jist-202605192320260519232507CMV Verlagkhoffmann@cmv-verlag.comCMV VerlagJournal of Information Systems and Telecommunication (JIST) jist2322-143731820241245SovonChakrabortyMuhammadBorhan Uddin TalukdarPortia SikdarJiaUddin318202411110.61186/jist.38322.12.45.1http://jist.ir/en/Article/38322http://jist.ir/en/Article/Download/38322http://jist.ir/en/Article/Download/38322http://jist.ir/en/Article/Download/38322http://jist.ir/en/Article/Download/38322http://jist.ir/en/Article/Download/38322http://jist.ir/en/Article/Download/38322http://jist.ir/en/Article/Download/38322[1] S. R. Bandekar and C. Vijayalakshmi, “Design and Analysis of Machine Learning Algorithms for the reduction of crime rates in India,” Procedia Computer Science, vol. 172. Elsevier BV, pp. 122–127, 2020. doi: 10.1016/j.procs.2020.05.018. [2] M. Pavel Rahman, A. K. M. Ifranul Hoque, Md. Faysal Ahmed, I. Iftekhirul, A. Alam, and N. Hossain, “Bangladesh Crime Reports Analysis and Prediction,” 2021 International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM). IEEE, Aug. 2021. doi: 10.1109/icsecs52883.2021.00089. [3] A. H. Mohd Hanif, N. Maarop, N. Kamaruddin, and G. N. Samy, “Machine Learning Approach in Predicting Fraudulent Job Advertisement,” International Journal of Academic Research in Business and Social Sciences, vol. 14, no. 1. Human Resources Management Academic Research Society (HRMARS), Jan. 12, 2024. doi: 10.6007/ijarbss/v14-i1/20532. [4] A. Alzubaidi, “Measuring the level of cyber-security awareness for cybercrime in Saudi Arabia,” Heliyon, vol. 7, no. 1. Elsevier BV, p. e06016, Jan. 2021. doi: 10.1016/j.heliyon.2021.e06016. [5] S. Lal, L. Tiwari, R. Ranjan, A. Verma, N. Sardana, and R. Mourya, “Analysis and Classification of Crime Tweets,” Procedia Computer Science, vol. 167. Elsevier BV, pp. 1911–1919, 2020. doi: 10.1016/j.procs.2020.03.211. [6] A. A. Biswas and S. Basak, “Forecasting the Trends and Patterns of Crime in Bangladesh using Machine Learning Model,” 2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT). IEEE, Sep. 2019. doi: 10.1109/icct46177.2019.8969031. [7] F. M. J. Mehedi Shamrat et al., “Sentiment analysis on twitter tweets about COVID-19 vaccines usi ng NLP and supervised KNN classification algorithm,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 23, no. 1. Institute of Advanced Engineering and Science, p. 463, Jul. 01, 2021. doi: 10.11591/ijeecs.v23.i1.pp463-470. [8] S. Aghababaei and M. Makrehchi, “Mining Social Media Content for Crime Prediction,” 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI). IEEE, Oct. 2016. doi: 10.1109/wi.2016.0089. [9] W. Li, L. Zhu, Y. Shi, K. Guo, and E. Cambria, “User reviews: Sentiment analysis using lexicon integrated two-channel CNN–LSTM family models,” Applied Soft Computing, vol. 94. Elsevier BV, p. 106435, Sep. 2020. doi: 10.1016/j.asoc.2020.106435. [10] Rahman, S., Hemel, J. N., Anta, S. J. A., Al Muhee, H., & Uddin, J. (2018, June). Sentiment analysis using R: An approach to correlate cryptocurrency price fluctuations with change in user sentiment using machine learning. In 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR) (pp. 492-497). IEEE. [11] S. Rahman, J. N. Hemel, S. J. A. Anta, H. Al Muhee, and J. Uddin, “Sentiment analysis using R: An approach to correlate cryptocurrency price fluctuations with change in user sentiment using machine learning,” In Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), 2018, pp. 492-497. [12] M. M. Rahman, Md. Aktaruzzaman Pramanik, R. Sadik, M. Roy, and P. Chakraborty, “Bangla Documents Classification using Transformer Based Deep Learning Models,” 2020 2nd International Conference on Sustainable Technologies for Industry 4.0 (STI). IEEE, Dec. 19, 2020. doi: 10.1109/sti50764.2020.9350394. [13] M. Singh, A. K. Jakhar, and S. Pandey, “Sentiment analysis on the impact of coronavirus in social life using the BERT model,” Social Network Analysis and Mining, vol. 11, no. 1. Springer Science and Business Media LLC, Mar. 19, 2021. doi: 10.1007/s13278-021-00737-z. [14] Z. Gao, A. Feng, X. Song, and X. Wu, “Target-Dependent Sentiment Classification With BERT,” IEEE Access, vol. 7. Institute of Electrical and Electronics Engineers (IEEE), pp. 154290–154299, 2019. doi: 10.1109/access.2019.2946594. [15] C. Sun, L. Huang, and X. Qiu, “Utilizing,” Proceedings of the 2019 Conference of the North. Association for Computational Linguistics, 2019. doi: 10.18653/v1/n19-1035. [16] S. Xie, J. Cao, Z. Wu, K. Liu, X. Tao, and H. Xie, “Sentiment Analysis of Chinese E-commerce Reviews Based on BERT,” 2020 IEEE 18th International Conference on Industrial Informatics (INDIN). IEEE, Jul. 20, 2020. doi: 10.1109/indin45582.2020.9442190. [17] Biswas, A., Chakraborty, S., Rifat, A. N. M. Y., Chowdhury, N. F., & Uddin, J. (2020, August). Comparative Analysis of Dimension Reduction Techniques Over Classification Algorithms for Speech Emotion Recognition. In International Conference for Emerging Technologies in Computing (pp. 170-184). Springer, Cham. [18] S. Thurner, R. Hanel, B. Liu and B. Corominas-Murtra “Understading Zipf’s law of word frequencies through sample space collapse in sentence formation,” Journal of The Royal Society Interface, vol. 12, no. 108, The Royal Society, p. 2-150330, Jul 2015, doi: 10.1098/rsif.2015.0330. [19] S. Nakagawa, P. C. D. Johnson, and H. Schielzeth, “The coefficient of determination R 2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded,” Journal of The Royal Society Interface, vol. 14, no. 134. The Royal Society, p. 20170213, Sep. 2017. doi: 10.1098/rsif.2017.0213. [20] H. Jing, C. Wang, L. Cheng, J. Qi, S. Jiang, and X. Zhang, “Automatic Development of Knowledge Graph Based on NLTK and Sentence Analysis,” 2021 3rd International Conference on Natural Language Processing (ICNLP). IEEE, Mar. 2021. doi: 10.1109/icnlp52887.2021.00015. [21] S. Ezhilarasi and P. U. Maheswari, “Depicting a Neural Model for Lemmatization and POS Tagging of Words from Palaeographic Stone Inscriptions,” 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, May 06, 2021. doi: 10.1109/iciccs51141.2021.9432315. [22] G. Y. Annum, “A Basic Strategy for Incorporating Lecture Notes with Audio-Visuals of Practical Activities to Foster Online Electronic Learning Implementation in Studio or Laboratory-Based Institutions,” Creative Education, vol. 14, no. 07. Scientific Research Publishing, Inc., pp. 1421–1439, 2023. doi: 10.4236/ce.2023.147090. [23] Lu, S., Wang, M., Liang, S., Lin, J., & Wang, Z. (2020, September). Hardware accelerator for multi-head attention and position-wise feed-forward in the transformer. In 2020 IEEE 33rd International System-on-Chip Conference (SOCC) (pp. 84-89). IEEE.25. M. A. Rahman and E. Kumar Dey, “Datasets for aspect-based sentiment analysis in bangla and its baseline evaluation,” Data, vol. 3, no. 2, pp. 1-15. [24] S. Chowdhury and W. Chowdhury, “Performing sentiment analysis in Bangla microblog posts,” 2014 International Conference on Informatics, Electronics & Vision (ICIEV). IEEE, May 2014. doi: 10.1109/iciev.2014.6850712. [25] M. H. Munna, M. R. I. Rifat, and A. S. M. Badrudduza, “Sentiment Analysis and Product Review Classification in E-commerce Platform,” 2020 23rd International Conference on Computer and Information Technology (ICCIT). IEEE, Dec. 19, 2020. doi: 10.1109/iccit51783.2020.9392710. [26] Md. H. Alam, M.-M. Rahoman, and Md. A. K. Azad, “Sentiment analysis for Bangla sentences using convolutional neural network,” 2017 20th International Conference of Computer and Information Technology (ICCIT). IEEE, Dec. 2017. doi: 10.1109/iccitechn.2017.8281840. [27] D. Sharma, M. Sabharwal, V. Goyal, and M. Vij, “Sentiment Analysis Techniques for Social Media Data: A Review,” First International Conference on Sustainable Technologies for Computational Intelligence. Springer Singapore, pp. 75–90, Nov. 02, 2019. doi: 10.1007/978-981-15-0029-9_7. Elham Askari3182024121910.61186/jist.39051.12.45.12http://jist.ir/en/Article/39051http://jist.ir/en/Article/Download/39051http://jist.ir/en/Article/Download/39051http://jist.ir/en/Article/Download/39051http://jist.ir/en/Article/Download/39051http://jist.ir/en/Article/Download/39051http://jist.ir/en/Article/Download/39051http://jist.ir/en/Article/Download/39051[1] H Ganji, “General Psychology”, Tehran:Savalan, Vol.350, 1386.(Persian). [2] F Lotfi Kashani, SH Vaziri, “Child's Pathological Psychology”, Tehran:Arasbaran, Vol.344, 1395.(Persian). [3] Karlson N, Kalat J, Bridola M, N Vatson, M Rosenzevig, “Physiological Psychology: An Introduction to Behavioral”, Cognitive, and Clinical Neuroscience, Tehran:Arasbaran, Vol.468, 1394.(Persian). [4] J Ledoux, “The Emotional Brain, Fear, and the Amygdala”, Cellular and Molcular Neurobioilogy. Vol.23, 2003, pp.727-738. [5] J Lin, J Zheng, “Modulating Amygdala–Hippocampal Network Communication: A Potential Therapy for Neuropsychiatric Disorders”, Neuropsychopharmacology. Vol.43, 2018, pp.218-219. [6] A Fallah, “Fear of Nervous Attacks and Earthquakes”, Daneshmand, Vol.53(9), 1394, pp.56-60.(Persian). [7] M Miyahara, T Harada, T Ruffman, N Sadato, T Iidaka, “Functional connectivity between amygdala -and facial regions involved in recognition of facial threat”, Soc Cogn Affect Neurosci, Vol.8(2), 2013, pp.181-189. [8] J Lin, J zheng, “Modulating Amygdala–Hippocampal Network Communication: A Potential Therapy for Neuropsychiatric Disorders”, Neuropsychopharmacol, Vol.43, 2018, pp.218–219. [9] M Kim, J Shin, J Taylor, A Mattek, S Chavez, P Whalen, “Intolerance of Uncertainty Predicts Increased Striatal Volume”, Emotion, Vol.12(6), 2017, pp.895-899. [10] H Yongzhen, H Kaiqi, W Liangsheng, T Dacheng, T Tieniu, L Xuelong, “Enhanced Biologically Inspired Model”, Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, 2008, Anchorage, AK, USA. [11] Fyaghouti, S motamed, “Recognition of Facial Expression of Emotions Based on Brain Emotional Learning (BEL) Model”, Advances in Cognitive Science, Vol.20(4), 2019, pp.46-61. [12] T Serre, L Wolf, T Poggio, “Object recognition with features inspired by visual cortex”, Proceeding of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005, San Diego, CA, USA. [13] J G Mutch, D Lowe, “Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields”, International Journal of Computer Vision, Vol.80(1), 2008, PP.45–57. [14] J Morén J, C Balkenius, “A computational model of emotional learning in the amygdala”, From animals to animats, Vol.6, 2006, PP.115-124. [15] J Morén J, “Emotion and learning- a computational model of the Amygdala [Ph.D. Dissertation]’, Lund, Sweden: Lund University; 2002. [16] E Lotfi, “Mathematical modeling of emotional brain for classification problems”, Proceeding of IAM, 2013 January, Vol. 2(1), PP.60-71. [17] M Wegrzyn, M Vogt, B Kireclioglu, J Schneider, J Kissler, “Mapping the emotional face. How individual face parts contribute to successful emotion recognition”, PLOS ONE, Vol.12(5), 2017. [18] Y Sima, J Yi, A Chen, Z Jin, “Automatic expression recognition of face image sequence based on key-frame generation and differential emotion feature”, Applied Soft Computing, Vol.113, 2021. [19] R Campbell, K Elgar, J Kuntsi, R Akers, J Terstegge, M Coleman, D Skuse, The classification of 'fear' from faces is associated with face recognition skill in women”, Neuropsychologia, Vol.40(6), 2002. [20] M Rinck, M A. Primbs, I A. M. Verpaalen, G Bijlstra, “Face masks impair facial emotion recognition and induce specifc emotion confusions”, Cognitive Research: Principles and Implications, Vol. 83, 2022. [21] W Mellouk, W Handouzi, “Facial emotion recognition using deep learning: review and insights” Proceeding of the 2nd international Workshop on the Future of Internet of Everything (FIoE), 2020 August 9-12, Leuven, Belgium. [22] D H Kim, W J Baddar, J Jang, Y M Ro, “Multi-Objective Based Spatio-Temporal Feature Representation Learning Robust to Expression Intensity Variations for Facial Expression Recognition”, IEEE Trans. Affect. Comput, Vol. 10(2), 2019, pp.223 236. [23] Z Yu, G Liu, Q Liu, J Deng, “Spatio-temporal convolutional features with nested LSTM for facial expression recognition”, Neurocomputing, Vol. 317, 2018, pp. 50 57. [24] D Liang, H Liang, Z Yu, Y Zhang, “Deep convolutional BiLSTM fusion network for facial expression recognition”, Vis. Comput, Vol. 36(3), pp. 499 508. [25] S Meshgini, A Aghagolzadeh, H Seyedarabi, “Face recognition using gabor-based direct linear discriminant analysis and support vector machine”, Computers & Electrical Engineering, Vol.39, 2013, pp.727–745. [26] C Liu, H Wechsler, “Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition”, IEEE Transactions on Image Processing, Vol.11, 2002, pp.467-476. [27] J K Kamarainen, V Kyrki, H Kalviainen, “Invariance properties of gabor filter-based features-overview and applications”, IEEE Transactions on Image Processing, Vol.15, 2006, pp.1088-1099. [28] L Shen, L Bai, M Fairhurst, “Gabor wavelets and general discriminant analysis for face identification and verification”, Image and Vision Computing, Vol.25, 2006, pp.553-563. [29] J G Daugman, “Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression”, IEEE Transactions on acoustics, speech, and signal processing, Vol.36(7), 1988, pp.1169-1179. [30] Y-L Boureau, J Ponce, Y LeCun, “A theoretical analysis of feature pooling in visual recognition”, Proceedings of the 27th international conference on machine learning (ICML-10), 2010, Haifa, Israel. [31] S M Tabatabaii, T M Nazeri, M Dastorani, “Archive of SID Performance comparison of GP, ANN, BCSD and SVM models for temperature simulation”, Journal of Meteorology and Atmospheric Sciences, Vol.1(1), 2018, pp.53-64. (Persian). [32] N Sebe, M.S Lew, I Cohen, A Garg, T.S Huang, “Emotion recognition using a Cauchy Naive Bayes classifier”, Proceeding of 16th International Conference on Pattern Recognition, 2002 August.AbolfazlEsfandiJavadAkbari TorkestaniAbbasKarimiFaranehZarafshan3182024294010.61186/jist.39109.12.45.29http://jist.ir/en/Article/39109http://jist.ir/en/Article/Download/39109http://jist.ir/en/Article/Download/39109http://jist.ir/en/Article/Download/39109http://jist.ir/en/Article/Download/39109http://jist.ir/en/Article/Download/39109http://jist.ir/en/Article/Download/39109http://jist.ir/en/Article/Download/39109[1] B. Varghese, and R. Buyya, “Next generation cloud computing: New trends and research directions,” Future Generation Computer Systems, vol. 79, pp. 849-861, 2018. [2] N. Moganarangan, R. Babukarthik, S. Bhuvaneswari et al., “A novel algorithm for reducing energy-consumption in cloud computing environment: Web service computing approach,” Journal of King Saud University-Computer and Information Sciences, vol. 28, no. 1, pp. 55-67, 2016. [3] A. Ghaffari, and A. Mahdavi, “Embedding Virtual Machines in Cloud Computing Based on Big Bang–Big Crunch Algorithm,” Journal of Information Systems and Telecommunication (JIST), vol. 28, no. 7, pp. 305-315, 2020. [4] L. F. Bittencourt, A. Goldman, E. R. Madeira et al., “Scheduling in distributed systems: A cloud computing perspective,” Computer science review, vol. 30, pp. 31-54, 2018. [5] N. Mansouri, and M. M. Javidi, “Cost-based job scheduling strategy in cloud computing environments,” Distributed and Parallel Databases, pp. 1-36, 2019. [6] U. Bhoi, and P. N. Ramanuj, “Enhanced max-min task scheduling algorithm in cloud computing,” International Journal of Application or Innovation in Engineering and Management (IJAIEM), vol. 2, no. 4, pp. 259-264, 2013. [7] Y. Mao, X. Chen, and X. Li, "Max–min task scheduling algorithm for load balance in cloud computing." pp. 457-465, 2014. [8] S. A. Hamad, and F. A. Omara, “Genetic-based task scheduling algorithm in cloud computing environment,” International Journal of Advanced Computer Science and Applications, vol. 7, no. 4, pp. 550-556, 2016. [9] A. Kaleeswaran, V. Ramasamy, and P. Vivekanandan, “Dynamic scheduling of data using genetic algorithm in cloud computing,” International Journal of Advances in Engineering & Technology, vol. 5, no. 2, pp. 327, 2013. [10] H. Aziza, and S. Krichen, “Bi-objective decision support system for task-scheduling based on genetic algorithm in cloud computing,” Computing, vol. 100, no. 2, pp. 65-91, 2018. [11] B. Keshanchi, A. Souri, and N. J. Navimipour, “An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing,” Journal of Systems and Software, vol. 124, pp. 1-21, 2017. [12] H. Y. Shishido, J. C. Estrella, C. F. M. Toledo et al., “Genetic-based algorithms applied to a workflow scheduling algorithm with security and deadline constraints in clouds,” Computers & Electrical Engineering, vol. 69, pp. 378-394, 2018. [13] M. A. Tawfeek, A. El-Sisi, A. E. Keshk et al., "Cloud task scheduling based on ant colony optimization." pp. 64-69, 2013. [14] C. Z. a. P. W. C. Liu, “A Task Scheduling Algorithm Based on Genetic Algorithm and Ant Colony Optimization in Cloud Computing,” in 13th International Symposium on Distributed Computing and Applications to Business, Engineering and Science, Xi'an, China, 2014, pp. 68-72. [15] X. Wei, “Task scheduling optimization strategy using improved ant colony optimization algorithm in cloud computing,” Journal of Ambient Intelligence and Humanized Computing, 2020/10/21, 2020. [16] F. Hemasian-Etefagh, and F. Safi-Esfahani, “Dynamic scheduling applying new population grouping of whales meta-heuristic in cloud computing,” The Journal of Supercomputing, vol. 75, no. 10, pp. 6386-6450, 2019. [17] N. Manikandan, N. Gobalakrishnan, and K. Pradeep, “Bee optimization based random double adaptive whale optimization model for task scheduling in cloud computing environment,” Computer Communications, vol. 187, pp. 35-44, 2022/04/01/, 2022. [18] Z. Liu, W. Qu, W. Liu et al., “Resource preprocessing and optimal task scheduling in cloud computing environments,” Concurrency and Computation: Practice and Experience, vol. 27, no. 13, pp. 3461-3482, 2015. [19] V. Priya, and C. N. K. Babu, “Moving average fuzzy resource scheduling for virtualized cloud data services,” Computer Standards & Interfaces, vol. 50, pp. 251-257, 2017. [20] S. Zhan, and H. Huo, “Improved PSO-based task scheduling algorithm in cloud computing,” Journal of Information & Computational Science, 2012. [21] A. Kamalinia, and A. Ghaffari, “Hybrid Task Scheduling Method for Cloud Computing by Genetic and PSO Algorithms,” Journal of Information Systems and Telecommunication (JIST), vol. 16, no. 4, pp. 1-10, 2016. [22] M. Masdari, F. Salehi, M. Jalali et al., “A survey of PSO-based scheduling algorithms in cloud computing,” Journal of Network and Systems Management, vol. 25, no. 1, pp. 122-158, 2017. [23] G. Babu, and K. Krishnasamy, “Task scheduling algorithm based on Hybrid Particle Swarm Optimization in cloud computing environment,” Journal of Theoretical and Applied Information Technology, vol. 55, pp. 33-38, 2013. [24] N. Mansouri, B. M. H. Zade, and M. M. Javidi, “Hybrid task scheduling strategy for cloud computing by modified particle swarm optimization and fuzzy theory,” Computers & Industrial Engineering, vol. 130, pp. 597-633, 2019. [25] X. Chen, and D. Long, “Task scheduling of cloud computing using integrated particle swarm algorithm and ant colony algorithm,” Cluster Computing, vol. 22, no. 2, pp. 2761-2769, 2019/03/01, 2019. [26] H. Naseri, S. Azizi, and A. Abdollahpouri, “BSFS: A Bidirectional Search Algorithm for Flow Scheduling in Cloud Data Centers,” Journal of Information Systems and Telecommunication (JIST), vol. 27, no. 7, pp. 175-183, 2020. [27] M. Shojafar, S. Javanmardi, S. Abolfazli et al., “FUGE: A joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method,” Cluster Computing, vol. 18, no. 2, pp. 829-844, 2015. [28] Y. Shi, L. Luo, and H. Guang, "Research on Scheduling of Cloud Manufacturing Resources Based on Bat Algorithm and Cellular Automata." pp. 174-177, 2019. [29] M. I. Khaleel, “Efficient job scheduling paradigm based on hybrid sparrow search algorithm and differential evolution optimization for heterogeneous cloud computing platforms,” Internet of Things, vol. 22, pp. 100697, 2023/07/01/, 2023. [30] H. G. S. Phani Praveen, Negar Shahabi, Fatemeh Izanloo, “A Hybrid Gravitational Emulation Local Search-Based Algorithm for Task Scheduling in Cloud Computing,” Mathematical Problems in Engineering, 2023. [31] S. Sahoo, B. Sahoo, and A. K. Turuk, "An Energy-Efficient Scheduling Framework for Cloud Using Learning Automata." pp. 1-5, 2018. [32] A. Yazidi, I. Hassan, H. L. Hammer et al., “Achieving Fair Load Balancing by Invoking a Learning Automata-Based Two-Time-Scale Separation Paradigm,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 8, pp. 3444-3457, 2021. [33] L. Zhu, K. Huang, Y. Hu et al., “A Self-Adapting Task Scheduling Algorithm for Container Cloud Using Learning Automata,” IEEE Access, vol. 9, pp. 81236-81252, 2021. [34] S. A. Murad, A. J. M. Muzahid, Z. R. M. Azmi et al., “A review on job scheduling technique in cloud computing and priority rule based intelligent framework,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 6, Part A, pp. 2309-2331, 2022/06/01/, 2022. [35] M. Masdari, and M. Zangakani, “Efficient task and workflow scheduling in inter-cloud environments: challenges and opportunities,” The Journal of Supercomputing, vol. 76, no. 1, pp. 499-535, 2020. [36] A. Arunarani, D. Manjula, and V. Sugumaran, “Task scheduling techniques in cloud computing: A literature survey,” Future Generation Computer Systems, vol. 91, pp. 407-415, 2019. [37] M. A. Rodriguez, and R. Buyya, “A taxonomy and survey on scheduling algorithms for scientific workflows in IaaS cloud computing environments,” Concurrency and Computation: Practice and Experience, vol. 29, no. 8, pp. e4041, 2017. [38] F. Wu, Q. Wu, and Y. Tan, “Workflow scheduling in cloud: a survey,” The Journal of Supercomputing, vol. 71, no. 9, pp. 3373-3418, 2015. [39] J. Kazemi Kordestani, M. Razapoor Mirsaleh, A. Rezvanian et al., "An Introduction to Learning Automata and Optimization," Advances in Learning Automata and Intelligent Optimization, J. Kazemi Kordestani, M. R. Mirsaleh, A. Rezvanian et al., eds., pp. 1-50, Cham: Springer International Publishing, 2021. [40] K. S. Narendra, and M. A. Thathachar, “Learning automata-a survey,” IEEE Transactions on systems, man, and cybernetics, no. 4, pp. 323-334, 1974. [41] K. S. Narendra, and M. A. Thathachar, Learning automata: an introduction: Courier corporation, 2012. [42] A. Rezvanian, A. M. Saghiri, S. M. Vahidipour et al., Recent advances in learning automata: Springer, 2018. [43] M. A. L. Thathachar, and B. R. Harita, “Learning automata with changing number of actions,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 17, no. 6, pp. 1095-1100, 1987. [44] R. N. Calheiros, R. Ranjan, C. A. De Rose et al., “Cloudsim: A novel framework for modeling and simulation of cloud computing infrastructures and services,” arXiv preprint arXiv:0903.2525, 2009. [45] R. N. Calheiros, R. Ranjan, A. Beloglazov et al., “CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,” Software: Practice and experience, vol. 41, no. 1, pp. 23-50, 2011. [46] R. D. Lakshmi, and N. Srinivasu, “A dynamic approach to task scheduling in cloud computing using genetic algorithm,” Journal of Theoretical & Applied Information Technology, vol. 85, no. 2, 2016. [47] J. Blazewicz, K. H. Ecker, E. Pesch et al., Scheduling Computer and Manufacturing Processes: Springer Science & Business Media, 2013.hamidjafaripourMohammadFathi3182024496110.61186/jist.40342.12.45.49http://jist.ir/en/Article/40342http://jist.ir/en/Article/Download/40342http://jist.ir/en/Article/Download/40342http://jist.ir/en/Article/Download/40342http://jist.ir/en/Article/Download/40342http://jist.ir/en/Article/Download/40342http://jist.ir/en/Article/Download/40342http://jist.ir/en/Article/Download/40342[1] Indu and R. Singh, “Trajectory planning and optimization for UAV communication: A review,” J. Discret. Math. Sci. Cryptogr., vol. 23, no. 2, pp. 475–483, 2020, doi: 10.1080/09720529.2020.1728901. [2]Y. Zeng, R. Zhang, and T. J. Lim, “Wireless communications with unmanned aerial vehicles: Opportunities and challenges,” IEEE Commun. Mag., vol. 54, no. 5, pp. 36–42, 2016, doi: 10.1109/MCOM.2016.7470933. [3] J. Ding, H. Mei, I. Chih-Lin, H. Zhang, and W. Liu, “Frontier progress of unmanned aerial vehicles optical wireless technologies,” Sensors (Switzerland), vol. 20, no. 19, pp. 1–35, 2020, doi: 10.3390/s20195476. [4] A. Al-Hourani, S. Kandeepan, and S. Lardner, “Optimal LAP altitude for maximum coverage,” IEEE Wirel. Commun. Lett., vol. 3, no. 6, pp. 569–572, 2014, doi: 10.1109/LWC.2014.2342736. [5] A. Al-Hourani, S. Chandrasekharan, G. Kaandorp, W. Glenn, A. Jamalipour, and S. Kandeepan, “Coverage and rate analysis of aerial base stations [Letter],” IEEE Trans. Aerosp. Electron. Syst., vol. 52, no. 6, pp. 3077–3081, 2016, doi: 10.1109/TAES.2016.160356. [6] S. H. Mostafavi Amjad, V. Solouk, and H. Kalbkhani, "Energy-Efficient User Pairing and Power Allocation for Granted Uplink-NOMA in UAV Communication Systems", JIST, Vol. 10, No. 4, 2022, 312-323. doi: 10.52547/jist.27369.10.40.312. [7] W. Shafik, S. M. Matinkhah, and M. Ghasemzadeh, "A Fast Machine Learning for 5G Beam Selection for Unmanned Aerial Vehicle Applications", JIST, Vol. 7, No. 4, 2019, 262-277. doi: 10.7508/jist.2019.04.003. [8] M. Alzenad, A. El-Keyi, and H. Yanikomeroglu, “3-D Placement of an Unmanned Aerial Vehicle Base Station for Maximum Coverage of Users with Different QoS Requirements,” IEEE Wirel. Commun. Lett., vol. 7, no. 1, pp. 38–41, 2018, doi: 10.1109/LWC.2017.2752161. [9] J. Lyu, Y. Zeng, R. Zhang, and T. J. Lim, “Placement Optimization of UAV-Mounted Mobile Base Stations,” IEEE Commun. Lett., vol. 21, no. 3, pp. 604–607, 2017, doi: 10.1109/LCOMM.2016.2633248. [10] R. I. Bor-Yaliniz, A. El-Keyi, and H. Yanikomeroglu, “Efficient 3-D placement of an aerial base station in next generation cellular networks,” 2016 IEEE Int. Conf. Commun. ICC 2016, 2016, doi: 10.1109/ICC.2016.7510820. [11] M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Mobile internet of things: Can UAVs provide an energy-efficient mobile architecture?,” 2016 IEEE Glob. Commun. Conf. GLOBECOM 2016 - Proc., 2016, doi: 10.1109/GLOCOM.2016.7841993. [12] S. Kumar, S. Suman, and S. De, “Backhaul and delay-aware placement of UAV-enabled base station,” INFOCOM 2018 - IEEE Conf. Comput. Commun. Work., pp. 634–639, 2018, doi: 10.1109/INFCOMW.2018.8406910. [13] M. Gruber, “Role of altitude when exploring optimal placement of UAV access points,” IEEE Wirel. Commun. Netw. Conf. WCNC, vol. 2016-Septe, no. Wcnc, 2016, doi: 10.1109/WCNC.2016.7565073. [14] Y. Chen, H. Zhang, and M. Xu, “The coverage problem in UAV network: A survey,” 5th Int. Conf. Comput. Commun. Netw. Technol. ICCCNT 2014, no. 3, pp. 3–7, 2014, doi: 10.1109/ICCCNT.2014.6963085. [15]Y. Zeng and R. Zhang, “Energy-Efficient UAV Communication with Trajectory Optimization,” IEEE Trans. Wirel. Commun., vol. 16, no. 6, 2017, doi: 10.1109/TWC.2017.2688328. [16] M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Efficient Deployment of Multiple Unmanned Aerial Vehicles for Optimal Wireless Coverage,” IEEE Commun. Lett., vol. 20, no. 8, pp. 1647–1650, 2016, doi: 10.1109/LCOMM.2016.2578312. [17] I. Valiulahi and C. Masouros, “Multi-UAV Deployment for Throughput Maximization in the Presence of Co-Channel Interference,” IEEE Internet Things J., vol. 8, no. 5, pp. 3605–3618, 2021, doi: 10.1109/JIOT.2020.3023010. [18] E. Chu, J. M. Kim, and B. C. Jung, “Interference Analysis of Directional UAV Networks: A Stochastic Geometry Approach,” Int. Conf. Ubiquitous Futur. Networks, ICUFN, vol. 2019-July, pp. 9–12, 2019, doi: 10.1109/ICUFN.2019.8806095. [19] W. Mei and R. Zhang, “Cooperative Downlink Interference Transmission and Cancellation for Cellular-Connected UAV: A Divide-and-Conquer Approach,” IEEE Trans. Commun., vol. 68, no. 2, pp. 1297–1311, 2020, doi: 10.1109/TCOMM.2019.2955953. [20] W. Lu, P. Si, G. Huang, H. Peng, S. Hu, and Y. Gao, “Interference Reducing and Resource Allocation in UAV-Powered Wireless Communication System,” 2020 Int. Wirel. Commun. Mob. Comput. IWCMC 2020, pp. 220–224, 2020, doi: 10.1109/IWCMC48107.2020.9148329. [21] A. A. Khuwaja, G. Zheng, Y. Chen, and W. Feng, “Optimum Deployment of Multiple UAVs for Coverage Area Maximization in the Presence of Co-Channel Interference,” IEEE Access, vol. 7, pp. 85203–85212, 2019, doi: 10.1109/ACCESS.2019.2924720. [22] M. Jacovic, O. Bshara, and K. R. Dandekar, “Waveform Design of UAV Data Links in Urban Environments for Interference Mitigation,” IEEE Veh. Technol. Conf., vol. 2018-Augus, pp. 1–5, 2018, doi: 10.1109/VTCFall.2018.8690581. [23] L. Zhou, X. Chen, M. Hong, S. Jin, and Q. Shi, “Efficient Resource Allocation for Multi-UAV Communication against Adjacent and Co-Channel Interference,” IEEE Trans. Veh. Technol., vol. 70, no. 10, pp. 10222–10235, 2021, doi: 10.1109/TVT.2021.3104279. [24] L. Xie, J. Xu, and Y. Zeng, “Common Throughput Maximization for UAV-Enabled Interference Channel with Wireless Powered Communications,” IEEE Trans. Commun., vol. 68, no. 5, pp. 3197–3212, 2020, doi: 10.1109/TCOMM.2020.2971488. [25] W. Tang, H. Zhang, Y. He, and M. Zhou, “Performance Analysis of Multi-Antenna UAV Networks with 3D Interference Coordination,” IEEE Trans. Wirel. Commun., 2021, doi: 10.1109/TWC.2021.3137347. [26] P. Li, L. Xie, J. Yao, and J. Xu, “Cellular-Connected UAV with Adaptive Air-to-Ground Interference Cancellation and Trajectory Optimization,” IEEE Commun. Lett., no. April, pp. 1–1, 2022, doi: 10.1109/lcomm.2022.3164905. [27] C. Gao, Z. Xue, W. Li, and W. Ren, “The influence of electromagnetic interference of HPM on UAV,” 2021 Int. Conf. Microw. Millim. Wave Technol. ICMMT 2021 - Proc., 2021, doi: 10.1109/ICMMT52847.2021.9617977. [28] T. Z. H. Ernest, A. S. Madhukumar, R. P. Sirigina, and A. K. Krishna, “Impact of Cellular Interference on Uplink UAV Communications,” IEEE Veh. Technol. Conf., vol. 2020-May, 2020, doi: 10.1109/VTC2020-Spring48590.2020.9128682. [29] J. Urama et al., “UAV-Aided Interference Assessment for Private 5G NR Deployments: Challenges and Solutions,” IEEE Commun. Mag., vol. 58, no. 8, pp. 89–95, 2020, doi: 10.1109/MCOM.001.00042.hasanAsil3182024202810.61186/jist.40851.12.45.20http://jist.ir/en/Article/40851http://jist.ir/en/Article/Download/40851http://jist.ir/en/Article/Download/40851http://jist.ir/en/Article/Download/40851http://jist.ir/en/Article/Download/40851http://jist.ir/en/Article/Download/40851http://jist.ir/en/Article/Download/40851http://jist.ir/en/Article/Download/40851[1] Saurabh gupta, Gopal Singh Tandel, Umashankar Pandey, "A Survey on Query Processing and Optimization in Relational Database Management System”, International Journal of Latest Trends in Engineering and Technology, Vol. 5 Issue 1 January 2015. [2] Amol Deshpande, Zachary Ives, and Vijay Shankar Raman. Adaptive query processing. Foundations and Trends in Databases, 1(1), 2007. To appear. January 7-10, 2007, Asilomar, California, USA. [3] Sybase, Performance and Tuning Series Query Processing and Abstract Plans, Sybase, Inc., One Sybase Drive, Dublin, CA 94568. [4] Navid mohseni, mehdi mokhtarpor, hosein shirgah,"Application of Ant Colony Algorithm in data mining”, National Conference on Emerging Trends in Computer Engineering and Data Recovery,2014. [5] Gupta, D.K.; Gupta, J.P.; Arora, Y.; Shankar, U., "Recursive Ant Colony optimization: a new technique for the estimation of function parameters from geophysical field data," Near Surface Geophysics , vol. 11, no. 3, pp.325-339. [6] Serap Ulusam Seçkiner, Yunus Eroğlu, Merve Emrullah, Türkay Dereli, Ant Colony optimization for continuous functions by using novel pheromone updating, Applied Mathematics and Computation Volume 219, Issue 9, 1 January 2013, Pages 4163-4175. [7] Xiao. M.Hu, J. ZHANG, and H. Chung, "An Intelligent Testing System Embedded with an Ant Colony Optimization Based Test Composition Method", IEEE Transactions on Systems, Man, and Cybernetics--Part C: Applications and Reviews, Vol. 39, No. 6, pp. 659-669, Dec 2009. [8] B. Pfahring, "Multi-agent search for open scheduling: adapting the Ant-Q formalism," Technical report TR-96-09, 1996. [9] A. Shmygelska, R. A. Hernández and H. H. Hoos, "An Ant Colony optimization algorithm for the 2D HP protein folding problem [permanent dead link]," Proceedings of the 3rd International Workshop on Ant Algorithms/ANTS 2002, Lecture Notes in Computer Science, vol.2463, pp.40-52, 2002. [10] Gupta, D.K.; Arora, Y.; Singh, U.K.; Gupta, J.P., "Recursive Ant Colony Optimization for estimation of parameters of a function," Recent Advances in Information Technology (RAIT), 2012 1st International Conference on, vol., no., pp.448-454, 15–17 March 2012 [11] Zhang, Y. (2013). "A Rule-Based Model for Bankruptcy Prediction Based on an Improved Genetic Ant Colony Algorithm". Mathematical Problems in Engineering. 2013: 753251. doi:10.1155/2013/753251. [12] Jevtić, A.; Melgar, I.; Andina, D. (2009). 2009 35th Annual Conference of IEEE Industrial Electronics. 35th Annual Conference of IEEE Industrial Electronics, 2009. IECON '09. pp. 3353–3358. doi:10.1109/IECON.2009.5415195. ISBN 978-1-4244-4648-3. S2CID 34664559. [13] Warner, Lars; Vogel, Ute (2008). Optimization of energy supply networks using Ant Colony optimization (PDF). Environmental Informatics and Industrial Ecology — 22nd International Conference on Informatics for Environmental Protection. Aachen, Germany: Shaker Verlag. ISBN 978-3-8322-7313-2. Retrieved 2018-10-09. [14] Zaidman, Daniel; Wolfson, Haim J. (2016-08-01). "PinaColada: peptide–inhibitor Ant Colony ad-hoc design algorithm". Bioinformatics. 32 (15): 2289–2296. doi:10.1093/bioinformatics/btw133. ISSN 1367-4803. PMID 27153578. [15] J. ZHANG, H. Chung, W. L. Lo, and T. Huang, "Extended Ant Colony Optimization Algorithm for Power Electronic Circuit Design", IEEE Transactions on Power Electronic. Vol.24,No.1, pp.147-162, Jan 2009. [16] L. Wang and Q. D. Wu, "Linear system parameters identification based on Ant system algorithm," Proceedings of the IEEE Conference on Control Applications, pp. 401-406, 2001. [17] Martins, Jean P.; Fonseca, Carlos M.; Delbem, Alexandre C. B. (25 December 2014). "On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem". Neurocomputing. 146: 17–29. doi:10.1016/j.neucom.2014.04.069. [18] L.M. Gambardella and M. Dorigo, "Solving Symmetric and Asymmetric TSPs by Ant Colonies", Proceedings of the IEEE Conference on Evolutionary Computation, ICEC96, Nagoya, Japan, May 20–22, pp. 622-627, 1996. [19] Mohammad_Reza Feizi_Derakhshi, Hasan Asil, Amir Asil, “Proposing a New Method for Query Processing Adaption in DataBase, JOURNAL OF COMPUTING,NY,USA, VOLUME 2, ISSUE 1, JANUARY 2010, ISSN 2151-961. [20] Mohammad_Reza Feizi_Derakhshi, Hasan Asil, Amir Asil " Proposing a New Method for Query Processing Adaption in Data Base " WCSET 2009: World Congress on Science, Engineering and Technology Dubai, United Arab Emirates VOLUME 37, January 28-30, 2009 ISSN 2070-3740. [21] D. Martens, M. De Backer, R. Haesen, J. VAnthienen, M. Snoeck, B. Baesens, Classification with Ant Colony Optimization, IEEE Transactions on Evolutionary Computation, volume 11, number 5, pages 651—665, 2007. [22] L.M. Gambardella and M. Dorigo, "Ant-Q: a reinforcement learning approach to the traveling salesman problem", Proceedings of ML-95, Twelfth International Conference on Machine Learning, A. Prieditis and S. Russell (Eds.), Morgan Kaufmann, pp. 252–260, 1995. [23] V. Donati, V. Darley, B. Ramachandran, "An Ant-Bidding Algorithm for Multistage Flowshop Scheduling Problem: Optimization and Phase Transitions", book chapter in Advances in Metaheuristics for Hard Optimization, Springer, ISBN 978-3-540-72959-4, pp.111-138, 2008. [24] Warner, Lars; Vogel, Ute (2008). Optimization of energy supply networks using Ant Colony optimization (PDF). Environmental Informatics and Industrial Ecology — 22nd International Conference on Informatics for Environmental Protection. Aachen, Germany: Shaker Verlag. ISBN 978-3-8322-7313-2. Retrieved 2018-10-09. [25] A. Deshpande and J. M. Hellerstein. Lifting the burden of history from adaptive query processing. In VLDB, 2004. [26] Bingsheng He, Qiong Luo,“Cache-Oblivious Query Processing” Biennial Conference on nnovative Data Systems Research (CIDR). [27] Wanhong Xu, “Xml Query Processing – SemAntic Cache System”, IJCSNS International Journal of Computer Science and Network Security, VOL.7 No.4, April 2007. [28] Elnaz zafarani , Mohammad_Reza Feizi_Derakhshi , Hasan Asil , Amir Asil “Presenting a New Method for Optimizing Join Queries Processing in Heterogeneous Distributed Databases. [29] Farhang Pedearan Moghadam, Hamid Maghsoudi, " Improved routing for load balancing in wireless sensor networks on the Internet of things, based on multiple Ant Colony algorithm", " Journal of Information Systems and Telecommunication (JIST) " Number 51, Volume 14 . [30] Farid Ahmadi ,Mohammad Pourmahmood Aghababa ,Hashem Kalbkhani, Nonlinear Regression Model Based on Fractional Bee Colony Algorithm for Loan Time Series, Journal of Information Systems and Telecommunication (JIST), 2022-04-21, Page: 141 – 1. [31] Luming Sun, Tao Ji, Cuiping Li, Hong ChenDeepO: A Learned Query Optimizer,SIGMOD '22: Proceedings of the 2022 International Conference on Management of DataJune 2022Pages 2421–2424https://doi.org/10.1145/3514221.3520167. [32] Ryan Marcus, Parimarjan Negi, Hongzi Mao, Nesime Tatbul, Mohammad Alizadeh, and Tim Kraska. 2021. Bao: Making Learned Query Optimization Practical. Proceedings of the 2021 International Conference on Management of Data (2021). [33] Kristian F. D. Rietveld and Harry A. G. Wijshoff,Redefining The Query Optimization Process,IEEE TKDE 2022,arXiv:2203.01079v1,January 2022. [34] Yakov Kuzin, Anna Smirnova, Evgeniy Slobodkin, George Chernishev, Query Processing and Optimization for a Custom Retrieval Language,Proceedings of the First Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning,October,2022. [35] Mohsin, S.A.; Younes, A.; Darwish, S.M. Dynamic Cost Ant Colony Algorithm to Optimize Query for Distributed Database Based on QuAntum-Inspired Approach. Symmetry 2021, 13, 70. https://doi.org/10.3390/sym13010070. [36] Zhiyong Ding,Study of Multi Ant Colony Genetic Algorithm in Query Optimization of Distributed Database, 2022 2nd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM),202250,10.52547/jist.16015.10.38.141. [37] Mladineo, Marko; Veza, Ivica; Gjeldum, Nikola (2017). "Solving partner selection problem in cyber-physical production networks using the HUMANT algorithm". International Journal of Production Research. 55 (9): 2506–2521. doi:10.1080/00207543.2016.1234084. S2CID 114390939. [38] Atieh Monemi Bidgoli, Hassan haghighi, Using Static Information of Programs to Partition the Input Domain in Search-based Test Data Generation, Journal of Information Systems and Telecommunication (JIST), 2021-01-13, Page: 219 – 229. [39] L. Bianchi, L.M. Gambardella et M.Dorigo, An Ant Colony optimization approach to the probabilistic traveling salesman problem, PPSN-VII, Seventh International Conference on Parallel Problem Solving from Nature, Lecture Notes in Computer Science, Springer Verlag, Berlin, Allemagne, 2002. [40] Babu Kumar Ajay Vikram Singh Parul Agarwal, AI based Computational Trust Model for Intelligent Virtual AssistAnt, Journal of Information Systems and Telecommunication (JIST), Issue 32 Vol. 8 Autumn 2020, Page: 263 - 271 10.29252/jist.8.32.263. [41] P. O’Neil and G. Graefe, “Multi-Table Joins Through Bitmapped Join Indices”, ACM SIGMOD, 1995”, WKDD2010, Phuket, Thailand, 9-10 January, 2010. [42] Rosa Karimi AdlEmail authorSeyed Mohammad Taghi Rouhani Rankoohi, "A new Ant Colony optimization-based algorithm for data allocation problem in distributed databases", Knowledge and Information Systems, September 2009, Volume 20, Issue 3, pp 349–373. [43] W. N. Chen and J. ZHANG "Ant Colony Optimization Approach to Grid Workflow Scheduling Problem with Various QoS Requirements", IEEE Transactions on Systems, Man, and Cybernetics--Part C: Applications and Reviews, Vol. 31, No. 1, pp.29-43, Jan 2009. [44] Jevtić, A.; Quintanilla-Dominguez, J.; Cortina-Janich’s, M.G.; Andina, D. (2009). Edge detection using Ant Colony search algorithm and multiscale contrast enhancement. IEEE International Conference on Systems, Man and Cybernetics, 2009. SMC 2009. pp. 2193–2198. doi:10.1109/ICSMC.2009.5345922. ISBN 978-1-4244-2793-2. S2CID 11654036. [45] O. Okobiah, S. P. MohAnty, and E. Kougianos, "Ordinary Kriging Metamodel-Assisted Ant Colony Algorithm for Fast Analog Design Optimization Archived March 4, 2016, at the Wayback Machine", in Proceedings of the 13th IEEE International Symposium on Quality Electronic Design (ISQED), pp. 458--463, 2012. [46] Gupta, D.K.; Arora, Y.; Singh, U.K.; Gupta, J.P., "Recursive Ant Colony Optimization for estimation of parameters of a function," Recent Advances in Information Technology (RAIT), 2012 1st International Conference on , vol., no., pp.448-454, 15–17 March 2012. [47] Muhammet Dursun Kaya, Hasan Asil, Dynamic Store Procedures in Database, German-Turkish Perspectives on IT and Innovation Management: Challenges and Approaches, 291-301,2018.YasmineAlmsrahadNasrollah Moghaddam Charkari3182024414810.61186/jist.40976.12.45.41http://jist.ir/en/Article/40976http://jist.ir/en/Article/Download/40976http://jist.ir/en/Article/Download/40976http://jist.ir/en/Article/Download/40976http://jist.ir/en/Article/Download/40976http://jist.ir/en/Article/Download/40976http://jist.ir/en/Article/Download/40976http://jist.ir/en/Article/Download/40976[1] M. Celliers and M. Hattingh, “A Systematic Review on Fake News Themes Reported in Literature,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2020, pp. 223–234. doi: 10.1007/978-3-030-45002-1_19. [2] X. Zhang and A. A. Ghorbani, “An overview of online fake news: Characterization, detection, and discussion,” Inf Process Manag, vol. 57, no. 2, p. 102025, 2020, doi: 10.1016/j.ipm.2019.03.004. [3] W. S. Paka, R. Bansal, A. Kaushik, S. Sengupta, and T. Chakraborty, “Cross-SEAN: A cross-stitch semi-supervised neural attention model for COVID-19 fake news detection,” Appl Soft Comput, vol. 107, p. 107393, 2021, doi: 10.1016/j.asoc.2021.107393. [4] Y. Wang et al., “Weak supervision for fake news detection via reinforcement learning,” AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, no. December 2019, pp. 516–523, 2020, doi: 10.1609/aaai.v34i01.5389. [5] N. Guimarães, Á. Figueira, and L. Torgo, “An organized review of key factors for fake news detection,” pp. 1–10, 2021, [Online]. Available: http://arxiv.org/abs/2102.13433. [6] S. Preston, A. Anderson, D. J. Robertson, M. P. Shephard, and N. Huhe, “Detecting fake news on Facebook: The role of emotional intelligence,” PLoS One, vol. 16, no. 3 March, pp. 1–13, 2021, doi: 10.1371/journal.pone.0246757. [7] P. Meel and D. K. Vishwakarma, “Fake news, rumor, information pollution in social media and web: A contemporary survey of state-of-the-arts, challenges and opportunities,” Expert Syst Appl, vol. 153, p. 112986, 2020, doi: 10.1016/j.eswa.2019.112986. [8] H. Allcott and M. Gentzkow, “Social media and fake news in the 2016 election,” Journal of Economic Perspectives, vol. 31, no. 2, pp. 211–236, 2017, doi: 10.1257/jep.31.2.211. [9] S. Raza and C. Ding, “Fake news detection based on news content and social contexts: a transformer-based approach,” Int J Data Sci Anal, vol. 13, no. 4, pp. 335–362, May 2022, doi: 10.1007/s41060-021-00302-z. [10] X. Zhang and A. A. Ghorbani, “An overview of online fake news: Characterization, detection, and discussion,” Inf Process Manag, vol. 57, no. 2, p. 102025, 2020, doi: 10.1016/j.ipm.2019.03.004. [11] N. Hoy and T. Koulouri, “A Systematic Review on the Detection of Fake News Articles,” Oct. 2021, [Online]. Available: http://arxiv.org/abs/2110.11240. [12] J. Jing, H. Wu, J. Sun, X. Fang, and H. Zhang, “Multimodal fake news detection via progressive fusion networks,” Inf Process Manag, vol. 60, no. 1, Jan. 2023, doi: 10.1016/j.ipm.2022.103120. [13] A. Biswas, D. Bhattacharya, K. Anil Kumar, and A. Professor, “DeepFake Detection using 3D-Xception Net with Discrete Fourier Transformation,” Journal of Information Systems and Telecommunication (JIST) 3, no. 35. 2021. 161. [14] S. Hangloo and B. Arora, “Combating multimodal fake news on social media: methods, datasets, and future perspective,” Multimed Syst, vol. 28, no. 6, pp. 2391–2422, Dec. 2022, doi: 10.1007/s00530-022-00966-y. [15] D. K. Vishwakarma, D. Varshney, and A. Yadav, “Detection and veracity analysis of fake news via scrapping and authenticating the web search,” Cogn Syst Res, vol. 58, pp. 217–229, Dec. 2019, doi: 10.1016/j.cogsys.2019.07.004. [16] D. Mangal and Di. K. Sharma, “Fake News Detection with Integration of Embedded Text Cues and Image Features,” ICRITO 2020 - IEEE 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), pp. 68–72, 2020, doi: 10.1109/ICRITO48877.2020.9197817. [17] A. Mahmoodzadeh, “Human Activity Recognition based on Deep Belief Network Classifier and Combination of Local and Global Features,” .” J. Inf. Syst. Telecommun 9, 2021. [18] Z. Jin, J. Cao, Y. Zhang, J. Zhou, and Q. Tian, “Novel Visual and Statistical Image Features for Microblogs News Verification,” IEEE Trans Multimedia, vol. 19, no. 3, pp. 598–608, 2017, doi: 10.1109/TMM.2016.2617078. [19] F. Marra, D. Gragnaniello, D. Cozzolino, and L. Verdoliva, “Detection of GAN-Generated Fake Images over Social Networks,” Proceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018, pp. 384–389, 2018, doi: 10.1109/MIPR.2018.00084. [20] B. Singh and D. K. Sharma, “SiteForge: Detecting and localizing forged images on microblogging platforms using deep convolutional neural network,” Comput Ind Eng, vol. 162, no. October, 2021, doi: 10.1016/j.cie.2021.107733. [21] J. Xue, Y. Wang, S. Xu, L. Shi, L. Wei, and H. Song, MVFNN: Multi-Vision Fusion Neural Network for Fake News Picture Detection, vol. 1300, no. 2018. Springer International Publishing, 2020. doi: 10.1007/978-3-030-63426-1_12. [22] C. Boididou et al., “Verifying multimedia use at MediaEval 2015,” CEUR Workshop Proc, vol. 1436, no. September, 2015. [23] I. B. K. Sudiatmika, F. Rahman, Trisno, and Suyoto, “Image forgery detection using error level analysis and deep learning,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 17, no. 2, pp. 653–659, 2019, doi: 10.12928/TELKOMNIKA.V17I2.8976. [24] N. Krawetz, “A Picture’s Worth... Digital Image Analysis and Forensics,” 2007. [Online]. Available: www.hackerfactor.com. [25] Paganini and Pierluigi, "Photo forensics: Detect Photoshop manipulation with error level analysis." Chief Information Security Officer at Bit4Id, 2013. [26] H. Farid, “Exposing Digital Forgeries from JPEG Ghosts,” IEEE transactions on information forensics and security 154-160, 4.1 .2009. [27] M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” 36th International Conference on Machine Learning, ICML 2019, vol. 2019-June, pp. 10691–10700, 2019. [28] W. and T. T. Jing Dong, “CASIA IMAGE TAMPERING DETECTION EVALUATION DATABASE Jing Dong , Wei Wang and Tieniu Tan Institute of Automation , Chinese Academy of Sciences,” pp. 422–426, 2013. [29] D. Khattar, M. Gupta, J. S. Goud, and V. Varma, “MvaE: Multimodal variational autoencoder for fake news detection,” The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, pp. 2915–2921, 2019, doi: 10.1145/3308558.3313552. [30] P. Qi, J. Cao, T. Yang, J. Guo, and J. Li, “Exploiting multi-domain visual information for fake news detection,” Proceedings - IEEE International Conference on Data Mining, ICDM, vol. 2019-Novem, no. Icdm, pp. 518–527, 2019, doi: 10.1109/ICDM.2019.00062. [31] F. Yu, Q. Liu, S. Wu, L. Wang, and T. Tan, “A Convolutional Approach for Misinformation Identification,” 2017. [Online]. Available: http://www.npr.org/2016/11/08/500686320/did-social-media-MotaharehGhavidelmeisamYadollahzadeh tabariMehdiGolsorkhtabaramiri3182024627110.61186/jist.41465.12.45.62http://jist.ir/en/Article/41465http://jist.ir/en/Article/Download/41465http://jist.ir/en/Article/Download/41465http://jist.ir/en/Article/Download/41465http://jist.ir/en/Article/Download/41465http://jist.ir/en/Article/Download/41465http://jist.ir/en/Article/Download/41465http://jist.ir/en/Article/Download/41465[1] A. Voulodimos, N. Doulamis, A. Doulami, and E. Protopapadakis, “Deep learning for computer vision: A brief review”, Computational intelligence and neuroscience, 2018. [2] M. Jahangiri, and S. Ghavami, “Hybrid fuzzy c-means clustering algorithm and multilayer perceptron for increasing the estimate accuracy of the geochemical element concentration case study: eastern zone of porphyry copper deposit of Sonajil”, Iranian Journal of Geology, Vol. 48, No. 48, pp. 0, 2019. [3] M. K. Pakhira, “A fast k-means algorithm using cluster shifting to produce compact and separate clusters”, Int J Eng, Vol. 28, No. 1, pp. 35-43, 2015. [4] M. Setnes, and U. Kaymak, “Extended fuzzy c-means with volume prototypes and cluster merging”, In Proceedings of the 6th European Conference on Intelligent Techniques and Soft Computing (EUFIT’98), 1998, pp. 1360-1364. [5] G. S. Budhi, R. Chiong, and Z. Wang, “Resampling imbalanced data to detect fake reviews using machine learning classifiers and textual-based features”, Multimedia Tools and Applications, Vol. 80, No. 9, pp. 13079-13097, 2021. [6] S. Cateni, V. Colla, A.Vignali, and M.Vannucci, “Data Pre-processing for Efficient Design of Machine Learning-Based Models to be Applied in the Steel Sector”, In Impact and Opportunities of Artificial Intelligence Techniques in the Steel Industry: Ongoing Applications, Perspectives and Future Trends, pp. 13-27, 2021. [7] Z. Abtahi, R. Sahraeian, and D. Rahmani, “A Stochastic Model for Prioritized Outpatient Scheduling in a Radiology Center”, International Journal of Engineering Transactions A: Basics, Vol. 33, No. 4, 2020. [8] J. MacQueen, “Classification and analysis of multivariate observations”, In 5th Berkeley Symp. Math. Statist. Probability, pp. 281-297, 1967. [9] T. Abukhalil, M. Patil, and T. Sobh, “A comprehensive survey on decentralized modular swarm robotic systems and deployment environments”, International Journal of Engineering (IJE), Vol. 7, No. 2, pp. 44, 2013. [10] C. Li, L. Liu, X. Sun, J. Zhao, and J. Yin,” Image segmentation based on fuzzy clustering with cellular automata and features weighting”, EURASIP Journal on Image and Video Processing, Nom. 1, pp. 1-11, 2019. [11] T. M. Silva filho, B. A. Pimentel, R. M. Souza, and A. L. Oliveira, “Hybrid methods for fuzzy clustering based on fuzzy c-means and improved particle swarm optimization”, Expert Systems with Applications, Vol. 42, No. 17-18, pp. 6315-6328, 2015. [12] S. Das, A. Abraham, and A. Konar, “Automatic clustering using an improved differential evolution algorithm”, IEEE Transactions on systems, man, and cybernetics-part A: Systems and Humans, Vol. 38, No. 1, pp. 218-237, 2007. [13] S. Paterlini and T. Krink, “Differential evolution and particle swarm optimization in partitional clustering”, Computational statistics & data analysis, Vol. 50, No. 5, pp. 1220-1247, 2006. [14] T. Niknam, M. Nayeripour, and B. B. Firouzi, “Application of a new hybrid optimization algorithm on cluster analysis”, In Proceedings of world academy of science, engineering and technology, Vol. 36, pp. 599, 2008. [15] K. Krishna, and M. N. Murty, “Genetic K-means algorithm”, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol. 29, No. 3, pp. 433-439, 1999. [16] H. Izakian, A. Abraham, and V. Snášel, “Fuzzy clustering using hybrid fuzzy c-means and fuzzy particle swarm optimization”, In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), pp. 1690-1694, 2009. [17] X. S. Yang, and S. Deb, “Cuckoo search: recent advances and applications”, Neural Computing and applications, Vol. 24, No. 1, pp. 169-174, 2014. [18] X. S.Yang, and S. Deb, “Engineering optimisation by cuckoo search” , International Journal of Mathematical Modelling and Numerical Optimisation, Vol. 1, No. 4, pp. 330-343, 2010. [19] R. Rajabioun, “Cuckoo optimization algorithm”, Applied soft computing, Vol. 11, No. 8, pp. 5508-5518, 2011. [20] H. Kahramanli, “A modified cuckoo optimization algorithm for engineering optimization”, International Journal of Future Computer and Communication, Vol. 1, No. 2, pp. 199, 2012. [21] M. Momeny, M. Agha Sarram, A.M. Latif, and R. Sheikhpour, “Improving the Architecture of Convolutional Neural Network for Classification of Images Corrupted by Impulse Noise”, Nashriyyah-i Muhandisi-i Barq va Muhandisi-i Kampyutar-i Iran, Vol. 76, No. 4, pp. 267, 2020. [22] M. Rohanian, M. Salehi, A. Darzi, and V. Ranjbar, “Convolutional Neural Networks for Sentiment Analysis in Persian Social Media”, arXiv preprint arXiv:2002.06233, 2020. [23] M. Mobini, G. Kaddoum, and M. Herceg, “Design of a SIMO deep learning-based chaos shift keying (DLCSK) communication system”, Sensors, Vol. 22, No. 1, pp. 333, 2022. [24] D. Madurasinghe, and G. K. Venayagamoorthy, “LVQ neural network for online identification of power system network branch events”, In 2020 Clemson University Power Systems Conference, 2020, pp. 1-7. [25] B. Zadrozny, and C. Elkan, “Transforming classifier scores into accurate multiclass probability estimates”, In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, 2002, pp. 694-699. [26] A. R. Mehrabian, and C. Lucas, “A novel numerical optimization algorithm inspired from weed colonization”, Ecological informatics, Vol. 1, No. 4, pp. 355-366, 2006. [27] K. J. Kim, and H. Ahn, “Using a clustering genetic algorithm to support customer segmentation for personalized recommender systems”, In International Conference on AI, Simulation, and Planning in High Autonomy Systems, 2004, pp. 409-415. [28] C. Mouton, J. C. Myburgh, and M. H. Davel, “Stride and translation invariance in CNNs.”, In Southern African Conference for Artificial Intelligence Research, 2021, pp. 267-281.HabibollahAsghariHeshaamFaili3182024728910.61186/jist.44445.12.45.72http://jist.ir/en/Article/44445http://jist.ir/en/Article/Download/44445http://jist.ir/en/Article/Download/44445http://jist.ir/en/Article/Download/44445http://jist.ir/en/Article/Download/44445http://jist.ir/en/Article/Download/44445http://jist.ir/en/Article/Download/44445http://jist.ir/en/Article/Download/44445[1] Bijankhan, M., Sheykhzadegan, J., Bahrani, M., & Ghayoomi, M. (2011). Lessons from building a Persian written corpus: Peykare. Language resources and evaluation, 45(2), 143-164. [2] Bahaadini, S., Sameti, H., & Khorram, S. (2011, September). Implementation and evaluation of statistical parametric speech synthesis methods for the Persian language. In Machine Learning for Signal Processing (MLSP), 2011 IEEE International Workshop on (pp. 1-6). IEEE. [3] Oskouipour, N. (2011). Converting Text to phoneme stream with the ability to recognizing ezafe marker and homographs applied to Persian speech synthesis. Msc. Thesis, Sharif University of Technology, Iran. [4] Maleki, J., Yaesoubi, M., & Ahrenberg, L. (2009, July). Applying Finite State Morphology to Conversion Between Roman and Perso-Arabic Writing Systems.Proceedings of the 2009 conference on Finite-State Methods and Natural Language Processing: Post-proceedings of the 7th International Workshop FSMNLP 2008 (pp. 215-223). [5] Nourian, A, Rasooli, M. S., Imany, M., and Faili, H., (2015) On the Importance of Ezafe Construction in Persian Parsing, The 53rd Annual Meeting of the Association for Computational Linguistics (ACL) and the 7h International Joint Conference on Natural Language Processing (IJCNLP), Beijing, China, July 2015., Volume 2: Short Papers: 877. [6] Asghari, H., Maleki, J., & Faili, H. (2014). A Probabilistic Approach to Persian Ezafe Recognition. 14th Conference of the European Chapter of the Association for Computational Linguistics(EACL 2014 ), 138. 26–30 April 2014, Gothenburg, Sweden. [7] Noferesti, S., and Shamsfard, M., (2014). A Hybrid Algorithm for Recognizing the Position of Ezafe Constructions in Persian Texts.International Journal of Artificial Intelligence and Interactive Multimedia (IJIMAI) 2(6): 17-25 (2014). [8] Isapour, S., Homayounpour, M. M., and Bijankhan, M. (2007). Identification of ezafe location in Persian language with Probabilistic Context Free Grammar, 13th Computer Association Conference, Kish Island, Iran. [9] Farghaly, A., (2004). Computer Processing of Arabic Script-based Languages: Current State and Future Directions. Workshop on Computational Approaches to Arabic Script-based Languages, COLING 2004, University of Geneva, Geneva, Switzerland, August 28, 2004. [10] Asgari, E., & Mofrad, M. R. (2016). Comparing Fifty Natural Languages and Twelve Genetic Languages Using Word Embedding Language Divergence (WELD) as a Quantitative Measure of Language Distance. arXiv preprint arXiv:1604.08561. [11] Marno, H., Langus, A., Omidbeigi, M., Asaadi, S., Seyed-Allaei, S., & Nespor, M. (2015). A new perspective on word order preferences: the availability of a lexicon triggers the use of SVO word order. Frontiers in Psychology, 6. [12] Moghaddam, M. D. (2001). Word order typology of Iranian languages. The Journal of Humanities of the Islamic Republic of Iran.–2001 (Spring), 8(2), 17-23. [13] Parsafar P. (2010). Syntax, Morphology, and Semantics of Ezafe. Iranian Studies [serial online]. December 2010; 43 (5): 637-666. Available in Academic Search Complete, Ipswich, MA. [14] Bögel, T., Butt, M., and Sulger, S., (2008). Urdu ezafe and the morphology-syntax interface. Proceedings of LFG08 (2008). CSLI Publications Stanford. [15] Estaji, A., and Jahangiri, N. (2006). The origin of kasre ezafe in Persian language. Journal of Persian language and literature, Vol. 47, pp. 69-82, Isfahan University, Iran. [16] Samvelian, P. (2007). The Ezafe as a head-marking inflectional affix: Evidence from Persian and Kurmanji Kurdish. Aspects of Iranian Linguistics: Papers in Honor of Mohammad Reza Bateni, 339-361. [17] Megerdoomian, K. (2000). A computational analysis of the Persian noun phrase. Memoranda in Computer and Cognitive Science MCCS-00-321, Computing Research Lab, New Mexico State University. [18] Bijankhan, M. (2005). A feasibility study on Ezafe Domain Analysis based on pattern matching method. Published by Research Institute on Culture, Art, and Communication, Tehran, Iran. [19] Zahedi, M. (1998). Design and Implementation of an Intelligent Program for Recognizing Short Vowels in Persian Text. Msc. Thesis, University of Tehran, Iran. [20] Mavvaji, V., and Eslami, M., (2012). Converting Persian Text to Phoneme Stream Based on a Syntactic Analyser. The first international conference on Persian text and speech, September 5,6, 2012, Semnan, Iran. [21] Razi, B., and Eshqi, M., (2012). Design of a POS tagger for Persian speech based on Neural Networks, 20th Conference on Electrical Engineering, 15-17 May 2012, Tehran, Iran. [22] Chen, X., Qiu, X., Zhu, C., Liu, P., & Huang, X. (2015). Long short-term memory neural networks for Chinese word segmentation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (pp. 1197-1206). [23] Ma, X., & Hovy, E. (2016). End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Vol. 1, pp. 1064-1074). [24] Cuong, N. V., Ye, N., Lee, W. S., & Chieu, H. L. (2014). Conditional random field with high-order dependencies for sequence labeling and segmentation. The Journal of Machine Learning Research, 15(1), 981-1009. [25] Farghaly, A., & Shaalan, K. (2009). Arabic natural language processing: Challenges and solutions. ACM Transactions on Asian Language Information Processing (TALIP), 8(4), 14. [26] Elshafei, M., Al-Muhtaseb, H., & Al-Ghamdi, M. (2006 a). Machine Generation of Arabic Diacritical Marks. MLMTA, 2006, 128-133. [27] Elshafei, M., Al-Muhtaseb, H., & Al-Ghamdi, M. (2006 b). Statistical methods for automatic diacritization of Arabic text. In The Saudi 18th National Computer Conference. Riyadh (Vol. 18, pp. 301-306). [28] Diab, M., Ghoneim, M., & Habash, N. (2007, September). Arabic diacritization in the context of statistical machine translation. In Proceedings of MT-Summit. [29] Habash, N., & Rambow, O. (2007, April). Arabic diacritization through full morphological tagging. In Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2007); Companion Volume, pp. 53-56, Association for Computational Linguistics. [30] Belinkov, Y., & Glass, J. (2015). Arabic diacritization with recurrent neural networks. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (pp. 2281-2285). [31] Abandah, G. A., Graves, A., Al-Shagoor, B., Arabiyat, A., Jamour, F., & Al-Taee, M. (2015). Automatic diacritization of Arabic text using recurrent neural networks. International Journal on Document Analysis and Recognition (IJDAR), 18(2), 183-197. [32] De Mareüil, P. B., Adda-Decker, M., & Gendner, V. (2003). Liaisons in French: a corpus-based study using morpho-syntactic information. In Proc. of the 15th International Congress of Phonetic Sciences. [33] Larson, R.K. (2009), Chinese as a reverse Ezafe language. Yuyanxue Luncong, Journal of Linguistics, 39: 30-85. Peking University. [34] Ling, W., Dyer, C., Black, A. W., Trancoso, I., Fermandez, R., Amir, S., ... & Luis, T. (2015). Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (pp. 1520-1530). [35] Peters, M., Ammar, W., Bhagavatula, C., & Power, R. (2017). Semi-supervised sequence tagging with bidirectional language models. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Vol. 1, pp. 1756-1765). [36] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. [37] LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989). Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4), 541-551. [38] Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016). Neural Architectures for Named Entity Recognition. In Proceedings of NAACL-HLT (pp. 260-270). [39] Koehn, P., Och, F. J., and Marcu, D. (2003). Statistical phrase-based translation. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1, NAACL ’03, pages 48–54, Stroudsburg, PA, USA. Association for Computational Linguistics. [40] Kalchbrenner, N. and Blunsom, P. (2013). Recurrent continuous translation models. In Proceedings of the ACL Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1700–1709. Association for Computational Linguistics. [41] Sutskever, I., Vinyals, O., and Le, Q. (2014). Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems (NIPS 2014). [42] Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014a). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078. [43] Hermann, K. M., & Blunsom, P. (2014). Multilingual models for compositional distributed semantics. arXiv preprint arXiv:1404.4641. [44] Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473. [45] Bijankhan, M. (2004). The Role of the Corpus in Writing a Grammar: An Introduction to a Software, Iranian Journal of Linguistics, Vol. 19, No. 2, fall and winter 2004. [46] Amiri, H., Hojjat, H., & Oroumchian, F. (2007). Investigation on a feasible corpus for Persian POS tagging. In Proceedings of the 12th International CSI Computer Conference (CSICC), 2007. [47] Mohtaj, Salar, Behnam Roshanfekr, Atefeh Zafarian, Habibollah Asghari, (2018), Parsivar: A Language Processing Toolkit for Persian, 11th edition of the Language Resources and Evaluation Conference (LREC 2018), 7-12 May 2018, Miyazaki (Japan). [48] Arppe, A. (2000). Developing a grammar checker for Swedish. In Proceedings of NODALIDA (Vol. 99, pp. 13-27). [49] Bernth, A. (1997, March). EasyEnglish: a tool for improving document quality. In Proceedings of the fifth conference on applied natural language processing (pp. 159-165). Association for Computational Linguistics. [50] Powers, D.M.W., (2011). Evaluation: from Precision, Recall, and F-measure to ROC, Informedness, Markedness, and Correlation. Journal of Machine Learning Technologies, 2(1), 37-63. [51] Bäck, T. (1996). Evolutionary algorithms in theory and practice. Oxford University Press. [52] Koehn, P., & Hoang, H. (2007, June). Factored Translation Models. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, In EMNLP-CoNLL 2007, pp. 868–876, Prague, June 2007. Association for Computational Linguistics. [53] Feng, Y., Cohn, T., & Du, X. (2014). Factored Markov translation with robust modeling. In Proceedings of the Eighteenth Conference on Computational Natural Language Learning (pp. 151-159). [54] Klein, G., Kim, Y., Deng, Y., Senellart, J., & Rush, A. (2017). OpenNMT: Open-Source Toolkit for Neural Machine Translation. Proceedings of ACL 2017, System Demonstrations, 67-72. [55] Brill, E. (1995). Transformation-based error-driven learning and natural language processing: A case study in part-of-speech tagging. Computational Linguistics, 21(4), 543-565. [56] Kahnemuyipour, Arsalan (2003). Syntactic categories and Persian stress. Natural Language & Linguistic Theory 21.2: 333-379. [57] Ghomeshi, J. (1996). Projection and Inflection: A Study of Persian Phrase Structure. Ph.D. Thesis, Graduate Department of Linguistics, University of Toronto. [58] Doostmohammadi, E., Nassajian, M., & Rahimi, A. (2020, November). Persian Ezafe Recognition Using Transformers and Its Role in Part-Of-Speech Tagging. In Findings of the Association for Computational Linguistics: EMNLP 2020 (pp. 961-971). [59] Larson, R., & Samiian, V. (2020). The Ezafe construction revisited. Advances in Iranian linguistics, 351, 173. [60] Ansari, A., Ebrahimian, Z., Toosi, R., & Akhaee, M. A. (2023, May). Persian Ezafeh Recognition using Transformer-Based Models. In 2023 9th International Conference on Web Research (ICWR) (pp. 283-288). IEEE. [61] Kharsa, R., Elnagar, A., & Yagi, S. (2024). BERT-Based Arabic Diacritization: A state-of-the-art approach for improving text accuracy and pronunciation. Expert Systems with Applications, p. 123416. [62] Lapointe, M., Kadim, A., & Dliou, A. (2023, November). Literature Review of Automatic Restoration of Arabic Diacritics. In 2023 IEEE International Conference on Advances in Data-Driven Analytics And Intelligent Systems (ADACIS) (pp. 1-5). IEEE.