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بهبود الگوریتم تناظریابی SIFT جهت تطبیق تصاویر ماهواره‌ای مرئی با استفاده از شبکه عصبی عمیق دوقلو Improvement of SIFT matching algorithm for matching visible satellite images using Siamese deep neural network 1 1 تطبیق تصاویر سنجش‌ازدور یک مرحله اساسی و پایه‌ای در بسیاری از کاربردهای پردازش تصویر است؛ برخلاف تصاویر معمولی، تصاویر سنجش‌ازدور به‌طورمعمول تحت تأثیر تغییرات پیچیده پس‌زمینه هستند که باعث دشواری تطبیق این دسته از تصاویر می‌شود. تصاویر مورد استفاده علاوه‌بر تغییرات شدید و غیرخطی در پس‌زمینه، شامل چالش‌هایی مانند تفاوت مقیاس، چرخش و زاویه دید متفاوت است. یکی از الگوریتم‌های پرکاربرد برای یافتن نقاط متناظر میان تصاویر، الگوریتم SIFT است که استفاده از این الگوریتم برای تطبیق تصاویر با ویژگی‌های گفته‌شده، در بسیاری از مواقع باعث تولید تطابق‌های اشتباه فراوان و تطابق‌های صحیح کم می‌شود؛ از طرفی روش‌های مبتنی بر یادگیری عمیق قادر به استخراج ویژگی‌های سطح متوسط و بالا و مقایسه آن‌ها برای تطبیق تصاویرند. با الهام از توانایی‌ها و پیشرفت‌های انجام‌شده در یادگیری عمیق، روشی برای استفاده هم‌زمان از الگوریتم SIFT و شبکه‌های عصبی عمیق برای تطبیق تصاویر سنجش‌ازدور معرفی شده‌است؛ بر اساس آزمایش‌های صورت‌گرفته بر روی مجموعه‌ای از تصاویر سنجش‌ازدور شامل 35 جفت تصویر و مقایسه نتایج نسبت به الگوریتم SIFT و الگوریتم‌های مبتنی بر یادگیری عمیق، روش پیشنهادی توانسته با کاهش تطابق‌های اشتباه و افزایش تطابق‌های صحیح، به دقت 849 درصد برسد. 2 Matching remote sensing images is a fundamental step in many image processing applications. Unlike regular images, remote sensing images often undergo complex and nonlinear background changes, making them difficult to match. They also pose challenges such as scale variations, rotation, and different viewing angles. One commonly used method for finding corresponding points between images is the Scale-Invariant Feature Transform (SIFT) algorithm; however, it often produces many incorrect matches when applied to such data. In contrast, deep learning-based approaches can extract and compare medium and high-level features for more accurate matching. Inspired by these advances, this work introduces a method that combines the SIFT algorithm with a Siamese deep neural network to improve the matching of remote sensing images. The proposed method modifies the conventional SIFT by adjusting its parameters to increase the proportion of correct to incorrect correspondences. After keypoints are extracted and described, initial correspondences are established. Then, for each matched point, a local patch is extracted based on the keypoint’s position, scale, and orientation. These patch pairs are input to a trained Siamese network that estimates the probability of a correct match. Matches with confidence below a threshold are rejected. This hybrid approach leverages the strengths of both traditional and deep learning-based techniques to enhance accuracy. The proposed approach introduces several key innovations, including optimized keypoint extraction to maximize true matches, patch-based feature representation aligned with local image geometry, and a neural network-based verification step to suppress incorrect matches. Based on experiments conducted on a dataset of 35 pairs of remote sensing images, and comparing the results with the SIFT algorithm and deep learning-based methods, the proposed approach achieved an accuracy of 0.849 by reducing false matches and increasing correct ones. 3 18 2023/12/23 1402/10/2 2025/07/21 1404/4/30 احمدرضا زارعی Ahmadreza Zarei دانش‌آموخته کارشناسی‌ارشد رشته مهندسی برق، دانشکده فنی و مهندسی، دانشگاه اصفهان، اصفهان، ایران ایران ahmadrezazarei92@gmail.com پیمان معلم Payman Moallem استاد گروه مهندسی برق، دانشکده فنی و مهندسی، دانشگاه اصفهان، اصفهان، ایران ایران p_moallem@eng.ui.ac.ir Image Keywords: Image Matching Remote Sensing SITF Algorithm Deep Learning Siamese Convolutional Neural Networks Matching Remote Sensing SITF Algorithm Deep Learning Siamese Convolutional Neural Networks. الگوریتم SIFT تطبیق تصاویر سنجش‌ازدور شبکه‌های عصبی کانولوشنی دوقلو 1. J. Ma, X. Jiang, A. Fan, J. Jiang, and J. Yan, "Image Matching from Handcrafted to Deep Features : A Survey," International Journal of Computer Vision, 2020. [DOI:10.1007/s11263-020-01359-2]##2. H. P. Moravec, "Techniques towards automatic visual obstacle avoidance," 1977.##3. C. Harris and M. Stephens, "A combined corner and edge detector," in Alvey vision conference, 1988, pp. 147-152.##4. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International journal of computer vision, vol. 60, no. 2, pp. 91-110, 2004. [DOI:10.1023/B:VISI.0000029664.99615.94]##5. H. Bay, T. Tuytelaars, and L. Van Gool, "Surf: Speeded up robust features," in European conference on computer vision, 2006, pp. 404-417. [DOI:10.1007/11744023_32]##6. F. Dellinger, J. Delon, Y. Gousseau, J. Michel, and F. Tupin, "SAR-SIFT: a SIFT-like algorithm for SAR images," IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 1, pp. 453-466, 2014. [DOI:10.1109/TGRS.2014.2323552]##7. S. Wang, H. You, and K. Fu, "BFSIFT: A novel method to find feature matches for SAR image registration," IEEE Geoscience and remote sensing letters, vol. 9, no. 4, pp. 649-653, 2011. [DOI:10.1109/LGRS.2011.2177437]##8. R. Song and J. Szymanski, "Well-distributed SIFT features," Electronics letters, vol. 45, no. 6, pp. 308-310, 2009. [DOI:10.1049/el.2009.2954]##9. L. Juan and O. Gwun, "A comparison of sift, pca-sift and surf," International Journal of Image Processing (IJIP), vol. 3, no. 4, pp. 143-152, 2009.##10. A. Sedaghat, M. Mokhtarzade, and H. Ebadi, "Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images," IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 49, NO. 11, no. January 2011. [DOI:10.1109/TGRS.2011.2144607]##۱۱. صداقت، امین، عبادی، حمید، مختارزاده، مهدی، "بهبود الگوریتم SIFT برای تطبیق تصاویر ماهواره‌ای"، مجله سنجش از دور و سامانه‌های اطلاعات جغرافیایی ایران، دوره ۲، شماره ۴، زمستان ۱۳۸۹.##11. A. Sedaghat, H. Ebadi, and M. Mokhtarzade, "Improving the SIFT algorithm in order to match satellite images,"Iranian Journal of Remote Sensing & GIS (GISG), vol. 2, no. 4, 2011.##۱۲. حسین‌نژاد، زهرا، نصری، مهدی، "موزاییک تصاویر طبیعی بر اساس حذف نقاط کلیدی زائد در الگوریتم SIFT و الگوریتم RANSAC تطبیقی"، پردازش علائم و داده‌ها، دوره ۱۸، شماره ۲، صفحات ۱۴۷-۱۶۲، ۱۴۰۰.##12. Z. Hossein-Nejad and M. Nasri, "Natural image mosaicing based on redundant keypoint elimination method in SIFT algorithm and adaptive RANSAC method," Signal and Data Processing, vol. 18, no. 2, pp. 147-162, 2021. [DOI:10.52547/jsdp.18.2.147]##13. Z. Yang, T. Dan, and Y. Yang, "Multi-temporal Remote Sensing Image Registration Using Deep Convolutional Features," IEEE Access, vol. PP, no. c, p. 1, 2018. [DOI:10.1109/ACCESS.2018.2853100]##14. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.##15. H. He, M. Chen, T. Chen, and D. Li, "Matching of Remote Sensing Images with Complex Background Variations via Siamese Convolutional Neural Network," pp. 1-23, 2018. [DOI:10.3390/rs10020355]##16. Y. Dong et al., "Local Deep Descriptor for Remote Sensing Image Feature Matching," Remote Sensing, pp. 1-21, 2019. [DOI:10.3390/rs11040430]##17. F. Ye, Y. Su, H. Xiao, X. Zhao, and W. Min, "Remote sensing image registration using convolutional neural network features," IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 2, pp. 232-236, 2018. [DOI:10.1109/LGRS.2017.2781741]##18. W. Ma et al., "Remote sensing image registration with modified SIFT and enhanced feature matching," IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 1, pp. 3-7, 2016. [DOI:10.1109/LGRS.2016.2600858]##19. W. Ma, J. Zhang, Y. Wu, L. Jiao, H. Zhu, and W. Zhao, "A Novel Two-Step Registration Method for Remote Sensing Images Based on Deep and Local Features," IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 7, pp. 4834-4843, 2019. [DOI:10.1109/TGRS.2019.2893310]##20. L. H. Hughes, M. Schmitt, L. Mou, Y. Wang, and X. X. Zhu, "Identifying corresponding patches in SAR and optical images with a pseudo-siamese CNN," IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 5, pp. 784-788, 2018. [DOI:10.1109/LGRS.2018.2799232]##21. B. Li, J. Zhang, B. Liu, Y. Xiang, and Y. Zhang, "An improved algorithm with SuperPoint+ SuperGlue network for UAV remote sensing image registration," in Proc. IGARSS - IEEE Int. Geosci. Remote Sens. Symp., pp. 9975-9978, 2024. [DOI:10.1109/IGARSS53475.2024.10640500]##22. D. Quan, Z. Wang, C. Lv, S. Wang, Y. Li, B. Ren, J. Chanussot, and L. Jiao, "LM-Net: A lightweight matching network for remote sensing image matching and registration," IEEE Trans. Geosci. Remote Sens., 2024. [DOI:10.1109/TGRS.2024.3509638]##23. P. Lindenberger, P. Sarlin, and M. Pollefeys, "LightGlue: Local feature matching at light speed," in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), 2023, pp. 17627-17638. [DOI:10.1109/ICCV51070.2023.01616]##24. W. Zhang, T. Li, Y. Zhang, G. Pei, X. Jiang, and Y. Yao, "LTFormer: A light-weight transformer-based self-supervised matching network for heterogeneous remote sensing images," Inf. Fusion, vol. 109, pp. 102425, 2024. [DOI:10.1016/j.inffus.2024.102425]##25. S. Ji, C. Zeng, Y. Zhang, and Y. Duan, "An evaluation of conventional and deep learning‐based image‐matching methods on diverse datasets," Photogramm. Rec., vol. 38, no. 182, pp. 137-159, 2023. [DOI:10.1111/phor.12445]##۲۶. جهانی، سید علی، محبی، کیوان، زمانی بروجنی، فرساد، "بهبود تشخیص صحنه در سنجش از راه‌دور با استفاده از یادگیری عمیق و انتخاب‌گر ویژگی"، پردازش علائم و داده‌ها، دوره ۲۱، شماره ۳، صفحات ۸۴-۶۹، ۱۴۰۳.##26. S. A. Jahani, K. Mohebbi, and F. Z. Boroujeni, "Improving Scene Recognition in Remote Sensing Using Deep Learning and Feature Selector," Signal and Data Processing, vol. 21, no. 3, pp. 69-84, 2024. [DOI:10.61186/jsdp.21.3.69]##27. J. Bromley, I. Guyon, Y. LeCun, E. Säckinger, and R. Shah, "Signature verification using a" siamese" time delay neural network," in Advances in neural information processing systems, 1994, pp. 737-744. [DOI:10.1142/9789812797926_0003]##28. D. Chicco, "Siamese Neural Networks: An Overview," Artificial Neural Networks, pp. 73-94, 2020. [DOI:10.1007/978-1-0716-0826-5_3]##29. S. Ioffe and C. Szegedy, "Batch Normalization : Accelerating Deep Network Training by Reducing Internal Covariate Shift," arXiv preprint arXiv:1502.03167, 2015.##30. M. Brown, G. Hua, and S. Winder, "Discriminative Learning of Local Image Descriptors," IEEE transactions on pattern analysis and machine intelligence, vol. 33, no. 1, pp. 43-57, 2011. [DOI:10.1109/TPAMI.2010.54]##31. C. Wu, L. Zhang, and L. Zhang, "A scene change detection framework for multi-temporal very high resolution remote sensing images," Signal Processing, vol. 124, pp. 184-197, 2016. [DOI:10.1016/j.sigpro.2015.09.020]##32. C. Wu, L. Zhang, and B. Du, "Kernel slow feature analysis for scene change detection," IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 4, pp. 2367-2384, 2017. [DOI:10.1109/TGRS.2016.2642125]##33. R. C. Daudt, B. Le Saux, A. Boulch, and Y. Gousseau, "Urban change detection for multispectral earth observation using convolutional neural networks," in IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, 2018, pp. 2115-2118. [DOI:10.1109/IGARSS.2018.8518015]##34. Satellite Imaging Corporation, "Remote sensing and satellite imaging services." [Online]. Available: https://www.satimagingcorp.com/. [Accessed: May 15, 2025].##35. A. Vedaldi and B. Fulkerson, "VLFeat - An open and portable library of computer vision algorithms," pp. 1-4, 2010. [DOI:10.1145/1873951.1874249]##1. J. Ma, X. Jiang, A. Fan, J. Jiang, and J. Yan, "Image Matching from Handcrafted to Deep Features : A Survey," International Journal of Computer Vision, 2020. [DOI:10.1007/s11263-020-01359-2]##2. H. P. Moravec, "Techniques towards automatic visual obstacle avoidance," 1977.##3. C. Harris and M. Stephens, "A combined corner and edge detector," in Alvey vision conference, 1988, pp. 147-152.##4. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International journal of computer vision, vol. 60, no. 2, pp. 91-110, 2004. [DOI:10.1023/B:VISI.0000029664.99615.94]##5. H. Bay, T. Tuytelaars, and L. Van Gool, "Surf: Speeded up robust features," in European conference on computer vision, 2006, pp. 404-417. [DOI:10.1007/11744023_32]##6. F. Dellinger, J. Delon, Y. Gousseau, J. Michel, and F. Tupin, "SAR-SIFT: a SIFT-like algorithm for SAR images," IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 1, pp. 453-466, 2014. [DOI:10.1109/TGRS.2014.2323552]##7. S. Wang, H. You, and K. Fu, "BFSIFT: A novel method to find feature matches for SAR image registration," IEEE Geoscience and remote sensing letters, vol. 9, no. 4, pp. 649-653, 2011. [DOI:10.1109/LGRS.2011.2177437]##8. R. Song and J. Szymanski, "Well-distributed SIFT features," Electronics letters, vol. 45, no. 6, pp. 308-310, 2009. [DOI:10.1049/el.2009.2954]##9. L. Juan and O. Gwun, "A comparison of sift, pca-sift and surf," International Journal of Image Processing (IJIP), vol. 3, no. 4, pp. 143-152, 2009.##10. A. Sedaghat, M. Mokhtarzade, and H. Ebadi, "Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images," IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 49, NO. 11, no. January 2011. [DOI:10.1109/TGRS.2011.2144607]##۱۱. صداقت، امین، عبادی، حمید، مختارزاده، مهدی، "بهبود الگوریتم SIFT برای تطبیق تصاویر ماهواره‌ای"، مجله سنجش از دور و سامانه‌های اطلاعات جغرافیایی ایران، دوره ۲، شماره ۴، زمستان ۱۳۸۹.##11. A. Sedaghat, H. Ebadi, and M. Mokhtarzade, "Improving the SIFT algorithm in order to match satellite images,"Iranian Journal of Remote Sensing & GIS (GISG), vol. 2, no. 4, 2011.##۱۲. حسین‌نژاد، زهرا، نصری، مهدی، "موزاییک تصاویر طبیعی بر اساس حذف نقاط کلیدی زائد در الگوریتم SIFT و الگوریتم RANSAC تطبیقی"، پردازش علائم و داده‌ها، دوره ۱۸، شماره ۲، صفحات ۱۴۷-۱۶۲، ۱۴۰۰.##12. Z. Hossein-Nejad and M. Nasri, "Natural image mosaicing based on redundant keypoint elimination method in SIFT algorithm and adaptive RANSAC method," Signal and Data Processing, vol. 18, no. 2, pp. 147-162, 2021. [DOI:10.52547/jsdp.18.2.147]##13. Z. Yang, T. Dan, and Y. Yang, "Multi-temporal Remote Sensing Image Registration Using Deep Convolutional Features," IEEE Access, vol. PP, no. c, p. 1, 2018. [DOI:10.1109/ACCESS.2018.2853100]##14. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.##15. H. He, M. Chen, T. Chen, and D. Li, "Matching of Remote Sensing Images with Complex Background Variations via Siamese Convolutional Neural Network," pp. 1-23, 2018. [DOI:10.3390/rs10020355]##16. Y. Dong et al., "Local Deep Descriptor for Remote Sensing Image Feature Matching," Remote Sensing, pp. 1-21, 2019. [DOI:10.3390/rs11040430]##17. F. Ye, Y. Su, H. Xiao, X. Zhao, and W. Min, "Remote sensing image registration using convolutional neural network features," IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 2, pp. 232-236, 2018. [DOI:10.1109/LGRS.2017.2781741]##18. W. Ma et al., "Remote sensing image registration with modified SIFT and enhanced feature matching," IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 1, pp. 3-7, 2016. [DOI:10.1109/LGRS.2016.2600858]##19. W. Ma, J. Zhang, Y. Wu, L. Jiao, H. Zhu, and W. Zhao, "A Novel Two-Step Registration Method for Remote Sensing Images Based on Deep and Local Features," IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 7, pp. 4834-4843, 2019. [DOI:10.1109/TGRS.2019.2893310]##20. L. H. Hughes, M. Schmitt, L. Mou, Y. Wang, and X. X. Zhu, "Identifying corresponding patches in SAR and optical images with a pseudo-siamese CNN," IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 5, pp. 784-788, 2018. [DOI:10.1109/LGRS.2018.2799232]##21. B. Li, J. Zhang, B. Liu, Y. Xiang, and Y. Zhang, "An improved algorithm with SuperPoint+ SuperGlue network for UAV remote sensing image registration," in Proc. IGARSS - IEEE Int. Geosci. Remote Sens. Symp., pp. 9975-9978, 2024. [DOI:10.1109/IGARSS53475.2024.10640500]##22. D. Quan, Z. Wang, C. Lv, S. Wang, Y. Li, B. Ren, J. Chanussot, and L. Jiao, "LM-Net: A lightweight matching network for remote sensing image matching and registration," IEEE Trans. Geosci. Remote Sens., 2024. [DOI:10.1109/TGRS.2024.3509638]##23. P. Lindenberger, P. Sarlin, and M. Pollefeys, "LightGlue: Local feature matching at light speed," in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), 2023, pp. 17627-17638. [DOI:10.1109/ICCV51070.2023.01616]##24. W. Zhang, T. Li, Y. Zhang, G. Pei, X. Jiang, and Y. Yao, "LTFormer: A light-weight transformer-based self-supervised matching network for heterogeneous remote sensing images," Inf. Fusion, vol. 109, pp. 102425, 2024. [DOI:10.1016/j.inffus.2024.102425]##25. S. Ji, C. Zeng, Y. Zhang, and Y. Duan, "An evaluation of conventional and deep learning‐based image‐matching methods on diverse datasets," Photogramm. Rec., vol. 38, no. 182, pp. 137-159, 2023. [DOI:10.1111/phor.12445]##۲۶. جهانی، سید علی، محبی، کیوان، زمانی بروجنی، فرساد، "بهبود تشخیص صحنه در سنجش از راه‌دور با استفاده از یادگیری عمیق و انتخاب‌گر ویژگی"، پردازش علائم و داده‌ها، دوره ۲۱، شماره ۳، صفحات ۸۴-۶۹، ۱۴۰۳.##26. S. A. Jahani, K. Mohebbi, and F. Z. Boroujeni, "Improving Scene Recognition in Remote Sensing Using Deep Learning and Feature Selector," Signal and Data Processing, vol. 21, no. 3, pp. 69-84, 2024. [DOI:10.61186/jsdp.21.3.69]##27. J. Bromley, I. Guyon, Y. LeCun, E. Säckinger, and R. Shah, "Signature verification using a" siamese" time delay neural network," in Advances in neural information processing systems, 1994, pp. 737-744. [DOI:10.1142/9789812797926_0003]##28. D. Chicco, "Siamese Neural Networks: An Overview," Artificial Neural Networks, pp. 73-94, 2020. [DOI:10.1007/978-1-0716-0826-5_3]##29. S. Ioffe and C. Szegedy, "Batch Normalization : Accelerating Deep Network Training by Reducing Internal Covariate Shift," arXiv preprint arXiv:1502.03167, 2015.##30. M. Brown, G. Hua, and S. Winder, "Discriminative Learning of Local Image Descriptors," IEEE transactions on pattern analysis and machine intelligence, vol. 33, no. 1, pp. 43-57, 2011. [DOI:10.1109/TPAMI.2010.54]##31. C. Wu, L. Zhang, and L. Zhang, "A scene change detection framework for multi-temporal very high resolution remote sensing images," Signal Processing, vol. 124, pp. 184-197, 2016. [DOI:10.1016/j.sigpro.2015.09.020]##32. C. Wu, L. Zhang, and B. Du, "Kernel slow feature analysis for scene change detection," IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 4, pp. 2367-2384, 2017. [DOI:10.1109/TGRS.2016.2642125]##33. R. C. Daudt, B. Le Saux, A. Boulch, and Y. Gousseau, "Urban change detection for multispectral earth observation using convolutional neural networks," in IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, 2018, pp. 2115-2118. [DOI:10.1109/IGARSS.2018.8518015]##34. Satellite Imaging Corporation, "Remote sensing and satellite imaging services." [Online]. Available: https://www.satimagingcorp.com/. [Accessed: May 15, 2025].##35. A. Vedaldi and B. Fulkerson, "VLFeat - An open and portable library of computer vision algorithms," pp. 1-4, 2010. [DOI:10.1145/1873951.1874249] ##

تحلیل ساختار ترافیکی جاده‌های استان چهارمحال‌وبختیاری با استفاده از رویکردهای داده‌کاوی Analysis of the traffic structure of the roads of Chahar Mahal and Bakhtiari province using data-mining approaches 1 1 مدیریت ترافیک جاده‌ای یکی از چالش‌های اساسی در نظام حمل‌ونقل است که تأثیر مستقیمی بر ایمنی عمومی، بهره‌وری اقتصادی و پایداری محیط‌زیست دارد. استان چهارمحال‌وبختیاری به‌دلیل موقعیت جغرافیایی راهبردی خود، نقش مهمی در اتصال مناطق مختلف کشور ایفا می‌کند؛ ازاین‌رو، تحلیل دقیق ساختار ترافیکی جاده‌های این استان گامی ضروری برای ارتقای کیفیت برنامه‌ریزی‌ها و تصمیم‌گیری‌های مبتنی بر داده در حوزه حمل‌ونقل محسوب می‌شود. در این پژوهش، داده‌های ترددشمار جاده‌ای استان در شهریورماه ۱۴۰۲ مورد استفاده قرار گرفته‌اند. این داده‌ها شامل متغیرهای کلیدی نظیر میانگین تردد وسایل نقلیه در پنج طبقه مختلف، میزان تخلفات سرعت غیرمجاز، فاصله غیرمجاز، سبقت غیرمجاز و سرعت متوسط در محورهای جاده‌ای هستند. تحلیل داده‌ها بر پایه سه الگوریتم یادگیری بدون نظارت شامل خوشه‌بندی k - میانگین، خوشه‌بندی سلسله‌مراتبی و رمزگذار خودکار انجام شده‌است. به‌منظور ارزیابی دقت و عملکرد این الگوریتم‌ها در خوشه‌بندی محورها، از سه شاخص معتبر سیلوئت، دیویس-بولدین و کالینسکی-هاراباسز بهره گرفته شده‌است. یافته‌ها نشان می‌دهند که مدل‌های رمزگذار خودکار و خوشه‌بندی سلسله‌مراتبی در مقایسه با روش سنتی   k -میانگین، دسته‌بندی دقیق‌تری از جاده‌ها ارائه داده و ساختار ترافیکی پنهان را بهتر آشکار می‌سازند؛ بر اساس نتایج، جاده‌های استان به دو خوشه متمایز تقسیم شده‌اند: خوشه نخست شامل محورهایی با بالاترین نرخ تردد و بیشترین میزان تخلفات سرعت و فاصله غیرمجاز است که نشانه‌ای از رفتارهای پرخطر رانندگی و ریسک بالای تصادف محسوب می‌شود؛ درحالی‌ که خوشه دوم محورهایی با الگوهای ترافیکی ایمن‌تر را در برمی‌گیرد. نوآوری اصلی این پژوهش در ترکیب هم‌زمان چند الگوریتم پیشرفته خوشه‌بندی و استفاده از شاخص‌های متنوع ارزیابی عملکرد است که موجب افزایش دقت تحلیل و استحکام نتایج شده‌است؛ افزون‌بر این، تلفیق متغیرهای فنی و رفتاری ترافیکی در یک چهارچوب داده‌محور، امکان استخراج بینش‌های عمیق‌تری از الگوهای ترافیکی را فراهم ساخته است. چهارچوب ارائه‌شده، قابلیت تعمیم به سایر مناطق کشور را دارد و می‌تواند به‌عنوان الگویی نوین در هوشمندسازی مدیریت شبکه‌های جاده‌ای به‌کار گرفته شود. پژوهش حاضر با فراهم‌سازی ابزارهای تحلیلی دقیق و عملیاتی، می‌تواند به ارتقای آگاهی تصمیم‌گیرندگان، بهینه‌سازی تخصیص منابع، طراحی استراتژی‌های ایمنی و درنهایت کاهش نرخ تصادفات منجر شود. به‌منظور تکمیل و توسعه این مسیر پژوهشی، پیشنهاد می‌شود مطالعات آتی به تحلیل‌های زمانی (فصلی)، ادغام متغیرهای انسانی، محیطی و توسعه مدل‌های پیش‌بینی ترکیبی در حوزه حمل‌ونقل بپردازند. 2 Road traffic management is a fundamental and multidimensional challenge within transportation systems, exerting a direct impact on public safety, economic efficiency, and environmental sustainability. The province of Chaharmahal and Bakhtiari, due to its strategic geographical location, plays a critical role in connecting various regions of Iran. Therefore, a precise analysis of the traffic structure of this province’s roadways is essential for improving the quality of data-driven planning and decision-making in the transportation sector. In this study, road traffic-counter data collected during September were utilized. These data include key variables such as the average number of vehicles in five different classes, instances of traffic violations (namely speeding, tailgating, and illegal overtaking), as well as the average speed on various road segments. The data were analyzed using three unsupervised learning algorithms: k-means clustering, hierarchical clustering, and autoencoder-based clustering. To assess the accuracy and performance of these algorithms in segment clustering, three well-established evaluation metrics—Silhouette score, Davies-Bouldin index, and Calinski-Harabasz index—were employed. The results demonstrate that the autoencoder and hierarchical clustering models offer a more accurate classification of road segments compared to the conventional k-means method, revealing latent traffic structures more effectively. Based on the findings, the roads in the province were categorized into two distinct clusters: the first cluster includes segments with the highest traffic volume and the highest rates of speeding and tailgating violations—indicative of risky driving behaviors and elevated accident risk. The second cluster encompasses segments characterized by safer traffic patterns. The primary contribution of this research lies in the integrated application of multiple advanced clustering algorithms alongside diverse performance evaluation metrics, which significantly enhance the precision and robustness of the analysis. Furthermore, the combination of technical and behavioral traffic variables within a unified data-driven framework enables the extraction of deeper insights into traffic behavior patterns. The proposed framework is generalizable to other regions and can serve as a novel model for the intelligent management of roadway networks.By providing accurate and actionable analytical tools, this study has the potential to support decision-makers in raising awareness, optimizing resource allocation, designing safety strategies, and ultimately reducing accident rates. To extend and enrich this line of research, future studies are encouraged to incorporate temporal (e.g., seasonal) analyses, integrate human and environmental variables, and develop hybrid predictive models in the transportation domain. 19 34 2023/12/232024/07/30 1403/5/9 2025/07/212025/07/21 1404/4/30 وحیده احراری Vahideh Ahrari استادیار گروه علوم کامپیوتر، دانشکده علوم ریاضی، دانشگاه شهرکرد، شهرکرد، ایران ایران vahideh.ahrari@sku.ac.ir رباب افشاری Robab Afshari استادیار گروه آمار، دانشکده علوم، دانشگاه زنجان، زنجان، ایران ایران afshari@znu.ac.ir Unsupervised learning clustering traffic structure analysis autoencoder speeding violation tailgating Chaharmahal and Bakhtiari Province. یادگیری بدون نظارت خوشه‌بندی تحلیل ساختار ترافیکی رمزگذار خودکار سرعت غیرمجاز فاصله غیرمجاز استان چهارمحال‌وبختیاری. 1. World Health Organization. (2018). Road traffic injuries. https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries.##2. C. Musingura, G. Lee, Y. Ahn, and K. Kim, "Mitigating Road Traffic Crashes in Urban Environments: A Case Study and Literature Review-based Approach," Authorea Preprints, 2023. [DOI:10.36227/techrxiv.24265186]##۳. کریمی، سیده فاطمه، خدابخش، مریم، «پیش‌بینی عملکرد نتایج پرس‌وجو با کمک روش‌های بدون نظارت»، پردازش علائم و داده‌ها، (۱) ۲۲، صفحات ۱۲-۳، ۱۴۰۴.##3. S. Karimi, M. Khodabakhsh, "Unsupervised Methods for Predicting Query Performance", Journal of Signal and Data Processing, 22 (1), pp. 3-12, 2025. [DOI:10.61186/jsdp.22.1.3]##۴. دانشپور نگین، برزگری علی، «روشی جدید در تشخیص تکراری رکوردها با استفاده از خوشه‌‌بندی سلسله‌مراتبی»، پردازش علائم و داده‌ها، (۴) ۱۸، صفحات ۳-۲۲، ۱۴۰۰.##4. N. Daneshpour, A. Barzegari, "A New Method for Duplicate Detection Using Hierarchical Clustering of Records", Journal of Signal and Data Processing; 18 (4) , pp. 3-22, 2022. [DOI:10.52547/jsdp.18.4.3]##5. R. Rossi, M. Gastaldi, and G. Gecchele, "Comparison of clustering methods for road group identification in FHWA traffic monitoring approach: Effects on AADT estimates," Journal of Transportation Engineering, vol. 140, no. 7, pp. 04014025, Jul. 2014. [DOI:10.1061/(ASCE)TE.1943-5436.0000676]##6. J. Wang and F. Biljecki, "Unsupervised machine learning in urban studies: A systematic review of applications," Cities, vol. 129, p. 103925, 2022. [DOI:10.1016/j.cities.2022.103925]##7. C. Bratsas et al., "A comparison of machine learning methods for the prediction of traffic speed in urban places," Sustainability, vol. 12, no. 1, p. 142, 2019. [DOI:10.3390/su12010142]##8. X. Cui et al., "Extracting main center pattern from road networks using density-based clustering with fuzzy neighborhood," ISPRS Int. J. Geo-Inf., vol. 8, no. 5, p. 238, 2019. [DOI:10.3390/ijgi8050238]##9. A. Aggarwal, A. Purwar, and S. Gulati, "An efficient technique to control road traffic using fuzzy neural network system," in Proc. 3rd Int. Conf. Reliability, Infocom Technol. Optim., Oct. 2014, pp. 1-6. [DOI:10.1109/ICRITO.2014.7014723]##10. Y. Liu and H. Wu, "Prediction of road traffic congestion based on random forest," in 2017 10th Int. Symp. Comput. Intell. Des. (ISCID), Vol. 2, Dec. 2017, pp. 361-364. [DOI:10.1109/ISCID.2017.216]##11. M. Akhtar and S. Moridpour, "A review of traffic congestion prediction using artificial intelligence," J. Adv. Transp., vol. 2021, no. 1, p. 8878011, 2021. [DOI:10.1155/2021/8878011]##12. S. Yang et al., "Analysis of traffic state variation patterns for urban road network based on spectral clustering," Adv. Mech. Eng., vol. 9, no. 9, p. 1687814017723790, 2017. [DOI:10.1177/1687814017723790]##13. Y. Haung, H. Zhen, and J.J. Yang, "Cluster-guided denoising graph auto-encoder for enhanced traffic data imputation and fault detection," Expert Systems with Applications, vol 261, p. 125531, Feb. 2025. [DOI:10.1016/j.eswa.2024.125531]##14. D. Shin et al., "Clustering and investigation of human driving bahavior using autoencoder and risk assessment," IEEE Access, 2025 Jan 15. [DOI:10.1109/ACCESS.2025.3529883]##15. F. Nielsen and F. Nielsen, "Hierarchical clustering," in Introduction to HPC with MPI for Data Science, pp. 195-211, 2016. [DOI:10.1007/978-3-319-21903-5_8]##16. Z. Zhang et al., "Hierarchical cluster analysis in clinical research with heterogeneous study population: highlighting its visualization with R," Annals of Translational Medicine, vol. 5, no. 4, 2017. [DOI:10.21037/atm.2017.02.05]##17. J. B. MacQueen, "Some methods for classification and analysis of multivariate observations," in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, L. M. Le Cam and J. Neyman, Eds. California: University of California Press, pp. 281-297, 1967.##18. Q. Wang, C. Wang, Z. Feng, and J. F. Ye, "Review of K-means clustering algorithm," Electronic Design Engineering, vol. 20, no. 7, pp. 21-24, 2012.##19. D. Steinley and M. J. Brusco, "Initializing k-means batch clustering: A critical evaluation of several techniques," Journal of Classification, vol. 24, pp. 99-121, 2007. [DOI:10.1007/s00357-007-0003-0]##20. D. Bank, N. Koenigstein, and R. Giryes, "Autoencoders," in Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, pp. 353-374, 2023. [DOI:10.1007/978-3-031-24628-9_16]##21. F. Ros and R. Riad, "Deep clustering techniques based on autoencoders," in Feature and Dimensionality Reduction for Clustering with Deep Learning, Cham: Springer Nature Switzerland, pp. 203-220, 2023. [DOI:10.1007/978-3-031-48743-9_11]##22. K. Berahmand et al., "Autoencoders and their applications in machine learning: a survey," Artificial Intelligence Review, vol. 57, no. 2, p. 28, 2024. [DOI:10.1007/s10462-023-10662-6]##23. I. Yatskiv and L. Gusarova, "The methods of cluster analysis results validation," in Proc. Int. Conf. RelStat, vol. 4, pp. 75-80, 2005. [DOI:10.1016/j.cmpb.2005.06.004]##24. Y. A. Wijaya et al., "Davies bouldin index algorithm for optimizing clustering case studies mapping school facilities," TEM J, vol. 10, no. 3, pp. 1099-1103, 2021. [DOI:10.18421/TEM103-13]##25. X. Wang and Y. Xu, "An improved index for clustering validation based on silhouette index and calinski-harabasz index," in IOP Conf. Ser., Mater. Sci. Eng., vol. 569, Aug. 2019, Art. no. 052024. [DOI:10.1088/1757-899X/569/5/052024]##26. D. J. Ketchen Jr. and C. L. Shook, "The application of cluster analysis in Strategic Management Research: An analysis and critique," Strategic Management Journal, vol. 17, no. 6, pp. 441-458, 1996. https://doi.org/10.1002/(SICI)1097-0266(199606)17:6<441::AID-SMJ819>3.0.CO;2-G [DOI:10.1002/(SICI)1097-0266(199606)17:63.0.CO;2-G]##1. World Health Organization. (2018). Road traffic injuries. https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries.##2. C. Musingura, G. Lee, Y. Ahn, and K. Kim, "Mitigating Road Traffic Crashes in Urban Environments: A Case Study and Literature Review-based Approach," Authorea Preprints, 2023. [DOI:10.36227/techrxiv.24265186]##۳. کریمی، سیده فاطمه، خدابخش، مریم، «پیش‌بینی عملکرد نتایج پرس‌وجو با کمک روش‌های بدون نظارت»، پردازش علائم و داده‌ها، (۱) ۲۲، صفحات ۱۲-۳، ۱۴۰۴.##3. S. Karimi, M. Khodabakhsh, "Unsupervised Methods for Predicting Query Performance", Journal of Signal and Data Processing, 22 (1), pp. 3-12, 2025. [DOI:10.61186/jsdp.22.1.3]##۴. دانشپور نگین، برزگری علی، «روشی جدید در تشخیص تکراری رکوردها با استفاده از خوشه‌‌بندی سلسله‌مراتبی»، پردازش علائم و داده‌ها، (۴) ۱۸، صفحات ۳-۲۲، ۱۴۰۰.##4. N. Daneshpour, A. Barzegari, "A New Method for Duplicate Detection Using Hierarchical Clustering of Records", Journal of Signal and Data Processing; 18 (4) , pp. 3-22, 2022. [DOI:10.52547/jsdp.18.4.3]##5. R. Rossi, M. Gastaldi, and G. Gecchele, "Comparison of clustering methods for road group identification in FHWA traffic monitoring approach: Effects on AADT estimates," Journal of Transportation Engineering, vol. 140, no. 7, pp. 04014025, Jul. 2014. [DOI:10.1061/(ASCE)TE.1943-5436.0000676]##6. J. Wang and F. Biljecki, "Unsupervised machine learning in urban studies: A systematic review of applications," Cities, vol. 129, p. 103925, 2022. [DOI:10.1016/j.cities.2022.103925]##7. C. Bratsas et al., "A comparison of machine learning methods for the prediction of traffic speed in urban places," Sustainability, vol. 12, no. 1, p. 142, 2019. [DOI:10.3390/su12010142]##8. X. Cui et al., "Extracting main center pattern from road networks using density-based clustering with fuzzy neighborhood," ISPRS Int. J. Geo-Inf., vol. 8, no. 5, p. 238, 2019. [DOI:10.3390/ijgi8050238]##9. A. Aggarwal, A. Purwar, and S. Gulati, "An efficient technique to control road traffic using fuzzy neural network system," in Proc. 3rd Int. Conf. Reliability, Infocom Technol. Optim., Oct. 2014, pp. 1-6. [DOI:10.1109/ICRITO.2014.7014723]##10. Y. Liu and H. Wu, "Prediction of road traffic congestion based on random forest," in 2017 10th Int. Symp. Comput. Intell. Des. (ISCID), Vol. 2, Dec. 2017, pp. 361-364. [DOI:10.1109/ISCID.2017.216]##11. M. Akhtar and S. Moridpour, "A review of traffic congestion prediction using artificial intelligence," J. Adv. Transp., vol. 2021, no. 1, p. 8878011, 2021. [DOI:10.1155/2021/8878011]##12. S. Yang et al., "Analysis of traffic state variation patterns for urban road network based on spectral clustering," Adv. Mech. Eng., vol. 9, no. 9, p. 1687814017723790, 2017. [DOI:10.1177/1687814017723790]##13. Y. Haung, H. Zhen, and J.J. Yang, "Cluster-guided denoising graph auto-encoder for enhanced traffic data imputation and fault detection," Expert Systems with Applications, vol 261, p. 125531, Feb. 2025. [DOI:10.1016/j.eswa.2024.125531]##14. D. Shin et al., "Clustering and investigation of human driving bahavior using autoencoder and risk assessment," IEEE Access, 2025 Jan 15. [DOI:10.1109/ACCESS.2025.3529883]##15. F. Nielsen and F. Nielsen, "Hierarchical clustering," in Introduction to HPC with MPI for Data Science, pp. 195-211, 2016. [DOI:10.1007/978-3-319-21903-5_8]##16. Z. Zhang et al., "Hierarchical cluster analysis in clinical research with heterogeneous study population: highlighting its visualization with R," Annals of Translational Medicine, vol. 5, no. 4, 2017. [DOI:10.21037/atm.2017.02.05]##17. J. B. MacQueen, "Some methods for classification and analysis of multivariate observations," in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, L. M. Le Cam and J. Neyman, Eds. California: University of California Press, pp. 281-297, 1967.##18. Q. Wang, C. Wang, Z. Feng, and J. F. Ye, "Review of K-means clustering algorithm," Electronic Design Engineering, vol. 20, no. 7, pp. 21-24, 2012.##19. D. Steinley and M. J. Brusco, "Initializing k-means batch clustering: A critical evaluation of several techniques," Journal of Classification, vol. 24, pp. 99-121, 2007. [DOI:10.1007/s00357-007-0003-0]##20. D. Bank, N. Koenigstein, and R. Giryes, "Autoencoders," in Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, pp. 353-374, 2023. [DOI:10.1007/978-3-031-24628-9_16]##21. F. Ros and R. Riad, "Deep clustering techniques based on autoencoders," in Feature and Dimensionality Reduction for Clustering with Deep Learning, Cham: Springer Nature Switzerland, pp. 203-220, 2023. [DOI:10.1007/978-3-031-48743-9_11]##22. K. Berahmand et al., "Autoencoders and their applications in machine learning: a survey," Artificial Intelligence Review, vol. 57, no. 2, p. 28, 2024. [DOI:10.1007/s10462-023-10662-6]##23. I. Yatskiv and L. Gusarova, "The methods of cluster analysis results validation," in Proc. Int. Conf. RelStat, vol. 4, pp. 75-80, 2005. [DOI:10.1016/j.cmpb.2005.06.004]##24. Y. A. Wijaya et al., "Davies bouldin index algorithm for optimizing clustering case studies mapping school facilities," TEM J, vol. 10, no. 3, pp. 1099-1103, 2021. [DOI:10.18421/TEM103-13]##25. X. Wang and Y. Xu, "An improved index for clustering validation based on silhouette index and calinski-harabasz index," in IOP Conf. Ser., Mater. Sci. Eng., vol. 569, Aug. 2019, Art. no. 052024. [DOI:10.1088/1757-899X/569/5/052024]##26. D. J. Ketchen Jr. and C. L. 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ارائه یک پروتکل جدید برای انتشار داده‌ها با حفظ حریم خصوصی در محیط‌های توزیع‌شده مبتنی بر مدل‌های احتمالاتی A Novel Privacy-Preserving Distributed Data Publishing Protocol Based on Probabilistic Models 1 1 امروزه بسیاری از خدمات در بخش خصوصی و دولتی به‌صورت الکترونیکی ارائه می‌شوند. اطلاعات ایجادشده به‌وسیله این خدمات به‌طورمعمول شامل رکوردهایی با اطلاعات حساس و خصوصی افرادی است که از این خدمات استفاده می‌کنند. سازمان‌های ارائه‌دهنده خدمات، موظف‌اند با ارائه خدمات امن و مورد اعتماد از نقض حریم خصوصی افراد جلوگیری کنند؛ از سوی دیگر اطلاعات ذخیره‌شده با حجم بالا منبع مناسبی برای کشف دانش به‌شمار می‌روند و با انتشار و اشتراک اطلاعات می‌توان به این مهم دست یافت که ممکن است به ایجاد چالش امنیتی و نقض حریم خصوصی منجر شود. راه‌کارهای ارائه‌شده در انتشار با حفظ محرمانگی داده‌های توزیع‌شده بیشتر با استفاده از روش‌های شخص سوم مورد اعتماد و محاسبات امن چندگانه همراه‌اند. این روش‌ها شامل مشکلات و چالش‌های متعدد امنیتی از قبیل ارتباط، هماهنگی و تبانی شرکت‌کنندگان در اشتراک داده، نبود شخص سوم قابل ‌اعتماد، چالش‌های امنیتی پروتکل‌های محاسبات امن چندگانه و همچنین حملات درون‌سازمانی مشارکت‌کنندگان است. در این مقاله علاوه‌بر مرور روش‌های حفظ محرمانگی و حریم خصوصی در داده‌های توزیع‌شده و بررسی نقاط ضعف و قدرت آن‌ها، طرح جدیدی بر پایه مدل احتمالاتی برای حفظ حریم خصوصی در حوزه توزیع‌شده ارائه می‌شود. نتایج به‌دست‌آمده از ارزیابی امنیتی نشان می‌دهد روش پیشنهادی، بدون نیاز به شخص سوم مورد اعتماد و یا استفاده از محاسبات امن چندگانه، علاوه‌بر مقاومت در برابر حملات خارجی، در مقابل حملات داخلی که توسط مشارکت‌کنندگان پی‌ریزی می‌شود نیز مقاومت مناسبی دارد؛ به‌نحوی ‌که در تمام حالات کمتر از یک درصد از رکوردهای منتشرشده نقض می‌شوند؛ همچنین نتایج حاصل از دقت  طبقه‌بندی نشان می‌دهد، در این روش با توجه به اشتراک داده، دقت داده‌ها نسبت به حالتی که تنها یک فراهم‌کننده قصد انتشار داده را دارد افزایش می‌یابد؛ اما از سوی دیگر استفاده از روش پیشنهادی افزایش سربار پردازشی را به‌دنبال دارد؛ به‌طوری ‌که این روش در تمامی حالات نسبت به روش پایه کندتر عمل می‌کند. 2 In the era of digital transformation, government agencies and corporations increasingly rely on electronic services, generating vast volumes of sensitive data stored in distributed databases. While these records hold immense potential for knowledge discovery through data mining, their publication or sharing raises critical privacy concerns, particularly when sensitive individual information is at risk. Traditional Privacy-Preserving Distributed Data Publishing (PPDDP) methods rely heavily on Trusted Third-Party (TTP) intermediaries and Secure Multi-Party Computation (SMC), which introduce systemic vulnerabilities such as communication bottlenecks, synchronization failures, insider attacks, and inherent distrust in centralized entities. In healthcare analytics, hospitals leverage patient data to enhance diagnostic precision, optimize clinical workflows, and advance preventive and precision medicine. Yet, reliance on siloed datasets from individual institutions often restricts model generalizability and impedes comprehensive insights into health outcomes. Patient health is a multidimensional construct influenced not only by genetic and biological factors but also by behavioral patterns and socio-environmental determinants. Cross-institutional collaboration integrating diverse datasets from geographically distributed sources is essential to develop robust analytical models. However, such collaboration raises critical privacy concerns, as centralized aggregation of sensitive data risks exposure to breaches or misuse. Our probabilistic framework for privacy-preserving distributed data publishing directly addresses this challenge. By eliminating dependencies on trusted third parties and secure multi-party computation, our approach enables secure, decentralized integration of heterogeneous healthcare data. Through uncertainty-aware probabilistic anonymization and adaptive noise injection, the framework ensures compliance with stringent privacy regulations (e.g., GDPR, CPRA, HIPAA) while preserving the analytical utility required for accurate, actionable health outcome predictions. This balance of utility and privacy empowers researchers to harness the full potential of distributed datasets without compromising individual confidentiality, ultimately fostering innovation in precision medicine and population health management. This paper introduces a novel probabilistic framework for privacy preservation in distributed environments, eliminating dependencies on TTP and SMC. Unlike existing approaches, this method leverages uncertainty-aware probabilistic models to dynamically anonymize and perturb data across distributed nodes while preserving global data utility. First a survey of privacy preservation data publishing methods is presented in this paper and then we discuss about prose and cons of the techniques. After this we present the model and its implementation details. The results obtained by security evaluations shows that the presented method will balance out the privacy security and the accuracy of distributed data better, using the probability model without needing a Trusted Third-Party and Secure Multi-party Computation. 35 58 2023/12/232024/07/302025/04/8 1404/1/19 2025/07/212025/07/212025/07/21 1404/4/30 الیاس مصیبی Elyas Mosayebi دانش‌آموخته کارشناسی‌ارشد گروه مهندسی کامپیوتر دانشگاه گیلان، رشت، ایران ایران elyas.mosayebi@gmail.com رضا ابراهیمی آتانی Reza Ebrahimi Atani دانشیار گروه مهندسی کامپیوتر دانشگاه گیلان، رشت، ایران ایران rebrahimi@guilan.ac.ir Data Mining Data Publishing Data sharing Privacy Preserving Security. اشتراک داده امنیت انتشار داده محرمانگی حفظ حریم خصوصی داده‌کاوی. 1. A. Majeed and S. 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شناسایی چندکلاسی سلول‌های ناهمگون خونی برمبنای الگوریتم شورایی و تجمیع توصیف‌گر بافت Identification, detection and classification and of multiclass heterogeneous blood cell series based on the council algorithm and aggregation of tissue descriptors 1 1 درک نحوه عملکرد سلول‌های بیولوژیکی و تمایز سلول‌ها از یکدیگر برای تشخیص بیماری و درمان اهمیت بالایی دارد؛ از نظر پزشکان متخصص در صورت آشکارشدن وجود ناهنجاری در مراحل نخستین شکل‌گیری تغییرات در سلول­های خونی، امکان درمان زودهنگام و جلوگیری از بروز عوارض آن وجود خواهد داشت. در گام نخست طرح پیشنهادی، ضرایب موجک تصویر به یک شبکه عصبی YOLO داده می‌شود تا مکان برگزیده از تصویر بافت برای بیرون‌آوردن ویژگی انتخاب شود. در ادامه از شبکه عصبی کانولوشنی، روش بهینه‌سازی عقاب طلایی (GEO) و سه دسته‌بند معروف شامل درخت تصمیم (DT)، بیزین ساده (NB) و K نزدیک‌ترین همسایه (KNN) به‌صورت دسته‌بند مشارکتی استفاده می‌شود تا بین سلول‌های خونی مختلف بر اساس مدل‌های آموزش‌دیده تمایز داده شود. نتایج شبیه‌سازی حاکی از آن است که مدل ارائه‌شده با دقت مناسبی به پیش‌بینی نوع سلول خونی بر مبنای آموزش داده‌‌شده به مدل پرداخته و توانسته ‌است به ‌دقت 95 درصد دست یابد.   2 Understanding the structural and morphological characteristics of blood cells plays a crucial role in the early diagnosis and treatment of hematological disorders. Manual inspection of blood smears under a microscope is still the standard approach in many laboratories; however, this process is subjective, time-consuming, and highly dependent on the expertise of the hematologist. To overcome these limitations, the present study introduces an intelligent hybrid framework for multiclass classification of heterogeneous blood cells based on the integration of deep learning and metaheuristic optimization techniques. In the proposed approach, the wavelet coefficients of microscopic images are first extracted to capture discriminative frequency-domain features. These coefficients are then fed into a YOLO-based convolutional neural network to detect candidate cell regions and identify spatial characteristics. A customized CNN architecture is subsequently employed for hierarchical feature learning, while a Golden Eagle Optimization (GEO) algorithm is utilized to perform feature selection and dimensionality reduction by eliminating redundant and less informative attributes. To achieve robust decision-making, three classical classifiers Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbors (KNN) are combined through a weighted voting ensemble strategy. The model was trained and validated on a dataset consisting of microscopic images of five major white blood cell types: lymphocytes, monocytes, eosinophils, basophils, and neutrophils. Quantitative evaluation was performed using precision, recall, F1-score, and accuracy metrics. Experimental results demonstrate that the proposed CNN GEO ensemble model achieves an overall accuracy of 95.7% and an average F1-score of 94.9%, outperforming comparable state-of-the-art methods such as CNN+SVM, PSO+KNN, and VGG-16 in both accuracy and computational efficiency. The findings highlight the capability of the proposed system to accurately distinguish among multiple blood cell categories, thereby providing a reliable and automated decision-support tool for early hematological diagnosis. Future work will focus on expanding the dataset and integrating domain adaptation mechanisms to further enhance cross-laboratory generalization. 59 76 2023/12/232024/07/302025/04/82023/08/17 1402/5/26 2025/07/212025/07/212025/07/212025/08/10 1404/5/19 امید اسلامی فر omid eslamifar دانشجوی دکتری گروه مهندسی برق، دانشکده فنی و مهندسی، دانشگاه آزاد اسلامی واحد ساوه، ساوه، ایران ایران O.eslamifar@iau-saveh.ac.ir محمدرضا سلطانی Mohammadreza soltani استادیار گروه مهندسی برق، دانشکده فنی و مهندسی، دانشگاه آزاد اسلامی واحد خمینی شهر، اصفهان، ایران ایران mrsoltani@iautiran.ac.ir سید محمد جلال رستگار فاطمی seyed Mohamadjalal Rastegar Fatemi استادیار گروه مهندسی برق، دانشکده فنی و مهندسی، دانشگاه آزاد اسلامی واحد ساوه، ساوه، ایران ایران Jalal.pe77@gmail.com blood cell type classification YOLO neural network golden eagle optimization. رسته سلول‌های خونی طبقه‌بندی شبکه عصبی YOLO بهینه‌سازی عقاب طلایی. 1. C. 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یک کاربرد از رویکرد تحلیل توپولوژیکی داده در طبقه‌بندی اشعار فارسی An application of the topological data analysis approach in Persian poetry classification 1 1 تحلیل توپولوژیکی داده شاخه‌ای بدیع و به‌سرعت در حال رشد در علوم داده است که مجموعه‌ای از ابزارهای هندسی و توپولوژیکی را برای استخراج ویژگی‌های مرتبط از داده پیچیده بُعد بالا فراهم می‌کند. در این مقاله، دو روش از بهترین روش‌های تحلیل توپولوژیکی داده؛ یعنی همولوژی ماندگار و نگاشت‌گر به‌منظور طبقه‌بندی اشعار دو تن از بهترین شعرای ایران؛ یعنی فردوسی و حافظ، به‌کار گرفته می‌شود. در این پژوهش از روش‌شناسی تحلیل متن سنتی فراتر رفته‌ تا کارآمدی و بهینه‌بودن تحلیل توپولوژیکی داده در زمینه متن‌کاوی و انتساب نویسنده، نشان داده شود. نکته نوآورانه این مقاله استفاده از تحلیل توپولوژیکی داده در انتساب نویسنده است که پیش‌تر نیز سابقه نداشته است؛ همچنین قابلیت بصری‌سازی نتایج با استفاده از نمودارهای پایا، بارکد و نگاشت‌گر که منحصر به تحلیل توپولوژیکی داده است و به‌سادگی قابل تفسیر توسط هر خواننده‌ای است، این امید را می‌دهد که از این به بعد تحلیل توپولوژیکی داده به‌عنوان یک دریچه جهت رمزگشایی در ادبیات و علوم‌انسانی بتواند مورداستفاده قرار گیرد. 2 This research delves into authorship attribution through an avant-garde lens, employing Topological Data Analysis (TDA) as a potent instrument to unravel intricate patterns within classical Persian poetry. The focal point of this study is the distinguished works of Ferdowsi and Hafez, two preeminent Persian poets, exploring the latent structures in their verses through the lenses of Persistent Homology and Mapper a pair of TDA methodologies. The discernment between Non-Semantic and Semantic authorship attribution methodologies lays the groundwork, elucidating the significance of capturing structural nuances in textual data. The main focus of this investigation revolves around the deployment of Persistent Homology a cutting-edge technique that transcends traditional text analysis methodologies. It operates in high-dimensional spaces, extracting topological features, and rendering them comprehensible through persistent diagrams. This paper meticulously unpacks the mathematical underpinnings of Persistent Homology, providing a stepwise exposition of its application, focusing on Homology, Simplicial Complex, and Group Theory. These foundational elements converge to empower extracting meaningful topological signatures from the poetic corpus. In tandem, Mapper, another TDA tool, unfolds as a pivotal player in this explorative journey. This algorithmic entity facilitates dimensionality reduction and simplicial complex construction to portray an accurate depiction of the intrinsic topological architecture residing in the dataset. The intricacies of Mapper's workflow from filter function selection to binning and clustering are meticulously detailed, forming a coherent narrative of its operational dynamics. Transitioning from theoretical discourse to practical implementation, this research adopts a case study approach, weaving Ferdowsi and Hafez's poetic masterpieces into the TDA tapestry. Beyond the mere application of algorithms, the study delves into the realm of accuracy assessments, subjecting the Mapper algorithm to rigorous tests, and gauging the precision of its poem classifications within identified clusters. An additional layer of complexity unfolds as the research embraces semantic clustering, elucidating thematic resonances embedded within the verses. The results borne out of this meticulous exploration not only underscore the efficacy of TDA methodologies in unveiling the intricate structures of Persian poetry but also offer a nuanced perspective on their interpretability and utility in the realm of authorship attribution. The poetic narrative, with its semantic richness and structural subtleties, emerges as a fertile ground for the application of TDA, pushing the boundaries of text classification methodologies. This research, therefore, contributes significantly to the evolving discourse on the intersection of literature and data science, offering a profound understanding of how TDA can be wielded as a transformative lens to decipher the profound threads of authorial expression. 77 90 2023/12/232024/07/302025/04/82023/08/172023/09/5 1402/6/14 2025/07/212025/07/212025/07/212025/08/102025/06/11 1404/3/21 نیره الیاسی Naiereh Elyasi استادیار دانشکده علوم ریاضی و کامپیوتر، دانشگاه خوارزمی، تهران، ایران ایران elyasi82@khu.ac.ir مهدی حسینی مقدم Mehdi Hosseini Moghadam کارشناس ارشد مهندسی داده، آکسفورد، انگلستان انگلستان m.h.moghadam1996@gmail.com Topological data analysis Persistent Homology Mapper Persian poems. تحلیل توپولوژیکی داده همولوژی ماندگار نگاشت‌گر اشعار فارسی. ۱. الیاسی نیره، تیموری حسین، پاک‌نیت سروش، مقدمه‌ای بر تحلیل توپولوژیکی داده: نظریه و رویکرد، انتشارات دانشگاه تفرش، ۱۴۰۰.##1. 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استفاده از رأی‌گیری بیشینه در شبکه‌های عصبی گراف برای تحلیل احساسات مبتنی بر جنبه Using Majority Voting in Graph Neural Networks for Aspect-Based Sentiment Analysis 1 1 درک عواطف و احساسات مبتنی بر جنبه، وظیفه‌ای کاربردی و مهم در حوزه پردازش زبان طبیعی است که دیدگاه‌های ظریفی را در داده‌های متنی آشکار می‌سازد و به تحلیل عمیق‌تر محتوای متنی کمک می‌کند. تجزیه‌وتحلیل احساسات در سطح جنبه، در تشخیص دقیق احساسات بیان‌شده نسبت به یافتن ویژگی‌های مثبت و منفی موضوعات مورد بحث، اهمیت فراوانی دارد و به‌همین دلیل در زمینه‌هایی مانند بررسی نظرات مشتریان، پایش شبکه‌های اجتماعی و بهبود سامانه‌های پیشنهاددهنده کاربردهای گسترده‌ای پیدا کرده‌است. در این مقاله، به تحلیل احساسات سطح جنبه با استفاده از شبکه‌های عصبی گراف (GCN) پرداخته می‌شود که این روش‌ها در ثبت روابط پیچیده درون داده‌ها نسبت به رویکردهای کلاسیک عملکرد بهتری داشته و برای تشخیص احساسات مرتبط با جنبه‌های خاص، گزینه‌ای مناسب به‌شمار می‌روند. روش پیشنهادی در این پژوهش شامل استفاده از ترکیب نتایج چند مدل پیاده‌سازی‌شده مبتنی بر الگوریتم‌های GCN بر روی مجموعه‌داده‌های معیار برای تحلیل احساسات سطح جنبه است. مدل‌های پیاده‌سازی‌شده شامل DualGCN، RDGCN، SSEGCN و R-GAT هستند که هرکدام با بهره‌گیری از دیدگاه‌ها و معماری‌های متفاوت، قابلیت‌های متنوعی در استخراج ویژگی‌های ساختاری و معنایی متن ارائه می‌دهند. این مدل‌ها با استفاده از رویکرد یادگیری جمعی که فرایند رأی‌گیری بیشینه را شامل می‌شود، ترکیب شده‌اند و درنتیجه پیشرفت‌های قابل‌ توجهی را نسبت به مدل‌های فردی نشان می‌دهند. مجموعه‌داده انتخابی این پژوهش شامل SemEval2014 (زیرمجموعه‌های رستوران 14 و لپ‌تاپ) و توییتر است. در مجموعه‌داده رستوران 14، مدل نهایی شاهد افزایش 2.15% در معیار دقت (Accuracy) و 2.8% در معیار امتیاز F1 (F1-Score) نسبت به مدل‌های پایه بوده‌است؛ همچنین، در مجموعه‌داده لپ‌تاپ افزایش قابل توجهی معادل 9.2 درصد در معیار دقت و 11.74 درصد در معیار امتیاز F1 مشاهده شده‌است؛ درنهایت، در مجموعه‌داده توییتر، افزایش 7.8% در معیار دقت و 8.7% در معیار امتیاز F1 به ثبت رسیده‌است. نتایج نشان می‌دهند که رویکرد جدید و پیش‌گام این پژوهش توانسته است بالاترین درصد دقت را نسبت به پژوهش‌های اخیر در این حوزه به‌دست آورد. 2 Aspect-Based Sentiment Analysis (ABSA) is a detailed subdomain of sentiment analysis that focuses on detecting sentiments toward specific aspects of entities, such as product features or service attributes, rather than providing a general sentiment polarity. This granular understanding is essential in domains such as customer feedback evaluation, social media opinion mining, and intelligent recommendation systems. However, capturing the syntactic and semantic dependencies required for accurate ABSA remains a challenge for conventional models. In this study, we propose an ensemble-based approach utilizing Graph Convolutional Networks (GCNs), which are particularly effective in learning structural relationships from sentence-level dependency trees. Our methodology involves the integration of four advanced GCN-based models: DualGCN, RDGCN, SSEGCN, and R-GAT. Each model offers distinct strengths, ranging from dual-graph encoding and reinforcement-driven attention mechanisms to syntax-aware semantic enhancements. These models are trained individually and then aggregated through a majority voting mechanism to create a robust ensemble capable of improved sentiment prediction at the aspect level. The models were evaluated on benchmark datasets including SemEval-2014 (Rest14 and Laptops subsets) and Twitter, covering both formal and informal texts. Extensive preprocessing was conducted to standardize input formats and ensure fair comparison across models. Moreover, training was performed using both GLoVE and BERT embeddings, allowing the ensemble to benefit from a diverse range of semantic features. The proposed majority voting strategy aggregates the predictions of individual models and determines the final sentiment class based on the most frequent output. In case of a tie, the model with the highest validation accuracy takes precedence. This strategy effectively combines the complementary capabilities of multiple GCN variants, leading to improved performance and stability across diverse datasets. Experimental results show that the proposed ensemble method significantly outperforms both baseline models and recent state-of-the-art methods. On the Rest14 dataset, the ensemble achieved an accuracy of 88.47%, improving upon the best recent model (SAGCN + BERT) by +1.34%. On the Laptops dataset, it attained 85.44%, exceeding SAGCN’s 85.12% by +0.32%. Similarly, on the Twitter dataset, our model reached 82.12%, surpassing SAGCN’s 81.45% by +0.67%. Additionally, compared to individual baseline models, the proposed method improved accuracy by 2.15% and F1-score by 2.8% on Rest14, 9.2% and 11.74% on Laptops, and 7.8% and 8.7% on Twitter, respectively. These improvements highlight the robustness of the ensemble in handling varying linguistic structures and domains. We also explored alternative ensemble strategies including weighted voting, neural fusion, and combined embedding approaches, yet none outperformed the majority voting strategy in consistency or accuracy. This further reinforces the effectiveness and simplicity of our proposed method .In conclusion, this research introduces a novel and practical ensemble technique for ABSA using multiple GCN models and a majority voting strategy. The method achieves state-of-the-art accuracy across multiple benchmarks and demonstrates strong generalization, making it a valuable contribution to aspect-level sentiment analysis. Future work may extend this approach to multilingual and domain-specific contexts or integrate large pretrained language models such as RoBERTa or GPT to further enhance contextual understanding. 91 102 2023/12/232024/07/302025/04/82023/08/172023/09/52024/07/8 1403/4/18 2025/07/212025/07/212025/07/212025/08/102025/06/112025/07/21 1404/4/30 علی بلوچی Ali Balouchi کارشناس‌ارشد مهندسی کامپیوتر، دانشگاه ایلام، ایلام، ایران ایران a.balouchi@ilam.ac.ir مظفر بگ محمدی Mozafar Bagmohmmadi دانشیار گروه مهندسی کامپیوتر، دانشگاه ایلام، ایلام، ایران ایران mozafar@ilam.ac.ir مجتبی کرمی Mojtaba Karmi استادیار گروه مهندسی کامپیوتر، دانشگاه ایلام، ایلام، ایران ایران m.karami@ilam.ac.ir Natural Language Processing Deep Learning Graph Neural Networks Aspect Level Sentiment Analysis Ensemble Learning. پردازش زبان طبیعی یادگیری عمیق شبکه‌های عصبی گراف تحلیل احساسات سطح جنبه یادگیری جمعی. W. Medhat, A. Hassan, and H. 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