., Shabir, M.Y., Ahmad, W.: A sentiment polarity categorization technique for online product reviews. IEEE Access 8, 3594–3605 (2019) </unstructured_citation></citation><citation key="ref32"><unstructured_citation>32.	Kim, Y., 2014. Convolutional Neural Networks for Sentence Classification. Doha, Qatar, s.n.</unstructured_citation></citation><citation key="ref33"><unstructured_citation>33.	Kraus, M.; Feuerriegel, S. Sentiment analysis based on rhetorical structure theory: Learning deep neural networks from discourse trees. Expert Syst. Appl. 2019, 118, 65–79. </unstructured_citation></citation><citation key="ref34"><unstructured_citation>34.	Kumar, S.; Gahalawat, M.; Roy, P.P.; Dogra, D.P.; Kim, B.-G.J.E. Exploring Impact of Age and Gender on Sentiment Analysis Using Machine Learning. Electronics 2020, 9, 374. </unstructured_citation></citation><citation key="ref35"><unstructured_citation>35.	LeCun, Y., Bengio, Y. &amp; Hinton, G., 2015. Deep learning. Nature, Volume 521, pp. 436-444.</unstructured_citation></citation><citation key="ref36"><unstructured_citation>36.	Li, L.; Goh, T.-T.; Jin, D. How textual quality of online reviews a_ect classification performance: A case of deep learning sentiment analysis. Neural Comput. Appl. 2018, 1–29. </unstructured_citation></citation><citation key="ref37"><unstructured_citation>37.	Liu, B., 2012. Sentiment Analysis and Opinion Mining. Synthesis lectures on human language technologies, pp. 1-167.</unstructured_citation></citation><citation key="ref38"><unstructured_citation>38.	Maas, A.L.; Daly, R.E.; Pham, P.T.; Huang, D.; Ng, A.Y.; Potts, C. Learning word vectors for sentiment analysis. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, Portland, OR, USA, 19–24 June 2011; pp. 142–150.</unstructured_citation></citation><citation key="ref39"><unstructured_citation>39.	Marzieh Fadaee, Arianna Bisazza, and Christof Monz. 2017. Data Augmentation for Low-Resource Neural Machine Translation. arXiv e-prints, page arXiv:1705.00440.</unstructured_citation></citation><citation key="ref40"><unstructured_citation>40.	Mousavirad, S.J. and Ebrahimpour-Komleh, H., (2014), Wrapper Feature Selection using Discrete Cuckoo Optimization Algorithm,  Austrian E-Journals of Universal Scientific Organization, Vol. 4(11), Apr, pp. 709-721.</unstructured_citation></citation><citation key="ref41"><unstructured_citation>41.	Onan, A.: Deep learning based sentiment analysis on product reviews on Twitter. In: Younas, M., Awan, I., Benbernou, S. (eds.) Innovate-Data 2019. CCIS, vol. 1054, pp. 80–91. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-27355-2_6</unstructured_citation></citation><citation key="ref42"><unstructured_citation>42.	Piryani, R., Madhavi, D. and Singh, V.K., (2017), “Analytical mapping of opinion mining and sentiment analysis research during 2000–2015,” Information Processing &amp; Management, vol. 53, no. 1, pp. 122–150.</unstructured_citation></citation><citation key="ref43"><unstructured_citation>43.	 Qiu, L. and Li, J., (2018), Sentiment analysis of short texts in microblog based on ependency parsing, springer: Cluster Computing,  Volume 21, Issue 1, pp 985-995.</unstructured_citation></citation><citation key="ref44"><unstructured_citation>44.	Roustaei, A. and Rastegari, H., (2018), Persian question classification using headword and semantic features, IEEE, Journal of Theoretical and Applied Information Technology, Vol 96(21), pp. 7206-7214.</unstructured_citation></citation><citation key="ref45"><unstructured_citation>45.	Schmitt, M.; Steinheber, S.; Schreiber, K.; Roth, B. Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks. arXiv 2018, arXiv:1808.09238.</unstructured_citation></citation><citation key="ref46"><unstructured_citation>46.	Shams, M., Shakery, A. &amp; Faili, H., (2012). A non-parametric LDA-based induction method for sentiment analysis. Shiraz, Iran, s.n.</unstructured_citation></citation><citation key="ref47"><unstructured_citation>47.	Shayaa, S. and et al., (2018), Sentiment Analysis of Big Data: Methods, Applications, and Open Challenges, IEEE. Translations and content mining are permitted for academic research only,Vol 6, pp. 37807-37827.</unstructured_citation></citation><citation key="ref48"><unstructured_citation>48.	Singh, V.K.; Mukherjee, M.; Mehta, G.K. Combining collaborative filtering and sentiment classification for improved movie recommendations. In Proceedings of the International Workshop on Multi-disciplinary Trends in Artificial Intelligence, Hyderabad, India, 7–9 December 2011; pp. 38–50.</unstructured_citation></citation><citation key="ref49"><unstructured_citation>49.	 Singhal, P.; Bhattacharyya, P. Sentiment Analysis and Deep Learning: A Survey; Center for Indian Language Technology, Indian Institute of Technology: Bombay, Indian, 2016.</unstructured_citation></citation><citation key="ref50"><unstructured_citation>50.	Sohrabi, M.K. and Roshani, R., (2017), Frequent itemset mining using cellular learning automata, Computers in Human Behavior, Vol 68, pp. 244-253.</unstructured_citation></citation><citation key="ref51"><unstructured_citation>51.	Stai, E.; Kafetzoglou, S.; Tsiropoulou, E.E.; Papavassiliou, S.J. A holistic approach for personalization, relevance feedback &amp; recommendation in enriched multimedia content. Multimed. Tools Appl. 2018, 77, 283–326.</unstructured_citation></citation><citation key="ref52"><unstructured_citation>52.	Tang, D., Qin, B., Liu, T.: Document modelling with gated recurrent neural network for sentiment classification. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1422–1432 (2015)</unstructured_citation></citation><citation key="ref53"><unstructured_citation>53.	Urologin, S., (2018), Sentiment Analysis Visualization and Classification of Summarized News Articles: A Novel Approach, (IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 9, No. 8, pp. 616-624.</unstructured_citation></citation><citation key="ref54"><unstructured_citation>54.	Wang, Y.; Wang, M.; Xu, W. A sentiment-enhanced hybrid recommender system for movie recommendation A big data analytics framework. Wirel. Commun. Mob. Comput. 2018, 2018. [CrossRef]</unstructured_citation></citation><citation key="ref55"><unstructured_citation>55.	Woolley, S.C., (2016), ‘‘Automating power: Social bot interference in global politics,’’ First Monday, vol. 21, no. 4.</unstructured_citation></citation><citation key="ref56"><unstructured_citation>56.	Wu, C.; Wu, F.; Wu, S.; Yuan, Z.; Liu, J.; Huang, Y. Semi-supervised dimensional sentiment analysis with variational autoencoder. Knowl. Based Syst. 2019, 165, 30–39. </unstructured_citation></citation><citation key="ref57"><unstructured_citation>57.	Yang, C.; Zhang, H.; Jiang, B.; Li, K.J. Aspect-based sentiment analysis with alternating coattention networks. Inf. Process. Manag. 2019, 56, 463–478. [CrossRef]</unstructured_citation></citation><citation key="ref58"><unstructured_citation>58.	Yao, Q.Z., Song, Z.L. and Peng, C., (2011), Research on text categorization based on LDA, Computer Engineering and Applications, Vol 47(13), pp. 150–153.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Trust Based Link Prediction Using Fuzzy Computational Model in Social Networks</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Fateme</given_name><surname>Hoseinkhani</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Ali</given_name><surname>Harounabadi</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>ُُُُSaeed</given_name><surname>Setayeshi</surname></person_name></contributors><publication_date media_type="online"><month>12</month><day>7</day><year>2024</year></publication_date><pages><first_page>229</first_page><last_page>246</last_page></pages><doi_data><doi>10.66224/jict.43131.16.61.229</doi><resource>http://jour.aicti.ir/en/Article/43131</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/en/Article/Download/43131</resource></item><item crawler="google"><resource>http://jour.aicti.ir/en/Article/Download/43131</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/en/Article/Download/43131</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/en/Article/Download/43131</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/en/Article/Download/43131</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/en/Article/Download/43131</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/en/Article/Download/43131</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	X.Li, H.Fang, and J.Zhang, “FILE: A novel framework for predicting social status in signed networks”, Thirty-Second AAAI Conference on Artificial Intelligence, AAAI18 - Artificial Intelligence and the Web, Vol.32, No.1, 2018, PP.330–337. </unstructured_citation></citation><citation key="ref2"><unstructured_citation>
[2]	K.Akilal, M.Omar, and H.Slimani, “Characterizing and using gullibility, competence, and reciprocity in a very fast and robust trust and distrust inference algorithm for weighted signed social networks”, Knowledge-Based Systems, Vol.192, 2020, PP.1-11. </unstructured_citation></citation><citation key="ref3"><unstructured_citation>
[3]	H.Shirgahi, M.Mohsenzadeh, and H.H.S.Javadi, “A new method of trust mirroring estimation based on social networks parameters by fuzzy system”, International Journal Machine Learning &amp; Cybernetics, Springer. Vol.9, 2018, PP.1153–1168. </unstructured_citation></citation><citation key="ref4"><unstructured_citation>
[4]	V.Kant, and KK.Bharadwaj, Fuzzy computational models of trust and distrust for enhanced recommendations, International Journal of Intelligent Systems, Vol.28, 2013, PP.332–365. </unstructured_citation></citation><citation key="ref5"><unstructured_citation>
[5]	N.Girdhar, S.Minz, and K.K.Bharadwaj, “Link prediction in signed social networks based on fuzzy computational model of trust and distrust”, Soft Computing, Vol.23, 2019, PP.12123–12138. </unstructured_citation></citation><citation key="ref6"><unstructured_citation>
[6]	Z.Duan, W.Xu, Y.Chen, and L.Ding, “ETBRec: a novel recommendation algorithm combining the double influence of trust relationship and expert users”, Applied Intelligence, Vol.52, 2022, PP.282–294.  </unstructured_citation></citation><citation key="ref7"><unstructured_citation>
[7]	N.D.Nur, A.H.Sitil, S.Muntadher, S.Firdaus, and N.Anuar, “Applications of link prediction in social networks: A review”, Journal of Network and Computer Appllicatins, Vol.166, 2020, PP.1-31. </unstructured_citation></citation><citation key="ref8"><unstructured_citation>
[8]	X.Zhu, and Y.Ma, “Sign Prediction on Social Networks Based Nodal Features”, Journal of Complexity, Vol.2020, 2020, PP.1-11. </unstructured_citation></citation><citation key="ref9"><unstructured_citation>
[9]	R.E.Veras De Sena Rosa, F.A.S.Guimarães, R.d.S.Mendonça and V.F.d.Lucena, “Improving Prediction Accuracy in Neighborhood-Based Collaborative Filtering by Using Local Similarity”, IEEE Access, Vol.8, 2020, PP.142795-142809. </unstructured_citation></citation><citation key="ref10"><unstructured_citation>
[10]	H.Ghorbanzadeh, A.Sheikhahmadi, M.Jalili, and S.Sulaimany, “A Hybrid Method of Link Prediction in Directed Graphs”, Expert Systems with Applications, Vol.165, 2020, PP.1-13. </unstructured_citation></citation><citation key="ref11"><unstructured_citation>
[11]	D.Wang, Da-wei, Y.Yih and M.Ventresca, “Improving neighbor-based collaborative filtering by using a hybrid similarity measurement”, Expert Systems with Applications, Vol.160, 2020, PP.1-23. </unstructured_citation></citation><citation key="ref12"><unstructured_citation>
[12]	X.Wang, Y.Chai, H.Li, and D.Wu, “Link prediction in heterogeneous information networks: An improved deep graph convolution approach”, Decision Support Systems, Vol.141, 2021, PP.113448-113460. </unstructured_citation></citation><citation key="ref13"><unstructured_citation>
[13]	H.Tahmasbi, M.Jalali, and H.Shakeri, “TSCMF: Temporal and social collective matrix factorization model for recommender systems”, Journal of Intelligence Information Systems, Vol.56, 2021, PP.169–187. </unstructured_citation></citation><citation key="ref14"><unstructured_citation>
[14]	C.He, H.Liu, Y.Tang, S.Liu, X.Fei, Q.Cheng, and H.Li, “Similarity preserving overlapping community detection in signed networks”, Future Generation Computer Systems, Vol.116, 2021, PP.275-290. </unstructured_citation></citation><citation key="ref15"><unstructured_citation>
[15]	R.I.Yaghi, H.Faris, I.Aljarah, A.M.Al-Zoubi, A.A.Heidari, and S.Mirjalili, “Link Prediction Using Evolutionary Neural Network Models”, Evolutionary Machine Learning Techniques, Vol.32, 2020, PP.85-112. </unstructured_citation></citation><citation key="ref16"><unstructured_citation>
[16]	R.E.Tillman, P.Vamsi, Ch.Jiahao, R.Prashant and M.Veloso, “Heuristics for Link Prediction in Multiplex Networks”, In Proceedings of ECAI'2020, 24th European Conference on Artificial Intelligence, Vol.325, 2020, PP.1938-1945. </unstructured_citation></citation><citation key="ref17"><unstructured_citation>
[17]	F.Guo, W.Zhou, Z.Wang, Ch.Ju, Sh.Ji, Q.Lu, "A link prediction method based on topological nearest-neighbors similarity in directed networks", Journal of Computational Science, Vol.69, 2023, PP.102002-102016. </unstructured_citation></citation><citation key="ref18"><unstructured_citation>
[18]	H.Liu, Z.Zhenzhen, B.Fan, H.Zeng, Y.Zhang, and G.Jiang, “PrGCN: Probability prediction with graph convolutional network for person re-identification”, Neurocomputing, Vol.423, 2021, PP.57-70. </unstructured_citation></citation><citation key="ref19"><unstructured_citation>
[19]	X.Hu, X.Xiong, Y.Wu, M.Shi, P.Wei, and Ch.Ma, "A Hybrid Clustered SFLA-PSO algorithm for optimizing the timely and real-time rumor refutations in Online Social Networks", Expert Systems with Applications, Vol.212, 2023, PP.118638-118670. </unstructured_citation></citation><citation key="ref20"><unstructured_citation>
[20]	Y.Xu, Z.Feng, X.Zhou, M.Xing, H.Wu, X.Xue, Sh.Chen, Ch.Wang and L.Qi, "Attention-based neural networks for trust evaluation in online social networks", Information Sciences, Vol.630, 2023, PP.507-522. </unstructured_citation></citation><citation key="ref21"><unstructured_citation>
[21]	M.Nooraei.Abadeh, M.Mirzaie, “A differential machine learning approach for trust prediction in signed social networks”, Supercomput, Vol.79, 2023, PP.9443–9466. </unstructured_citation></citation><citation key="ref22"><unstructured_citation>
[22]	T.Zhang, W.Li, L.Wang, and J.Yang, “Social recommendation algorithm based on stochastic gradient matrix decomposition in social network”, Journal of Ambient Intelligence and Humanized Computing. Vol.11, 2020, PP.601-608. </unstructured_citation></citation><citation key="ref23"><unstructured_citation>
[23]	P.Srilatha, R.Manjula, and C.P.Kumar, “Link Prediction on Social Attribute Network Using Lévy Flight Firefly Optimization”, Advances in Artificial Intelligence and Data Engineering,  Vol.1133, 2021, PP.1299-1309. </unstructured_citation></citation><citation key="ref24"><unstructured_citation>
[24]	E.Nasiri, K.Berahmand, and Y.Li, “Robust graph regularization nonnegative matrix factorization for link prediction in attributed networks”, Multimedia Tools and Applications, Vol.82, 2023, PP.3745–3768. </unstructured_citation></citation><citation key="ref25"><unstructured_citation>
[25]	S.Ghasemi, and A.Zarei., “Improving link prediction in social networks using local and global features: a clustering-based approach, Progress in Artificial”, Intelligence, Vol.11, 2022,  PP.79–92. </unstructured_citation></citation><citation key="ref26"><unstructured_citation>
[26]	Suryakant, and T.Mahara, A New Similarity Measure Based on Mean Measure of Divergence for Collaborative Filtering in Sparse Environment, Procedia Computer Science, Vol.89, 2016, PP.450–456. </unstructured_citation></citation><citation key="ref27"><unstructured_citation>
[27]	https://snap.stanford.edu/data/soc-Epinions1. html,Last Visited (01, October. 2022). </unstructured_citation></citation><citation key="ref28"><unstructured_citation>
[28]	http://snap.stanford.edu/data/soc-Slashdot0902.html, Last Visited (01, October.2022). </unstructured_citation></citation><citation key="ref29"><unstructured_citation>
[29]	J.Golbeck, “Combining provenance with trust in social networks for semantic content filtering”, International Provenance and Annotation Workshop, Vol.4145, 2006, PP.101–108. </unstructured_citation></citation><citation key="ref30"><unstructured_citation>


</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Energy-Efficient Fixed-Point Hardware Accelerator for Embedded DNNs</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Marzie</given_name><surname>Mastalizade</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Ali</given_name><surname>Ansarmohammadi</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Najme</given_name><surname>Nazari</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Mostafa</given_name><surname>Salehi</surname></person_name></contributors><publication_date media_type="online"><month>12</month><day>7</day><year>2024</year></publication_date><pages><first_page>37</first_page><last_page>54</last_page></pages><doi_data><doi>10.66224/jict.43395.16.61.37</doi><resource>http://jour.aicti.ir/en/Article/43395</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/en/Article/Download/43395</resource></item><item crawler="google"><resource>http://jour.aicti.ir/en/Article/Download/43395</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/en/Article/Download/43395</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/en/Article/Download/43395</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/en/Article/Download/43395</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/en/Article/Download/43395</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/en/Article/Download/43395</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	V. Sze, Y.-H. Chen, T.-J. Yang, and J. S. Emer, "Efficient processing of deep neural networks: A tutorial and survey," Proceedings of the IEEE, vol. 105, no. 12, pp. 2295-2329, 2017.
</unstructured_citation></citation><citation key="ref2"><unstructured_citation>
[2]	Y. He, P. Liu, Z. Wang, Z. Hu, and Y. Yang, "Filter pruning via geometric median for deep convolutional neural networks acceleration," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 4340-4349. 
</unstructured_citation></citation><citation key="ref3"><unstructured_citation>
[3]	Z. Zhuang et al., "Discrimination-aware channel pruning for deep neural networks," Advances in neural information processing systems, vol. 31, 2018.
</unstructured_citation></citation><citation key="ref4"><unstructured_citation>
[4]	Z. Liu, M. Sun, T. Zhou, G. Huang, and T. Darrell, "Rethinking the value of network pruning," arXiv preprint arXiv:1810.05270, 2018.
</unstructured_citation></citation><citation key="ref5"><unstructured_citation>
[5]	C. Baskin et al., "Nice: Noise injection and clamping estimation for neural network quantization," Mathematics, vol. 9, no. 17, p. 2144, 2021.
</unstructured_citation></citation><citation key="ref6"><unstructured_citation>
[6]	Y. Bhalgat, J. Lee, M. Nagel, T. Blankevoort, and N. Kwak, "Lsq+: Improving low-bit quantization through learnable offsets and better initialization," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020, pp. 696-697. 
</unstructured_citation></citation><citation key="ref7"><unstructured_citation>
[7]	S.-E. Chang et al., "RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions," in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 5251-5260. 
</unstructured_citation></citation><citation key="ref8"><unstructured_citation>
[8]	J. Choi, Z. Wang, S. Venkataramani, P. I.-J. Chuang, V. Srinivasan, and K. Gopalakrishnan, "Pact: Parameterized clipping activation for quantized neural networks," arXiv preprint arXiv:1805.06085, 2018.
</unstructured_citation></citation><citation key="ref9"><unstructured_citation>
[9]	M. Courbariaux, Y. Bengio, and J.-P. David, "Training deep neural networks with low precision multiplications," arXiv preprint arXiv:1412.7024, 2014.
</unstructured_citation></citation><citation key="ref10"><unstructured_citation>
[10]	Z. Dong, Z. Yao, A. Gholami, M. W. Mahoney, and K. Keutzer, "Hawq: Hessian aware quantization of neural networks with mixed-precision," in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 293-302. 
</unstructured_citation></citation><citation key="ref11"><unstructured_citation>
[11]	S. K. Esser, J. L. McKinstry, D. Bablani, R. Appuswamy, and D. S. Modha, "Learned step size quantization," arXiv preprint arXiv:1902.08153, 2019.
</unstructured_citation></citation><citation key="ref12"><unstructured_citation>
[12]	M. Ghasemzadeh, M. Samragh, and F. Koushanfar, "ReBNet: Residual binarized neural network," in 2018 IEEE 26th annual international symposium on field-programmable custom computing machines (FCCM), 2018: IEEE, pp. 57-64. 
</unstructured_citation></citation><citation key="ref13"><unstructured_citation>
[13]	T. Chen et al., "Diannao: A small-footprint high-throughput accelerator for ubiquitous machine-learning," ACM SIGARCH Computer Architecture News, vol. 42, no. 1, pp. 269-284, 2014.
</unstructured_citation></citation><citation key="ref14"><unstructured_citation>
[14]	Y. Chen et al., "Dadiannao: A machine-learning supercomputer," in 2014 47th Annual IEEE/ACM International Symposium on Microarchitecture, 2014: IEEE, pp. 609-622. 
</unstructured_citation></citation><citation key="ref15"><unstructured_citation>
[15]	P. Gysel, J. Pimentel, M. Motamedi, and S. Ghiasi, "Ristretto: A framework for empirical study of resource-efficient inference in convolutional neural networks," IEEE transactions on neural networks and learning systems, vol. 29, no. 11, pp. 5784-5789, 2018.
</unstructured_citation></citation><citation key="ref16"><unstructured_citation>
[16]	P. Gysel, M. Motamedi, and S. Ghiasi, "Hardware-oriented approximation of convolutional neural networks," arXiv preprint arXiv:1604.03168, 2016.
</unstructured_citation></citation><citation key="ref17"><unstructured_citation>
[17]	S. Sharify et al., "Laconic deep learning inference acceleration," in 2019 ACM/IEEE 46th Annual International Symposium on Computer Architecture (ISCA), 2019: IEEE, pp. 304-317. 
</unstructured_citation></citation><citation key="ref18"><unstructured_citation>
[18]	S. Ghodrati, H. Sharma, C. Young, N. S. Kim, and H. Esmaeilzadeh, "Bit-parallel vector composability for neural acceleration," in 2020 57th ACM/IEEE Design Automation Conference (DAC), 2020: IEEE, pp. 1-6. 
</unstructured_citation></citation><citation key="ref19"><unstructured_citation>
[19]	S. Jung et al., "Learning to quantize deep networks by optimizing quantization intervals with task loss," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 4350-4359. 
</unstructured_citation></citation><citation key="ref20"><unstructured_citation>
[20]	C. Gong et al., "µl2q: An ultra-low loss quantization method for DNN compression," in 2019 International Joint Conference on Neural Networks (IJCNN), 2019: IEEE, pp. 1-8. 
</unstructured_citation></citation><citation key="ref21"><unstructured_citation>
[21]	R. Gong et al., "Differentiable soft quantization: Bridging full-precision and low-bit neural networks," in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 4852-4861. 
</unstructured_citation></citation><citation key="ref22"><unstructured_citation>
[22]	M. Nikolić et al., "Bitpruning: Learning bitlengths for aggressive and accurate quantization," arXiv preprint arXiv:2002.03090, 2020.
</unstructured_citation></citation><citation key="ref23"><unstructured_citation>
[23]	N. Nazari, M. Loni, M. E. Salehi, M. Daneshtalab, and M. Sjodin, "Tot-net: An endeavor toward optimizing ternary neural networks," in 2019 22nd Euromicro Conference on Digital System Design (DSD), 2019: IEEE, pp. 305-312. 
</unstructured_citation></citation><citation key="ref24"><unstructured_citation>
[24]	S. A. Mirsalari, N. Nazari, S. A. Ansarmohammadi, M. E. Salehi, and S. Ghiasi, "E2BNet: MAC-free yet accurate 2-level binarized neural network accelerator for embedded systems," Journal of Real-Time Image Processing, vol. 18, pp. 1285-1299, 2021.
</unstructured_citation></citation><citation key="ref25"><unstructured_citation>
[25]	S. A. Mirsalari, N. Nazari, S. A. Ansarmohammadi, S. Sinaei, M. E. Salehi, and M. Daneshtalab, "ELC-ECG: Efficient LSTM Cell for ECG classification based on quantized architecture," in 2021 IEEE International Symposium on Circuits and Systems (ISCAS), 2021: IEEE, pp. 1-5. 
</unstructured_citation></citation><citation key="ref26"><unstructured_citation>
[26]	M. E. Salehi, "Binary neural networks," 2020.
</unstructured_citation></citation><citation key="ref27"><unstructured_citation>
[27]	N. Nazari, S. A. Mirsalari, S. Sinaei, M. E. Salehi, and M. Daneshtalab, "Multi-level binarized lstm in eeg classification for wearable devices," in 2020 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), 2020: IEEE, pp. 175-181. 
</unstructured_citation></citation><citation key="ref28"><unstructured_citation>
[28]	S. Gupta, A. Agrawal, K. Gopalakrishnan, and P. Narayanan, "Deep learning with limited numerical precision," in International conference on machine learning, 2015: PMLR, pp. 1737-1746. 
</unstructured_citation></citation><citation key="ref29"><unstructured_citation>
[29]	F. Asim, J. Park, A. Azamat, and J. Lee, "Centered Symmetric Quantization for Hardware-Efficient Low-Bit Neural Networks," 2022: British Machine Vision Association (BMVA). 
</unstructured_citation></citation><citation key="ref30"><unstructured_citation>
[30]	P. Judd et al., "Reduced-precision strategies for bounded memory in deep neural nets," arXiv preprint arXiv:1511.05236, 2015.
</unstructured_citation></citation><citation key="ref31"><unstructured_citation>
[31]	X. Zhao, Y. Wang, X. Cai, C. Liu, and L. Zhang, "Linear symmetric quantization of neural networks for low-precision integer hardware," 2020.
</unstructured_citation></citation><citation key="ref32"><unstructured_citation>
[32]	K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778. 
</unstructured_citation></citation><citation key="ref33"><unstructured_citation>
[33]	J. L. McKinstry et al., "Discovering low-precision networks close to full-precision networks for efficient inference," in 2019 Fifth Workshop on Energy Efficient Machine Learning and Cognitive Computing-NeurIPS Edition (EMC2-NIPS), 2019: IEEE, pp. 6-9.
</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Providing a Blockchain-Based Method to Protect Users’ Privacy in Social Networks</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Ibrahim</given_name><surname>Zamani Babgohari</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Monireh</given_name><surname>Hosseini</surname></person_name></contributors><publication_date media_type="online"><month>12</month><day>7</day><year>2024</year></publication_date><pages><first_page>205</first_page><last_page>217</last_page></pages><doi_data><doi>10.66224/jict.43768.16.61.205</doi><resource>http://jour.aicti.ir/en/Article/43768</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/en/Article/Download/43768</resource></item><item crawler="google"><resource>http://jour.aicti.ir/en/Article/Download/43768</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/en/Article/Download/43768</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/en/Article/Download/43768</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/en/Article/Download/43768</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/en/Article/Download/43768</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/en/Article/Download/43768</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1] H. Gao , J. Hu , T. Huang , J. Wang , Y. Chen , 2011,Security issues in online social networks, IEEE Internet Computer. 15 (4), pp. 56–63.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>
 [2] M. Fire , R. Goldschmidt , Y. Elovici , 2014, Online social networks: threats and solutions, IEEE Communications Surveys &amp; Tutorials,16 (4),pp. 5-15.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>
[3] Gianluca Lax , Antonia Russo, Lara Saidia Fascì, A Blockchain-based approach for matching desired and real privacy settings of social network users, Information Sciences,pp.2-4,(2021)</unstructured_citation></citation><citation key="ref4"><unstructured_citation>
 [4] Diaa Salama Abd Elminaam, Hatem Mohamed Abdual Kader, and Mohiy Mohamed Hadhoud,2010, Evaluating The Performance of SymmetricEncryption Algorithms, International Journal of Network Security, May 2010</unstructured_citation></citation><citation key="ref5"><unstructured_citation>
[5] A.M. Alattar, N.D. Memon, C.D. Heitzenrater, Media Watermarking, (2015)</unstructured_citation></citation><citation key="ref6"><unstructured_citation>
[6] A. Zigomitros, A. Papageorgiou, C. Patsakis, Social network content management through watermarking, in: Proceedings of the 11th International Conference on Trust, Security and Privacy in Computing and Communications, IEEE,(2012)</unstructured_citation></citation><citation key="ref7"><unstructured_citation>
[7] C. Ho Sin, N.A. Kim, B.W. Go, K.S. Min, J.D. Lee, J.H. Park, Realizing the right to be forgotten in an environment, in: H. Jeong, M.S. Obaidat, N. Yen, J. Park (Eds.), Advances in Computer Science and Its Applications, Lecture Notes in Electrical Engineering, (2014)</unstructured_citation></citation><citation key="ref8"><unstructured_citation>
 [8] C. Patsakis, A. Zigomitros, A. Papageorgiou, E. Galván-López, Distributing privacy policies over multimedia content across multiple online social networks, (2014) </unstructured_citation></citation><citation key="ref9"><unstructured_citation>
[9] K. Thongkor, N. Mettripun, T. Pramoun, T. Amornraksa, Image watermarking based on DWT coefficients modification for social networking services, in: Proceedings of the 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), IEEE, (2013)</unstructured_citation></citation><citation key="ref10"><unstructured_citation>
[10] A.C. Squicciarini, H. Xu, X.L. Zhang, CoPE: enabling collaborative privacy management in online social networks, J. Am. Soc. Inf. Sci. Technol. (2011) </unstructured_citation></citation><citation key="ref11"><unstructured_citation>
[11] A.C. Squicciarini, M. Shehab, J. Wede, Privacy policies for shared content in social network sites, VLDB J. (2010) </unstructured_citation></citation><citation key="ref12"><unstructured_citation>
[12] H. Hu, G.J. Ahn, J. Jorgensen, Multiparty access control for online social networks: model and mechanisms, IEEE Trans.  (2013)</unstructured_citation></citation><citation key="ref13"><unstructured_citation>
[13] F. Li, K. Wu, J. Lei, M. Wen, Z. Bi, C. Gu, Steganalysis over large-scale social networks with high-order joint features and clustering ensembles, IEEE Trans. Inf. Forensic Secur. (2016) </unstructured_citation></citation><citation key="ref14"><unstructured_citation>
[14] N. Venkatachalam, R. Anitha, A multi-feature approach to detect Stegobot: a covert multimedia social network botnet, Multimed. Tools. Appl. (2017) </unstructured_citation></citation><citation key="ref15"><unstructured_citation>
[15] V. Natarajan, S. Sheen, R. Anitha, Multilevel analysis to detect covert social botnet in multimedia social networks, Comput. (2015) </unstructured_citation></citation><citation key="ref16"><unstructured_citation>
[16] M. Tierney, I. Spiro, C. Bregler, L. Subramanian, Cryptagram: photo privacy for online social media, in: Proceedings of the first ACM conference on Online social networks,(2013)</unstructured_citation></citation><citation key="ref17"><unstructured_citation>
[17] S. Rathore, P. Kumar Sharma, V. Loia , Y. Jeong, J. Park ,  Social network security: Issues, challenges, threats, and solutions,2017, Information Sciences,pp. 2-24.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>

[18] P. Savla, L.D. Martino, Content analysis of privacy policies for health social networks, in: Proceedings of the International Symposium on Policies for Distributed Systems and Networks,( 2012)</unstructured_citation></citation><citation key="ref19"><unstructured_citation>
[19] X. Liu, Q. Liu, T. Peng, J. Wu, Dynamic access policy in cloud-based personal health record (PHR) systems, Inf. Sci.  (2017) </unstructured_citation></citation><citation key="ref20"><unstructured_citation>
[20] G. Yan, G. Chen, S. Eidenbenz, N. Li, Malware propagation in online social networks: nature, dynamics, and defense implications, in: Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security, (2011)</unstructured_citation></citation><citation key="ref21"><unstructured_citation>
[21] H. Zhu, C. Huang, H. Li, MPPM: Malware propagation and prevention model in online social network, IEEE International Conference on Communications Workshops (ICC), IEEE, (2014)</unstructured_citation></citation><citation key="ref22"><unstructured_citation>
[22] W. Xu, F. Zhang, S. Zhu, Toward worm detection in online social networks, in: Proceedings of the 26th Annual Computer Security Applications Conference,( 2010)</unstructured_citation></citation><citation key="ref23"><unstructured_citation>
[23] G. Danezis, P. Mittal, SybilInfer: detecting sybil nodes using social networks, in: NDSS, (2009)</unstructured_citation></citation><citation key="ref24"><unstructured_citation>
[24] G. Wang, F. Musau, S. Guo, M.B. Abdullahi, Neighbor similarity trust against sybil attack in P2P e-commerce, Trans. (2015)</unstructured_citation></citation><citation key="ref25"><unstructured_citation>
[25] Panagiotis Ilia, Iasonas Polakis, Elias Athanasopoulos, Federico Maggi, Sotiris Ioannidis,Preventing Privacy Leakage From Photos in
Social Networks,2015</unstructured_citation></citation><citation key="ref26"><unstructured_citation>
[26] Seyyed Mohammad Safi, Ali Movaghar, Mohammad Ghorbani,Privacy protection scheme for mobile social network,2022</unstructured_citation></citation><citation key="ref27"><unstructured_citation>
[27] Sun, X., Yao, Y., Xia, Y., Liu, X., Chen, J., Wang, Z., 2016. Towards Efficient Sharing of
Encrypted Data in Cloud-Based Mobile Social Network. KSII Transactions on
Internet and Information Systems 10 (4), 1892–1903.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>

[28] N. Tran, J. Li, L. Subramanian, S.S. Chow, Optimal sybil-resilient node admission control, in: Proceedings of the INFOCOM, IEEE, (2011)</unstructured_citation></citation><citation key="ref29"><unstructured_citation>
[29] W. Wei, F. Xu, C.C. Tan, Q. Li, SybilDefender: a defense mechanism for Sybil attacks in large social networks, IEEE Trans. (2013)</unstructured_citation></citation><citation key="ref30"><unstructured_citation>
[30] A. Hai Wang, Don’t follow me: spam detection in twitter, in: Proceedings of the International Conference on Security and Cryptography (SECRYPT),IEEE, (2010)</unstructured_citation></citation><citation key="ref31"><unstructured_citation>
[31] F. Ahmed, M. Abulaish, A generic statistical approach for spam detection in Online Social Networks,(2013) </unstructured_citation></citation><citation key="ref32"><unstructured_citation>
[32] H. Gao, Y. Chen, K. Lee, D. Palsetia, A.N. Choudhary, Towards online spam filtering in social networks, (2012)</unstructured_citation></citation><citation key="ref33"><unstructured_citation>
[33] M. Fire, R. Goldschmidt, Y. Elovici, Online social networks: threats and solutions, (2014) </unstructured_citation></citation><citation key="ref34"><unstructured_citation>
[34] D.H. Lee, Personalizing information using users’ online social networks: a case study of CiteULike, (2015) </unstructured_citation></citation><citation key="ref35"><unstructured_citation>
[35] D. Wang, N. Wang, P. Wang, S. Qing, Preserving privacy for free: efficient and provably secure two-factor authentication scheme with user anonymity, (2015) </unstructured_citation></citation><citation key="ref36"><unstructured_citation>
[36] T. Stein, E. Chen, K. Mangla, Facebook immune system, in: Proceedings of the 4th Workshop on Social Network Systems,( 2011)</unstructured_citation></citation><citation key="ref37"><unstructured_citation>
[37] G. Kontaxis, I. Polakis, S. Ioannidis, E.P. Markatos, Detecting social network profile cloning, in: Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), IEEE, (2011)</unstructured_citation></citation><citation key="ref38"><unstructured_citation>
[38] Z. Shan, H. Cao, J. Lv, C. Yan, A. Liu, Enhancing and identifying cloning attacks in online social networks, in: Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication, ( 2013)</unstructured_citation></citation><citation key="ref39"><unstructured_citation>
[39] Raúl Pardo a, Musard Balliu a, Gerardo Schneider, Formalising privacy policies in social networks, Journal of Logical and Algebraic Methods in Programming,2017</unstructured_citation></citation><citation key="ref40"><unstructured_citation>
[40] Joaquim Marquesa, Carlos Serrãob, Improving content privacy on social networks using open digital rights management solutions, International Conference on Project MANagement,2013</unstructured_citation></citation><citation key="ref41"><unstructured_citation>
[41] Shaukat Ali ,Naveed Islam , Azhar Rauf , Ikram Ud Din  and Mohsen Guizani and Joel J. P. C. Rodrigues, Privacy and Security Issues in Online Social Networks,future internet,2018</unstructured_citation></citation><citation key="ref42"><unstructured_citation>
[42] Nader Yahya Alkeinaya, Norita Md. Norwawi, International Conference on Innovation, Management and Technology Research, User Oriented Privacy Model for Social Networks,2013</unstructured_citation></citation><citation key="ref43"><unstructured_citation>
[43] Xun Yi, Elisa Bertino, Fang-Yu Rao, Kwok-Yan Lam, Surya Nepal and Athman Bouguettaya, Privacy-Preserving User Profile Matching in Social Networks ,IEEE,2018</unstructured_citation></citation><citation key="ref44"><unstructured_citation>
[44] ERFAN AGHASIAN, SAURABH GARG, (Member, IEEE), LONGXIANG GAO, (Member, IEEE),SHUI YU,Senior Member, IEEE) AND JAMES MONTGOMERY, (Member, IEEE) , Scoring Users’ Privacy Disclosure Across Multiple Online Social Networks,IEEE,2017</unstructured_citation></citation><citation key="ref45"><unstructured_citation>
[45] Xiaoyun He, Jaideep Vaidya, Basit Shafiq, Nabil Adam, Vijay Atluri, Preserving Privacy in Social Networks: A Structure-Aware Approach,IEEE,2009</unstructured_citation></citation><citation key="ref46"><unstructured_citation>
[46] KAH MENG CHONG AND AMIZAH MALIP, Trace Me If You Can: An Unlinkability Approach for Privacy-Preserving in Social Networks,IEEE,2018</unstructured_citation></citation><citation key="ref47"><unstructured_citation>
[47] Tsan-sheng Hsu, Churn-Jung Liau ,Da-Wei Wang, A logical framework for privacy-preserving social network publication, Journal of Applied Logic,2014</unstructured_citation></citation><citation key="ref48"><unstructured_citation>
[48] Abdullah Al Hasib, Threats of Online Social Networks, International Journal of Computer Science and Network Security,2009</unstructured_citation></citation><citation key="ref49"><unstructured_citation>
[49] Gianluca Lax , Antonia Russo, Lara Saidia Fascì, A Blockchain-based approach for matching desired and real privacy settings of social network users, Information Sciences,2021</unstructured_citation></citation><citation key="ref50"><unstructured_citation>
[50] Largo Bruno Pontecorvo, When Blockchain meets Online Social Networks, Pervasive and Mobile Computing,2020</unstructured_citation></citation><citation key="ref51"><unstructured_citation>
[51] Le Jiang , Xinglin Zhang, A Blockchain-Based Decentralized Online Social Network, IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, VOL. 6, NO. 6, DECEMBER 2019</unstructured_citation></citation><citation key="ref52"><unstructured_citation>
[52] https://www.guru99.com/</unstructured_citation></citation><citation key="ref53"><unstructured_citation>
[53] Aamer Nadeem, Dr M. Younus Javed," A  Performance Comparison of Data Encryption Algorithms", International Conference on Information and Communication Technologies, 27-28 August 2005,doi: 10.1109/ICICT.2005.1598556</unstructured_citation></citation><citation key="ref54"><unstructured_citation>
[54] D. Commey,S. Griffith Klogo,J. Dzisi Gadze," Performance comparison of 3DES, AES, Blowfish and RSA for Dataset Classification and Encryption in Cloud
Data Storage",International Journal of Computer Applications,vol. 177, February 2020</unstructured_citation></citation><citation key="ref55"><unstructured_citation>
[55] Chandrashekhar B,Dr. Mohamed Abdul Waheed," Analysis of Possible Attacks on Data and Possible Solutions with
Comparative Analysis of Various Encryption Algorithms and Evaluation",  International Journal of Innovative Research in Engineering &amp; Management (IJIREM),vol. 9,  April 2022,doi: https://doi.org/10.55524/ijirem.2022.9.2.7</unstructured_citation></citation><citation key="ref56"><unstructured_citation>
[56] C. Rathod,A. Gonsai," Performance Analysis of AES, Blowfish and Rijndael: Cryptographic Algorithms for Audio", Rising Threats in Expert Applications and Solutions, 
pp. 203–209,02 October 2020, doi: 10.1007/978-981-15-6014-9_24</unstructured_citation></citation><citation key="ref57"><unstructured_citation>
[57] H. Alabdulrazzaq, M. Alenezi," Performance Analysis and Evaluation of Cryptographic Algorithms: DES, 3DES, Blowfish, Twofish, and Threefish ", International Journal of Communication Networks and Information Security (IJCNIS),vol. 14, No. 1, April 2022</unstructured_citation></citation><citation key="ref58"><unstructured_citation>
[58] Bin Zhou ,Jian Pei," Preserving Privacy in Social Networks Against Neighborhood Attacks ", IEEE 24th International Conference on Data Engineering, 07-12 April 2008,doi:  10.1109/ICDE.2008.4497459</unstructured_citation></citation><citation key="ref59"><unstructured_citation>
[59] A.Jain,S.Ranjan Sahoo,J.Kaubiyal," Online social networks security and privacy: comprehensive review and analysis ", Complex &amp; Intelligent Systems, Vol. 7, pp. 2157–2177, 01 June 2021,doi: 10.1007/S40747-021-00409-7</unstructured_citation></citation><citation key="ref60"><unstructured_citation>
[60] Seyed Hossein Mousavi , Hamid Barati," SECURITY AND PRIVACY IN SOCIAL NETWORKS ", Journal of Positive School Psychology, Vol. 6, No. 5 ,2022</unstructured_citation></citation><citation key="ref61"><unstructured_citation>
[61] Ahmed Al-Charchafchi, S. Manickam,Zakaria N. M. Alqattan," Threats Against Information Privacy and Security in Social Networks: A Review ", Springer Nature Singapore Pte Ltd, pp. 358–372, 2020,doi: https://doi.org/10.1007/978-981-15-2693-0_26</unstructured_citation></citation><citation key="ref62"><unstructured_citation>
[62] R. Malekhosseini, M. Hosseinzadeh,K. Navi," An investigation into the requirements of privacy in social networks and factors contributing to users’ concerns about violation of their privacy ", Social Network Analysis and Mining,11 june 2018,doi: https://doi.org/10.1007/s13278-018-0518-x</unstructured_citation></citation><citation key="ref63"><unstructured_citation>
[63] P.K. Paul, P.S. Aithal,R.Saavedra, Su. Ghosh4," Blockchain Technology and its Types—A Short Review ", International Journal of Applied Science and Engineering, December 2021 </unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Trust Management Based on User Feedback in Cloud Computing Environment by Using Cuckoo Optimization Algorithm</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Muqtada</given_name><surname>Soleimani Mobarake</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Golnaz</given_name><surname>Aghaee</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Babak</given_name><surname>Nikmard</surname></person_name></contributors><publication_date media_type="online"><month>12</month><day>7</day><year>2024</year></publication_date><pages><first_page>147</first_page><last_page>164</last_page></pages><doi_data><doi>10.66224/jict.44218.16.61.147</doi><resource>http://jour.aicti.ir/en/Article/44218</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/en/Article/Download/44218</resource></item><item crawler="google"><resource>http://jour.aicti.ir/en/Article/Download/44218</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/en/Article/Download/44218</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/en/Article/Download/44218</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/en/Article/Download/44218</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/en/Article/Download/44218</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/en/Article/Download/44218</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]  Jens Lansing , Ali Sunyaev, “Trust in CloudComputing: Conceptual Typology and Trust-   BuildingAntecedents”, ACM SIGMIS Database SystemsVolume 47Issue 2May 2016,pp 58–96</unstructured_citation></citation><citation key="ref2"><unstructured_citation>
[2] Huang, J., Nicol, D.M, “Trust mechanisms for cloud computing”, Journal of Cloud  Computing Advances, Systems and Applications volume 2, Article number: 9 (2013) , pp 1-14. </unstructured_citation></citation><citation key="ref3"><unstructured_citation>
[3] F. Azzedin and M. Maheswaran, "Towards Trust-Aware Resource Management in Grid Computing  Systems," 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid CCGRID'02), Berlin, Germany, 2002, pp. 452-452. </unstructured_citation></citation><citation key="ref4"><unstructured_citation>
[4] Abhishek Kesarwani, Pabitra Mohan Khilar, " Development of trustbased access control models using fuzzy logic in cloud computing " Journal of King Saud University - Computer and Information  Sciences, Volume 34, Issue 5, 2022, pp1958-1967. </unstructured_citation></citation><citation key="ref5"><unstructured_citation>
[5] P. Gupta, M. K. Goyal and P. Kumar, "Trust and reliabilitybased load balancing algorithm for cloud . IaaS," 2013 3rd IEEE International Advance Computing Conference (IACC), Ghaziabad, India, 2013, pp. 65-69. </unstructured_citation></citation><citation key="ref6"><unstructured_citation>
[6] Mohammad Javad Salehi1, Mehrdad Ashtiani2* and Behrouz Minaei Bidgoli Cloud Service Selection based on the Credibility persistency of Users’Feedbacks, Volume 7, Issue 1 December 2018Pages 29-41. </unstructured_citation></citation><citation key="ref7"><unstructured_citation>
[7] Tabassum N. Mujawar, Lokesh B. Bhajantri, “Behavior and feedbackbased trust computation in cloud environment”, Journal of King Saud University - Computer and Information Sciences, Volume 34, Issue 8, PartA,2022, Pages 4956-4967. </unstructured_citation></citation><citation key="ref8"><unstructured_citation>
[8] Aghaee Ghazvini, G., Mohsenzadeh, M., Nasiri, R. et al. " A new multi-level trust management framework (MLTM) for solving the invalidity and sparse problems of user feedback ratings in cloud environments”. J Supercomput 77, (2021), pp2326–2354 (2021). </unstructured_citation></citation><citation key="ref9"><unstructured_citation>
[9] H. Hassan, A. I. El-Desouky, A. Ibrahim, E. -S. M. El-Kenawy and R. Arnous, "Enhanced QoS-Based Model for Trust Assessment in Cloud Computing Environment," in IEEE Access, 2020, vol. 8, pp. 43752-43763. </unstructured_citation></citation><citation key="ref10"><unstructured_citation>
[10] Aghaee Ghazvini, G, Mohsenzadeh, M, Nasiri, R, Masoud Rahmani, A. MMLT: A mutual multilevel  trust framework based on trusted third parties in multicloud environments. Software Pract .Exper. 2020; 50: 1203– 1227, pp 1-25. </unstructured_citation></citation><citation key="ref11"><unstructured_citation>
[11] Noor, T.H., Sheng, Q.Z. (2011). “Trust as a Service: A Framework for Trust Management in Cloud Environments” In: Bouguettaya, A., Hauswirth, M., Liu, L. (eds) Web Information System Engineering – WISE 2011. WISE 2011. Lecture Notes in Computer Science, vol 6997. Springer, Berlin, Heidelberg.pp315-317. </unstructured_citation></citation><citation key="ref12"><unstructured_citation>
[12] Shakeel Ahmad, Bashir Ahmad, Sheikh Muhammad Saqib and Rashid Muhammad Khattak, “Trust Model : Cloud’s Provider and Cloud’s User”,International Journal of Advanced Science and   Technology Vol.44,July,2012. </unstructured_citation></citation><citation key="ref13"><unstructured_citation>
[13] Saurabh Kumar Garg, Steve Versteeg, Rajkumar Buyya, A framework for ranking of cloud computing services, Future Generation Computer Systems, Volume 29, Issue 4,2013, Pages 1012-1023, </unstructured_citation></citation><citation key="ref14"><unstructured_citation>
[14] Muhammad Kashif Naseer, Sohail Jabbar, Dr.  Irfan Zafar, “ ANovel Trust Model for Selection of . Cloud Service Provider”, (IEEE 2014) ,2014. </unstructured_citation></citation><citation key="ref15"><unstructured_citation>
[15] Paul Manuel, 2015. "A trust model of cloud computing based on Quality of Service," Annals of . Operations Research, Springer, vol. 233(1), pages 281-292, October. </unstructured_citation></citation><citation key="ref16"><unstructured_citation>
[16] Talal H. Noor, Quan Z. Sheng, Athman Bouguettaya ,Trust Management in Cloud Services, Springer, 2014,pp1-119. </unstructured_citation></citation><citation key="ref17"><unstructured_citation>
[17] Ricardo Perez-Truglia, Markets, trust and cultural biases: evidence from eBay, Journal of Behavioral and Experimental Economics, Volume 72,2018, pp 17-27. </unstructured_citation></citation><citation key="ref18"><unstructured_citation>
[18] Rajabioun, Ramin. (2011). Cuckoo Optimization Algorithm. Applied Soft Computing. 11. pp5508–5518. </unstructured_citation></citation><citation key="ref19"><unstructured_citation>
[19] J. Gibson, R. Rondeau, D. Eveleigh and Q. Tan, "Benefits and challenges of three cloud computing service models," 2012 Fourth International Conference on Computational Aspects of Social Networks (CASoN), Sao Carlos, Brazil, 2012, pp. 198-205</unstructured_citation></citation><citation key="ref20"><unstructured_citation>.
[20] Sana, Kouchi &amp; Nacer, Hassina. (2022). Service Selection in Cloud Computing Environment by Using Cuckoo Search. 10.1007/978-3-030-91738-8_2 pp219-227. </unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Improving Code Coverage Metrics for Discovering Vulnerabilities in Stateful Network Protocols using Hybrid Fuzzing</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Hamid</given_name><surname>Rezaei Rahvard</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Mehdi</given_name><surname>Salkhordeh Haghighi</surname></person_name></contributors><publication_date media_type="online"><month>12</month><day>7</day><year>2024</year></publication_date><pages><first_page>72</first_page><last_page>84</last_page></pages><doi_data><doi>10.66224/jict.44407.16.61.72</doi><resource>http://jour.aicti.ir/en/Article/44407</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/en/Article/Download/44407</resource></item><item crawler="google"><resource>http://jour.aicti.ir/en/Article/Download/44407</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/en/Article/Download/44407</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/en/Article/Download/44407</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/en/Article/Download/44407</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/en/Article/Download/44407</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/en/Article/Download/44407</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>M. Zalewski, “American fuzzy lop - a security-orientedfuzzer.”,2021,https://lcamtuf.coredump.cx/afl
</unstructured_citation></citation><citation key="ref2"><unstructured_citation>
Cui, Lei, Jiancong Cui, Zhiyu Hao, Lun Li, Zhenquan Ding, and Yongji Liu. "An empirical study of vulnerability discovery methods over the past ten years." Computers &amp; Security, 2022
</unstructured_citation></citation><citation key="ref3"><unstructured_citation>
F. Rustamov, J. Kim, J. Yu, and J. Yun, “Exploratory review of hybrid fuzzing for automated vulnerability detection,” IEEE Access, vol. 9, pp. 131166–131190, 2021.
</unstructured_citation></citation><citation key="ref4"><unstructured_citation>
[4] Zhou, Shunfan, Zhemin Yang, Dan Qiao, Peng Liu, Min Yang, Zhe Wang, and Chenggang Wu. "Ferry:{State-Aware} Symbolic Execution for Exploring {State-Dependent} Program Paths." In 31th USENIX Security Symposium (USENIX Security 22), pp. 4365-4382. 2022.
</unstructured_citation></citation><citation key="ref5"><unstructured_citation>
V.-T. Pham, M. Böhme, and A. Roychoudhury, “Aflnet: a greybox fuzzer for network protocols,” in 2020 
</unstructured_citation></citation><citation key="ref6"><unstructured_citation>
IEEE 13th International Conference on Software Testing, Validation and Verification (ICST), pp. 460-465, IEEE, 2020
</unstructured_citation></citation><citation key="ref7"><unstructured_citation>
S. Schumilo, C. Aschermann, A. Abbasi, S. Wörner, and T. Holz, “Nyx: Greybox hypervisor fuzzing using fast snapshots and affne types,” in 30th USENIX Security Symposium (USENIX Security 21), pp. 2597–2614, 2021
</unstructured_citation></citation><citation key="ref8"><unstructured_citation>
S. Schumilo, C. Aschermann, A. Jemmett, A. Abbasi, and T. Holz, “Nyx-net: network fuzzing with incremental snapshots,” in Proceedings of the Seventeenth European Conference on Computer Systems, pp. 166–180, 2022.
</unstructured_citation></citation><citation key="ref9"><unstructured_citation>
J. Li, S. Li, G. Sun, T. Chen, and H. Yu, “Snpsfuzzer: A fast greybox fuzzer for stateful network protocols using snapshots,” IEEE Transactions on Information Forensics and Security, vol. 17, pp. 2673–2687, 2022.
</unstructured_citation></citation><citation key="ref10"><unstructured_citation>
R. Natella, “Stateafl: Greybox fuzzing for stateful network servers,” Empirical Software Engineering, vol. 27, no. 7, p. 191, 2022
</unstructured_citation></citation><citation key="ref11"><unstructured_citation>
J. Ba, M. Böhme, Z. Mirzamomen, and A. Roychoudhury, “Stateful greybox fuzzing,” in 31st USENIX Security Symposium (USENIX Security 22), pp. 3255–3272, 2022.
</unstructured_citation></citation><citation key="ref12"><unstructured_citation>
C. Aschermann, S. Schumilo, A. Abbasi, and T. Holz, “Ijon: Exploring deep state spaces via fuzzing,” in 2020 IEEE Symposium on Security and Privacy (SP), pp. 1597–1612, IEEE, 2020.
</unstructured_citation></citation><citation key="ref13"><unstructured_citation>
Y. Shoshitaishvili, R. Wang, C. Salls, N. Stephens, M. Polino, A. Dutcher, J. Grosen, S. Feng, C. Hauser, C. Kruegel, and G. Vigna, “SoK: (State of) The Art of War: Offensive Techniques in Binary Analysis,” in IEEE Symposium on Security and Privacy, 2016
</unstructured_citation></citation><citation key="ref14"><unstructured_citation>
V. Chipounov, V. Kuznetsov, and G. Candea, “S2e: A platform for in-vivo multipath analysis of software systems,” Acm Sigplan Notices, vol. 46, no. 3, pp. 265–278,2011.
</unstructured_citation></citation><citation key="ref15"><unstructured_citation>
I. Yun, S. Lee, M. Xu, Y. Jang, and T. Kim, “{QSYM}: A practical concolic execution engine tailored for hybrid fuzzing,” in 27th USENIX Security Symposium (USENIX Security 18), pp. 745–761, 2018.
</unstructured_citation></citation><citation key="ref16"><unstructured_citation>

S. Poeplau and A. Francillon, “Symbolic execution with {SymCC}: Don’t interpret, compile!,” in 29th USENIX Security Symposium (USENIX Security 20), pp. 181–198, 2020.
</unstructured_citation></citation><citation key="ref17"><unstructured_citation>
S. Poeplau and A. Francillon, “Symqemu: Compilation-based symbolic execution for binaries,” in NDSS 2021, Network and Distributed System Security Symposium, Internet Society, 2021.
</unstructured_citation></citation><citation key="ref18"><unstructured_citation>
L. Borzacchiello, E. Coppa, and C. Demetrescu, “Fuzzolic: Mixing fuzzing and concolic execution,” Computers &amp; Security, vol. 108, p. 102368, 2021
</unstructured_citation></citation><citation key="ref19"><unstructured_citation>
S. Zhou, Z. Yang, D. Qiao, P. Liu, M. Yang, Z. Wang, and C. Wu, “Ferry:{StateAware} symbolic execution for exploring {State-Dependent} program paths,” in 31st USENIX Security Symposium (USENIXSecurity 22), pp. 4365–4382, 2022.
</unstructured_citation></citation><citation key="ref20"><unstructured_citation>
D. Bruening and T. Garnett, “Building dynamic instrumentation tools with dynamorio,” in Proc. Int. Conf. IEEE/ACM Code Generation and Optimi zation (CGO), Shen Zhen, China, 2013.
</unstructured_citation></citation><citation key="ref21"><unstructured_citation>
F. Saudel and J. Salwan, “Triton: A dynamic symbolic execution framework,” in
Symposium sur la sécurité des technologies de l’information et des communications, SSTIC, France, Rennes, pp. 31–54, 2015.
</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Energy Efficient Target (Rhynchophorus Ferrugineus) Tracking in Wireless Sensor Network Using the Cat Swarm Optimization Algorithm and Fuzzy Logic</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Shayesteh</given_name><surname>Tabatabaei</surname></person_name></contributors><publication_date media_type="online"><month>12</month><day>7</day><year>2024</year></publication_date><pages><first_page>165</first_page><last_page>179</last_page></pages><doi_data><doi>10.66224/jict.44533.16.61.165</doi><resource>http://jour.aicti.ir/en/Article/44533</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/en/Article/Download/44533</resource></item><item crawler="google"><resource>http://jour.aicti.ir/en/Article/Download/44533</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/en/Article/Download/44533</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/en/Article/Download/44533</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/en/Article/Download/44533</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/en/Article/Download/44533</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/en/Article/Download/44533</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>Simon, G., Maróti, M., Lédeczi, Á., Balogh, G., Kusy, B., Nádas, A., ... &amp; Frampton, K. (2004, November). Sensor network-based countersniper system. In Proceedings of the 2nd international conference on Embedded networked sensor systems (pp. 1-12). </unstructured_citation></citation><citation key="ref2"><unstructured_citation> 
Tabatabaei, S. (2021). A Novel Method for Optimizing Energy Consumption in Applications for Detecting Palm Rhynchophorus Ferrugineus in WSNs Using Data mining and Q-Learning. Wireless Personal Communications, 121(1), 1-17. </unstructured_citation></citation><citation key="ref3"><unstructured_citation> 
عباسی ج،. دبیری ح،. امیری ع،. " آفت قرنطینه ای سرخرطومی حنایی خرما "، ج 1، انتشارات  مدیریت هماهنگی ترویج کشاورزی استان فارس، ص 14، بهار 1396 https://agrilib.areeo.ac.ir/book_3292.pdf. </unstructured_citation></citation><citation key="ref4"><unstructured_citation> 
Lima, M. C. F., de Almeida Leandro, M. E. D., Valero, C., Coronel, L. C. P., &amp; Bazzo, C. O. G. (2020). Automatic detection and monitoring of insect pests—A review. Agriculture, 10(5), 161. </unstructured_citation></citation><citation key="ref5"><unstructured_citation> 
Suganya, S. (2008, July). A cluster-based approach for collaborative target tracking in wireless sensor networks. In 2008 First International Conference on Emerging Trends in Engineering and Technology (pp. 276-281). IEEE. </unstructured_citation></citation><citation key="ref6"><unstructured_citation> 
Wang, Z., Li, H., Shen, X., Sun, X., &amp; Wang, Z. (2008, April). Tracking and predicting moving targets in hierarchical sensor networks. In 2008 IEEE International Conference on Networking, Sensing and Control (pp. 1169-1173). IEEE. </unstructured_citation></citation><citation key="ref7"><unstructured_citation> 
Balasubramanian, S., Jayaweera, S. K., &amp; Namuduri, K. R. (2005, March). Energy-aware, collaborative tracking with ad-hoc wireless sensor networks. In IEEE Wireless Communications and Networking Conference, 2005 (Vol. 3, pp. 1878-1883). IEEE. </unstructured_citation></citation><citation key="ref8"><unstructured_citation> 
Li, D., Wong, K. D., Hu, Y. H., &amp; Sayeed, A. M. (2002). Detection, classification, and tracking of targets. IEEE signal processing magazine, 19(2), 17-29. </unstructured_citation></citation><citation key="ref9"><unstructured_citation> 
Liu, H. Q., So, H. C., Chan, F. K. W., &amp; Lui, K. W. K. (2009). Distributed particle filter for target tracking in sensor networks. Progress In Electromagnetics Research C, 11, 171-182. </unstructured_citation></citation><citation key="ref10"><unstructured_citation> 
Madhavi, K. R., Nawi, M. N. M., Reddy, B. B., Baboji, K., Kishore, K. H., &amp; Manikanthan, S. V. (2023). Energy efficient target tracking in wireless sensor network using PF-SVM (particle filter-support vector machine) technique. Measurement: Sensors, 26, 100667. </unstructured_citation></citation><citation key="ref11"><unstructured_citation> 
Xiang, S., &amp; Yang, J. (2023). A novel adaptive deployment method for the single-target tracking of mobile wireless sensor networks. Reliability Engineering &amp; System Safety, 234, 109135. </unstructured_citation></citation><citation key="ref12"><unstructured_citation> 
Qu, Z., Xu, H., Zhao, X., Tang, H., Wang, J., &amp; Li, B. (2022). A fault-tolerant sensor scheduling approach for target tracking in wireless sensor networks. Alexandria Engineering Journal, 61(12), 13001-13010. </unstructured_citation></citation><citation key="ref13"><unstructured_citation> 
Munjani, J., &amp; Joshi, M. (2021). A non-conventional lightweight Auto Regressive Neural Network for accurate and energy efficient target tracking in Wireless Sensor Network. ISA transactions, 115, 12-31. </unstructured_citation></citation><citation key="ref14"><unstructured_citation> 
Sahoo, B. M., Pandey, H. M., &amp; Amgoth, T. (2022). A genetic algorithm inspired optimized cluster head selection method in wireless sensor networks. Swarm and Evolutionary Computation, 75, 101151. </unstructured_citation></citation><citation key="ref15"><unstructured_citation> 
Sadrishojaei, M., Navimipour, N. J., Reshadi, M., &amp; Hosseinzadeh, M. (2022). A new clustering-based routing method in the mobile internet of things using a krill herd algorithm. Cluster Computing, 25(1), 351-361. </unstructured_citation></citation><citation key="ref16"><unstructured_citation> 
Srinivas, P., &amp; Swapna, P. (2022). Quantum tunicate swarm algorithm based energy aware clustering scheme for wireless sensor networks. Microprocessors and Microsystems, 94, 104653. </unstructured_citation></citation><citation key="ref17"><unstructured_citation> 
Amutha, J., Sharma, S., &amp; Sharma, S. K. (2022). An energy efficient cluster based hybrid optimization algorithm with static sink and mobile sink node for Wireless Sensor Networks. Expert Systems with Applications, 203, 117334. </unstructured_citation></citation><citation key="ref18"><unstructured_citation> 
Mansour, R. F., Alsuhibany, S. A., Abdel-Khalek, S., Alharbi, R., Vaiyapuri, T., Obaid, A. J., &amp; Gupta, D. (2022). Energy Aware Fault Tolerant Clustering with Routing Protocol for Improved Survivability in Wireless Sensor Networks. Computer Networks, 109049. </unstructured_citation></citation><citation key="ref19"><unstructured_citation> 
Kaedi, M., Bohlooli, A., &amp; Pakrooh, R. (2022). Simultaneous optimization of cluster head selection and inter-cluster routing in wireless sensor networks using a 2-level genetic algorithm. Applied Soft Computing, 128, 109444. </unstructured_citation></citation><citation key="ref20"><unstructured_citation> 
Malisetti, N., &amp; Pamula, V. K. (2022). Energy efficient cluster based routing for wireless sensor networks using moth levy adopted artificial electric field algorithm and customized grey wolf optimization algorithm. Microprocessors and Microsystems, 93, 104593. </unstructured_citation></citation><citation key="ref21"><unstructured_citation> 
Chu, S. C., Tsai, P. W., &amp; Pan, J. S. (2006). Cat swarm optimization. In PRICAI 2006: Trends in Artificial Intelligence: 9th Pacific Rim International Conference on Artificial Intelligence Guilin, China, August 7-11, 2006 Proceedings 9 (pp. 854-858). Springer Berlin Heidelberg. </unstructured_citation></citation><citation key="ref22"><unstructured_citation> 
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353. </unstructured_citation></citation><citation key="ref23"><unstructured_citation> 

</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Generating Personalized Explanations for Twitter List Recommendations Using Semantic Similarity of Hashtags</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Havva</given_name><surname>Alizadeh Noughabi</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Behshid</given_name><surname>Behkamal</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Saleheh</given_name><surname>Naseri</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Mohsen</given_name><surname>Kahani</surname></person_name></contributors><publication_date media_type="online"><month>12</month><day>7</day><year>2024</year></publication_date><pages><first_page>195</first_page><last_page>204</last_page></pages><doi_data><doi>10.66224/jict.45492.16.61.195</doi><resource>http://jour.aicti.ir/en/Article/45492</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/en/Article/Download/45492</resource></item><item crawler="google"><resource>http://jour.aicti.ir/en/Article/Download/45492</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/en/Article/Download/45492</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/en/Article/Download/45492</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/en/Article/Download/45492</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/en/Article/Download/45492</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/en/Article/Download/45492</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	S. de la Rouviere and K. Ehlers, “Lists as coping strategy for information overload on Twitter,” presented at the Proceedings of the 22nd International Conference on World Wide Web, 2013, pp. 199–200.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>
[2]	V. Rakesh, D. Singh, B. Vinzamuri, and C. K. Reddy, “Personalized recommendation of twitter lists using content and network information,” presented at the Proceedings of the International AAAI Conference on Web and Social Media, 2014, pp. 416–425.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>
[3]	L. Chen, Y. Zhao, S. Chen, H. Fang, C. Li, and M. Wang, “iplug: Personalized list recommendation in twitter,” presented at the Web Information Systems Engineering–WISE 2013: 14th International Conference, Nanjing, China, October 13-15, 2013, Proceedings, Part II 14, Springer, 2013, pp. 88–103.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>
[4]	C.-H. Tsai and P. Brusilovsky, “The effects of controllability and explainability in a social recommender system,” User Modeling and User-Adapted Interaction, vol. 31, pp. 591–627, 2021.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>
[5]	D. Shmaryahu, G. Shani, and B. Shapira, “Post-hoc Explanations for Complex Model Recommendations using Simple Methods.,” presented at the IntRS@ RecSys, 2020, pp. 26–36.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>
[6]	Y. Zhang and X. Chen, “Explainable recommendation: A survey and new perspectives,” Foundations and Trends® in Information Retrieval, vol. 14, no. 1, pp. 1–101, 2020.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>
[7]	I. Nunes and D. Jannach, “A systematic review and taxonomy of explanations in decision support and recommender systems,” User Modeling and User-Adapted Interaction, vol. 27, pp. 393–444, 2017.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>
[8]	K. Balog and F. Radlinski, “Measuring recommendation explanation quality: The conflicting goals of explanations,” presented at the Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, 2020, pp. 329–338.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>
[9]	J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>
[10]	M. Caro-Martínez, G. Jiménez-Díaz, and J. A. Recio-García, “Conceptual modeling of explainable recommender systems: an ontological formalization to guide their design and development,” Journal of Artificial Intelligence Research, vol. 71, pp. 557–589, 2021.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>
[11]	C. Nóbrega and L. Marinho, “Towards explaining recommendations through local surrogate models,” presented at the Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 2019, pp. 1671–1678.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>
[12]	G. Peake and J. Wang, “Explanation mining: Post hoc interpretability of latent factor models for recommendation systems,” presented at the Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining, 2018, pp. 2060–2069.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>
[13]	C. Lonjarret, C. Robardet, M. Plantevit, R. Auburtin, and M. Atzmueller, “Why should i trust this item? explaining the recommendations of any model,” presented at the 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, 2020, pp. 526–535.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>
[14]	Y. Zhang, G. Lai, M. Zhang, Y. Zhang, Y. Liu, and S. Ma, “Explicit factor models for explainable recommendation based on phrase-level sentiment analysis,” presented at the Proceedings of the 37th international ACM SIGIR conference on Research &amp; development in information retrieval, 2014, pp. 83–92.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>
[15]	N. Wang, H. Wang, Y. Jia, and Y. Yin, “Explainable recommendation via multi-task learning in opinionated text data,” presented at the The 41st International ACM SIGIR Conference on Research &amp; Development in Information Retrieval, 2018, pp. 165–174.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>
[16]	H. Wang et al., “Ripplenet: Propagating user preferences on the knowledge graph for recommender systems,” presented at the Proceedings of the 27th ACM international conference on information and knowledge management, 2018, pp. 417–426.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>
[17]	Q. Ai, V. Azizi, X. Chen, and Y. Zhang, “Learning heterogeneous knowledge base embeddings for explainable recommendation,” Algorithms, vol. 11, no. 9, p. 137, 2018.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>
[18]	Y. Xian, Z. Fu, S. Muthukrishnan, G. De Melo, and Y. Zhang, “Reinforcement knowledge graph reasoning for explainable recommendation,” presented at the Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, 2019, pp. 285–294.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>
[19]	F. Fusco, M. Vlachos, V. Vasileiadis, K. Wardatzky, and J. Schneider, “RecoNet: An Interpretable Neural Architecture for Recommender Systems.,” presented at the IJCAI, 2019, pp. 2343–2349.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>
[20]	Y. Lu, R. Dong, and B. Smyth, “Why I like it: multi-task learning for recommendation and explanation,” presented at the Proceedings of the 12th ACM Conference on Recommender Systems, 2018, pp. 4–12.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>
[21]	Z. Chen et al., “Co-attentive multi-task learning for explainable recommendation.,” presented at the IJCAI, 2019, pp. 2137–2143.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>
[22]	X. Wang, X. He, F. Feng, L. Nie, and T.-S. Chua, “Tem: Tree-enhanced embedding model for explainable recommendation,” presented at the Proceedings of the 2018 world wide web conference, 2018, pp. 1543–1552.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>
[23]	W. Ma et al., “Jointly learning explainable rules for recommendation with knowledge graph,” presented at the The world wide web conference, 2019, pp. 1210–1221.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>
[24]	W. Sherchan, S. Nepal, and C. Paris, “A survey of trust in social networks,” ACM Computing Surveys (CSUR), vol. 45, no. 4, pp. 1–33, 2013.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>
[25]	B. Wang, M. Ester, J. Bu, and D. Cai, “Who also likes it? generating the most persuasive social explanations in recommender systems,” presented at the Proceedings of the AAAI Conference on Artificial Intelligence, 2014.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>
[26]	C. Shi, Z. Zhang, P. Luo, P. S. Yu, Y. Yue, and B. Wu, “Semantic path based personalized recommendation on weighted heterogeneous information networks,” presented at the Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, 2015, pp. 453–462.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>
[27]	Y. Zhang, X. Xu, H. Zhou, and Y. Zhang, “Distilling structured knowledge into embeddings for explainable and accurate recommendation,” presented at the Proceedings of the 13th international conference on web search and data mining, 2020, pp. 735–743.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>
[28]	J. Zheng, Z. Qin, S. Wang, and D. Li, “Attention-based explainable friend link prediction with heterogeneous context information,” Information Sciences, vol. 597, pp. 211–229, 2022.</unstructured_citation></citation><citation key="ref29"><unstructured_citation>
[29]	Z. Ren, S. Liang, P. Li, S. Wang, and M. de Rijke, “Social collaborative viewpoint regression with explainable recommendations,” presented at the Proceedings of the tenth ACM international conference on web search and data mining, 2017, pp. 485–494.</unstructured_citation></citation><citation key="ref30"><unstructured_citation>
[30]	Y. Wu and M. Ester, “Flame: A probabilistic model combining aspect based opinion mining and collaborative filtering,” presented at the Proceedings of the eighth ACM international conference on web search and data mining, 2015, pp. 199–208.</unstructured_citation></citation><citation key="ref31"><unstructured_citation>
[31]	Y. Zhang, “Incorporating phrase-level sentiment analysis on textual reviews for personalized recommendation,” presented at the Proceedings of the eighth ACM international conference on web search and data mining, 2015, pp. 435–440.</unstructured_citation></citation><citation key="ref32"><unstructured_citation>
[32]	D. Kim, Y. Jo, I.-C. Moon, and A. Oh, “Analysis of twitter lists as a potential source for discovering latent characteristics of users,” presented at the ACM CHI workshop on microblogging, Citeseer, 2010.</unstructured_citation></citation><citation key="ref33"><unstructured_citation>
[33]	C. Lu, W. Lam, and Y. Zhang, “Twitter user modeling and tweets recommendation based on wikipedia concept graph,” presented at the Workshops at the Twenty-Sixth AAAI conference on artificial intelligence, 2012.</unstructured_citation></citation><citation key="ref34"><unstructured_citation>
[34]	R. Egger and J. Yu, “A topic modeling comparison between lda, nmf, top2vec, and bertopic to demystify twitter posts,” Frontiers in sociology, vol. 7, p. 886498, 2022.</unstructured_citation></citation><citation key="ref35"><unstructured_citation>
[35]	M. Grootendorst, “BERTopic: Neural topic modeling with a class-based TF-IDF procedure,” arXiv preprint arXiv:2203.05794, 2022.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Investigating an Approach to Identify and Classify Mutants Based on the Characteristics of Mutants with Machine Learning Algorithms</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Zeinab</given_name><surname>Asghari</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Bahman</given_name><surname>Arasteh</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Abbas</given_name><surname>Koochari</surname></person_name></contributors><publication_date media_type="online"><month>12</month><day>7</day><year>2024</year></publication_date><pages><first_page>180</first_page><last_page>192</last_page></pages><doi_data><doi>10.66224/jict.46174.16.61.180</doi><resource>http://jour.aicti.ir/en/Article/46174</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/en/Article/Download/46174</resource></item><item crawler="google"><resource>http://jour.aicti.ir/en/Article/Download/46174</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/en/Article/Download/46174</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/en/Article/Download/46174</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/en/Article/Download/46174</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/en/Article/Download/46174</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/en/Article/Download/46174</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>1.	Richard A. DeMillo, Richard J. Lipton, and Fred G. Sayward. "Hints on test data selection: Help for the practicing programmer." IEEE Computer, 11(4): 34-41, 1978.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>
2.	Jeff Offutt and Ammei Lee. "An empirical evaluation of weak mutation." IEEE Transactions on Software Engineering, 24(8): 649-660, 1998.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>
3.	Mark Harman. "The current state and future of search-based software engineering." In Proceedings of the 28th International Conference on Software Engineering (ICSE'06), pages 342-351. ACM, 2006.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>
4.	Simon Poulding and James A. Jones. "An empirical study of the robustness of MacOS applications using random testing." In Proceedings of the 10th International Workshop on Dynamic Analysis (WODA'12), pages 35-40. IEEE, 2012.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>
5.	López, J., Kushik, N., &amp; Yevtushenko, N. (2018). Source Code Optimization using Equivalent Mutants. Inf. Softw. Technol., 103, 138-141. https://doi.org/10.1016/j.infsof.2018.06.013.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>
6.	Ghiduk, A., Girgis, M., &amp; Shehata, M. (2019). Employing Dynamic Symbolic Execution for Equivalent Mutant Detection. IEEE Access, 7, 163767-163777. https://doi.org/10.1109/ACCESS.2019.2952246.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>
7.	Zeinab Asghari, Bahman Arasteh, and Abbas Koochari. 2024. Effective Software Mutation-Test Using Program Instructions Classification. J. Electron. Test. 39, 5–6 (Dec 2023), 631–657. https://doi.org/10.1007/s10836-023-06089-0</unstructured_citation></citation><citation key="ref8"><unstructured_citation>
8.	Ghiduk, A., Girgis, M., &amp; Shehata, M. (2019). Employing Dynamic Symbolic Execution for Equivalent Mutant Detection. IEEE Access, 7, 163767-163777. https://doi.org/10.1109/ACCESS.2019.2952246.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>
9.	Jammalamadaka, K., &amp; Parveen, N. (2021). Equivalent mutant identification using hybrid wavelet convolutional rain optimization. Software: Practice and Experience, 52, 576 - 593. https://doi.org/10.1002/spe.3038.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>
10.	Souza, B., &amp; Gheyi, R. (2020). A Lightweight Technique to Identify Equivalent Mutants. . https://doi.org/10.5753/CBSOFT_ESTENDIDO.2020.14630.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>
11.	Tenorio, M., Lopes, R., Fechine, J., Marinho, T., &amp; Costa, E. (2019). Subsumption in Mutation Testing: An Automated Model Based on Genetic Algorithm. 16th International Conference on Information Technology-New Generations (ITNG 2019). https://doi.org/10.1007/978-3-030-14070-0_24.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>
12.	Fernandes, L., Ribeiro, M., Gheyi, R., Delamaro, M., Guimarães, M., &amp; Santos, A. (2022). Put Your Hands In The Air! Reducing Manual Effort in Mutation Testing. Proceedings of the XXXVI Brazilian Symposium on Software Engineering. https://doi.org/10.1145/3555228.3555233.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>
13.	Abuljadayel, A., &amp; Wedyan, F. (2018). An Approach for the Generation of Higher Order Mutants Using Genetic Algorithms. International Journal of Intelligent Systems and Applications, 10, 34-45. https://doi.org/10.5815/IJISA.2018.01.05.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>
14.	Basile, D., Beek, M., Cordy, M., &amp; Legay, A. (2020). Tackling the equivalent mutant problem in real-time systems: the 12 commandments of model-based mutation testing. Proceedings of the 24th ACM Conference on Systems and Software Product Line: Volume A - Volume A. https://doi.org/10.1145/3382025.3414966.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>
15.	Naeem, M., Lin, T., Naeem, H., &amp; Liu, H. (2020). A machine learning approach for classification of equivalent mutants. Journal of Software: Evolution and Process, 32. https://doi.org/10.1002/smr.2238.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>
16.	Ayad, A., Marsit, I., Loh, J., Omri, M., &amp; Mili, A. (2019). Estimating the Number of Equivalent Mutants. 2019 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW), 112-121. https://doi.org/10.1109/ICSTW.2019.00039.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>
17.	Ayad, A., &amp; Mili, A. (2020). Automated Estimation of the Rate of Equivalent Mutants. 2020 International Conference on Computational Science and Computational Intelligence (CSCI), 1794-1799. https://doi.org/10.1109/CSCI51800.2020.00331.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>
18.	Kazerouni, A., Davis, J., Basak, A., Shaffer, C., Servant, F., &amp; Edwards, S. (2021). Fast and accurate incremental feedback for students' software tests using selective mutation analysis. J. Syst. Softw., 175, 110905. https://doi.org/10.1016/j.jss.2021.110905.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>
19.	Devroey, X., Perrouin, G., Papadakis, M., Legay, A., Schobbens, P., &amp; Heymans, P. (2018). Model-based mutant equivalence detection using automata language equivalence and simulations. J. Syst. Softw., 141, 1-15. https://doi.org/10.1016/j.jss.2018.03.010.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>
20.	Marsit, I., Ayad, A., Kim, D., Latif, M., Loh, J., Omri, M., &amp; Mili, A. (2021). The ratio of equivalent mutants: A key to analyzing mutation equivalence. J. Syst. Softw., 181, 111039. https://doi.org/10.1016/J.JSS.2021.111039.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>
21.	 Khan, R., &amp; Amjad, M. (2019). Mutation-based genetic algorithm for efficiency optimisation of unit testing. Int. J. Adv. Intell. Paradigms, 12, 254-265. https://doi.org/10.1504/IJAIP.2019.10019862.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>
22.	Petrović, G., Ivankovic, M., Fraser, G., &amp; Just, R. (2021). Practical Mutation Testing at Scale: A view from Google. IEEE Transactions on Software Engineering, 48, 3900-3912. https://doi.org/10.1109/TSE.2021.3107634.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>
23.	Naeem, M., Lin, T., Naeem, H., Ullah, F., &amp; Saeed, S. (2019). Scalable Mutation Testing Using Predictive Analysis of Deep Learning Model. IEEE Access, 7, 158264-158283. https://doi.org/10.1109/ACCESS.2019.2950171.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>The Local Strategic Model of Smart Police of The Islamic Republic of Iran</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Ahmad</given_name><surname>Dolatkhah</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Mohammad Reza</given_name><surname>Movahedi Sefat</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Mehrab</given_name><surname>Ramak</surname></person_name></contributors><publication_date media_type="online"><month>12</month><day>7</day><year>2024</year></publication_date><pages><first_page>109</first_page><last_page>122</last_page></pages><doi_data><doi>10.66224/jict.46184.16.61.109</doi><resource>http://jour.aicti.ir/en/Article/46184</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/en/Article/Download/46184</resource></item><item crawler="google"><resource>http://jour.aicti.ir/en/Article/Download/46184</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/en/Article/Download/46184</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/en/Article/Download/46184</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/en/Article/Download/46184</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/en/Article/Download/46184</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/en/Article/Download/46184</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation> اردلان، محمدرضا؛ قنبري، سيروس؛ نصيري وليک بني، فخرالسادات؛ بهشتيراد، رقيه" نقش رهبري خدمتگزار در ارتقاي اعتماد سازماني با نقش ميانجي توانمندسازي." فصلنامه مطالعات اندازه گيري و ارزشيابي آموزشي، دوره سوم، شماره چهارم، 1392 </unstructured_citation></citation><citation key="ref2"><unstructured_citation>
]2[. اوجاقي، علي." بررسي نقش ميانجي گروه هاي تسهيم تجربه بر رابطه ساختار سازماني ارگانيک و مديريت دانش در سازمانهاي نظامي". مديريت نظامي، دوره سوم، شماره نوزدهم، 1398</unstructured_citation></citation><citation key="ref3"><unstructured_citation>
]3[.الهي، شعبان و آذر، عادل. "سيستم هاي هوشمند اطلاعاتي مديريت، رويکرد فازي، عصبي" فصلنامه علمي پژوهشي مدرس، 157-135. 1378</unstructured_citation></citation><citation key="ref4"><unstructured_citation>
]4[.حقاني، محمود؛ شيباني، حسن؛ کرباسيان، سعيد. "‌سنجش سرمايه هاي فکري به منظور توسعه منابع انساني در شرکتهاي خودروسازي." رهيافتي نو در مديريت آموزشي، دوره نهم، شماره چهل و سوم، 1397 </unstructured_citation></citation><citation key="ref5"><unstructured_citation>
]5[.حمیدزاده, مهرداد, پورابراهیمی, علیرضا, طلوعی اشلقی, عباس, معتدل, محمدرضا. "امنیت سازمان هوشمند مبتنی بر هستان شناسی با رویکرد مفهوم سازی شبکه". نشریه"فناوری اطلاعات و ارتباطات انتظامی دوره چهارم شماره سیزدهم، 1402</unstructured_citation></citation><citation key="ref6"><unstructured_citation>
]6[.شاه محمدی, غلامرضا, درویشی سرنابادی, رضا, قمری, محمدرضا. 'الگوی پلیس هوشمند در فرماندهی انتظامی جمهوری اسلامی ایران', پژوهش های مدیریت انتظامی، دوره هجدهم، شماره اول 1402</unstructured_citation></citation><citation key="ref7"><unstructured_citation>
]7[. شاه محمدي، غلامرضا، اکباتاني، سميه "آسيب شناسي هوشمندسازي مرزهاي جمهوري اسلامي ايران" اولين همايش ملي رويکردهاي نوين در مديريت مرز" دانشگاه علوم انتظامي امين.  1401</unstructured_citation></citation><citation key="ref8"><unstructured_citation>
]8[. قنواتي زاده درويش، ويدا و گودرزي، علي." هوشمند سازي و تاثير تکنولوژي ديجيتال در حوزه هاي مديريت منابع انساني" ،دومين کنفرانس ملي آينده نگري در روانشناسي و علوم تربيتي،شيراز، 1401</unstructured_citation></citation><citation key="ref9"><unstructured_citation>
]9[. درویشی, صیاد. "تأثیرکیفیت خدمات پلیس هوشمند بر مشارکت شهروندان در پیشگیری از توزیع مواد مخدر". مجله علمی "مدیریت سرمایه اجتماعی،  دوره هفتم، شماره چهارم، 1399</unstructured_citation></citation><citation key="ref10"><unstructured_citation>
[10]. Katz, Charles M. &amp; Kurtenbach, Mike &amp; Choate, David E. &amp; White, Michael D. 2019. Phoenix, Arizona, Smart Policing Initiative: Evaluating the Impact of Police Officer Body- Worn Cameras, Bureau of Justice Assistance, NCJRS Abstract</unstructured_citation></citation><citation key="ref11"><unstructured_citation>.
[11]. Coldren Jr., James R. &amp; Huntoon, Alissa &amp; Medaris, Michael.”Introducing Smart Policing: Foundations, Principles, and Practice,” Police quarterly , Vol(16), No(3), PP. 275-286, 2013.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>
[12]. Arcos, R. (2016). Public relations strategic intelligence: Intelligence analysis, communication and influence. Public Relations Review, Vol(42), No(2), PP.264-270, 2016</unstructured_citation></citation><citation key="ref13"><unstructured_citation>

]13[.درستکار یاقوتی, بهنام." بازیابی ویدئو مبتنی بر محتوا با استفاده از شبکه عصبی عمیق برای کشف علمی جرائم در پلیس هوشمند". نشریه"فناوری اطلاعات و ارتباطات انتظامی، دوره چهارم، شماره سیزدهم، 1402.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>
]14[. امیری, نسرین, فرورقی, کیوان, قادری, محمد رضا." آنتن شکاف دار موجبری با شکافِ بدون خَمش در دیواره باریک برای کاربردهای پهپادی در باندX " نشریه"فناوری اطلاعات و ارتباطات انتظامی، دوره چهارم، شماره پانزدهم، 1402.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>
]15[. گرامی, محسن, مبین علی, افسانه, هاشم‌پور, راهب, یزدانیان, وحید. تحلیل رفتار کاربران در فروشگاه های اینترنتی با استفاده از یادگیری عمیق". نشریه"فناوری اطلاعات و ارتباطات انتظامی، دوره چهارم، شماره سیزدهم، 1402</unstructured_citation></citation><citation key="ref16"><unstructured_citation>
]16[.لک, بهزاد, بختیاری, سعید, محمد زاده مهنه, حمید رضا, امینی, ابوالفضل." تحلیل تاثیر ابزارهای فناوری اطلاعات در هوشمندسازی پلیس در عرصه مبارزه با قاچاق از گمرکات مرزی"" نشریه"فناوری اطلاعات و ارتباطات انتظامی، دوره چهارم، شماره پانزدهم، 1402.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>
]17[.حیدری, محمدرضا, کریمی زند, مهدی, کرامتی, محمدعلی, آرائی, وحید, موسوی, سید عبداله امین. شناسایی و اولویت بندی عوامل موثر بر حکمرانی نوین مبتنی بر کارکردهای هوش مصنوعی فرماندهی انتظامی ج.ا.ا "فناوری اطلاعات و ارتباطات انتظامی، دوره چهارم، شماره چهاردهم، 1402.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>
]18[.قيصري ، نورالله" اقتدار، جذابيت، هوشمندي و معماري ناجاي آينده،" فصلنامه ي مطالعات راهبردي ناجا، سال سوم ، شماره هشتم، 1397</unstructured_citation></citation><citation key="ref19"><unstructured_citation>
]19[.قلاوند, کورش, حاجی ملی میرزایی, حامد. مسئله شناسی نظام انتظامی فضای مجازی جمهوری اسلامی ایران. نشریه"فناوری اطلاعات و ارتباطات انتظامی، دوره چهارم، شماره پانزدهم، 1402.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>
]20[.مجدي، علي اکبر؛ تيموري، محمود؛ ميرمحمدتبار، سيد احمد."تحليل و واکاوي عوامل مؤثر بر يادگيري سازماني؛ فراتحليلي از تحقيقات موجود" فصلنامه علوم اجتماعي، دوره پانزدهم، شماره اول، 1397</unstructured_citation></citation><citation key="ref21"><unstructured_citation>


[21]. AlegreJ., ChivaR.; Assessing impact of organizational learning capability on product performance; Technovation, No. 28, , pp.315–326. 2014</unstructured_citation></citation><citation key="ref22"><unstructured_citation>
[22]. Al-Kasasbeh, M. M. (2019). The Impact of E-Management in Achieving Competitive Advantages. International Journal of Adaeh, 10, 101–120.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>
[23]. Afzal, M., &amp; Panagiotopoulos, P. (2020). Smart policing: A critical review of the literature. In International Conference on Electronic Government (pp. 59-70).</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Providing an Intrusion Detection System in the Industrial Internet of Things Using the Gray Wolf Algorithm</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Sajad</given_name><surname>Alimohamadi</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Mohammad</given_name><surname>Fathi</surname></person_name></contributors><publication_date media_type="online"><month>12</month><day>7</day><year>2024</year></publication_date><pages><first_page>218</first_page><last_page>228</last_page></pages><doi_data><doi>10.66224/jict.46422.16.61.218</doi><resource>http://jour.aicti.ir/en/Article/46422</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/en/Article/Download/46422</resource></item><item crawler="google"><resource>http://jour.aicti.ir/en/Article/Download/46422</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/en/Article/Download/46422</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/en/Article/Download/46422</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/en/Article/Download/46422</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/en/Article/Download/46422</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/en/Article/Download/46422</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>‎[1]‎	X.Fei and G. Tian, "Fault Identification and ‎Analysis of Communication NetworkBased ‎on Deep Learning," 2022.‎</unstructured_citation></citation><citation key="ref2"><unstructured_citation>‎[2]‎	J. Wan, J. Li, M. Imran and D. Li, "A ‎blockchain-based solution for enhancing ‎security and privacy in smart factory," IEEE ‎Transactions on Industrial Informatics, vol. ‎‎15, no. 6, pp. 3652-3660, 2019.‎</unstructured_citation></citation><citation key="ref3"><unstructured_citation>‎[3]‎	A. R. Sadeghi, C. Wachsmann and M. ‎Waidner, "Security and privacy challenges in ‎industrial internet of thing," In 2015 52nd ‎ACM/EDAC/IEEE Design Automation ‎Conference (DAC), pp. 1-6, June 2015.‎</unstructured_citation></citation><citation key="ref4"><unstructured_citation>‎[4]‎	J. Sengupta, S. Ruj and S. D. Bit, "A ‎comprehensive survey on attacks, security ‎issues and blockchain solutions for IoT and ‎IIoT," Journal of Network and Computer ‎Applications, p. 149, 2020.‎</unstructured_citation></citation><citation key="ref5"><unstructured_citation>‎[5]‎	M. Zolanvari, M. A. Teixeira, L. Gupta and ‎K. Khan, "Machine learning-based network ‎vulnerability analysis of industrial Internet of ‎Things," IEEE Internet of Things Journal, vol. ‎‎6, no. 4, pp. 6822-6834, 2019.‎</unstructured_citation></citation><citation key="ref6"><unstructured_citation>‎[6]‎	A. Deshpande, P. Pitale and S. Sanap, ‎‎"Industrial automation using Internet of ‎Things (IOT)," International Journal of ‎Advanced Research in Computer Engineering ‎&amp; Technology (IJARCET), vol. 5, no. 2, pp. ‎‎266-269, 2016.‎</unstructured_citation></citation><citation key="ref7"><unstructured_citation>‏[7]‏	ر. آرزم‎ ‎‏ و ع. براتی، "ارائه روشی برای احراز هویت در اینترنت اشیاء مبتنی بر ‏موقعیت گیرنده‌های‎ Wi-Fi ‎و فناوری زنجیره بلوکی‎"‎، پنجمین اجلاس ملی ‏محاسبات توزیعی و پردازش داده‌های بزرگ، ص ۱۸‏‎, ‎‏۱۳۹۸‏‎.‎</unstructured_citation></citation><citation key="ref8"><unstructured_citation>‏[8]‏	ع. سید ترابی‎ and ‎ر. پهلوان, "رمزگذاری در احراز هویت دستگاه‌های اینترنت ‏اشیا‎"‎، چهارمین اجلاس ملی ایده‌های نوین در فنی و مهندسی، ص 24، 1398.‏</unstructured_citation></citation><citation key="ref9"><unstructured_citation>‏[9]‏	ح. عیسی لو‎ and ‎ع. سلیمانی، "ارائه روشی کم‌بار برای احراز هویت اشیاء در ‏اینترنت اشیا‎"‎، کنگره ملی تحقیقات بنیادین در مهندسی کامپیوتر و فناوری ‏اطلاعات، ص 15، 1398.‏</unstructured_citation></citation><citation key="ref10"><unstructured_citation>‎[10]‎	A. Derhab, M. Guerroumi, A. Gumaei, L. ‎Maglaras, M. A. Ferrag, M. Mukherjee, et al., ‎‎"Blockchain and random subspace learning-‎based ids for sdn-enabled industrial iot ‎security", Sensors, vol. 19, no. 14, pp. 3119, ‎‎2019.‎</unstructured_citation></citation><citation key="ref11"><unstructured_citation>‎[11]‎	D. K. K. Reddy, H. Behera, J. Nayak, B. ‎Naik, U. Ghosh and P. K. Sharma, "Exact ‎greedy algorithm based split finding approach ‎for intrusion detection in fog-enabled iot ‎environment", Journal of Information Security ‎and Applications, vol. 60, pp. 102866, 2021.‎</unstructured_citation></citation><citation key="ref12"><unstructured_citation>‎[12]‎	F. Zhang, H. A. D. E. Kodituwakku, J. ‎W. Hines and J. Coble, "Multilayer data-‎driven cyber-attack detection system for ‎industrial control systems based on network ‎system and process data", IEEE Transactions ‎on Industrial Informatics, vol. 15, no. 7, pp. ‎‎4362-4369, 2019.‎</unstructured_citation></citation><citation key="ref13"><unstructured_citation>‎[13]‎	O. Eigner, P. Kreimel, P. Tavolato and P. ‎Kreimel, "Detection of man-in-the-middle ‎attacks on industrial control networks," in In ‎‎2016 International Conference on Software ‎Security and Assurance (ICSSA), 2016.‎</unstructured_citation></citation><citation key="ref14"><unstructured_citation>‎[14]‎	M. Zolanvari, . M. A. Teixeira, L. Gupta, . ‎K. M. Khan and R. Jain, "Machine Learning-‎Based Network Vulnerability Analysis of ‎Industrial Internet of Things," Internet of ‎Things Journal, vol. 6, no. 4, pp. 6822-6833, ‎‎2019.‎</unstructured_citation></citation><citation key="ref15"><unstructured_citation>‎[15]‎	S. Mirjalili, S. M. Mirjalili, and A. Lewis, ‎‎“Grey Wolf Optimizer,” Advances in ‎Engineering Software, vol. 69, pp. 46–61, ‎Mar. 2014.‎</unstructured_citation></citation><citation key="ref16"><unstructured_citation>‎[16]‎	C. M. Bishop, Pattern Recognition and ‎Machine Learning. Springer, 2016.‎</unstructured_citation></citation><citation key="ref17"><unstructured_citation>‎[17]‎	M. Tavallaee, E. Bagheri, W. Lu, and A. A. ‎Ghorbani, “A detailed analysis of the KDD ‎CUP 99 data set.” 2009 IEEE Symposium on ‎Computational Intelligence for Security and ‎Defense Applications, 2009.‎</unstructured_citation></citation><citation key="ref18"><unstructured_citation>‏[18]‏	ل. عجمي بختياروند و‎ ‎ز. بهشتی، "روشی نوين برای خوشه‌بندی داده‌ها با استفاده از ‏الگوريتم بهينه‌سازی چهارگرگ خاكستری‎,"‎‏ نشريه مهندسی برق و مهندسی ‏كامپيوتر ايران، ب- مهندسي كامپيوتر، ص.ص 274-261،شماره 4، سال 19.‏‎ ‎</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Provide A Lightweight Encryption Solution To secure Data In The Internet Of Things </title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>wahab</given_name><surname>aminiazar</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>rasoul</given_name><surname>farahi</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>fattameh</given_name><surname>dashti</surname></person_name></contributors><publication_date media_type="online"><month>12</month><day>7</day><year>2024</year></publication_date><pages><first_page>282</first_page><last_page>294</last_page></pages><doi_data><doi>10.66224/jict.46598.16.61.282</doi><resource>http://jour.aicti.ir/en/Article/46598</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/en/Article/Download/46598</resource></item><item crawler="google"><resource>http://jour.aicti.ir/en/Article/Download/46598</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/en/Article/Download/46598</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/en/Article/Download/46598</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/en/Article/Download/46598</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/en/Article/Download/46598</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/en/Article/Download/46598</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	Hassija V, Chamola V, Saxena V, Jain D, Goyal P, Sikdar B. A survey on IoT security: application areas, security threats, and solution architectures. IEEE Access. 2019 Jun 20;7:82721-43.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>
[2]	Ammar M, Russello G, Crispo B. Internet of Things: A survey on the security of IoT frameworks. Journal of Information Security and Applications. 2018 Feb 1;38:8-27.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>
[3]	Mrabet H, Belguith S, Alhomoud A, Jemai A. A survey of IoT security based on a layered architecture of sensing and data analysis. Sensors. 2020 Jun 28;20(13):3625.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>
[4]	HaddadPajouh H, Dehghantanha A, Parizi RM, Aledhari M, Karimipour H. A survey on internet of things security: Requirements, challenges, and solutions. Internet of Things. 2021 Jun 1;14:100129.
[5]	Mousavi SK, Ghaffari A, Besharat S, Afshari H. Security of internet of things based on cryptographic algorithms: a survey. Wireless Networks. 2021 Feb;27(2):1515-55.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>
[6]	Fraga-Lamas P, Fernández-Caramés TM, Suárez-Albela M, Castedo L, González-López M. A review on internet of things for defense and public safety. Sensors. 2016 Oct 5;16(10):1644.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>
[7]	Mousavi SK, Ghaffari A, Besharat S, Afshari H. Improving the security of internet of things using cryptographic algorithms: a case of smart irrigation systems. Journal of Ambient Intelligence and Humanized Computing. 2021 Feb;12(2):2033-51.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>
[8]	D. Wang, W. Li, and P. Wang, “Measuring TwoFactor Authentication Schemes for Real-Time Data Access in Industrial Wireless Sensor Networks,” IEEE Trans. Ind. Informatics, vol. 14, no. 9, pp. 4081–4092, 2018. </unstructured_citation></citation><citation key="ref8"><unstructured_citation>
[9]	C. M. Chen, S. Liu, X. Li, S. Kumari, and L. Li, “Design and Analysis of a Provable Secure TwoFactor Authentication Protocol for Internet of Things,” Secur. Commun. Networks, vol. 2022. </unstructured_citation></citation><citation key="ref9"><unstructured_citation>
[10]	Xia Z, Liu Y, Hsu CF, Chang CC. Cryptanalysis and improvement of a group authentication scheme with multiple trials and multiple authentications. Security and Communication Networks. 2020 Jul 13;2020:1-8.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>
[11]	El Mouaatamid O, Lahmer M, Belkasmi M. A scalable group authentication scheme based on combinatorial designs with fault tolerance for the Internet of things. SN Computer Science. 2020 Jul;1:1-3.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>
[12]	Yao Y, Chang X, Mišić J, Mišić VB. Lightweight batch AKA scheme for user-centric ultra-dense networks. IEEE Transactions on Cognitive Communications and Networking. 2020 Mar 20;6(2):597-606.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>
[13]	Sun Y, Cao J, Ma M, Zhang Y, Li H, Niu B. EAP-DDBA: efficient anonymity proximity device discovery and batch authentication mechanism for massive D2D communication devices in 3GPP 5G HetNet. IEEE transactions on dependable and secure computing. 2020 Apr 23;19(1):370-87.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>
[14]	Park K, Noh S, Lee H, Das AK, Kim M, Park Y, Wazid M. LAKS-NVT: Provably secure and lightweight authentication and key agreement scheme without verification table in medical internet of things. IEEE Access. 2020 Jun 29;8:119387-404.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>
[15]	Zhu L, Xiang H, Zhang K. A Light and Anonymous Three-Factor Authentication Protocol for Wireless Sensor Networks. Symmetry 2022, 14, 46. Optimization and Applications of Modern Wireless Networks and Symmetry. 2021:3.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>
[16]	Wu F, Li X, Sangaiah AK, Xu L, Kumari S, Wu L, Shen J. A lightweight and robust two-factor authentication scheme for personalized healthcare systems using wireless medical sensor networks. Future Generation Computer Systems. 2018 May 1;82:727-37.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>
[17]	Shreya S, Chatterjee K, Singh A. A smart secure healthcare monitoring system with Internet of Medical Things. Computers and Electrical Engineering. 2022 Jul 1;101:107969.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>
[18]	Adhikary T, Jana AD, Chakrabarty A, Jana SK. The internet of things (iot) augmentation in healthcare: An application analytics. ICICCT 2019–System Reliability, Quality Control, Safety, Maintenance and Management: Applications to Electrical, Electronics and Computer Science and Engineering. 2020:576-83.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>
[19]	Dhillon PK, Kalra S. Elliptic curve cryptography for real time embedded systems in IoT networks. In2016 5th international conference on wireless networks and embedded systems (WECON) 2016 Oct 14 (pp. 1-6). IEEE.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>
[20]	Durairaj M, Muthuramalingam K. A new authentication scheme with elliptical curve cryptography for internet of things (IoT) environments. Int. J. Eng. Technol. 2018;7(2.26):119-24.
</unstructured_citation></citation></citation_list></journal_article></journal></body></doi_batch>