﻿<?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-1405070418</doi_batch_id><timestamp>14050704183545</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>2</month><day>26</day><year>2026</year></publication_date><journal_volume><volume>17</volume></journal_volume><issue>66</issue></journal_issue><journal_article publication_type="full_text"><titles><title>Enteprise Ontology Based on Intelligent Agents. Case Study: Knowledge Based Production Export Actors</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Mohammad Rahim</given_name><surname>Banakar</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>ُShaban</given_name><surname>Elahi</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Shaghayegh</given_name><surname>Sahraee</surname></person_name></contributors><publication_date media_type="online"><month>2</month><day>26</day><year>2026</year></publication_date><pages><first_page>1</first_page><last_page>19</last_page></pages><doi_data><doi>10.66224/jict.41455.17.66.1</doi><resource>http://jour.aicti.ir/fa/Article/41455</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/fa/Article/Download/41455</resource></item><item crawler="google"><resource>http://jour.aicti.ir/fa/Article/Download/41455</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/fa/Article/Download/41455</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/fa/Article/Download/41455</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/fa/Article/Download/41455</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/fa/Article/Download/41455</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/fa/Article/Download/41455</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	S. S. Rao and A. Nayak, “Enterprise ontology model for tacit knowledge externalization in socio-technical enterprise,” Interdiscip. J. Information, Knowledge, Manag., vol. 12, pp. 99–124, 2017.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>[2]	M. Brahimi, “An agents’ model using ontologies and web services for creating and managing virtual enterprises,” Int. J. Comput. Digit. Syst., vol. 8, no. 1, pp. 1–9, 2019, doi: 10.12785/ijcds/080101.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>[3]	T. R. Gruber, “Toward Principles for the Design of Ontologies,” International Journal of Human-Computer Studies, vol. 43, no. 5–6. pp. 907–928, 1995.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>[4]	D. Monticolo, I. Lahoud, and P. C. Barrios, “OCEAN: A multi agent system dedicated to knowledge management,” J. Ind. Inf. Integr., vol. 17, p. 100124, 2020, doi: 10.1016/j.jii.2019.100124.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>[5]	S. Zidat and F. Marir, “An Approach to the Acquisition of Tacit Knowledge Based on an Ontological Model Department of Computer Science , Chahid Mostefa Ben Boulaid , University of College of Technological Innovation , Zayed University , Dubai , United Arab Corresponding Author :,” J. King Saud Univ. - Comput. Inf. Sci., 2018, doi: 10.1016/j.jksuci.2018.09.012.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>[6]	A. Di Iorio and D. Rossi, “Capturing and managing knowledge using social software and semantic web technologies,” Inf. Sci. (Ny)., vol. 432, pp. 1–21, 2018, doi: 10.1016/j.ins.2017.12.009.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>[7]	Jan Andreasik, Knowledge management model based on the enterprise ontology for the KB DSS system of enterprise situation assessment in the SME sector, vol. 787. Springer International Publishing, 2019. doi: 10.1007/978-3-319-94229-2_15.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>[8]	B. Okreša Ɖurić, J. Rincon, C. Carrascosa, M. Schatten, and V. Julian, “MAMbO5: a new ontology approach for modelling and managing intelligent virtual environments based on multi-agent systems,” J. Ambient Intell. Humaniz. Comput., no. 0123456789, 2018, doi: 10.1007/s12652-018-1089-4.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>[9]	D. Chumachenko and I. Meniailov, “Development of an intelligent agent-based model of the epidemic process of syphilis,” csit, vol. 2, pp. 17–20, 2019.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>[10]	Y. Wang, L. Wang, and C. Wang, “Research on Ontology-Based Tacit Knowledge Mining for Aerospace Enterprise,” J. Phys. Conf. Ser., vol. 1087, no. 3, 2018, </unstructured_citation></citation><citation key="ref11"><unstructured_citation>[11]	A. Smirnov, A., Levashova, T. and Kashevnik, Enterprise Ontology for Service Interoperability in Socio-Cyber-Physical Systems. In Enterprise Interoperability VIII, vol. 9. Springer International Publishing, 2019. doi: 10.1007/978-3-030-13693-2.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>[12]	Y. Chemlal, “Onto-agent-SSSN: An ontology model to facilitate reactive reasoning in multi-agent systems within a business intelligence network,” Int. J. Reason. Intell. Syst., vol. 11, no. 3, pp. 282–291, 2019, doi: 10.1504/IJRIS.2019.102635.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>[13]	J. U. the blockchain using enterprise ontology. de Kruijff, J. and Weigand, H., 2017, “Understanding the Blockchain Using Enterprise Ontology,” Int. Conf. Adv. Inf. Syst. Eng. Springer, Cham., pp. 29–43, 2017, doi: 10.1007/978-3-319-59536-8.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>[14]	J. Liu et al., “Grid workflow validation using ontology-based tacit knowledge: A case study for quantitative remote sensing applications,” Comput. Geosci., vol. 98, pp. 46–54, 2017, doi: 10.1016/j.cageo.2016.10.002.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>[15]	J. Cordeiro, “Analysing Enterprise Ontology and Its Suitability for Model-Based Software Development,” vol. 2, pp. 257–269, 2019, doi: 10.1007/978-3-030-24854-3_19.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>[16]	M. A. Musa and M. S. Othman, “Knowledge map and enterprise ontology for enhancing business process reengineering in healthcare: A case of radiology department,” Int. J. Enterp. Inf. Syst., vol. 12, no. 2, pp. 26–46, 2016, doi: 10.4018/IJEIS.2016040103.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>[17]	توقعی، محسن و بهشت‌زاده کیایی، منیره و رضایی، سپیده، 1395, “بررسی بکارگیری هستان‌شناسی در ساماندهی جریان دانش ضمنی سازمان,” in همایش ملی دانش و فناوری مهندسی برق، کامپیوتر و مکانیک ایران, 1395, vol. https://ci.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>[18]	A. M. Pinto-Llorente, M. C. Sánchez-Gómez, and A. Pedro Costa, “Qualitative and Mixed Methods Researches in Social Sciences,” ACM Int. Conf. Proceeding Ser., pp. 193–196, 2020, doi: 10.1145/3434780.3436696.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>[19]	A. Van Den Berg and M. Struwig, “Guidelines for Researchers Using an Adapted Consensual Qualitative Research Approach in... by Academic Conferences and publishing International - Issuu,” Electron. J. Bus. Res. Methods, vol. 15, no. 2, pp. 109–119, 2017.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>[20]	Y. Xiao and M. Watson, “Guidance on Conducting a Systematic Literature Review,” J. Plan. Educ. Res., vol. 39, pp. 93–112, 2019, doi: 10.1177/0739456X17723971.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>[21]	L. K. Nelson, “Computational Grounded Theory : A Methodological Framework,” Sociol. Methods Res., vol. 49, pp. 3–42, 2020, doi: 10.1177/0049124117729703.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>[22]	R. T. Webster, J. and Watson, “Analyzing the past to prepare for the future: Writing a literature review,” MIS Q., vol. 26, no. 2, pp. xiii–xxiii, 2002.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>[23]	K. Dal, S. Mendes, R. Cristina, and D. C. Pereira, “USE OF THE BIBLIOGRAPHIC REFERENCE MANAGER IN THE SELECTION OF PRIMARY STUDIES IN INTEGRATIVE REVIEWS,” Texto Context., vol. 28, pp. 1–13, 2019, doi: 10.1590/1980-265X-TCE-2017-0204.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>[24]	Dermeval, Diego, Jéssyka Vilela, Ig Ibert Bittencourt, Jaelson Castro, Seiji Isotani, Patrick Brito, “Applications of ontologies in requirements engineering : a systematic review of the literature,” Requir. Eng., vol. 21, no. 4, pp. 405–437, 2016, doi: 10.1007/s00766-015-0222-6.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>[25]	M. Dadkhah, S. Araban, and S. Paydar, “A systematic literature review on semantic web enabled software testing,” J. Syst. Softw., vol. 162, p. 110485, 2020, doi: 10.1016/j.jss.2019.110485. </unstructured_citation></citation><citation key="ref26"><unstructured_citation>[26]	J. Gharib, M., Giorgini, P. and Mylopoulos, “Towards an ontology for privacy requirements via a systematic literature review,” in 36th International Conference on Conceptual Modeling (ER), 2017, vol. 10650, pp. 193–208. doi: 10.1007/978-3-319-69904-2_16.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>[27]	F. Messaoudi, R., Mtibaa, A., Vacavant, A., Gargouri, F. and Jaziri, “Ontologies for Liver Diseases Representation : A Systematic Literature Review,” J. Digit. Imaging, pp. 1–11, 2019, doi: 10.1007/s10278-019-00303-2.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>[28]	H. wiesche, Manuel, Jurisch, Marlen C, Yetton, Philip W and Krcmar, “Grounded Theory Methodology in Information Systems Research,” MIS Q., vol. 41, no. 3, pp. 685–701, 2017, doi: 10.25300/MISQ/2017/41.3.02.</unstructured_citation></citation><citation key="ref29"><unstructured_citation>[29]	L. &amp; Guba, Competing Paradigms in Qualitative Research. 1994.</unstructured_citation></citation><citation key="ref30"><unstructured_citation>[30]	J. E. Douglas and M. Bryon, “Interview data on severe behavioural eating difficulties in young children,” Arch. Dis. Child., vol. 75, no. 4, pp. 304–308, 1996, doi: 10.1136/adc.75.4.304.</unstructured_citation></citation><citation key="ref31"><unstructured_citation>[31]	T. Hovorushchenko and O. Pavlova, Method of activity of ontology-based intelligent agent for evaluating initial stages of the software lifecycle, vol. 836. Springer International Publishing, 2019. doi: 10.1007/978-3-319-97885-7_17.</unstructured_citation></citation><citation key="ref32"><unstructured_citation>[32]	V. R. Sampath Kumar et al., “Ontologies for industry 4.0,” Knowl. Eng. Rev., vol. 34, pp. 1–14, 2019, doi: 10.1017/S0269888919000109.</unstructured_citation></citation><citation key="ref33"><unstructured_citation>[33]	[33] Smith, B. (2020) ‘Ontology and Its Applications II’, Iranian Research Institute for Information Science and Technology, 36(1), pp. 271–294.</unstructured_citation></citation><citation key="ref34"><unstructured_citation>[34]	de Franco Rosa, Ferrucio et al. (2018) Towards an Ontology of Security Assessment: A Core Model Proposal Security assessment ·Information security ·Knowledge formalization ·OWL ·Ontology 12.1 Introduction. Springer International Publishing. Available at: https://doi.org/10.1007/978-3-319-77028-4_12.</unstructured_citation></citation><citation key="ref35"><unstructured_citation>[35]	Degbelo, A. (2017) ‘A snapshot of ontology evaluation criteria and strategies’, ACM International Conference Proceeding Series, 2017-Septe(September), pp. 1–8. doi: 10.1145/3132218.3132219.</unstructured_citation></citation><citation key="ref36"><unstructured_citation>[36]	Raad, J. and Cruz, C. (2018) ‘A Survey on Ontology Evaluation Methods’, Quarterly Knowledge and Information Management Journal, 6(2), pp. 25–34. doi: 10.30473/MRS.2020.48615.1402.</unstructured_citation></citation><citation key="ref37"><unstructured_citation>[37]	Chumachenko, D. and Meniailov, I. (2019) ‘Development of an intelligent agent-based model of the epidemic process of syphilis’, csit. IEEE, 2, pp. 17–20.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>A Self-supervised Sensors’ Anomaly Detection Scheme in Industrial Control Systems based on Ensemble Deep Learning </title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Armin</given_name><surname>Salimi-Badr</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Athena</given_name><surname>Abdi</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Afshin</given_name><surname>Souzani</surname></person_name></contributors><publication_date media_type="online"><month>2</month><day>26</day><year>2026</year></publication_date><pages><first_page>69</first_page><last_page>86</last_page></pages><doi_data><doi>10.66224/jict.47707.17.66.69</doi><resource>http://jour.aicti.ir/fa/Article/47707</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/fa/Article/Download/47707</resource></item><item crawler="google"><resource>http://jour.aicti.ir/fa/Article/Download/47707</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/fa/Article/Download/47707</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/fa/Article/Download/47707</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/fa/Article/Download/47707</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/fa/Article/Download/47707</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/fa/Article/Download/47707</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	E. Knapp, Industrial Network Security: Securing critical infrastructure networks for smart grid, SCADA, and other Industrial Control Systems. Elsevier, 2024</unstructured_citation></citation><citation key="ref2"><unstructured_citation>[2]	R. Radvanovsky and J. Brodsky, Handbook of SCADA. Boca Raton Crc Press, 2016.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>[3]	B. Zhu and S. Sastry, “Scada-specific intrusion detection/prevention systems: a survey and taxonomy,” in Proceedings of the 1st Workshop on Secure Control Systems (SCS), 2010.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>[4]	B. Kim, M. Alawami, E. Kim, S. Oh, J. Park, H. Kim, “A comparative study of time series anomaly detection models for industrial control systems,” Sensors, vol. 23, p. 1310, January 2023.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>[5]	M. Nawrocki, M, T. Schmidt, M. Wählisch, “Uncovering Vulnerable Industrial Control Systems from the Internet Core,” In Proceedings of the IEEE/IFIP Network Operations and Management Symposium, Budapest, Hungary, 20–24 April 2020.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>[6]	A. Di Pinto, Y. Dragoni, A. Carcano, “The First ICS Cyber Attack on Safety Instrument Systems,” In Proceedings of the Black Hat USA, Las Vegas, NV, USA, 4–9 August 2018.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>[7]	K. D. Gupta, K. Singhal, D. K. Sharma, N. Sharma, and S. J. Malebary, “Fuzzy Controller-empowered Autoencoder Framework for anomaly detection in Cyber Physical Systems,” Computers &amp; Electrical Engineering, vol. 108, p. 108685, May 2023.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>[8]	D. Pliatsios, P. Sarigiannidis, T. Lagkas, and A. Sarigiannidis, “A survey on SCADA systems: secure protocols, incidents, threats and tactics,” IEEE Communications Surveys &amp; Tutorials, vol. 22, pp.1942-1976, April 2020. </unstructured_citation></citation><citation key="ref9"><unstructured_citation>[9]	Y. Yang, K. McLaughlin, T. Littler, S. Sezer, B. Pranggono, and H. Wang, “Intrusion detection system for IEC 60870-5-104 based scada networks,” in Proceeding IEEE Power &amp; Energy Society General Meeting, 2013.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>[10]	S. Alem, D. Espes, L. Nana, E. Martin, F. De Lamotte, “A novel bi-anomaly-based intrusion detection system approach for industry 4.0,” Future Generation Computer Systems, vol. 145, pp.267-283, August 2023. </unstructured_citation></citation><citation key="ref11"><unstructured_citation>[11]	F. Skopik, I. Friedberg, and R. Fiedler, “Dealing with advanced persistent threats in smart grid ict networks,” in Proceeding Innovative Smart Grid Technologies Conference (ISGT), 2014.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>[12]	I. Friedberg, F. Skopik, G. Settanni, and R. Fiedler, “Combating advanced persistent threats: From network event correlation to incident detection,” Computers &amp; Security, vol. 48, pp. 35–57, 2015.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>[13]	F. Zhang, H. Kodituwakku, J. Hines, 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, pp.4362-4369, January 2019.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>[14]	GR. MR, N. Somu, A. Mathur, “A Multilayer Perceptron Model for Anomaly Detection in Water Treatment Plants,” International Journal of Critical Infrastructure Protection, vol. 31, p. 100393, December 2020.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>[15]	R. Khalil, N. Saeed, M. Masood, Y. Fard, M. Alouini, T. Al-Naffouri, “Deep learning in the industrial internet of things: Potentials, challenges, and emerging applications,” IEEE Internet of Things Journal, vol. 8, pp. 11016-11040, 2021.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>[16]	H. Mao, M. Alizadeh, I. Menache, S. Kandula, “Resource management with deep reinforcement learning,” in Proceedings of the 15th ACM Workshop on Hot Topics in Networks, 2016.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>[17]	Y. Lu, S. Chai, Y. Suo, F. Yao, C. Zhang, “Intrusion detection for Industrial Internet of Things based on deep learning,” Neurocomputing, vol. 564, 2024.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>[18]	J. Audibert, P. Michiardi, F. Guyard, S. Marti, M. Zuluaga, “USAD: Unsupervised Anomaly Detection on Multivariate Time Series,” In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining, 2020.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>[19]	A. Deng, B. Hooi, “Graph Neural Network-Based Anomaly Detection in Multivariate Time Series,” In Proceedings of the AAAI Conference on Artificial Intelligence, 2021.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>[20]	Z. Li, Y. Zhao, J. Han, Y. Su, R. Jiao, X. Wen, D. Pei, “Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding,” In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining, 2021.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>[21]	A. Koay, R. Ko, H. Hettema, K. Radke, “Machine learning in industrial control system (ICS) security: current landscape, opportunities and challenges,” Journal of Intelligent Information Systems, vol. 60, pp. 377-405, 2023.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>[22]	M. Nankya, R. Chataut, R. Akl, “Securing industrial control systems: components, cyber threats, and machine learning-driven defense strategies,” Sensors, vol. 23, p. 8840, 2023.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>[23]	L. Yuan, X. Ya, C. Long, P. Guojun, Y. Danfeng “Deep Learning-Based Anomaly Detection in Cyber-Physical Systems: Progress and Opportunities,” ACM Computing Surveys, vol. 54, pp. 1-36, 2021. </unstructured_citation></citation><citation key="ref24"><unstructured_citation>[24]	W. Hilal, S. Gadsden, J. Yawney, “Financial fraud: a review of anomaly detection techniques and recent advances,” Expert systems With applications, vol. 193, p. 116429, 2022.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>[25]	A. Sgueglia, A. Sorbo, C. Visaggio, G. Canfora, ’A systematic literature review of IoT time series anomaly detection solutions,’ Future Generation Computer Systems, Vol. 134, PP. 170-186, 2022.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>[26]	A.Cook, G. Mısırlı, Z. Fan, “Anomaly Detection for IoT Time-Series Data: A Survey,” IEEE Internet of Things Journal, December 2019.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>[27]	L. Erhan, M. Ndubuaku, M. Di Mauro, W. Song, M. Chen, G. Fortino, O. Bagdasar, A. Liotta, ’Smart anomaly detection in sensor systems: A multi-perspective review’, Information Fusion,2020.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>[28]	A. Blázquez-García, A. Conde, U. Mori, J. Lozano, “A review on outlier/anomaly detection in time series data,” ACM computing surveys (CSUR), vol. 54, pp. 1-33, 2021.</unstructured_citation></citation><citation key="ref29"><unstructured_citation>[29]	M. Van Onsem, D. De Paepe, S. Hautte, P. Bonte, V. Ledoux, A. Lejon, S. Van Hoecke, “Hierarchical pattern matching for anomaly detection in time series,” Computer Communications, vol. 193, pp. 75-81, 2022.</unstructured_citation></citation><citation key="ref30"><unstructured_citation>[30]	C. Feng, T. Li and D. Chana, "Multi-level Anomaly Detection in Industrial Control Systems via Package Signatures and LSTM Networks," 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2017.</unstructured_citation></citation><citation key="ref31"><unstructured_citation>[31]	Y. Lai, J. Zhang, and Z. Liu, “Industrial Anomaly Detection and Attack Classification Method Based on Convolutional Neural Network,” Security and Communication Networks, vol. 2019, pp. 1–11, Sep. 2019.</unstructured_citation></citation><citation key="ref32"><unstructured_citation>[32]	M. Kravchik and A. Shabtai, “Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks,” Proceedings of the 2018 Workshop on Cyber-Physical Systems Security and PrivaCy, 2018.</unstructured_citation></citation><citation key="ref33"><unstructured_citation>[33]	A. Abdi, A. Ghasemi-Tabar, "ARAD: Automated and Real-Time Anomaly Detection in Sensors of Autonomous Vehicles Through a Lightweight Supervised Learning Approach," IEEE Access, vol. 12, pp. 90432-90441, 2024</unstructured_citation></citation><citation key="ref34"><unstructured_citation>[34]	L. Yuan, X. Ya, C. Long, P. Guojun, Y. Danfeng “Deep Learning-Based Anomaly Detection in Cyber-Physical Systems: Progress and Opportunities,” ACM Computing Surveys, vol. 54, pp. 1-36, 2021. </unstructured_citation></citation><citation key="ref35"><unstructured_citation>[35]	Y. Wu, H. Dai, H. Tang, H, “Graph neural networks for anomaly detection in industrial internet of things,” IEEE Internet of Things Journal, vol. 9, pp. 9214-9231, 2021.</unstructured_citation></citation><citation key="ref36"><unstructured_citation>[36]	Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, pp. 436-444, 2015.</unstructured_citation></citation><citation key="ref37"><unstructured_citation>[37]	A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. “O’Reilly Media, Inc.,” 2022.</unstructured_citation></citation><citation key="ref38"><unstructured_citation>[38]	H. Mao, M. Alizadeh, I. Menache, S. Kandula, “Resource management with deep reinforcement learning,” in Proceedings of the 15th ACM Workshop on Hot Topics in Networks. ACM, 2016, pp. 50-56.</unstructured_citation></citation><citation key="ref39"><unstructured_citation>[39]	Y. Lu, S. Chai, Y. Suo, F. Yao, C. Zhang, “Intrusion detection for Industrial Internet of Things based on deep learning,” Neurocomputing, vol. 564, 2024.</unstructured_citation></citation><citation key="ref40"><unstructured_citation>[40]	Y. LeCun, Generalization and network design strategies, Technical Report, CRG-TR-89-4, University of Toronto, 1989.</unstructured_citation></citation><citation key="ref41"><unstructured_citation>[41]	M.T. Jones, A beginner’s guide to artificial intelligence, machine learning, and cognitive computing, Technical Report, IBM, 2017.</unstructured_citation></citation><citation key="ref42"><unstructured_citation>[42]	E. Sisinni, A. Saifullah, S. Han, U. Jennehag, M. Gidlund, “Industrial internet of things: Challenges, opportunities, and directions,” IEEE transactions on industrial informatics, vol. 14, pp. 4724-4734, 2018.</unstructured_citation></citation><citation key="ref43"><unstructured_citation>[43]	J. Yu, H. Yin, X. Xia, T. Chen, J. Li and Z. Huang, "Self-Supervised Learning for Recommender Systems: A Survey," in IEEE Transactions on Knowledge and Data Engineering, vol. 36, pp. 335-355, Jan. 2024. </unstructured_citation></citation><citation key="ref44"><unstructured_citation>[44]	J. Gui, T. Chen, J. Zhang, Q. Cao, Z. Sun, H. Luo, D. Tao, “A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends,” IEEE Transactions on Pattern Analysis and Machine Intelligence, June 2024.</unstructured_citation></citation><citation key="ref45"><unstructured_citation>[45]	A. Mathur, N. Tippenhauer, “SWaT: A Water Treatment Testbed for Research and Training on ICS Security,” In Proceedings of the International Workshop on Cyber-Physical Systems for Smart Water Networks, 2016.</unstructured_citation></citation><citation key="ref46"><unstructured_citation>[46]	M, Macas, W. Chunming, “Enhanced Cyber-Physical Security through Deep Learning Techniques,” In Proceedings of the CPS Summer School PhD Workshop, 2019.</unstructured_citation></citation><citation key="ref47"><unstructured_citation>[47]	A. Abdulaal, Z. Liu, T. Lancewicki, “ Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization,” In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining, 2021.</unstructured_citation></citation><citation key="ref48"><unstructured_citation>[48]	M. Aslam, A. Tufail, L. Chandratilak De Silva, R. Anna Awg Haji Mohd Apong, A. Namoun. "An improved autoencoder-based approach for anomaly detection in industrial control systems." Systems Science &amp; Control Engineering, vol. 12, 2024.</unstructured_citation></citation><citation key="ref49"><unstructured_citation>[49]	A. Gómez, L. Fernández Maimó, A. Huertas Celdrán, F. García Clemente. "SUSAN: A Deep Learning based anomaly detection framework for sustainable industry," Sustainable Computing: Informatics and Systems, vol. 37, 2023.</unstructured_citation></citation><citation key="ref50"><unstructured_citation>[50]	S. Tang, Y. Ding, M. Zhao, H. Wang, "SAKMR: Industrial control anomaly detection based on semi-supervised hybrid deep learning," Peer-to-Peer Networking and Applications, vol. 17, 2024.</unstructured_citation></citation><citation key="ref51"><unstructured_citation>[51]	L. Pinto, L. Herrera, Y. Donoso, J. Gutierrez. "Enhancing Critical Infrastructure Security: Unsupervised Learning Approaches for Anomaly Detection." International Journal of Computational Intelligence Systems, vol. 17, 2024.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Security Evaluation of Information Systems with Systems Dynamics Approach (Study Case: Agriculture Bank)</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Amirhossein</given_name><surname>Abdolalipour</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Mohsen</given_name><surname>Shafiee</surname></person_name></contributors><publication_date media_type="online"><month>2</month><day>26</day><year>2026</year></publication_date><pages><first_page>54</first_page><last_page>68</last_page></pages><doi_data><doi>10.66224/jict.48325.17.66.54</doi><resource>http://jour.aicti.ir/fa/Article/48325</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/fa/Article/Download/48325</resource></item><item crawler="google"><resource>http://jour.aicti.ir/fa/Article/Download/48325</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/fa/Article/Download/48325</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/fa/Article/Download/48325</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/fa/Article/Download/48325</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/fa/Article/Download/48325</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/fa/Article/Download/48325</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	Moore, A., &amp; Warkentin, M. “Cybersecurity: Principles and Practices”. Pearson.2019.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>[2]	Osmanbegović, E., Piric, N., &amp; Suljic, M. “Information Security Controls As Determinant Of Continuity Of Information System Work”. Vol. XV, Issue 2, 35-42, 2017.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>[3]	Böhme, R., &amp; Moore, T. (2023). The Economics of Cybersecurity: Principles and Policy Options. Annual Review of Economics, 15, 567-592.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>[4]	Bock, S. “Human Error and Cybersecurity in the Banking Sector”. Journal of Banking Technology, 15(2), 123-135.2021.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>[5]	Alshaikh, M., Maynard, S. B., Ahmad, A., &amp; Chang, S. (2023). A Human-Centric Risk-Based Investment Model for Information Security: Empirical Evidence from the Financial Sector. Computers &amp; Security, 128, 103234.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>[6]	Lubua, E.W., Semlambo, A.A., &amp; Mkude, C.G. “Factors Affecting the Security of Information Systems in Africa: A Literature Review”. University of Dar es Salaam Library Journal, 17(2), 94-114.2022. </unstructured_citation></citation><citation key="ref7"><unstructured_citation>[7]	Alizadeh, A., Chehrehpak, M., Nasr, A.K., &amp; Zamanifard, S. “An empirical study on effective factors on adoption of cloud computing in electronic banking: a case study of Iran banking sector”. Int. J. Bus. Inf. Syst., 33, 408-428.2020.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>[8]	Khan, H. U., Malik, M. Z., Nazir, S. , and Khan,F., "Utilizing Bio Metric System for Enhancing Cyber Security in Banking Sector: A Systematic Analysis," in IEEE Access, vol. 11, pp. 80181-80198.2023.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>[9]	Rapina, R., Carolina, Y., Setiawan, S., Gania, A., Sandra, L.M., Darmasetiawan, J.B., &amp; Fuentes, R.O. “Empirical Study on Banking in Indonesia: Factors Affecting Information Systems Quality”. Proceedings of the 2020 12th International Conference on Information Management and Engineering. 2020.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>[10]	Alsalamah, A. “Security Risk Management in Online System”. 5th Intl Conf on Applied Computing and Information Technology/4th Intl Conf on Computational Science/Intelligence and Applied Informatics/2nd Intl Conf on Big Data, Cloud Computing, Data Science (ACIT-CSII-BCD), 119-124.2017.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>[11]	Lestari, D., Tama, A., Karlina, S., Sultan, A., &amp; Tarwoto, T. “Factors Affecting Security Information Systems: Information Security, Threats and Cyber Attack, Physical Security, and Information Technology”. International Journal of Informatics and Information Systems, 7(1), 16-21.2024. </unstructured_citation></citation><citation key="ref12"><unstructured_citation>[12]	Noubissi, A.C., Iguchi-Cartigny, J., &amp; Lanet, J. “Hot updates for Java based smart cards”. IEEE 27th International Conference on Data Engineering Workshops, 168-173.2011. </unstructured_citation></citation><citation key="ref13"><unstructured_citation>[13]	Putra Utama, F., &amp; Hilmi Nurhadi, R.M. “Uncovering the Risk of Academic Information System Vulnerability through PTES and OWASP Method”, COMMIT (Communication and Information Technology) Journal. 18(1), 39-51.2024.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>[14]	Smith, J. (2023). The Role of Artificial Intelligence in Banking Risk Management. Journal of Banking and Finance, 134, 1-10.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>[15]	Rajendran, S. R., N. F., Dipu, Tarek, S., H. M., Kamali, Farahmandi F. and Tehranipoor, M., "Exploring the Abyss? Unveiling Systems-on-Chip Hardware Vulnerabilities Beneath Software," in IEEE Transactions on Information Forensics and Security, vol. 19, pp. 3914-3926, 2024.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>[16]	ENISA (European Union Agency for Cybersecurity). (2022). Threat Landscape for Information Integrity in Financial Services.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>[17]	Duddu, S., Rishita sai, A., Sowjanya, C.L., Rao, G.R., &amp; Siddabattula, K. (2020). Secure Socket Layer Stripping Attack Using Address Resolution Protocol Spoofing. 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), 973-978.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>[18]	Gai, K., Qiu, M., &amp; Qiu, L. (2022). Security and Privacy Issues: A Survey on FinTech in Banking Systems. Future Generation Computer Systems, 135, 386-399.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>[19]	Brown, L., &amp; Green, T. (2022). The Impact of Data Types on Cyber Threats in Financial Institutions. International Journal of Cyber Studies, 9(2), 123-139.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>[20]	Li, Z., Xu, W., Shi, H., Zhang, Y., &amp; Yan, Y. “Security and Privacy Risk Assessment of Energy Big Data in Cloud Environment”. Computational intelligence and neuroscience, 2398460. 2021. https://doi.org/10.1155/2021/2398460 (Retraction published Comput Intell Neurosci. 2023 Oct 18; 2023:9896475. doi: 10.1155/2023/9896475).</unstructured_citation></citation><citation key="ref21"><unstructured_citation>[21]	Blesswin, J., Mary, S.J., Suryawanshi, S., Kshirsagar, V.G., Pabalkar, S.Y., Venkatesan, M., &amp; Karunya, C.E. “Secure transmission of grayscale images with triggered error visual sharing”. Journal of Autonomous Intelligence. 2023.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>[22]	اکبرنژاد، ابوالقاسم و چشک، کریم، "اولویت‌بندی مؤلفه‌های اثرگذار بر سیاست دفاعی- امنیتی جمهوری اسلامی ایران". 1399</unstructured_citation></citation><citation key="ref23"><unstructured_citation>[23]	جلالی، محمد و افشاری، مریم و مزینانیان، زینب،"تأثیر ابعاد زیست‌محیطی تغییرات اقلیمی بر امنیت ملی". 1399.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>[24]	Alsmadi, I., &amp; Zarour, M. (2023). Cybersecurity in Banking: Risks, Challenges, and Solutions. Journal of Banking and Financial Technology, 7(1), 21-34.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>[25]	خون جوش, ف.خ. و عاشوری, م. "بررسی تأثیر تنظیمات پارامترهای سخت‌افزاری بر انرژی مصرفی در الگوریتم ضرب برداری ماتریس‌های تنک بر روی پردازنده‌های گرافیکی" فصلنامه فناوری اطلاعات و ارتباطات ایران، (9)31، 78-67. 1398.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>[26]	Lee, S. Y. (2022). Physical Security Threats to Banking Information Systems. Journal of Financial Risk Management, 11(3), 1-12.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>[27]	Hassan, R., Bandi, C., Tsai, M., Golchin, S., P D, S.M., Rafatirad, S., &amp; Salehi, S. (2023). Automated Supervised Topic Modeling Framework for Hardware Weaknesses. 2023 24th International Symposium on Quality Electronic Design (ISQED), 1-8.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>[28]	Shehab, R., s.alismail, A., Amin Almaiah, D.M., Alkhdour, D.T., AlWadi, D.B., &amp; Alrawad, D.M. “Assessment of Cybersecurity Risks and threats on Banking and Financial Services. Journal of Internet Services and Information Security” 14(3), 167-190.2024.</unstructured_citation></citation><citation key="ref29"><unstructured_citation>[29]	White, R., &amp; Black, S. “Historical Cyber Attacks and Their Future Implications for Banks. Cybersecurity Review”, 15(1), 88-102.2023.</unstructured_citation></citation><citation key="ref30"><unstructured_citation>[30]	Shams, S., &amp; Soltanifar, M. (2023). The Impact of Cyberattacks on Customer Trust in the Banking Sector: Evidence from Emerging Markets. Journal of Financial Crime, 30(2), 545-562.</unstructured_citation></citation><citation key="ref31"><unstructured_citation>[31]	Lavanya, M., &amp; Mangayarkarasi, D.S. “A Review on Detection of Cybersecurity Threats in Banking Sectors Using AI Based Risk Assessment”. Journal of Electrical Systems. Vol. 20 No. 6s, 1359-1365.2024.</unstructured_citation></citation><citation key="ref32"><unstructured_citation>[32]	Dawodu, S.O., Omotosho, A., Akindote, O.J., Adegbite, A.O., &amp; Ewuga, S.K. “CYBERSECURITY RISK ASSESSMENT IN BANKING: METHODOLOGIES AND BEST PRACTICES”. Computer Science &amp; IT Research Journal, 4(3), 220-243. 2023.</unstructured_citation></citation><citation key="ref33"><unstructured_citation>[33]	عزیزی سرخانی, محمدجواد و کردلوئی, حمیدرضا. "بررسی ابزارهای امنیتی بانکداری الکترونیک در بخش بانکداری دولتی بانک‌های هند با مروری بر جهانی شدن". دانش سرمایه‌گذاری، (18) 5، 262-253، 1395.</unstructured_citation></citation><citation key="ref34"><unstructured_citation>[34]	فرزام نیا، نیما، عبدی, بهنام و رضائیان، علی. "ارائه الگوی حکمرانی خوب امنیت فضای سایبری در سازمان‌های دفاعی"، فصلنامه مدیریت نظامی، (77)20، 81-120. 1399.</unstructured_citation></citation><citation key="ref35"><unstructured_citation>[35]	Dhanya, C., &amp; Ramya, K. “Impact of System-Level Indicators of Chatbots on Perceived Usefulness and Intention to use for Banking Services”. The Review of Finance and Banking, 16(1), 43-55.2024.</unstructured_citation></citation><citation key="ref36"><unstructured_citation>[36]	Fatoki, J.O. “The influence of cyber security on financial fraud in the Nigerian banking industry”. International Journal of Science and Research Archive, 9(02), 503–515.2023.</unstructured_citation></citation><citation key="ref37"><unstructured_citation>[37]	شفیعی نیک‌آبادی، محسن، حکاکی، امیر و غلامشاهی، سارا. "مدلی پویا جهت ارزیابی امنیت سیستم‌های اطلاعاتی با استفاده از رویکرد پویایی‌شناسی سیستم‌ها" ، فصلنامه رشد فناوری، (16)64، 61-52. 1399.</unstructured_citation></citation><citation key="ref38"><unstructured_citation>[38]	Damenu, T.K., &amp; Beaumont, C. “Analysing information security in a bank using soft systems methodology”. Inf. Comput. Secur., 25, 240-258.2017.</unstructured_citation></citation><citation key="ref39"><unstructured_citation>[39]	Cheng, L., Liu, F., Yao, D., &amp; Wang, X. (2022). ATM Security: Threats, Vulnerabilities, and Countermeasures in the Era of Digital Banking. Computers &amp; Security, 119, 102765.</unstructured_citation></citation><citation key="ref40"><unstructured_citation>[40]	Sarumi, J.A., Longe, O.B., &amp; Adelodun, F.O. “An Empirical Evaluation of the Effectiveness of the Computer-Based Network Security and Firewall in Banking Systems”. Advances in Multidisciplinary and scientific Research Journal Publication, 10(1), 21-33. 2022.</unstructured_citation></citation><citation key="ref41"><unstructured_citation>[41]	Ewuga, S.K., Egieya, Z.E., Omotosho, A., &amp; Adegbite, A.O. “ISO 27001 IN BANKING: AN EVALUATION OF ITS IMPLEMENTATION AND EFFECTIVENESS IN ENHANCING INFORMATION SECURITY”. Finance &amp; Accounting Research Journal, 5(12), 405-426.2024.</unstructured_citation></citation><citation key="ref42"><unstructured_citation>[42]	Somogyi, T., &amp; Nagy, R. “The Impact of the War in Ukraine on the Information Security of the European Union’s Banking Industry – A Case Study of Hungary And Slovakia”. CONTEMPORARY MILITARY CHALLENGES, 25, 23 - 32. 2023.</unstructured_citation></citation><citation key="ref43"><unstructured_citation>[43]	Al-Hadhrami, A., Alghamdi, A., &amp; Alfarraj, O. (2022). Perceived Security Threats and Their Impact on the Adoption of Accounting Information Systems in the Banking Sector. Journal of Information Security and Applications, 68, 103236.</unstructured_citation></citation><citation key="ref44"><unstructured_citation>[44]	Zhou, Y., Li, X., &amp; Wang, J. (2023). Security Assessment and Vulnerability Analysis of Online Banking Systems: Recent Advances and Challenges. Computers &amp; Security, 126, 103140.</unstructured_citation></citation><citation key="ref45"><unstructured_citation>[45]	Pérez, J., et al. "Risk Assessment of Cloud Migration in Banking Sector." Journal of Financial Services Technology.2020.</unstructured_citation></citation><citation key="ref46"><unstructured_citation>[46]	پیکری، حمیدرضا و بنازاده، بابک. "رابطۀ آگاهی از امنیت اطلاعات با قصد نقض امنیت اطلاعات با نقش میانجی هنجارهای فردی و خودکنترلی عنوان مکرر: قصد نقض امنیت اطلاعات". پژوهش‌های راهبردی مسائل اجتماعی، (4)7، 41-58، 1397.</unstructured_citation></citation><citation key="ref47"><unstructured_citation>[47]	Zhou, Y., Li, X., &amp; Wang, J. (2023). Security Assessment and Vulnerability Analysis of Online Banking Systems: Recent Advances and Challenges. Computers &amp; Security, 126, 103140. </unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>A Framework for Contextual Regulation Needs of Artificial Intelligence</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Hadi</given_name><surname>Sadjadi</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Helia</given_name><surname>Yousefnejad</surname></person_name></contributors><publication_date media_type="online"><month>2</month><day>26</day><year>2026</year></publication_date><pages><first_page>20</first_page><last_page>33</last_page></pages><doi_data><doi>10.66224/jict.49043.17.66.20</doi><resource>http://jour.aicti.ir/fa/Article/49043</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/fa/Article/Download/49043</resource></item><item crawler="google"><resource>http://jour.aicti.ir/fa/Article/Download/49043</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/fa/Article/Download/49043</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/fa/Article/Download/49043</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/fa/Article/Download/49043</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/fa/Article/Download/49043</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/fa/Article/Download/49043</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1] 	C. Rigano, "Using artificial intelligence to address criminal justice needs," National Institute of Justice Journal, vol. 280, no. 17, pp. 1-10, 2019. </unstructured_citation></citation><citation key="ref2"><unstructured_citation>[2] 	E. Musk, Interviewee, Elon Musk Warns Governors: Artificial Intelligence Poses 'Existential Risk'. [Interview]. 27 November 2017.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>[3] 	D. B. Audretsch, C. S. Hayter and A. N. Link, "Concise guide to entrepreneurship, technology and innovation," Edward Elgar Publishing, 2015.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>[4] 	N. Maslej, L. Fattorini, E. Brynjolfsson, J. Etchemendy, K. Ligett, T. Lyons and J. Manyika, "The AI index 2023 annual report," Stanford University, Stanford, 2023.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>[5] 	H. Mollazadeh, A. Hashempoor, A. Sharifian, H. Namazi, S. Mirzaei and M. Dehghan, "Artificial intelligence technology and strategic approaches of governments," Vice-Presidency for science and technology affairs, Tehran, 2021.[In persian]</unstructured_citation></citation><citation key="ref6"><unstructured_citation>[6] 	"An Introduction to the Philosophy of Artificial Intelligence," National Cyberspace Center Research Institute, Tehran, 2019. [In persian]</unstructured_citation></citation><citation key="ref7"><unstructured_citation>[7] 	S. M. M. Ghamami, "Editor's Note: An Introduction to Why Legislate Artificial Intelligence," Quarterly Journal of Government and Law, vol. 5, no. 1, pp. 1-8, 2024. [In persian]</unstructured_citation></citation><citation key="ref8"><unstructured_citation>[8] 	P. G. R. de Almeida, C. D. dos Santos and J. S. Farias, "Artificial intelligence regulation: a framework for governance," Ethics and Information Technology, vol. 23, no. 3, pp. 505-525, 2021. </unstructured_citation></citation><citation key="ref9"><unstructured_citation>[9] 	M. C. Buiten, "Towards intelligent regulation of artificial intelligence," European Journal of Risk Regulation, vol. 10, no. 1, pp. 41-59, 2019. </unstructured_citation></citation><citation key="ref10"><unstructured_citation>[10] 	J. Pigatto, M. Datysgeld and L. Silva, "Internet governance is what global stakeholders make of it: a tripolar approach," Revista Brasileira de Política Internacional, vol. 64, no. 2, p. e011, 2021. </unstructured_citation></citation><citation key="ref11"><unstructured_citation>[11] 	"The EU Artificial Intelligence Act," 2024.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>[12] 	C. Novelli, M. Taddeo and L. Floridi, "Accountability in artificial intelligence: what it is and how it works," Ai &amp; Society, vol. 39, no. 4, pp. 1871-1882, 2024. </unstructured_citation></citation><citation key="ref13"><unstructured_citation>[13] 	M. Almada and N. Petit, "The EU AI Act: Between the rock of product safety and the hard place of fundamental rights," Common market law review, vol. 62, no. 1, 2025. </unstructured_citation></citation><citation key="ref14"><unstructured_citation>[14] 	S. Musch, M. C. Borrelli and C. Kerrigan, "Balancing AI innovation with data protection: A closer look at the EU AI Act," Journal of Data Protection &amp; Privacy, vol. 6, no. 2, pp. 135-152, 2023. </unstructured_citation></citation><citation key="ref15"><unstructured_citation>[15] 	C. Panigutti, R. Hamon, I. Hupont, D. Fernandez Liorca, D. Fano Yela, H. Junklewitz and E. Gomez, "The role of explainable AI in the context of the AI Act," in Proceedings of the 2023 ACM conference on fairness, accountability, and transparency, 2023. </unstructured_citation></citation><citation key="ref16"><unstructured_citation>[16] 	T. Tzimas, "Algorithmic transparency and explainability under eu law," European Public Law, vol. 29, no. 4. </unstructured_citation></citation><citation key="ref17"><unstructured_citation>[17] 	L. Deck, J. L. Müller, C. Braun and D. Zipperling, "Implications of the AI Act for Non-Discrimination Law and Algorithmic Fairness," arXiv preprint arXiv:2403.20089, 2025. </unstructured_citation></citation><citation key="ref18"><unstructured_citation>[18] 	K. Meding, "It's complicated. The relationship of algorithmic fairness and non-discrimination regulations in the EU AI Act," arXiv preprint arXiv:2501.12962, 2025. </unstructured_citation></citation><citation key="ref19"><unstructured_citation>[19] 	J. Davidovic, "On the purpose of meaningful human control of AI," Frontiers in big data, vol. 5, 2023. </unstructured_citation></citation><citation key="ref20"><unstructured_citation>[20] 	M. Hedlund and E. Persson, "Expert responsibility in AI development," AI &amp; SOCIETY, vol. 39, no. 2, pp. 453-464, 2024. </unstructured_citation></citation><citation key="ref21"><unstructured_citation>[21] 	B. N. Schilit and M. M. Theimer, "Disseminating active map information to mobile hosts," IEEE network, vol. 8, no. 5, pp. 22-32, 1994. </unstructured_citation></citation><citation key="ref22"><unstructured_citation>[22]B. Bukenya, S. Hickey and S. King, "Understanding the role of context in shaping social accountability interventions: towards an evidence-based approach," Institute for Development Policy and Management, University of Manchester, Manchester, 2012.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>[23]J. T. Gonzales, "Implications of AI innovation on economic growth: a panel data study," Journal of Economic Structures, vol. 12, no. 1, p. 13, 2023. </unstructured_citation></citation><citation key="ref24"><unstructured_citation>[24]M. A. Trabelsi, "The impact of artificial intelligence on economic development," Journal of Electronic Business &amp; Digital Economics, 2024. </unstructured_citation></citation><citation key="ref25"><unstructured_citation>[25] "Economic impacts of artificial intelligence," European Parliamentary Research Service, 2019.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>[26] A.-Μ. Kanzola, K. Papaioannou and P. Petrakis, "Unlocking society's standings in artificial intelligence," Technological Forecasting and Social Change, vol. 200, pp. 106-123, 2024. </unstructured_citation></citation><citation key="ref27"><unstructured_citation>[27] T. Firoozan Sarnaghi and F. Ahmadzadeh, "Global Competitiveness Index (GCI) and Classification of Countries Based on Ethical Criteria," Journal of Decision Engineering, vol. 3, no. 9, pp. 103-138, 2019. [In persian]</unstructured_citation></citation><citation key="ref28"><unstructured_citation>[28] E. K. Ghani, N. Ariffin and C. Sukmadilaga, "Factors influencing artificial intelligence adoption in publicly listed manufacturing companies: a technology, organisation, and environment approach," International Journal of Applied Economics, Finance and Accounting, vol. 14, no. 2, pp. 108-117, 2022. </unstructured_citation></citation><citation key="ref29"><unstructured_citation>[29]R. Kabalisa and J. Altmann, "AI technologies and motives for AI adoption by countries and firms: a systematic literature review," in Economics of Grids, Clouds, Systems, and Services: 18th International Conference, GECON 2021, 2021. </unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>An Architecture For Processes Analysis in Smart Factories Based on Big Data, Process Mining, and Machine Learning Techniques</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Alireza</given_name><surname>Olyai</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Shideh</given_name><surname>Saraeian</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Ali</given_name><surname>Nodehi</surname></person_name></contributors><publication_date media_type="online"><month>2</month><day>26</day><year>2026</year></publication_date><pages><first_page>34</first_page><last_page>53</last_page></pages><doi_data><doi>10.66224/jict.49107.17.66.34</doi><resource>http://jour.aicti.ir/fa/Article/49107</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/fa/Article/Download/49107</resource></item><item crawler="google"><resource>http://jour.aicti.ir/fa/Article/Download/49107</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/fa/Article/Download/49107</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/fa/Article/Download/49107</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/fa/Article/Download/49107</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/fa/Article/Download/49107</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/fa/Article/Download/49107</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	Mabkhot. M, Al-Ahmari. A, Salah. B, and Alkhalefah. H, “Requirements of the smart factory system: a survey and perspective,” Machines, vol. 6, no. 2, pp. 23, 2018.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>[2]	Chen. M, Mao. S, and Liu. Y, "Big data: A survey,” Mobile networks and applications, vol. 19, no. 2, pp. 171-209, 2014.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>[3]	Lee. J, Ardakani. H. D., Yang. S, and Bagheri. B, “Industrial big data analytics and cyber-physical systems for future maintenance &amp; service innovation,” Procedia Cirp, vol. 38, pp. 3-7, 2015.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>[4]	Olyai. A, Saraeian. S, and Nodehi. A, “Process mining-based business process management architecture: A case study in smart factories,” Scientia Iranica, vol. 31, no. 14, 2024.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>[5]	Zur Muehlen. M, Workﬂow-based Process Controlling: Foundation, Design and Application of workﬂow-driven Process Information Systems, Logos Velrag Berlin, 2004.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>[6]	Polyvyanyy. A, Ouyang. C, Barros. A, and van der Aalst. W. M, “Process querying: Enabling business intelligence through query-based process analytics,” Decision Support Systems, vol. 100, pp. 41-56, 2017.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>[7]	Liu. X, Iftikhar. N, and Xie. X, “Survey of real-time processing systems for big data,” In Proceedings of the 18th International Database Engineering &amp; Applications Symposium, Porto, Portugal, July 7-9, 2014, ACM, 2014, pp. 356-361.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>[8]	Nagdive. A S and Tugnayat. R M, “A review of Hadoop ecosystem for bigdata,” Int. J. Comput. Appl, vol. 180, no.14, pp. 35-40, 2018.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>[9]	Shaikh. E, Mohiuddin. I, Alufaisan. Y, and Nahvi. I, “Apache spark: A big data processing engine,” In 2019 2nd IEEE Middle East and North Africa COMMunications Conference (MENACOMM), Manama, Bahrain, November 19-21, 2019, IEEE ,2019, pp. 1-6.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>[10]	Salloum. S, Dautov. R, Chen. X, Peng. P X, and Huang. J Z, “Big data analytics on Apache Spark,” International Journal of Data Science and Analytics, vol. 1, pp.145-164 ,2016.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>[11]	Sahal. R, Breslin. J G, and Ali. M I, “Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case,” Journal of Manufacturing Systems, vol. 54, pp. 138-151, 2020.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>[12]	Vora. M N, “Hadoop-HBase for large-scale data,” In Proceedings of 2011 International Conference on Computer Science and Network Technology, Harbin, China, December 24-26, 2011, IEEE, 2011, pp. 601-605.‏ </unstructured_citation></citation><citation key="ref13"><unstructured_citation>[13]	Thusoo. A and et al., “Hive: a warehousing solution over a map-reduce framework,” Proceedings of the VLDB Endowment, vol. 2, no.2, pp.1626-1629, 2009.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>[14]	Bansal. K, Chawla. P, and Kurle. P, “Analyzing performance of apache pig and apache hive with Hadoop,” In Engineering Vibration, Communication and Information Processing: ICoEVCI, India, Springer Singapore, 2019, pp. 41-51.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>[15]	Alexakis. T, Peppes. N, Demestichas. K, and Adamopoulou. E, “ A distributed big data analytics architecture for vehicle sensor data,” Sensors, vol. 23, no. 1, pp. 357, 2022.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>[16]	Manogaran. G and et al., “A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system,” Future Generation Computer Systems, vol. 82, pp. 375-387, 2018.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>[17]	Biswas. S and Sen. J, “A proposed architecture for big data driven supply chain analytics,” arXiv preprint arXiv:1705.04958, pp. 7-34, 2017.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>[18]	Almutairi. L, Abugabah, A, Alhumyani H, and Mohamed. A. A, “Intelligent biomedical image classification in a big data architecture using metaheuristic optimization and gradient approximation”, Wireless Networks, vol.30, no. 8, pp. 7087-7108, 2024.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>[19]	Pastor-Galindo. J and et al., “A Big Data architecture for early identification and categorization of dark web sites”, Future Generation Computer Systems, vol. 157, pp. 67-81, 2024.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>[20]	Siriweera. A and Paik. I, “AutoBDA: Model-driven Reference Architecture for Automated Big Data Analysis Framework”, IEEE Transactions on Services Computing, 2025.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>[21]	Theodorakopoulos. L, Theodoropoulou. A, Kampiotis. G, and Kalliampakou. I, “NeuralACT: Accounting Analytics using Neural Network for Real-time Decision Making from Big Data”, IEEE Access, 2025.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>[22]	Nauman. M and et al., “The Role of Big Data Analytics in Revolutionizing Diabetes Management and Healthcare Decision-Making”, IEEE Access, 2025.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>[23]	Gohar. M and et al., “A big data analytics architecture for the internet of small things,” IEEE Communications Magazine, vol. 56, no. 2, pp.128-133, 2018.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>[24]	Constante-Nicolalde. F V, Pérez-Medina. J L, and Guerra-Terán. P, “A proposed architecture for iot big data analysis in smart supply chain fields,” In The international conference on advances in emerging trends and technologies, Cham: Springer International Publishing, 2019, pp. 361-374. </unstructured_citation></citation><citation key="ref25"><unstructured_citation>[25]	Salierno. G, Morvillo. S, Leonardi. L, and Cabri. G, “An architecture for predictive maintenance of railway points based on big data analytics,” In International Conference on Advanced Information Systems Engineering, Cham: Springer International Publishing, 2020, pp. 29-40. </unstructured_citation></citation><citation key="ref26"><unstructured_citation>[26]	Simaković. M N, Cica. Z G, and Masnikosa. I B. "Big Data architecture for mobile network operators,” In 2021 15th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS), Nis, Serbia, October 20-22, 2021, IEEE, 2021, pp. 283-286.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>[27]	Raif. M, Chehri. A, and Saadane. R, “Data architecture and big data analytics in smart cities,” Procedia Computer Science, vol. 207, pp. 4123-4131.2022.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>[28]	Ahaidous. K, Tabaa. M, and Hachimi. H, “Towards IoT-Big Data architecture for future education,” Procedia Computer Science, vol. 220, pp. 348-355.2023.</unstructured_citation></citation><citation key="ref29"><unstructured_citation>[29]	Mills. N and et al., “A cloud-based architecture for explainable Big Data analytics using self-structuring Artificial Intelligence,” Discover Artificial Intelligence, vol.4, no. 1, pp.33, 2024.</unstructured_citation></citation><citation key="ref30"><unstructured_citation>[30]	Werner. S, and Tai. S,” A reference architecture for serverless big data processing”, Future Generation Computer Systems, vol. 155, pp. 179-192, 2024.</unstructured_citation></citation><citation key="ref31"><unstructured_citation>[31]	Ismail. A, Sazali. F. H, Jawaddi. S. N. A, and Mutalib. S, “ Stream ETL framework for twitter-based sentiment analysis: Leveraging big data technologies”, Expert Systems with Applications, vol. 261, pp. 125523, 2025.</unstructured_citation></citation><citation key="ref32"><unstructured_citation>[32]	Saraswat. J. K and Choudhari. S,” Integrating big data and cloud computing into the existing system and performance impact: A case study in manufacturing”, Technological Forecasting and Social Change, vol. 210, pp. 123883, 2025.</unstructured_citation></citation><citation key="ref33"><unstructured_citation>[33]	Çınar. Z M and et al., “Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0,” Sustainability, vol.12, no. 19, pp. 8211, 2020.</unstructured_citation></citation><citation key="ref34"><unstructured_citation>[34]	Maier. A, Schriegel. S, and Niggemann. O, “Big data and machine learning for the smart factory—Solutions for condition monitoring, diagnosis and optimization,” Industrial Internet of Things: Cyber manufacturing Systems, pp. 473-485, 2017.</unstructured_citation></citation><citation key="ref35"><unstructured_citation>[35]	Cho. S and et al., “A hybrid machine learning approach for predictive maintenance in smart factories of the future,” In Advances in Production Management Systems. Smart Manufacturing for Industry 4.0: IFIP WG 5.7 International Conference, APMS 2018, Seoul, Korea, August 26-30, 2018, Proceedings, Part II, Springer International Publishing, 2018, pp. 311-317.</unstructured_citation></citation><citation key="ref36"><unstructured_citation>[36]	Halimaa. A and Sundarakantham. K, “Machine learning based intrusion detection system,” In 2019 3rd International conference on trends in electronics and informatics (ICOEI), Tirunelveli, India, April 23-25, 2019, IEEE, 2019, pp. 916-920.</unstructured_citation></citation><citation key="ref37"><unstructured_citation>[37]	Saraeian. S, Shirazi. B, and Motameni. H, “Optimal autonomous architecture for uncertain processes management,” Information Sciences, vol. 501, pp. 84-99, 2019.</unstructured_citation></citation><citation key="ref38"><unstructured_citation>[38]	Saraeian. S, and Shirazi. B, “Process mining-based anomaly detection of additive manufacturing process activities using a game theory modeling approach,” Computers &amp; Industrial Engineering, vol. 146, pp. 106584, 2020.</unstructured_citation></citation><citation key="ref39"><unstructured_citation>[39]	Theis. J, Galanter. W L, Boyd. A D, and Darabi. H, “Improving the in-hospital mortality prediction of diabetes ICU patients using a process mining/deep learning architecture,” IEEE Journal of Biomedical and Health Informatics, vol. 26, no.1, pp. 388-399, 2021.</unstructured_citation></citation><citation key="ref40"><unstructured_citation>[40]	Ehsani. M and et al., “Machine learning for predicting concrete carbonation depth: A comparative analysis and a novel feature selection,” Construction and Building Materials, vol. 417, pp. 135331, 2024.</unstructured_citation></citation><citation key="ref41"><unstructured_citation>[41]	Xu. Q, “Application of an Intelligent English Text Classification Model with Improved KNN Algorithm in the Context of Big Data in Libraries”, Systems and Soft Computing, pp. 200186, 2025.</unstructured_citation></citation><citation key="ref42"><unstructured_citation>[42]	Alsayat. A and et al., “Enhancing cardiac diagnostics: A deep learning ensemble approach for precise ECG image classification”, Journal of Big Data, vol. 12, no., pp.7, 2025.</unstructured_citation></citation><citation key="ref43"><unstructured_citation>[43]	Alizadeh. S, and Norani. A, “ICMA: a new efficient algorithm for process model discovery,” Applied Intelligence, vol. 48, no.11, pp. 4497-4514, 2018.</unstructured_citation></citation><citation key="ref44"><unstructured_citation>[44]	Van der Aalst. W, Weijters. T, and Maruster. L, “Workflow mining: Discovering process models from event logs,” IEEE transactions on knowledge and data engineering, vol.16, no. 9, pp. 1128-1142, 2004.</unstructured_citation></citation><citation key="ref45"><unstructured_citation>[45]	Weijters. AJMM, Van der Aalst. WMP, Medeiros. AK, “Process mining with the heuristics miner algorithm,” TU Eindhoven: BETA Working Paper Series, 2006.</unstructured_citation></citation><citation key="ref46"><unstructured_citation>[46]	Van der Werf. J M E, van Dongen. B F, Hurkens. C A, and Serebrenik. A, “Process discovery using integer linear programming,” In International conference on applications and theory of petri nets, Springer, Berlin, Heidelberg, 2008, pp. 368-387. </unstructured_citation></citation><citation key="ref47"><unstructured_citation>[47]	Günther. C W, and Van Der Aalst. W M, “Fuzzy mining–adaptive process simplification based on multi-perspective metrics,” In International conference on business process management, Springer Berlin Heidelberg, 2007, pp. 328-343.</unstructured_citation></citation><citation key="ref48"><unstructured_citation>[48]	Van Der Aalst. W M P, Process Mining-Data Science in Action, 2rd ed., Springer, Berlin, Heidelberg, 2016.</unstructured_citation></citation><citation key="ref49"><unstructured_citation>[49]	Leemans. S J, Fahland. D, and Van Der Aalst. W M, “Discovering block-structured process models from event logs containing infrequent behavior,” In Business Process Management Workshops: BPM 2013 International Workshops, Beijing, China, August 26, 2013, Revised Papers 11, Springer international publishing, 2014, pp. 66-78. </unstructured_citation></citation><citation key="ref50"><unstructured_citation>[50]	Leemans. S J, Fahland. D, and Van der Aalst. W M, “Scalable process discovery and conformance checking,” Software &amp; Systems Modeling, vol. 17, pp. 599-631, 2018.</unstructured_citation></citation><citation key="ref51"><unstructured_citation>[51]	Genkin. M, Dehne. F, Shahmirza. A, Navarro. P, and Zhou. S, “Autonomic Architecture for Big Data Performance Optimization”, In Intelligent Systems Conference, Cham: Springer Nature Switzerland, 2024, pp. 475-496.</unstructured_citation></citation><citation key="ref52"><unstructured_citation>[52]	Pohar. M, Blas. M, and Turk. S, “Comparison of logistic regression and linear discriminant analysis: a simulation study,” Metodoloski zvezki, vol.1, no. 1, pp.143, 2004.</unstructured_citation></citation><citation key="ref53"><unstructured_citation>[53]	Kumar. V, “Evaluation of computationally intelligent techniques for breast cancer diagnosis,” Neural Computing and Applications, vol. 33, no.8, pp. 3195-3208, 2021.</unstructured_citation></citation><citation key="ref54"><unstructured_citation>[54]	Bettacchi. A, Polzonetti. A, and Re. B, “Understanding production chain business process using process mining: a case study in the manufacturing scenario”, In Advanced Information Systems Engineering Workshops: CAiSE 2016 International Workshops, Ljubljana, Slovenia, June 13-17, 2016, Proceedings 28, Springer International Publishing, 2016, pp. 193-203.</unstructured_citation></citation><citation key="ref55"><unstructured_citation>[55]	Ahmed. S F and et al. “Deep learning modelling techniques: current progress, applications, advantages, and challenges”, Artificial Intelligence Review, vol.56, no. 11, pp. 13521-13617, 2023.</unstructured_citation></citation><citation key="ref56"><unstructured_citation>[56]	Bailly. A and et al. “Effects of dataset size and interactions on the prediction performance of logistic regression and deep learning models”, Computer Methods and Programs in Biomedicine, vol. 213, pp. 106504, 2022.</unstructured_citation></citation><citation key="ref57"><unstructured_citation>[57]	Lu. Y and et al. “Comparison of machine learning and logistic regression models for predicting emergence delirium in elderly patients: A prospective study”, International Journal of Medical Informatics, vol. 199, pp. 105888, 2025.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Personalization of the Radiology Residency Training Process Using Deep Learning and Interactive Extraction of Diagnostic Error Patterns</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Seyed Ali </given_name><surname>Kianmehr</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Mahdi</given_name><surname>Hashemzadeh</surname></person_name></contributors><publication_date media_type="online"><month>2</month><day>26</day><year>2026</year></publication_date><pages><first_page>102</first_page><last_page>127</last_page></pages><doi_data><doi>10.66224/jict.49269.17.66.102</doi><resource>http://jour.aicti.ir/fa/Article/49269</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/fa/Article/Download/49269</resource></item><item crawler="google"><resource>http://jour.aicti.ir/fa/Article/Download/49269</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/fa/Article/Download/49269</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/fa/Article/Download/49269</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/fa/Article/Download/49269</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/fa/Article/Download/49269</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/fa/Article/Download/49269</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	A. P. Brady, "Error and discrepancy in radiology: inevitable or avoidable?," (in eng), Insights Imaging, vol. 8, no. 1, pp. 171-182, 2017, doi: 10.1007/s13244-016-0534-1.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>[2]	S. Waite et al., "A Review of Perceptual Expertise in Radiology-How it develops, How we can test it, and Why humans still matter in the era of Artificial Intelligence," Academic Radiology, vol. 27, no. 1, pp. 26-38, 2020/01/01/ 2020, doi: https://doi.org/10.1016/j.acra.2019.08.018.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>[3]	J. Irvin et al., "CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 590-597, 07/17 2019, doi: 10.1609/aaai.v33i01.3301590.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>[4]	M. Perumal, A. Nayak, R. P. Sree, and M. Srinivas, "INASNET: Automatic identification of coronavirus disease (COVID-19) based on chest X-ray using deep neural network," ISA Transactions, 2022/03/03/ 2022, doi: https://doi.org/10.1016/j.isatra.2022.02.033.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>[5]	B. Qi et al., GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-ray images. 2021.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>[6]	V. Sorin, Y. Barash, E. Konen, and E. Klang, "Deep Learning for Natural Language Processing in Radiology—Fundamentals and a Systematic Review," Journal of the American College of Radiology, vol. 17, no. 5, pp. 639-648, 2020/05/01/ 2020, doi: https://doi.org/10.1016/j.jacr.2019.12.026.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>[7]	L. Guo, A. Tahir, D. Zhang, Z. Wang, and R. Ward, Automatic Medical Report Generation: Methods and Applications. 2024.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>[8]	M. Mazurowski, J. Baker, H. Barnhart, and G. Tourassi, "Individualized computer-aided education in mammography based on user modeling: Concept and preliminary experiments," Medical physics, vol. 37, pp. 1152-60, 03/01 2010, doi: 10.1118/1.3301575.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>[9]	M. Wang, M. Wang, L. J. Grimm, and M. A. Mazurowski, "A computer vision-based algorithm to predict false positive errors in radiology trainees when interpreting digital breast tomosynthesis cases," Expert Systems with Applications, vol. 64, pp. 490-499, 2016/12/01/ 2016, doi: https://doi.org/10.1016/j.eswa.2016.08.023.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>[10]	M. Wang et al., "Predicting false negative errors in digital breast tomosynthesis among radiology trainees using a computer vision-based approach," Expert Systems with Applications, vol. 56, pp. 1-8, 2016/09/01/ 2016, doi: https://doi.org/10.1016/j.eswa.2016.01.053.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>[11]	S. Burti, A. Zotti, and T. Banzato, "Role of AI in diagnostic imaging error reduction," Frontiers in Veterinary Science, vol. 11, 08/30 2024, doi: 10.3389/fvets.2024.1437284.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>[12]	M. A. Mazurowski and G. D. Tourassi, "Exploring the potential of collaborative filtering for user-adaptive mammography education," in Proceedings of the 2011 Biomedical Sciences and Engineering Conference: Image Informatics and Analytics in Biomedicine, 15-17 March 2011 2011, pp. 1-4, doi: 10.1109/BSEC.2011.5872325. </unstructured_citation></citation><citation key="ref13"><unstructured_citation>[13]	S. Voisin, F. Pinto, G. Morin-Ducote, K. B. Hudson, and G. D. Tourassi, "Predicting diagnostic error in radiology via eye-tracking and image analytics: preliminary investigation in mammography," (in eng), Med Phys, vol. 40, no. 10, p. 101906, Oct 2013, doi: 10.1118/1.4820536.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>[14]	H. Lin, X. Yang, and W. Wang, "A content-boosted collaborative filtering algorithm for personalized training in interpretation of radiological imaging," Journal of digital imaging, vol. 27, pp. 449-456, 2014.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>[15]	M. A. Mazurowski, J. Zhang, J. Y. Lo, C. M. Kuzmiak, S. V. Ghate, and S. Yoon, "Modeling resident error-making patterns in detection of mammographic masses using computer-extracted image features: preliminary experiments," in Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment, 2014, vol. 9037: SPIE, pp. 197-202. </unstructured_citation></citation><citation key="ref16"><unstructured_citation>[16]	J. Zhang, J. Y. Lo, C. M. Kuzmiak, S. V. Ghate, S. C. Yoon, and M. A. Mazurowski, "Using computer‐extracted image features for modeling of error‐making patterns in detection of mammographic masses among radiology residents," Medical physics, vol. 41, no. 9, p. 091907, 2014.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>[17]	J. Zhang, J. I. Silber, and M. A. Mazurowski, "Modeling false positive error making patterns in radiology trainees for improved mammography education," Journal of biomedical informatics, vol. 54, pp. 50-57, 2015.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>[18]	Z. Gandomkar, K. Tay, W. Ryder, P. Brennan, and C. Mello-Thoms, Predicting radiologists' true and false positive decisions in reading mammograms by using gaze parameters and image-based features. 2016, p. 978715.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>[19]	B. Ibragimov and C. Mello-Thoms, "The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review," IEEE journal of biomedical and health informatics, vol. PP, 02/29 2024, doi: 10.1109/JBHI.2024.3371893.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>[20]	H. Wainer, N. J. Dorans, R. Flaugher, B. F. Green, and R. J. Mislevy, Computerized Adaptive Testing: A Primer. Taylor &amp; Francis, 2000.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>[21]	W. A. Sands, B. K. Waters, and J. R. McBride, Computerized adaptive testing: From inquiry to operation. American Psychological Association, 1997.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>[22]	E. E. Roskam and P. G. Jansen, "A new derivation of the Rasch model," in Advances in Psychology, vol. 20: Elsevier, 1984, pp. 293-307.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>[23]	A. D. Mead, "An introduction to multistage testing," Applied Measurement in Education, vol. 19, no. 3, pp. 185-187, 2006.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>[24]	Y. Zhuang, Q. Liu, Z. Huang, Z. Li, S. Shen, and H. Ma, "Fully adaptive framework: Neural computerized adaptive testing for online education," in Proceedings of the AAAI conference on artificial intelligence, 2022, vol. 36, no. 4, pp. 4734-4742. </unstructured_citation></citation><citation key="ref25"><unstructured_citation>[25]	D. F. Mujtaba and N. R. Mahapatra, "Artificial intelligence in computerized adaptive testing," in 2020 International Conference on Computational Science and Computational Intelligence (CSCI), 2020: IEEE, pp. 649-654. </unstructured_citation></citation><citation key="ref26"><unstructured_citation>[26]	D. Kermany. Large dataset of labeled optical coherence tomography (OCT) and Chest X-Ray images, Mendeley, 2018, doi: 10.17632/RSCBJBR9SJ.3.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>[27]	D. S. Kermany et al., "Identifying medical diagnoses and treatable diseases by image-based deep learning," Cell, vol. 172, no. 5, pp. 1122-1131.e9, 2018/2 2018, doi: 10.1016/j.cell.2018.02.010.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>[28]	J. Cohen, P. Morrison, L. Dao, K. Roth, T. Duong, and M. Ghassemi, COVID-19 Image Data Collection: Prospective Predictions Are the Future. 2020.</unstructured_citation></citation><citation key="ref29"><unstructured_citation>[29]	A. Chung, "Actualmed COVID-19 chest x-ray data initiative," 2020. [Online]. Available: https://github.com/agchung/Actualmed-COVID-chestxray-dataset.</unstructured_citation></citation><citation key="ref30"><unstructured_citation>[30]	A. Chung, "Figure 1 COVID-19 chest x-ray data initiative," 2020. [Online]. Available: https://github.com/agchung/Figure1-COVID-chestxray-dataset.</unstructured_citation></citation><citation key="ref31"><unstructured_citation>[31]	P. Garg, M. Gautam, B. Chugh, and K. Dwivedi, "Employing transfer learning techniques for COVID-19 detection using chest X-ray," International Journal of Advances in Applied Sciences, vol. 13, p. 680, 09/01 2024, doi: 10.11591/ijaas.v13.i3.pp680-688.</unstructured_citation></citation><citation key="ref32"><unstructured_citation>[32]	G. Huang, Z. Liu, L. V. D. Maaten, and K. Q. Weinberger, "Densely Connected Convolutional Networks," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 21-26 July 2017 2017, pp. 2261-2269, doi: 10.1109/CVPR.2017.243. </unstructured_citation></citation><citation key="ref33"><unstructured_citation>[33]	J. Yu et al., "A Unified Adaptive Testing System Enabled by Hierarchical Structure Search," in Forty-first International Conference on Machine Learning. </unstructured_citation></citation><citation key="ref34"><unstructured_citation>[34]	Q. Liu et al., "Survey of computerized Adaptive Testing: A machine learning perspective," 2024 2024, doi: 10.48550/ARXIV.2404.00712.</unstructured_citation></citation><citation key="ref35"><unstructured_citation>[35]	S. J. Chen, A. Choi, and A. Darwiche, "Computer Adaptive Testing Using the Same-Decision Probability," in BMA@ UAI, 2015, pp. 34-43. </unstructured_citation></citation><citation key="ref36"><unstructured_citation>[36]	J.-J. Vie, F. Popineau, É. Bruillard, and Y. Bourda, "A Review of Recent Advances in Adaptive Assessment," in Learning Analytics: Fundaments, Applications, and Trends: A View of the Current State of the Art to Enhance e-Learning, A. Peña-Ayala Ed. Cham: Springer International Publishing, 2017, pp. 113-142.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Enhancing Edge Computing Efficiency Using Autoencoding</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Mahdi</given_name><surname>Tatar</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Fatemeh</given_name><surname>Nasiri</surname></person_name></contributors><publication_date media_type="online"><month>2</month><day>26</day><year>2026</year></publication_date><pages><first_page>189</first_page><last_page>197</last_page></pages><doi_data><doi>10.66224/jict.49801.17.66.189</doi><resource>http://jour.aicti.ir/fa/Article/49801</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/fa/Article/Download/49801</resource></item><item crawler="google"><resource>http://jour.aicti.ir/fa/Article/Download/49801</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/fa/Article/Download/49801</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/fa/Article/Download/49801</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/fa/Article/Download/49801</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/fa/Article/Download/49801</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/fa/Article/Download/49801</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1] Veeramachaneni, V. (2025). Edge Computing: Architecture, Applications, and Future Challenges in a Decentralized Era. Recent Trends in Computer Graphics and Multimedia Technology, 7(1), 8-23.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>[2] Alnoman, A., Sharma, S. K., Ejaz, W., &amp; Anpalagan, A. (2019). Emerging edge computing technologies for distributed IoT systems. IEEE Network, 33(6), 140-147.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>[3] Torabi, H., Khazaei, H., &amp; Litoiu, M. (2024, May). A Learning-Based Caching Mechanism for Edge Content Delivery. In Proceedings of the 15th ACM/SPEC International Conference on Performance Engineering (pp. 236-246).</unstructured_citation></citation><citation key="ref4"><unstructured_citation> [4] Malandrino, F., Chiasserini, C. F., &amp; Dell’Aera, G. M. (2021). Edge-powered assisted driving for connected cars. IEEE Transactions on Mobile Computing, 22(2), 874-889.</unstructured_citation></citation><citation key="ref5"><unstructured_citation> [5] Adeniyi, O., Sadiq, A. S., Pillai, P., Aljaidi, M., &amp; Kaiwartya, O. (2024). Securing mobile edge computing using hybrid deep learning method. Computers, 13(1), 25.</unstructured_citation></citation><citation key="ref6"><unstructured_citation> [6] Bourechak, A., Zedadra, O., Kouahla, M. N., Guerrieri, A., Seridi, H., &amp; Fortino, G. (2023). At the confluence of artificial intelligence and edge computing in iot-based applications: A review and new perspectives. Sensors, 23(3), 1639.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>[7] T. Tran and D. Pompili, “Adaptive bitrate video caching and pro- cessing in mobile-edge computing networks,” IEEE Transactions on Mobile Computing, 2018.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>[8] J. George and S. Sebastian, “Cooperative caching strategy for video streaming in mobile networks,” in Emerging Technological Trends (ICETT), International Conference on. IEEE, 2016, pp. 1–7.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>[9] S. Zhang, P. He, K. Suto, P. Yang, L. Zhao, and X. Shen, “Coop- erative edge caching in user-centric clustered mobile networks,” IEEE Transactions on Mobile Computing, vol. 17, no. 8, pp. 1791– 1805, 2018.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>[10] A. Gharaibeh, A. Khreishah, B. Ji,  and  M.  Ayyash,  “A  prov- ably efficient online collaborative caching algorithm for multicell- coordinated systems,” IEEE Transactions on Mobile Computing, vol. 15, no. 8, pp. 1863–1876, 2016.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>[11] H. Mouss, D. Mouss, N. Mouss, and L. Sefouhi, “Test of page- hinckley, an approach for fault detection in an agro-alimentary production system,” in 2004 5th Asian Control Conference (IEEE Cat. No. 04EX904), vol. 2. IEEE, 2004, pp. 815–818.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>[12] A. Maskooki, G. Sabatino, and N. Mitton, “Analysis and perfor- mance evaluation of the next generation wireless networks,” in Modeling and Simulation of Computer Networks and Systems. Else- vier, 2015, pp. 601–627.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>[13] X. Xia, F. Chen, G. Cui, M. Abdelrazek, J. Grundy, H. Jin, and Q. He, “Budgeted data caching based on k-median in mobile edge computing,” in 27th IEEE International Conference on Web Services. IEEE, 2020.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>[14] W. Shi and S. Dustdar, “The promise of edge computing,” Com- puter, vol. 49, no. 5, pp. 78–81, 2016.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>[15] U. Drolia, K. Guo, J. Tan, R. Gandhi, and P. Narasimhan, “Cachier: Edge-caching for recognition applications,” in 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2017, pp. 276–286.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>[16] E. Zeydan, E. Bastug, M. Bennis, M. A. Kader, I. A. Karatepe, A. S. Er, and M. Debbah, “Big data caching for networking: Moving from cloud to edge,” IEEE Communications Magazine, vol. 54, no. 9, pp. 36–42, 2016.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>[17] R. Halalai, P. Felber, A.-M. Kermarrec, and F. Ta¨ıani, “Agar: A caching system for erasure-coded data,” in 2017 IEEE 37th Interna- tional Conference on Distributed Computing Systems (ICDCS). IEEE, 2017, pp. 23–33.</unstructured_citation></citation><citation key="ref18"><unstructured_citation> [18] X. Cao, J. Zhang, and H. V. Poor, “An optimal auction mechanism for mobile edge caching,” in 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2018, pp. 388– 399.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>[19] X. Zhang and Q.  Zhu,  “Collaborative  hierarchical  caching  over 5g edge computing mobile wireless networks,” in 2018 IEEE International Conference on Communications (ICC). IEEE, 2018, pp. 1–6.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>[20] W. Liu, Y. Jiang, S. Xu, G. Cao, W. Du, and Y. Cheng, “Mobility- aware video prefetch caching and replacement strategies in mobile-edge computing networks,” in 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 2018, pp. 687–694.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>[21] M. Chen, Y. Qian, Y. Hao, Y. Li, and J. Song, “Data-driven comput- ing and caching in 5g networks: Architecture and delay analysis,” IEEE Wireless Communications, vol. 25, no. 1, pp. 70–75, 2018.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>[22] Xia, Xiaoyu, et al. "Graph-based data caching optimization for edge computing." Future generation computer systems 113 (2020): 228-239.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>[23] Liu, Ying, et al. "Data caching optimization in the edge computing environment." IEEE Transactions on Services Computing (2020)</unstructured_citation></citation><citation key="ref24"><unstructured_citation>[24] Safavat, Sunitha, Naveen Naik Sapavath, and Danda B. Rawat. "Recent advances in mobile edge computing and content caching." Digital Communications and Networks 6.2 (2020): 189-194.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>[25] Wang, Xiaofei, et al. "In-edge ai: Intelligentizing mobile edge computing, caching and communication by federated learning." Ieee Network 33.5 (2019): 156-165.</unstructured_citation></citation><citation key="ref26"><unstructured_citation> [26] Xu, D., Li, T., Li, Y., Su, X., Tarkoma, S., Jiang, T., ... &amp; Hui, P. (2021). Edge intelligence: Empowering intelligence to the edge of network. Proceedings of the IEEE, 109(11), 1778-1837.</unstructured_citation></citation><citation key="ref27"><unstructured_citation> [27] Ullah, I., &amp; Mahmoud, Q. H. (2022). Design and development of RNN anomaly detection model for IoT networks. IEEE Access, 10, 62722-62750.</unstructured_citation></citation><citation key="ref28"><unstructured_citation> [28] Abusitta, A., de Carvalho, G. H., Wahab, O. A., Halabi, T., Fung, B. C., &amp; Al Mamoori, S. (2023). Deep learning-enabled anomaly detection for IoT systems. Internet of Things, 21, 100656.</unstructured_citation></citation><citation key="ref29"><unstructured_citation> [29] Rafique, S. H., Abdallah, A., Musa, N. S., &amp; Murugan, T. (2024). Machine learning and deep learning techniques for internet of things network anomaly detection—current research trends. Sensors, 24(6), 1968.</unstructured_citation></citation><citation key="ref30"><unstructured_citation> [30] Wang, Y., Qin, G., &amp; Liang, Y. (2025). A reliability anomaly detection method based on enhanced GRU-Autoencoder for Vehicular Fog Computing services. Computers &amp; Security, 150, 104217.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Presenting a Hybrid Model on Machine Learning and Principal Component Analysis for Action Detection in the Internet of Things </title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Zahra</given_name><surname>Shahpar</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Mohammadreza</given_name><surname>Badragheh</surname></person_name></contributors><publication_date media_type="online"><month>2</month><day>26</day><year>2026</year></publication_date><pages><first_page>178</first_page><last_page>188</last_page></pages><doi_data><doi>10.66224/jict.49811.17.66.178</doi><resource>http://jour.aicti.ir/fa/Article/49811</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/fa/Article/Download/49811</resource></item><item crawler="google"><resource>http://jour.aicti.ir/fa/Article/Download/49811</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/fa/Article/Download/49811</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/fa/Article/Download/49811</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/fa/Article/Download/49811</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/fa/Article/Download/49811</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/fa/Article/Download/49811</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	S. Sadhwani, B. Manibalan, R. Muthalagu, and P. Pawar, "A lightweight model for DDoS attack detection using machine learning techniques," Applied Sciences, vol. 13, no. 17, p. 9937, 2023.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>[2]	T. Zhang, L. Gao, C. He, M. Zhang, B. Krishnamachari, and A. S. Avestimehr, "Federated learning for the internet of things: Applications, challenges, and opportunities," IEEE Internet of Things Magazine, vol. 5, no. 1, pp. 24-29, 2022.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>[3]	B. B. Zarpelão, R. S. Miani, C. T. Kawakani, and S. C. De Alvarenga, "A survey of intrusion detection in Internet of Things," Journal of Network and Computer Applications, vol. 84, pp. 25-37, 2017.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>[4]	M. Ahmid and O. Kazar, "A comprehensive review of the internet of things security," Journal of Applied Security Research, vol. 18, no. 3, pp. 289-305, 2023.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>[5]	N. Dat-Thinh, H. Xuan-Ninh, and L. Kim-Hung, "MidSiot: A multistage intrusion detection system for internet of things," Wireless Communications and Mobile Computing, vol. 2022, no. 1, p. 9173291, 2022.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>[6]	L. Strous, S. von Solms, and A. Zúquete, "Security and privacy of the Internet of Things," Computers &amp; Security, vol. 102, p. 102148, 2021.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>[7]	S. Pandey and B. Bhushan, "Recent Lightweight cryptography (LWC) based security advances for resource-constrained IoT networks," Wireless Networks, vol. 30, no. 4, pp. 2987-3026, 2024.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>[8]	P. Fusco, A. Montefusco, G. P. Rimoli, F. Palmieri, and M. Ficco, "TinyML-Based Intrusion Detection System for Handling Class Imbalance in IoT-Edge Domain Using Siamese Neural Network on MCU," in International Conference on Advanced Information Networking and Applications, 2025: Springer, pp. 389-402. </unstructured_citation></citation><citation key="ref9"><unstructured_citation>[9]	K. A. Da Costa, J. P. Papa, C. O. Lisboa, R. Munoz, and V. H. C. de Albuquerque, "Internet of Things: A survey on machine learning-based intrusion detection approaches," Computer Networks, vol. 151, pp. 147-157, 2019.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>[10]	E. Konstantopoulou, G. Athanasiou, and N. Sklavos, "Review and Analysis of FPGA and ASIC Implementations of NIST Lightweight Cryptography Finalists," ACM Computing Surveys, vol. 57, no. 10, pp. 1-35, 2025.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>[11]	H. Griffioen and C. Doerr, "Examining Mirai's battle over the Internet of Things," in Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, 2020, pp. 743-756. </unstructured_citation></citation><citation key="ref12"><unstructured_citation>[12]	M. Kintzlinger and N. Nissim, "Keep an eye on your personal belongings! The security of personal medical devices and their ecosystems," Journal of biomedical informatics, vol. 95, p. 103233, 2019.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>[13]	M. Ahmed, A. N. Mahmood, and J. Hu, "A survey of network anomaly detection techniques," Journal of   Network and Computer Applications, vol. 60, pp. 19-31, 2016.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>[14]	A. A. Diro and N. Chilamkurti, "Distributed attack detection scheme using deep learning approach for Internet of Things," Future Generation Computer Systems, vol. 82, pp. 761-768, 2018.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>[15]	S. D. Babar and P. N. Mahalle, "A hash key-based key management mechanism for cluster-based wireless sensor network," Journal of Cyber Security and Mobility, pp. 73-88, 2016.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>[16]	A. Fatani, A. Dahou, M. A. Al-Qaness, S. Lu, and M. A. Elaziz, "Advanced feature extraction and selection approach using deep learning and Aquila optimizer for IoT intrusion detection system," Sensors, vol. 22, no. 1, p. 140, 2021.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>[17]	R. A. Disha and S. Waheed, "Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique," Cybersecurity, vol. 5, no. 1, p. 1, 2022.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>[18]	M. Mohy-Eddine, A. Guezzaz, S. Benkirane, and M. Azrour, "An effective intrusion detection approach based on ensemble learning for IIoT edge computing," Journal of Computer Virology and Hacking Techniques, vol. 19, no. 4, pp. 469-481, 2023.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>[19]	R. S. Tiwari, D. Lakshmi, T. K. Das, A. K. Tripathy, and K.-C. Li, "A lightweight optimized intrusion detection system using machine learning for edge-based IIoT security," Telecommunication Systems, pp. 1-20, 2024.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>[20]	M. J. Awan et al., "Real-time DDoS attack detection system using big data approach," Sustainability, vol. 13, no. 19, p</unstructured_citation></citation><citation key="ref21"><unstructured_citation>[21]	W. Elmasry, A. Akbulut, and A. H. Zaim, "A Design of an Integrated Cloud-based Intrusion Detection System with Third Party Cloud Service" Open Computer Science, vol. 11, no. 1, 2021, pp. 365-379. https://doi.org/10.1515/comp-2020-0214.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>[22]	M. Sarhan, S. Layeghy, N. Moustafa, and M. Portmann, "Netflow datasets for machine learning-based network intrusion detection systems," in Big Data Technologies and Applications: 10th EAI International Conference, BDTA 2020, and 13th EAI International Conference on Wireless Internet, WiCON 2020, Virtual Event, December 11, 2020, Proceedings 10, 2021: Springer, pp. 117-135.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>[23]	M. S. M. AL-inizi, Y. T. Alzubaidi, S. H. Oleiwi, N. A. A. Zahra, and J. F. Yonan, "Improvement Networks Intrusion Detection System Using Artificial Neural Networks (ANN)," in International Conference On Innovative Computing And Communication, 2024: Springer, pp. 571-587.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Message Transfer Protocol Between Social Messengers</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Hamzeh</given_name><surname>Sezavar</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Hamed</given_name><surname>Monkaresi</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Hassan</given_name><surname>Nickaein</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Mehdi</given_name><surname>Mozaffari</surname></person_name></contributors><publication_date media_type="online"><month>2</month><day>26</day><year>2026</year></publication_date><pages><first_page>87</first_page><last_page>101</last_page></pages><doi_data><doi>10.66224/jict.49885.17.66.87</doi><resource>http://jour.aicti.ir/fa/Article/49885</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/fa/Article/Download/49885</resource></item><item crawler="google"><resource>http://jour.aicti.ir/fa/Article/Download/49885</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/fa/Article/Download/49885</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/fa/Article/Download/49885</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/fa/Article/Download/49885</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/fa/Article/Download/49885</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/fa/Article/Download/49885</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	J. Gottfried, “Americans’ Social Media Use,” Pew Research Center, Jan. 13, 2024. [Online]. Available: https://www.pewresearch.org/internet/2024/01/31/americans-social-media-use/. [Accessed: Aug. 22, 2024].</unstructured_citation></citation><citation key="ref2"><unstructured_citation>[2]	M. Anderson, M. Faverio, and J. Gottfried, “Teens, Social Media and Technology 2023,” Pew Research Center, Dec. 11, 2023. [Online]. Available: https://www.pewresearch.org/internet/2023/12/11/teens-social-media-and-technology-2023/. [Accessed: Aug. 22, 2024].</unstructured_citation></citation><citation key="ref3"><unstructured_citation>[3]	M. Nurudeen, S. Abdul-Samad, E. Owusu-Oware, G. Y. Koi-Akrofi, and H. A. Tanye, “Measuring the effect of social media on student academic performance using a social media influence factor model,” Educ. Inform. Technol., vol. 28, pp. 1165-1188, 2023. doi: 10.1007/s10639-022-11196-0.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>[4]	K. K. Kapoor, K. Tamilmani, N. P. Rana, P. Patil, Y. K. Dwivedi, and S. Nerur, “Advances in Social Media Research: Past, Present and Future,” Inf. Syst. Frontiers, vol. 20, pp. 531-558, 2018. doi: 10.1007/s10796-017-9810-y.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>[5]	S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system,” 2008, pp. 1-8.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>[6]	T. Madiega, “Digital services act,” European Parliamentary Research Service, PE, pp. 1-8, 2020.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>[7]	C. O’Halloran, “Social media giants face annual audits under new EU law,” The Journal.ie, Aug. 2023. [Online]. Available: https://www.thejournal.ie/social-media-audits-digital-services-act-6151679-Aug2023/. [Accessed: Nov. 20, 2023].</unstructured_citation></citation><citation key="ref8"><unstructured_citation>[8]	R. Flynn, “RCS Interconnect Hub: Driving global interconnectivity of RCS,” Openmind Networks, pp. 3-10, 2013</unstructured_citation></citation><citation key="ref9"><unstructured_citation>[9]	J. Simmons, J. Mackenzie, T. Martin, T. Ralston, and D. Almeida, “Matrix,” [Online]. Available: https://matrix.org/. [Accessed: Jul. 20, 2023], 2018</unstructured_citation></citation><citation key="ref10"><unstructured_citation>[10]	S. Ferretti, M. Zichichi, and J. Sparber, “Blockchain-based end-to-end encryption for Matrix instant messaging,” 2021</unstructured_citation></citation><citation key="ref11"><unstructured_citation>[11]	European Parliament and Council of the European Union, “Regulation (EU) 2022/1925 of 14 September 2022 on contestable and fair markets in the digital sector and amending Directives (EU) 2019/1937 and (EU) 2020/1828 (Digital Markets Act),” Official Journal of the European Union, L 265, pp. 1-66, Oct. 12, 2022.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>[12]	European Parliament and Council of the European Union, “Regulation (EU) 2022/1925 on contestable and fair markets in the digital sector,” Official Journal of the European Union, L 265, Oct. 12, 2022. [Online]. Available: https://eur-lex.europa.eu/eli/reg/2022/1925/oj. [Accessed: Aug. 22, 2024].</unstructured_citation></citation><citation key="ref13"><unstructured_citation>[13]	F. Liberatore, “DMA: EU Publishes The New Digital Markets Act,” [Online]. Available: https://www.privacyworld.blog/2022/10/dma-eu-publishes-the-new-digital-markets-act/. [Accessed: Aug. 22, 2024].</unstructured_citation></citation><citation key="ref14"><unstructured_citation>[14]	S. Amaro, “EU announces sweeping new rules that could force breakups and hefty fines for Big Tech,” [Online]. Available: https://www.cnbc.com/2020/12/15/digital-markets-act-eus-new-rules-on-big-tech.html. [Accessed: Aug. 22, 2024].</unstructured_citation></citation><citation key="ref15"><unstructured_citation>[15]	European Commission, “Proposal for a Regulation of the European Parliament and of the Council on European Data Governance (Data Governance Act),” EUR-Lex, Nov. 25, 2020. [Online]. Available: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52020PC0842. [Accessed: Aug. 22, 2024].</unstructured_citation></citation><citation key="ref16"><unstructured_citation>[16]	F. S. Morton and C. Caffarra, “The European Commission Digital Markets Act: A translation,” Jan. 5, 2021. [Online]. Available: https://cepr.org/voxeu/columns/european-commission-digital-markets-act-translation. [Accessed: Aug. 22, 2024].</unstructured_citation></citation><citation key="ref17"><unstructured_citation>[17]	M. Mariniello and J. Anderson, “Regulating big tech: the Digital Markets Act,” [Online]. Available: https://www.bruegel.org/blog-post/regulating-big-tech-digital-markets-act. [Accessed: Aug. 22, 2024].</unstructured_citation></citation><citation key="ref18"><unstructured_citation>[18]	K. Holt, “EU confirms the six tech giants subject to its strict new competition laws,” [Online]. Available: https://www.engadget.com/eu-confirms-the-six-tech-giants-subject-to-its-strict-new-competition-laws-161917822.html. [Accessed: May 22, 2024].</unstructured_citation></citation><citation key="ref19"><unstructured_citation>[19]	European Commission, “Commission Proposes New EU Cybersecurity Strategy,” [Online]. Available: https://ec.europa.eu/commission/presscorner/detail/en/ip_22_6423. [Accessed: Aug. 22, 2024].</unstructured_citation></citation><citation key="ref20"><unstructured_citation>[20]	M. Murgia, “Facebook Fined €110m by European Commission over WhatsApp Deal,” Financial Times, [Online]. Available: https://www.ft.com/content/a2dadc48-3bb1-11e7-821a-6027b8a20f23. [Accessed: Aug. 22, 2024].</unstructured_citation></citation><citation key="ref21"><unstructured_citation>[21]	A. Satariano, “Google Fined $5.1 Billion by E.U. in Android Antitrust Case,” The New York Times, [Online]. Available: https://www.nytimes.com/2018/07/18/technology/google-eu-android-fine.html. [Accessed: Aug. 22, 2024].</unstructured_citation></citation><citation key="ref22"><unstructured_citation>[22]	D. Brouwer, “Making messaging interoperability with third parties safe for users in Europe,” Mar. 6, 2024. [Online]. Available: https://engineering.fb.com/2024/03/06/security/whatsapp-messenger-messaging-interoperability-eu/. [Accessed: Aug. 22, 2024].</unstructured_citation></citation><citation key="ref23"><unstructured_citation>[23]	K. Holt, “Meta explains how third-party apps will hook into Messenger and WhatsApp,” Mar. 6, 2024. [Online]. Available: https://www.engadget.com/meta-explains-how-third-party-apps-will-hook-into-messenger-and-whatsapp-192532065.htm. [Accessed: Aug. 22, 2024].</unstructured_citation></citation><citation key="ref24"><unstructured_citation>[24]	B. Vigliarolo, “Meta killing off Instagram, Messenger cross-platform chatting,” Dec. 5, 2023. [Online]. Available: https://www.theregister.com/2023/12/05/meta_instagram_messenger. [Accessed: Aug. 22, 2024].</unstructured_citation></citation><citation key="ref25"><unstructured_citation>[25]	“WhatsApp, Instagram, and Messenger will be integrated: cross-chatting option will be available,” Joy of Android, Sep. 29, 2021. [Online]. Available: https://joyofandroid.com/news/whatsapp-instagram-and-messenger-will-be-integrated-cross-chatting-option-will-be-available/. [Accessed: Aug. 22, 2024].</unstructured_citation></citation><citation key="ref26"><unstructured_citation>[26]	N. Sarwar, “WhatsApp Cross-Chat With Messenger &amp; Instagram Will Be Optional... For Now,” Sep. 28, 2021. [Online]. Available: https://screenrant.com/whatsapp-cross-chat-facebook-messenger-instagram-optional-interoperability/. [Accessed: Aug. 22, 2024].</unstructured_citation></citation><citation key="ref27"><unstructured_citation>[27]	“An Update on How We’re Building Safe and Secure Third-Party Chats for Users in Europe,” Sep. 6, 2024. [Online]. Available: https://about.fb.com/news/2024/09/an-update-on-how-were-building-safe-and-secure-third-party-chats-for-users-in-europe/. [Accessed: Aug. 22, 2024].</unstructured_citation></citation><citation key="ref28"><unstructured_citation>[28]	P. Sawers, “Inside Matrix, the protocol that might finally make messaging apps interoperable,” TechCrunch, Dec. 30, 2022. [Online]. Available: https://techcrunch.com/2022/12/30/inside-matrix-the-protocol-that-might-finally-make-messaging-apps-interoperable/. [Accessed: Aug. 22, 2024].</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Modeling the Future Efficiency of the Green Supply Chain of the Poultry Farming Industry Using Multistage DEA and Artificial Neural Networks</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Tahere</given_name><surname>Torkashvand</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 Hossein</given_name><surname>Darvish Motevalli</surname></person_name></contributors><publication_date media_type="online"><month>2</month><day>26</day><year>2026</year></publication_date><pages><first_page>128</first_page><last_page>148</last_page></pages><doi_data><doi>10.66224/jict.50079.17.66.128</doi><resource>http://jour.aicti.ir/fa/Article/50079</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/fa/Article/Download/50079</resource></item><item crawler="google"><resource>http://jour.aicti.ir/fa/Article/Download/50079</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/fa/Article/Download/50079</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/fa/Article/Download/50079</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/fa/Article/Download/50079</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/fa/Article/Download/50079</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/fa/Article/Download/50079</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	"Theories in sustainable supply chain management: a structured literature review." International Journal of Physical Distribution &amp; Logistics Management. International Journal of Physical Distribution &amp; Logistics Management 45 (1-2): 16–42,2015.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>[2]	Akanmode, E., et al. "Prediction of poultry yield using data mining techniques." Int J Innov Eng Sci Res 2: 16-32, 2018.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>[3]	Salehi moghadam,S. and Darvish motevalli,M. H. A New Model for the four-level multi-product supply chain optimization based on stochastic demand with probability distribution function. (e231650). Karafan Journal, 22(2), 2025.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>[4]	Alinezhad, A. and A. Taherinezhad . "Performance Evaluation of Production Chain using Two-Stage DEA Method (Case Study: Iranian Poultry Industry)." new economy and trad 16(3): 105-130, 2021.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>[5]	Amini, A., et al. "Evaluation of green supply chain performance using network data envelopment analysis." International Journal of Green Economics 13(3-4): 187-201, 2019.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>[6]	Kheyrati L, Darvish Motevalli M H. Presenting a Mathematical Model for Optimizing the Cold Food Supply Chain During Disasters. Disaster Prev. Manag. Know.; 14 (3) :292-309, 2024.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>[7]	Amirbeygi, F., et al. "Evaluation of green supply chain performance using balanced scorecard and data envelopment analysis." Journal of Industrial Engineering and Management Studies 9(2): 64-85, 2023.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>[8]	Anouze, A. L. M. and I. Bou-Hamad. "Data envelopment analysis and data mining to efficiency estimation and evaluation." International Journal of Islamic and Middle Eastern Finance and Management 12(2): 169-190, 2019.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>[9]	Chen, Y., Cook, W. D., Ning, L., &amp; Zhu, J. "Additive efficiency decomposition in two-stage DEA." European journal of operational research, 196(193), 1170-1176, 2009.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>[10]	Krakovsky, R., Forgac, R., 2011. Neural network approach to multidimensional data classification via clustering. In: IEEE. 9th International Symposium on Intelli gent Systems and Informatics, pp. 169e174.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>[11]	Depi. "Structure of Victoria’s Chicken Meat Industry." Department of Environment and Primary Industries, Victoria, Australia. Accessed November, 2013.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>[12]	Emrouznejad, A. and M. Tavana. Performance measurement with fuzzy data envelopment analysis, Springer, 2013.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>[13]	Ibiwoye, A., Ajibola, E., Sogunro, A.B., Artificial neural network model for predicting insurance insolvency. Int. J. Manag. Bus. Res. 2 (1), 59e68, 2012.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>[14]	Färe, R., &amp; Whittaker, G. "An intermediate input model of dairy production." Journal of Agricultural Economics, 46, 201–213, 1995.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>[15]	Geng, R., et al. "The relationship between green supply chain management and performance: A meta-analysis of empirical evidences in Asian emerging economies." International journal of production economics 183: 245-258, 2017.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>[16]	Gupta, M. K. and P. Chandra. "Effects of similarity/distance metrics on k-means algorithm with respect to its applications in iot and multimedia: a review." Multimedia Tools and Applications 81(26): 37007-37032, 2022.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>[17]	Melchiorre, C., Matteucci, M., Azzoni, A., Zanchi, A., Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorpho 94 (3), 379e400, 2008.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>[18]	Han, Y.M., Geng, Z.Q. and Zhu, Q.X., “Energy optimization and prediction of complex petrochemical industries using an improved artificial neural network approach integrating data envelopment analysis”, Energy Conversion and Management, Vol. 124, pp. 73-83, 2016.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>[19]	Charnes, A., Cooper, W.W. and Rhodes, E., “Measuring the efficiency of decision-making units”, European Journal of Operational Research, Vol. 2 No. 6, pp. 429-444, 1998.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>[20]	Kwon, H.B., Lee, J. and Roh, J.J., “Best performance modeling using complementary DEA-ANN approach: application to Japanese electronics manufacturing firms”, Benchmarking: An International Journal, Vol. 23 No. 3, pp. 704-721, 2016.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>[21]	Selim, S. and Bursalıoglu, S.A., “Efficiency of higher education in Turkey: a bootstrapped twostage DEA approach 1”, International Journal of Statistics and Applications, Vol. 5 No. 2, pp. 56-67, 2015.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>[22]	Agasisti, T. and Ricca, L., “Comparing the efficiency of Italian public and private universities (2007-2011): an empirical analysis”, Italian Economic Journal, Vol. 2 No. 1, pp. 57-89, 2016.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>[23]	Kao, C., &amp; Hwang, S. N.  "Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan." European Journal of Operational Research: 185(181), 418-429, 2008.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>[24]	Kao, C. "Efficiency decomposition for general multi-stage systems in data envelopment analysis." European Journal of Operational Research 232(1): 117-124, 2014.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>[25]	Kao, C. "Network data envelopment analysis: A review." European Journal of Operational Research 239(1): 1-16, 2014.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>[26]	Khashei, M. and M. Bijari. "An artificial neural network (p, d, q) model for timeseries forecasting." Expert Systems with applications 37(1): 479-489, 2010.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>[27]	Adler, N., Martini, G. and Volta, N., “Measuring the environmental efficiency of the global aviation fleet”, Transportation Research Part B: Methodological, Vol. 53, pp. 82-100, 2013.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>[28]	Kumar, S., &amp; Putnam, V. "Cradle to cradle: Reverse logistics strategies and opportunities across three industry sectors." International journal of production economics: 115(112), 305-315, 2018.</unstructured_citation></citation><citation key="ref29"><unstructured_citation>[29]	Liu, J.S., Lu, L.Y. and Lu, W.M., “Research fronts and prevailing applications in data envelopment analysis”, Data Envelopment Analysis, Springer, Boston, MA, pp. 543-574, 2016.</unstructured_citation></citation><citation key="ref30"><unstructured_citation>[30]	Alizadeh, A. and Omrani, H., “An integrated multi response Taguchi-neural network-robust data envelopment analysis model for CO2 laser cutting”, Measurement, Vol. 131, pp. 69-78, 2019.</unstructured_citation></citation><citation key="ref31"><unstructured_citation>[31]	Li, Y., Chen, Y., Liang, L., &amp; Xie, J. "DEA models for extended two-stage network structures." Omega: 40(45), 611-618, 2012.</unstructured_citation></citation><citation key="ref32"><unstructured_citation>[32]	Ghasemi, N., Najafi, E., Lotfi, F.H. and Sobhani, F.M., “Assessing the performance of organizations with the hierarchical structure using data envelopment analysis: an efficiency analysis of Farhangian University”, Measurement, Vol. 156, 107609, 2002.</unstructured_citation></citation><citation key="ref33"><unstructured_citation>[33]	Misiunas, N., et al. "DEANN: A healthcare analytic methodology of data envelopment analysis and artificial neural networks for the prediction of organ recipient functional status." Omega 58: 46-54, 2016.</unstructured_citation></citation><citation key="ref34"><unstructured_citation>[34]	Manasakis, C., Apostolakis, A. and Datseris, G., “Using data envelopment analysis to measure hotel efficiency in Crete”, International Journal of Contemporary Hospitality Management, Vol. 25 No. 4, pp. 510-535, 2013.</unstructured_citation></citation><citation key="ref35"><unstructured_citation>[35]	Nguyen, H.G. Using neutral work in predicting corporate failure. J. Soc. Sci. 1 (4), 199e202, 2005.</unstructured_citation></citation><citation key="ref36"><unstructured_citation>[36]	Nandy, A. and P. K. Singh. "Farm efficiency estimation using a hybrid approach of machine-learning and data envelopment analysis: Evidence from rural eastern India." Journal of Cleaner Production 267: 122106, 2020.</unstructured_citation></citation><citation key="ref37"><unstructured_citation>[37]	Bhanot, N. and Singh, H., “Benchmarking the performance indicators of Indian Railway container business using data envelopment analysis”, Benchmarking: An International Journal, Vol. 21 No. 1, pp. 101-120, 2014.</unstructured_citation></citation><citation key="ref38"><unstructured_citation>[38]	Pratap, S., Jauhar, S.K., Paul, S.K. and Zhou, F., “Stochastic optimization approach for green routing and planning in perishable food production”, Journal of Cleaner Production, Vol. 333, 130063, 2022.</unstructured_citation></citation><citation key="ref39"><unstructured_citation>[39]	Carlucci, D., Renna, P. and Schiuma, G., “Evaluating service quality dimensions as antecedents to outpatient satisfaction using back propagation neural network”, Health Care Management Science, Vol. 16 No. 1, pp. 37-44, 2013.</unstructured_citation></citation><citation key="ref40"><unstructured_citation>[40]	Athanassopoulos, A.D. and Curram, S.P., “A comparison of data envelopment analysis and artificial neural networks as tools for assessing the efficiency of decision-making units”, Journal of the Operational Research Society, Vol. 47 No. 8, pp. 1000-1016, 1996.</unstructured_citation></citation><citation key="ref41"><unstructured_citation>[41]	Rezaee, M. J. "Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange." Physica A: Statistical Mechanics and its Applications 489: 78-93, 2018.</unstructured_citation></citation><citation key="ref42"><unstructured_citation>[42]	Paradi, J.C. and Zhu, H., “A survey on bank branch efficiency and performance research with data envelopment analysis”, Omega, Vol. 41 No. 1, pp. 61-79, 2013.</unstructured_citation></citation><citation key="ref43"><unstructured_citation>[43]	Panapakidis, I.P. and Dagoumas, A.S., “Day-ahead natural gas demand forecasting based on the combination of wavelet transform and ANFIS/genetic algorithm/neural network model”, Energy, Vol. 118, pp. 231-245, 2017.</unstructured_citation></citation><citation key="ref44"><unstructured_citation>[44]	Shabanpour, H., et al. "Forecasting efficiency of green suppliers by dynamic data envelopment analysis and artificial neural networks." Journal of Cleaner Production 142: 1098-1107, 2017.</unstructured_citation></citation><citation key="ref45"><unstructured_citation>[45]	Shamsuddoha, M. "Integrated supply chain model for sustainable poultry production in Bangladesh: a system dynamics approach."2014.</unstructured_citation></citation><citation key="ref46"><unstructured_citation>[46]	Tone, K., &amp; Tsutsui, M. "Dynamic DEA: A slacks-based measure approach." Omega: 38(33-34), 145-156, 2010.</unstructured_citation></citation><citation key="ref47"><unstructured_citation>[47]	Kwon, H.B.Exploring the predictive potential of artificial neural networks in conjunction with DEA in railroad performance modeling. International Journal of Production Economics, 183(A), 159-170, 2017.</unstructured_citation></citation><citation key="ref48"><unstructured_citation>[48]	Zhang, G. "Forecasting with artificial neural networks: The state of the art." International journal of forecasting 14(1): 35-62, 2018.</unstructured_citation></citation><citation key="ref49"><unstructured_citation>[49]	Kuo, R.J., Wang, Y.C. and Tien, F.C, “Integration of artificial neural network and MADA methods for green supplier selection”, Journal of Cleaner Production, (18)-12. 1161-1170, 2010.</unstructured_citation></citation><citation key="ref50"><unstructured_citation>[50]	Misiunas, N., Oztekin, A., Chen, Y. and Chandra, K, “DEANN: a healthcare analytic methodology of data envelopment analysis and artificial neural networks for the prediction of organ recipient functional status”, Omega, Vol. 58, pp. 46-54, 2016.</unstructured_citation></citation><citation key="ref51"><unstructured_citation>[51]	Rezaei, J., Tavasszy, L., &amp; Tavakkoli-Moghaddam, R. (2023). A hybrid ANN–DEA model for performance prediction in green supply chains. Journal of Cleaner Production, 414, 137594, 2023.</unstructured_citation></citation><citation key="ref52"><unstructured_citation>[52]	Han, S., &amp; Lee, J. (2022). Forecasting supply chain efficiency using multi-layer neural networks and DEA integration. Expert Systems with Applications, 202, 117267, 2022.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Identifying the Determinants of Blockchain Technology Adoption and Proposing a Hybrid Adoption Model Based on TAM and TOE Using the K-means Clustering Method</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Atefeh</given_name><surname>Farazmand</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Abouzar</given_name><surname>Arabsorkhi</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Seyed ahmad</given_name><surname>Yazdian</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Abbas</given_name><surname>Saghaei</surname></person_name></contributors><publication_date media_type="online"><month>2</month><day>26</day><year>2026</year></publication_date><pages><first_page>149</first_page><last_page>164</last_page></pages><doi_data><doi>10.66224/jict.50322.17.66.149</doi><resource>http://jour.aicti.ir/fa/Article/50322</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/fa/Article/Download/50322</resource></item><item crawler="google"><resource>http://jour.aicti.ir/fa/Article/Download/50322</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/fa/Article/Download/50322</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/fa/Article/Download/50322</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/fa/Article/Download/50322</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/fa/Article/Download/50322</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/fa/Article/Download/50322</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	Taherdoost, H. Blockchain Innovations, Applications, and Future Prospects. Electronics 2024, 13, 422. https://doi.org/10.3390/electronics13020422.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>[2]	U. Farooq, K. Shahzad, Z. Guan, and A. Rauf, “Unlocking the potential of blockchain technology in China’s supply chain: a survey of industry professionals,” Journal of Entrepreneurship and Public Policy, vol. 13, no. 2, pp. 333–356, 2024. 2</unstructured_citation></citation><citation key="ref3"><unstructured_citation>[3]	M. Ghaly, E. Elbeltagi, A. Elsmadony, and M. A. Tantawy, “Integration of Blockchain-Enabled smart contracts in construction: SWOT framework and social network analysis,” Civil Engineering Journal, vol. 10, no. 5, pp. 1662–1697, 2024. 4</unstructured_citation></citation><citation key="ref4"><unstructured_citation>[4]	N. Kshetri, "1 Blockchain’s roles in meeting key supply chain management objectives," International Journal of information management, vol. 39, pp. 80-89, 2018.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>[5]	M. Iansiti and K. R. Lakhani, "The truth about blockchain," Harvard business review, vol. 95, no. 1, pp. 118-127, 2017.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>[6]	K. R. Lakhani, &amp; McAfee, A. , "What Every CEO Needs to Know About the Cloud," Harvard Business Review, 2011.: https://www.researchgate.net/publication/293431811_What_every_CEO_needs_to_know_about_the_cloud.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>[7]	M. C. Lacity, "Addressing key challenges to making enterprise blockchain applications a reality," MIS Q. Executive, vol. 17, no. 3, p. 3, 2018.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>[8]	C. Boonmee, J. Mangkalakeeree, and Y. Jeong, “Towards sustainable digital transformation: AI adoption barriers and enablers among SMEs in Northern Thailand,” Sustainable Futures, vol. 10, p. 101169, 2025.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>[9]	E. Asante Boakye, H. Zhao, B. N. Kwame Ahia, and M. Adu-Damoah, “Modeling the adoption enablers of blockchain technology–based supply chain financing: an integrative dual DOI–TOE analysis,” Journal of the International Council for Small Business, pp. 1–22, 2025.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>[10]	S. Gaehtgens, &amp; Allan, A, "Trust and the evolution of digital business ecosystems," Gartner Research, 2017.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>[11]	F. R. Edwards and F. S. Mishkin, "The decline of traditional banking: Implications for financial stabilityand regulatory policy," ed: National Bureau of Economic Research Cambridge, Mass., USA, 1995.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>[12]	M. O. Akintunde and H. O. Amuda, “Predictors of adoption of blockchain technology by academic libraries in Nigeria,” Library Hi Tech, vol. 43, no. 4–5, pp. 1273–1291, 2025.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>[13]	H. Wu, W. Zhong, B. Zhong, H. Li, J. Guo, and I. Mehmood, “Barrier identification, analysis and solutions of blockchain adoption in construction: a fuzzy DEMATEL and TOE integrated method,” Engineering, Construction and Architectural Management, vol. 32, no. 1, pp. 409–426, 2025.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>[14]	H. Taherdoost, "A review of technology acceptance and adoption models and theories," Procedia manufacturing, vol. 22, pp. 960-967, 2018.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>[15]	H. Taherdoost, "A critical review of blockchain acceptance models—blockchain technology adoption frameworks and applications," Computers, vol. 11, no. 2, p. 24, 2022.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>[16]	H. Taherdoost and M. Masrom, "An examination of smart card technology acceptance using adoption model," in Proceedings of the ITI 2009 31st international conference on information technology interfaces, 2009: IEEE, pp. 329-334. </unstructured_citation></citation><citation key="ref17"><unstructured_citation>[17]	F. D. Davis, "Perceived usefulness, perceived ease of use, and user acceptance of information technology," MIS quarterly, pp. 319-340, 1989.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>[18]	C. Low, Y. Chen, and M. Wu, "Understanding the determinants of cloud computing adoption," Industrial management &amp; data systems, vol. 111, no. 7, pp. 1006-1023, 2011.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>[19]	J. D. Bryan and T. Zuva, "A review on TAM and TOE framework progression and how these models integrate," Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 137-145, 2021.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>[20]	K. Zhu, K. L. Kraemer, V. Gurbaxani, and S. X. Xu, "Migration to open-standard interorganizational systems: Network effects, switching costs, and path dependency," MIS quarterly, pp. 515-539, 2006.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>[21]	M. Intan Salwani, G. Marthandan, M. Daud Norzaidi, and S. Choy Chong, "E‐commerce usage and business performance in the Malaysian tourism sector: empirical analysis," Information management &amp; computer security, vol. 17, no. 2, pp. 166-185, 2009.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>[22]	J. Xu, "Research on application of BIM 5D technology in central grand project," Procedia engineering, vol. 174, pp. 600-610, 2017.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>[23]	H. O. Awa, O. U. Ojiabo, and B. C. Emecheta, "Integrating TAM, TPB and TOE frameworks and expanding their characteristic constructs for e-commerce adoption by SMEs," Journal of Science &amp; Technology Policy Management, vol. 6, no. 1, pp. 76-94, 2015.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>[24]	C. Carnaghan and K. Klassen, "Exploring the determinants of web-based e-business evolution in Canada," 2007.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>[25]	S. Almekhlafi and N. Al-Shaibany, "The literature review of blockchain adoption," Asian Journal of Research in Computer Science, vol. 7, no. 2, pp. 29-50, 2021.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>[26]	S. A. Borhani, J. Babajani, I. Raeesi Vanani, S. Sheri Anaqiz, and M. Jamaliyanpour, "Adopting blockchain technology to improve financial reporting by using the technology acceptance model (TAM)," International Journal Of Finance &amp; Managerial Accounting, vol. 6, no. 22, pp. 155-171, 2021.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>[27]	J. M. Woodside, F. K. Augustine Jr, and W. Giberson, "Blockchain technology adoption status and strategies," Journal of International Technology and Information Management, vol. 26, no. 2, pp. 65-93, 2017.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>[28]	T. Clohessy and T. Acton, "Investigating the influence of organizational factors on blockchain adoption: An innovation theory perspective," Industrial Management &amp; Data Systems, vol. 119, no. 7, pp. 1457-1491, 2019.</unstructured_citation></citation><citation key="ref29"><unstructured_citation>[29]	S. ShafeeN, "E-learning Technology Acceptance Model with cultural factors. Liverpool John Moores University: School of Computing and Mathematical Sciences," MSc Dissertation, April, 2011. </unstructured_citation></citation><citation key="ref30"><unstructured_citation>[30]	P. A. Pavlou, "Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model," International journal of electronic commerce, vol. 7, no. 3, pp. 101-134, 2003.</unstructured_citation></citation><citation key="ref31"><unstructured_citation>[31]	T. Pikkarainen, K. Pikkarainen, H. Karjaluoto, and S. Pahnila, "Consumer acceptance of online banking: an extension of the technology acceptance model," Internet research, vol. 14, no. 3, pp. 224-235, 2004.</unstructured_citation></citation><citation key="ref32"><unstructured_citation>[32]	M. Ervasti and H. Helaakoski, "Case study of application-based mobile service acceptance and development in Finland," International Journal of Information Technology and Management, vol. 9, no. 3, pp. 243-259, 2010.</unstructured_citation></citation><citation key="ref33"><unstructured_citation>[33]	G. Müller-Seitz, K. Dautzenberg, U. Creusen, and C. Stromereder, "Customer acceptance of RFID technology: Evidence from the German electronic retail sector," Journal of retailing and consumer services, vol. 16, no. 1, pp. 31-39, 2009.</unstructured_citation></citation><citation key="ref34"><unstructured_citation>[34]	V. Kumar, S. Kumar, A. Mewada, and S. Akhtar, “Understanding blockchain technology adoption in human resource management: A technology acceptance model (TAM) approach,” in Applications of Blockchain Technology, Chapman and Hall/CRC, 2025, pp. 35–46.</unstructured_citation></citation><citation key="ref35"><unstructured_citation>[35]	O. Ukoha, H. O. Awa, C. A. Nwuche, I. F. Asiegbu, and E. Cohen, "Analysis of Explanatory and Predictive Architectures and the Relevance in Explaining the Adoption of IT in SMEs," Interdisciplinary Journal of Information, Knowledge &amp; Management, vol. 6, 2011.</unstructured_citation></citation><citation key="ref36"><unstructured_citation>[36]	I. Troshani, C. Jerram, and S. Rao Hill, "Exploring the public sector adoption of HRIS," Industrial Management &amp; Data Systems, vol. 111, no. 3, pp. 470-488, 2011.</unstructured_citation></citation><citation key="ref37"><unstructured_citation>[37]	H.-d. Yang and Y. Yoo, "It's all about attitude: revisiting the technology acceptance model," Decision support systems, vol. 38, no. 1, pp. 19-31, 2004.</unstructured_citation></citation><citation key="ref38"><unstructured_citation>[38]	K. Zhu, K. L. Kraemer, and S. Xu, "The process of e-business assimilation in organizations: A technology diffusion perspective," Management Science, vol. 52, no. 10, pp. 1557-1576, 2006.</unstructured_citation></citation><citation key="ref39"><unstructured_citation>[39]	L. Raymond, F. Bergeron, and S. Blili, "The assimilation of E‐business in manufacturing SMEs: Determinants and effects on growth and internationalization," Electronic Markets, vol. 15, no. 2, pp. 106-118, 2005.</unstructured_citation></citation><citation key="ref40"><unstructured_citation>[40]	N. Schillewaert, M. J. Ahearne, R. T. Frambach, and R. K. Moenaert, "The adoption of information technology in the sales force," Industrial marketing management, vol. 34, no. 4, pp. 323-336, 2005.</unstructured_citation></citation><citation key="ref41"><unstructured_citation>[41]	W.-W. Wu, "Developing an explorative model for SaaS adoption," Expert systems with applications, vol. 38, no. 12, pp. 15057-15064, 2011.</unstructured_citation></citation><citation key="ref42"><unstructured_citation>[42]	M. D. Williams, Y. K. Dwivedi, B. Lal, and A. Schwarz, "Contemporary trends and issues in IT adoption and diffusion research," Journal of Information Technology, vol. 24, no. 1, pp. 1-10, 2009.</unstructured_citation></citation><citation key="ref43"><unstructured_citation>[43]	S. Xu, K. Zhu, and J. Gibbs, "Global technology, local adoption: A Cross‐Country investigation of internet adoption by companies in the United States and China," Electronic markets, vol. 14, no. 1, pp. 13-24, 2004.</unstructured_citation></citation><citation key="ref44"><unstructured_citation>[44]	T. S. Teo, S. Lin, and K.-h. Lai, "Adopters and non-adopters of e-procurement in Singapore: An empirical study," Omega, vol. 37, no. 5, pp. 972-987, 2009.</unstructured_citation></citation><citation key="ref45"><unstructured_citation>[45]	I. Arpaci, Y. C. Yardimci, S. Ozkan, and O. Turetken, "Organizational adoption of information technologies: A literature review," International Journal of ebusiness and egovernment Studies, vol. 4, no. 2, pp. 37-50, 2012.</unstructured_citation></citation><citation key="ref46"><unstructured_citation>[46]	M.-J. Pan and W.-Y. Jang, "Determinants of the adoption of enterprise resource planning within the technology-organization-environment framework: Taiwan's communications industry," Journal of Computer information systems, vol. 48, no. 3, pp. 94-102, 2008.</unstructured_citation></citation><citation key="ref47"><unstructured_citation>[47]	Y. Teng, K. C. Shang, H. C. Wang, S. Y. Kuo, and C. S. Lu, “The implementation of blockchain adoption in China’s manufacturing industry: the technology organization environment (TOE) method,” Humanities and Social Sciences Communications, vol. 12, no. 1, pp. 1–11, 2025.</unstructured_citation></citation><citation key="ref48"><unstructured_citation>[48]	Y.-M. Wang, Y.-S. Wang, and Y.-F. Yang, "Understanding the determinants of RFID adoption in the manufacturing industry," Technological forecasting and social change, vol. 77, no. 5, pp. 803-815, 2010.</unstructured_citation></citation><citation key="ref49"><unstructured_citation>[49]	K.-W. Wen and Y. Chen, "E-business value creation in Small and Medium Enterprises: a US study using the TOE framework," International Journal of Electronic Business, vol. 8, no. 1, pp. 80-100, 2010.</unstructured_citation></citation><citation key="ref50"><unstructured_citation>[50]	T. Oliveira and M. F. Martins, "Literature review of information technology adoption models at firm level," Electronic journal of information systems evaluation, vol. 14, no. 1, pp. pp110‑121-pp110‑121, 2011.</unstructured_citation></citation><citation key="ref51"><unstructured_citation>[51]	X. Qin, Y. Shi, K. Lyu, and Y. Mo, "Using a TAM-TOE model to explore factors of Building Information Modelling (BIM) adoption in the construction industry," 2020.</unstructured_citation></citation><citation key="ref52"><unstructured_citation>[52]	A. Legesse, B. Beshah, E. Berhan, and E. Tesfaye, “Exploring the influencing factors of blockchain technology adoption in national quality infrastructure: a Dual-Stage structural equation model and artificial neural network approach using TAM-TOE framework,” Cogent Engineering, vol. 11, no. 1, p. 2369220, 2024.</unstructured_citation></citation><citation key="ref53"><unstructured_citation>[53]	J. Chen, A. Q. Abdul-Hamid, and S. Zailani, “Blockchain adoption for a circular economy in the Chinese automotive industry: Identification of influencing factors using an integrated TOE-TAM model,” Sustainability, vol. 16, no. 24, p. 10817, 2024.</unstructured_citation></citation><citation key="ref54"><unstructured_citation>[54]	Ntoyanto Ceki, Babalwa &amp; Moloi, Tankiso. (2025). Technology Adoption Framework for Supreme Audit Institutions Within the Hybrid TAM and TOE Model. Journal of Risk and Financial Management. 18. 409.</unstructured_citation></citation><citation key="ref55"><unstructured_citation>[55]	L. A. Palinkas, S. M. Horwitz, C. A. Green, J. P. Wisdom, N. Duan, and K. Hoagwood, "Purposeful sampling for qualitative data collection and analysis in mixed method implementation research," Administration and policy in mental health and mental health services research, vol. 42, pp. 533-544, 2015.</unstructured_citation></citation><citation key="ref56"><unstructured_citation>[56]	H. Kallio, A. M. Pietilä, M. Johnson, and M. Kangasniemi, "Systematic methodological review: developing a framework for a qualitative semi‐structured interview guide," Journal of advanced nursing, vol. 72, no. 12, pp. 2954-2965, 2016.</unstructured_citation></citation><citation key="ref57"><unstructured_citation>[57]	Guest, G., Bunce, A., &amp; Johnson, L. (2006). How Many Interviews Are Enough? An Experiment with Data Saturation and Variability. Field Methods, 18(1), 59–82. </unstructured_citation></citation><citation key="ref58"><unstructured_citation>[58]	Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). Thousand Oaks, CA: SAGE Publications.</unstructured_citation></citation><citation key="ref59"><unstructured_citation>[59]	S. S. Chawathe, "Clustering blockchain data," Clustering Methods for Big Data Analytics: Techniques, Toolboxes and Applications, pp. 43-72, 2019.</unstructured_citation></citation><citation key="ref60"><unstructured_citation>[60]	E. U. Oti, M. O. Olusola, F. C. Eze, and S. U. Enogwe, "Comprehensive review of K-Means clustering algorithms," criterion, vol. 12, pp. 22-23, 2021.</unstructured_citation></citation><citation key="ref61"><unstructured_citation>[61]	E. Pantano and L. Di Pietro, "Understanding consumer’s acceptance of technology-based innovations in retailing," Journal of technology management &amp; innovation, vol. 7, no. 4, pp. 1-19, 2012.</unstructured_citation></citation><citation key="ref62"><unstructured_citation>[62]	M. Yektai, &amp; Ranjbarnoshari, A. , "model for cloud computing adoption in IT outsourcing.," Journal of Future Studies in Management (Management Researches), 2016. [Online]. Available: https://sid.ir/paper/204257/en (Note: Original article in Persian).</unstructured_citation></citation><citation key="ref63"><unstructured_citation>[63]	C. Lazim, N. D. B. Ismail, and M. Tazilah, "Application of technology acceptance model (TAM) towards online learning during covid-19 pandemic: Accounting students perspective," International Journal of Business, Economics and Law, vol. 24, no. 1, pp. 13-20, 2021.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>7-PORTNon-blocking Optical Router Design and Efficient Routing Algorithm in 3D Mesh Optical Network on Chip</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Sanaz</given_name><surname>Asadinia</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Elham</given_name><surname>Yaghoubi</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>mostafa</given_name><surname>sadeghi</surname></person_name></contributors><publication_date media_type="online"><month>2</month><day>26</day><year>2026</year></publication_date><pages><first_page>165</first_page><last_page>177</last_page></pages><doi_data><doi>10.66224/jict.51065.17.66.165</doi><resource>http://jour.aicti.ir/fa/Article/51065</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/fa/Article/Download/51065</resource></item><item crawler="google"><resource>http://jour.aicti.ir/fa/Article/Download/51065</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/fa/Article/Download/51065</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/fa/Article/Download/51065</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/fa/Article/Download/51065</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/fa/Article/Download/51065</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/fa/Article/Download/51065</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1] L. Weichen, T. Guiyu, L. Mengquan, “Autonomous Temperature Sensing for Optical Network-on-Chip”. Journal of Systems Architecture, January 2020, 101650. </unstructured_citation></citation><citation key="ref2"><unstructured_citation>[2] HUSEYúIN T, KAYHAN M, “Scheduling Computation and Communication on a Software-Defined Photonic Network-on-Chip Architecture for High-Performance Real-Time Systems”, Journal of Systems Architecture, October 2018, Pages 54-71. </unstructured_citation></citation><citation key="ref3"><unstructured_citation>[3] S. Asadinia, M. Mehrabi, E. Yaghoubi, “Surix: Non‑blocking and low insertion loss micro‑ring resonator‑based optical router for photonic network on chip,” The Journal of Supercomputing, https://doi.org/10.1007/s11227-020-03442-4, 2020. </unstructured_citation></citation><citation key="ref4"><unstructured_citation>[4] S. Asadinia, E. Yaghoubi, M. Mehrabi, “3D Mesh ONoC: Design of low Insertion Loss and Non-blocking Optical Router and Efficient Routing Algorithm,” 14th International Conference on Information and Knowledge Technology (IKT), 2023, DOI: 10.1109/IKT62039.2023.10433045. </unstructured_citation></citation><citation key="ref5"><unstructured_citation>[5] A.W. Poon, F. Xu, and X. Luo, “Cascaded active silicon micro resonator array cross-connect circuits for WDM networkson-chip,” in Proc. SPIE, vol. 6898, pp. 689812-689812-10, 2008.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>[6] Y. Ye et al., “3-D mesh-based optical network-on-chip for multiprocessor system-on-chip,” IEEE Trans. Comput. -Aided Des. Integr. Circuits Syst., vol. 32, no. 4, pp. 584–596, Apr. 2013.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>[7] Ben Ahmed A, Ben Abdallah A,” Hybrid silicon-photonic network-on-chip for future generations of high-performance many-core systems, “The Journal of Supercomputing, DOI: 10.1007/s11227-015-1539-0.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>[8] A. Reza, Sarbazi-Azad H, A. Khademzadeh, H. Shabani, Niazmand B,” A loss aware scalable topology for photonic on chip interconnection networks, “The Journal of Supercomputing, DOI: 10.1007/s11227-013-1026-4. </unstructured_citation></citation><citation key="ref9"><unstructured_citation>[9] Pengxing Guo, Weigang Hou, Lei Guo, Wei Sun, Chuang Liu, Hainan Bao, Luan H. K. Duong, and Weichen Liu,”  Fault-Tolerant Routing Mechanism in 3D Optical Network-on-Chip based on Node Reuse,” IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS. </unstructured_citation></citation><citation key="ref10"><unstructured_citation>[10] J. Hao, Ting Zh, Yunchou Zh, X. Yuhao, D. Jincheng, Lei Zh, D. Jianfeng, Xin F and Lin Y, "Six- port optical switch for cluster-mesh photonic network-on-chip", Nanophotonics; 7(5): 827–835, 2018: 827–835,2018.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>[11] A. Shacham, K. Bergman, and L. P. Carloni, “Photonic networks-on-chip for future generations of chip multiprocessors,” IEEE Trans. Comput., vol. 57, no. 9, pp. 1246–1260, Sep. 2008.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>[12] Y. Xie, W. Zhao, W. Xu, Y. Huang, and Z. Zhang, “Performance optimization and evaluation for mesh-based optical networks-on-chip,” IEEE Photon. J., vol. 7, no. 4, Aug. 2015, Art. No. 7801412.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>[13] J. H. Lau, “Through-Silicon Vias for 3D Integration,” New York, NY, USA: McGraw-Hill, 2012, ISBN-13 978-0071785143.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>[14] K. Zhu, H. Gu, Y. Yang, W. Tan, and B. Zhang, “A 3D multilayer optical network on chip based on mesh topology,” Photon. Netw. Commun. vol. 2016, no. 3, pp. 293–299, 2016.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>[15] J. H. Lee, “Insertion Loss-Aware Routing Analysis and Optimization for a Fat-Tree-Based Optical Network-on-Chip”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 37. No. 3, pp. 559-572. </unstructured_citation></citation><citation key="ref16"><unstructured_citation>[16] P. Guo, W. Hou, L. Guo, Q. Yang, Y. Ge, Liang H,” Low Insertion Loss and Non-Blocking Microring-Based Optical Router for 3D Optical Network-on-Chip,” IEEE Photonics Journal. DOI:10.1109/JPHOT.2018.2796094. </unstructured_citation></citation><citation key="ref17"><unstructured_citation>[17] P. Guo, W. Hou, and L. Guo, “Designs of low insertion loss optical router and reliable routing for 3D optical network on-chip,” Sci. China-Inf. Sci., vol. 59, no. 10, 2016, Art. No. 102302.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>[18] W. Hou, L. Guo, Q. Cai, and L. Zhu, “3D Torus ONoC: Topology design, router modeling and adaptive routing algorithm,” in Proc. IEEE Int. Conf. Opt. Commun. Netw, 2014, pp. 1–4.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>[19] S. Asadinia, M. Mehrabi, E. Yaghoubi, “Non-Blocking and Multi Wavelength Optical Router Design based on Mach-zehnder Interferometer in 3-D Optical Network on Chip,” Majlesi Journal of Electrical Engineering, 2021, DOI: https://doi.org/10.52547/mjee.15.2.73. </unstructured_citation></citation><citation key="ref20"><unstructured_citation>[20] N. Dahir, T. Mak, Al-Dujaily R, Yakovlev A,” Highly adaptive and deadlock-free routing for three-dimensional networks-on-chip. “IET Computers &amp; Digital Techniques. 2013, Vol. 7, No. 6, pp. 255-263.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>[21] Chiu G.M,” The odd-even turn model for adaptive routing. IEEE Transactions on Parallel and Distributed Systems, “2000, Vol. 11, No. 7, pp. 729-738. </unstructured_citation></citation><citation key="ref22"><unstructured_citation>[22] P. Bahrebar, Stroobandt D,” The Hamiltonian-based odd–even turn model for maximally adaptive routing in 2D mesh networks-on-chip. “Computers &amp; Electrical Engineering, 2015, Vol. 45, No. pp. 386-401. </unstructured_citation></citation><citation key="ref23"><unstructured_citation>[23] A. Shacham, K. Bergman, Carloni L.P,” On the Design of a Photonic Network-on-Chip. “First International Symposium on Networks-on-Chip (NOCS'07), 2007, pp. 53-64, 7-9. </unstructured_citation></citation><citation key="ref24"><unstructured_citation>[24] Huaxi Gu, Jiang Xu, 2009. "Design of 3D Optical Network on Chip", in Proc. Conf., 2009, IEEE.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>[25] Yaoyao Ye, Xiaowen Wu, Mahdi N,” 3-D Mesh-Based Optical Network-on-Chip for Multiprocessor System-on-Chip.” IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2013, VOL. 32, NO. 4 </unstructured_citation></citation><citation key="ref26"><unstructured_citation>[26] Kexin Zhu, Huaxi GU, Yintang Yang, Wei Tan, Bowen Zhang,” A 3D multilayer optical network on chip based on mesh topology. “Photon Netw Commun. 2016, DOI 10.1007/s11107-016-0627-2. </unstructured_citation></citation><citation key="ref27"><unstructured_citation>[27] Muhammad R.Y, Ning Wu, Gaizhen Yan, A. Tanveer, Jinbao Z and Yuanyuan Z,” HoneyComb ROS: A 6 * 6 Non-Blocking Optical Switch with Optimized Reconfiguration for ONoCs. “Electronics 2019, 8, 844; doi:10.3390/electronics8080844. </unstructured_citation></citation><citation key="ref28"><unstructured_citation>[28] J. Chan, G. Hendry, A. Biberman, K. Bergman, Carloni L.P,” Phoenixsim: a simulator for hysical-layer analysis of chip-scale photonic interconnection networks.” 2010, Proceedings of theConference on Design Automation and Test in Europe 691–696. </unstructured_citation></citation><citation key="ref29"><unstructured_citation>[29] A. Varga, Hornig R,” An overview of the OMNeT++simulation environment. “In: Proceedings of the 1st International Conference on Simulation Tools and Techniques for Communications, Networks and Systems and Workshops, 2008, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), p 60. </unstructured_citation></citation><citation key="ref30"><unstructured_citation>[30] Varga A,” The OMNeT++discrete event simulation system. “In: Proceedings of the European Simulation Multi conference (ESM’2001), vol S 185. sn, p 65 The OMNeT++discrete event simulation system. </unstructured_citation></citation><citation key="ref31"><unstructured_citation>[31] J. Chan, A. Biberman, Lee B.G, Bergmann K,” Insertion loss analysis in a photonic interconnection network for on-chip and off-chip communications. “21st Annual Meeting of the IEEE Lasers and Electro-Optics Society, 2008, pp. 300-301, 9-13. </unstructured_citation></citation><citation key="ref32"><unstructured_citation>[32] R. Ji, L. Yang, L. Zhang, Y. Tian, J. Ding, H. Chen, Y. Lu, P. Zhou, Zhu W,” Microring-resonator-based four-port optical router for photonic networks-on-chip.” Optics Express. Vol. 19, No. 20, pp. 18945-18955. </unstructured_citation></citation><citation key="ref33"><unstructured_citation>[33] Chaudhari B. S, Patil S. S,” Optimized designs of low loss non-blocking optical router for ONoC applications.” 2019, (IJIT). DOI: 10.1007/s41870-019-00298-7. </unstructured_citation></citation><citation key="ref34"><unstructured_citation>[34] J. Chan, G. Hendry, K. Bergman, Carloni L.P,” Physical-Layer Modeling and System-Level Design of Chip-Scale Photonic Interconnection Networks.” IEEE Trans. Computer-Aided Design of Integrated Circuits and Systems. 2011, vol. 30, no. 10, pp. 1507-1520. DOI:10.1109/TCAD.2011.2157157. </unstructured_citation></citation><citation key="ref35"><unstructured_citation>[35] Lee B. G, A. Biberman, D. Po, M. Lipson, Bergman K,” All-Optical Comb Switch for Multi wavelength Message Routing in Silicon Photonic Networks.” 2008, IEEE Photonics Technology Letters, vol. 20, no. 10, pp. 767-769. </unstructured_citation></citation><citation key="ref36"><unstructured_citation>[36] N Bagheri Renani, E Yaghoubi,” A Review of Optical Routers in Photonic Networks-on-Chip,” A Literature Survey. J. ADV COMP ENG TECHNOL. 2018, 4(3) pp. 143-154 </unstructured_citation></citation><citation key="ref37"><unstructured_citation>[37] N.B. Renani, E. Yaghoubi, N Sadehnezhad, et al. “NLR-OP: a high-performance optical router based on North-Last turning model for multicore processors.” J Supercomput ,2022, 78, 2442–2476. DOI: 10.1007/s11227-021-03920-3.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>Analyzing the Relationship between the Digital Economy and the GDP of Iran and Malaysia Using Long Short-Term Memory Neural Networks</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Mohammad Kazem</given_name><surname>Sayadi</surname></person_name></contributors><publication_date media_type="online"><month>2</month><day>26</day><year>2026</year></publication_date><pages><first_page>237</first_page><last_page>257</last_page></pages><doi_data><doi>10.66224/jict.51605.17.66.237</doi><resource>http://jour.aicti.ir/fa/Article/51605</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/fa/Article/Download/51605</resource></item><item crawler="google"><resource>http://jour.aicti.ir/fa/Article/Download/51605</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/fa/Article/Download/51605</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/fa/Article/Download/51605</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/fa/Article/Download/51605</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/fa/Article/Download/51605</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/fa/Article/Download/51605</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1] K. Schwab, The Fourth Industrial Revolution, Penguin Books Limited, 2017.</unstructured_citation></citation><citation key="ref2"><unstructured_citation> [2] R. Bukht and R. Heeks, Defining, Conceptualising and Measuring the Digital Economy, Development Informatics Working Paper No. 68, Aug. 2017. [Online]. Available: http://dx.doi.org/10.2139/ssrn.3431732</unstructured_citation></citation><citation key="ref3"><unstructured_citation>[3] A. Goldfarb, C. Tucker, "Digital economics," Journal of Economic Literature, vol. 57, no. 1, pp. 3–43, 2019.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>[4] K. Hafner و M. Lyon, Where Wizards Stay Up Late: The Origins of the Internet, New York: Touchstone, Simon &amp; Schuster, 1996.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>[5] S. Greenstein, How the Internet Became Commercial: Innovation, Privatization, and the Birth of a New Network, Princeton and Oxford: Princeton University Press, 2015.</unstructured_citation></citation><citation key="ref6"><unstructured_citation> [6] N. Moradhassel and B. Mohebikhah, “Estimating the Value of Digital Economy Core Spillover in Iran,” Journal of Information and Communication Technology, vol. 15, no. 57, pp. 111–121, 2023, doi: 10.61186/jict.44104.15.57.111.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>[7] L. Xia, S. Baghaie, and S. M. Sajadi, “The digital economy: Challenges and opportunities in the new era of technology and electronic communications,” Ain Shams Engineering Journal, vol. 15, no. 2, p. 102411, 2024.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>[8] Z. Chen and R. Xing, “Digital economy, green innovation and high-quality economic development,” International Review of Economics &amp; Finance, vol. 99, p. 104029, 2025.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>[9] A. Raihan, “A review of the potential opportunities and challenges of the digital economy for sustainability,” Innovation and Green Development, vol. 3, no. 4, p. 100174, 2024.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>[10] J. Mokyr, “Long-term economic growth and the history of technology,” in Handbook of Economic Growth, vol. 1, Part B, P. Aghion and S. N. Durlauf, Eds. Elsevier, 2005, pp. 1113–1180. doi: 10.1016/S1574-0684(05)01017-8.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>[11] E. Brynjolfsson و L. M. Hitt, "Computing Productivity: Firm-Level Evidence," Review of Economics and Statistics, vol. 85, no. 4, pp. 793–808, 2003.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>[12] A. Agrawal, N. Lacetera, and E. Lyons, "Does standardized information in online markets disproportionately benefit job applicants from less developed countries?," Journal of International Economics, vol. 103, pp. 1–12, 2016, doi: 10.1016/j.jinteco.2016.08.003.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>[13] E. Brynjolfsson, Y. Hu, و M. D. Smith, "Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety at Online Booksellers," Management Science, vol. 49, no. 11, pp. 1580–1596, 2003.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>[14] G. J. Stigler, "The Economics of Information," Journal of Political Economy, vol. 69, no. 3, pp. 213–225, 1961.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>[15] P. A. Diamond, "A Model of Price Adjustment," Journal of Economic Theory, vol. 3, no. 2, pp. 156–168, 1971.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>[16] C. Shapiro و H. R. Varian, Information Rules: A Strategic Guide to the Network Economy, Cambridge: Harvard Business School Press, 1998.</unstructured_citation></citation><citation key="ref17"><unstructured_citation>[17] H. R. Varian, Intermediate Microeconomics: A Modern Approach, 8th ed., New York: W.W. Norton &amp; Company, 2010.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>[18] M. Spence, "Government and economics in the digital economy," Journal of Government and Economics, vol. 3, 2021, Art. no. 100020.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>[19] م. مهرکام، م.ت. تقوی فرد، و ا. جهانگرد، "مدلی برای تحلیل نقش فناوری اطلاعات و ارتباطات در رشد تولید ناخالص داخلی ایران با رویکرد پویایی سیستم"، رساله دکتری مدیریت فناوری اطلاعات، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبائی، 1400.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>[20] P. Koutroumpis, "The Economic Impact of Broadband on Growth: A Simultaneous Approach," Telecommunications Policy, vol. 33, no. 9, 2009.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>[21] R. Katz و P. Koutroumpis, "Measuring Socio-Economic Digitization: A Paradigm Shift," Social Science Research Network, 2012.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>[22] R. Bukht و R. Heeks, "Defining, conceptualising and measuring the digital economy," Development Informatics Working Paper, no. 68, 2017.</unstructured_citation></citation><citation key="ref23"><unstructured_citation> [23] H. Aly, "Digital transformation, development and productivity in developing countries: is artificial intelligence a curse or a blessing?," Review of Economics and Political Science, vol. 7, no. 4, pp. 238–256, 2022.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>[24] L. Mićić, "Digital transformation and its influence on GDP," Economics, vol. 5, no. 2, pp. 135–147, 2017, doi: 10.1515/eoik-2017-0028.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>[25] E. Roszko-Wójtowicz و M. M. Grzelak, "Macroeconomic stability and the level of competitiveness in EU member states: A comparative dynamic approach," Oeconomia Copernicana, vol. 11, no. 4, pp. 657–688, 2020, doi: 10.24136/oc.2020.027.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>[26] A. Małkowska, M. Urbaniec, و M. Kosała, "The impact of digital transformation on European countries: Insights from a comparative analysis," Equilibrium. Quarterly Journal of Economics and Economic Policy, vol. 16, no. 2, pp. 325–355, 2021, doi: 10.24136/eq.2021.012.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>[27] ع. پناهی‌فرد، م. پیری، و س. کیان‌پور، "بررسی تأثیر اقتصاد دیجیتال در بازاریابی بر توسعه صادرات و رشد اقتصادی همدان: رویکرد توابع کاپیولا"، فصلنامه تخصصی رشد فناوری، دوره 20، شماره 78، 1403.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>[28] ف. توسلی، ف. دژپسند، و ع. عرب مازار، "بررسی اثر دیجیتالی شدن اقتصاد بر رشد اقتصادی ایران"، پایان‌نامه کارشناسی ارشد، دانشکده علوم اقتصادی و سیاسی، دانشگاه شهید بهشتی، 1399.</unstructured_citation></citation><citation key="ref29"><unstructured_citation>[29] م.ع. مرادی و م.رضا هدایتی، "طراحی مدل تکاملی گذار ایران به اقتصاد دیجیتال"، فصلنامه پژوهشنامه اقتصادی، سال هجدهم، شماره 68، صص. 219-251، 1397.</unstructured_citation></citation><citation key="ref30"><unstructured_citation>[30] ک. امامی، "آیا افزایش سهم فناوری اطلاعات و ارتباطات از تولید ناخالص داخلی در کشور ایران ضروری است؟"، فصلنامه پژوهشنامه اقتصادی، سال هجدهم، شماره 68، بهار 1397، صص. 45-74، 1396.</unstructured_citation></citation><citation key="ref31"><unstructured_citation>[31] س. مشیری، "برآورد آثار مستقیم و سرریز سرمایه‌گذاری در فناوری اطلاعات و ارتباطات بر تولید صنایع ایران با تأکید بر نقش سرمایه‌ی انسانی و ظرفیت جذب"، فصلنامه تحقیقات اقتصادی، جلد 52، شماره 2، صص. 395-426، 1396.</unstructured_citation></citation><citation key="ref32"><unstructured_citation>[32] ص. معتقد، ه. رنجبر، و س. دایی کریم‌زاده، "رابطه فناوری اطلاعات و ارتباطات، بخش‌های صادراتی و غیرصادراتی و رشد اقتصادی در ایران: تعمیم مدل فدر"، مدلسازی اقتصادی، جلد 8، شماره 4 (پیاپی 28)، صص. 27-44، 1393.</unstructured_citation></citation><citation key="ref33"><unstructured_citation>[33] م.ع. مرادی، م. کبریایی، و م. گنجی، "تأثیر فناوری اطلاعات و ارتباطات بر رشد اقتصادی کشورهای اسلامی منتخب"، فصلنامه اقتصاد و تجارت نوین، شماره 29 و 30، پاییز 1391، صص. 79-108، 1391.</unstructured_citation></citation><citation key="ref34"><unstructured_citation>[34] A. L. Gómez and S. J. López, "Innovation and transformation: Keys to the success of SMES in the digital age," Journal of Economics, Innovative Management and Entrepreneurship, vol. 2, no. 3, 2024.</unstructured_citation></citation><citation key="ref35"><unstructured_citation>[35] W. Zhang, S. Zhao, X. Wan, and Y. Yao, "Study on the effect of digital economy on high-quality economic development in China," PLOS ONE, vol. 16, no. 9, p. e0257365, 2021.</unstructured_citation></citation><citation key="ref36"><unstructured_citation>[36] Jiao S, Sun Q. Digital Economic Development and Its Impact on Economic Growth in China: Research Based on the Prespective of Sustainability. Sustainability. 2021; 13(18):10245. https://doi.org/10.3390/su131810245</unstructured_citation></citation><citation key="ref37"><unstructured_citation>[37] Solomon, E. M., &amp; van Klyton, A. (2020). The impact of digital technology usage on economic growth in Africa. Utilities policy, 67, 101104.</unstructured_citation></citation><citation key="ref38"><unstructured_citation>[38] Niebel, T. (2018). ICT and economic growth–Comparing developing, emerging and developed countries. World development, 104, 197-211.</unstructured_citation></citation><citation key="ref39"><unstructured_citation>[39] Fernández-Portillo, A., Almodóvar-González, M., &amp; Hernández-Mogollón, R. (2020). Impact of ICT development on economic growth. A study of OECD European union countries. Technology in Society, 63, 101420.</unstructured_citation></citation><citation key="ref40"><unstructured_citation>[40] Latif, Z., Latif, S., Ximei, L., Pathan, Z. H., Salam, S., &amp; Jianqiu, Z. (2018). The dynamics of ICT, foreign direct investment, globalization and economic growth: Panel estimation robust to heterogeneity and cross-sectional dependence. Telematics and informatics, 35(2), 318-328.</unstructured_citation></citation><citation key="ref41"><unstructured_citation>[41] Palvia, P., Baqir, N., &amp; Nemati, H. (2018). ICT for socio-economic development: A citizens’ perspective. Information &amp; Management, 55(2), 160-176.</unstructured_citation></citation><citation key="ref42"><unstructured_citation>[42] S. Bandyopadhyay, "Knowledge-Based Economic Development: Mass Media and the Weightless Economy," STICERD, London School of Economics and Oriel College, Oxford University, 2009.</unstructured_citation></citation><citation key="ref43"><unstructured_citation>[43] L. Raffestin, "ICT Spending and Inflation at the Sectorial Level," University of Paris, 2011.</unstructured_citation></citation><citation key="ref44"><unstructured_citation>[44] M. Kabza, "Artificial intelligence in financial services–benefits and costs," in Innovation in Financial services, Routledge, 2020, pp. 183-198.</unstructured_citation></citation><citation key="ref45"><unstructured_citation> [45] S. Mullainathan و J. Spiess, "Machine learning: an applied econometric approach," Journal of Economic Perspectives, vol. 31, pp. 87–106, 2017.</unstructured_citation></citation><citation key="ref46"><unstructured_citation>[46] Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press</unstructured_citation></citation><citation key="ref47"><unstructured_citation>[47] Angrist JD, Pischke JS. 2008. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton, NJ: Princeton Univ. Press</unstructured_citation></citation><citation key="ref48"><unstructured_citation>[48] Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. 2008. Top 10 algorithms in data mining. Knowl. Inform. Syst. 14:1–37</unstructured_citation></citation><citation key="ref49"><unstructured_citation>[49] Athey, S., &amp; Imbens, G. W. (2019). Machine learning methods that economists should know about. Annual Review of Economics, 11(1), 685-725.</unstructured_citation></citation></citation_list></journal_article><journal_article publication_type="full_text"><titles><title>A Review of Multimedia Recommender Systems</title></titles><contributors><person_name contributor_role="author" sequence="first"><given_name>Saeedeh</given_name><surname>Momtazi</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Zahra</given_name><surname>Pourbahman</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Mohammad Reza</given_name><surname>Azizi</surname></person_name><person_name contributor_role="author" sequence="additional"><given_name>Meysam</given_name><surname>Bagheri</surname></person_name></contributors><publication_date media_type="online"><month>2</month><day>26</day><year>2026</year></publication_date><pages><first_page>198</first_page><last_page>237</last_page></pages><doi_data><doi>10.66224/jict.52833.17.66.198</doi><resource>http://jour.aicti.ir/fa/Article/52833</resource><collection property="crawler-based"><item crawler="iParadigms"><resource>http://jour.aicti.ir/fa/Article/Download/52833</resource></item><item crawler="google"><resource>http://jour.aicti.ir/fa/Article/Download/52833</resource></item><item crawler="msn"><resource>http://jour.aicti.ir/fa/Article/Download/52833</resource></item><item crawler="altavista"><resource>http://jour.aicti.ir/fa/Article/Download/52833</resource></item><item crawler="yahoo"><resource>http://jour.aicti.ir/fa/Article/Download/52833</resource></item><item crawler="scirus"><resource>http://jour.aicti.ir/fa/Article/Download/52833</resource></item></collection><collection property="text-mining"><item><resource mime_type="application/pdf">http://jour.aicti.ir/fa/Article/Download/52833</resource></item></collection></doi_data><citation_list><citation key="ref1"><unstructured_citation>[1]	Deldjoo, Y., et al. Multimedia recommender systems. in Proceedings of the 12th ACM Conference on Recommender Systems. 2018.</unstructured_citation></citation><citation key="ref2"><unstructured_citation>[2]	Deldjoo, Y., et al. MMTF-14K: a multifaceted movie trailer feature dataset for recommendation and retrieval. in Proceedings of the 9th ACM Multimedia Systems Conference. 2018.</unstructured_citation></citation><citation key="ref3"><unstructured_citation>[3]	Chong, D., Deep dive into netflix’s recommender system. 2020, Medium.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>[4]	Amato, F., et al., SOS: A multimedia recommender System for Online Social networks. Future generation computer systems, 2019. 93: p. 914-923.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>[5]	Liang, T., et al., A hybrid recommendation model based on estimation of distribution algorithms. Journal of Computational Information Systems, 2014. 10(2): p. 781-788.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>[6]	Chen, Y.-L., Y.-H. Yeh, and M.-R. Ma, A movie recommendation method based on users' positive and negative profiles. Information Processing &amp; Management, 2021. 58(3): p. 102531.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>[7]	Pradeep, N., et al., Content based movie recommendation system. International Journal of Research in Industrial Engineering, 2020. 9(4): p. 337-348.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>[8]	Singh, R.H., et al., Movie recommendation system using cosine similarity and KNN. International Journal of Engineering and Advanced Technology, 2020. 9(5): p. 556-559.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>[9]	Almeida, M.S. and A. Britto. MOEA-RS: A Content-Based Recommendation System Supported by a Multi-objective Evolutionary Algorithm. in International Conference on Artificial Intelligence and Soft Computing. 2020. Springer.</unstructured_citation></citation><citation key="ref10"><unstructured_citation>[10]	Meel, P., et al. Movie Recommendation Using Content-Based and Collaborative Filtering. in International Conference on Innovative Computing and Communications. 2021. Springer.</unstructured_citation></citation><citation key="ref11"><unstructured_citation>[11]	Singla, R., et al. FLEX: A Content Based Movie Recommender. in 2020 International Conference for Emerging Technology (INCET). 2020. IEEE.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>[12]	Sottocornola, G., et al. Towards a deep learning model for hybrid recommendation. in Proceedings of the International Conference on Web Intelligence. 2017.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>[13]	Yin, H., et al., Dynamic user modeling in social media systems. ACM Transactions on Information Systems (TOIS), 2015. 33(3): p. 1-44.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>[14]	Albanese, M., et al., A multimedia recommender system. ACM Transactions on Internet Technology (TOIT), 2013. 13(1): p. 1-32.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>[15]	Rajasekar, R., Radhakrishnan, N., Sridar, K., Viji, C., Mohanraj, M., Kalpana, C., &amp; Rajkumar, N. (2025). Intelligent movie recommendation system. Salud, Ciencia y Tecnología-Serie de Conferencias, (4), 1438.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>[16]	Shashaani, S. (2024, October). Explainability in music recommender system. In Proceedings of the 18th ACM Conference on Recommender Systems (pp. 1395-1401).</unstructured_citation></citation><citation key="ref17"><unstructured_citation>[17]	Van Den Oord, A., S. Dieleman, and B. Schrauwen. Deep content-based music recommendation. in Neural Information Processing Systems Conference (NIPS 2013). 2013. Neural Information Processing Systems Foundation (NIPS).</unstructured_citation></citation><citation key="ref18"><unstructured_citation>[18]	Wang, X. and Y. Wang. Improving content-based and hybrid music recommendation using deep learning. in Proceedings of the 22nd ACM international conference on Multimedia. 2014.</unstructured_citation></citation><citation key="ref19"><unstructured_citation>[19]	Liu, N.-H., Comparison of content-based music recommendation using different distance estimation methods. Applied intelligence, 2013. 38(2): p. 160-174.</unstructured_citation></citation><citation key="ref20"><unstructured_citation>[20]	Bogdanov, D., et al., Semantic audio content-based music recommendation and visualization based on user preference examples. Information Processing &amp; Management, 2013. 49(1): p. 13-33.</unstructured_citation></citation><citation key="ref21"><unstructured_citation>[21]	Wang, X., D. Rosenblum, and Y. Wang. Context-aware mobile music recommendation for daily activities. in Proceedings of the 20th ACM international conference on Multimedia. 2012.</unstructured_citation></citation><citation key="ref22"><unstructured_citation>[22]	Barragáns-Martínez, A.B., et al., Exploiting social tagging in a web 2.0 recommender system. IEEE Internet Computing, 2010. 14(6): p. 23-30.</unstructured_citation></citation><citation key="ref23"><unstructured_citation>[23]	Chiliguano, P. and G. Fazekas. Hybrid music recommender using content-based and social information. in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2016. IEEE.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>[24]	Chou, S.-Y., et al. Addressing cold start for next-song recommendation. in Proceedings of the 10th ACM Conference on Recommender Systems. 2016.</unstructured_citation></citation><citation key="ref25"><unstructured_citation>[25]	R. Turrin, A.C., R. Pagano, M. Quadrana, and P. Cremonesi, Large scale music recommendation, in LSRS. 2015.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>[26]	Vasile, F., E. Smirnova, and A. Conneau. Meta-prod2vec: Product embeddings using side-information for recommendation. in Proceedings of the 10th ACM Conference on Recommender Systems. 2016.</unstructured_citation></citation><citation key="ref27"><unstructured_citation>[27]	Wang, X., et al., Exploration in interactive personalized music recommendation: a reinforcement learning approach. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 2014. 11(1): p. 1-22.</unstructured_citation></citation><citation key="ref28"><unstructured_citation>[28]	Rao, A., et al. Supervised Feature Learning for Music Recommendation. in International Symposium on Signal Processing and Intelligent Recognition Systems. 2020. Springer.</unstructured_citation></citation><citation key="ref29"><unstructured_citation>[29]	Hosseinzadeh Aghdam, M., et al. Adapting recommendations to contextual changes using hierarchical hidden markov models. in Proceedings of the 9th ACM Conference on Recommender Systems. 2015.</unstructured_citation></citation><citation key="ref30"><unstructured_citation>[30]	Jannach, D., L. Lerche, and I. Kamehkhosh. Beyond" hitting the hits" Generating coherent music playlist continuations with the right tracks. in Proceedings of the 9th ACM Conference on Recommender Systems. 2015.</unstructured_citation></citation><citation key="ref31"><unstructured_citation>[31]	Vall, A., et al. A hybrid approach to music playlist continuation based on playlist-song membership. in Proceedings of the 33rd Annual ACM Symposium on Applied Computing. 2018.</unstructured_citation></citation><citation key="ref32"><unstructured_citation>[32]	Vall, A., et al. Music playlist continuation by learning from hand-curated examples and song features: Alleviating the cold-start problem for rare and out-of-set songs. in Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems. 2017.</unstructured_citation></citation><citation key="ref33"><unstructured_citation>[33]	Gharahighehi, A. and C. Vens, Personalizing diversity versus accuracy in session-based recommender systems. SN Computer Science, 2021. 2(1): p. 1-12.</unstructured_citation></citation><citation key="ref34"><unstructured_citation>[34]	Engelbert, B.B., M.B.; Kruthoff-Bruwer, R.; Morisse, K. A user supporting personal video recorder by implementing a generic Bayesian classifier based recommendation system. in Proceedings of the 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops). 2011.</unstructured_citation></citation><citation key="ref35"><unstructured_citation>[35]	Quan, J.-C.C., S.-B, A Hybrid Recommender System Based on AHP That Awares Contexts with Bayesian Networks for Smart TV, in International Conference on Hybrid Artificial Intelligence Systems (HAIS 2014). 2014.</unstructured_citation></citation><citation key="ref36"><unstructured_citation>[36]	Oh, J.S., Y.; Kim, J.; Humayoun, M.; Park, Y.H.; Yu, H. Time-dependent user profiling for TV recommendation. in Proceedings of the 2nd International Conference on Cloud and Green Computing and 2nd International Conference on Social Computing and Its Applications, CGC/SCA 2012. 2012.</unstructured_citation></citation><citation key="ref37"><unstructured_citation>[37]	Turrin, R.C., A.; Cremonesi, P.; Pagano, R. Time-based TV programs prediction. in Proceedings of the 1st Workshop on Recommender Systems for Television and Online Video (RecSysTV) 2014. 2014.</unstructured_citation></citation><citation key="ref38"><unstructured_citation>[38]	da Silva, F.S., L.G.P. Alves, and G. Bressan, Personal TVware: an infrastructure to support the context-aware recommendation for personalized digital TV. International Journal of Computer Theory and Engineering, 2012. 4(2): p. 131.</unstructured_citation></citation><citation key="ref39"><unstructured_citation>[39]	Zibriczky, D., et al. Personalized recommendation of linear content on interactive TV platforms: beating the cold start and noisy implicit user feedback. in UMAP workshops. 2012.</unstructured_citation></citation><citation key="ref40"><unstructured_citation>[40]	Song, S., H. Moustafa, and H. Afifi, Advanced IPTV services personalization through context-aware content recommendation. IEEE Transactions on Multimedia, 2012. 14(6): p. 1528-1537.</unstructured_citation></citation><citation key="ref41"><unstructured_citation>[41]	Chaudhry, M., et al. Heterogeneous information network based TV program recommendation. in Proceedings of the 16th International Symposium on Advanced Intelligent Systems. 2015.</unstructured_citation></citation><citation key="ref42"><unstructured_citation>[42]	Barraza-Urbina, A., et al., Using social media data for online television recommendation services at RTÉ Ireland. 2015.</unstructured_citation></citation><citation key="ref43"><unstructured_citation>[43]	Hromic, H., et al., Event panning in a stream of big data. Research Day 2013 Schedule, 2012: p. 44.</unstructured_citation></citation><citation key="ref44"><unstructured_citation>[44]	Yuan, J., et al., Context-aware LDA: Balancing relevance and diversity in TV content recommenders. 2015.</unstructured_citation></citation><citation key="ref45"><unstructured_citation>[45]	Symeonidis, P., et al. Recommending the video to watch next: an offline and online evaluation at YOUTV. de. in Fourteenth ACM conference on recommender systems. 2020.</unstructured_citation></citation><citation key="ref46"><unstructured_citation>[46]	Basilico, J. Recent Trends in Personalization: A Netflix Perspective. 2019. ICML.</unstructured_citation></citation><citation key="ref47"><unstructured_citation>[47]	Covington, P., J. Adams, and E. Sargin. Deep neural networks for youtube recommendations. in Proceedings of the 10th ACM conference on recommender systems. 2016.</unstructured_citation></citation><citation key="ref48"><unstructured_citation>[48]	Implementing the YouTube Recommendations Paper in TensorFlow — Part 1. Available from: https://theiconic.tech/implementing-the-youtube-recommendations-paper-in-tensorflow-part-1-d1e1299d5622.</unstructured_citation></citation><citation key="ref49"><unstructured_citation>[49]	Krawiec, T., The Amazon Recommendations Secret to Selling More Online. Rejoiner http://rejoiner. com/resources/amazon-recommendations-secret-selling-online/website visited, 2018: p. 1-18.</unstructured_citation></citation><citation key="ref50"><unstructured_citation>[50]	Mangalindan, J. Amazon’s recommendation secret. 2012; Available from: https://fortune.com/2012/07/30/amazons-recommendation-secret/.</unstructured_citation></citation><citation key="ref51"><unstructured_citation>[51]	Linden, G., B. Smith, and J. York, Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing, 2003. 7(1): p. 76-80.</unstructured_citation></citation><citation key="ref52"><unstructured_citation>[52]	Baatarjav, E.-A., S. Phithakkitnukoon, and R. Dantu. Group recommendation system for facebook. in OTM Confederated International Conferences" On the Move to Meaningful Internet Systems". 2008. Springer.</unstructured_citation></citation><citation key="ref53"><unstructured_citation>[53]	Facebook recommender system. Available from: https://github.com/facebookresearch/dlrm.</unstructured_citation></citation><citation key="ref54"><unstructured_citation>[54]	Metaxiotis, K., et al., Decision support through knowledge management: the role of the artificial intelligence. Information Management &amp; Computer Security, 2003.</unstructured_citation></citation><citation key="ref55"><unstructured_citation>[55]	Wondercube® TV Broadcasting GSD/HD digital broadcast. Available from: http://www.jvc.slak.si/pdf/tv%20broadcasting.pdf.</unstructured_citation></citation><citation key="ref56"><unstructured_citation>[56]	Recommendation Systems: Applications, Examples &amp; Benefits. Available from: https://research.aimultiple.com/recommendation-system/#amazoncom.</unstructured_citation></citation><citation key="ref57"><unstructured_citation>[57]	Optimizely. Available from: https://aimultiple.com/.</unstructured_citation></citation><citation key="ref58"><unstructured_citation>[58]	CRM. Available from: https://www.cnbc.com/quotes/CRM.</unstructured_citation></citation><citation key="ref59"><unstructured_citation>[59]	Clarifai. Available from: https://www.clarifai.com/.</unstructured_citation></citation><citation key="ref60"><unstructured_citation>[60]	Jinni. Available from: http://www.jinni.com/.</unstructured_citation></citation><citation key="ref61"><unstructured_citation>[61]	RottenTomatoes. Available from: www.rottentomatoes.com.</unstructured_citation></citation><citation key="ref62"><unstructured_citation>[62]	MovieLens 100K Dataset. Available from: https://grouplens.org/datasets/movielens/100k/.</unstructured_citation></citation><citation key="ref63"><unstructured_citation>[63]	Criticker. Available from: http://www.criticker.com/.</unstructured_citation></citation><citation key="ref64"><unstructured_citation>[64]	Bestsimilar. Available from: bestsimilar.com.</unstructured_citation></citation><citation key="ref65"><unstructured_citation>[65]	PEACH. 2021 [cited 2021 1 June 2021]; Available from: https://peach.ebu.io/.</unstructured_citation></citation><citation key="ref66"><unstructured_citation>[66]	Sidana, S., Recommendation systems for online advertising. 2018, Université Grenoble Alpes.</unstructured_citation></citation><citation key="ref67"><unstructured_citation>[67]	Netflix Prize Data Set. Available from: https://academictorrents.com/details/9b13183dc4d60676b773c9e2cd6de5e5542cee9a.</unstructured_citation></citation><citation key="ref68"><unstructured_citation>[68]	Eliashberg, J., et al., Demand-driven scheduling of movies in a multiplex. International Journal of Research in Marketing, 2009. 26(2): p. 75-88.</unstructured_citation></citation><citation key="ref69"><unstructured_citation>[69]	Zarandi, M.H.F., et al., A state of the art review of intelligent scheduling. Artificial Intelligence Review, 2020. 53(1): p. 501-593.</unstructured_citation></citation><citation key="ref70"><unstructured_citation>[70]	Brown, D.E., J.A. Marin, and W.T. Scherer, A survey of intelligent scheduling systems, in Intelligent Scheduling Systems. 1995, Springer. p. 1-40.</unstructured_citation></citation><citation key="ref71"><unstructured_citation>[71]	Horen, J.H., Scheduling of network television programs. Management Science, 1980. 26(4): p. 354-370.</unstructured_citation></citation><citation key="ref72"><unstructured_citation>[72]	Welbank, M., A review of knowledge acquisition techniques for expert systems. 1983: Martlesham Consultancy Services Martlesham Heath, Ipswich.</unstructured_citation></citation><citation key="ref73"><unstructured_citation>[73]	Piroozfard, H., K.Y. Wong, and A. Hassan, A hybrid genetic algorithm with a knowledge-based operator for solving the job shop scheduling problems. Journal of Optimization, 2016. 2016.</unstructured_citation></citation><citation key="ref74"><unstructured_citation>[74]	Mitchell, T.M., Machine learning. 1997.</unstructured_citation></citation><citation key="ref75"><unstructured_citation>[75]	Kocsis, T., et al., Case-Based Reasoning system for mathematical modelling options and resolution methods for production scheduling problems: Case representation, acquisition and retrieval. Computers &amp; Industrial Engineering, 2014. 77: p. 46-64.</unstructured_citation></citation><citation key="ref76"><unstructured_citation>[76]	Panaggio, M.J., et al., Prediction and Optimal Scheduling of Advertisements in Linear Television. arXiv preprint arXiv:1608.07305, 2016.</unstructured_citation></citation><citation key="ref77"><unstructured_citation>[77]	Ghassemi Tari, F. and R. Alaei, Scheduling TV commercials using genetic algorithms. International Journal of Production Research, 2013. 51(16): p. 4921-4929.</unstructured_citation></citation><citation key="ref78"><unstructured_citation>[78]	AlShami, H., Optimising television programming and scheduling. 2017: Lancaster University (United Kingdom).</unstructured_citation></citation><citation key="ref79"><unstructured_citation>[79]	A case study of designing TV schedules. Available from: http://www.kr.inf.uc3m.es/wp-content/uploads/2019/12/AI4SE-Meer.pdf.</unstructured_citation></citation><citation key="ref80"><unstructured_citation>[80]	Le, Q. and T. Mikolov. Distributed representations of sentences and documents. in International conference on machine learning. 2014. PMLR.</unstructured_citation></citation><citation key="ref81"><unstructured_citation>[81]	He, X., et al. Neural collaborative filtering. in Proceedings of the 26th international conference on world wide web. 2017.</unstructured_citation></citation><citation key="ref82"><unstructured_citation>[82]	Cheng, H.-T., et al. Wide &amp; deep learning for recommender systems. in Proceedings of the 1st workshop on deep learning for recommender systems. 2016.</unstructured_citation></citation><citation key="ref83"><unstructured_citation>[83]	Roshdy, Youssef, and Mennat Allah Hassan. "An Efficient Content-Based Video Recommendation." Journal of Computing and Communication 1.1 (2022): 48-64.</unstructured_citation></citation><citation key="ref84"><unstructured_citation>[84]	Markapudi, Baburao, et al. "Content-based video recommendation system (CBVRS): a novel approach to predict videos using multilayer feed forward neural network and Monte Carlo sampling method." Multimedia Tools and Applications 82.5 (2023): 6965-6991. </unstructured_citation></citation><citation key="ref85"><unstructured_citation>[85]	Ali, Shaik Faizan Roshan, et al. "Recommender System using Audio and Lyrics." 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2023.</unstructured_citation></citation><citation key="ref86"><unstructured_citation>[86]	Burch, Charats, Robert Sprowl, and Mehmet Ergezer. "A multi-user virtual world with music recommendations and mood-based virtual effects." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 37. No. 13. 2023.</unstructured_citation></citation><citation key="ref87"><unstructured_citation>[87]	Dudekula, Khasim Vali, et al. "Convolutional Neural Network-Based Personalized Program Recommendation System for Smart Television Users." Sustainability 15.3 (2023): 2206.</unstructured_citation></citation><citation key="ref88"><unstructured_citation>[88]	Boeker, Maximilian and Aleksandra Urman. “An Empirical Investigation of Personalization Factors on TikTok.” Proceedings of the ACM Web Conference 2022 (2022): n. pag.</unstructured_citation></citation><citation key="ref89"><unstructured_citation>[89]	Liu, Zhuoran, et al. "Monolith: real time recommendation system with collisionless embedding table." arXiv preprint arXiv:2209.07663 (2022).</unstructured_citation></citation><citation key="ref90"><unstructured_citation>[90]	Ansari, Farooq. The Spotify Audio Features Hit Predictor Dataset (1960-2019). 1, 4TU.Centre for Research Data, 6 Apr. 2020, doi:10.4121/UUID:D77E74B0-66BC-47AC-8B25-5796D3084478.</unstructured_citation></citation><citation key="ref91"><unstructured_citation>[91]	Moura, Luan; Fontelles, Emanuel; Sampaio, Vinicius; França, Mardônio (2020), “Music Dataset: Lyrics and Metadata from 1950 to 2019”, Mendeley Data, V2, doi: 10.17632/3t9vbwxgr5.2</unstructured_citation></citation><citation key="ref92"><unstructured_citation>[92]	Deldjoo, Y., Schedl, M., Hidasi, B., Wei, Y., He, X. (2022). Multimedia Recommender Systems: Algorithms and Challenges. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-2197-4_25</unstructured_citation></citation><citation key="ref93"><unstructured_citation>[93]	Deldjoo, Yashar and Schedl, Markus and Cremonesi, Paolo and Pasi, Gabriella, “Recommender Systems Leveraging Multimedia Content”, ACM Computing Surveys, 2020, 53, 5,</unstructured_citation></citation><citation key="ref94"><unstructured_citation>[94]	A Survey of Multimedia Recommender Systems: Challenges and Opportunities Mouzhi Ge (1Faculty of Informatics, Masaryk University, Brno 60200, Czech Republic) and Fabio Persia (2Faculty of Computer Science, Free University of Bozen-Bolzano, Bozen-Bolzano, 39100, Italy) International Journal of Semantic Computing 2017 11:03, 411-428</unstructured_citation></citation><citation key="ref95"><unstructured_citation>[95]	Jiang, Y., Xia, L., Wei, W., Luo, D., Lin, K., &amp; Huang, C. (2024, October). Diffmm: Multi-modal diffusion model for recommendation. In Proceedings of the 32nd ACM International Conference on Multimedia (pp. 7591-7599).</unstructured_citation></citation><citation key="ref96"><unstructured_citation>[96]	Malitesta, D., Cornacchia, G., Pomo, C., Merra, F. A., Di Noia, T., &amp; Di Sciascio, E. (2025). Formalizing multimedia recommendation through multimodal deep learning. ACM Transactions on Recommender Systems, 3(3), 1-33. </unstructured_citation></citation></citation_list></journal_article></journal></body></doi_batch>