ijece-140502292014050229202437CMV Verlagkhoffmann@cmv-verlag.comCMV VerlagNashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iranijece16823745632025224rezakhaleghi farRezamohammadiMohammadNassiriSakinesohrabi63202526927710.66224/ijece.42261.22.4.269http://ijece.org/fa/Article/42261http://ijece.org/fa/Article/Download/42261http://ijece.org/fa/Article/Download/42261http://ijece.org/fa/Article/Download/42261http://ijece.org/fa/Article/Download/42261http://ijece.org/fa/Article/Download/42261http://ijece.org/fa/Article/Download/42261http://ijece.org/fa/Article/Download/42261[1] Z. Jalali Khalil Abadi, N. Mansouri, and M. Khalouie, "Task scheduling in fog environment-challenges, tools & methodologies: a review," Computer Science Review, vol. 48, Article ID: 100550, May 2023. [2] R. K. Naha, et al., "Fog computing: survey of trends, architectures, requirements, and research directions," IEEE Access, vol. 6, pp. 47980-48009, 2018. [3] H. Alhussian, N. Zakaria, and A. Patel, "An unfair semi-greedy real-time multiprocessor scheduling algorithm," Computers & Electrical Engineering, vol. 50, pp. 143-165, Feb. 2016. [4] R. Thakur, G. Sikka, U. Bansal, J. Giri, and S. Mallik, "Deadline-aware and energy efficient IoT task scheduling using fuzzy logic in fog computing," Multimedia Tools and Applications, vol. 2024, 2024. [5] M. S. U. Islam, A. Kumar, and Y. C. Hu, "Context-aware scheduling in fog computing: a survey, taxonomy, challenges and future directions," J. of Network and Computer Applications, vol. 180, Article ID: 103008, Apr. 2021. [6] A. Raglin, S. Metu, S. Russell, and P. Budulas, "Implementing internet of things in a military command and control environment," in Proc. of SPIE, the Inte. Society for Optical Engineering, Article ID: 1020708, Anaheim, CA, USA, 9-13 Apr 2017. [7] Maniah, B. Soewito, F. Lumban Gaol, and E. Abdurachman, "A systematic literature review: risk analysis in cloud migration," J. of King Saud University-Computer and Information Sciences, vol. 34, no. 6, pt. B, pp. 3111-3120, Jun. 2021. [8] P. Schulz, et al., "Latency critical IoT applications in 5G: perspective on the design of radio interface and network architecture," IEEE Communications Magazine, vol. 55, no. 2, pp. 70-78, Feb. 2017. [9] P. Mach and Z. Becvar, "Mobile edge computing: a survey on architecture and computation offloading," IEEE Communications Surveys & Tutorials, vol. 19, no. 3, pp. 1628-1656, Thirdquarter 2017. [10] K. Dolui and S. K. Datta, "Comparison of edge computing implementations: fog computing, cloudlet and mobile edge computing," Global Internet of Things Summit, 6 pp., Geneva, Switzerland, 6-9 Jun. 2017. [11] C. M. Huang, et al., "V2V data offloading for cellular network based on the software defined network (SDN) inside mobile edge computing (MEC) architecture," IEEE Access, vol. 6, pp. 17741-17755, 2018. [12] D. Dey, S. Chandra, and N. Ghosh, "HessianAuth: an ECC-based distributed and efficient authentication mechanism for 6LoWPAN networked IoT devices," in Proc. of the 24th Int. Conf. on Distributed Computing and Networking, pp. 227-236, Kharagpur, India, 4-7 Jan. 2023. [13] K. Sood, S. Yu, and Y. Xiang, "Software-defined wireless networking opportunities and challenges for internet-of-things: a review," IEEE Internet of Things J., vol. 3, no. 4, pp. 453-463, Aug. 2016. [14] F. M. Talaat, H. A. Ali, M. S. Saraya, and A. I. Saleh, "Effective scheduling algorithm for load balancing in fog environment using CNN and MPSO," Knowledge and Information Systems, vol. 64, pp. 773-797, 2022. [15] A. Najafizadeh, A. Salajegheh, A. M. Rahmani, and A. Sahafi, "Multi-objective task scheduling in cloud-fog computing using goal programming approach," Cluster Computing, vol. 25, no. 1, pp. 141- 165, Feb. 2022. [16] L. Mas Manchón, J. Vilaplana, J. Mateo, and F. Solsona, "A queuing theory model for fog computing," The J. of Supercomputing, vol. 78, no. 8, pp. 11138-11155, Feb. 2022. [17] I. Z. Yakubu, L. Muhammed, Z. A. Musa, Z. I. Matinja, and I. M. Adamu, "A multi agent based dynamic resource allocation in fog-cloud computing environment," Trends in Sciences, vol. 18, no. 22, Article ID: 413, 15 Nov. 2021. [18] S. Misra and N. Saha, "Detour: dynamic task offloading in software-defined fog for IoT applications," IEEE J. on Selected Areas in Communications, vol. 37, no. 5, pp. 1159-1166, May 2019. [19] M. Abbasi, E. Mohammadi Pasand, and M. R. Khosravi, "Workload allocation in IoT-fog-cloud architecture using a multi-objective genetic algorithm," J. of Grid Computing, vol. 18, no. 1, pp. 43-56, Jan. 2020. [20] K. Alwasel, et al., "IoTSim-Osmosis: a framework for modeling and simulating IoT applications over an edge-cloud continuum," J. of Systems Architecture, vol. 116, Article ID: 101956, Jun. 2021.AtenaAbidiHaniye JamahmoodiZahra Heydaran Daroogheh Amnyiehimanzabbah63202529530310.66224/ijece.43241.22.4.295http://ijece.org/fa/Article/43241http://ijece.org/fa/Article/Download/43241http://ijece.org/fa/Article/Download/43241http://ijece.org/fa/Article/Download/43241http://ijece.org/fa/Article/Download/43241http://ijece.org/fa/Article/Download/43241http://ijece.org/fa/Article/Download/43241http://ijece.org/fa/Article/Download/43241[1] M. Zreik, et al., "Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis," Med Image Anal, vol. 44, no. 3, pp. 72-85, Feb. 2018. [2] M. Q. Zhang, et al., "Clinical features of 2019 novel coronavirus pneumonia in the early stage from a fever clinic in Beijing," Case Reports, vol. 43, no. 3, pp. 215-218, May 2020. [3] R. Ouni and H. Alhichri, Cross-Dataset Domain Adaptation for the Classification of COVID-19 Using Chest Computed Tomography Images, arXiv preprint arXiv:2311.08524, 2023. [4] L. Gaur, U. Bhatia, N. Z. Jhanjhi, G. Muhammad, and M. Masud, "Medical image-based detection of COVID-19 using deep convolution neural networks," Multimedia Systems, vol. 29, pp. 1729-1738, 2023. [5] W. Zhao, Z. Zhong, X. Xie, Q. Yu, and J. Liu, "Relation between chest CT findings and clinical conditions of coronavirus disease (covid-19) pneumonia: a multicenter study," American J. of Roentgenology, vol. 214, no. 5, pp. 1072-1077, May 2020. [6] O. Gozes, et al., Rapid AI Development Cycle for the Coronavirus (Covid-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring Using Deep Learning CT Image Analysis, arXiv preprint. arXiv:2003.05037, 2020. [7] T. Ai, et al., "Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases" Radiology, vol. 296, no. 2, pp. E32-E40, Aug. 2020. [8] C. Zheng, et al., "Deep learning-based detection for COVID-19 from chest CT using weak label," IEEE Trans. on Medical Imaging, vol. 39, no. 8, pp. 2615-2625, Aug. 2020. [9‌ م. معلم و ع. ا. پویان، "کشف ناهنجاری با استفاده از کد کننده خودکار مبتنی بر بلوک‌های "LSTM، مجله مدل¬سازی در مهندسی، سال 17، شماره 56، صص. 211-191، اردیبهشت 1398. [10] L. Wang, Y. Lin, and A. Wong, "COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images," Scientific Reports, vol. 10, Article ID: 19549, 2020. [11] ع. ماروسی، ا. ذباح، م. مقربی، س. ا. یثربی و ک. لایقی، " بهبود تشخیص بیماری سرطان پستان با استفاده از سیستم استنتاج عصبی- فازی تطبیقی"، کارافن، سال 19، شماره 3، صص. 392-377، آذر 1401. [12] M. Loey, F. Smarandache, and N. E. M. Khalifa, "Within the lack of COVID-19 benchmark dataset: a novel GAN with deep transfer learning for corona-virus detection in chest X-ray images," Symmetry, vol. 12, no. 4, Article ID: 0651, 2020. [13] X. Yang, et al., "Deep learning signature based on staging CT for preoperative prediction of sentinel lymph node metastasis in breast cancer," Academic Radiology, vol. 27, no. 9, pp. 1226-1233, Sept. 2019. [14] E. Cortés and S. Sánchez, "Deep Learning Transfer with AlexNet for chest X-ray COVID-19 recognition," IEEE Latin America Trans., vol. 19, no. 6, pp. 944-951, Jun. 2021. [15] S. Akter, F. J. M. Shamrat, S. Chakraborty, A. Karim, and S. Azam, "COVID-19 detection using deep learning algorithm on chest X-ray images," Biology, vol. 10, no. 11, Article ID: 1174, 2020. [16] Y. Kaya, Z. Yiner, M. Kaya, and F. Kuncan, "A new approach to COVID-19 detection from x-ray images using angle transformation with GoogleNet and LSTM," Measurement Science and Technology, vol. 33, no. 12, Article ID: 124011, Dec. 2022. [17] I. Zabbah, K. Layeghi, and R. Ebrahimpour, "Improving the diagnosis of COVID-19 by using a combination of deep learning Models," Journal of Electrical and Computer Engineering Innovations, vol. 10, no. 2, pp. 411-424, Jul. 2020. [18] ا. ذباح، ع. ماروسی و ر. ابراهیم¬پور، "تشخیص هوشمند بیماری کوید19 با استفاده از ترکیب ویژگی های عمیق و تحلیل مولفه اصلی"، پردازش سیگنال پیشرفته، در حال انتشار. [19] V. X. Nunes, et al., "A novel web platform for covid-19 diagnosis using x-ray exams and deep learning techniques," in Proc. Int. Joint Conf. on Neural Networks, 8 pp., Shenzhen, China, 18-22 Jul. 2019. [20] P. Silva, E. Luz, G. Silva, G. Moreira, R. Silva, D. Lucio, and D. Menotti, "COVID-19 detection in CT images with deep learning: a voting-based scheme and cross-datasets analysis," Informatics in Medicine Unlocked, vol. 20, Article ID: 100427, 2020. [21] ر. نوپور، ج. شنبه¬زاده و ه. کاظمی آرپناهی، "پیشنهاد یک راهکار فناورانه موثر جهت تشخیص زودهنگام بیماری کووید-19: مطالعه مبتنی بریادگیری ماشین داده محور"¬، مجله انفورماتیک سلامت و زیست پزشکی، سال 7، شماره 1، صص. 78-68، بهار 1400.MostafaSabzekarShimaBaradaran NejadMahdiKhazaiepoorMehdiKherad63202524525810.66224/ijece.43556.22.4.245http://ijece.org/fa/Article/43556http://ijece.org/fa/Article/Download/43556http://ijece.org/fa/Article/Download/43556http://ijece.org/fa/Article/Download/43556http://ijece.org/fa/Article/Download/43556http://ijece.org/fa/Article/Download/43556http://ijece.org/fa/Article/Download/43556http://ijece.org/fa/Article/Download/43556[1] [1] A. Reihanian, M. R. Feizi-Derakhshi, and H. S. Aghdasi, "An enhanced multi-objective biogeography-based optimization for overlapping community detection in social networks with node attributes," Information Sciences, vol. 622, pp. 903-929, Apr. 2023. [2] S. K. Gupta and D. P. Singh, "Seed community identification framework for community detection over social media," Arab. J. Sci. Eng., vol. 48, no. 2, pp. 1829-1843, 2023. [3] S. Mishra, S. S. Singh, S. Mishra, and B. Biswas, "Multi-objective based unbiased community identification in dynamic social networks," Comput. Commun., vol. 214, pp. 18-32, 15 Jan. 2024. [4] M. Sabzekar, S. BaradaranNejad, and M. Khazaeipoor, "A node-centric approach for community detection in dynamic networks," J. Electr. Comput. Eng. Innov., vol. 12, no. 2, pp. 305-318, Jul. 2024. [5] X. Li, Y. Xin, C. Zhao, Y. Yang, and Y. Chen, "Graph convolutional networks for privacy metrics in online social networks," Applied Sciences, vol. 10, no. 4, Article ID: 1327, 2020. [6] Z. Ma and S. Nandy, "Community detection with contextual multilayer networks," IEEE Trans. Inf. Theory, vol. 69, no. 5, pp. 3203-3239, May 2023. [7] M. Rostami, M. Oussalah, K. Berahmand, and V. Farrahi, "Community detection algorithms in healthcare applications: a systematic review," IEEE Access, vol. 11, pp. 30247-30272, 2023. [8] F. S. Gharehchopogh, "An improved harris hawks optimization algorithm with multi-strategy for community detection in social network," J. Bionic Eng., vol. 20, pp. 1175-1197, 2023. [9] D. Jin, et al., "A survey of community detection approaches: from statistical modeling to deep learning," IEEE Trans. Knowl. Data Eng., vol. 35, no. 2, pp. 1149-1170, Feb. 2023. [10] M. Seifikar, S. Farzi, and M. Barati, "C-Blondel: an efficient louvain-based dynamic community detection algorithm," IEEE Trans. Comput. Soc. Syst., vol. 7, no. 2, pp. 308-318, Apr. 2020. [11] S. Ahajjam, M. El Haddad, and H. Badir, "A new scalable leader-community detection approach for community detection in social networks," Soc. Networks, vol. 54, pp. 41-49, Jul. 2018. [12] S. Bahadori, H. Zare, and P. Moradi, "PODCD: probabilistic overlapping dynamic community detection," Expert Syst. Appl., vol. 174, Article ID: 114650, 15 Jul. 2021. [13] C. Bothorel, V. L. Dao, and P. Lenca, "Community structure: a comparative evaluation of community detection methods," Netw. Sci., vol. 8, no. 1, pp. 1-41, Mar. 2020. [14] S. Fortunato, "Community detection in graphs," Phys. Rep., vol. 486, no. 3-5, pp. 75-174, Feb. 2010. [15] M. E. J. Newman, "Modularity and community structure in networks," in Proc. Natl. Acad. Scivol. 103, no. 23, pp. 8577-8582, Jun. 2006. [16] W. Li, X. Zhou, C. Yang, Y. Fan, Z. Wang, and Y. Liu, "Multi-objective optimization algorithm based on characteristics fusion of dynamic social networks for community discovery," Inf. Fusion, vol. 79, no. C, pp. 110-123, Mar. 2022. [17] Q. Ni, J. Guo, W. Wu, and H. Wang, "Influence-based community partition with sandwich method for social networks," IEEE Trans. Comput. Soc. Syst., vol. 10, no. 2, pp. 819-830, Apr. 2023. [18] H. Long, X. Li, X. Liu, and W. Wang, "BBTA: detecting communities incrementally from dynamic networks based on tracking of backbones and bridges," Appl. Intell., vol. 53, pp. 1084-1100, 2023. [19] H. Safdari, M. Contisciani, and C. De Bacco, "Reciprocity, community detection, and link prediction in dynamic networks," J. Phys. Complex., vol. 3, no. 1, Article ID: 15010, 2022. [20] S. Kumar, A. Mallik, and S. S. Sengar, "Community detection in complex networks using stacked autoencoders and crow search algorithm," J. Supercomput., vol. 79, no. 3, pp. 3329-3356, 2023. [21] T. Ma, Q. Liu, J. Cao, Y. Tian, A. Al-Dhelaan, and M. Al-Rodhaan, "LGIEM: global and local node influence based community detection," Futur. Gener. Comput. Syst., vol. 105, pp. 533-546, Apr. 2020. [22] T. Wang, S. Chen, X. Wang, and J. Wang, "Label propagation algorithm based on node importance," Phys. A Stat. Mech. its Appl., vol. 551, Article ID: 124137, Aug. 2020. [23] C. Li, et al., "NANI: an efficient community detection algorithm based on nested aggregation of node influence," 2017. [24] D. Zhuang, M. Chang, and M. Li, "DynaMo: dynamic community detection by incrementally maximizing modularity," IEEE Trans. Knowl. Data Eng., vol. 33, no. 5, pp 1934-1945, May 2021. [25] S. A. Seyedi, A. Lotfi, P. Moradi, and N. N. Qader, "Dynamic graph-based label propagation for density peaks clustering," Expert Syst. Appl., vol. 115, pp. 314-328, Jan. 2019. [26] F. Liu, J. Wu, C. Zhou, and J. Yang, "Evolutionary community detection in dynamic social networks," in Proc. Int. Joint Conf. on Neural Networks, 7 pp. , Budapest, Hungary, 14-19 Jul. 2019. [27] X. Su, J. Cheng, H. Yang, M. Leng, W. Zhang, and X. Chen, "IncNSA: detecting communities incrementally from time-evolving networks based on node similarity," Int. J. Mod. Phys. C, vol. 31, no. 7, Article ID: 2050094, 2020. [28] M. Cordeiro, R. P. Sarmento, and J. Gama, "Dynamic community detection in evolving networks using locality modularity optimization," Soc. Netw. Anal. Min., vol. 6, Article ID: 15, 2016. [29] J. Leskovec, J. Kleinberg, and C. Faloutsos, "Graphs over time: densification laws, shrinking diameters and possible explanations," in Proc. of the 11th ACM SIGKDD Int. Conf. on Knowledge Discovery in Data Mining, pp. 177-187, Chicago, IL, USA, 21-24 Aug. 2005. [30] A. Paranjape, A. R. Benson, and J. Leskovec, "Motifs in temporal networks," in Proc. of the 10th ACM Int. Conf. on Web Search and Data Mining, pp. 601-610, Cambridge, UK, 6-10 Dec. 2017. [31] P. Panzarasa, T. Opsahl, and K. M. Carley, "Patterns and dynamics of users' behavior and interaction: network analysis of an online community," J. Am. Soc. Inf. Sci. Technol., vol. 60, no. 5, pp. 911-932, May 2009.KimiaGhasemiZeinabMovahedi63202525926810.66224/ijece.45773.22.4.259http://ijece.org/fa/Article/45773http://ijece.org/fa/Article/Download/45773http://ijece.org/fa/Article/Download/45773http://ijece.org/fa/Article/Download/45773http://ijece.org/fa/Article/Download/45773http://ijece.org/fa/Article/Download/45773http://ijece.org/fa/Article/Download/45773http://ijece.org/fa/Article/Download/45773[1] Statista Research Department, Internet of Things - Number of Connected Devices Worldwide 2015-2025, https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/, Date Published: Nov 27, 2016, Last Access on Feb. 2024. [2] C. Mateos, K. R. Choo, and A. Zunino, "Sharpening the edge: towards improved edge computing environment for mobile and IoT applications," Future Gener. Comput. Syst., vol. 107, pp. 1130-1133, Jun. 2020. [3] W. Zhuang, Q. Ye, F. Lyu, N. Cheng, and J. Ren, "SDN/NFV-empowered future IoV with enhanced communication, computing, and caching," Proceedings of the IEEE, vol. 108, no. 2, pp. 274-291, Feb. 2020. [4] T. Zhang, Y. Xu, J. Loo, D. Yang, and L. Xiao, "Joint computation and communication design for UAV-assisted mobile edge computing in IoT," IEEE Trans. Ind. Inform., vol. 16, no. 8, pp. 5505-5516, Oct. 2020. [5] Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, "A survey on mobile edge computing: the communication perspective," IEEE Commun. Surv. Tut., vol. 19, no. 4, pp. 2322-2358, Jan. 2017. [6] X. Xiong, K. Zheng, L. Lei, and L. Hou, "Resource allocation based on deep reinforcement learning in IoT edge computing," IEEE J. Sel. Areas Commun., vol. 38, no. 6, pp. 1133-1146, Apr. 2020. [7] Z. Chang, L. Liu, X. Guo, and Q. Sheng, "Dynamic resource allocation and computation offloading for IoT fog computing system," IEEE Trans. Ind. Inform., vol. 17, no. 5, pp. 3348-3357, Jun. 2021. [8] X. Hu, K. Wong, K. Yang, and Z. Zheng, "UAV-assisted relaying and edge computing: scheduling and trajectory optimization," IEEE Trans. Wirel. Commun., vol. 18, no. 10, pp. 4738-4752, Dec. 2019. [9] M. Li, N. Cheng, J. Gao, Y. Wang, L. Zhao, and X. Shen, "Energy efficient UAV-assisted mobile edge computing: resource allocation and trajectory optimization," IEEE Trans. Veh. Technol., vol. 69, no. 3, pp. 3424-3438, Jan. 2020. [10] L. D. Nguyen, A. E. Kalør, I. L. Mayorga, and P. Popovski, "Trusted wireless monitoring based on distributed ledgers over NB-IoT connectivity," IEEE Commun. Mag., vol. 58, no. 6, pp. 77-83, Apr. 2020. [11] M. Pradhan and J. Noll, "Security, privacy, and dependability evaluation in verification and validation life cycles for military IoT systems," IEEE Commun. Mag., vol. 58, no. 8, pp. 14-20, Aug. 2020. [12] Z. Tan, H. Qu, J. Zhao, S. Zhou, and W. Wang, "UAV-aided edge/fog computing in smart IoT community for social augmented reality," IEEE Internet Things J., vol. 7, no. 6, pp. 4872-4884, Feb. 2020. [13] F. Zhou, Y. Wu, R. Q. Hu, and Y. Qian, "Computation rate maximization in UAV-enabled wireless-powered mobile-edge computing systems," IEEE J. Sel. Areas Commun., vol. 36, no. 9, pp. 1927-1941, Jun. 2018. [14] L. Li, T. Q. S. Quek, J. Ren, H. H. Yang, Z. Chen, and Y. Zhang, "An incentive-aware job offloading control framework for multi-access edge computing," IEEE Trans. Mobile Comput., vol. 20, no. 1, pp. 63-75, Jan. 2021. [15] K. Poularakis, J. Llorca, A. M. Tulino, I. J. Taylor, and L. Tassiulas, "Service placement and request routing in MEC networks with storage, computation, and communication constraints," IEEE/ACM Trans. Netw., vol. 28, no. 3, pp. 1047-1060, Apr. 2020. [16] K. Poularakis, J. Llorca, A. M. Tulino, I. J. Taylor, and L. Tassiulas, "Service placement and request routing in MEC networks with storage, computation, and communication constraints," IEEE/ACM Trans. Netw., vol. 28, no. 3, pp. 1047-1060, Apr. 2020. [17] T. Ouyang, Z. Zhou, and X. Chen, "Follow me at the edge: mobilityaware dynamic service placement for mobile edge computing," IEEE J. Sel. Areas Commun., vol. 36, no. 10, pp. 2333-2345, Sep. 2018. [18] S. Wang, Y. Guo, N. Zhang, P. Yang, A. Zhou, and X. Shen, "Delayaware microservice coordination in mobile edge computing: a reinforcement learning approach," IEEE Trans. Mobile Comput., vol. 20, no. 3, pp. 939-951, Dec. 2021. [19] Y. Liu, Y. Li, Y. Niu, and D. Jin, "Joint optimization of path planning and resource allocation in mobile edge computing," IEEE Trans. Mobile Comput., vol. 19, no. 9, pp. 2129-2144, Sept. 2020. [20] F. Guo, H. Zhang, H. Ji, X. Li, and V. C. Leung, "Joint trajectory and computation offloading optimization for UAV-assisted MEC with NOMA," in Proc. IEEE Conf. Comput. Commun. Workshops, 6 pp., Paris, France, 29 Apr.-2 May 2019. [21] S. Jeong, O. Simeone, and J. Kang, "Mobile edge computing via a UAV-mounted cloudlet: optimization of bit allocation and path planning," IEEE Trans. Veh. Technol., vol. 67, no. 3, pp. 2049-2063, Sept. 2018. [22] Y. Qian, et al., "User association and path planning for UAV-aided mobile edge computing with energy restriction," IEEE Wirel. Commun. Lett., vol. 8, no. 5, pp. 1312-1315, Apr. 2019. [23] X. Chen, et al., "Distributed computation offloading and trajectory optimization in multi-uav-enabled edge computing," IEEE Internet of Things, vol. 9, no. 20, pp. 20096-20110, May 2022. [24] Q. Ye, et al., "Learningbased computing task offloading for autonomous driving: a load balancing perspective," in Proc. IEEE Int. Conf. Commun., 6 pp., Montreal, Canada, 14-23 Jun. 2021. [25] I. A. Elgendy, S. Meshoul, and M. Hammad, "Joint task offloading, resource allocation, and load-balancing optimization in multi-UAV-aided MEC systems," Applied Sciences, vol. 13, no. 4, Article ID: 2625, Feb. 2023. [26] B. Li, et al., "Robust computation offloading and trajectory optimization for multi-UAV-assisted MEC: a multi-agent DRL approach," IEEE Internet of Things J., vol. 11, no. 3, pp. 4775-4786, Aug. 2023. [27] Z. Yu, Y. Gong, S. Gong, and Y. Guo, "Joint task offloading and resource allocation in UAV-enabled mobile edge computing," IEEE Internet Things J., vol. 7, no. 4, pp. 3147-3159, Jan. 2020. [28] Y. Wang, Z. Ru, K. Wang, and P. Huang, "Joint deployment and task scheduling optimization for large-scale mobile users in multi-UAV-enabled mobile edge computing," IEEE Trans. Cybern., vol. 50, no. 9, pp. 3984-3997, Sept. 2020. [29] A. Ranjha, G. Kaddoum, M. Rahim, and K. Dev, "URLLC in UAV-enabled multicasting systems: a dual time and energy minimization problem using UAV speed, altitude and beamwidth," Computer Communications, vol. 187, pp. 125-133, Apr. 2022. [30] N. Lin, H. Tang, L. Zhao, S. Wan, A. Hawbani, and M. Guizani, "A PDDQNLP algorithm for energy efficient computation offloading in UAV-assisted MEC," IEEE Trans. on Wireless Communications, vol. 22, no. 12, pp. 8876-8890, Dec. 2023. [31] M. Ma and Z. Wang, "Distributed offloading for multi-uav swarms in mec-assisted 5G heterogeneous networks," Drones, vol. 7, no. 4, Article ID: 226, Mar. 2023.M.YaghoubzadehA.Ebrahimi moghadamM.KhademiH.Sadoghi Yazdi63202527828610.66224/ijece.47047.22.4.278http://ijece.org/fa/Article/47047http://ijece.org/fa/Article/Download/47047http://ijece.org/fa/Article/Download/47047http://ijece.org/fa/Article/Download/47047http://ijece.org/fa/Article/Download/47047http://ijece.org/fa/Article/Download/47047http://ijece.org/fa/Article/Download/47047http://ijece.org/fa/Article/Download/47047[1] S. Usmani and J. A. Shamsi, "News sensitive stock market prediction: literature review and suggestions," Peer J. Comput. Sci., vol. 7, no. 4, Article ID: e490, 2021. [2] F. G. Monteiro and D. R. Ferreira, "How much does stock Prediction improve with sentiment analysis?" in Proc. 5th ECML PKDD Workshop, pp. 16-31, Ghent, Belgium, 18-18 Sept. 2020. [3] O. Bustos, "Stock market movement forecast: a systematic review," Expert Syst. Appl., vol. 156, Article ID: 113464, Oct. 2020. [4] K. Fataliyev, A. Chivukula, M. Prasad, and W. Liu, Stock Market Analysis with Text Data: A Review, arXiv preprint arXiv:2106.12985. [5] M. Rodríguez-Ibánez, A. Casánez-Ventura, F. Castejón-Mateos, and P.-M. Cuenca-Jiménez, "A review on sentiment analysis from social media platforms," Expert Systems with Applications, vol. 223, Article ID: 119862, 2021. [6] P. Sudhir and V. D. Suresh, "Comparative study of various approaches, applications and classifiers for sentiment analysis," in Glob. Transitions Proc., vol. 2, no. 2, pp. 205-211, Nov. 2021. [7] C. Michele, et al., "Machine learning sentiment analysis, COVID-19 news and stock market reactions," Research in International Business and Finance, vol. 64, Article ID: 101881, Jan. 2023. [8] D. Kelvin, et al., "Financial sentiment analysis: techniques and applications," ACM Computing Surveys, vol. 56, no. 9, Article ID: 220, Sept. 2024. [9] D. Nabanita, et al., "Effect of public sentiment on stock market movement prediction during the COVID-19 outbreak," Social Network Analysis and Mining, vol. 12, no. 1, Article ID: 92, 2022. [10] N. Rouf, et al., "Stock market prediction using machine learning techniques: a decade survey on methodologies, recent developments, and future directions," Electron., vol. 10, no. 21, Article ID: 2127, 2021. [11] N. Jing, Z. Wu, and H. Wang, "A hybrid model integrating deep learning with investor sentiment analysis for stock price prediction," Expert Syst. Appl., vol. 178, Article ID: 115019, Sept. 2021. [12] D. Kumar, P. K. Sarangi, and R. Verma, "A systematic review of stock market prediction using machine learning and statistical techniques," in Mater. Today Proc., vol. 49, pt. 8., pp. 3187-3191, 2022. [13] C. Chou, J. Park, and E. Chou, "Predicting stock closing price after COVID-19 based on sentiment analysis and LSTM," in Proc. IEEE Adv. Inf. Technol. Electron. Autom. Control Conf., pp. 2752-2756, Chongqing, China, 12-14 Mar. 2021. [14] M. Peji, Text Mining for Big Data Analysis in Financial Sector: A Literature Review, 2019. [15] K. Mishev, A. Gjorgjevikj, I. Vodenska, L. T. Chitkushev, and D. Trajanov, "Evaluation of sentiment analysis in finance: from lexicons to transformers," IEEE Access, vol. 8, pp. 131662-131682, 2020. [16] Y. Qi, "Stock prediction under COVID-19 based on LSTM," in Proc. IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers, pp. 93-98, Dalian, China, 4-16 Apr. 2021. [17] N. Christina and C. Tjortjis, "A methodology for stock movement prediction using sentiment analysis on Twitter and stocktwits data," in Proc. 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conf., vol. 7 pp., Preveza, Greece, 24-26 Sept.2021. [18] C. Liapis, A. Karanikola, and S. Kotsiantis, "Investigating deep stock market forecasting with sentiment analysis," Entropy, vol. 25, no. 2, Article ID: 219, Feb. 2023. [19] W. Zhaoxia, et al., "Learning-based stock trending prediction by incorporating technical indicators and social media sentiment," Cognitive Computation, vol. 15, no. 3, pp. 1092-1102, 2023. [20] N. Zaman, et al., "Stock market prediction based on machine learning and social sentiment analysis," TechRxiv. Mar. 27, 2023., DOI: 10.36227/techrxiv.22315069.v1. [21] D. Narayana, et al., "Stock price prediction using sentiment analysis and deep learning for Indian markets," arXiv preprint arXiv:2204.05783, 2022. [22] K. Jihwan, H. Kim, and S. Y. Choi, "Forecasting the S&P 500 index using mathematical-based sentiment analysis and deep learning models: a FinBERT transformer model and LSTM," Axioms, vol. 12, no. 9, Article ID: 835, Sept. 2023. [23] Q. Xiao and B. Ihnaini, "Stock trend prediction using sentiment analysis," Peer J. Computer Science, vol. 9, Article ID; e1293, 2023. [24] Y. Qiu, Z. Song, and Z. Chen, "Short-term stock trends prediction based on sentiment analysis and machine learning," Soft Comput., vol. 26, no. 5, pp. 2209-2224, Mar. 2022. [25] J. Liu, J. Leu, and S. Holst, "Stock price movement prediction based on stocktwits investor sentiment using FinBERT and ensemble SVM," Peer J. Computer Science, vol. 9, Article ID; e1403, 2023. [26] R. Ren, D. D. Wu, and D. D. Wu, "Forecasting stock market movement direction using sentiment analysis and support vector machine," IEEE Syst. J., vol. 13, no. 1, pp. 760-770, Mar. 2019.ReyhaneRashidpourAli-MohammadZareh-Bidoki63202528729410.66224/ijece.47119.22.4.287http://ijece.org/fa/Article/47119http://ijece.org/fa/Article/Download/47119http://ijece.org/fa/Article/Download/47119http://ijece.org/fa/Article/Download/47119http://ijece.org/fa/Article/Download/47119http://ijece.org/fa/Article/Download/47119http://ijece.org/fa/Article/Download/47119http://ijece.org/fa/Article/Download/47119[1] E. Convey, "Porn sneaks way back on web," The Boston Herald, vol. 28, 1996. [2] M. De Kunder, "he Size of the World Wide Web (The Internet), https://www.worldwidewebsize.com, Retrived 2024. [3] A. Shahzad, N. M. Nawi, M. Z. Rehman, and A. Khan, "An improved framework for content‐and link‐based web‐spam detection: a combined approach," Complexity, vol. 2021, Article ID: 6625739, 18 pp., 2021. [4] C. Castillo, Web Spam Collections, https://chato.cl/webspam/datasets/uk2007, Retrived 2024. [5] T. Chen and C. Guestrin, "Xgboost: a scalable tree boosting system," in Proc. ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 785-794, San Francisco, CA, USA, 13-17 Aug. 2016. [6] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: synthetic minority over-sampling technique," Artificial Intelligence Research, vol. 16, no. 1, pp. 321-357, Jan. 2002. [7] J. Liu, Y. Su, S. Lv, and C. Huang, "Detecting web spam based on novel features from web page source code," Security and Communication Networks, vol. 2020, Article ID: 6662166, 14 pp., 2020. [8] F. Asdaghi and A. Soleimani, "An effective feature selection method for web spam detection," Knowledge-Based Systems, vol. 166, pp. 198-206, Feb. 2019. [9] A. Ntoulas, M. Najork, M. Manasse, and D. Fetterly, "Detecting spam web pages through content analysis," in Proc. World Wide Web, pp. 83-92, Edinburgh, Scotland, 23-26 May 2006. [10] L. Becchetti, C. Castillo, D. Donato, S. Leonardi, and R. Baeza-Yates, "Using rank propagation and probabilistic counting for link-based spam detection," in Proc. the WebKDD, 10 pp., 2006. [11] R. Baeza-Yates, P. Boldi, and C. Castillo, "Generalizing PageRank: damping functions for link-based ranking algorithms," in Proc. Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 308-315, Seattle, WA, USA, 6-11 Aug. 2006. [12] M. Yu, J. Zhang, J. Wang, J. Gao, T. Xu, and R. Yu, "The research of spam web page detection method based on web page differentiation and concrete clusters centers," in Proc. Int. Conf. on Wireless Algorithms, Systems, and Applications, pp. 820-826, Tianjin, China, 20-22 Jun. 2018. [13] J. J. Whang, Y. S. Jeong, I. Dhillon, S. Kang, and J. Lee, "Fast asynchronous antitrust rank for web spam detection," in Proc. WSDM Workshop on Misinformation and Misbehavior Mining on the Web, 4 pp., Marina Del Rey, CA, USA, 5-9 Feb. 2018. [14] Z. Gyöngyi, H. Garcia-Molina, and J. Pedersen, "Combating web spam with trustrank," in Proc.Very Large Data Bases, vol. 30, pp. 576-587, Toronto, Canada, 31 Aug.-3 Sept. 2004. [15] M. Sobek, Pr0-Google’s Pagerank 0 Penalty, http://pr.efactory.de/e-pr0.shtml, Retrived 2024. [16] D. Liu and J. Lee, "CNN based malicious website detection by invalidating multiple web spams," IEEE Access, vol. 8, pp. 97258-97266, 2020. [17] X. Zhuang, Y. Zhu, Q. Peng, and F. Khurshid, "Using deep belief network to demote web spam," Future Generation Computer Systems, vol. 118, pp. 94-106, May 2021. [18] C. Wei, Y. Liu, M. Zhang, S. Ma, L. Ru, and K. Zhang, "Fighting against web spam: a novel propagation method based on click-through data," in Proc. Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 395-404, Portland, ON, USA, 12-16 Aug. 2012. [19] A. Heydari, M. A. Tavakoli, N. Salim, and Z. Heydari, "Detection of review spam: a survey," Expert Systems with Applications, vol. 42, no. 7, pp. 3634-3642, May 2015. [20] S. Brin and L. Page, "The anatomy of a large-scale hypertextual web search engine," Computer Networks and ISDN Systems, vol. 30, no. 1-7, pp. 107-117, Apr. 1998. [21] D. Sculley, Kaggle: Your Machine Learning and Data Science Community, https://www.kaggle.com, Retrived 2024. [22] X. Ren, Knowledge Dscovery in Data and Data-Mining, https://kdd.org/, Retrieved 2024. [23] T. Wongvorachan, S. He, and O. Bulut, "A comparison of undersampling, oversampling, and SMOTE methods for dealing with imbalanced classification in educational data mining," Information, vol. 14, no. 1, Article ID: 54, 2023. [24] Y. Zhang, L. Deng, and B. Wei, "Imbalanced data classification based on improved random-SMOTE and feature standard deviation," Mathematics, vol. 12, no. 11, Article ID: 1709, 2024.