ijece-202605192320260519230931CMV Verlagkhoffmann@cmv-verlag.comCMV VerlagNashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iranijece16823745772024221Mohammad EbrahimShenassaBehroozMinaei-Bidgoli772024303810.66224/ijece.38931.22.1.30http://ijece.org/en/Article/38931http://ijece.org/en/Article/Download/38931http://ijece.org/en/Article/Download/38931http://ijece.org/en/Article/Download/38931http://ijece.org/en/Article/Download/38931http://ijece.org/en/Article/Download/38931http://ijece.org/en/Article/Download/38931http://ijece.org/en/Article/Download/38931[1] K. Woodsend, Y. Feng, and M. Lapata, "Title generation with quasi-synchronous grammar," in Proc. EMNLP Conf. Empir. Methods Nat. Lang. Process, pp. 513-523, Cambridge, MA, USA, 9-11 Oct. 2010. [2] Y. Liu and M. Lapata, "Text summarization with pretrained encoders," in Proc. of the Conf. on Empirical Methods in Natural Language Processing and the 9th Int. Joint Conf. on Natural Language Processing, EMNLP-IJCNLP'19, pp. 3728-3738, Hong Kong, China, 3-7 Nov. 2019. [3] M. Farahani, M. Gharachorloo, and M. Manthouri, "Leveraging parsBERT and pretrained mT5 for persian abstractive text summarization," in Proc. 26th Int. Comput. Conf. Comput. Soc. Iran, CSICC'21, 6 pp., Tehran, Iran, 3-4 Mar. 2021. [4] M. E. Shenassa and B. Minaei-Bidgoli, "ElmNet: a benchmark dataset for generating headlines from Persian papers," Multimed. Tools Appl., vol. 81, no. 2, pp. 1853-1866, Jan. 2022. [5] B. Dorr, D. Zajic, and R. Schwartz, "Hedge trimmer," in Proc. of the HLT-NAACL 03 on Text Summarization Workshop, vol. 5, 8 pp., Stroudsburg, PA, USA, 31-31 May 2003. [6] L. Vanderwende, H. Suzuki, and C. Brockett, "Microsoft research at DUC2006: task-focused summarization with sentence simplification and lexical expansion," in Proc. of Document Understanding Workshop, DUC'06, pp. 70-77, New York, NY, USA, 6-8 Jun. 2006. [7] J. M. Conroy, J. D. Schlesinger, D. P. O'leary, and J. Goldstein, "Back to basics: CLASSY 2006," in Proc. of Document Understanding Workshop, DUC'06, pp. 150-158, New York, NY, USA, 6-8 Jun. 2006. [8] K. Knight and D. Marcu, "Summarization beyond sentence extraction: a probabilistic approach to sentence compression," Artif. Intell., vol. 139, no. 1, pp. 91-107, Jul. 2002. [9] M. Galley and K. McKeown, "Lexicalized markov grammars for sentence compression," in Proc. The Conf. of the North American Chapter of the Association for Computational Linguistics, Hlt-Naacl'07, pp. 180-187, 2007. [10] J. Turner and E. Charniak, "Supervised and unsupervised learning for sentence compression," in Proc. of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 290-297, Ann Arbor, MI, USA, Jun. 2005. [11] E. Alfonseca, D. Pighin, and G. Garrido, "Heady: news headline abstraction through event pattern clustering," in Proc. 51st Annu. Meet. Assoc. Comput. Linguist. Conf., ACL'13, vol. 1, pp. 1243-1253, Sofia, Bulgaria, 4-9 Aug. 2013. [12] K. Filippova, E. Alfonseca, C. A. Colmenares, L. Kaiser, and O. Vinyals, "Sentence compression by deletion with LSTMs," in Proc. Conf. on Empirical Methods in Natural Language Processing, EMNLP'15, pp. 360-368, Lisbon, Portugal, 17-21 Sept.2015. [13] W. Che, Y. Zhao, H. Guo, Z. Su, and T. Liu, "Sentence compression for aspect-based sentiment analysis," IEEE/ACM Trans. Audio Speech Lang. Process., vol. 23, no. 12, pp. 2111-2124, Dec. 2015. [14] Z. Wei, Y. Liu, C. Li, and W. Gao, "Using tweets to help sentence compression for news highlights generation," Social Media Content Analysis: Natural Language Processing and Beyond, vol. 3, pp. 309-320, Nov. 2017. [15] M. Banko, V. O. Mittal, and M. J. Witbrock, "Headline generation based on statistical translation," in Proc. of the 38th Annual Meeting on Association for Computational Linguistics, pp. 318-325, Hong Kong, China, 3-6 Oct. 2000. [16] R. Sun, Y. Zhang, M. Zhang, and D. Ji, "Event-driven headline generation," in Proc. ACL-IJCNLP 2015-53rd Annual Meeting of the Association for Computational Linguistics and the 7th Int.Joint Conf. on Natural Language Processing of the Asian Federation of Natural Language Processing, pp. 462-472, Beijing, China, 26-31 Jul. 2015. [17] S. Chopra, M. Auli, and A. M. Rush, "Abstractive sentence summarization with attentive recurrent neural networks," in Proc. of the 2016 Conf. of the North American Chapter of the Association for Computational Linguistics, pp. 93-98, San Diego, CA, USA, 12-17 Jun. 2016. [18] R. Nallapati, B. Zhou, C. dos Santos, C. Gulcehre, and B. Xiang, "Abstractive text summarization using sequence-to-sequence RNNs and Beyond," in Proc. 20th SIGNLL Conf. Comput. Nat. Lang. Learn., pp. 280-290, Berlin, Germany, 11-12 Aug. 2016. [19] A. See, P. J. Liu, and C. D. Manning, "Get to the point: summarization with pointer-generator networks," in Proc. 55th Annu. Meet. Assoc. for Comput. Linguist., vol. 1, pp. 1073-1083, Vancouver, Canada, 30 Jul.-4 Aug. 2017. [20] J. Gehring, M. Auli, D. Grangier, D. Yarats, and Y. N. Dauphin, "Convolutional sequence to sequence learning," in Proc. of the 34th Int. Conf. on Machine Learning, ICM'17L, pp. 1243-1252, Sydney, Australia, 6-11 Aug. 2017. [21] P. Kouris, G. Alexandridis, and A. Stafylopatis, "Abstractive text summarization based on deep learning and semantic content generalization," in Proc. 57th Annual Meeting of the Association for Computational Linguistics, Conf., ACL'19, pp. 5082-5092, Florence, Italy, 28 Jul.-2 Aug. 2020. [22] G. Klein, Y. Kim, Y. Deng, J. Senellart, and A. M. Rush, "OpenNMT: open-source toolkit for neural machine translation," in Proc. 55th Annual Meeting of the Association for Computational Linguistics, Proc. of System Demonstrations, ACL'17, pp. 67-72, Vancouver, Canada, 30 Jul.-4 Aug. 2017. [23] Y. Liu, et al., RoBERTa: A Robustly Optimized BERT Pretraining Approach, http://arxiv.org/abs/1907.11692, 2019. [24] C. Raffel, et al., "Exploring the limits of transfer learning with a unified text-to-text transformer," J. Mach. Learn. Res., vol. 21, pp. 1-67, 2020. [25] M. Lewis, et al., "BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension," in Proc. of the Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 7871-7880, 5-10 Jul. 2020. [26] Z. Yang, et al., "XLNet: generalized autoregressive pretraining for language understanding," in Proc. 33rd Conference on Neural Information Processing Systems, pp. 5753-5763, Vancouver, Canada, 8-14 Dec. 2019. [27] K. Song, B. Wang, Z. Feng, L. Ren, and F. Liu, "Controlling the amount of verbatim copying in abstractive summarization," in Proc. 34th AAAI Conf. on Artificial Intelligence, AAAI'20, pp. 8902-8909, New York, NY, USA, 7-12 Feb. 2020. [28] D. Bahdanau, K. H. Cho, and Y. Bengio, "Neural machine translation by jointly learning to align and translate," in Proc. 3rd Int. Conf. on Learning Representations, ICLR'15., 15 pp., San Diego, CA, USA, 7-9 May 2015. [29] J. Pennington, R. Socher, and C. D. Manning, "GloVe: global vectors for word representation," in Proc. of the Conf. on Empirical Methods in Natural Language Processing, EMNLP'14, pp. 1532-1543, Doha, Qatar.25-29 Oct. 2014. [30] A. Vaswani, et al., "Attention is all you need," in Proc. of the 31st Int. Conf. on Neural Information Processing Systems, NIPS'27, pp. 5999-6009, Long Beach, CA, USA, 4-9 Dec. 2017. [31] HAZM, "Python library for digesting Persian text," Sobhe, https://github.com/sobhe/hazm%0Ahttps://github.com/sobhe/hazm, 2014. [32] ن. غنی و ن. ریاحی، "خلاصه‌سازی چکیده‌ای متون فارسی با رویکرد مبتنی بر گراف،" مجموعه مقالات سیزدهمین کنفرانس بین‌المللی فناوری اطلاعات،کامپیوتر و مخابرات، 22 ص.، تفلیس،گرجستان، 14 آبان 1400. [33] M. Moradi, M. Dashti, and M. Samwald, "Summarization of biomedical articles using domain-specific word embeddings and graph ranking," J. Biomed. Inform., vol. 107, Article ID: 103452. Jul. 2020. [34] D. Anand and R. Wagh, "Effective deep learning approaches for summarization of legal texts," J. King Saud Univ.-Comput. Inf. Sci., vol. 34, no. 5, pp. 2141-2150, May 2022. [35] J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, "BERT: pre-training of deep bidirectional transformers for language understanding," in Proc. Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT'19, pp. 4171-4186, Minneapolis, MN, USA3-7 Jun. 2019. [36] C. Y. Lin, " ROUGE: a package for automatic evaluation of summaries," in Proc. of the Workshop on Text Summarization Branches Out, pp. 74–81, Barcelona, Spain, 25–26 July 2004. [37] T. A. Dang and N. T. T. Nguyen, "Abstractive text summarization using pointer-generator networks with pre-trained word embedding," in Proc. ACM Int. Conf. Proc. Series, pp. 473-478, Hanoi, Viet Nam4-6 Dec. 2019. [38] D. Nam, J. Yasmin, and F. Zulkernine, "Effects of pre-trained word embeddings on text-based deception detection," in Proc. IEEE 18th Int. Conf. on Dependable, Autonomic and Secure Computing, IEEE 18th Int. Conf. on Pervasive Intelligence and Computing, IEEE 6th Int. Conf. on Cloud and Big Data Computing and IEEE 5th Int. Conf. on Cybe Conf on Cyber Science and Technology Congress, pp. 437-443, Calgary, Canada, 17-22 Aug. 2020. [39] R. Weng, H. Yu, S. Huang, S. Cheng, and W. Luo, "Acquiring knowledge from pre-trained model to neural machine translation," in Proc. 34th AAAI Conf. on Artificial Intelligence, AAAI'20, pp. 9266-9273, New York, NY, USA, 7-12 Feb. 2020. [40] S. Gehrmann, Y. Deng, and A. M. Rush, "Bottom-up abstractive summarization," in Proc. of the Conf. on Empirical Methods in Natural Language Processing, EMNLP'18, pp. 4098-4109, Brussels, Belgium, 31 Oct.-4 Nov. 2018. [41] K. Ethayarajh, "How contextual are contextualized word representations? comparing the geometry of BERT, ELMO, and GPT-2 embeddings," in Proc. Conf. on Empirical Methods in Natural Language Processing and 9th Inte. Joint Conf. on Natural Language Processing, EMNLP-IJCNLP'19, pp. 55-65, Hong Kong, China, 3-7 Nov. 2019. [42] I. Beltagy, M. E. Peters, and A. Cohan, Longformer: The Long-Document Transformer, [Online] Available: http://arxiv.org/abs/2004.05150, 2020.FarnazHoseiniHamedSepehrzadeh772024122010.66224/ijece.39347.22.1.12http://ijece.org/en/Article/39347http://ijece.org/en/Article/Download/39347http://ijece.org/en/Article/Download/39347http://ijece.org/en/Article/Download/39347http://ijece.org/en/Article/Download/39347http://ijece.org/en/Article/Download/39347http://ijece.org/en/Article/Download/39347http://ijece.org/en/Article/Download/39347[1] A. Waqar, W. Tian, S. Ud Din, D. Iradukunda, and A. Aman Khan, "Classical and modern face recognition approaches: a complete review," J. of Multimedia Tools and Applications, vol. 80, no. 3, pp. 4825-4880, Jan. 2021. [2] S. Koosha and M. Amini, "Machine learning and deep learning: a review of methods and applications," J. of World Information Technology and Engineering, vol. 10, no. 7, pp. 3897-3904, May 2023. [3] ف. حسینی، ا. طبیب¬زاده لمر و سید مهدی میرکاظمی نیارق، "تشخیص چهره با داده¬های ناقص توسط شبکه عصبی کانولوشنی عمیق "(DCNN)، پذیرفته برای انتشار در مجله علمی محاسبات نرم، مرداد 1403. [4] R. Mayank Kumar and D. Kumar Singh, "A comprehensive survey on techniques to handle face identity threats: challenges and opportunities," J. of Multimedia Tools and Applications, vol. 82, no. 2, pp. 1669-1748, Jan. 2023. [5] G. Soumia, D. Benboudjema, and W. Pieczynski, "A new hybrid model of convolutional neural networks and hidden Markov chains for image classification," J. of Neural Computing and Applications, vol. 35, no. 24, pp. 17987-18002, Aug. 2023. [6] N. Yifan and W. Deng, "Federated learning for face recognition with gradient correction," J. of Proc. of the AAAI Conf. on Artificial Intelligence, vol. 36, no. 2, pp. 1999-2007, Jun. 2023. [7] R. Ramin, "Cuckoo optimization algorithm," J. of Applied Soft Computing, vol. 11, no. 8, pp. 5508-5518, Dec. 2023. [8] A. Manar Abdulkareem, "Cuckoo search algorithm: review and its application," Tikrit J. of Pure Science, vol. 26, no. 2, pp. 137-144, Apr. 2021. [9] D. Garg, P. Jain, K. Kotecha, P. Goel, and V. Varadarajan, "An efficient multi-scale anchor box approach to detect partial faces from a video sequence," J. of Big Data and Cognitive Computing, vol. 6, no. 1, pp. 1-9, Jan. 2022. [10] H. Yan, X. Wang, Y. Liu, Y. Zhang, and H. Li, "A new face detection method based on Faster RCNN," J. of Physics: Conf. Series, vol. 1754, no. 1, Article ID: 012209, 5 pp., 2021. [11] H. S. Avani, A. Turkar, and C. D. Divya, "Face detection and natural language processing system using artificial intelligence," In: G., Ranganathan, J., Chen, and A. Rocha, (eds) Inventive Communication and Computational Technologies, LNNS, vol. 89, pp. 773-780, 2020. [12] S. Tangina, M. Delowar Hossain, N. Hasan Zead, N. Alam Sarker, and J. Fardoush, "A new approach for efficient face detection using bpv algorithm based on mathematical modeling," in Proc. of Int. Joint Conf. on Computational Intelligence, pp. 345-358, Dhaka, Bangladesh, 20-21 Nov. 2020. [13] A. Seema and J. A. Kendule, "Face detection and recognition using combined DRLBP and sift features with fuzzy classifier," in Proc. of International Joint Conference on Advances in Computational Intelligence, pp. 133-143, 20-21 Nov. 2020. [14] A. Elmahmudi and H. Ugail, "Deep face recognition using imperfect facial data," J. of Future Generation Computer Systems, vol. 99, no. 1, pp. 213-225, Oct. 2019. [15] R. Vinothkanna and T. Vijayakumar, "Using contourlet transform based RBFN classifier for face detection and recognition," in Proc. Int. Conf. on ISMAC in Computational Vision and Bio-Engineering pp. 1911-1920, Palladam, India, May 2020. [16] Z. Huiling and K. Lam, "Age-invariant face recognition based on identity inference from appearance age," J. of Pattern Recognition, vol. 76, no. 1, pp. 191-202, Apr. 2018. [17] H. Sungeun, W. Im, J. Ryu, and H. S. Yang, "SSPP-DAN: deep domain adaptation network for face recognition with single sample per person," in Proc. IEEE Int. Conf. on Image Processing, pp. 825-829, Beijing, China, 17-20 Sept. 2017. [18] A. Vinay, G. Kathiresan, D. Akhil Mundroy, H. Nihar Nandan, C. Sureka, K. Balasubramanya Murthy, and S. Natarajan, "Face recognition using filtered eoh-sift," J. of Procedia Computer Science, vol. 79, no. 1, pp. 543-552, Jan. 2016. [19] S. Syed Afaq Ali, M. Bennamoun, and F. Boussaid, "Iterative deep learning for image set based face and object recognition," J. of Neurocomputing, vol. 174, no. 1, pp. 866-874, Jan. 2016. [20] A. Wadhah, W. Elhamzi, I. Charfi, and M. Atri, "A hybrid feature extraction approach for brain MRI classification based on bag-of-words," J. of Biomedical Signal Processing and Control, vol. 48, no. 1, pp. 144-152, Feb. 2019. [21] J. Brendan and H. S. Le, "Precision-recall versus accuracy and the role of large data sets," J. of Proc. of the AAAI Conf. on Artificial Intelligence, vol. 33, no. 1, pp. 4039-4048, Jul. 2019.YaserJamshidiRaziyeh SadatOkhovat772024475310.66224/ijece.39847.22.1.47http://ijece.org/en/Article/39847http://ijece.org/en/Article/Download/39847http://ijece.org/en/Article/Download/39847http://ijece.org/en/Article/Download/39847http://ijece.org/en/Article/Download/39847http://ijece.org/en/Article/Download/39847http://ijece.org/en/Article/Download/39847http://ijece.org/en/Article/Download/39847[1] M. Hassaballah, M. A. Kenk, K. Muhammad, and S. Minaee, "Vehicle detection and tracking in adverse weather using a deep learning framework," IEEE Trans. on Intelligent Transportation Systems, vol. 22, no. 7, pp. 4230-4242, Jul. 2020. [2] M. Hassaballah, M. A. Kenk, and I. M. El-Henawy, "Local binary pattern-based on-road vehicle detection in urban traffic scene," Pattern Analysis and Applications, vol. 23, no. 4, pp. 1505-1521, 2020. [3] P. Kumar Bhaskar and S. Yong, "Image processing based vehicle detection and tracking method," in Proc. Int. Conf. on Computer and Information Sciences, ICCOINS'14, 5 pp., Kuala Lumpur, Malaysia, 03-05 Jun. 2014. [4] W. Chu, Y. Liu, C. Shen, D. Cai, and X. S. Hua, "Multi-task vehicle detection with region-of-interest voting," IEEE Trans. Image Process., vol. 27, no. 1, pp. 432-441, Jan. 2018. [5] L. Zhang, et al., "Vehicle object detection based on improved retinanet," J. of Physics: Conf. Series, vol. 1757, no. 1, Article ID: 012070, 2021. [6] J. Shin, et al., "Real-time vehicle detection using deep learning scheme on embedded system," in Proc. 9th Int. Conf. on Ubiquitous and Future Networks, ICUFN'17, pp. 272-274, Milan, Italy, 4-7 Jul. 2017. [7] H. Wang, et al., "A comparative study of state-of-the-art deep learning algorithms for vehicle detection," IEEE Intelligent Transportation Systems Magazine, vol. 11, no. 2, pp. 82-95, Summer 2019. [8] H. Kuang, X. Zhang, Y. J. Li, L. L. H. Chan, and H. Yan, "Nighttime vehicle detection based on bio inspired image eenhancement and weighted score-level feature fusion," IEEE Trans. Intell. Transp. Syst., vol. 18, no. 4, pp. 927-936, Apr. 2017. [9] C. Sakaridis, D. Dai, and L. Van Gool, "Semantic foggy scene understanding with synthetic data," Int. J. Comput. Vis., vol. 126, no. 9, pp. 973-992, Sept. 2018. [10] C. Hodges, M. Bennamoun, and H. Rahmani, "Single image dehazing using deep neural networks," Pattern Recognit. Lett, vol. 128, pp. 70-77, Dec. 2019. [11] B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao, "Dehazenet: an end-to-end system for single image haze removal," IEEE Trans. on Image Processing, vol. 25, pp. 5187-5198, 2016. [12] S. Li, et al., "Single image deraining: a comprehensive benchmark analysis," in Proc. of the IEEE/CVF Conf. Comput. Vis. Pattern Recognit, pp. 3838-3847, Long Beach, CA, USA, 15-20 Jun. 2019. [13] Y. L. Chen and C. T. Hsu, "A generalized low-rank appearance model for spatio-temporally correlated rain streaks," in Proc. of the IEEE Int. Conf. on Computer Vision, pp. 1968-1975, Sydney, Australia, 1-8 Dec. 2013. [14] L. Zhu, C. W. Fu, D. Lischinski, and P. A. Heng, "Joint bi-layer optimization for single-image rain streak removal," in Proc. of the IEEE In. Conf. on Computer Vision, pp. 2526-2534, Venice, Italy, 27-29 Oct. 2017. [15] D. Sudha and J. Priyadarshini, "An intelligent multiple vehicle detection and tracking using modified vibe algorithm and deep learning algorithm," Soft Computing, vol. 24, no. 22, pp. 17417-17429, Nov. 2020. [16] X. Han, J. Chang, and K. Wang, "Real-time object detection based on YOLO-V2 for tiny vehicle object," Procedia Computer Science, vol. 183, pp. 61-72, 2021. [17] M. Shafiee, B. Chywl, F. Li, and A. Wong, Fast YOLO: A Fast You Only Look Once System for Real-Time Embedded Object Setection in Video, arXiv preprint arXiv:1709.05943, 2017. [18] S. R. Sree, S. B. Vyshnavi, and N. Jayapandian, "Real-world application of machine learning and deep learning," in Proc. of the Int. Conf. on Smart Systems and Inventive Technology, ICSSIT'19, pp. 1069-1073, Tirunelveli, India, 27-29 Nov. 2019. [19] M. A. Ponti, L. S. F. Ribeiro, T. S. Nazare, T. Bui, and J. Collomosse, "Everything you wanted to know about deep learning for computer vision but were afraid to ask," in Proc. of the 30th SIBGRAPI Conf. on Graphics, Patterns and Images Tutorials, SIBGRAPI-T'17, pp. 17-41, Niteroi, Brazil, 17-18 Oct. 2017. [20] S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: towards real-time object detection with region proposal networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, pp. 137-1149, Jun. 2017. [21] R. Girshick, "Fast R-CNN," in Proc. of the IEEE Int. Conf. on Computer Vision, ICCV'15, pp. 1440-1448, Santiago, Chile, 07-13 Dec. 2015. [22] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: unified, real-time object detection," in Proc. of the IEEE Conf. Computer Vision and Pattern Recognition, CVPR'16, pp. 779-788, Las Vegas, NV, USA, 27-30 Jun. 2016. [23] W. Liu, et al., "SSD: single shot multibox detector," in Proc. of the 14th European Conference, Computer Vision, ECCV'16, pp. 21-37, Amsterdam, The Netherlands, 11-14 Oct. 2016. [24] T. Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, "Focal loss for dense object detection," in Proc. of the IEEE Int. Conf. on Computer Vision, pp. 2980-2988, Venice, Italy, 22-29 Oct. 2017. [25] T. Y et al., "Feature pyramid networks for object detection," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 2117-2125, Honolulu, HI, USA, 21-26 Jul. 2017. [26] T. Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, "Focal loss for dense object detection," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 42, no. 2, pp. 318 - 327, Feb. 2017.Seyyed AbedHosseiniM.Houshmand772024394610.66224/ijece.40455.22.1.39http://ijece.org/en/Article/40455http://ijece.org/en/Article/Download/40455http://ijece.org/en/Article/Download/40455http://ijece.org/en/Article/Download/40455http://ijece.org/en/Article/Download/40455http://ijece.org/en/Article/Download/40455http://ijece.org/en/Article/Download/40455http://ijece.org/en/Article/Download/40455[1] A. S. A. Hans and S. Rao, "A CNN-LSTM based deep neural networks for facial emotion detection in videos," International J. of Advances in Signal and Image Sciences, vol. 7, no. 1, pp. 11-20, Jun. 2021. [2] L. Mou, et al., "Driver stress detection via multimodal fusion using attention-based CNN-LSTM," Expert Systems with Applications, vol. 173, Article ID: 114693, Jul. 2021. [3] N. S. Suhaimi, J. Mountstephens, and J. Teo, "EEG-based emotion recognition: a state-of-the-art review of current trends and opportunities," Computational Intelligence and Neuroscience, vol. 2020, Article ID: 8875426, 16 Sept. 2020. [4] Y. Kim, H. Lee, and E. M. Provost, "Deep learning for robust feature generation in audiovisual emotion recognition," in Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, pp. 3687-3691, Vancouver, Canada, 26-31 May 2013. [5] C. Herrando, J. Jiménez-Martínez, M. J. Martín-De Hoyos, and E. Constantinides, "Emotional contagion triggered by online consumer reviews: evidence from a neuroscience study," J. of Retailing and Consumer Services, vol. 67, Article ID: 102973, Jul. 2022. [6] M. Ali, A. H. Mosa, F. Al Machot, and K. Kyamakya, "EEG-based emotion recognition approach for e-healthcare applications," in Proc. 8th Int. Conf. on Ubiquitous and Future Networks, pp. 946-950, Vienna, Austria, 5-8 Jul. 2016. [7] S. A. Hosseini, M. A. Khalilzadeh, and S. Changiz, "Emotional stress recognition system for affective computing based on bio-signals," J. of Biological Systems, vol. 18, no. spec01, pp. 101-114, 2010. [8] A. Sakalle, P. Tomar, H. Bhardwaj, D. Acharya, and A. Bhardwaj, "A LSTM based deep learning network for recognizing emotions using wireless brainwave driven system," Expert Systems with Applications, vol. 173, Article ID: 114516, Jul. 2021. [9] C. Li, Z. Bao, L. Li, and Z. Zhao, "Exploring temporal representations by leveraging attention-based bidirectional LSTM-RNNs for multi-modal emotion recognition," Information Processing & Management, vol. 57, no. 3, Article ID: 102185, May 2020. [10] A. Bhattacharyya, R. K. Tripathy, L. Garg, and R. B. Pachori, "A novel multivariate-multiscale approach for computing EEG spectral and temporal complexity for human emotion recognition," IEEE Sensors J., vol. 21, no. 3, pp. 3579-3591, Feb. 2021. [11] Y. Luo, et al., "EEG-based emotion classification using spiking neural networks," IEEE Access, vol. 8, pp. 46007-46016, 2020. [12] Y. Wang, et al., "EEG-based emotion recognition with prototype-based data representation," in Proc. 41st Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, pp. 684-689, Berlin, Germany, 23-27 Jul. 2019. [13] M. M. Rahman, et al., "Recognition of human emotions using EEG signals: a review," Computers in Biology and Medicine, vol. 136 Article ID: 104696, Sept. 2021. [14] J. Yang, X. Huang, H. Wu, and X. Yang, "EEG-based emotion classification based on bidirectional long short-term memory network," Procedia Computer Science, vol. 174, pp. 491-504, 2020. [15] R. Andreasson, B. Alenljung, E. Billing, and R. Lowe, "Affective touch in human-robot interaction: conveying emotion to the nao robot," International J. of Social Robotics, vol. 10, no. 4, pp. 473-491, Dec. 2018. [16] X. Wang, Y. Ren, Z. Luo, W. He, J. Hong, and Y. Huang, "Deep learning-based EEG emotion recognition: current trends and future perspectives," Frontiers in Psychology, vol. 14, Article ID: 1126994, Feb. 2023. [17] M. K. Chowdary, J. Anitha, and D. J. Hemanth, "Emotion recognition from EEG signals using recurrent neural networks," Electronics, vol. 11, no. 15, Article ID: 2387, Jul. 2022. [18] R. C. Dhingra and S. Ram Avtar Jaswal, "Emotion recognition based on EEG using DEAP dataset," European J. of Molecular & Clinical Medicine, vol. 8, no. 3, pp. 3509-3517, 2021. [19] N. Zhuang, et al., "Emotion recognition from EEG signals using multidimensional information in EMD domain," BioMed Research International, vol. 2017, Article ID: 8317357, 2017. [20] V. M. Joshi and R. B. Ghongade, "IDEA: intellect database for emotion analysis using EEG signal," J. of King Saud University-Computer and Information Sciences, vol. 34, no. 7, pp. 4433-4447, Jul. 2022. [21] O. Atila and A. Şengür, "Attention guided 3D CNN-LSTM model for accurate speech based emotion recognition," Applied Acoustics, vol. 182, Article ID: 108260, Nov. 2021. [22] X. Zheng and W. Chen, "An attention-based bi-LSTM method for visual object classification via EEG," Biomedical Signal Processing and Control, vol. 63, Article ID: 102174, Jan. 2021. [23] D. Huang, et al., "Differences first in asymmetric brain: a bi-hemisphere discrepancy convolutional neural network for EEG emotion recognition," Neurocomputing, vol. 448, pp. 140-151, 11 Aug. 2021. [24] S. Koelstra, et al., "Deap: a database for emotion analysis; using physiological signals," IEEE Trans. on Affective Computing, vol. 3, no. 1, pp. 18-31, Jun. 2011. [25] M. Algarni, F. Saeed, T. Al-Hadhrami, F. Ghabban, and M. Al-Sarem, "Deep learning-based approach for emotion recognition using electroencephalography (EEG) signals using bi-directional long short-term memory (Bi-LSTM)," Sensors, vol. 22, no. 8, Article ID: 2976, Apr. 2022. [26] W. L. Zheng and B. L. Lu, "Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks," IEEE Trans. on Autonomous Mental Development, vol. 7, no. 3, pp. 162-175, Sep. 2015. [27] Q. Ma, M. Wang, L. Hu, L. Zhang, and Z. Hua, "A novel recurrent neural network to classify EEG signals for customers’ decision-making behavior prediction in brand extension scenario," Frontiers in Human Neuroscience, vol. 15, Article ID: 610890, Mar. 2021. [28] S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780, Nov. 1997. [29] S. Kumar, A. Sharma, and T. Tsunoda, "Brain wave classification using long short-term memory network based OPTICAL predictor," Scientific Reports, vol. 9, Article ID: 9153, Jun. 2019. [30] M. Z. I. Ahmed and N. Sinha, "EEG-based emotion classification using LSTM under new paradigm," Biomedical Physics & Engineering Express, vol. 7, no. 6, Article ID: 065018, Sept. 2021. [31] G. Liu and J. Guo, "Bidirectional LSTM with attention mechanism and convolutional layer for text classification," Neurocomputing, vol. 337, pp. 325-338, 14 Apr. 2019. [32] J. C. Nunez, R. Cabido, J. J. Pantrigo, A. S. Montemayor, and J. F. Velez, "Convolutional neural networks and long short-term memory for skeleton-based human activity and hand gesture recognition," Pattern Recognition, vol. 76, pp. 80-94, Apr. 2018. [33] K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber, "LSTM: a search space odyssey," IEEE Trans. on Neural Networks and Learning Systems, vol. 28, no. 10, pp. 2222-2232, Mar. 2015. [34] T. Shen, et al., "Disan: directional self-attention network for rnn/cnn-free language understanding," in Proc. of the AAAI Conf. on Artificial Intelligence, pp. 5446-5455, New Orleans, LA, USA, 2-7 Feb. 2018. [35] S. Mirsamadi, E. Barsoum, and C. Zhang, "Automatic speech emotion recognition using recurrent neural networks with local attention," in Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, pp. 2227-2231, New Orleans, LA, USA, 5-9 Mar. 2017. [36] R. Dutta and M. Majumder, "Attention-based bidirectional LSTM with embedding technique for classification of COVID-19 articles," Intelligent Decision Technologies, vol. 16, no. 1, pp. 205-215, Apr. 2022.ZakariaRaeisiFazlloahAdibniaMahdiYazdian77202411110.66224/ijece.40928.22.1.1http://ijece.org/en/Article/40928http://ijece.org/en/Article/Download/40928http://ijece.org/en/Article/Download/40928http://ijece.org/en/Article/Download/40928http://ijece.org/en/Article/Download/40928http://ijece.org/en/Article/Download/40928http://ijece.org/en/Article/Download/40928http://ijece.org/en/Article/Download/40928[1] J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, "Internet of Things (IoT): a vision, architectural elements and future directions," Future Generation Computer Systems, vol. 29, no. 7, pp. 1645-1660, Sep. 2013. [2] R. Kushwah, P. K. Batra, and A. Jain, "Internet of things architectural elements, challenges and future directions," in Proc. 6th Int. Conf. on Signal Processing and Communication, ICSC'20, 5 pp., Noida, India, 5-7 Mar. 2020. [3] A. Koohang, C. S. Sargent, J. H. Nord, and J. Paliszkiewicz, "Internet of Things (IoT): from awareness to continued use," International J. of Information Management, vol. 62, Article ID: 102442-, Feb. 2020. [4] U. Farooq, N. Tariq, M. Asim, T. Baker, and A. Al-Shamma'a, "Machine learning and the internet of things security: solutions and open challenges," J. of Parallel and Distributed Computing, vol. 162, pp. 89-104, Apr. 2022. [5] P. Mishra, A. Biswal, S. Garg, R. Lu, M. Tiwary, and D. Puthal, "Software defined internet of things security: properties, state of the art, and future research," IEEE Wireless Communications, vol. 27, no. 3, pp. 10-16, Jun. 2020. [6] A. E. Omolara, et al., "The internet of things security: a survey encompassing unexplored areas and new insights," Computers & Security, vol. 112, Article ID: 102494, Jan. 2022. [7] X. Guo, H. Lin, Z. Li, and M. Peng, "Deep-reinforcement-learning-based QoS-aware secure routing for SDN-IoT," IEEE Internet of Things J., vol. 7, no. 7, pp. 6242-6251, Dec. 2019. [8] P. K. Sharma, J. H. Park, Y. S. Jeong, and J. H. Park, "SHSec: SDN based secure smart home network architecture for internet of things," Mobile Networks and Applications, vol. 24, pp. 913-924 2018. [9] S. Rathore, B. W. Kwon, and J. H. Park, "BlockSecIoTNet: blockchain-based decentralized security architecture for IoT network," J. of Network and Computer Applications, vol. 143, pp. 167-177, Oct. 2019. [10] H. Honar Pajooh, M. Rashid, F. Alam, and S. Demidenko, "Multi-layer blockchain-based security architecture for internet of things," Sensors, vol. 21, no. 3, Article ID: 772, 2021. [11] A. Dawoud, S. Shahristani, and C. Raun, "Deep learning and software-defined networks: towards secure IoT architecture," Internet of Things, vol. 3-4, pp. 82-89, Oct. 2018. [12] N. McKeown, et al., "OpenFlow: enabling innovation in campus networks," ACM Sigcomm Computer Communication Review, vol. 38, no. 2, pp. 69-74, Apr. 2008. [13] M. Babiker Mohamed, et al., "A comprehensive survey on secure software‐defined network for the Internet of Things," Trans. on Emerging Telecommunications Technologies, vol. 33, no. 1, Article ID: e4391, Jan. 2022. [14] D. Sovilj, P. Budnarain, S. Sanner, G. Salmon, and M. Rao, "A comparative evaluation of unsupervised deep architectures for intrusion detection in sequential data streams," Expert Systems with Applications, vol. 159, Article ID: 113577, Nov. 2020. [15] C. W. Chang, C. Y. Chang, and Y. Y. Lin, "A hybrid CNN and LSTM-based deep learning model for abnormal behavior detection," Multimedia Tools and Applications, vol. 81, no. 2, pp. 1-19, Apr. 2022. [16] K. Smagulova and A. P. James, "A survey on LSTM memristive neural network architectures and applications," the European Physical J. Special Topics, vol. 228, no. 10, pp. 2313-2324, Oct. 2019. [17] N. Alqudah, M. Y. Qussai, "Machine learning for traffic analysis: a review," Procedia Computer Science, vol. 170, pp. 911-916, 2020. [18] S. K. Tayyaba, M. A. Shah, O. A. Khan, and A. W. Ahmed, "Software defined network (SDN) based internet of things (IoT) a road ahead," in Proc. of the International Conf. on Future Networks and Distributed Systems, Article ID: 15, 8 pp., Cambridge, UK, 19-20, Jul. 2017. [19] R. Bhatia, S. Benno, J. Esteban, T. V. Lakshman, and J. Grogan, "Unsupervised machine learning for network-centric anomaly detection in IoT," in Proc. of the 3rd ACM Context Workshop on Big Data, Machine Learning and Artificial Intelligence for Data Communication Networks, pp. 42-48, Orlando, FL, USA, 9-9 Dec .2019. [20] D. Arellanes and K. K. Lau, "Evaluating IoT service composition mechanisms for the scalability of IoT systems," Future Generation Computer Systems, vol. 108, pp. 827-848, Mar. 2020. [21] S. Bera, S. Misra, and A. V. Vasilakos, "Software-defined networking for internet of things: a survey," IEEE Internet of Things J., vol. 4, no. 6, pp. 1994-2008, Aug. 2017. [22] M. Singh and G. Baranwal, "Quality of Service (QoS) in internet of things," in Proc. 3rd Int. Conf. on Internet of Things: Smart Innovation and Usages, IoT-SIU'18, 6 pp., Bhimtal, India, 23-24 Feb. 2018. [23] T. A. Nguyen, D. Min, and E. Choi, "A hierarchical modeling and analysis framework for availability and security quantification of IoT infrastructures," Electronics, vol. 9, no. 1, Article ID: 155, Jan. 2020. [24] R. Swami, M. Dave, and V. Ranga, "Voting‐based intrusion detection framework for securing software‐defined networks," Concurrency and Computation: Practice and Experience, vol. 32, no. 24, Article ID: e5927, 25 Dec. 2020. [25] I. Rabet, et al., "SDMob: SDN-based mobility management for IoT networks," J. of Sensor and Actuator Networks, vol. 11, no. 1, Article ID: 8, 2022. [26] B. Alzahrani and N. Fotiou, "Enhancing internet of things security using software-defined networking," J. of Systems Architecture, vol. 110, Article ID: pp. 101779, Nov. 2020. [27] A. Hamza, H. H. Gharakheili, and V. Sivaraman, "Combining MUD policies with SDN for IoT intrusion detection," in Proc. of the Workshop on IoT Security and Privacy, 7 pp., Budapest, Hungary, 20-20, Aug. 2018. [28] G. Shravanya, N. H. Swati, R. P. Rustagi, and O. Sharma, "Securing distributed SDN controller network from induced DoS attacks," in Proc., IEEE International Conf. on Cloud Computing in Emerging Markets, CCEM'19, pp. 9-16, Bengaluru, India, 19-20 Sept. 2019. [29] A. Hamza, H. H. Gharakheili, T. A. Benson, and V. Sivaraman, "Detecting volumetric attacks on lot devices via SDN-based monitoring of mud activity," in Proc. of the ACM Symp. on SDN Research, pp. 36-48, San Jose, CA, USA, 3-4 Apr. 2019. [30] C. Xu, H. Lin, Y. Wu, X. Guo, and W. Lin, "An SDN FV-based DDoS defense technology for smart cities," IEEE Access, vol. 7, pp. 137856-137874, 2019. [31] O. Salman, I. H. Elhajj, A. Chehab, and A. Kayssi, "A machine learning based framework for IoT device identification and abnormal traffic detection," Trans. on Emerging Telecommunications Technologies, vol. 33, no. 3, Article ID: e3743, Mar. 2022. [32] F. A. F. Silveira, F. Lima-Filho, F. S. D. Silva, A. D. M. B. Junior, and L. F. Silveira, "Smart detection-IoT: a DDoS sensor system for Internet of Things," in Proc. Int. Conf. on Systems, Signals, and Image Processing, IWSSIP'20, pp. 343-348, Niteroi, Brazil, 1-3 Jul. 2020. [33] A. Wani and S. Revathi, "DDoS detection and alleviation in IoT using SDN (SDN IoT-DDoS-DA)," J. of the Institution of Engineers (India): Series B, vol. 101, no. 3, pp. 117-128, Apr. 2020. [34] M. P. Novaes, L. F. Carvalho, J. Lloret, and M. L. Proenca, "Long short-term memory and fuzzy logic for anomaly detection and mitigation in software-defined network environment," IEEE Access, vol. 8, pp. 83765-83781, 2020. [35] Y. Meidan, et al., "A novel approach for detecting vulnerable IoT devices connected behind a home NAT," Computers & Security, vol. 97, Article ID: 101968, Oct. 2020. [36] S. H. Khan, A. R. Arko, and A. Chakrabarty, "Anomaly detection in IoT using machine learning," In: S. Misra, A. K. Tyagi, V. Piuri, and L. Garg (Eds.), Artificial Intelligence for Cloud and Edge Computing Springer, Chap, pp. 237-254, 2022. [37] M. Abdullahi, et al., "Detecting cybersecurity attacks in internet of things using artificial intelligence methods: a systematic literature review," Electronics, vol. 11, no. 2, Article ID: e3743198, 2022. [38] M. V. Assis, L. F. Carvalho, J. Lloret, and M. L. Proença Jr, "A GRU deep learning system against attacks in software defined networks," J. of Network and Computer Applications, vol. 177, Article ID: 102942, 2021. [39] A. S. Alshra'a, A. Farhat, and J. Seitz, "Deep learning algorithms for detecting denial of service attacks in software-defined networks," Procedia Computer Science, vol. 191pp. 254-263, 2021. [40] A. Likas, N. Vlassis, and J. J. Verbeek, "The global k-means clustering algorithm," Pattern Recognition, vol. 36, no. 2, pp. 451-461, Feb. 2003. [41] J. Zhang, Y. Ling, X. Fu, X. Yang, G. Xiong, and R. Zhang, "Model of the intrusion detection system based on the integration of spatial-temporal features," Computers & Security, vol. 89, Article ID: 101681, Feb. 2020.NegarSarsharMehdiBekrani772024546010.66224/ijece.41576.22.1.54http://ijece.org/en/Article/41576http://ijece.org/en/Article/Download/41576http://ijece.org/en/Article/Download/41576http://ijece.org/en/Article/Download/41576http://ijece.org/en/Article/Download/41576http://ijece.org/en/Article/Download/41576http://ijece.org/en/Article/Download/41576http://ijece.org/en/Article/Download/41576[1] T. Tripathy and C. Novak, Acoustic Beamforming: Automotive Applications for Noise, Vibrations and Harshness, University of Windsor, 2017. [2] J. Benesty, C. Pan, and J. Chen, Fundamentals of Differential Beamforming, Singapore: Springer, Apr. 2016. [3] U. Michel, "History of acoustic beamforming," in Proc. 1st Berlin Beamforming Conf., 17 pp., Berlin, Germany, 21-22 Nov. 2006. [4] J. Benesty, J. Chen, and Y. Huang, Microphone Array Signal Processing, Berlin: Springer-Verlag, 2008. [5] M. Bekrani and M. Lotfizad, "A modified wavelet-domain adaptive filtering algorithm for stereophonic acoustic echo cancellation in the teleconferencing application," in Proc. Int. Symp. on Telecommunications, pp. 548-554, Tehran, Iran, 27-28 Aug. 2008. [6] M. Bekrani, A. W. H. Khong, and M. Lotfizad, "Neural network based adaptive echo cancellation for stereophonic teleconferencing application," in Proc. IEEE Int. Conf. on Multimedia and Expo, Singapore, Singapore, pp. 1172-1177, 19-23 Jul. 2010. [7] م. بکرانی، "طراحی و تحلیل یک الگوریتم وفقی LMS/Newton بهبودیافته در کاربرد حذف پژواک آکوستیکی،" نشریه مهندسی برق و مهندسی کامپيوتر ايران، ب- مهندسی کامپیوتر، سال 18، شماره 2، صص. 116-105، تابستان 1399. [8] M. Bekrani and H. Zayyani, "A weighted soft-max PNLMS algorithm for sparse system identification," International J. of Information and Communication Technology Research, vol. 8, no. 3, pp. 7-14, Summer 2016. [9] W. Yang, G. Huang, J. Benesty, and J. Chen, "On the design of flexible kronecker product beamformers with linear microphone arrays," in Proc. Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP'19, pp. 441-445, Brighton, UK, 12-17 May 2019. [10] G. Huang, J. Benesty, I. Cohen, and J. Chen, "A simple theory and new method of differential beamforming with uniform linear microphone arrays," IEEE/ACM Trans. on Audio, Speech, and Language Processing, vol. 28, pp. 1079-1093, 2020. [11] G. Huang, Y. Wang, J. Benesty, I. Cohen, and J. Chen, "Combined differential beamforming with uniform linear microphone arrays," in Proc. Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP'21, pp. 781-785, Toronto, Canada, 6-11 Jun. 2021. [12] G. Huang, J. Chen, J. Benesty, I. Cohen, and X. Zhao, "Steerable differential beamformers with planar microphone arrays," EURASIP J. on Audio, Speech, and Music Processing, vol. 2020, Article ID: 15, 18 pp., 2020. [13] G. Huang, J. Chen, and J. Benesty, "On the design of differential beamformers with arbitrary planar microphone array geometry," J. of the Acoustical Society of America, vol. 144, no. 1, Article ID: EL66, Jul. 2018. [14] X. Wang, G. Huang, I. Cohen, J. Benesty, and J. Chen, "Robust steerable differential beamformers with null constraints for concentric circular microphone arrays," in Proc. Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP'21, pp. 4465-4469, Toronto, Canada, 6-11 Jun. 2021. [15] G. Huang, I. Cohen, J. Chen, and J. Benesty, "Continuously steerable differential beamformers with null constraints for circular microphone arrays," J. of the Acoustical Society of America, vol. 148, no. 3, pp. 1248-1258, Sept. 2020. [16] G. Huang, J. Chen, and J. Benesty, "Insights into frequency-invariant beamforming with concentric circular microphone arrays," IEEE/ACM Trans. on Audio, Speech, and Language Processing, vol. 26, no. 12, pp. 2305-2318, Dec. 2018. [17] G. Huang, J. Benesty, and J. Chen, "Design of robust concentric circular differential microphone arrays," J. of the Acoustical Society of America, vol. 141, no. 5, pp. 3236-3249, May 2017. [18] J. Benesty, I. Cohen, and J. Chen, Fundamentals of Signal Enhancement and Array Signal Processing, John Wiley & Sons, Dec. 2017. [19] G. Huang, J. Benesty, I. Cohen, and J. Chen, "Differential beamforming on graphs," IEEE/ACM Trans. on Audio, Speech, and Language Processing, vol. 28, pp. 901-913, Feb. 2020. [20] G. Huang, J. Benesty, J. Chen, and I. Cohen, "Robust and steerable kronecker product differential beamforming with rectangular microphone arrays," in Proc. Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP'20, pp. 211-215, Barcelona, Spain, 4-8 May 2020. [21] M. Bekrani, A. H. T. Nguyen, and A. W. H. Khong, "An adaptive non-linear process for under-determined virtual microphone beamforming," in Proc. Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP'21, pp. 4495-4499, Toronto, Canada, 6-11 Jun. 2021. [22] G. Itzhak and I. Cohen, "Differential and constant-beamwidth beamforming with uniform rectangular arrays," in Proc. Int. Workshop on Acoustic Signal Enhancement, WAENC'22, 5 pp., Bamberg, Germany, 5-8 Sept. 2022. [23] X. Wang, I. Cohen, J. Benesty, and J. Chen, "Study of the null directions on the performance of differential beamformers," in Proc. Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP'22, pp. 4928-4932, Singapore, Singapore, 23-27 May 2022. [24] G. Itzhak, I. Cohen, and J. Benesty, "Multistage approach for steerable differential beamforming with rectangular arrays," Speech Communication, vol. 142, pp. 61-76, Jul. 2022. [25] A. A. Nugraha, K. Sekiguchi, M. Fontaine, Y. Bando, and K. Yoshii, "DNN-free low-latency adaptive speech enhancement based on frame-online beamforming powered by block-online FastMNMF," in Proc. Int. Workshop on Acoustic Signal Enhancement, IWAENC'22, 5 pp., Bamberg, Germany, 5-8 Sept. 2022. [26] H. Kim, K. Kang, and J. W. Shin, "Factorized MVDR deep beamforming for multi-channel speech enhancement," IEEE Signal Processing Letters, vol. 29, pp. 1898-1902, 2022. [27] X. Xiao, et al., "Deep beamforming networks for multi-channel speech recognition," in Proc. Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP'16, pp. 5745-5749, Shanghai, China, 20-25 Mar. 2016. [28] I. McCowan, Microphone Arrays: A Tutorial, Queensland University, Australia, pp. 1-38, Apr. 2001. [29] E. A. P. Habets, J. Benesty, I. Cohen, S. Gannot, and J. Dmochowski, "New insights into the MVDR beamformer in room acoustics," IEEE Trans. on Audio, Speech, and Language Processing, vol. 18, no. 1, pp. 158-170, Jun. 2009. [30] T. Dietzen, S. Doclo, M. Moonen, and T. V. Waterschoot, "Integrated sidelobe cancellation and linear prediction Kalman filter for joint multi-microphone speech dereverberation, interfering speech cancellation, and noise reduction," IEEE/ACM Trans. on Audion, Speech, and Language Processing, vol. 28, pp. 740-754, 2020.shekoofebostanAli-MohammadZare-BidokiMohammad-RezaPajoohan772024212910.66224/ijece.43081.22.1.21http://ijece.org/en/Article/43081http://ijece.org/en/Article/Download/43081http://ijece.org/en/Article/Download/43081http://ijece.org/en/Article/Download/43081http://ijece.org/en/Article/Download/43081http://ijece.org/en/Article/Download/43081http://ijece.org/en/Article/Download/43081http://ijece.org/en/Article/Download/43081[1] Y. Yum, et al., "A word pair dataset for semantic similarity and relatedness in Korean medical vocabulary: reference development and validation," JMIR Medical Informatics, vol. 9, no. 6, Article ID: e29667, Jun. 2021. [2] E. Hindocha, V. Yazhiny, A. Arunkumar, and P. Boobalan, "Short-text semantic similarity using GloVe word embedding," International Research J. of Engineering and Technology, vol. 6, no. 4, pp. 553-558, Apr. 2019. [3] J. Zhang, Y. Liu, J. Mao, W. Ma, and J. Xu, "User behavior simulation for search result re-ranking," ACM Trans. on Information Systems, vol. 41, no. 1, Article ID: 5, 35 pp., Jan. 2023. [4] V. Zosimov and O. Bulgakova, "Usage of inductive algorithms for building a search results ranking model based on visitor rating evaluations," in Proc. IEEE 13th Int. Scientific and Technical Conf. on Computer Sciences and Information Technologies, CSIT'18, pp. 466-469, Lviv, Ukraine, 11-14 Sept. 2018. [5] B. Mitra and N. Craswell, Neural Models for Information Retrieval, arXiv preprint arXiv:1705.01509, vol. 1, 2017. [6] V. Gupta, A. Dixit, and S. Sethi, "A comparative analysis of sentence embedding techniques for document ranking," J. of Web Engineering, vol. 21, no. 7, pp. 2149-2186, 2022. [7] J. Pennington, R. Socher, C. Ma, and C. Manning, "GloVe: global vectors for word representation," in Proc. Conf. on Empirical Methods in Natural Language Processing, EMNLP'14, pp. 1532-1543, Doha, Qatar, 25-29 Oct. 2014. [8] T. Mikolov, K. Chen, G. Corrado, and J. Dea, "Efficient estimation of word representations in vector space," in Proc. In. Conf. on Learning Representations, ICLR'13, 12 pp., Scottsdale, AZ, USA, 2-4 May 2013. [9] P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, "Enriching word vectors with subword information," Trans. of the Association for Computational Linguistics, vol. 5, pp. 135-146, 2017. [10] M. E. Peters, et al., "Deep contextualized word representations," in Proc. Conf. of the North American Chapter of the Association of Computational Linguistics, NAACL-HLT'18, 11 pp., New Orleans, LA, USA, 1-6 Jun. 2018. [11] J. Devlin, M. W. Chang, and K. L. Kristina, "BERT: pre-training of deep bidirectional transformers for language understanding," in Proc. Conf. of the North American Chapter of the Association of Computational Linguistics, NAACL-HLT'18, 16 pp., New Orleans, LA, USA, 1-6 Jun. 2018. [12] T. Brown, et al., "Language models are few-shot learners," in Proc. 34th Conf. on Neural Information Processing Systems, NeurIPS'20, 25 pp., Vancouver, Canada, 6-12 Dec. 2020. [13] P. Sherki, S. Navali, and R. Inturi, "Retaining semantic data in binarized word embedding," in ¬Proc. IEEE 15th Int. Conf. on Semantic Computing, ICSC'21, pp. 130-133, Laguna Hills, CA, USA, 27-29 Jan. 2021. [14] L. Shaohua, C. Tat-Seng, Z. Jun, and C. Miao, Generative Topic Embedding: A Continuous Representation of Documents (Extended Version with Proofs), arXiv preprint arXiv:1606.02979, vol. 1, 2016. [15] B. Mitra, E. Nalisnick, N. Craswell, and R. Caruana, "A dual embedding space model for document ranking," in Proc. 25th Int. Conf. Companion on World Wide Web, WWW'16, 10 pp., Montreal, Canada, 11-15 Apr. 2016. [16] M. Dehghani, H. Zamani, A. Severyn, and J. Kamps, "Neural ranking models with weak supervision," in Proc. of the 40th Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, SIGIR '17, pp. 65-74, Tokyo, Japan, 7-11 Aug. 2017. [17] C. Xiong, Z. Dai, and J. Callan, "End-to-end neural ad-hoc ranking with kernel pooling," in Proc. of the 40th Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 55-64, Tokyo, Japan, 7-11 Aug. 2017. [18] R. Brochier, A. Guille, and J. Velcin, "Global vectors for node representations," in Proc. ACM World Wide Web Conf., WWW'19, San Francisco, pp. 2587-2593, San Francisco, CA, USA, 13-17 May 2019. [19] A. Gourru and J. Velcin, "Gaussian embedding of linked documents from a pretrained semantic space," in Proc. 29th Int. Joint Conf. on Artificial Intelligence, IJCAI'20, pp. 3912-3918, Yokohama, Japan, 8-10 Jan. 2021. [20] R. Menon, J. Kaartik, and K. Nambiar, "Improving ranking in document based search systems," in Proc. 4th Int. Conf. on Trends in Electronics and Informatics, ICOEI'20, pp. 914-921, Tirunelveli, India, 15-17 Jun. 2020. [21] J. Li, C. Guo, and Z. Wei, "Improving document ranking with relevance-based entity embeddings," in Proc. 8th Int. Conf. on Big Data and Information Analytics, BigDIA'22, China, pp. 186-192, Guiyang, China, 24-25 Aug. 2022. [22] S. Han, X. Wang, M. Bendersky, and M. Najork, Learning-to-Rank with BERT in TF-Ranking, Google Research Tech Report, 2020. [23] ش. بستان، ع. زارع بیدکی و م. ر. پژوهان، "درون¬سازی معنایی واژه¬ها با استفاده از BERT روی وب فارسی،" نشریه مهندسی برق و مهندسی کامپیوتر ایران، ب- مهندسی کامپیوتر، سال 21، شماره 2، صص. 100-89، تابستان 1402. [24] M. Farahani, M. Gharachorloo, M. Farahani, and M. Manthouri, "Parsbert: transformer-based model for Persian language understanding," Neural Processing Letters, vol. 53, pp. 3831-3847, 2021. [25] D. Yang and Y. Yin, "Evaluation of taxonomic and neural embedding methods for calculating semantic similarity," Natural Language Engineering, vol. 28, no. 6, pp. 733-761, Nov. 2022. [26] R. Mihalcea, C. Corley, and C. Strapparava, "Corpus-based and knowledge-based measures of text semantic similarity," in Proc. 21st National Conf. on Artificial Intelligence, vol. 1, pp. 775-780, Boston, MA, USA, 16-20 Jul. 2006. [27] K. Jarvelin and J. Kekalainen, "Cumulated gain-based evaluation of IR techniques," ACM Trans. on Information Systems, vol. 20, no. 4, pp. 422-446, Oct. 2002.