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پیش‌بینی عملکرد نتایج پرس‌وجو با کمک روش‌های بدون نظارت Unsupervised Methods for Predicting Query Performance 1 1 در سال­‌های اخیر، استفاده از موتورهای جست‌وجو افزایش روزافزون داشته و نیاز به توسعه روش‌های دقیق‌تر بازیابی و رتبه‌بندی اسناد بیشتر شده‌است؛ درنتیجه پیش­بینی عملکرد موتورهای جست‌وجو، یکی از الزامات و چالش‌­های بازیابی اطلاعات محسوب می‌شود. اگر بتوان عملکرد پرس­‌وجوها را پیش از مرحله بازیابی یا بعد از آن تخمین زد، می­‌توان اقدامات خاصی را برای بهبود بازیابی انجام داد. پیش‌بینی عملکرد پرس‌وجو بر تخمین دشواری برآوردن درخواست کاربر برای یک روش بازیابی خاص متمرکز است. این پژوهش، به بررسی عملکرد پرس‌وجو با کمک روش‌های پس از بازیابی می‌پردازد؛ در این راستا از روش‌­های بدون نظارت استفاده می‌شود و به خوشه­‌بندی و اندازه­‌گیری معیارهای مختلف جهت ارزیابی عملکرد پاسخ‌­دهی پرس‌­وجوها می‌­پردازیم؛ درنهایت کار خود را با روش‌­های بدون نظارت موجود در ادبیات این حوزه مقایسه خواهیم کرد. نتایج نشان می‌دهد روش پیشنهادی پژوهش حاضر توانست ضریب اسپیرمن را در مجموعه داده TREC DL 2019 و DL-Hard به ترتیب 0.009 و 0.163 و در مجموعه داده TREC DL 2020 ضریب پیرسون را 0.037 نسبت به بهترین کار موجود افزایش دهد. 2 With the rapid increase in the use of search engines, the need for developing more effective information retrieval and ranking methods has become critical. One of the key challenges in information retrieval is predicting query performance, which involves estimating how well a search engine can fulfill a user's information need. Accurate prediction of query performance allows search engines to take adaptive actions, such as query reformulation or ranking adjustment, to enhance retrieval effectiveness. Query Performance Prediction (QPP) methods fall into two main categories: pre-retrieval prediction and post-retrieval prediction. Pre-retrieval predictors estimate query difficulty before the retrieval process, relying on linguistic and statistical query features rather than retrieved documents. In contrast, post-retrieval prediction methods assess query performance based on the ranking list and document collection, providing deeper insights into retrieval effectiveness. In this study, we propose a novel unsupervised post-retrieval QPP method that evaluates query performance by analyzing the clustering behavior of retrieved documents. Our method defines five new metrics—CC, DCIC, DCNIC, DCNICR, and CCR— to measure the distribution and coherence of retrieved documents. These metrics help assess query difficulty by capturing how documents group into clusters, identifying outlier documents that do not fit well into clusters, and evaluating the overall structure of retrieved results. By leveraging these metrics, our approach provides a more fine-grained estimation of query performance without requiring human-labeled data. To evaluate the effectiveness of the proposed method, we conduct experiments on three datasets: TREC DL 2019, TREC DL 2020, and DL-Hard. The results demonstrate that our approach improves Spearman's correlation coefficient by 0.009 and 0.163 on the TREC DL 2019 and DL-Hard datasets, respectively. Additionally, it increases Pearson’s correlation coefficient by 0.037 on the TREC DL 2020 dataset compared to state-of-the-art unsupervised QPP methods. These improvements indicate that clustering-based QPP methods can effectively capture query difficulty and retrieval quality without the need for external supervision. 3 12 2023/11/13 1402/8/22 2025/03/8 1403/12/18 سیده فاطمه کریمی Seyedehfatemeh Karimi کارشناس‌ارشد مهندسی کامپیوتر، دانشگاه فردوسی مشهد، مشهد، ایران ایران ftmkm9776@gmail.com مریم خدابخش Maryam Khodabakhsh استادیار دانشکده مهندسی کامپیوتر، دانشگاه صنعتی شاهرود، شاهرود، ایران ایران m_khodabakhsh@shahroodut.ac.ir Query Performance Prediction Information Retrieval Search Engines پیش‌بینی عملکرد پرس‌وجو بازیابی اطلاعات موتورهای جست‌وجو M. Khodabakhsh and E. Bagheri, "Learning to rank and predict: Multi-task learning for ad hoc retrieval and query performance prediction," Inf. Sci. (Ny)., vol. 639, 2023, doi: 10.1016/j.ins.2023.119015.##N. Arabzadeh, M. Khodabakhsh, and E. Bagheri, "BERT-QPP: Contextualized Pre-trained transformers for Query Performance Prediction," Int. Conf. Inf. Knowl. Manag. Proc., pp. 2857-2861, 2021, doi: 10.1145/3459637.3482063.##J. ForutanRad, M. HourAli, and M. KeyvanRad, "Farsi Question and Answer Dataset (FarsiQuAD)," Signal Data Process., vol. 20, no. 4, 2024, doi: 10.61186/jsdp.20.4.107.##O. Khattab and M. Zaharia, "ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT," SIGIR 2020 - Proc. 43rd Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., pp. 39-48, 2020, doi: 10.1145/3397271.3401075.##L. Xiong et al., "Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval," ICLR 2021 - 9th Int. Conf. Learn. Represent., pp. 1-16, 2021.##T. Formal, C. Lassance, B. Piwowarski, and S. Clinchant, From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective, vol. 1, no. 1. Association for Computing Machinery, 2022. doi: 10.1145/3477495.3531857.##R. Nogueira, Z. Jiang, R. Pradeep, and J. Lin, "Document ranking with a pretrained sequence-to-sequence model," Find. Assoc. Comput. Linguist. Find. ACL EMNLP 2020, pp. 708-718, 2020, doi: 10.18653/v1/2020.findings-emnlp.63.##T. A. Nakamura, P. H. Calais, D. de C. Reis, and A. P. Lemos, "An anatomy for neural search engines," Inf. Sci. (Ny)., vol. 480, pp. 339-353, 2019, doi: 10.1016/j.ins.2018.12.041.##G. Hirst, J. Lin, R. Nogueira, and A. Yates, "Pretrained Transformers for Text Ranking: BERT and beyond," Synth. Lect. Hum. Lang. Technol., vol. 14, no. 4, pp. 1-325, 2021, doi: 10.2200/S01123ED1V01Y2021 08HLT053.##K. Sparck Jones, S. Walker, and S. E. Robertson, "Probabilistic model of information retrieval: Development and comparative experiments. Part 2," Inf. Process. Manag., vol. 36, no. 6, pp. 809-840, 2000, doi: 10.1016/S0306-4573(00)00016-9.##J. Lafferty and C. Zhai, "Document language models, query models, and risk minimization for information retrieval," SIGIR Forum (ACM Spec. Interes. Gr. Inf. Retrieval), vol. 51, no. 2, pp. 111-119, 2001, doi: 10.1145/383952.383970.##E. Bagheri and F. N. Al-Obeidat, "A Latent Model for Ad Hoc Table Retrieval," Adv. Inf. Retr., vol. 12036, pp. 86-93, 2020, [Online]. Available: https://api.semanticscholar.org/Corpus ID:215746638##N. Arabzadeh, F. Zarrinkalam, J. Jovanovic, and E. Bagheri, "Neural embedding-based metrics for pre-retrieval query performance prediction," Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 12036 LNCS, pp. 78-85, 2020, doi: 10.1007/978-3-030-45442-5_10.##C. Hauff, D. Hiemstra, and F. De Jong, "A survey of pre-retrieval query performance predictors," Int. Conf. Inf. Knowl. Manag. Proc., pp. 1419-1420, 2008, doi: 10.1145/1458082.1458311.##G. Faggioli, O. Zendel, J. S. Culpepper, N. Ferro, and F. Scholer, "sMARE: a new paradigm to evaluate and understand query performance prediction methods," Inf. Retr. J., vol. 25, no. 2, pp. 94-122, 2022, doi: 10.1007/s10791-022-09407-w.##H. Roitman, S. Erera, and G. Feigenblat, "A study of query performance prediction for answer quality determination," ICTIR 2019 - Proc. 2019 ACM SIGIR Int. Conf. Theory Inf. Retr., pp. 43-46, 2019, doi: 10.1145/3341981.3344219.##G. Faggioli, T. Formal, S. Marchesin, S. Clinchant, N. Ferro, and B. Piwowarski, "Query Performance Prediction for Neural IR: Are We There Yet?," Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 13980 LNCS, pp. 232-248, 2023, doi: 10.1007/978-3-031-28244-7_15.##S. Sarnikar, Z. Zhang, and J. Zhao, "Query-Performance Prediction for Effective Query Routing in Domain-Specific Repositories," J. Assoc. Inf. Sci. Technol., vol. 65, 2014, doi: 10.1002/asi.23072.##D. Carmel and E. Yom-Tov, Estimating the query difficulty for information retrieval. Morgan & Claypool Publishers, 2010.##J. S. Culpepper, G. Faggioli, N. Ferro, and O. Kurland, "Topic Difficulty: Collection and Query Formulation Effects," ACM Trans. Inf. Syst., vol. 40, no. 1, Sep. 2021, doi: 10.1145/3470563.##G. Faggioli, S. Lupart, S. Marchesin, N. Ferro, and B. Piwowarski, "Towards Query Performance Prediction for Neural Information Retrieval : Challenges and Opportunities," pp. 51-63, doi: 10.1145/3578337.3605142.##M. Khodabakhsh and E. Bagheri, "Semantics-enabled query performance prediction for ad hoc table retrieval," Inf. Process. Manag., vol. 58, no. 1, p. 102399, 2021, doi: 10.1016/j.ipm.2020.102399.##S. Cronen-Townsend, Y. Zhou, and W. B. Croft, "Predicting query performance," SIGIR Forum (ACM Spec. Interes. Gr. Inf. Retrieval), pp. 299-306, 2002, doi: 10.1145/564426.564429.##F. Raiber and O. Kurland, "Query-performance prediction: Setting the expectations straight," SIGIR 2014 - Proc. 37th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., pp. 13-22, 2014, doi: 10.1145/2600428.2609581.##H. Roitman, S. Erera, O. Sar-Shalom, and B. Weiner, "Enhanced mean retrieval score estimation for query performance prediction," ICTIR 2017 - Proc. 2017 ACM SIGIR Int. Conf. Theory Inf. Retr., no. October, pp. 35-42, 2017, doi: 10.1145/3121050.3121051.##H. Roitman, S. Erera, and B. Weiner, "Robust standard deviation estimation for query performance prediction," ICTIR 2017 - Proc. 2017 ACM SIGIR Int. Conf. Theory Inf. Retr., pp. 245-248, 2017, doi: 10.1145/3121050.3121087.##J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of deep bidirectional transformers for language understanding," NAACL HLT 2019 - 2019 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. - Proc. Conf., vol. 1, no. Mlm, pp. 4171-4186, 2019.##M. and et al. Robertson, Stephen E and Walker, Steve and Jones, Susan and Hancock-Beaulieu, Micheline M and Gatford, "Okapi at TREC-3," Nist Spec. Publ. Sp, vol. 109, p. 109, 1995.##J. Sander, M. Ester, H. P. Kriegel, and X. Xu, "Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications," Data Min. Knowl. Discov., vol. 2, no. 2, pp. 169-194, 1998, doi: 10.1023/A:1009745219419.##N. Craswell, B. Mitra, E. Yilmaz, and D. Campos, "Overview of the TREC 2019 deep learning track," pp. 1-22, 2020, [Online]. Available: http://arxiv.org/abs/2102.07662##N. Craswell, B. Mitra, E. Yilmaz, and D. Campos, "Overview of the TREC 2020 deep learning track," pp. 1-13, 2021, [Online]. Available: http://arxiv.org/abs/2102.07662##I. MacKie, J. Dalton, and A. Yates, "How Deep is your Learning: The DL-HARD Annotated Deep Learning Dataset," SIGIR 2021 - Proc. 44th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., no. July, pp. 2335-2341, 2021, doi: 10.1145/3404835.3463 262.##Y. Z. and W. B. Croft, "Query performance prediction in web search environments," in " in Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007, pp. 543-550.##D. M. Blei, T. L. Griffiths, M. I. Jordan, and J. B. Tenenbaum, "Hierarchical topic models and the nested Chinese restaurant process," Adv. Neural Inf. Process. Syst., no. May 2004, 2004.##A. Singh, D. Ganguly, S. Datta, and C. Macdonald, Unsupervised Query Performance Prediction for Neural Models with Pairwise Rank Preferences, vol. 1, no. 1. Association for Computing Machinery, 2023. doi: 10.1145/3539618.3592082.##M. Khodabakhsh and E. Bagheri, "Learning to rank and predict: Multi-task learning for ad hoc retrieval and query performance prediction," Inf. Sci. (Ny)., vol. 639, 2023, doi: 10.1016/j.ins.2023.119015.##N. Arabzadeh, M. Khodabakhsh, and E. Bagheri, "BERT-QPP: Contextualized Pre-trained transformers for Query Performance Prediction," Int. Conf. Inf. Knowl. Manag. Proc., pp. 2857-2861, 2021, doi: 10.1145/3459637.3482063.##J. ForutanRad, M. HourAli, and M. KeyvanRad, "Farsi Question and Answer Dataset (FarsiQuAD)," Signal Data Process., vol. 20, no. 4, 2024, doi: 10.61186/jsdp.20.4.107.##O. Khattab and M. Zaharia, "ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT," SIGIR 2020 - Proc. 43rd Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., pp. 39-48, 2020, doi: 10.1145/3397271.3401075.##L. Xiong et al., "Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval," ICLR 2021 - 9th Int. Conf. Learn. Represent., pp. 1-16, 2021.##T. Formal, C. Lassance, B. Piwowarski, and S. Clinchant, From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective, vol. 1, no. 1. Association for Computing Machinery, 2022. doi: 10.1145/3477495.3531857.##R. Nogueira, Z. Jiang, R. Pradeep, and J. Lin, "Document ranking with a pretrained sequence-to-sequence model," Find. Assoc. Comput. Linguist. Find. ACL EMNLP 2020, pp. 708-718, 2020, doi: 10.18653/v1/2020.findings-emnlp.63.##T. A. Nakamura, P. H. Calais, D. de C. Reis, and A. P. Lemos, "An anatomy for neural search engines," Inf. Sci. (Ny)., vol. 480, pp. 339-353, 2019, doi: 10.1016/j.ins.2018.12.041.##G. Hirst, J. Lin, R. Nogueira, and A. Yates, "Pretrained Transformers for Text Ranking: BERT and beyond," Synth. Lect. Hum. Lang. Technol., vol. 14, no. 4, pp. 1-325, 2021, doi: 10.2200/S01123ED1V01Y2021 08HLT053.##K. Sparck Jones, S. Walker, and S. E. Robertson, "Probabilistic model of information retrieval: Development and comparative experiments. Part 2," Inf. Process. Manag., vol. 36, no. 6, pp. 809-840, 2000, doi: 10.1016/S0306-4573(00)00016-9.##J. Lafferty and C. Zhai, "Document language models, query models, and risk minimization for information retrieval," SIGIR Forum (ACM Spec. Interes. Gr. Inf. Retrieval), vol. 51, no. 2, pp. 111-119, 2001, doi: 10.1145/383952.383970.##E. Bagheri and F. N. Al-Obeidat, "A Latent Model for Ad Hoc Table Retrieval," Adv. Inf. Retr., vol. 12036, pp. 86-93, 2020, [Online]. Available: https://api.semanticscholar.org/Corpus ID:215746638##N. Arabzadeh, F. Zarrinkalam, J. Jovanovic, and E. Bagheri, "Neural embedding-based metrics for pre-retrieval query performance prediction," Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 12036 LNCS, pp. 78-85, 2020, doi: 10.1007/978-3-030-45442-5_10.##C. Hauff, D. Hiemstra, and F. De Jong, "A survey of pre-retrieval query performance predictors," Int. Conf. Inf. Knowl. Manag. Proc., pp. 1419-1420, 2008, doi: 10.1145/1458082.1458311.##G. Faggioli, O. Zendel, J. S. Culpepper, N. Ferro, and F. Scholer, "sMARE: a new paradigm to evaluate and understand query performance prediction methods," Inf. Retr. J., vol. 25, no. 2, pp. 94-122, 2022, doi: 10.1007/s10791-022-09407-w.##H. Roitman, S. Erera, and G. Feigenblat, "A study of query performance prediction for answer quality determination," ICTIR 2019 - Proc. 2019 ACM SIGIR Int. Conf. Theory Inf. Retr., pp. 43-46, 2019, doi: 10.1145/3341981.3344219.##G. Faggioli, T. Formal, S. Marchesin, S. Clinchant, N. Ferro, and B. Piwowarski, "Query Performance Prediction for Neural IR: Are We There Yet?," Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 13980 LNCS, pp. 232-248, 2023, doi: 10.1007/978-3-031-28244-7_15.##S. Sarnikar, Z. Zhang, and J. Zhao, "Query-Performance Prediction for Effective Query Routing in Domain-Specific Repositories," J. Assoc. Inf. Sci. Technol., vol. 65, 2014, doi: 10.1002/asi.23072.##D. Carmel and E. Yom-Tov, Estimating the query difficulty for information retrieval. Morgan & Claypool Publishers, 2010.##J. S. Culpepper, G. Faggioli, N. Ferro, and O. Kurland, "Topic Difficulty: Collection and Query Formulation Effects," ACM Trans. Inf. Syst., vol. 40, no. 1, Sep. 2021, doi: 10.1145/3470563.##G. Faggioli, S. Lupart, S. Marchesin, N. Ferro, and B. Piwowarski, "Towards Query Performance Prediction for Neural Information Retrieval : Challenges and Opportunities," pp. 51-63, doi: 10.1145/3578337.3605142.##M. Khodabakhsh and E. Bagheri, "Semantics-enabled query performance prediction for ad hoc table retrieval," Inf. Process. Manag., vol. 58, no. 1, p. 102399, 2021, doi: 10.1016/j.ipm.2020.102399.##S. Cronen-Townsend, Y. Zhou, and W. B. Croft, "Predicting query performance," SIGIR Forum (ACM Spec. Interes. Gr. Inf. Retrieval), pp. 299-306, 2002, doi: 10.1145/564426.564429.##F. Raiber and O. Kurland, "Query-performance prediction: Setting the expectations straight," SIGIR 2014 - Proc. 37th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., pp. 13-22, 2014, doi: 10.1145/2600428.2609581.##H. Roitman, S. Erera, O. Sar-Shalom, and B. Weiner, "Enhanced mean retrieval score estimation for query performance prediction," ICTIR 2017 - Proc. 2017 ACM SIGIR Int. Conf. Theory Inf. Retr., no. October, pp. 35-42, 2017, doi: 10.1145/3121050.3121051.##H. Roitman, S. Erera, and B. Weiner, "Robust standard deviation estimation for query performance prediction," ICTIR 2017 - Proc. 2017 ACM SIGIR Int. Conf. Theory Inf. Retr., pp. 245-248, 2017, doi: 10.1145/3121050.3121087.##J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of deep bidirectional transformers for language understanding," NAACL HLT 2019 - 2019 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. - Proc. Conf., vol. 1, no. Mlm, pp. 4171-4186, 2019.##M. and et al. Robertson, Stephen E and Walker, Steve and Jones, Susan and Hancock-Beaulieu, Micheline M and Gatford, "Okapi at TREC-3," Nist Spec. Publ. Sp, vol. 109, p. 109, 1995.##J. Sander, M. Ester, H. P. Kriegel, and X. Xu, "Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications," Data Min. Knowl. Discov., vol. 2, no. 2, pp. 169-194, 1998, doi: 10.1023/A:1009745219419.##N. Craswell, B. Mitra, E. Yilmaz, and D. Campos, "Overview of the TREC 2019 deep learning track," pp. 1-22, 2020, [Online]. Available: http://arxiv.org/abs/2102.07662##N. Craswell, B. Mitra, E. Yilmaz, and D. Campos, "Overview of the TREC 2020 deep learning track," pp. 1-13, 2021, [Online]. Available: http://arxiv.org/abs/2102.07662##I. MacKie, J. Dalton, and A. Yates, "How Deep is your Learning: The DL-HARD Annotated Deep Learning Dataset," SIGIR 2021 - Proc. 44th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., no. July, pp. 2335-2341, 2021, doi: 10.1145/3404835.3463 262.##Y. Z. and W. B. Croft, "Query performance prediction in web search environments," in " in Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007, pp. 543-550.##D. M. Blei, T. L. Griffiths, M. I. Jordan, and J. B. Tenenbaum, "Hierarchical topic models and the nested Chinese restaurant process," Adv. Neural Inf. Process. Syst., no. May 2004, 2004.##A. Singh, D. Ganguly, S. Datta, and C. Macdonald, Unsupervised Query Performance Prediction for Neural Models with Pairwise Rank Preferences, vol. 1, no. 1. Association for Computing Machinery, 2023. doi: 10.1145/3539618.3592082.## ##

بهینه‌سازی مسیر درایوتست در شبکه‌های تلفن همراه Drive Test Route Optimization in Mobile Networks 1 1 عملکرد سلول‌ها در ناحیه دسترسی رادیویی شبکه‌های تلفن همراه به‌صورت مستمر پایش می‌شود تا عمل‌گرها بتوانند کیفیت و پوشش شبکه را در برابر تغییرات محیطی و الگوی مصرف کاربران بهینه‌سازی کنند. درایوتست یک روش مرسوم برای جمع‌آوری داده در مورد وضعیت شبکه در موقعیت‌های مختلف جغرافیایی است؛ در این روش، تمام نقاط جغرافیایی شامل تمامی خیابان‌ها و معابر یک بخش از نقشه پیمایش می‌شود تا وضعیت سرویس‌دهی شبکه در این نقاط مشخص شود. عمل‌گرها از داده‌های ارزشمند به‌دست‌آمده از این روش برای بهینه‌سازی شبکه‌های تلفن همراه استفاده می‌کنند. این فرایند هزینه زیادی برای عمل‌گرهای شبکه به همراه دارد و زمان‌بر است. در این مقاله، روش جدیدی برای بهینه‌سازی فرایند جمع‌آوری داده‌ها در درایوتست‌ شبکه‌های تلفن همراه ارائه می‌شود تا به ‌جای پیمایش تمامی معابر در یک منطقه، یک مسیر جایگزین و کوتاه‌تر پیمایش شود و بتوان با هزینه کمتری به نقاطی که RSRP نامناسبی در یک بخش از شهر دارند، دست‌یافت؛ علاوه‌بر این، در این مقاله معیاری برای ارزیابی یک مسیر برای انجام درایوتست معرفی شده‌ که تابعی از میزان موفقیت این مسیر در یافتن نقاط دارای RSRP نامناسب و مسافت لازم برای پیمایش این مسیر است؛ درنهایت، مسیر پیشنهادی در این روش نیز با استفاده از داده‌های واقعی درایوتست‌های قبلی و معیار معرفی‌شده ارزیابی شده‌است. 2 The design and maintenance of the radio access network (RAN) in mobile telecommunications requires meticulous monitoring of network performance to ensure optimal quality and coverage. Drive testing is a prevalent methodology for collecting data on network status across delineated geographical areas. This process involves systematically traversing various routes, including streets and pathways, to assess network performance metrics such as Reference Signal Received Power (RSRP). Although drive testing provides essential insights for identifying regions with inadequate signal quality, it is inherently resource-intensive, involving considerable time and financial expenditures. This paper proposes an innovative optimization methodology aimed at enhancing the efficiency of data collection during drive tests. The proposed approach is organized into four fundamental steps: (1) partitioning the map into smaller sections, (2) selecting critical points within each section, (3) employing map-matching techniques to accurately align these points with actual streets and pathways, and (4) determining the optimal route for traversing the critical points. A rectangular area of interest is selected and divided into K smaller sub-regions, within which M critical points are identified according to a predefined criterion. These points, which may not initially correspond with the existing street network, are corrected through map-matching techniques to ensure feasible traversal paths. Lastly, an optimization algorithm is utilized to compute the shortest route that encompasses all identified critical points. The efficacy of the proposed method is assessed through experimental studies that manipulate key parameters K (denoting the number of sub-regions) and M (indicating the maximum critical points per zone). The evaluation emphasizes two critical metrics: the total distance traveled and the success rate in detecting areas with RSRP values below -100 dBm. Results indicate that the proposed approach significantly decreases the distance required for drive testing while achieving substantial coverage of areas with weak signals. For example, in an experiment where the optimized route covered only 18.54 kilometers—equivalent to 34% of the distance of an entire drive test—it successfully identified 70% of regions with poor RSRP. Furthermore, this paper introduces a criterion for assessing the effectiveness of drive test routes, highlighting the balance between route length and coverage of low-signal areas. The findings substantiate that adequate data for network performance analysis can be secured with considerable savings in both cost and time compared to conventional exhaustive drive testing. While this study concentrates on RSRP measurements within 4G networks, the proposed methodology is adaptable for other metrics, including Reference Signal Received Quality (RSRQ). Future research may also examine the integration of alternative data sources, such as satellite imagery, to further refine map partitioning and critical point selection, thereby enhancing the overall efficacy of the proposed method. 13 24 2023/11/132024/06/28 1403/4/8 2025/03/82024/12/4 1403/9/14 علی نظری Ali Nazari کارشناس‌ارشد مهندسی کامپیوتر گرایش شبکه‌های کامپیوتری، دانشگاه علم و صنعت ایران، تهران، ایران ایران ali4nazari4@gmail.com مصطفی فلاح Mostafa Fallah دانشجوی کارشناسی‌ارشد مهندسی کامپیوتر گرایش شبکه‌های کامپیوتری، دانشگاه علم و صنعت ایران، تهران، ایران ایران mostafa_fallah@comp.iust.ac.ir محمدجواد طاهری Mohammad Javad Taheri کارشناس‌ارشد مهندسی کامپیوتر گرایش شبکه‌های کامپیوتری، دانشگاه علم و صنعت ایران، تهران، ایران ایران taheri_mo96@comp.iust.ac.ir ابوالفضل دیانت Abolfazl Diyanat استادیار دانشکده مهندسی کامپیوتر، دانشگاه علم و صنعت ایران، تهران، ایران ایران adiyanat@iust.ac.ir Mobile Networks Drive Test Route Optimization Reference Signal Received Power (RSRP) شبکه‌های تلفن همراه درایوتست بهینه‌سازی مسیر قدرت سیگنال دریافتی A. 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Benn, "Gendt: mobile network drive testing made efficient with generative modeling," in Proceedings of the 18th International Conference on emerging Networking EXperiments and Technologies, 2022, pp. 43-58.##M. F. A. Fauzi, R. Nordin, N. F. Abdullah, H. A. H. Alobaidy, and M. Behjati, "Machine Learning-Based Online Coverage Estimator (MLOE): Advancing Mobile Network Planning and Optimization," IEEE Access, vol. 11, pp. 3096-3109, 2023.##P. Chao, Y. Xu, W. Hua, and X. Zhou, "A survey on map-matching algorithms," in Databases Theory and Applications: 31st Australasian Database Conference, ADC 2020, Melbourne, VIC, Australia, February 3-7, 2020, Proceedings 31, 2020, pp. 121-133.##W. Li, Y. Chen, S. Wang, H. Li, and Q. Fan, "A Novel Map Matching Method Based on Improved Hidden Markov and Conditional Random Fields Model," Int J Digit Earth, vol. 17, no. 1, p. 2328366, 2024.##S. Gupta, A. Rana, and V. 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Ylvisaker, "Minimax and maximin distance designs," J Stat Plan Inference, vol. 26, no. 2, pp. 131-148, 1990.##Neshan, "Map Matching API," 2023.##Neshan, "Neshan Direction API Documentation."##A. Taufique, M. Jaber, A. Imran, Z. Dawy, and E. Yacoub, "Planning wireless cellular networks of future: Outlook, challenges and opportunities," IEEE Access, vol. 5, pp. 4821-4845, 2017.##T. R. A. Network, "Study on minimization of drive-tests in next generation networks;(release 9)," 3GPP TR 36.805.##L. M. Silalahi, S. Budiyanto, F. A. Silaban, I. U. V. Simanjuntak, and A. D. Rochendi, "Improvement of Quality and Signal Coverage LTE in Bali Province Using Drive Test Method," in 2021 International Seminar on Intelligent Technology and Its Applications (ISITIA), 2021, pp. 376-380.##M. Skocaj et al., "Cellular network capacity and coverage enhancement with MDT data and deep reinforcement learning," Comput Commun, vol. 195, pp. 403-415, 2022.##J. Thrane, M. Artuso, D. Zibar, and H. L. 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Fan, "A Novel Map Matching Method Based on Improved Hidden Markov and Conditional Random Fields Model," Int J Digit Earth, vol. 17, no. 1, p. 2328366, 2024.##S. Gupta, A. Rana, and V. Kansal, "Comparison of Heuristic techniques: A case of TSP," in 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2020, pp. 172-177.##N. Christofides, "The vehicle routing problem," Revue française d'automatique, informatique, recherche opérationnelle. Recherche opérationnelle, vol. 10, no. V1, pp. 55-70, 1976.##ابراهیمی مود سپهر، جاویدی محمد مسعود، خسروی محمدرضا. ارائه الگوریتم جست‌وجوی گرانشی مقید و حل مسأله مسیریابی وسایل نقلیه. پردازش علائم و داده‌ها. ۱۴۰۰; ۱۸ (۴) :۲۳-۳۶##S. Ebrahimi Mood, M. M. Javidi, and M. R. Khosravi, "Proposing a Constrained-GSA for the Vehicle Routing Problem," Signal and Data Processing, vol. 18, no. 4, pp. 23-36, Mar. 2022, doi: 10.52547/jsdp.18.4.23.##E. B. Tirkolaee, A. Goli, S. Gütmen, G.-W. Weber, and K. 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جست‌وجوی معماری شبکه‌های عصبی عمیق آگاه از منابع در سامانه‌های نهفته چند‌هسته‌ای Resource-Aware Neural Architecture Search for Multicore Embedded Real-Time Systems 1 1 ایجاد شبکه‌های عصبی به ‌صورت دستی فرایندی کند و مبتنی بر آزمون‌وخطا است که با افزایش تعداد پارامترها یا لایه‌ها، به طور قابل‌توجهی هزینه‌بر و غیر‌بهینه می‌شود؛ برای حل این مشکل، الگوریتم‌های خودکار جست‌وجوی معماری شبکه (NAS) معرفی شده‌اند که به‌تازگی توانسته‌اند در مجموعه‌داده‌های مختلفی مانند CIFAR-10، ImageNet و Penn Tree Bank به دقت‌های بالایی دست یابند. این الگوریتم‌ها قادرند فضای وسیعی از معماری‌ها با ویژگی‌های متنوع مانند عمق، عرض، نوع اتصالات و عملیات‌ها را جست‌وجو کرده و معماری‌های بهینه را کشف کنند؛ بااین‌حال، یکی از چالش‌های اصلی NAS زمان جست‌وجوی طولانی آن‌هاست که ممکن است به ده‌ها هزار ساعت GPU برسد؛ هرچند با پژوهش‌های اخیر این زمان به ده‌ها ساعت کاهش یافته‌است؛ علاوه‌بر این، این روش‌ها بر حسب معمول تنها بر بهبود دقت شبکه تمرکز می‌کنند و به معیارهای مهمی مانند سرعت شبکه و منابع مصرفی توجهی ندارند. این مسئله استفاده مستقیم از آن‌ها را در سامانه‌های نهفته با محدودیت منابع مانند قدرت پردازشی، حافظه و انرژی مصرفی دشوار می‌سازد؛ بنابراین، نیاز به روش‌های جست‌وجوی آگاه از محدودیت‌های سامانه وجود دارد. در این مقاله، روشی بر اساس کاهش گرادیان ارائه می‌شود که به طور خودکار شبکه‌هایی مناسب برای اجرا بر روی پردازنده‌های چندهسته‌ای بدون GPU طراحی می‌کند. در این روش، یک اَبَرشبکه با مسیرهای موازی و بلوک‌های محاسباتی ایجاد می‌شود. برای انتخاب عملیات مناسب در هر بلوک از مسیر، از تعدادی متغیر تصمیم‌گیری استفاده می‌‌شود؛ همچنین، برای بهره‌مندی مسیرهای موازی از نتایج میانی یکدیگر و افزایش دقت شبکه، علاوه‌بر تصمیم‌گیری در مورد عملیات هر بلوک، درباره نقاط همگام‌سازی نیز تصمیم‌گیری می‌شود؛ سپس با آموزش متغیرهای تصمیم‌گیری (نوع بلوک و نقاط همگام‌سازی) هم‌زمان با وزن‌های اصلی شبکه، زیرشبکه مناسبی انتخاب می‌شود. در روش ارائه‌شده به‌دلیل استفاده از روش کاهش گرادیان، تنها دو بار فرایند آموزش انجام می‌شود. این امر زمان اجرا را نسبت به روش‌های مبتنی بر جست‌وجوی تکاملی و یادگیری تقویتی کاهش قابل‌توجهی می‌دهد؛ علاوه‌بر این، با درنظرگرفتن محدودیت‌های سامانه هدف مانند تعداد هسته‌ها و حافظه مصرفی، می‌توان به معماری مناسب‌تری نسبت به سایر روش‌ها دست یافت. آزمایش‌های انجام‌شده بر روی مجموعه‌داده CIFAR-10 نشان می‌دهد که روش پیشنهادی می‌تواند با زمان جست‌وجوی بسیار کم به دقت مناسبی دست یابد که کارایی آن را برای سامانه‌های نهفته با منابع محدود تأیید می‌کند. 2 Creating neural networks in a non-automatic way is a slow process based on trial and error. When the number of network parameters or the number of layers increases, the non-automatic method becomes very expensive and the final result may be suboptimal. Automatic network architecture search algorithms are used to solve this problem. Recently, these algorithms have been able to achieve high accuracies on various datasets such as CIFAR-10, ImageNet, and Penn Tree Bank. These algorithms have the ability to search a wide space of architectures with different characteristics such as network depth, width, connection method, and operations in order to discover architectures with appropriate accuracy. However, one of the traditional challenges of these algorithms is their high search time (Approximately tens of thousands of GPU hours), which has been reduced to tens of hours with new research. Another challenge that usually exists in these methods is their focus on improving network accuracy, while other criteria such as network speed and consumed resources are not taken into account. As a result, these methods cannot be used directly to find the optimal architecture in embedded systems that have limited resources such as processing power, memory, and energy consumption. Therefore, search methods should be devised that are aware of these limitations. Research has been done in this field in recent years, but these methods do not focus specifically on coarse-grained multi-core architectures that do not have a GPU. In this article, we present a method for the automatic design of networks that are suitable for running on multi-core processors. In this method, based on gradient descent, a SuperNet with parallel paths and computational blocks is created. The number of parallel paths is equal to or less than the number of cores. We use a series of decision variables to select appropriate operations in each block of the path. In addition to deciding on the operations performed in each block, deciding is also made regarding synchronization points to utilize the intermediate results of parallel paths and improve the network's accuracy. Then, by training the decision variables (block type and synchronization points) simultaneously with the main network weights, an appropriate subnetwork is selected. Due to the use of the gradient descent method in this approach, the training process is performed only twice, resulting in the final structure of the network. As a result, it has a much lower execution time compared to other methods based on evolutionary search and reinforcement learning. Additionally, considering the constraints of the target system, such as the number of cores and memory consumption, can lead to a more suitable architecture compared to other methods. Experiments conducted on the CIFAR-10 dataset demonstrate that the proposed method can achieve satisfactory accuracy with very little search time. 25 38 2023/11/132024/06/282024/01/23 1402/11/3 2025/03/82024/12/42025/03/8 1403/12/18 سهیل رستاری Soheil Rastari کارشناس ارشد دانشکده برق و کامپیوتر، دانشگاه صنعتی قم، قم، ایران ایران rastari.s@qut.ac.ir مرتضی محجل کفشدوز Morteza Mohajjel kafshdooz استادیار، دانشکده برق و کامپیوتر، دانشگاه صنعتی قم، قم، ایران ایران mohajjel@qut.ac.ir محبوبه شمسی Mahboubeh Shamsi دانشیار، دانشکده برق و کامپیوتر، دانشگاه صنعتی قم، قم، ایران ایران shamsi@qut.ac.ir Neural network architecture search embedded systems parallelization multi-core processors gradient descent method. جست‌وجوی معماری شبکه‌های عصبی سامانه‌های نهفته موازی‌سازی پردازنده‌های چند‌هسته‌ای روش کاهش گرادیان. T. Elsken, J. H.Metzen, and F. Hutter, "Neural Architecture Search: A Survey." arXiv, Apr. 26, 2019.##M. Wistuba, A. Rawat, and T. 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تشخیص پیام‌های درخواست و غیر درخواست در شبکه‌های اجتماعی با رویکردهای ترکیبی Recognizing request and non-request messages in social networks with combined approaches 1 1 امروزه با رشد روزافزون استفاده از شبکه­‌های اجتماعی، حجم داده‌های تولیدشده روبه‌افزایش است؛ از طرفی کسب‌وکارهای زیادی در شبکه‌های اجتماعی مختلف فعالیت دارند؛ به همین دلیل تشخیص نیازمندی­‌های کاربران برای بازاریابان در شبکه­‌های اجتماعی یکی از نیازمندی‌­های توسعه کسب‌وکارهای اینترنتی و تجارت الکترونیکی است؛ ازاین‌رو تشخیص خودکار پیام‌­های درخواست و به نوعی فیلترینگ آن­‌ها در متون فارسی حائز اهمیت است. پژوهش حاضر با هدف بهبود تشخیص پیام‌­های درخواست در مجموعه پیام‌­های ارسال‌شده در پیام­رسان­‌ها انجام ­شده‌است. امروزه شبکه‌­های اجتماعی به ‌راحتی در دسترس‌اند؛ ازاین‌رو پیام‌­ها در شبکه­‌های اجتماعی متفاوت با متون ادبی است. پیام‌­ها در شبکه­‌های اجتماعی دارای داده­‌های اضافی و عامیانه‌اند؛ از طرف دیگر واژه‌ها نیز شامل غلط­‌های املایی فراوان‌اند؛ بنابراین مقابله با این پیام‌­ها یک چالش محسوب می­‌شود. در این پژوهش ابتدا پیش‌­پردازش و حذف داده‌­های اضافی مورد بررسی قرار گرفته‌است. برای مقابله با دیگر چالش­‌های مطرح‌شده روش پیشنهادی در حین حفظ ارزش واژه‌ها، با غلط­‌های املایی نیز مقابله کرده‌است. پس از استخراج ویژگی­‌های مناسب، یک مدل ترکیبی مبتنی بر شبکه‌­های عصبی عمیق برای فرایند تشخیص پیام‌­های درخواست و طبقه‌بندی طراحی شد. در مرحله ارزیابی، آزمایش­‌های جامعی برای تحلیل عملکرد مدل پیشنهادی پیاده­‌سازی شد. مطابق نتایج به‌دست‌آمده precision، recall و f-score روش پیشنهادی، تقریباً برابر 90درصد است و در مقایسه با روش­‌های پیشین ارائه‌شده، به طور میانگین 5 درصد بهبود یافت. 2 The aim of the request recognition task in social networks is to understand the intent behind the posts, comments, or messages shared by users. Many businesses are actively present on various social networks, making it crucial to identify user needs for marketers in this space to foster the growth of online businesses and e-commerce. Detecting request messages automatically and filtering them is essential. However, social network messages often contain slang and numerous spelling errors, posing challenges for research in this domain. While extensive research has been conducted in English, studies on this task in Persian are limited. Telegram stands out as the most popular social network in Iran, with a large Persian-speaking user base. This study utilized a standard labeled Persian dataset from Telegram for training and testing purposes, comprising 85741 messages from the platform, evenly split between request and non-request categories. To tackle the significant challenges posed by sarcastic messages and spelling mistakes on social media platforms, we devised a multi-step hybrid strategy. The initial step involves preprocessing. Social media data typically consists of unstructured and slang-ridden user messages, necessitating preprocessing to enhance Persian text processing and reduce slang usage. The pre-processing phase is crucial when dealing with social media platforms. Because Telegram is unique compared to other platforms the data cleaning process varies. This study's accomplishment includes developing a unique dataset and filtering out noise from Telegram enhancing improvement in the pre-processing phase. Also, this involves normalizing different word forms, such as "beautiful" and "beauty," to maintain the integrity of word meanings. The subsequent step focuses on feature extraction. Various approaches to feature extraction come with their own set of advantages and drawbacks. Hence, we employed hybrid feature extraction methods to address this complexity. While Tf-Idf methods assess word importance without considering meaning, FastText retains semantic similarity. By combining the bag of words and FastText methods, our research aims to enhance accuracy. The final step involves classification, where deep learning networks are utilized to evaluate these features. Experimental findings indicate that our final model achieves precision, recall, and f-score rates of nearly 90%, representing a 5% improvement on average compared to previous methodologies. 39 52 2023/11/132024/06/282024/01/232024/04/18 1403/1/30 2025/03/82024/12/42025/03/82024/12/4 1403/9/14 پردیس مرادبیکی pardis moradbeiki دانشجوی دکترای دانشکده مهندسی برق و کامپیوتر دانشگاه صنعتی اصفهان، ایران ایران p.moradbeiki@ec.iut.ac.ir علیرضا بصیری alireza basiri استادیار دانشکده برق و کامپیوتر، دانشگاه صنعتی اصفهان، اصفهان، ایران ایران basiri@iut.ac.ir e-commerce request detection social networks messaging deep-learning based method. تجارت الکترونیکی تشخیص درخواست شبکه‌های اجتماعی پیام‌رسان روش مبتنی بر یادگیری عمیق خبرگزاری جمهوری اسلامی (2021) ، آخرین وضعیت شبکه‌های اجتماعی، https://www.irna.ir/news/##https://www.irna.ir/news/ (2021)##آ. پیک، م. زارع چاهوکی، م. آقاصرام، «تشخیص پیام‌های درخواست در پیام‌رسان تلگرام مبتنی بر اثربخشی کاهش وضعیت ها در مدل مخفی مارکوف»، دومین کنفرانس بین‌المللی پژوهش‌های کاربردی در علوم برق و کامپیوتر، 1397.##A. peyk, M. Zare Chahooki, "Detection of request messages in Telegram messenger based on the effectiveness of reducing situations in the hidden Markov model", Second International Conference on Applied Research in Electrical and Computer Science, 2019.##ع. محمدی، م. رضاییان، «تعیین قطبیت نظرات کاربران و تشخیص درخواست ها با کمک تکنیک‌های یادگیری عمیق در تلگرام»، چهارمین کنفرانس ملی تحقیقات کاربردی در مهندسی برق، مکانیک، کامپیوتر و فناوری اطلاعات، 1397.##A. Mohammadi, M. Rezaee, "Determining the polarity of users' opinions and recognizing requests with the help of deep learning techniques in Telegram", Fourth National Conference on Applied Research in Electrical Engineering, Mechanics, Computer and Information Technology, 2019.##م. دیانتی، م. صدرالدینی، ا. راسخ، ح. تقی‌زاده، «روشی مستقل از زبان جهت ریشه‌یابی کلمات با استفاده از معیار شباهت»، یازدهمین کنفرانس سراسری سیستم های هوشمند، 1391.##M. Dianati, M. Sadredini, "A language-independent method for rooting words using similarity criteria", 11th Iranian Conference on Intelligent Systems, 2013.##S. Moro, P. Cortez, P. Rita, "Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation", Expert Systems with Applications, vol. 42, no. 3, pp. 1314-24, 2015.##M. Yang, B. Jiang, Y. Wang, T. Hao, Y. Liu, "News text mining-based business sentiment analysis and its significance in economy", Frontiers in Psychology, vol. 13, pp. 1-7 , 2022.##H. Hassani, C. Beneki, S. Unger, MT. Mazinani, "Text mining in big data analytics", Big Data and Cognitive Computing, vol. 4, no. 1, 2020.##A. Gasparetto, M. Marcuzzo, A. Zangari, A. Albarelli, "A survey on text classification algorithms: From text to predictions", Information, vol. 13, no. 2, pp. 83, 2023.##F. Greco, A. Polli, "Emotional Text Mining: Customer profiling in brand management", International Journal of Information Management , vol. 51, pp. 101934, 2020.##A. Akundi, B. Tseng, J. Wu, E. Smith, "Text mining to understand the influence of social media applications on smartphone supply chain", Procedia Computer Science, vol. 140, pp. 87-94, 2018.##W. He, S. Zha, L. Li, "Social media competitive analysis and text mining: A case study in the pizza industry", International journal of information management, vol. 33, no. 3, pp. 464-72, 2013.##W. Souma, I. Vodenska, H. Aoyama, "Enhanced news sentiment analysis using deep learning methods", Journal of Computational Social Science, vol. 2, no. 1, pp. 33-46, 2019.##S. Mohan, S. Mullapudi, S. Sammeta, "Stock price prediction using news sentiment analysis", In2019 IEEE fifth international conference on big data computing service and applications (BigDataService), pp. 205-208, 2019.##S. Negash, "Business intelligence", Communications of the association for information systems, vol. 13, no. 15, pp. 177-195, 2004.##H. Chen, RHL. Chiang, VC. Storey, "Business intelligence and analytics: From big data to big impact", MIS quarterly, vol. , no. 1, pp. 1165-1188, 2012.##J. Park, V. Barash, C. Fink, M. Cha, "Emoticon style: Interpreting differences in emoticons across cultures", In Proceedings of the international AAAI conference on web and social media, vol. 7, no. 1, pp. 466-475, 2013.##K. Spirovski, E. Stevanoska, A. Kulakov, "Comparison of different model's performances in task of document classification", In Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics, Novi Sad, Serbia, pp. 1-12, 2018.##KS. Kyaw, P. Tepsongkroh, C. Thongkamkaew, "Business Intelligent Framework Using Sentiment Analysis for Smart Digital Marketing in the E-Commerce Era", Asia Social, vol. 16, no. 3, pp. e252965-e252965, 2023.##D. Yan, K. Li, S. Gu, L. Yang, "Network-based bag-of-words model for text classification", IEEE Access, vol. 8, pp. 82641-82652, 2020.##WA. Qader, MM. Ameen, "An overview of bag of words; importance, implementation, applications, and challenges", In2019 international engineering conference (IEC) , pp. 200-204, 2019.##C. Niu, W. Zhang, S. Byna, Y. Chen, "Kv2vec: A Distributed Representation Method for Key-value Pairs from Metadata Attributes", IEEE High Performance Extreme Computing Conference (HPEC), pp. 1-7, 2022.##J. Cai, J. Luo, S. Wang, S. Yang, "Feature selection in machine learning: A new perspective", Neurocomputing, vol. 300, pp. 70-79, 2023.##DH. Hubel, TN. Wiesel, "Receptive fields, binocular interaction and functional architecture in the cat's visual cortex", The Journal of physiology, vol. 160, no. 1, pp. 106-154, 1962.##A. Joulin, E. Grave, P. Bojanowski, M. Douze, "Fasttext. zip: Compressing text classification models", arXiv preprint, 2016.##P. Bojanowski, E. Grave, A. Joulin, "Enriching word vectors with subword information", Transactions of the association for computational linguistics, vol. 5, pp. 135-146, 2017.##JL. Elman, "Finding structure in time", Cognitive Science, vol. 14, no. 2, pp. 179-211, 1990.##S. Hochreiter, J. Schmidhuber, "Long short-term memory", Neural computation, vol. 9, no. 8, pp. 1735-1780, 1997.##خبرگزاری جمهوری اسلامی (2021) ، آخرین وضعیت شبکه‌های اجتماعی، https://www.irna.ir/news/##https://www.irna.ir/news/ (2021)##آ. پیک، م. زارع چاهوکی، م. آقاصرام، «تشخیص پیام‌های درخواست در پیام‌رسان تلگرام مبتنی بر اثربخشی کاهش وضعیت ها در مدل مخفی مارکوف»، دومین کنفرانس بین‌المللی پژوهش‌های کاربردی در علوم برق و کامپیوتر، 1397.##A. peyk, M. Zare Chahooki, "Detection of request messages in Telegram messenger based on the effectiveness of reducing situations in the hidden Markov model", Second International Conference on Applied Research in Electrical and Computer Science, 2019.##ع. محمدی، م. رضاییان، «تعیین قطبیت نظرات کاربران و تشخیص درخواست ها با کمک تکنیک‌های یادگیری عمیق در تلگرام»، چهارمین کنفرانس ملی تحقیقات کاربردی در مهندسی برق، مکانیک، کامپیوتر و فناوری اطلاعات، 1397.##A. Mohammadi, M. Rezaee, "Determining the polarity of users' opinions and recognizing requests with the help of deep learning techniques in Telegram", Fourth National Conference on Applied Research in Electrical Engineering, Mechanics, Computer and Information Technology, 2019.##م. دیانتی، م. صدرالدینی، ا. راسخ، ح. تقی‌زاده، «روشی مستقل از زبان جهت ریشه‌یابی کلمات با استفاده از معیار شباهت»، یازدهمین کنفرانس سراسری سیستم های هوشمند، 1391.##M. Dianati, M. Sadredini, "A language-independent method for rooting words using similarity criteria", 11th Iranian Conference on Intelligent Systems, 2013.##S. Moro, P. Cortez, P. Rita, "Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation", Expert Systems with Applications, vol. 42, no. 3, pp. 1314-24, 2015.##M. Yang, B. Jiang, Y. Wang, T. Hao, Y. Liu, "News text mining-based business sentiment analysis and its significance in economy", Frontiers in Psychology, vol. 13, pp. 1-7 , 2022.##H. Hassani, C. Beneki, S. Unger, MT. Mazinani, "Text mining in big data analytics", Big Data and Cognitive Computing, vol. 4, no. 1, 2020.##A. Gasparetto, M. Marcuzzo, A. Zangari, A. Albarelli, "A survey on text classification algorithms: From text to predictions", Information, vol. 13, no. 2, pp. 83, 2023.##F. Greco, A. Polli, "Emotional Text Mining: Customer profiling in brand management", International Journal of Information Management , vol. 51, pp. 101934, 2020.##A. Akundi, B. Tseng, J. Wu, E. Smith, "Text mining to understand the influence of social media applications on smartphone supply chain", Procedia Computer Science, vol. 140, pp. 87-94, 2018.##W. He, S. Zha, L. Li, "Social media competitive analysis and text mining: A case study in the pizza industry", International journal of information management, vol. 33, no. 3, pp. 464-72, 2013.##W. Souma, I. Vodenska, H. Aoyama, "Enhanced news sentiment analysis using deep learning methods", Journal of Computational Social Science, vol. 2, no. 1, pp. 33-46, 2019.##S. Mohan, S. Mullapudi, S. Sammeta, "Stock price prediction using news sentiment analysis", In2019 IEEE fifth international conference on big data computing service and applications (BigDataService), pp. 205-208, 2019.##S. Negash, "Business intelligence", Communications of the association for information systems, vol. 13, no. 15, pp. 177-195, 2004.##H. Chen, RHL. Chiang, VC. Storey, "Business intelligence and analytics: From big data to big impact", MIS quarterly, vol. , no. 1, pp. 1165-1188, 2012.##J. Park, V. Barash, C. Fink, M. Cha, "Emoticon style: Interpreting differences in emoticons across cultures", In Proceedings of the international AAAI conference on web and social media, vol. 7, no. 1, pp. 466-475, 2013.##K. Spirovski, E. Stevanoska, A. Kulakov, "Comparison of different model's performances in task of document classification", In Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics, Novi Sad, Serbia, pp. 1-12, 2018.##KS. Kyaw, P. Tepsongkroh, C. Thongkamkaew, "Business Intelligent Framework Using Sentiment Analysis for Smart Digital Marketing in the E-Commerce Era", Asia Social, vol. 16, no. 3, pp. e252965-e252965, 2023.##D. Yan, K. Li, S. Gu, L. 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Joulin, "Enriching word vectors with subword information", Transactions of the association for computational linguistics, vol. 5, pp. 135-146, 2017.##JL. Elman, "Finding structure in time", Cognitive Science, vol. 14, no. 2, pp. 179-211, 1990.##S. Hochreiter, J. Schmidhuber, "Long short-term memory", Neural computation, vol. 9, no. 8, pp. 1735-1780, 1997.## ##

مسیریابی هوشمند ماشین حمل پول در شبکه ترافیک شهری شیراز Intelligent routing of the money-carrying vehicle in the urban traffic network of Shiraz 1 1 با رشد و توسعه شهری، نیاز به سامانه‌های حمل‌ونقل امن و بهینه در حوزه ترابری شهری بیش از پیش احساس می‌شود. یکی از چالش‌های مهم در این زمینه، مسیریابی ماشین‌های حمل پول بانک‌ها به‌دلیل ماهیت حساس و خطرناک آن، نیازمند برنامه‌ریزی دقیق و هوشمند است. چالش‌های اصلی این حوزه شامل امنیت، زمان‌بندی، ترافیک و انتخاب مسیر بهینه است. این مقاله با هدف ارائه روشی نوآورانه برای مسیریابی هوشمند ماشین‌های حمل پول، مسئله مسیریابی شهری را به مسئله فروشنده دوره‌گرد (TSP) نگاشت می‌کند. در این روش، از الگوریتم بهینه‌سازی کلونی مورچه‌ها (ACO) استفاده شده است و محدودیت‌های واقعی مانند ترافیک سنگین، امنیت پایین و خطرات جاده‌ای به‌عنوان وزن‌های اضافی درنظر گرفته می‌شوند. داده‌های مورد استفاده شامل نقشه ترافیک شهری شیراز و موقعیت‌های بانک‌های مختلف است. نتایج نشان می‌دهد که روش پیشنهادی قادر است مسیرهایی را انتخاب کند که از مناطق پرترافیک، جرم‌خیز و خطرناک دوری کرده و در عین حال، زمان سفر را بهینه‌سازی کند. این روش نه‌تنها امنیت و کارایی را افزایش می‌دهد؛ بلکه به کاهش هزینه‌های عملیاتی نیز کمک می‌کند. با توجه به ارزیابی‌های مقایسه‌ای انجام‌شده، الگوریتم پیشنهادی با ترکیب عواملی مانند داده‌های ترافیکی، نرخ امنیت و شرایط رانندگی و جاده، راه‌حلی واقعی‌تر و جامع‌تر نسبت به مدل‌های دیگر ارائه کرده‌است. این رویکرد در ایجاد تعادل بین جنبه‌های چندوجهی مسیریابی مانند ایمنی، وقت‌شناسی و هزینه کارآمد بوده‌است. توانایی این الگوریتم برای اجتناب از مسیرهایی با ترافیک سنگین، نرخ پایین امنیت و شرایط نامناسب جاده، ایمنی و کاهش زمان سفر در ماشین‌های حمل پول را به طور چشم‌گیری افزایش داده‌است؛ همچنین، روش پیشنهادی با نگاشت مسئله مسیریابی شهری به مسئله فروشنده دوره‌گرد و استفاده از الگوریتم ACO توانسته است، مسیرهایی بهینه و ایمن ارائه دهد. این روش در مقایسه با مقالات دیگر، از نظر دقت، زمان اجرا، مصرف انرژی و امنیت عملکرد بهتری دارد. استفاده از داده‌های ترافیکی واقعی و توانایی تطبیق با تغییرات پویا، اعتبار و کاربردی‌بودن روش پیشنهادی را افزایش داده‌است. در کل، این مقاله با ارائه یک روش نوآورانه برای مسیریابی هوشمند خودروهای حمل پول، گامی مهم در جهت بهبود کارایی و امنیت عملیات بانکی برداشته است. 2 With the rapid growth and development of urban areas, the demand for secure and efficient transportation systems in urban logistics has become increasingly critical. One of the most pressing challenges in this domain is the routing of bank cash-in-transit (CIT) vehicles, which, due to their sensitive and high-risk nature, require precise and intelligent planning. The primary challenges for CIT vehicles include ensuring security, optimizing timing, managing traffic congestion, and selecting the most efficient routes. This paper proposes an innovative method for the intelligent routing of CIT vehicles by mapping the urban routing problem to the Traveling Salesman Problem (TSP). The proposed approach leverages the Ant Colony Optimization (ACO) algorithm, enhanced with real-world constraints such as heavy traffic, low-security areas, and road hazards, which are incorporated as additional weights in the optimization process. The data used in this study includes the urban traffic map of Shiraz, Iran, and the locations of various banks. The results demonstrate that the proposed method effectively selects routes that avoid high-traffic zones, crime-prone areas, and hazardous roads while optimizing travel time. This approach not only enhances security and operational efficiency but also contributes to reducing operational costs. The core innovation of this research lies in its ability to map the urban routing problem to the TSP, a well-known combinatorial optimization problem, and to utilize the ACO algorithm, which is inspired by the foraging behavior of ants. In nature, ants leave pheromone trails to communicate and find the shortest path between their nest and food sources. Similarly, the ACO algorithm employs artificial ants to explore possible routes, leaving virtual pheromones to guide subsequent ants toward optimal paths. In this study, the ACO algorithm is further enhanced by incorporating heuristic information such as traffic volume, security rates, and unsafe driving conditions, which are treated as critical factors in the routing process. The implementation of the proposed method utilizes real-world data from the urban traffic map of Shiraz, including the locations of eight major banks and the routes connecting them. The distances between these locations are calculated using the Haversine formula, which accounts for the Earth's curvature to provide accurate geographical distances. The algorithm is tested with various parameters, including different numbers of artificial ants (ranging from 10 to 200), evaporation rates (0.1 to 0.5), and exploration-exploitation trade-offs (alpha and beta values). The results show that the proposed method can effectively identify routes that minimize travel time while avoiding high-traffic areas, crime-prone zones, and hazardous roads. One of the key contributions of this research is the integration of multiple heuristic factors into the ACO algorithm. Traditional routing algorithms often focus solely on minimizing distance or travel time, neglecting critical real-world constraints. In contrast, the proposed method assigns weights to factors such as traffic volume, security levels, and unsafe driving conditions, allowing the algorithm to prioritize safer and more efficient routes. For example, routes passing through areas with high crime rates or heavy traffic are penalized, reducing their likelihood of being selected. This approach ensures that the final route is not only the shortest but also the safest and most reliable. Comparative evaluations indicate that the proposed algorithm offers a more realistic and comprehensive solution compared to other models. By balancing multifaceted aspects of routing such as safety, timeliness, and cost, the method proves to be highly effective. The algorithm's ability to avoid routes with heavy traffic, low security, and poor road conditions significantly enhances the safety and time efficiency of CIT vehicles. Furthermore, the proposed method represents a significant step forward in improving the efficiency and security of banking operations by providing an innovative approach to intelligent routing for CIT vehicles. By mapping the urban routing problem to the TSP and utilizing the ACO algorithm with real-world constraints, the method delivers optimal and secure routes. In comparison to other studies, the proposed method demonstrates superior performance in terms of accuracy, execution time, energy consumption, and security. The use of real-time traffic data and the algorithm's ability to adapt to dynamic changes further enhance the practicality and reliability of the proposed solution. This adaptability makes the method particularly suitable for urban environments with fluctuating traffic patterns and evolving security challenges. In conclusion, this paper presents a novel and effective approach to intelligent routing for CIT vehicles in urban environments. By combining the strengths of the ACO algorithm with real-world constraints, the proposed method offers a comprehensive solution that balances efficiency, security, and reliability. Future work could explore the integration of machine learning techniques to further enhance the algorithm's predictive capabilities, enabling it to anticipate and respond to emerging traffic and security challenges proactively. This research marks a significant advancement in the field of urban logistics, providing a robust framework for the safe and efficient routing of high-risk transportation systems. 53 70 2023/11/132024/06/282024/01/232024/04/182024/04/3 1403/1/15 2025/03/82024/12/42025/03/82024/12/42025/03/15 1403/12/25 سمیرا اسدزاده samira asadzadeh دانشجوی دکترای گروه مهندسی کامپیوتر، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران ایران samira.assadzadeh@gmail.com الهام پروین نیا Elham parvinnia دانشیار گروه مهندسی کامپیوتر، واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران ایران Elham.parvinnia@iau.ac.ir Intelligent routing Ant Colony Optimization Traveling Salesman Problem route optimization traffic security مسیریابی هوشمند الگوریتم کلونی مورچه‌ها مسئله فروشنده دوره‌گرد بهینه‌سازی مسیر امنیت ترافیک. M. A. Fadhel, A. M. Duhaim, A. Saihood, A. Sewify, M. N. Al-Hamadani, A. S. Albahri, ... & Y. 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Jung, "ACO-based traffic routing method with automated negotiation for connected vehicles," Complex & Intelligent Systems, vol. 9, no. 1, pp. 625-636, Feb. 2023.##T. H. Nguyen and J. J. Jung, "Ant colony optimization-based traffic routing with intersection negotiation for connected vehicles," Applied Soft Computing, vol. 112, p. 107828, Nov. 1, 2021.##H. Han, J. Tang, and Z. Jing, "Wireless sensor network routing optimization based on improved ant colony algorithm in the Internet of Things," Heliyon, vol. 10, no. 1, Jan. 15, 2024.##O. Sbayti and K. Housni, "A new routing method based on ant colony optimization in vehicular ad-hoc network," Statistics, Optimization & Information Computing, vol. 12, no. 1, pp. 167-181, 2024.##B. Wu, Z. Zuo, M. Zhou, X. Wan, X. Zhao, and S. Yang, "A Multi-Objective Ant Colony System-Based Approach to Transit Route Network Adjustment," IEEE Transactions on Intelligent Transportation Systems, Jan. 16, 2024.##R. Ramamoorthy, "An Enhanced Location-Aided Ant Colony Routing for Secure Communication in Vehicular Ad Hoc Networks," Human-Centric Intelligent Systems, pp. 1-28, Jan. 10, 2024.##N. Saeedi and S. Babaie, "A New Hybrid Routing Algorithm based on Genetic Algorithm and Simulated Annealing for Vehicular Ad hoc Networks," JSDP, vol. 19, no. 2, p. 5, 2022.##S. Borumand, A. Hesampour, M. Kuchaki, and M. Rafsanjani, "Intuitionistic fuzzy logic for adaptive energy efficient routing in mobile ad-hoc networks," JSDP, vol. 18, no. 1, pp. 12-3, 2021.##J. Ćelić, B. Mandžuka, V. Tomas, and F. Tadić, "Driver-centric urban route planning: Smart search for parking," Sustainability, vol. 16, no. 2, p. 856, 2024.##K. Haseeb, A. Rehman, T. Saba, S. A. Bahaj, H. Wang, and H. Song, "Efficient and trusted autonomous vehicle routing protocol for 6G networks with computational intelligence," ISA Transactions, vol. 132, pp. 61-68, 2023.##J. Bai, J. Sun, Z. Wang, X. Zhao, A. Wen, C. Zhang, and J. Zhang, "An adaptive intelligent routing algorithm based on deep reinforcement learning," Computer Communications, vol. 216, pp. 195-208, 2024.##B. Bai, Y. Li, S. Ding, L. Qiao, Y. Wu, and X. Li, "Research on relieve of traffic congestion based on optimization of DQN algorithm in CamTra recognition system," Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 09544070241280671, 2024.##M. Scianna, "The AddACO: A bio-inspired modified version of the ant colony optimization algorithm to solve travel salesman problems," Mathematics and Computers in Simulation, vol. 218, pp. 357-382, 2024.##M. A. Fadhel, A. M. Duhaim, A. Saihood, A. Sewify, M. N. Al-Hamadani, A. S. Albahri, ... & Y. Gu, "Comprehensive systematic review of information fusion methods in smart cities and urban environments," Information Fusion, vol. 102317, 2024.##D. N. 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طبقه‌بندی خودکار فازفولاد در تصاویر میکروسکوپ الکترونی روبشی Automated Classification of Steel Phases in Scanning Electron Microscope Images 1 1 خواص فولادها وابسته به اجزای ریزساختاری به نام فاز است که در فرایند تولید آن شکل می‌گیرد. فازهای مختلف فولاد در تصاویر میکروسکوپی سطح فولاد قابل مشاهده‌اند. تشخیص و طبقه‌بندی خودکار انواع فازها از روی تصاویر منجر به درک بهتری از خواص فولاد با سرعت و دقت بالاتر می‌شود. در این مقاله برای نخستین‌بار، روشی برای طبقه‌بندی خودکار و هوشمند فازهای فولاد از روی تصاویر میکروسکوپی ارائه می‌شود. این کار نیازمند تعریف و استخراج ویژگی‌های مناسب بافت منحصربه‌فرد این تصاویر و بخش‌بندی تصاویر به مناطقی با اشکال بسیار نامنظم بر اساس ویژگی‌های استخراج شده‌است؛ بدین منظور ابتدا تصویر ورودی بلوک‌بندی شده و برای هر بلوک به‌صورت مستقل یک سری از ویژگی‌های بافت تصویر استخراج می‌شوند. تعداد این ویژگی‌ها به‌وسیله روش تحلیل مؤلفه‌های اصلی کاهش یافته و سپس به شبکه عصبی بیشینه‌هموار برای طبقه‌بندی هر بلوک از تصویر داده می‌شوند. نتایج پیاده‌سازی نشان می‌دهد که دقت روش پیشنهادی در طبقه‌بندی دو فاز سوزنی و مرزدانه‌ای بالای 99 درصد و همچنین در طبقه‌بندی سه فاز مرزدانه‌ای، سوزنی و ویدمن اشتاتن بالای 86 درصد است. 2 The properties of steels are intrinsically dependent on their microstructural components, known as phases, which form during the manufacturing process. Different steel phases can be observed in microscopic images of steel surfaces. Automatic detection and classification of these phases from images can significantly enhance the understanding of steel properties with improved speed and accuracy. This paper introduces, for the first time, an intelligent and automated method for classifying steel phases from microscopic images. This process requires defining and extracting suitable texture features unique to these images and segmenting the images into highly irregular regions based on the extracted features. To achieve this, the input image is initially divided into blocks, and texture features are extracted independently for each block. The dimensionality of these features is then reduced using Principal Component Analysis, and the refined features are subsequently fed into a Softmax neural network for classification. The implementation results indicate that the proposed method achieves an accuracy of over 99% in distinguishing between two phases: acicular ferrite and granular ferrite. Furthermore, it attains an accuracy exceeding 86% when classifying three phases: granular ferrite, acicular ferrite, and Widmanstätten ferrite. This suggests that the widely used and conventional k-means clustering method, as a traditional machine learning approach, is incapable of effectively distinguishing microscopic steel phase blocks using extracted texture features. Notably, as of the writing of this paper, no prior research has been conducted on the automatic classification of different ferrite phases, making this study a novel contribution to the field. In this research, an automated classification algorithm for ferrite phase structures in SEM images of steel is proposed using texture feature extraction methods and machine learning models. The dataset comprises images of 1024×768 resolution, which were divided into 128×128 blocks, with classification performed independently for each block. Due to the limited number of blocks available for training machine learning models, data augmentation techniques such as rotation and scaling were applied to increase the dataset size. Various image processing methods were used to extract 128 texture features. These extracted features were then used to classify different ferrite phases using two machine learning models: k-means clustering and the Softmax neural network. Additionally, PCA was employed to reduce feature dimensionality, which positively impacted the classification of granular and acicular ferrite. While k-means clustering, as a conventional and widely used machine learning method, failed to achieve satisfactory classification accuracy, the proposed approach using a smooth maximum neural network demonstrated exceptional performance. Despite the complex and irregular nature of ferrite shapes, the selected features and the proposed algorithm successfully achieved over 99% accuracy for two-phase classification and over 86% accuracy for three-phase classification. 71 82 2023/11/132024/06/282024/01/232024/04/182024/04/32023/09/26 1402/7/4 2025/03/82024/12/42025/03/82024/12/42025/03/152025/03/8 1403/12/18 زهرا فیروز مهجن آبادی Zahra Firuz Mahjanabadi کارشناس‌ارشد گروه مهندسی مخابرات، دانشکده مهندسی برق و کامپیوتر، دانشگاه سیستان و بلوچستان، زاهدان، ایران ایران zahra.firooz2020@gmail.com پوریا جعفری Pouria Jafari استادیار گروه مهندسی برق و الکترونیک، دانشکده مهندسی برق و کامپیوتر، دانشگاه سیستان و بلوچستان، زاهدان، ایران ایران pjafari@ece.usb.ac.ir مهدی رضایی Mehdi Rezaei دانشیار گروه مهندسی برق و الکترونیک، دانشکده مهندسی برق و کامپیوتر، دانشگاه سیستان و بلوچستان، زاهدان، ایران ایران mehdi.rezaei@ece.usb.ac.ir Scanning Electron Microscope Automated Classification Steel Phases Neural Networks K-Means Algorithm. تصاویر میکروسکوپی الکترونی روبشی فازهای فولاد طبقه‌بندی خودکار شبکه عصبی مصنوعی بیشینه‌هموار الگوریتم کی-میانگین. 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مروری بر روش‌های ردیابی مبتنی بر بینایی؛ ویژگی‌های زمانی و مکانی A Review of Vision-Based Tracking Methods: Temporal and Spatial Features 1 1 ردیابی مبتنی بر بینایی که یکی از پرچالش‌ترین زمینه‌های موجود در بینایی ماشین است به معنای دنبال‌کردن یک یا چند هدف در دنباله‌ای از تصاویر است؛ چالش‌هایی نظیر تغییر در ظاهر هدف، پوشانده‌شدن با عوامل محیطی، حرکات سریع و ناگهانی که هر یک زمینه پژوهشی فعالی را به خود اختصاص داده‌اند. دو شاخص مهم در عملکرد یک ردیاب، سرعت اجرا و دقت آن هستند. با ساده‌ترشدن الگوریتم سرعت افزایش‌ می‌یابد؛ اما از دقت کاسته می‌شود و تعامل میان این دو، موضوع مهمی به‌ویژه در پیاده‌سازی‌های عملی است. در این پژوهش به بررسی جامع و پیاده‌سازی الگوریتم‌های ردیابی مختلف پرداخته و روش‌های مناسب با کاربردهای عملی معرفی شده‌است. از سوی دیگر ساختارهای مختلف ردیابی بررسی و بر اساس ویژگی‌های مکانی، زمانی، بصری و حرکتی دسته‌بندی شده‌اند؛ همچنین به‌دلیل توسعه روش‌های یادگیری عمیق و تأثیر آن‌ها در ردیابی، معماری‌های عمیق، پایگاه‌های داده، مجموعه‌های آموزشی، روش‌ها و استانداردهای ارزیابی معرفی شده و افق پیشِ‌روی این حوزه مورد بحث قرار گرفته‌است. بررسی‌ها نشان می‌دهد که ویژگی‌های زمانی و حرکتی علی‌رغم تأثیر مطلوب بر عملکرد ردیابی کمتر مورد توجه قرار گرفته‌اند. با توسعه شبکه‌های عمیق حافظه‌دار استفاده از این ویژگی‌ها روبه‌افزایش بوده و سهم بیشتری را در ردیابی به خود اختصاص داده‌اند؛ ازاین‌رو ویژگی‌های زمانی و حرکتی با تمرکز بیشتری بررسی شده‌اند. 2 Vision-based object tracking, as one of the most challenging fields in machine vision, means following the target(s) in the sequence of image frames in the presence of various challenges. In general, tracking algorithms can be classified to the single-target and multi-target based on the number of objects that should be tracked in frames. Trackers use two basic features in tracking: the appearance and motion. The appearance features are extracted from independent images but the motion features are produced through sequence of frames. According to the evaluations, motion models improve the tracking performance and take less process compared to the appearance features. Our investigations show that in contrast of single-target algorithms, the multi-target algorithms consider more contribution for the motion models, and due to the multiplicity of objectives in the scene they focus less on the appearance features. Despite the wide range of methods and significant progress in machine vision, reliable and flawless performance cannot be expected in the use of tracking algorithms with real-time criteria. This will be aggravated if one of the challenges occurs. Challenges such as sudden and fast movements by the target, occlusion by obstacles or other targets in the scene, extreme changes in the appearance and dimensions of the target, as well as entering and exiting the scene, which cause tracking algorithms to fail. Having a good trade-off between the accuracy and the execution speed is one of the main problems for applied tracking algorithms. Detection algorithms, which are known to detect different targets in an independent image, have shown acceptable accuracy, but it is not possible to use them in every frame for a real-time tracking, because either due to the high processing volume of these algorithms, the execution speed of the detector is limited or they are only able to identify certain classes. But the purpose of a general tracking is to follow an object in a sequence of images regardless of its type and class as well as considering temporal and spatial dependencies among successive frames. With the development of recurrent neural networks and their great ability to process sequential data such as text, audio and video, their use in tracking algorithms is increasing. The use of these networks has helped to improve the performance of tracking algorithms due to their short-term and long-term memory in maintaining important features during tracking. Different methods of integrating convolutional and recurrent neural networks are presented and showed grate performance in tracking, but the main drawback of most of them is the low execution speed of the algorithms. Our studies show that direct feeding the high-dimensional inputs, such as features extracted from images, to the recurrent networks greatly reduces their processing speed. Therefore, in some methods with the approach of real-time execution, the dimensions of the recurrent networks input are downsampled and reduced to the smaller size, although the accuracy is also slightly reduced. Our investigations show that the use of motion models in single-target tracking algorithms is less explored compared to the multi-target methods. Meanwhile, the studies show the success of these models in improving tracking performance. Before the introduction of convolutional networks and their remarkable success in extracting deep features from the image, motion models were mostly used, but in recent methods, especially in single-target trackers, appearance features are used more. In single-target algorithms, the presence of only one object in the image and less computational volume compared to multi-target algorithms allows for more free use of appearance features, but this is not possible in multi-target tracking due to the multiplicity of targets so the motion models are more useful in these algorithms. Therefore, in this paper, a more detailed investigation of motion models and their effect on tracking performance is done. The results show that motion models have a profound effect on improving tracking performance while being simple and impose low processing volume. In this paper, a comprehensive review and implementation of different tracking algorithms is discussed and appropriate methods are introduced for practical implementations. On the other hand, different tracking structures are investigated and categorized based on spatial, temporal, appearance and motion features. Also, due to the development of deep learning methods and their impact on tracking, deep architectures, training datasets and standard evaluation methods are studied and the future horizon of this field is discussed. Our studies show that temporal and motion features have received less attention despite their favorable impact on tracking performance. With the development of deep memory networks, the use of these features is increasing and they have taken a greater portion in tracking. 83 112 2023/11/132024/06/282024/01/232024/04/182024/04/32023/09/262023/11/24 1402/9/3 2025/03/82024/12/42025/03/82024/12/42025/03/152025/03/82024/12/4 1403/9/14 محمدحسین بیات Mohammad Hosein Bayat دانشجوی دکتری مکاترونیک، دانشکدگان علوم و فناوری‌های میان‌رشته‌ای، دانشگاه تهران، تهران، ایران ایران mhbayat@ut.ac.ir بهرام تارویردی زاده Bahram Tarvirdizadeh دانشیار گروه مکاترونیک، دانشکدگان علوم و فناوری‌های میان‌رشته‌ای، دانشگاه تهران، تهران، ایران ایران bahram@ut.ac.ir محمد شهبازی Mohammad Shahbazi استادیار گروه ساخت و تولید، دانشکده مکانیک، دانشگاه علم و صنعت، تهران، ایران ایران shahbazi@iust.ac.ir Vision-Based Object Tracking Appearance Features Motion Features Deep Learning Machine Vision. ردیابی مبتنی بر بینایی ویژگی‌های ظاهری ویژگی‌های حرکتی یادگیری عمیق بینایی ماشین. M. Biglari, A. Soleimani, and H. 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مروری نقادانه بر روش‌های بازیابی محتوامحور و معناگرای تصاویر a Critical Survey on Content-Based & Semantic Image Retrieval – Abstract 1 1 تعداد، تنوع و پیچیدگی محتوای تصویری در دنیای رقمی به‌سرعت در حال افزایش است و این موضوع نیاز به طراحی و پیاده‌سازی سامانه‌های جویش و بازیابی محتوای تصویری را بسیار محسوس کرده‌است؛ در حال حاضر با مقیاس عظیمی از داده‌های تصویری در فضای وب روبه‌روهستیم که راه‌کارهای معمولِ مبتنی بر فراداده‌های دستی و انسانی پاسخ‌گوی تنوع و تعداد بسیار زیاد آن‌ها نیست. حجم عظیم داده‌های تولیدی در محیط وب، بدون راه‌کاری با دقت و سرعت بالا در درک و بازیابی آن‌ها، به آرشیو‌های ابدی رقمی خواهند پیوست و هرگز دوباره پیدا نخواهند شد. در سال‌های اخیر تلاش‌های بسیاری برای بازیابی این تصاویر، به‌‌ویژه در مبحث «بازیابی ‌محتوامحور» (CBIR) و «بازیابی معناگرای‌» (SIR) تصویر شده‌است. سامانه‌های بازیابی محتوامحور و معناگرای تصویر، توانایی جست‌وجو و بازیابی تصاویر بر اساس محتوای درونی و معانی سطح بالای انسانی را دارد، نه فراداده‌هایی‌ که‌ ممکن است، همراه با آن ثبت شده باشند. این مقاله، مروری جامع بر آخرین پیشرفت‌ها در زمینۀ بازیابی محتوامحور تصاویر در سال‌های اخیر ارائه کرده و تلاش دارد با رویکردی نقادانه، نقاط مثبت و منفی هر حوزۀ پژوهشی مطرح در مبحث بازیابی محتوامحور را بیان کند و نمایی کلی از چهارچوب این فرایند و پیشرفت‌های این حوزه ارائه دهد که شامل زمینه‌هایی همچون پیش‌پردازش تصویر، استخراج و تعبیۀ ویژگی‌ها (Feature Embedding)، یادگیری ماشینی، مجموعه‌داده‌های مطرح در این حوزه، تطبیق شباهت و ارزیابی عملکرد است؛ درنهایت، رویکردهای پژوهشیِ اصیل، چالش‌ها و پیشنهادهایی برای پیشرفت بهتر پژوهش‌ها در این حوزه ارائه شده‌است.   2 The rapid increase in the volume, diversity, and complexity of visual content in the digital world has made the need for designing and implementing visual content search and retrieval systems highly evident. Currently, we are facing a massive scale of visual data on the web, for which the conventional approaches based on manual and human-generated metadata are not sufficient to handle the diversity and sheer volume. The enormous volume of data generated on the web, without a high-accuracy and high-speed solution for understanding and retrieving it, will join the digital archives forever and never be found again. Recently, there have been significant efforts for retrieving these images, particularly in the fields of Content-Based Image Retrieval (CBIR) and Semantic Image Retrieval (SIR). Content-based and semantic image retrieval systems have the capability to search and retrieve images based on their internal content and high-level human-understandable semantics, rather than just the metadata that may be associated with them. This paper provides a comprehensive review of the latest advancements in the field of content-based image retrieval in recent years. It aims to critically discuss the strengths and weaknesses of each research area in content-based retrieval, and provide an overall framework of this process and the progress made in areas such as image preprocessing, feature extraction and embedding, machine learning, benchmark datasets, similarity matching, and performance evaluation. Finally, the paper presents novel research approaches, challenges, and suggestions for better advancing research in this field. The sections of the paper are organized as follows: After the introduction, Section 2 describes the components of a CBIR system framework, and with a cursory look at classical and traditional methods, it will delve into the workings of modern approaches and their associated challenges. In Section 3, we will provide an overview of the concept of "relevance feedback" and explain the need for this method to enhance the retrieval performance in CBIR systems, followed by an introduction to the prominent solutions in this domain. Finally, in Section 4, we will present a review of the image datasets commonly used in the field of content-based image retrieval, along with a discussion of their characteristics. IGiven the recent advancements in the field of computer vision and image processing, especially in the area of "image-text relationship" and how to integrate the two to improve retrieval performance, the focus of a large part of this study has been on the solutions in this area and the performance of the prominent methods. The current main research in this field is monopolized by large companies and organizations with access to vast financial resources, which has slowed down the progress of research and academic work in this field. These companies, with access to unimaginable data and financial resources, have trained well-known and sometimes unknown models on a very large scale (billions of images and texts), and after the training is complete, they have placed the final model in various web services without publishing the details of the research conducted. The important point is that the scale law applies in this field, and any entity that has more access to computational and storage resources will be able to train better and more accurate models, which has made it less possible for small research units and universities to enter this field and wait for the publication of research by the aforementioned organizations and companies. There is a dire need to introduce effective solutions in this field that require limited resources and are capable of achieving high accuracy and competitiveness with the massive models, with a fraction of the budget required to train them. This has happened in the field of large language models, and after two years, multiple research groups have been able to achieve the accuracy of the Chat-GPT4 language model from OpenAI and with the ability to run on home devices, and it is necessary for research in this field to shift from focusing on achieving accuracy with greater scale to focusing on achieving accuracy with lower cost, otherwise this field will remain in the monopoly of companies focused on greater profits 113 141 2023/11/132024/06/282024/01/232024/04/182024/04/32023/09/262023/11/242024/07/13 1403/4/23 2025/03/82024/12/42025/03/82024/12/42025/03/152025/03/82024/12/42025/03/15 1403/12/25 محمد مهدی حاجی اسمعیلی Mohammad Mahdi Haji-Esmaeili دانشجوی دکترای فناوری اطلاعات دانشگاه تربیت مدرس، تهران، ایران ایران neltherion@gmail.com غلامعلی منتظر Gholamali Montazer استاد گروه مهندسی فناوری اطلاعات دانشگاه تربیت مدرس، تهران، ایران ایران montazer@modares.ac.ir Content-Based Image Retrieval Image Processing Computer Vision Machine Learning Deep Learning Semantic Gap. بازیابی محتوامحور تصاویر پردازش تصویر بینایی ماشین یادگیری ماشینی یادگیری ژرف شکاف معنایی. 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