jict-202606051820260605180403CMV Verlagkhoffmann@cmv-verlag.comCMV VerlagJournal of Information and Communication Technologyjict2717-041122720231453HosseinAbbasimehrRezaPaki227202311310.66224/jict.41374.14.53.1http://jour.aicti.ir/en/Article/41374http://jour.aicti.ir/en/Article/Download/41374http://jour.aicti.ir/en/Article/Download/41374http://jour.aicti.ir/en/Article/Download/41374http://jour.aicti.ir/en/Article/Download/41374http://jour.aicti.ir/en/Article/Download/41374http://jour.aicti.ir/en/Article/Download/41374http://jour.aicti.ir/en/Article/Download/41374[1] H. Haberleitner, H. Meyr, and A. Taudes, "Implementation of a demand planning system using advance order information," International Journal of Production Economics, vol. 128, pp. 518-526, 2010.[2] H. Abbasimehr, M. Shabani, and M. Yousefi, "An optimized model using LSTM network for demand forecasting," Computers & Industrial Engineering, vol. 143, p. 106435, 2020/05/01/ 2020.[3] M. A. Villegas, D. J. Pedregal, and J. R. Trapero, "A support vector machine for model selection in demand forecasting applications," Computers & Industrial Engineering, vol. 121, pp. 1-7, 2018/07/01/ 2018.[4] N. J. Johannesen, M. Kolhe, and M. Goodwin, "Relative evaluation of regression tools for urban area electrical energy demand forecasting," Journal of Cleaner Production, vol. 218, pp. 555-564, 2019.[5] M. Q. Raza, M. Nadarajah, and C. Ekanayake, "Demand forecast of PV integrated bioclimatic buildings using ensemble framework," Applied energy, vol. 208, pp. 1626-1638, 2017.[6] J. W. Taylor, "A comparison of univariate time series methods for forecasting intraday arrivals at a call center," Management Science, vol. 54, pp. 253-265, 2008.[7] R. Law, G. Li, D. K. C. Fong, and X. Han, "Tourism demand forecasting: A deep learning approach," Annals of Tourism Research, vol. 75, pp. 410-423, 2019.[8] F. Martínez, M. P. Frías, M. D. Pérez, and A. J. Rivera, "A methodology for applying k-nearest neighbor to time series forecasting," Artificial Intelligence Review, vol. 52, pp. 2019–2037, 2019.[9] A. Sagheer and M. Kotb, "Time series forecasting of petroleum production using deep LSTM recurrent networks," Neurocomputing, vol. 323, pp. 203-213, 2019.[10] I. Maqsood, M. R. Khan, and A. Abraham, "An ensemble of neural networks for weather forecasting," Neural Computing & Applications, vol. 13, pp. 112-122, 2004.[11] H. Abbasimehr and M. Shabani, "A new framework for predicting customer behavior in terms of RFM by considering the temporal aspect based on time series techniques," Journal of Ambient Intelligence and Humanized Computing, 2020/05/05 2020.[12] H. Abbasimehr and R. Paki, "Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization," Chaos, Solitons & Fractals, vol. 142, p. 110511, 2021.[13] K. Bandara, H. Hewamalage, Y.-H. Liu, Y. Kang, and C. Bergmeir, "Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation," arXiv preprint arXiv:2008.02663, 2020.[14] K. Bandara, C. Bergmeir, and S. Smyl, "Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach," Expert Systems with Applications, vol. 140, p. 112896, 2020/02/01/ 2020.[15] C. Bergmeir, Rob J. Hyndman, and José M. Benítez, "Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation," International Journal of Forecasting, vol. 32, pp. 303-312, 2016/04/01/ 2016.[16] A. R. S. Parmezan, V. M. Souza, and G. E. Batista, "Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model," Information Sciences, vol. 484, pp. 302-337, 2019.[17] H. Shi, S. Hu, and J. Zhang, "LSTM based prediction algorithm and abnormal change detection for temperature in aerospace gyroscope shell," International Journal of Intelligent Computing and Cybernetics, vol. 12, pp. 274-291, 2019.[18] T. Fischer and C. Krauss, "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, vol. 270, pp. 654-669, 2018.[19] I. E. Livieris, E. Pintelas, and P. Pintelas, "A CNN–LSTM model for gold price time-series forecasting," Neural computing and applications, vol. 32, pp. 17351-17360, 2020.[20] S. Mehtab and J. Sen, "Stock price prediction using convolutional neural networks on a multivariate timeseries," arXiv preprint arXiv:2001.09769, 2020.[21] F. Shahid, A. Zameer, and M. Muneeb, "Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM," Chaos, Solitons & Fractals, vol. 140, p. 110212, 2020/11/01/ 2020.[22] S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural computation, vol. 9, pp. 1735-1780, 1997.[23] K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber, "LSTM: A Search Space Odyssey," IEEE Transactions on Neural Networks and Learning Systems, vol. 28, pp. 2222-2232, 2017.[24] I. Goodfellow, Y. Bengio, and A. Courville, Deep learning: MIT press, 2016.[25] W. Rawat and Z. Wang, "Deep convolutional neural networks for image classification: A comprehensive review," Neural computation, vol. 29, pp. 2352-2449, 2017.[26] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, et al., "Attention is all you need," in 31st International Conference on Neural Information Processing Systems, Long Beach, California, USA, 2017, pp. 6000–6010.[27] G. E. P. Box and D. R. Cox, "An Analysis of Transformations," Journal of the Royal Statistical Society: Series B (Methodological), vol. 26, pp. 211-243, 1964.[28] R. B. Cleveland, W. S. Cleveland, J. E. McRae, and I. Terpenning, "STL: A seasonal-trend decomposition," Journal of official statistics, vol. 6, pp. 3-73, 1990.[29] S. N. Lahiri, Resampling methods for dependent data: Springer Science & Business Media, 2013.ShaghayeghhajiabdollahMitraMirzarezaeeMir MohsenPedram2272023142310.66224/jict.41375.14.53.14http://jour.aicti.ir/en/Article/41375http://jour.aicti.ir/en/Article/Download/41375http://jour.aicti.ir/en/Article/Download/41375http://jour.aicti.ir/en/Article/Download/41375http://jour.aicti.ir/en/Article/Download/41375http://jour.aicti.ir/en/Article/Download/41375http://jour.aicti.ir/en/Article/Download/41375http://jour.aicti.ir/en/Article/Download/41375[1]. Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up: sentiment classification using machine learning techniques. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, (pp. 79–86)[2]. Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowledge-based systems, 89, 14-46.[3]. Wandra, K. H., & Barot, M. SARCASM DETECTION IN SENTIMENT ANALYSIS.[4]. Dashtipour, K., Hussain, A., Zhou, Q., Gelbukh, A., Hawalah, A. Y., & Cambria, E. (2016, November). PerSent: a freely available Persian sentiment lexicon. In International Conference on Brain Inspired Cognitive Systems (pp. 310-320). Springer, Cham.[5]. [4] Hardeniya, T., & Borikar, D. A. (2016). Dictionary based approach to sentiment analysis-a review. International Journal of Advanced Engineering, Management and Science, 2(5), 239438.[6]. Khan, A., Baharudin, B., Lee, L. H., & Khan, K. (2010). A review of machine learning algorithms for text-documents classification. Journal of advances in information technology, 1(1), 4-20.[7]. Wilson, T., Wiebe, J., & Hoffmann, P. (2005, October). Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of human language technology conference and conference on empirical methods in natural language processing (pp. 347-354).[8]. Wu, H. H., CHARNG-RURNG TSAI, A. N. G. E. L. A., TZONG-HAN TSAIi, R. I. C. H. A. R. D., & YUNG-JEN HSU, J. A. N. E. (2013). Building a Graded Chinese Sentiment Dictionary Based on Commonsense Knowledge for Sentiment Analysis of Song Lyrics. Journal of Information Science & Engineering, 29(4).[9]. Shams, M., Shakery, A., & Faili, H. (2012, May). A non-parametric LDA-based induction method for sentiment analysis. In The 16th CSI international symposium on artificial intelligence and signal processing (AISP 2012) (pp. 216-221). IEEE.[10]. Basiri, M. E., Naghsh-Nilchi, A. R., & Ghasem-Aghaee, N. (2014). Sentiment prediction based on dempster-shafer theory of evidence. Mathematical Problems in Engineering, 2014.[11]. Dashtipour, K., Gogate, M., Adeel, A., Hussain, A., Alqarafi, A., & Durrani, T. (2017, July). A comparative study of persian sentiment analysis based on different feature combinations. In International Conference in Communications, Signal Processing, and Systems (pp. 2288-2294). Springer, Singapore[12]. Heikal, M., Torki, M., & El-Makky, N. (2018). Sentiment analysis of Arabic Tweets using deep learning. Procedia Computer Science, 142, 114-122.[13]. Rahimi, Z., Noferesti, S., & Shamsfard, M. (2019). Applying data mining and machine learning techniques for sentiment shifter identification. Language Resources and Evaluation, 53(2), 279-302.[14]. Dashtipour, K., Gogate, M., Adeel, A., Ieracitano, C., Larijani, H., & Hussain, A. (2018, July). Exploiting deep learning for Persian sentiment analysis. In International conference on brain inspired cognitive systems (pp. 597-604). Springer, Cham.[15]. Alharbi, A. S. M., & de Doncker, E. (2019). Twitter sentiment analysis with a deep neural network: An enhanced approach using user behavioral information. Cognitive Systems Research, 54, 50-61.[16]. Prasad, A. G., Sanjana, S., Bhat, S. M., & Harish, B. S. (2017, October). Sentiment analysis for sarcasm detection on streaming short text data. In 2017 2nd International Conference on Knowledge Engineering and Applications (ICKEA) (pp. 1-5). IEEE.[17]. Ren, Y., Ji, D., & Ren, H. (2018). Context-augmented convolutional neural networks for twitter sarcasm detection. Neurocomputing, 308, 1-7.[18]. Mehndiratta, P., Sachdeva, S., & Soni, D. (2017). Detection of sarcasm in text data using deep convolutional neural networks. Scalable Computing: Practice and Experience, 18(3), 219-228.[19]. Ahuja, R., Bansal, S., Prakash, S., Venkataraman, K., & Banga, A. (2018). Comparative study of different sarcasm detection algorithms based on behavioral approach. Procedia computer science, 143, 411-418.Rohanian, Morteza & Salehi, Mostafa & Darzi, Ali & Ranjbar, Vahid. (2020). Convolutional Neural Networks for Sentiment Analysis in Persian Social Media.MohammadPishdarYounesSeifi2272023243710.66224/jict.41376.14.53.24http://jour.aicti.ir/en/Article/41376http://jour.aicti.ir/en/Article/Download/41376http://jour.aicti.ir/en/Article/Download/41376http://jour.aicti.ir/en/Article/Download/41376http://jour.aicti.ir/en/Article/Download/41376http://jour.aicti.ir/en/Article/Download/41376http://jour.aicti.ir/en/Article/Download/41376http://jour.aicti.ir/en/Article/Download/41376[1] Kamble, A., Malemath, V. S., & Patil, D. (2017). Security attacks and secure routing protocols in RPL-based Internet of Things: Survey. 2017 International Conference on Emerging Trends & Innovation in ICT (ICEI). Published. https://doi.org/10.1109/etiict.2017.7977006[2] Adat, V., & Gupta, B. B. (2017). Security in Internet of Things: issues, challenges, taxonomy, and architecture. Telecommunication Systems, 67(3), 423–441. https://doi.org/10.1007/s11235-017-0345-9[3] Lamaazi, H., & Benamar, N. (2020). A comprehensive survey on enhancements and limitations of the RPL protocol: A focus on the objective function. Ad Hoc Networks, 96, 102001. https://doi.org/10.1016/j.adhoc.2019.102001[4] Winter, T., Ed., Thubert, P., Ed., Brandt, A., Hui, J., Kelsey, R., Levis, P., Pister, K., Struik, R., Vasseur, JP., and R. Alexander, "RPL: IPv6 Routing Protocol for Low-Power and Lossy Networks", RFC 6550, DOI 10.17487/RFC6550, March 2012, <https://www.rfc-editor.org/info/rfc6550 >.[5] Iova, O., Picco, P., Istomin, T., & Kiraly, C. (2016). RPL: The Routing Standard for the Internet of Things. . . Or Is It? IEEE Communications Magazine, 54(12), 16–22. https://doi.org/10.1109/mcom.2016.1600397cm[6] Kamgueu, P. O., Nataf, E., & Ndie, T. D. (2018). Survey on RPL enhancements: A focus on topology, security and mobility. Computer Communications, 120, 10–21. https://doi.org/10.1016/j.comcom.2018.02.011[7] Coloured Petri Nets: Modelling and Validation of Concurrent Systems by Kurt Jensen (2009–07-01). (2021). Springer. [8] Zaitsev, D. A., & Shmeleva, T. R. (2019). Modeling With Colored Petri Nets. Automated Systems in the Aviation and Aerospace Industries, 378–404. https://doi.org/10.4018/978-1-5225-7709-6.ch014[9] Chen, Y., Hou, K., Chanet, J., & GHOLAMI, K. (2013). A RPL based Adaptive and Scalable Data-collection Protocol module for NS-3 simulation platform. NICST 2013. [10] Bartolozzi, L., Pecorella, T., & Fantacci, R. (2012). ns-3 RPL module: IPv6 Routing Protocol for Low power and Lossy Networks. ACM. https://doi.org/10.4108/icst.simutools.2012.247718[11] Karkazis, P., Trakadas, P., Zahariadis, T., Hatziefremidis, A., & Leligou, H. C. (2012, June). RPL modeling in J-Sim platform. 2012 Ninth International Conference on Networked Sensing (INSS). https://doi.org/10.1109/inss.2012.6240559[12] Tripathi, J., de Olive, J. C., & Vasseur, J. P. (2010, March). A performance evaluation study of RPL: Routing Protocol for Low power and Lossy Networks. 2010 44th Annual Conference on Information Sciences and Systems (CISS). https://doi.org/10.1109/ciss.2010.5464820[13] Becker, M., Kuladinithi, K., & Görg, C. (2012). Modelling and Simulating the Trickle Algorithm. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 135–144. https://doi.org/10.1007/978-3-642-30422-4_10[14] Kristensen, L. M., & Veiset, V. (2016). Transforming CPN Models into Code for TinyOS: A Case Study of the RPL Protocol. Application and Theory of Petri Nets and Concurrency, 135–154. https://doi.org/10.1007/978-3-319-39086-4_10[15] Wallgren, L., Raza, S., & Voigt, T. (2013). Routing Attacks and Countermeasures in the RPL-Based Internet of Things. International Journal of Distributed Sensor Networks, 9(8), 794326. https://doi.org/10.1155/2013/794326[16] Yu, W., Jia, M., Fang, X., Lu, Y., & Xu, J. (2020). Modeling and analysis of medical resource allocation based on Timed Colored Petri net. Future Generation Computer Systems, 111, 368–374. https://doi.org/10.1016/j.future.2020.05.010[17] Shahidinejad, A., Ghobaei-Arani, M., & Esmaeili, L. (2019). An elastic controller using Colored Petri Nets in cloud computing environment. Cluster Computing, 23(2), 1045–1071. https://doi.org/10.1007/s10586-019-02972-8[18] Cavone, G., Dotoli, M., & Seatzu, C. (2018). A Survey on Petri Net Models for Freight Logistics and Transportation Systems. IEEE Transactions on Intelligent Transportation Systems, 19(6), 1795–1813. https://doi.org/10.1109/tits.2017.2737788[19] Bhandari, G. P., & Ratneshwer. (2018). Dependency-based fault diagnosis approach for SOA-based systems using Colored Petri Nets. Journal of King Saud University - Computer and Information Sciences. Published. https://doi.org/10.1016/j.jksuci.2018.12.002[20] Kaid, H., Al-Ahmari, A., Li, Z., & Davidrajuh, R. (2019). Single Controller-Based Colored Petri Nets for Deadlock Control in Automated Manufacturing Systems. Processes, 8(1), 21. https://doi.org/10.3390/pr8010021َAmir HosseinMozayaniSajjad Faraji DizajiHosseinKarimi2272023375910.66224/jict.41377.14.53.37http://jour.aicti.ir/en/Article/41377http://jour.aicti.ir/en/Article/Download/41377http://jour.aicti.ir/en/Article/Download/41377http://jour.aicti.ir/en/Article/Download/41377http://jour.aicti.ir/en/Article/Download/41377http://jour.aicti.ir/en/Article/Download/41377http://jour.aicti.ir/en/Article/Download/41377http://jour.aicti.ir/en/Article/Download/413771. Simon, J. D. (1982). Political risk assessment-past trends and future-prospects. Columbia Journal of World Business, 17(3), 62-71.2. Bouchet, M. H., Clark, E., & Groslambert, B. (2003). Country risk assessment: A guide to global investment strategy. John Wiley & Sons.3. Bhalla, B. (1983). How corporations should weigh up country risk. Euromoney, June, 66, 72.4. حلافی، حمیدرضا، اقبالی، علیرضا، گسکری، ریحانه (1383)، انحراف نرخ ارز واقعی و رشد اقتصادی در اقتصاد ایران، پژوهشنامه اقتصادی، 14، 198-167.5. اثنی عشری، ابوالقاسم، ندری، کامران، ابوالحسنی، اصغر، مهرگان، نادر و بابایی سمیرمی‌، محمدرضا (1395)، تأثیر تکانه‌های قیمت نفت بر تورم، رشد و پول، مطالعه موردی ایران، فصلنامه علمی‌ پژوهشی پژوهش‌های رشد و توسعه اقتصادی، 22، 102-85.6. Elder, J., & Serletis, A. (2010). Oil price uncertainty. Journal of Money, Credit and Banking, 42(6), 1137-1159.7. Miller, K. D. (1992). A framework for integrated risk management in international business. Journal of international business studies, 23(2), 311-331.8. Alon, I., & Herbert, T. T. (2009). A stranger in a strange land: Micro political risk and the multinational firm. Business Horizons, 52(2), 127-137.9. فتحی، سعید، کبیری‌پور، سعید (1391)، بررسی ابعاد مختلف ریسک سیاسی و تأثیر آن بر سرمایه‌گذاری مستقیم خارجی، دوماهنامه بررسی‌های بازرگانی، 52.10. Busse, M., & Hefeker, C. (2007). Political risk, institutions and foreign direct investment. European journal of political economy, 23(2), 397-415.11. Sameti, M., Kenary, S. S., & Gharakhani, S. (2014). The Investigation of Internet Effect on Financial Corruption Case study: Iran and Some Selected Developing Countries (2002-2009). International Journal of Academic Research in Business and Social Sciences, 4(7), 2222 6990.12. Elbahnasawy, N. G. (2014). E-government, internet adoption, and corruption: an empirical investigation. World Development, 57, 114-126.Dedrick, J., Gurbaxani, V., & Kraemer, K. L. (2003). Information technology and economic performance: A critical review of the empirical evidence. ACM Computing Surveys (CSUR), 35(1), 1-28.ترابی، تقی؛ حاجی حسنی، 13. فرزانه، 1389، تأثیر فناوري اطلاعات و ارتباطات بر یکپارچگی اقتصادي در کشورهاي در حال توسعه منتخب، فصلنامه مدلسازي اقتصادي، سال سوم، شمارۀ 2، ص 153-172. 14. www.itu.int 15. شریفی رنانی، حسین؛ مشرف جوادی، محمدحسین؛ حاجی‌پور، محسن، بررسی فناوری اطلاعات در کاهش فساد اداری از طریق شفافیت اطلاعات، بهبود پاسخ‌گویی و ارتقاء اعتماد و درستی: مورد مطالعه شعب بانک صادرات شهر اصفهان، پردازش و مدیریت اطلاعات، 1392، 28، 3، 679-659.16. Gupta, B., Dasgupta, S., & Gupta, A. (2008). Adoption of ICT in a government organization in a developing country: An empirical study. The Journal of Strategic Information Systems, 17(2), 140-154.17. Ojha, A., Palvia, S., & Gupta, M. P. (2008). A model for impact of e-government on corruption: Exploring theoretical foundations. Critical thinking in e-governance, 160-170.Picci, L. (2006). The quantitative evaluation of the economic impact of e-government: A structural modelling approach. Information Economics and Policy, 18(1), 107-123.18. Jorgenson, D. W. (2001). Information technology and the US economy. American Economic Review, 91(1), 1-32.19. Ulrich B, (2002), La Soci´et´e du Risque: Sur la voie d’une nouvelle modernit´e. Paris: Fayard.20. Perrow C, 1999, Organisations `a hauts risques et “accidents normaux”, Actes de la XIV◦ s´eance du s´eminaire du programme Risques Collectifs et Situations de crise, CNRS, Paris, 2 juin.21. Mozayani, A., & Moradhasel, N. (2020). Assessing the Virtual Space Impact on Productivity & Efficiency of National Product in Iran. Journal of Development and Capital, 5(2), 37-78.22. مزیني، اميرحسين، سحابي، بهرام، ممالکي، مهرفام (1398)، اثر توسعه فناوري اطلاعات و ارتباطات بر مشارکت زنان در اقتصاد ایران، فصلنامه علمي-پژوهشي زن و جامعه.23. مهرگان، نادر، سحابي، بهرام، محمداميني، مریم (1394)، تأثير شاخص توسعه فناوري اطلاعات و ارتباطات IDI بر فساد اداري در کشورها با درآمد متوسط، فصلنامه نظریه هاي کاربردي اقتصاد، 2 ،. 60 – 4324. محمدزاده، پرویز، نجار باذوق قره چپق، هدی (1393)، تأثیر (ICT) بر کارایی تولیدی در کشورهای درحال توسعه‌ی منتخب، فصلنامه تحقیقات اقتصادی، 3، 698-677.25. کریم‌زادگان، سعيده، سلاطين، پروانه (1396)، تأثير فناوري اطلاعات و ارتباطات بر اقتصاد سلامت، فصلنامه مدلسازي اقتصادي، 1 ، .146 – 12926. Cioaca, S. I., Cristache, S. E., Vuță, M., Marin, E., & Vuță, M. (2020). Assessing the Impact of ICT Sector on Sustainable Development in the European Union: An Empirical Analysis Using Panel Data. Sustainability, 12(2), 592.27. Ali, M. S. B. (2020). Does ICT promote democracy similarily in developed and developing countries? A linear and nonlinear panel threshold framework. Telematics and Informatics, 50, 101382.28. Iqbal, Kashif, Hui Peng, and Muhammad Hafeez. "Analyzing the effect of ICT on migration and economic growth in belt and road (BRI) countries." Journal of International Migration and Integration 21.1 (2020): 307-318.29. Alshubiri, F., Jamil, S. A., & Elheddad, M. (2019). The impact of ICT on financial development: Empirical evidence from the Gulf Cooperation Council countries. International Journal of Engineering Business Management, 11.30. Biagi, F., & Falk, M. (2017). The impact of ICT and e-commerce on employment in Europe. Journal of Policy Modeling, 39(1), 1-18.31. Latif, Z., Latif, S., Ximei, L., Pathan, Z. H., Salam, S., & 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), 318-328.32. اشرف‌زاده، سیدحمیدرضا؛ مهرگان، نادر، 1387، اقتصادسنجی پانل دیتا، مؤسسه تحقیقات تعاون، دانشگاه تهران، تهران.33. www.imf.org34. www.prsgroup.com35. Weare, C. (2002). The Internet and democracy: The causal links between technology and politics. International Journal of Public Administration, 25(5), 659-691.36. Aday, S., Farrell, H., Lynch, M., Sides, J., Kelly, J., & Zuckerman, E. (2010). Blogs and bullets: New media in contentious politics. United States Institute of Peace, 65,1 31.37. Hollyer, James R., B. Peter Rosendorff, and James Raymond Vreeland. "Transparency, protest, and autocratic instability." American Political Science Review 109.4 (2015): 764-784.38. Haiqing, Y. U. (2004). The power of thumbs: The politics of SMS in urban China. Graduate journal of Asia-Pacific studies, 2(2), 30-43.MohsenSeyyedi SaraviMohammadrezaBinesh MarvastiSeyedeh LeiliMirtaheriSeyyed AmirAsghari2272023637710.66224/jict.41378.14.53.63http://jour.aicti.ir/en/Article/41378http://jour.aicti.ir/en/Article/Download/41378http://jour.aicti.ir/en/Article/Download/41378http://jour.aicti.ir/en/Article/Download/41378http://jour.aicti.ir/en/Article/Download/41378http://jour.aicti.ir/en/Article/Download/41378http://jour.aicti.ir/en/Article/Download/41378http://jour.aicti.ir/en/Article/Download/41378[1] M. Rasti, A. R. Sharafat, and J. Zander, “A Distributed Dynamic Target-SIR-Tracking Power Control Algorithm for Wireless Cellular Networks,” IEEE Trans. Veh. Technol., vol. 59, no. 2, pp. 906–916, Feb. 2010.[2] Carlos Gandarillas ; Carlos Martín-Engeños ; Héctor López Pombo ; Antonio G. Marques, “Dynamic transmit-power control for WiFi access points based on wireless link occupancy 2014 IEEE Wireless Communications and Networking Conference (WCNC), 2014.[3] M. Rasti, A. R. Sharafat, and J. Zander, “Pareto and Energy-Efficient Distributed Power Control with Feasibility Check in Wireless Networks,” IEEE Trans. Inf. Theory, Volume: 57, no. 1, pp. 245–255, Jan. 2011.[4] Sheyda Zarandi ; Mehdi Rasti, “Energy efficient resource allocation in two-tier heterogeneous network with inband full-duplex communications”, IEEE Conferences Iranian Conference on Electrical Engineering (ICEE), pp. 2072 – 2077, 2017.[5] Kamal Singh, “Optimal Power Control in Decentralized Gaussian Multiple Access Channels”, IEEE Communications Letters, Volume: 22, Journal Article, Publisher: IEEE, 2018 .[6] Sixing Yin; Lihua Li; F. Richard Yu. “Resource Allocation and Base station Placement in Downlink Cellular Networks Assisted by Multiple Wireless Powered UAVs”. IEEE Transactions on Vehicular Technology, pp. 2171 – 2184, 2020.[7] Z. Chen; k. Chi; K. Zheng; Y. Li; X. Liu. "Common Throughput Maximization in Wireless Powered Communication Network with Non-C\Orthogonal Multiple Access". IEEE Transactions on Vehicular Technology, pp:7692 – 7706, 2020.[8] J. Zhao; F. Shen; J. Joung. Throughput Maximization with Rate-Dependent Power Consumption in Battery-Limited Multiuser Networks. IEEE Transactions on Vehicular Technology; pp: 1141 – 1146, 2020.FatemehAlimoradiFarzanehRahmaniLeilaRabieiMohammadKhansariMojtabaMazoochi2272023789510.66224/jict.41379.14.53.78http://jour.aicti.ir/en/Article/41379http://jour.aicti.ir/en/Article/Download/41379http://jour.aicti.ir/en/Article/Download/41379http://jour.aicti.ir/en/Article/Download/41379http://jour.aicti.ir/en/Article/Download/41379http://jour.aicti.ir/en/Article/Download/41379http://jour.aicti.ir/en/Article/Download/41379http://jour.aicti.ir/en/Article/Download/41379S. Long, X. He and C. Yao, "Scene Text Detection and Recognition: The Deep Learning Era," International Journal of Computer Vision, vol. 129, p. 161–184, 2021.X. Chen, L. Jin, Y. Zhu, C. Luo and T. Wang, "Text Recognition in the Wild: A Survey," ACM Computing Surveys, vol. 54, no. 2, pp. 1-35, 2021.C. Zhang, W. Ding, G. Peng, F. Fu and W. Wang, "Street View Text Recognition With Deep Learning for Urban Scene Understanding in Intelligent Transportation Systems," IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 7, pp. 4727-4743, 2021.A. Shinde and M. Patil, "Street View Text Detection Methods: Review Paper," International Conference on Artificial Intelligence and Smart Systems (ICAIS), March 25-27, 2021, Coimbatore, India, pp. 961-965, 2021.F. Borisyuk, A. Gordo and V. Sivakumar, "Rosetta: Large Scale System for Text Detection and Recognition in Images," KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, July, 2018, London, United Kingdom, pp. 71-79, 2018.W. Huang, Z. Lin, J. Yang and J. Wang, "Text Localization in Natural Images Using Stroke Feature Transform and Text Covariance Descriptors," IEEE International Conference on Computer Vision, Dec. 1-8, 2013, Sydney, NSW, Australia, pp. 1241-1248, 2013.X. Zhou, C. Yao, H. Wen, Y. Wang, S. Zhou, W. He and J. Liang, "EAST: An Efficient and Accurate Scene Text Detector," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA, pp. 2642-2651, 2017.B. Shi, X. Bai and C. Yao, "An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 11, pp. 2298-2304, 2017.Z. Liu, Y. Li, F. Ren, W. L. Goh and H. Yu, "SqueezedText: A real-time scene text recognition by binary convolutional encoder-decoder network," 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, February 2-7, 2018, New Orleans, Lousiana, USA, pp. 7194-7201, 2018.M. Liao, B. Shi, X. Bai, X. Wang and W. Liu, "TextBoxes: A Fast Text Detector with a Single Deep Neural Network," AAAI, February 4 – 9, 2017, San Francisco, California, USA, pp. 4161-4167, 2017.Y. Liu, C. Shen, L. Jin, T. He, P. Chen, C. Liu and H. Chen, "ABCNet v2: Adaptive Bezier-Curve Network for Real-time End-to-end Text Spotting," IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-1, 2021.M. Bušta, Y. Patel and J. Matas, "E2E-MLT - An Unconstrained End-to-End Method for Multi-language Scene Text," Computer Vision – ACCV 2018 Workshops, December 2–6, 2018, Perth, Australia, pp. 127-143, 2019.L. Xing, Z. Tian, W. Huang and M. R. Scott, "Convolutional Character Networks," 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Oct. 27 - Nov. 2, 2019, Seoul, Korea (South), pp. 9125-9135 2019.M. Busta, L. Neumann and J. Matas, "Deep TextSpotter: An End-To-End Trainable Scene Text Localization and Recognition Framework," IEEE International Conference on Computer Vision (ICCV), Oct. 22-29, 2017, Venice, Italy, pp. 2204-2212, 2017.V. Khare, P. Shivakumara, P. Raveendran and M. Blumenstein, "A blind deconvolution model for scene text detection and recognition in video," Pattern Recognition, vol. 54, pp. 128-148, 2016.A. Gupta, A. Vedaldi and A. Zisserman, "Synthetic Data for Text Localisation in Natural Images," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA, pp. 2315-2324, 2016.Z. Zhong, L. Jin and S. Huang, "DeepText: A new approach for text proposal generation and text detection in natural images," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), March 5-9, 2017, New Orleans, LA, USA, pp. 1208-1212, 2017.W. Liu, C. Chen, K. Y. K. Wong, Z. Su and J. Han, "STAR-Net: A SpaTial Attention Residue Network for Scene Text Recognition," BMVC, September 19-22, 2016, York, UK, pp. 43.1-43.13, 2016.P. He, W. Huang, Y. Qiao, C. C. Loy and X. Tang, "Reading scene text in deep convolutional sequences," AAAI'16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12–17, 2016, Phoenix, Arizona USA, pp. 3501–3508, 2016.C. Y. Lee and S. Osindero, "Recursive Recurrent Nets with Attention Modeling for OCR in the Wild," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA, pp. 2231-2239, 2016.M. Jaderberg, K. Simonyan, A. Vedaldi and A. Zisserman, "Reading Text in the Wild with Convolutional Neural Networks," International Journal of Computer Vision, vol. 116, pp. 1-20, 2016.Y. Dai, Z. Huang, Y. Gao and K. Chen, "Fused Text Segmentation Networks for Multi-oriented Scene Text Detection," 2018 24th International Conference on Pattern Recognition (ICPR), Aug. 20-24, 2018, Beijing, China, pp. 3604-3609, 2018.D. He, X. Yang, C. Liang, Z. Zhou, A. G. Ororbia, D. Kifer and C. L. Giles, "Multi-scale FCN with Cascaded Instance Aware Segmentation for Arbitrary Oriented Word Spotting in the Wild," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA, pp. 474-483, 2017.P. He, W. Huang, T. He, Q. Zhu, . Y. Qiao and X. Li, "Single Shot Text Detector with Regional Attention," IEEE International Conference on Computer Vision (ICCV), Oct. 22-29, 2017, Venice, Italy, pp. 3066-3074, 2017.Y. Liu and L. Jin, "Deep Matching Prior Network: Toward Tighter Multi-oriented Text Detection," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA, pp. 3454-3461, 2017.M. Samaee and H. Tavakoli, "Farsi Text Localization in Natural Scene Images," International Journal of Computer Science and Information Security (IJCSIS), vol. 15, no. 2, pp. 22-30, 2017.B. Shi, X. Bai and S. Belongie, "Detecting Oriented Text in Natural Images by Linking Segments," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA, pp. 3482-3490, 2017.Y. Wu and P. Natarajan, "Self-Organized Text Detection with Minimal Post-processing via Border Learning," IEEE International Conference on Computer Vision (ICCV), Oct. 22-29, 2017, Venice, Italy, pp. 5010-5019, 2017.Y. Gao, Y. Chen, J. Wang and H. Lu, "Reading Scene Text with Attention Convolutional Sequence Modeling," arXiv preprint arXiv:1709.04303v1, 2017.S. Bin Ahmed, S. Naz, M. I. Razzak and R. Yousaf, "Deep learning based isolated Arabic scene character recognition," 1st International Workshop on Arabic Script Analysis and Recognition (ASAR), April 3-5, 2017, Nancy, France, pp. 46-51, 2017.Z. Cheng, F. Bai, Y. Xu, G. Zheng, S. Pu and S. Zhou, "Focusing Attention: Towards Accurate Text Recognition in Natural Images," IEEE International Conference on Computer Vision (ICCV), Oct. 22-29, 2017, Venice, Italy, pp. 5086-5094, 2017.F. Yin, Y. C. Wu, X. Y. Zhang and C. L. Liu, "Scene Text Recognition with Sliding Convolutional Character Models," arXiv preprint arXiv:1709.01727v1, 2017.H. Li, P. Wang and C. Shen, "Towards End-to-End Text Spotting with Convolutional Recurrent Neural Networks," IEEE International Conference on Computer Vision (ICCV), Oct. 22-29, 2017, Venice, Italy, pp. 5248-5256, 2017.S. Zhang, Y. Liu, L. Jin and C. Luo, "Feature Enhancement Network: A Refined Scene Text Detector," Proceedings of the AAAI Conference on Artificial Intelligence, February 2-7, 2018, New Orleans, Lousiana, USA, vol. 32, no. 1, pp. 2612-2619, 2018.D. Deng, H. Liu, X. Li and D. Cai, "PixelLink: Detecting Scene Text via Instance Segmentation," The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), February 2-7, 2018, New Orleans, Lousiana, USA, pp. 6773-6780, 2018.M. Liao, Z. Zhu, B. Shi, G. S. Xia and X. Bai, "Rotation-Sensitive Regression for Oriented Scene Text Detection," IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA, pp. 5909-5918, 2018.P. Lyu, C. Yao, W. Wu, S. Yan and X. Bai, "Multi-oriented Scene Text Detection via Corner Localization and Region Segmentation," IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA, pp. 7553-7563, 2018.J. Ma, W. Shao, H. Ye, L. Wang, H. Wang, Y. Zheng and X. Xue, "Arbitrary-Oriented Scene Text Detection via Rotation Proposals," IEEE Transactions on Multimedia, vol. 20, no. 11, p. 3111–3122, 2018.F. Bai, Z. Cheng, Y. Niu, S. Pu and S. Zhou, "Edit Probability for Scene Text Recognition," IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA, pp. 1508-1516, 2018.Z. Cheng, Y. Xu, F. Bai, Y. Niu, S. Pu and S. Zhou, "AON: Towards Arbitrarily-Oriented Text Recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 18-23, 2018, Salt Lake City, UT, USA, pp. 5571-5579, 2018.X. Liu, D. Liang, S. Yan, D. Chen, Y. Qiao and J. Yan, "FOTS: Fast Oriented Text Spotting with a Unified Network," IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA, pp. 5676-5685, 2018.C. Bartz, H. Yang and C. Meinel, "SEE: Towards Semi-Supervised End-to-End Scene Text Recognition," AAAI Conference on Artificial Intelligence, February 2-7, 2018, New Orleans, Louisiana, USA, pp. 6674-6681, 2018.T. He, Z. Tian, W. Huang, C. Shen, Y. Qiao and C. Sun, "An End-to-End TextSpotter with Explicit Alignment and Attention," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 18-23, 2018, Salt Lake City, UT, USA, pp. 5020-5029, 2018.J. Ghavidel, A. Ahmadyfard and M. Zahedi, "Natural scene text localization using edge color signature," International Journal of Nonlinear Analysis and Applications, vol. 10, no. 1, pp. 229-237, 2019.Y. Baek, B. Lee, D. Han, S. Yun and H. Lee, "Character Region Awareness for Text Detection," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA, pp. 9357-9366, 2019.Y. Liu, L. Jin, . S. Zhang, C. Luo and S. Zhang, "Curved scene text detection via transverse and longitudinal sequence connection," Pattern Recognition, vol. 90, pp. 337-345, 2019.Z. Tian, M. Shu, P. Lyu, R. Li, C. Zhou, X. Shen and J. Jia, "Learning Shape-Aware Embedding for Scene Text Detection," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA, pp. 4229-4238, 2019.W. Wang, E. Xie, X. Li, W. Hou, T. Lu, G. Yu and S. Shao, "Shape Robust Text Detection With Progressive Scale Expansion Network," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA, pp. 9328-9337, 2019.C. Zhang, B. Liang, Z. Huang, M. En, J. Han, E. Ding and X. Ding, "Look More Than Once: An Accurate Detector for Text of Arbitrary Shapes," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA, pp. 10544-10553, 2019.Z. Zhong, L. Sun and Q. Huo, "Improved localization accuracy by LocNet for Faster R-CNN based text detection in natural scene images," Pattern Recognition, vol. 96, 2019.C. Luoa, L. Jin and Z. Sun, "MORAN: A Multi-Object Rectified Attention Network for scene text," Pattern Recognition, vol. 90, pp. 109-118, 2019.Y. Zhu, S. Wang, Z. Huang and K. Chen, "Text Recognition in Images Based on Transformer with Hierarchical Attention," in IEEE International Conference on Image Processing (ICIP), Sept. 22-25, 2019, Taipei, Taiwan, pp. 1945-1949, 2019.M. Liao, J. Zhang, Z. Wan, F. Xie, J. Liang, P. Lyu, C. Yao and X. Bai, "Scene Text Recognition from Two-Dimensional Perspective," Proceedings of the AAAI Conference on Artificial Intelligence, January 27 – February 1, 2019, Honolulu, Hawaii, USA, vol. 33, no. 01, pp. 8714-8721, 2019.W. Feng, W. He, F. Yin, X. Y. Zhang and C. L. Liu, "TextDragon: An End-to-End Framework for Arbitrary Shaped Text Spotting," Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Oct. 27 - Nov. 2, 2019, Seoul, Korea (South), pp. 9076-9085, 2019.S. X. Zhang, X. Zhu, J. B. Hou, C. Liu, C. Yang, H. Wang and X. C. Yin, "Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 13-19, 2020, Seattle, WA, USA, pp. 9696-9705, 2020.S. Saha, N. Chakraborty, S. Kundu, S. Paula, A. F. Mollah, S. Basu and R. Sarkar, "Multi-lingual scene text detection and language identification," Pattern Recognition Letters, vol. 138, pp. 16-22, 2020.H. Liu, A. Guo, D. Jiang, Y. Hu and B. Ren, "PuzzleNet: Scene Text Detection by Segment Context Graph Learning," arXiv preprint arXiv:2002.11371, 2020.M. Fasha, B. Hammo, N. Obeid and J. Alwidian, "A Hybrid Deep Learning Model for Arabic Text Recognition," (IJACSA) International Journal of Advanced Computer Science and Applications, vol. 11, no. 8, pp. 122-130, 2020.Z. Qiao, Y. Zhou, D. Yang, Y. Zhou and W. Wang, "SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition," in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 13-19, 2020, Seattle, WA, USA, pp. 13525-13534, 2020.X. Chen, T. Wang, Y. Zhu, L. Jin and C. Luo, "Adaptive embedding gate for attention-based scene text recognition," Neurocomputing, vol. 381, pp. 261-271, 2020.Y. Liu, H. Chen, C. Shen, T. He, L. Jin and L. Wang, "ABCNet: Real-Time Scene Text Spotting With Adaptive Bezier-Curve Network," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 13-19, 2020, Seattle, WA, USA, pp. 9806-9815, 2020.L. Qiao, S. Tang, Z. Cheng, Y. Xu, Y. Niu, S. Pu and F. Wu, "Text Perceptron: Towards End-to-End Arbitrary-Shaped Text Spotting," Proceedings of the AAAI Conference on Artificial Intelligence, February 7–12, 2020, New York Hilton Midtown, New York, New York, USA, vol. 34, no. 7, pp. 11899-11907, 2020.H. Wang, P. Lu, H. Zhang, M. Yang, X. Bai, Y. Xu, M. He, Y. Wang and W. Liu, "All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting," Proceedings of the AAAI Conference on Artificial Intelligence, February 7–12, 2020, New York Hilton Midtown, New York, New York, USA, vol. 34, no. 07, pp. 12160-12167, 2020.X. Qin, Y. Zhou, Y. Guo, D. Wu, Z. Tian, N. Jiang, H. Wang and W. Wang, "Mask is All You Need: Rethinking Mask R-CNN for Dense and Arbitrary-Shaped Scene Text Detection," ACM MULTIMEDIA, October 20-24, 2021, Chengdu, China, 2021.Y. Zhu and J. Du, "TextMountain: Accurate scene text detection via instance segmentation," Pattern Recognition, vol. 110, 2021.C. Ma, L. Sun, Z. Zhong and Q. Huo, "ReLaText: Exploiting visual relationships for arbitrary-shaped scene text detection with graph convolutional networks," Pattern Recognition, vol. 111, 2021.N. Lu, W. Yu, X. Qi, Y. Chen, P. Gong, R. Xiao and X. Bai, "MASTER: Multi-aspect non-local network for scene text recognition," Pattern Recognition, vol. 117, 2021.Q. Lin, C. Luo, L. Jin and S. Lai, "STAN: A sequential transformation attention-based network for scene text recognition," Pattern Recognition, vol. 111, 2021.H. Hassan, A. El-Mahdy and M. E. Hussein, "Arabic Scene Text Recognition in the Deep Learning Era: Analysis on a Novel Dataset," IEEE Access, vol. 9, pp. 107046-107058, 2021.B. Esfahbod and R. Pournader, "FarsiTEX and the Iranian TEX Community," TUGboat, vol. 23, no. 1, pp. 41-45, 2002.M. Darab and M. Rahmati, "A Hybrid Approach to Localize Farsi Text in Natural Scene Images," Procedia Computer Science, vol. 13, pp. 171-184, 2012.P. Arbeláez, M. Maire, C. Fowlkes and J. Malik, "Contour Detection and Hierarchical Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 5, pp. 898-916, 2011.F. Liu, C. Shen and G. Lin, "Deep convolutional neural fields for depth estimation from a single image," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA, pp. 5162-5170, 2015.P. Pérez, M. Gangnet and A. Blake, "Poisson image editing," ACM Transactions on Graphics, vol. 22, no. 3, p. 313–318, 2003.F. Zhan , . S. Lu and C. Xue, "Verisimilar Image Synthesis for Accurate Detection and Recognition of Texts in Scenes," in Computer Vision – ECCV 2018, September 8-14, 2018, Munich, Germany, pp 257-273, 2018.M. Liao, B. Song, S. Long, . M. He, C. Yao and X. Bai, "SynthText3D: synthesizing scene text images from 3D virtual worlds," in Science China Information Sciences, vol. 63, no. 2, pp. 120105:1-120105:14, 2020.W. Qiu and A. Yuille, "UnrealCV: Connecting Computer Vision to Unreal Engine," in Computer Vision – ECCV 2016 Workshops, October 8-10 and 15-16, 2016, Amsterdam, The Netherlands, Springer, Cham, pp. 909-916, 2016.S. Long and C. Yao, "UnrealText: Synthesizing Realistic Scene Text Images from the Unreal World," arXiv preprint arXiv:2003.10608, 2020.J. Pont-Tuset, P. Arbeláez, J. T. Barron, F. Marques and J. Malik, "Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 1, pp. 128-140, 2017.I. Laina, C. Rupprecht, V. Belagiannis, F. Tombari and N. Navab, "Deeper Depth Prediction with Fully Convolutional Residual Networks," Fourth International Conference on 3D Vision (3DV), Oct. 25-28, 2016, Stanford, CA, USA, pp. 239-248, 2016.S. M. Lucas, A. Panaretos, L. Sosa, A. Tang, S. Wong and R. Young, "ICDAR 2003 robust reading competitions," Seventh International Conference on Document Analysis and Recognition, Aug. 6-6, 2003, Edinburgh, UK, pp. 682-687, 2003.K. Wang, B. Babenko and . S. Belongie, "End-to-end scene text recognition," International Conference on Computer Vision, Nov. 6-13, 2011, Barcelona, Spain, pp. 1457-1464, 2011.A. Mishra, K. Alahari and C. V. Jawahar, "Scene Text Recognition using Higher Order Language Priors," Proceedings of British Machine Vision Conference, September 3-7, 2012, Guildford, UK, pp. 127.1-127.11, 2012.D. Karatzas, . F. Shafait, S. Uchida, M. Iwamura, L. G. i. Bi, S. R. Mestre, J. Mas, D. F. Mota, J. A. Almazàn and L. P. d. l. Heras, "ICDAR 2013 Robust Reading Competition," 12th International Conference on Document Analysis and Recognition, Aug. 25-28, 2013, Washington, DC, USA, pp. 1484-1493, 2013.A. Davoudi, "This is a modified version of Ankush's code for generating synthetic text images which support right-to-left languages such as Persian and Arabic.," [Online]. Available: https://github.com/adavoudi/SynthText. [Accessed 22 06 2021].S. T. Piantadosi, "Zipf’s word frequency law in natural language: A critical review and future directions," Psychonomic bulletin & review, vol. 21, no. 5, p. 1112–1130, 2014.MahboubehZandiehSepidehAdabiSamanehYazdani22720239611010.66224/jict.41380.14.53.96http://jour.aicti.ir/en/Article/41380http://jour.aicti.ir/en/Article/Download/41380http://jour.aicti.ir/en/Article/Download/41380http://jour.aicti.ir/en/Article/Download/41380http://jour.aicti.ir/en/Article/Download/41380http://jour.aicti.ir/en/Article/Download/41380http://jour.aicti.ir/en/Article/Download/41380http://jour.aicti.ir/en/Article/Download/41380] R., Hassanzadeh, A., Movaghar, and H.R., Hassanzadeh, “A Multi-Dimensional Fairness Combinatorial Double-Sided Auction Model in Cloud Environment”, 8th International Symposium on Telecommunications (IST), IEEE, pp. 672-677, 2016.[2] L., Dierks, and S., Seuken, “Cloud Pricing: The Spot Market Strikes Back”, Management Science, 2021, DOI: https://doi.org/10.1287/mnsc.2020.3907.[3] C., Wu, A.N., Toosi, R., Buyya, and K., Ramamohanarao, “Hedonic Pricing of Cloud Computing Services”, IEEE Transactions on Cloud Computing, 9 (1), pp. 182-196 , 2021.[4] N., Sultan, “Making Use of Cloud Computing for Healthcare Provision: Opportunities and Challenges”, International Journal of Information Management, 34(2), pp. 177– 184, 2014.[5] S.R., Dibaj, A., Miri, and S.A., Mostafavi, “A Cloud Dynamic Online Double Auction Mechanism (DODAM) for Sustainable Pricing”, Telecommunication Systems, 75(4), pp. 461-480, 2020.[6] P., Rad-Jahanbani, S., Adabi, and A., Rezaee, “A New Multi-agent Group-buying Auction for Automated VM-to-Customer Mapping”, Journal of Organizational Computing and Electronic Commerce, 31(1), pp. 35-58, 2020.[7] R., Ananthakumer, K., Kartheeban, “Resource Allocation Using Dynamic Pricing Auction Mechanism for Supporting Emergency Demands in Cloud Computing”, Journal of Parallel and Distributed Computing, 2021, DOI:https://doi.org/10.1016/j.jpdc.2021.07.016.[8] J., Rong, T., Qin, and B., An, “Competitive Cloud Pricing for Long-Term Revenue Maximization”, Journal of Computer Science and Technology, 34(3), pp. 645-656, 2019.[9] L., Mashayekhy, M. M., Nejad, D., Grosu, and A.V., Vasilakos, “An Online Mechanism for Resource Allocation and Pricing in Clouds”, IEEE Transactions on Computers, 65(4), pp. 1172-1184, 2016.[10] G.V., Prasad, A.S., Prasad, and Sh., Rao, “A Combinatorial Auction Mechanism for Multiple Resource Procurement in Cloud Computing”, IEEE Transactions on Cloud Computing, 6(4), pp. 904-914, 2015.[11] S., Li, J., Huang and B., Cheng, “A Price-Incentive Resource Auction Mechanism Balancing the Interests Between Users and Cloud Service Provider”, IEEE Transactions on Network and Service Management, 18(2), pp. 2030-2045, 2020.[12] S., Adabi, F., Alayin, and A., Sharifi, “A new flexible pricing mechanism considering price-quality relation for cloud resource allocation”, Evolving Systems, 12, pp. 541-565, 2021.[13] B., Javed, P., Bloodsworth, R.U., Rasool, K., Munir and O., Rana, “Cloud Market Maker: An Automated Dynamic Pricing Marketplace for Cloud Users”, Future Generation Computer Systems, 54, pp. 52-67, 2016.[14] R., Lu, Y., Liang, Q., Ling, Ch., Li, and, W., Wu, “Double Auction and Profit Maximization Mechanism for Jobs with Heterogeneous Durations in Cloud Federations”, Journal of Cloud Computing, 10(1), pp. 1-22, 2021.[15] A., Abdiansah and R., Wardoya, “Time Complexity Analysis of Support Vector Machines (SVM) in LibSVM”, International Journal of Computer Applications, 128(3), 2015.]16[ قاسمیان، نفیسه، آخوندزاده هنزائی، مهدی، "مقایسه روش های شبکه عصبی مصنوعی، ماشین بردار پشتیبان و درخت تصمیم گیری در شناسایی ابر در تصاویر ماهواره¬ای لندست 8"، نشریه علمی-ترویجی مهندسی نقشه برداری و اطلاعات مکانی، دوره هفتم، شماره 4، آبان 1395 .[17] M., Rahmani, M., Momeni, “Alzheimer Speech Signal Analysis of Persian Speaking Alzheimer’s Patients”, Computational Intelligence in Electrical Engineering, 11(1), pp. 81-94, 2020.]18[ اشرفی اصفهانی، حمیدرضا، "روشی برای طبقه بندی داده¬ها با استفاده از الگوریتم¬های داده کاوی داده¬های بزرگ"، کنگره ملی تحقیقات بنیادین در مهندسی کامپیوتر و فن آوری اطلاعات،تهران، 1398 .[19] V.S., Noble, “What is a Support Vector Machine?”, Nature Publishing Group , 24, pp. 1565-1567, 2006.jalalzareRosaHendijani227202311113910.66224/jict.41381.14.53.111http://jour.aicti.ir/en/Article/41381http://jour.aicti.ir/en/Article/Download/41381http://jour.aicti.ir/en/Article/Download/41381http://jour.aicti.ir/en/Article/Download/41381http://jour.aicti.ir/en/Article/Download/41381http://jour.aicti.ir/en/Article/Download/41381http://jour.aicti.ir/en/Article/Download/41381http://jour.aicti.ir/en/Article/Download/41381[1] World Bank. (2015). Data: Services, etc., Value Added (%GDP). Available at: http://data.worldbank.org/indicator/NV.SRV.TETC.ZS.[2] Santa, R., MacDonald, J., & Ferrer, M. (2019). The role of trust in e-Government effectiveness, operational effectiveness and user satisfaction: Lessons from Saudi Arabia in e-G2B. International Journal of Government Information Quarterly, 36, 39–50[3] Verkijika, S., & De Wet, L. (2018). E-government adoption in sub-Saharan Africa. International Journal of Electronic Commerce Research and Applications, 30, 83-93[4] Twizeyimana, J. & Andersson, A. (2019). The public value of E-Government – A literature review. International Journal of Government Information Quarterly, 36(2), 167-178[5] Weerakkody, V., El-Haddadeh, R., & Omar, A. (2019). A case analysis of E-government service delivery through a service chain dimension. International Journal of Information Management, 47, 233–238[6] Heintzman, R., & Brian, M. (2005). People, service and trust: is there a public sector service value chain. International Review of Administrative Sciences, 71(4), 550–575[7] Beynon-Davies, P., & Martin, S. (2004). Electronic local government and the modernisation agenda: Progress and prospects for public service improvement. International Journal of Local Government Studies, 30(2), 214–229.[8] Holden, S. H., & Fletcher, P. D. (2005). The virtual value chain and E-Government partnership: Non-monetary agreements in the IRS E-File program. International Journal of Public Administration, 28, 643–664.[9] McAdam, R., Hazlett, S. A., & Johnston, S. (2011). Establishing exante public construction supply chain guidelines in the public procurement of schools’ estate. International Journal of Public Sector Management, 24(3), 250–273.[10] Seepma, A.P., de Blok, C., & Van Donk, D.P. (2020). Designing digital public service supply chains four country-based cases in criminal justice. International Journal of Supply Chain Management, 26(3), 418-446.[11] Ellram, L., W. & Billington, C. (2004). Understanding and managing the Services supply chain. International Journal of Supply Chain Management, 40(4), 17–32.[12] Boon-itt, S. & Pongpanarat, C. (2011). Measuring Service Supply Chain Management Processes: The Application of the Q-Sort Technique. International Journal of Innovation, Management and Technology, 2(3), 217-223[13] Boon-itt, S., Wong, Y., & Wong, C. (2017). Service supply chain management process capabilities: Measurement development. International Journal of Production Economics, 193, 1–11.[14] Sampson, S. E., Spring, M. (2012). Customer Roles in Service Supply Chains and Opportunities for Innovation. International Journal of Service Industry Management, 48(4), 30-50[15] Sampson, S. E. (2000). Customer-supplier duality and bidirectional supply chains in service organizations. International Journal of Service Industry Management, 11(4), 348-364[16] Shahin, A. (2010). SSCM: Service supply chain management. International Journal of Logistics Systems & Management. 6(1), 60-75.[17] Taghipour, A., Murat, S., Huang, P. (2021). E-Supply Chain Management: A review. International Journal of e-Education e-Business e-Management and e-Learning, 11(2), 51–61.[18] Lee, H. & Whang, S. (2001). E-Business and supply chain integration, Stanford University, USA.[19] Norris, G., Hurley, J.R., Hartley, K.M., Dunleavy, J.R. & Balls, J.D. (2002). E-Business and ERP: Transforming the Enterprise. International Journal of Quality & Reliability Management, 19(4), 486-487.[20] Cheng, K. (2005). E-Manufacturing: Fundamentals and Applications, Leeds Metropolitan University, UK.[21] Fitzgerald, L. (1988). Performance measurement in service businesses. International Journal of Operations & Production Management, 8(3), 109-116.[22] Cho, D. W., Lee, Y. H., Ahn, S. H. & Hwang, M. K. (2012). A framework for measuring the performance of service supply chain management. International Journal of Computers & Industrial Engineering, 62, 801–818.[23]رضائي‌پندری عباس، آذر عادل، تقوی الهوردی (1393). ارائه مدل ارزیابی عملکرد ‌زنجیره ‌تأمين خدمات با رویکرد نگاشت شناختي فازی(مورد مطالعه: صنعت بیمه). فصلنامه چشم انداز‌ مديريت‌صنعتي، شماره 16،ص 75 -93.[24] مستقیمی محمودرضا، رمضانیان محمدرحیم، اسماعیل زاده محمد (1394). شناسایی و اولویت بندی معیارهایی برای ارزیابی عملکرد زنجیرة ‌تأمين خدمات. فصلنامه مديريت‌صنعتي، شماره 7 ،ص 151 -174.[25] Chithambaranathan, P., Subramanian, N., Gunasekaran, A., & Palaniappan, P. (2015). Service supply chain environmental performance evaluation using grey based hybrid MCDM approach. International Journal of Production Economics, 166, 163-176.[26] شاهین آرش، مهرپور هدی، کرباسیان مهدی (1396). ارائه چارچوبی یکپارچه به منظور انتخاب شاخص‌های ارزیابی عملکرد در ‌زنجیره ‌تأمين خدمات. فصلنامه علوم مدیریت ایران، شماره1،ص 45 -25.[27] Tseng, M., et al. (2018). A framework for evaluating the performance of sustainable service supply chain management under uncertainty. International Journal of Production Economics, 195, 359-372.[28] Katok E., Thomas D., Davis A. (2008). Inventory service-level agreements as coordination mechanisms: The effect of review periods. International Journal of Manufacturing & Service Operations Management, 10 (4), 1-16[29] Hasija S., Pinker E.J., Shumsky R.A. (2008). Call center outsourcing contracts under information asymmetry. International Journal of Management Science, 54 (4) ،793-807[30] Demirkan H., Cheng H. (2008). The risk and information sharing of application services supply chain. European Journal of Operational Research, 187 (3) ،765-784.[31] Cheng, F., Yang, S., & Xijun, M. (2011). Equilibrium Conditions in Service Supply Chain. International Journal of Procedia Engineering, 15(4), 5100 – 5104.[32] Liu W., Xie D., Xu X. (2013). Quality supervision and coordination of logistic service supply chain under multi-period conditions. International Journal of Production Economics, 142 (2), 353-361[33] Liu W., Wang S., Chen L. (2017). The role of control power allocation in service supply chains: Model analysis and empirical examination. International Journal of Production Economics, 23 (3), 176-190.[34] Acharya, C., Ojha, D., Patel, P., & Gokhale, R. (2019). Modular interconnected processes, fluid partnering, and innovation speed: A loosely coupled systems perspective on B2B service supply chain management. International Journal of Industrial Marketing Management, 89,209-219.[35] عطایی محمود(1389). تصمیم گیری چند معیاره. انتشارات دانشگاه صنعتی شاهرود، شاهرود.[36] Triantaphyllou, E., Triantaphyllou, B., Shu, S., Nieto, S., & Ray, T. (1998). Multi Criteria Decision Making: An Operations Research Approach. International Journal of Encyclopedia of Electrical and Electronics Engineering, 15, 175-186[37] Buchanan, J., Shepperd, PH., Vanderpooten, D. (1999). Project Ranking Using the ELECTRE Method, Publisher Department of Management Systems, University of Waikato.[38] Aboubakar, M., Kellil, M., & Roux, R. (2021). A review of IoT network management: Current status and perspectives. International Journal of King Saud University - Computer and Information Sciences, 6, 1–14[39] Srinivasan, K., Sundar. K. & Tridas. M. (1994). Impact of electronic data interchangeTechnology on JIT shipments. International Journal of Management Science, 40(10), 1291-1304[40] خوراکيان عليرضا، نيک خواه فرخاني زهرا، بوستاني راد مينا، لقماني‌دوين شکوفه (1398). تبيين و طبقه بندي مولفه هاي انسجام سازماني. فصلنامه مطالعات رفتار سازماني، شماره 2، ص 83 -124.[41] Ellram, L., Tate, W. & Billington, C. (2004). Understanding and managing the services supply chain. International Journal of Supply Chain Management, 40(4): 17–32.[42] Baltacioglu, T., Ada, E., Kaplan, M.D., Yurt and, O., Cem Kaplan, Y. (2007). A new framework for service supply chains. International Journal of the Service Industries, 27 (2), 105–124.[43] Wen, X., Xu, C., Hu, Q. (2016). Dynamic capacity management with uncertain demand and Dynamic price. International Journal of Production Economics, 175, 121–131.[44] Campbell, A.J. (2003). Creating customer knowledge competence: managing CRM programs strategically. International Journal of Industrial Marketing Management, 32(5), 375–383.[45] Yang, Y. F. (2012). Service capabilities and customer relationship management: an investigation of the banks in Taiwan. International Journal of Service Industries. 32 (6), 937–960.S. MohammadRazavizadehNasimMohammadi227202314014810.66224/jict.41382.14.53.140http://jour.aicti.ir/en/Article/41382http://jour.aicti.ir/en/Article/Download/41382http://jour.aicti.ir/en/Article/Download/41382http://jour.aicti.ir/en/Article/Download/41382http://jour.aicti.ir/en/Article/Download/41382http://jour.aicti.ir/en/Article/Download/41382http://jour.aicti.ir/en/Article/Download/41382http://jour.aicti.ir/en/Article/Download/41382F. Al-Turjman, E. Ever, and H. Zahmatkesh, “Small cells in the forthcoming 5G/IoT: Traf_c modelling and deployment overview,” IEEE Commun. Surv. Tuts., vol. 21, no. 1, pp. 28_65, 1st Quart., 2019.J. Ye, X. Ge, G. Mao, and Y. Zhong, “5G ultradense networks with nonuniform distributed users,” IEEE Trans. Veh. Technol., vol. 67, no. 3, pp. 2660–2670, March 2018.C. Chen and X. Zhao, “Cell boundary user performance in multi-user MIMO poisson oronoi cell,” IEEE Commun. Lett., vol. 22, no. 4, pp. 772 – 775, 2018.X. Zou, G. Cui, M. Tang, and W. Wang, “Base station density bounded by maximum outage probability in massive MIMO system,” in Proc. IEEE VTC, May 2015, pp. 1–5.Q. H. Spencer, C. B. Peel, A. L. Swindlehurst and M. Haardt, “An introduction to the multi-user MIMO downlink,” IEEE Commun. Magazine, vol. 42, pp: 60-67, Oct. 2004.S. Kusaladharma, W.-P. Zhu and W. Ajib, “Exact outage analysis for stochastic cellular networks under multi-user MIMO,” IEEE 17th Annual Consumer Communications & Networking Conference (CCNC), Jan. 2020.A. Kuchar, J.-P. Rossi, and E. Bonek, “Directional macro-cell channel characterization from urban measurements,” IEEE Transactions on Antennas and Propagation, vol. 48, no. 2, pp. 137–146, Feb. 2000.Q-A Nadeem, A. Kammoun and M-S Alouini, “Elevation beamforming with full dimension mimo architectures in 5G systems: a tutorial,” IEEE Communications Surveys & Tutorials, vol. 21, 2019.S. M. Razavizadeh, M. Ahn and I. Lee, “three-dimensional beamforming: a new enabling technology for 5G wireless networks,” IEEE Signal Processing Magazine, vol. 31, 2014.W. Lee, S-R. Lee, H-B. Kong, S. Lee and I. Lee, “Downlink vertical beamforming design for active antenna systems,” IEEE Transactions on Communications, vol. 62, 2014.M. Baianifar, S. M. Razavizadeh, S. K-Moghaddam and T. Svensson, “Effect of users height distribution on the coverage of mmwave cellular networks with 3D beamforming,” IEEE Access, vol. 7, pp: 68091 – 68105, May 2019.S. Atapattu, P. Dharmawansa, C. Tellambura and J. Evans, “Exact outage analysis of multiple-user downlink with MIMO matched-filter precoding,” IEEE Commun. Lett., vol. 21, no. 12, pp: 2754-2757, Dec. 2017.Ch. Li, J. Zhang and K. B. Letaief, “Performance analysis of SDMA in multicell wireless networks,” IEEE Global Communications Conference (GLOBECOM), Dec. 2013.Zh. Shi, G. Yang, Y. Fu, H. Wang and Sh. Ma, “ Performance analysis of MIMO-NOMA systems with randomly deployed users,” IEEE Global Communications Conference (GLOBECOM), Dec. 2018.X. Yu, Ch. Li, J. Zhang and K. B. Letaief, stochastic geometry analysis of multi-antenna wireless networks, Springer, 1st ed. 2019.S. M. Razavizadeh and T. Svensson, “3D beamforming in reconfigurable intelligent surfaces-assisted wireless communication networks,” 24th International ITG Workshop on Smart Antennas (WSA), Feb. 2020.Zh. Chen, L. Qiu and X. Liang, “Area spectral efficiency analysis and energy consumption minimization in multi-antenna poisson distributed networks,” IEEE Trans. Wireless Commun., vol. 15, pp: 4862-4874, 2016.M. Joham, W. Utschick and J. A. Nossek, “linear transmit processing in MIMO communications systems,” IEEE Trans. Signal Process., vol. 53, no. 8, pp: 2700-2712, Aug. 2005.VahidSafari dehnavimasoudshafiee227202314916710.66224/jict.41383.14.53.149http://jour.aicti.ir/en/Article/41383http://jour.aicti.ir/en/Article/Download/41383http://jour.aicti.ir/en/Article/Download/41383http://jour.aicti.ir/en/Article/Download/41383http://jour.aicti.ir/en/Article/Download/41383http://jour.aicti.ir/en/Article/Download/41383http://jour.aicti.ir/en/Article/Download/41383http://jour.aicti.ir/en/Article/Download/41383[1]. Buettner R. Predicting user behavior in electronic markets based on personality-mining in large online social networks: A personality-based product recommender framework. The International Journal on Networked Business. Springer, 2016. pp. 1–19.[2]. Mosavi A, Vaezipour A. Developing effective tools for predictive analytics and informed decision. University of Tallinn, Technical Report. 2013.[3] Jones ER. Neural Networks’ Role in Predictive Analytics. DM Review Special Report. 2008.[4] Krauss C, Do XA, Huck N. Deep neural networks, gradient boosted trees, random forests: Statistical arbitrage on the S&P 500. Eur J Oper Res. 2017;259:689–702.[5] Hiransha M, Gopalakrishnan EA, Menon VK, Soman KP. NSE stock market prediction using deep-learning models. Procedia Comput Sci. 2018;132: 1351–1362.[6] Abraham A, Nath B, Mahanti PK. Hybrid intelligent systems for stock market analysis. In: Proceedings of International Conference on Computational Science. San Francisco, CA. Springer-Verlag, Berlin Heidelberg, 2001. pp. 337–345.[7] Vijaya P, Raju G, Ray SK. Artificial neural network-based merging score for Meta search engine. J Central South Univ. 2016;23:2604–2615.[8] Evans C, Pappas K, Xhafa P. Utilizing artificial neural networks and genetic algorithms to build an algo-trading model intra-day foreign exchange speculation. Math Comput Model. 2013;58:1249–1266.[9] Sun BQ, Guo H, Karimi HR, et al. Prediction of stock market futures based on fuzzy sets and multivariate fuzzy time series. Neurocomputing. 2015; 151:1528–1536.[10] Efendi R, Arbaiy N, Deris MM. A new procedure in stock market forecasting based on fuzzy random auto-regression time series model. Inform Sci. 2018;441:113–132.[11] Khedr AE, Yaseen N. Predicting stock market behavior using data mining technique and news sentiment analysis. Int J Intell Syst Appl. 2017;9:22.[12] Ahmadi E, Jasemi M, Monplaisir L, et al. New efficient hybrid candlestick technical analysis model for stock market timing on the basis of the Support Vector Machine and Heuristic Algorithms of Imperialist Competition and Genetic. Expert Syst Appl. 2018;94:21–31.[13]. Safari Dehnavi, V., & Shafiee, M. (2020). »LQR for Generalized Systems Using Metaheuristic Algorithms Based on Disturbance Observer«. 28th Iranian Conference on Electrical Engineering (ICEE), 2020, 08, 04, Tabriz, Iran.[14]. Sabri, M. (2018). »Stabilization and control of power system using meta-heuristic algorithms«. Karafan (TVU), 14 (42), 33-55.[15]. Pourdadashi Komachali, F., & Shafiee, M. (2020). »Sensor fault diagnosis in fractional-order singular systems using unknown input observer«. International Journal of Systems Science, 51 (1), 116-132.‏[16] Huang, W. (2007). »Neural networks in finance and economics forecasting«. International Journal of Information Technology & Decision Making, 6 (1), 113-140.‏[17] Dennis, O. & Mossman, CH. (2003). »Neural network forecasts of Canadian stock returns using accounting ratios«. International Journal of Forecasting, 19 (3), 453-465.‏[18] Kelly, J. (2011). »The current stock of money: an aggregation theoretic measure of narrowly defined money«. Applied Economics Letters, 18 (7), 659-664.‏[19] Moayedi, H. (2020). »Optimization of ANFIS with GA and PSO estimating α ratio in driven piles«. Engineering with Computers, 36 (1), 227-238.‏[20] Sajadi, A. (2020). »Estimation of cetane numbers of biodiesel and diesel oils using regression and PSO-ANFIS models«. Renewable Energy, 158 (1), 465-473.‏[21] Çakıt, E. (2020). »Assessing safety at work using an adaptive neuro-fuzzy inference system (ANFIS) approach aided by partial least squares structural equation modeling (PLS-SEM)«. International Journal of Industrial Ergonomics, 76(1), 102925- (in press).‏[22] Choo, K. (2018). »Symmetries and many-body excitations with neural-network quantum states«. Physical review letters, 121 (16), 167-204.‏[23] Dai, A., & Quoc V. (2020). »Training a document classification neural network«, U.S. Patent No. 10,528,866. 7.‏[24] Shqair, M. (2020). »Adaptation of conformable residual power series scheme in solving nonlinear fractional quantum mechanics problems«. Applied Sciences, 10 (3), 890-912.‏[25] Erik, C., Gálvez, J., & Avalos, O. (2020). »Gravitational Search Algorithm for Non-linear System Identification Using ANFIS-Hammerstein Approach«. Recent Metaheuristics Algorithms for Parameter Identification (Springer), 854 (1), 97-134.‏[26] Bobyr, M., & Sergey G. (2020). »A nonlinear method of learning neuro-fuzzy models for dynamic control systems«. Applied Soft Computing, 88 (1), 106030-106044.‏[27] SafariDehnavi, Vahid, and Masoud Shafiee. "The prediction of stock value by using the proposed fuzzy neural network and hybrid algorithm." Karafan Quarterly Scientific Journal 18.1 (2021): 203-220.‏[28] Pourzamani, Z. & Miralavi, H. (2018). »Provide a model for stock price forecasting using meta-innovative methods and neural networks«. Quarterly Journal of Financial Management and Securities, 10 (40), 57-83 (in Persian).[29] Samadipour, S., Matinfard, R. & Torkashvand, A. (2020). »Investigating and predicting the decline or growth of stock companies in a certain period of time using data mining classification technique«. The Third International Conference on New Strategies in Engineering, Information Science and Technology in the Next Century, 2020, 05, 20, Tehran, Iran (in Persian).ALI ASGHARANSARIMohsenEslamiMohammad JavadDehghaniSaeidehParsaei Fard227202316818810.66224/jict.41384.14.53.168http://jour.aicti.ir/en/Article/41384http://jour.aicti.ir/en/Article/Download/41384http://jour.aicti.ir/en/Article/Download/41384http://jour.aicti.ir/en/Article/Download/41384http://jour.aicti.ir/en/Article/Download/41384http://jour.aicti.ir/en/Article/Download/41384http://jour.aicti.ir/en/Article/Download/41384http://jour.aicti.ir/en/Article/Download/41384[1] Cisco public (2020). Cisco Annual Internet Report (2018–2023). Available: https://www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.pdf[2] ITU-R M.2370-0 (2015). IMT traffic estimates for the years 2020 to 2030, .https://www.itu.int/dms_pub/itu-r/opb/rep/R-REP-M.2370-2015-PDF-E.pdf.[3] impacts-5g-productivity-economic-growth, https://www.communications.gov.au/publications/impacts-5g-productivity-and-economic-growth.[4] Wu, Q., Li, G. Y., Chen, W., Ng, D. W. K., & Schober, R. (2017). An overview of sustainable green 5G networks. IEEE Wireless Communications, 24(4), 72-80.[5] Peng, M., Sun, Y., Li, X., Mao, Z., & Wang, C. (2016). Recent advances in cloud radio access networks: System architectures, key techniques, and open issues. IEEE Communications Surveys & Tutorials, 18(3), 2282-2308.[6] Wu, J., Zhang, Z., Hong, Y., & Wen, Y. (2015). Cloud radio access network (C-RAN): a primer. IEEE network, 29(1), 35-41.[7] Peng, M., Li, Y., Zhao, Z., & Wang, C. (2015). System architecture and key technologies for 5G heterogeneous cloud radio access networks. IEEE network, 29(2), 6-14.[8] Peng, M., Li, Y., Jiang, J., Li, J., & Wang, C. (2014). Heterogeneous cloud radio access networks: A new perspective for enhancing spectral and energy efficiencies. IEEE wireless communications, 21(6), 126-135.[9] Peng, M., Zhang, K., Jiang, J., Wang, J., & Wang, W. (2014). Energy-efficient resource assignment and power allocation in heterogeneous cloud radio access networks. IEEE Transactions on Vehicular Technology, 64(11), 5275-5287.[10] Kitindi, E. J., Fu, S., Jia, Y., Kabir, A., & Wang, Y. (2017). Wireless network virtualization with SDN and C-RAN for 5G networks: Requirements, opportunities, and challenges. IEEE Access, 5, 19099-19115.[11] Topoloi, S. G., & Borcoci, E. (2018, June). Software Defined Networking and Network Function Virtualisation Cooperation-Experiments. In 2018 International Conference on Communications (COMM) (pp. 281-286). IEEE.[12] Chartsias, P. K., Amiras, A., Plevrakis, I., Samaras, I., Katsaros, K., Kritharidis, D., & Escalona, E. (2017, June). SDN/NFV-based end to end network slicing for 5G multi-tenant networks. In 2017 European Conference on Networks and Communications (EuCNC) (pp. 1-5). IEEE.[13] Marzouk, F., Barraca, J. P., & Radwan, A. (2020). On Energy Efficient Resource Allocation in Shared RANs: Survey and Qualitative Analysis. IEEE Communications Surveys & Tutorials, 22(3), 1515-1538.[14] Oladejo, S. O., & Falowo, O. E. (2017, October). 5G network slicing: A multi-tenancy scenario. In 2017 Global Wireless Summit (GWS) (pp. 88-92). IEEE.[15] Björnson, E., Hoydis, J., & Sanguinetti, L. (2017). Massive MIMO networks: Spectral, energy, and hardware efficiency. Foundations and Trends in Signal Processing, 11(3-4), 154-655.[16] Wang, L., Wong, K. K., Elkashlan, M., Nallanathan, A., & Lambotharan, S. (2016). Secrecy and energy efficiency in massive MIMO aided heterogeneous C-RAN: A new look at interference. IEEE Journal of Selected Topics in Signal Processing, 10(8), 1375-1389.[17] Zhou, F., Wu, Y., Hu, R. Q., Wang, Y., & Wong, K. K. (2018). Energy-efficient NOMA enabled heterogeneous cloud radio access networks. IEEE Network, 32(2), 152-160.[18] Al-Abbasi, Z. Q., Rabie, K., & So, D. K. C. (2021). EE Optimization for Downlink NOMA-based Multi-Tier CRANs. IEEE Transactions on Vehicular Technology.[19] Schneir, J. R., Konstantinou, K., Bradford, J., Zimmermann, G., Droste, H., Palancar, R. C., & Ajibulu, A. (2020). Cost assessment of multi-tenancy for a 5G broadband network in a dense urban area. Digital Policy, Regulation and Governance.[20] Schneir, J. R., Konstantinou, K., Bradford, J., Zimmermann, G., Droste, H., Canto, R., & Ajibulu, A. (2017). Cost analysis of a 5G network with Multi-Tenancy options.[21] Baghani, M., Parsaeefard, S., Derakhshani, M., & Saad, W. (2019). Dynamic non-orthogonal multiple access and orthogonal multiple access in 5G wireless networks. IEEE Transactions on Communications, 67(9), 6360-6373.[22] Amani, N., Pedram, H., Taheri, H., & Parsaeefard, S. (2018). Energy-efficient resource allocation in heterogeneous cloud radio access networks via BBU offloading. IEEE Transactions on Vehicular Technology, 68(2), 1365-1377.[23] Zhao, W., & Wang, S. (2016, May). Remote radio head selection for power saving in cloud radio access networks. In 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring) (pp. 1-5). IEEE.[24] Tohidi, M., Bakhshi, H., & Parsaeefard, S. (2020). Joint uplink and downlink delay‐aware resource allocation in C‐RAN. Transactions on Emerging Telecommunications Technologies, 31(3), e3778.[25] Kazmi, S. A., Tran, N. H., Ho, T. M., & Hong, C. S. (2017). Hierarchical matching game for service selection and resource purchasing in wireless network virtualization. IEEE Communications Letters, 22(1), 121-124.[26] Ye, J., & Zhang, Y. J. (2019). Pricing-based resource allocation in virtualized cloud radio access networks. IEEE Transactions on Vehicular Technology, 68(7), 7096-7107.[27] Wang, G., Feng, G., Tan, W., Qin, S., Wen, R., & Sun, S. (2017, December). Resource allocation for network slices in 5G with network resource pricing. In GLOBECOM 2017-2017 IEEE Global Communications Conference (pp. 1-6). IEEE.[28] Jiang, M., Condoluci, M., & Mahmoodi, T. (2017, May). Network slicing in 5G: An auction-based model. In 2017 IEEE International Conference on Communications (ICC) (pp. 1-6). IEEE.[29] Morcos, M., Chahed, T., Chen, L., Elias, J., & Martignon, F. (2018). A two-level auction for resource allocation in multi-tenant C-RAN. Computer Networks, 135, 240-252.[30] Zhang, Y., Bi, S., & Zhang, Y. J. (2016, December). A two-stage spectrum leasing optimization framework for virtual mobile network operators. In 2016 IEEE International Conference on Communication Systems (ICCS) (pp. 1-6). IEEE.[31] Zhu, K., & Hossain, E. (2015). Virtualization of 5G cellular networks as a hierarchical combinatorial auction. IEEE Transactions on Mobile Computing, 15(10), 2640-2654.[32] Hao, Y., Ni, Q., Li, H., & Hou, S. (2017). On the energy and spectral efficiency tradeoff in massive MIMO-enabled HetNets with capacity-constrained backhaul links. IEEE Transactions on Communications, 65(11), 4720-4733.[33] Parsaeefard, S., Dawadi, R., Derakhshani, M., Le-Ngoc, T., & Baghani, M. (2017). Dynamic resource allocation for virtualized wireless networks in massive-MIMO-aided and fronthaul-limited C-RAN. IEEE Transactions on Vehicular Technology, 66(10), 9512-9520.[34] Liu, Y., Derakhshani, M., Parsaeefard, S., Lambotharan, S., & Wong, K. K. (2018). Antenna Allocation and Pricing inVirtualized Massive MIMO Networks via Stackelberg Game. IEEE Transactions on Communications, 66(11), 5220-5234.[35] Wang, X. (2017). Spectrum and energy efficiency of uplink massive MIMO system with D2D underlay. Future Internet, 9(2), 12.[36] Li, Y., Tao, C., Mezghani, A., Swindlehurst, A. L., Seco-Granados, G., & Liu, L. (2016). Optimal design of energy and spectral efficiency tradeoff in one-bit massive MIMO systems. arXiv preprint arXiv:1612.03271.[37] Hu, Y., Ji, B., Huang, Y., Yu, F., & Yang, L. (2015). Energy-efficient resource allocation in uplink multiuser massive MIMO systems. International Journal of Antennas and Propagation, 2015.[38] Parsaeefard, S., Dawadi, R., Derakhshani, M., & Le-Ngoc, T. (2016). Joint user-association and resource-allocation in virtualized wireless networks. IEEE Access, 4, 2738-2750.[39] Chiang, M. (2005). Geometric programming for communication systems. Now Publishers Inc.[40] Chiang, M., Tan, C. W., Palomar, D. P., O'neill, D., & Julian, D. (2007). Power control by geometric programming. IEEE transactions on wireless communications, 6(7), 2640-2651.[41] Xu, G. (2014). Global optimization of signomial geometric programming problems. European journal of operational research, 233(3), 500-510.[42] Tuy, H. (1995). DC optimization: theory, methods and algorithms. In Handbook of global optimization (pp. 149-216). Springer, Boston, MA.[43] Grant, M. and Boyd, S. (2017). CVX: MATLAB software for disciplined convex programming, in Version 2.1. http://cvxr.com/cvx.RezaBahriMahdiFasanghariAhmadrezaEskandariVahidYazdanian227202319019910.66224/jict.41385.14.53.190http://jour.aicti.ir/en/Article/41385http://jour.aicti.ir/en/Article/Download/41385http://jour.aicti.ir/en/Article/Download/41385http://jour.aicti.ir/en/Article/Download/41385http://jour.aicti.ir/en/Article/Download/41385http://jour.aicti.ir/en/Article/Download/41385http://jour.aicti.ir/en/Article/Download/41385http://jour.aicti.ir/en/Article/Download/41385[1] S. i. Kwak, B. C. Kim, and H. D. Choi, “Analysis of isotropic deviation of the electric-fields probe in the personal exposure measurement meter," ICTC 2012, 235-237, Oct. 2012.[2] S. i. Kwak, J. H. Kwon and Y. J. Yoon, "Experimental Results of an E-Field Probe Using Variable Resistors to Improve Performance," in IEEE Antennas and Wireless Propagation Letters, vol. 15, pp. 1369-1372, 2016.[3] S. I. Kwak, J. H. Kwon and Y. J. Yoon, "Design of the E-field probe for mobile communication bands in the personal exposure meter," The 18th IEEE International Symposium on Consumer Electronics (ISCE 2014), Jeju, Korea (South), 2014, pp. 1-2.[4] S. I. Kwak, J. H. Kwon and Y. J. Yoon, "Design of the E-field probe with variable resistors," 2014 International Symposium on Antennas and Propagation Conference Proceedings, Kaohsiung, Taiwan, 2014, pp. 353-354.[5] S. I. Kwak, B. C. Kim, H. D. Choi, and Y.J. Yoon, “Design of the electric-fields probe in the personal exposure meter,” PIERS Proceedings, Stockholm, Sweden, Aug. 12-15, 2013.[6] E. S. Pires, P. I. L. Ferreira, G. Fontgalland, M. A. B. de Melo, R. M. Valle and T. P. Vuong, "Design of a UWB antenna for sensor and wireless systems applications," 2008 IEEE International Conference on Ultra-Wideband, Hannover, Germany, 2008, pp. 185-188.[7] M. Kanda, "Standard probes for electromagnetic field measurements," in IEEE Transactions on Antennas and Propagation, vol. 41, no. 10, pp. 1349-1364, Oct. 1993.[8] X. Zhang and B. Huang, "The design of the elliptic monopole omnidirectional antenna," 2017 Sixth Asia-Pacific Conference on Antennas and Propagation (APCAP), Xi'an, China, 2017, pp. 1-3.[9] R.A. Sadeghzadeh, M.A. Honarvar, A.R. Eskandari, “Coplanar-fed UWB elliptical patch antenna with notched band characteristics,” Progress In Electromagnetics Research Symposium Proceedings, Xi’an, China, March 22–26, 2010.[10] M. N. Hasan and M. Seo, "A Planar 3.4 -9 GHz UWB Monopole Antenna," 2018 International Symposium on Antennas and Propagation (ISAP), Busan, Korea (South), 2018, pp. 1-2.[11] C. Li, X. Zhu, P. Liu and C. Yu, "Novel wideband omnidirectional monopole antenna for indoor communication environment," 12th European Conference on Antennas and Propagation (EuCAP 2018), London, UK, 2018, pp. 1-3.[12] M. Arrawatia, M. S. Baghini and G. Kumar, "Broadband omnidirectional antenna for GSM900, GSM1800, 3G, 4G and Wi-Fi applications," 2017 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, San Diego, CA, USA, 2017, pp. 329-330.[13] L. Zhao, Z. Chen and J. Wang, "A Wideband Dual-Polarized Omnidirectional Antenna for 5G/WLAN," in IEEE Access, vol. 7, pp. 14266-14272, 2019.[14] J. Xie, Y. Gao and C. Y. Xia, "A design of three-band monopole antenna applied to 4G/5G," 2020 International Conference on Computer Communication and Network Security (CCNS), Xi'an, China, 2020, pp. 99-103.[15] N. N. Al-Areqi, N. Seman and T. A. Rahman, "Parallel-coupled line bandpass filter design using different substrates for fifth generation wireless communication applications," 2015 International Symposium on Antennas and Propagation (ISAP), Hobart, TAS, Australia, 2015, pp. 1-4.[16] Y. Guan, Y. Wu and M. M. Tentzeris, "A Bidirectional Absorptive Common-Mode Filter Based on Interdigitated Microstrip Coupled Lines for 5G “Green” Communications," in IEEE Access, vol. 8, pp. 20759-20769, 2020.[17] W. Zhang, K. Ma, H. Zhang and H. Fu, "Design of a Compact SISL BPF With SEMCP for 5G Sub-6 GHz Bands," in IEEE Microwave and Wireless Components Letters, vol. 30, no. 12, pp. 1121-1124.[18] S. Lookian and A.R. Eskandari, “A compact balanced filter with quadruple-mode resonators for UWB application”, Journal of Instrumentation 15 (07), P07003, 2020.[19] Y. I. A. Al-Yasir, N. OjaroudiParchin, A. Abdulkhaleq, K. Hameed, M. Al-Sadoon and R. Abd-Alhameed, "Design, Simulation and Implementation of Very Compact Dual-band Microstrip Bandpass Filter for 4G and 5G Applications," 2019 16th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD), Lausanne, Switzerland, 2019, pp. 41-44.[20] David M. Pozar, Microwave Engineering, 4th Edition, Wiley, 2011.MehdiMorsaliAbolfazlToroghi HaghighatSasanHosseinali-Zade227202320022310.66224/jict.41386.14.53.200http://jour.aicti.ir/en/Article/41386http://jour.aicti.ir/en/Article/Download/41386http://jour.aicti.ir/en/Article/Download/41386http://jour.aicti.ir/en/Article/Download/41386http://jour.aicti.ir/en/Article/Download/41386http://jour.aicti.ir/en/Article/Download/41386http://jour.aicti.ir/en/Article/Download/41386http://jour.aicti.ir/en/Article/Download/413861. Adhianto, L. et al. 2010. “HPCTOOLKIT: Tools for Performance Analysis of Optimized Parallel Programs.” Concurrency Computation Practice and Experience 22(6): 685–701.2. Afzal, Shahbaz, and G. Kavitha. 2019. “Load Balancing in Cloud Computing – A Hierarchical Taxonomical Classification.” Journal of Cloud Computing 8(1).3. Agarwal, Tusha, and Abhishek Saxena. 2018. “A Review On Load Balancing Algorithm in Cloud Computing Using Restful Web Services.” International Journal of Computer Sciences and Engineering 6(7): 704–7.4. Ahmad, Mohammad Oqail, and Rafiqul Zaman Khan. 2018. “Load Balancing Tools and Techniques in Cloud Computing: A Systematic Review.” Advances in Intelligent Systems and Computing 554(November 2017): 181–95.5. Alam, Mahfooz, and Zaki Ahmad Khan. 2017. “Issues and Challenges of Load Balancing Algorithm in Cloud Computing Environment.” Indian Journal of Science and Technology 10(25): 1–12.6. Alankar, Bhavya et al. 2020. “Experimental Setup for Investigating the Efficient Load Balancing Algorithms on Virtual Cloud.” Sensors (Switzerland) 20(24): 1–26.7. Anna Victoria Oikawa, C. R., Vinicius Freitas, Marcio Castro, and Laercio L. Pilla. 2020. “Adaptive Load Balancing Based on Machine Learning for Iterative Parallel Applications.” Proceedings - 2020 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2020: 94–101.8. Aruna, M., D. Bhanu, and S. Karthik. 2019. “An Improved Load Balanced Metaheuristic Scheduling in Cloud.” Cluster Computing 22(March): 10873–81.9. Ashouraei, Mehran, Seyed Nima Khezr, Rachid Benlamri, and Nima Jafari Navimipour. 2018. “A New SLA-Aware Load Balancing Method in the Cloud Using an Improved Parallel Task Scheduling Algorithm.” Proceedings - 2018 IEEE 6th International Conference on Future Internet of Things and Cloud, FiCloud 2018: 71–76.10. Attiya, Ibrahim, Mohamed Abd Elaziz, and Shengwu Xiong. 2020. “Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm.” Computational Intelligence and Neuroscience 2020.11. Balaji, K., P. Sai Kiran, and M. Sunil Kumar. 2021. “An Energy Efficient Load Balancing on Cloud Computing Using Adaptive Cat Swarm Optimization.” Materials Today: Proceedings.12. Basu, Sayantani, G. Kannayaram, Somula Ramasubbareddy, and C. Venkatasubbaiah. 2019. “Improved Genetic Algorithm for Monitoring of Virtual Machines in Cloud Environment.” Smart Innovation, Systems and Technologies 105: 319–26. http://dx.doi.org/10.1007/978-981-13-1927-3_34.13. Bhargavi, K., B. Sathish Babu, and Jeremy Pitt. 2020. “Performance Modeling of Load Balancing Techniques in Cloud: Some of the Recent Competitive Swarm Artificial Intelligence-Based.” Journal of Intelligent Systems 30(1): 40–58.14. Černý, V. 1985. “Thermodynamical Approach to the Traveling Salesman Problem: An Efficient Simulation Algorithm.” Journal of Optimization Theory and Applications 45(1): 41–51.15. Chauhan, Prof Samir, and Jinal Patel. 2019. “Load Balancing in Cloud Computing Using Machine Learning Techniques.” JASC: Journal of Applied Science and Computations VI(Iv): 3533–41. 16. Dasgupta, Kousik et al. 2013. “A Genetic Algorithm (GA) Based Load Balancing Strategy for Cloud Computing.” Procedia Technology 10: 340–47. http://dx.doi.org/10.1016/j.protcy.2013.12.369.17. Davidović, Tatjana, Dušan Teodorović, and Milica Šelmić. 2015. “Bee Colony Optimization Part I: The Algorithm Overview.” Yugoslav Journal of Operations Research 25(1): 33–56.18. Dhiman, Gaurav, and Amandeep Kaur. 2018. “Optimizing the Design of Airfoil and Optical Buffer Problems Using Spotted Hyena Optimizer.” Designs 2(3): 1–16.19. Djennane, Nabila, Rachida Aoudjit, and Samia Bouzefrane. 2018. “Energy-Efficient Algorithm for Load Balancing and VMs Reassignment in Data Centers.” Proceedings - 2018 IEEE 6th International Conference on Future Internet of Things and Cloud Workshops, W-FiCloud 2018: 225–30.20. Fahim, Youssef et al. 2018. “Load Balancing in Cloud Computing Using Meta-Heuristic Algorithm.” Journal of Information Processing Systems 14(3): 569–89.21. Gavvala, Siva Kumar, Chandrashekar Jatoth, G. R. Gangadharan, and Rajkumar Buyya. 2019. “QoS-Aware Cloud Service Composition Using Eagle Strategy.” Future Generation Computer Systems 90: 273–90.22. Geethu, Gopinath P.P., and Shriram K. Vasudevan. 2015. “An In-Depth Analysis and Study of Load Balancing Techniques in the Cloud Computing Environment.” Procedia Computer Science 50: 427–32. http://dx.doi.org/10.1016/j.procs.2015.04.009.23. Gulbaz, Rohail et al. 2021. “Balancer Genetic Algorithm-a Novel Task Scheduling Optimization Approach in Cloud Computing.” Applied Sciences (Switzerland) 11(14).24. Kaur, Amanpreet, and Bikrampal Kaur. 2019. “Load Balancing Optimization Based on Hybrid Heuristic-Metaheuristic Techniques in Cloud Environment.” Journal of King Saud University - Computer and Information Sciences (xxxx). https://doi.org/10.1016/j.jksuci.2019.02.010.25. Krishnaveni, H., and V. Sinthu Janita. 2019. “Modified Artificial Fish Swarm Algorithm for Efficient Task Scheduling in Cloud Environment.” International Journal of Computer Sciences and Engineering 7(5): 1363–71.26. Kruekaew, Boonhatai, and Warangkhana Kimpan. 2020. “Enhancing of Artificial Bee Colony Algorithm for Virtual Machine Scheduling and Load Balancing Problem in Cloud Computing.” International Journal of Computational Intelligence Systems 13(1): 496–510.27. Kumar, Arun Rana, Ayodeji Olalekan Salau, Swati Gupta, and Sandeep Arora. 2018. “A Survey of Machine Learning Methods for IoT and Their Future Applications.” Amity Journal of Computational Sciences 2(2): 1–5. www.amity.edu.in/ajcs.28. Kumar, Jitendra, Ashutosh Kumar Singh, and Anand Mohan. 2020. “Resource-Efficient Load-Balancing Framework for Cloud Data Center Networks.” ETRI Journal 43(June 2019): 53–63.29. Kumar, Kethavath Prem, Thirumalaisamy Ragunathan, Devara Vasumathi, and Pamulapati Krishna Prasad. 2020. “An Efficient Load Balancing Technique Based on Cuckoo Search and Firefly Algorithm in Cloud.” International Journal of Intelligent Engineering and Systems 13(3): 422–32.30. Kumar Mishra, Sambit, Bibhudatta Sahoo, and Paramita Parida. 2018. “Load Balancing in Cloud Computing: A Big Picture Q.” https://doi.org/10.1016/j.jksuci.2018.01.003 (August 21, 2021).31. Kumar, Pawan, and Rakesh Kumar. 2019. “Issues and Challenges of Load Balancing Techniques in Cloud Computing: A Survey.” ACM Computing Surveys 51(6).32. Li, Kun et al. 2011. “Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization.” Proceedings - 2011 6th Annual ChinaGrid Conference, ChinaGrid 2011: 3–9.33. Lim, Jongbeom, and Daewon Lee. 2020. “A Load Balancing Algorithm for Mobile Devices in Edge Cloud Computing Environments.” Electronics (Switzerland) 9(4): 1–13.34. Mallikarjuna, B., and P. Venkata Krishna. 2015. “OLB: A Nature Inspired Approach for Load Balancing in Cloud Computing.” Cybernetics and Information Technologies 15(4): 138–48.35. Manasrah, Ahmad M., and Hanan Ba Ali. 2018. “Workflow Scheduling Using Hybrid GA-PSO Algorithm in Cloud Computing.” Wireless Communications and Mobile Computing 2018.36. Milani, Alireza Sadeghi, and Nima Jafari Navimipour. 2016. “Load Balancing Mechanisms and Techniques in the Cloud Environments: Systematic Literature Review and Future Trends.” Journal of Network and Computer Applications 71: 86–98.37. Mishra, Kaushik, Jharashree Pati, and Santosh Kumar Majhi. 2020. “A Dynamic Load Scheduling in IaaS Cloud Using Binary JAYA Algorithm.” Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2020.12.001.38. Mohammad Oqail Ahmad and Rafiqul Zaman Khan. 2018. 554 Advances in Computer and Computational Sciences.39. de Monts, Robert et al. 2016. “Defined Categories of Security as a Service.” Cloud Security Alliance –. https://downloads.cloudsecurityalliance.org/assets/research/security-as-a-service/csa-categories-securities-prep.pdf.40. Patel, Karan D., and Tosal M. Bhalodia. 2019. “An Efficient Dynamic Load Balancing Algorithm for Virtual Machine in Cloud Computing.” In 2019 International Conference on Intelligent Computing and Control Systems, ICCS 2019, , 145–50.41. Pattnaik, Saumendra, Jyoti Prakash Mishra, Bidush Kumar Sahoo, and Binod Kumar Pattanayak. 2021. “Load Balancing in Cloud Computing Environment Using CloudSim.” Smart Innovation, Systems and Technologies 194(01): 197–205.42. Prassanna, J., and Neelanarayanan Venkataraman. 2019. “Threshold Based Multi-Objective Memetic Optimized Round Robin Scheduling for Resource Efficient Load Balancing in Cloud.” Mobile Networks and Applications 24(4): 1214–25.43. Ramadhan, Gema, Tito Waluyo Purboyo, Roswan Latuconsina, and A Round Robin. 2018. “Experimental Model for Load Balancing in Cloud Computing Using Throttled Algorithm.” International Journal of Applied Engineering Research 13(2): 1139–43. https://www.ripublication.com/ijaer18/ijaerv13n2_42.pdf.44. Ramasubbareddy, Somula et al. 2019. “Analysis of Load Balancing Algorithms Using Cloud Analyst.” International Journal of Recent Technology and Engineering 7(6): 684–87.45. Rani, Mercy Gnana et al. 2014. “Artificial Fish Swarm Load Balancing and Job Migration Task with Overloading Detection in Cloud Computing Environments.” International Review on Computers and Software (IRECOS) 9(4): 727–34. https://www.praiseworthyprize.org/jsm/index.php?journal=irecos&page=article&op=view&path[]=15345 (August 29, 2021).46. Shahid, Muhammad Asim et al. 2020. “A Comprehensive Study of Load Balancing Approaches in the Cloud Computing Environment and a Novel Fault Tolerance Approach.” IEEE Access 8(c): 130500–526.47. Sui, Xin et al. 2019. “Virtual Machine Scheduling Strategy Based on Machine Learning Algorithms for Load Balancing.” Eurasip Journal on Wireless Communications and Networking 2019(1).48. Talaat, Fatma M. et al. 2020. “A Load Balancing and Optimization Strategy (LBOS) Using Reinforcement Learning in Fog Computing Environment.” Journal of Ambient Intelligence and Humanized Computing 11(11): 4951–66. https://doi.org/10.1007/s12652-020-01768-8.49. Talbi, El-Ghazali. 2009. “Frontmatter Enhanced Reader.Pdf.” : 25.50. ———. 2020. “Machine Learning into Metaheuristics: A Survey and Taxonomy of Data-Driven Meta-Heuristics.” : 1–30. https://hal.inria.fr/hal-02745295.51. Téllez, Nadim, Miguel Jimeno, Augusto Salazar, and Elias D. Nino-Ruiz. 2018. “A Tabu Search Method for Load Balancing in Fog Computing.” International Journal of Artificial Intelligence 16(2): 106–35.52. Thanka, M. Roshni, P. Uma Maheswari, and E. Bijolin Edwin. 2019. “An Improved Efficient: Artificial Bee Colony Algorithm for Security and QoS Aware Scheduling in Cloud Computing Environment.” Cluster Computing 22: 10905–13.53. UMA, Mrs. R., M . BALA SARASWATHY. 2019. “OPTIMIZATION ALGORITHMS IN LOAD BALANCING: A STUDY.” XII(Iv): 1–22.54. Venkata Rao, R. 2016. “Jaya: A Simple and New Optimization Algorithm for Solving Constrained and Unconstrained Optimization Problems.” International Journal of Industrial Engineering Computations 7(1): 19–34.55. Viana, Monique Simplicio, Orides Morandin Junior, and Rodrigo Colnago Contreras. 2020. “A Modified Genetic Algorithm with Local Search Strategies and Multi-Crossover Operator for Job Shop Scheduling Problem.” Sensors (Switzerland) 20(18): 1–32.56. Wang, Chuan’An et al. 2017. “A Switch Migration-Based Decision-Making Scheme for Balancing Load in SDN.” IEEE Access 5(c): 4537–44.57. Wang, Chunpu, Chen Feng, and Julian Cheng. 2018. “Distributed Join-the-Idle-Queue for Low Latency Cloud Services.” IEEE/ACM Transactions on Networking 26(5): 2309–19.58. Wang, Lijuan, and Jun Shen. 2016. “Multi-Phase Ant Colony System for Multi-Party Data-Intensive Service Provision.” IEEE Transactions on Services Computing 9(2): 264–78.59. Xu, Peng, Guimin He, Zhenhao Li, and Zhongbao Zhang. 2018. “An Efficient Load Balancing Algorithm for Virtual Machine Allocation Based on Ant Colony Optimization.” International Journal of Distributed Sensor Networks 14(12).60. Yaashuwanth, C et al. 2012. “Performance Comparison of Priority Rule Scheduling Algorithms Using Different Inter Arrival Time Jobs in Grid Environment.” International Journal of Grid and Distributed Computing 6(4): 157–68.ZahraAbbasiSomayehFatahiMohammad Javad Ershadi227202322423610.66224/jict.41387.14.53.224http://jour.aicti.ir/en/Article/41387http://jour.aicti.ir/en/Article/Download/41387http://jour.aicti.ir/en/Article/Download/41387http://jour.aicti.ir/en/Article/Download/41387http://jour.aicti.ir/en/Article/Download/41387http://jour.aicti.ir/en/Article/Download/41387http://jour.aicti.ir/en/Article/Download/41387http://jour.aicti.ir/en/Article/Download/41387• Abbas, A. M., & Kure, O. (2010). Quality of Service in mobile ad hoc networks: a survey. International journal of ad hoc and ubiquitous computing, 6(2), 75-98.• Aznoli, F., & Navimipour, N. J. (2017). Cloud services recommendation: Reviewing the recent advances and suggesting the future research directions. Journal of Network and Computer Applications, 77, 73-86.• Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation computer systems, 25(6), 599-616.• Del Castillo, A. S., & Sardi, N. (2012). ISO standards and the quality concept applied to anesthesia services. Revista Colombiana de Anestesiología, 40(1), 14-16.• Erdil, S. T., & Yıldız, O. (2011). Measuring service quality and a comparative analysis in the passenger carriage of airline industry. Procedia-Social and Behavioral Sciences, 24, 1232-1242.• Gorla, N., Somers, T. M., & Wong, B. (2010). Organizational impact of system quality, information quality, and service quality. The Journal of Strategic Information Systems, 19(3), 207-228.• Gui, Z., Yang, C., Xia, J., Huang, Q., Liu, K., Li, Z., & Jin, B. (2014). A service brokering and recommendation mechanism for better selecting cloud services. PloS one, 9(8), e105297.• Kersten, W., & Koch, J. (2010). The effect of quality management on the service quality and business success of logistics service providers. International Journal of Quality & Reliability Management.• Kwon, H. K., & Seo, K. K. (2013). A decision-making model to choose a cloud service using fuzzy AHP. Advanced Science and Technology Letters, 35(1), 93-96.• Li, A., Yang, X., Kandula, S., & Zhang, M. (2010, November). CloudCmp: comparing public cloud providers. In Proceedings of the 10th ACM SIGCOMM conference on Internet measurement (pp. 1-14).• Ma, S. P., Lan, C. W., & Li, C. H. (2015). Contextual service discovery using term expansion and binding coverage analysis. Future Generation Computer Systems, 48, 73-81.• Mell, P., & Grance, T. (2011). The NIST definition of cloud computing.• Mohammadi, M., & Rezaei, J. (2020). Evaluating and comparing ontology alignment systems: An MCDM approach. Journal of Web Semantics, 64, 100592.• Nawaz, F., Asadabadi, M. R., Janjua, N. K., Hussain, O. K., Chang, E., & Saberi, M. (2018). An MCDM method for cloud service selection using a Markov chain and the best-worst method. Knowledge-Based Systems, 159, 120-131.• Sharabi, M. (2013). Managing and improving service quality in higher education. International Journal of Quality and Service Sciences.• Sun, L., Dong, H., Hussain, F. K., Hussain, O. K., & Chang, E. (2014). Cloud service selection: State-of-the-art and future research directions. Journal of Network and Computer Applications, 45, 134-150.• Zhao, C., Zhang, S., Liu, Q., Xie, J., & Hu, J. (2009, September). Independent tasks scheduling based on genetic algorithm in cloud computing. In 2009 5th international conference on wireless communications, networking and mobile computing (pp. 1-4). IEEE.Maede Amini vlashaniayoubmohamadianSeyed MohammadbagherJafari227202323726610.66224/jict.41388.14.53.237http://jour.aicti.ir/en/Article/41388http://jour.aicti.ir/en/Article/Download/41388http://jour.aicti.ir/en/Article/Download/41388http://jour.aicti.ir/en/Article/Download/41388http://jour.aicti.ir/en/Article/Download/41388http://jour.aicti.ir/en/Article/Download/41388http://jour.aicti.ir/en/Article/Download/41388http://jour.aicti.ir/en/Article/Download/41388Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics, 182, 113–131. https://doi.org/10.1016/j.ijpe.2016.08.018Amankwah-Amoah, J., & Adomako, S. (2019). Big data analytics and business failures in data-Rich environments: An organizing framework. Computers in Industry, 105, 204–212. https://doi.org/10.1016/j.compind.2018.12.015Barney, J. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108Barrales-Molina, V., Martínez-López, F. J., & Gázquez-Abad, J. C. (2014). Dynamic marketing capabilities: Toward an integrative framework. International Journal of Management Reviews, 16(4), 397–416. https://doi.org/10.1111/ijmr.12026Božič, K., & Dimovski, V. (2019). Business intelligence and analytics use, innovation ambidexterity, and firm performance: A dynamic capabilities perspective. Journal of Strategic Information Systems, 28(4), 101578. https://doi.org/10.1016/j.jsis.2019.101578Braganza, A., Brooks, L., Nepelski, D., Ali, M., & Moro, R. (2017). Resource management in big data initiatives: Processes and dynamic capabilities. Journal of Business Research, 70, 328–337. https://doi.org/10.1016/j.jbusres.2016.08.006Branda, A. F., Lala, V., & Gopalakrishna, P. (2018). The marketing analytics orientation (MAO) of firms: identifying factors that create highly analytical marketing practices. Journal of Marketing Analytics, 6(3), 84–94. https://doi.org/10.1057/s41270-018-0036-8Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oaBruni, D. S., & Verona, G. (2009). Dynamic marketing capabilities in science-based firms: An exploratory investigation of the pharmaceutical industry. British Journal of Management, 20(SUPP. 1). https://doi.org/10.1111/j.1467-8551.2008.00615.xBullini Orlandi, L. (2016). Organizational capabilities in the digital era: Reframing strategic orientation. Journal of Innovation & Knowledge, 1(3), 156–161. http://creativecommons.org/licenses/ by-nc-nd/4.0/)Bullini Orlandi, L., Zardini, A., & Rossignoli, C. (2020). Organizational technological opportunism and social media: The deployment of social media analytics to sense and respond to technological discontinuities. Journal of Business Research, 112(November 2019), 385–395. https://doi.org/10.1016/j.jbusres.2019.10.070Camilleri, E., & Miah, S. (2018). A Consumer Analytics Framework for Enabling Data-Driven Marketing Intervention Methods. Proceedings - 2017 4th Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2017, 8–12. https://doi.org/10.1109/APWConCSE.2017.00011Cao, G., Duan, Y., & El Banna, A. (2019). A dynamic capability view of marketing analytics: Evidence from UK firms. Industrial Marketing Management, 76(May 2018), 72–83. https://doi.org/10.1016/j.indmarman.2018.08.002Cao, G., & Tian, N. (2020). Enhancing customer-linking marketing capabilities using marketing analytics. Journal of Business and Industrial Marketing, 35(7), 1289–1299. https://doi.org/10.1108/JBIM-09-2019-0407Cao, G., Tian, N., & Blankson, C. (2021). Big Data, Marketing Analytics, and Firm Marketing Capabilities. Journal of Computer Information Systems, 00(00), 1–10. https://doi.org/10.1080/08874417.2020.1842270Cepeda-Carrion, G., Cegarra-Navarro, J. G., & Jimenez-Jimenez, D. (2012). The effect of absorptive capacity on innovativeness: Context and information systems capability as catalysts. British Journal of Management, 23(1), 110–129. https://doi.org/10.1111/j.1467-8551.2010.00725.xCôrte-Real, N., Oliveira, T., & Ruivo, P. (2017). Assessing business value of Big Data Analytics in European firms. Journal of Business Research, 70, 379–390. https://doi.org/10.1016/j.jbusres.2016.08.011Day, G. S. (2011). Closing the marketing capabilities gap. Journal of Marketing, 75(4), 183–195. https://doi.org/10.1509/jmkg.75.4.183De Luca, L. M., Herhausen, D., Troilo, G., & Rossi, A. (2020). How and when do big data investments pay off? The role of marketing affordances and service innovation. Journal of the Academy of Marketing Science. https://doi.org/10.1007/s11747-020-00739-xden Hertog, P., van der Aa, W., & de Jong, M. W. (2010). Capabilities for managing service innovation: Towards a conceptual framework. Journal of Service Management, 21(4), 490–514. https://doi.org/10.1108/09564231011066123Duan, Y., & Edwards, J. S. (2020). Understanding the Impact of Business Analytics on Innovation (Vol. 44). European Journal of Operational Research.Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic capabilities: What are they? Strategic Management Journal, 21(10–11), 1105–1121. https://doi.org/10.1002/1097-0266(200010/11)21:10/11<1105::AID-SMJ133>3.0.CO;2-EElsharnouby, T. H., & Elbanna, S. (2021). Change or perish: Examining the role of human capital and dynamic marketing capabilities in the hospitality sector. Tourism Management, 82(April 2020), 104184. https://doi.org/10.1016/j.tourman.2020.104184Ghasemaghaei, M., Ebrahimi, S., & Hassanein, K. (2018). Data analytics competency for improving firm decision making performance. Journal of Strategic Information Systems, 27(1), 101–113. https://doi.org/10.1016/j.jsis.2017.10.001Gnizy, I. (2019). Big data and its strategic path to value in international firms. International Marketing Review, 36(3), 318–341. https://doi.org/10.1108/IMR-09-2018-0249Guo, H., Xu, H., Tang, C., Liu-Thompkins, Y., Guo, Z., & Dong, B. (2018). Comparing the impact of different marketing capabilities: Empirical evidence from B2B firms in China. In Journal of Business Research (Vol. 93, pp. 79–89). https://doi.org/10.1016/j.jbusres.2018.04.010Hallikainen, H., Savimäki, E., & Laukkanen, T. (2020). Fostering B2B sales with customer big data analytics. Industrial Marketing Management, 86(June 2018), 90–98. https://doi.org/10.1016/j.indmarman.2019.12.005Janssen, M., van der Voort, H., & Wahyudi, A. (2017). Factors influencing big data decision-making quality. Journal of Business Research, 70, 338–345. https://doi.org/10.1016/j.jbusres.2016.08.007Johnson, D. S., Muzellec, L., Sihi, D., & Zahay, D. (2019). The marketing organization’s journey to become data-driven. Journal of Research in Interactive Marketing, 13(2), 162–178. https://doi.org/10.1108/JRIM-12-2018-0157Kachouie, R., Mavondo, F., & Sands, S. (2018). Dynamic marketing capabilities view on creating market change. European Journal of Marketing, 52(5–6), 1007–1036. https://doi.org/10.1108/EJM-10-2016-0588Lin, C., & Kunnathur, A. (2019). Strategic orientations, developmental culture, and big data capability. Journal of Business Research, 105(November 2018), 49–60. https://doi.org/10.1016/j.jbusres.2019.07.016Louro, A. C., Brandão, M. M., Jaklič, J., & Sarcinelli, A. (2019). How can customer analytics capabilities influence organizational performance? a moderated mediation analysis. Brazilian Business Review, 16(4), 370–382. https://doi.org/10.15728/bbr.2019.16.4.4Moreno, V., Cavazotte, F., & de Souza Carvalho, W. (2020). Business intelligence and analytics as a driver of dynamic and operational capabilities in times of intense macroeconomic turbulence. Journal of High Technology Management Research, xxxx, 100389. https://doi.org/10.1016/j.hitech.2020.100389Raguseo, E., & Vitari, C. (2018). Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects. International Journal of Production Research, 56(15), 5206–5221. https://doi.org/10.1080/00207543.2018.1427900Rialti, R., Zollo, L., Ferraris, A., & Alon, I. (2019). Big data analytics capabilities and performance: Evidence from a moderated multi-mediation model. Technological Forecasting and Social Change, 149(October), 119781. https://doi.org/10.1016/j.techfore.2019.119781Roach, D., Ryman, J., Jones, R., & Ryman, H. (2018). Enhancing Innovativeness: The Role of Dynamic Marketing Capabilities. Canadian Journal of Administrative Sciences, 35(4), 563–576. https://doi.org/10.1002/cjas.1473Shan, S., Luo, Y., Zhou, Y., & Wei, Y. (2019). Big data analysis adaptation and enterprises’ competitive advantages: the perspective of dynamic capability and resource-based theories. Technology Analysis and Strategic Management, 31(4), 406–420. https://doi.org/10.1080/09537325.2018.1516866Singh, S. K., & El-Kassar, A. N. (2019). Role of big data analytics in developing sustainable capabilities. Journal of Cleaner Production, 213, 1264–1273. https://doi.org/10.1016/j.jclepro.2018.12.199Teece, D. J. (2007). The Effect of Firm Compensation Structures on the Mobility and Entrepreneurship of Extreme Performers. Strategic Management Journal, 28(June), 1319–1350. https://doi.org/10.1002/smjTeece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(April 1991), 509–533. https://doi.org/10.1093/0199248540.003.0013Torres, R., Sidorova, A., & Jones, M. C. (2018). Enabling firm performance through business intelligence and analytics: A dynamic capabilities perspective. Information and Management, 55(7), 822–839. https://doi.org/10.1016/j.im.2018.03.010Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. fan, Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009Wang, C. L., & Ahmed, P. K. (2007). Dynamic capabilities: A review and research agenda. International Journal of Management Reviews, 9(1), 31–51. https://doi.org/10.1111/j.1468-2370.2007.00201.xWang, E. T. G., Hu, H. F., & Hu, P. J. H. (2013). Examining the role of information technology in cultivating firms’ dynamic marketing capabilities. Information and Management, 50(6), 336–343. https://doi.org/10.1016/j.im.2013.04.007Wang, S., Yeoh, W., Richards, G., Wong, S. F., & Chang, Y. (2019). Harnessing business analytics value through organizational absorptive capacity. Information and Management, 56(7). https://doi.org/10.1016/j.im.2019.02.007Warner, K. S. R., & Wäger, M. (2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), 326–349. https://doi.org/10.1016/j.lrp.2018.12.001Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97–121. https://doi.org/10.1509/jm.15.0413Wieczorek, A., & Pfajfar, G. (2018). DYNAMIC MARKETING CAPABILITIES. 360.Zeng, J., & Khan, Z. (2019). Value creation through big data in emerging economies: The role of resource orchestration and entrepreneurial orientation. Management Decision, 57(8), 1818–1838. https://doi.org/10.1108/MD-05-2018-0572Zollo, M., & Winter, S. G. (2002). Deliberate learning and the evolution of dynamic capabilities. Organization Science, 13(3), 339–351. https://doi.org/10.1287/orsc.13.3.339.278mohsentaheriniamahdiEsmaeiliBehroozMinaei227202326729210.66224/jict.41389.14.53.267http://jour.aicti.ir/en/Article/41389http://jour.aicti.ir/en/Article/Download/41389http://jour.aicti.ir/en/Article/Download/41389http://jour.aicti.ir/en/Article/Download/41389http://jour.aicti.ir/en/Article/Download/41389http://jour.aicti.ir/en/Article/Download/41389http://jour.aicti.ir/en/Article/Download/41389http://jour.aicti.ir/en/Article/Download/41389With the incredible development of social networks, many marketers have exploited the opportunities, and attempt to find influential people within online social networks to influence other people. This problem is known as the Influence Maximization Problem. Efficiency and effectiveness are two important criteria in the production and analysis of influence maximization algorithms. Some of researchers improved these two issues by exploiting the communities’ structure as a very useful feature of social networks. This paper aims to provide a comprehensive review of the state of the art algorithms of the influence maximization problem with special emphasis on the community detection-based approaches