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روشی جدید در تشخیص تکراری رکوردها با استفاده از خوشه‌‌بندی سلسله مراتبی A New Method for Duplicate Detection Using Hierarchical Clustering of Records 1 1 به‌دلیل اهمیت بالای کیفیت داده‌‌ها در عملکرد سامانه‌های نرم‌‌افزاری، فرآیند پاکسازی داده به‌خصوص تشخیص رکوردهای تکراری، طی سالیان اخیر یکی از مهم‌‌ترین حوزه‌‌های علوم رایانه به حساب آمده است. در این مقاله روشی برای تشخیص رکوردهای تکراری ارائه شده است که با خوشه‌‌بندی سلسله‌‌مراتبی رکوردها بر اساس ویژگی‌‌های مناسب در هر سطح، میزان شباهت میان رکوردها تخمین زده می‌‌شود. این کار سبب می‌‌شود تا خوشه‌‌هایی در سطح آخر به‌دست آیند که رکوردهای درون آن‌‌ها بسیار مشابه یکدیگر باشند. برای کشف رکوردهای تکراری نیز مقایسه تنها بر روی رکوردهای درون یک خوشه از سطح آخر انجام می‌‌گیرد. همچنین در این مقاله برای مقایسه میان رکوردها، یک تابع تشابه نسبی بر پایه تابع فاصله ویرایشی ارائه شده که دقت بسیار بالایی به همراه دارد. مقایسه نتایج ارزیابی سامانه نشان می‌‌دهد که روش ارائه‌شده، در زمان کمتری، 90% تکراری‌‌های موجود را با دقت 97% کشف می‌‌کند و بهبود داشته است. 2 Accuracy and validity of data are prerequisites of appropriate operations of any software system. Always there is possibility of occurring errors in data due to human and system faults. One of these errors is existence of duplicate records in data sources. Duplicate records refer to the same real world entity. There must be one of them in a data source, but for some reasons like aggregation of data sources and human faults in data entry, it is possible to appear several copies of an entity in a data source. This problem leads to error occurrence in operations or output results of a system; also, it costs a lot for related organization or business. Therefore, data cleaning process especially duplicate record detection, became one of the most important area of computer science in recent years. Many solutions presented for detecting duplicates in different situations, but they almost are all time-consuming. Also, the volume of data is growing up every day. hence, previous methods don’t have enough performance anymore. Incorrect detection of two different records as duplicates, is another problem that recent works are being faced. This becomes important because duplicates will usually be deleted and some correct data will be lost. So it seems that presenting new methods is necessary. In this paper, a method has been proposed that reduces required volume of process using hierarchical clustering with appropriate features. In this method, similarity between records has been estimated in several levels. In each level, a different feature has been used for estimating similarity between records. As a result, clusters that contain very similar records will be created in the last level. The comparisons are done on these records for detecting duplicates. Also, in this paper, a relative similarity function has been proposed for comparing between records. This function has high precision in determining the similarity. Eventually, the evaluation results show that the proposed method detects 90% of duplicate records with 97% accuracy in less time and results have improved. 3 22 2019/06/18 1398/3/28 2020/08/18 1399/5/28 نگین دانشپور Negin Daneshpour دانشگاه تربیت دبیر شهید رجایی ایران ndaneshpour@sru.ac.ir علی برزگری Ali Barzegari دانشگاه آزاد اسلامی واحد علوم تحقیقات ایران ali.barzegari70@gmail.com Duplicate Record Detection Data Cleaning Hierarchical Clustering Similarity Function Feature Selection تشخیص تکراری پاک‌سازی داده خوشه‌‌بندی سلسله‌‌مراتبی تابع تشابه انتخاب ویژگی [1] E. Rahm, and H.H. Do, "Data cleaning: Problems and current approaches", IEEE Data Eng. Bull., 23(4), pp. 3-13, 2000.##[2] L. Bradji, and M. Boufaida, "Knowledge based data cleaning for data warehouse quality", in Digital Information Processing and Communications., Springer. pp. 373-384, 2011.##[3] D.K. Koshley, and R. Halder, "Data cleaning: An abstraction-based approach. in Advances in Computing, Communications and Informatics (ICACCI)," 2015 International Conference on. 2015. IEEE.##[4] M. Alian, , A. Awajan, and B. Ramadan, "Unsupervised learning blocking keys technique for indexing Arabic entity resolution", International Journal of Speech Technology, pp. 1-8, 2018.##[5] Y. Li, , H. Wang, and H. Gao, "Efficient entity resolution based on sequence rules", in Advanced Research on Computer Science and Information Engineering, Springer. pp. 381-388, 2011.##[6] https://www.reddit.com/r/datasets/ comments/3bxlg7/i_have_every_publicly_available_reddit_comment/.##[7] Y. Altowim, , D.V. Kalashnikov, and S. Mehrotra, "ProgressER: Adaptive Progressive Approach to Relational Entity Resolution", ACM Transactions on Knowledge Discovery from Data (TKDD),vol. 12(3), pp. 33, 2018.##[8] J.H. Martin, and D. Jurafsky, "Speech and language processing", International Edition, vol. 710: pp. 25, 2000.##[9] B. Hussain, et al., ''An evaluation of clustering algorithms in duplicate detection,'' Technical Report CSRG-620, University of Toronto, Department of Computer Science, 2013.##[10] M.A. Hernández, and S.J. Stolfo, ''Real-world data is dirty: Data cleansing and the merge/purge problem'', Data mining and knowledge discovery, vol. 2(1), pp. 9-37, 1998.##[11] L. He, et al, ''An efficient data cleaning algorithm based on attributes selection'', in Computer Sciences and Convergence Information Technology (ICCIT), 2011 6th International Conference on. 2011. IEEE.##[12] T. Smith, and M. Waterman, ªIdentification of Common Molecular Subsequences. º J. Molecular Biology, vol. 147, pp. 195-197, 1981.##[13] Li, M., Q. Xie, and Q. Ding, An Improved Data Cleaning Algorithm Based on SNM, in Cloud Computing and Security. 2015, Springer. p. 259-269.##[14] T. Wang, et al, ''SIER: An Efficient Entity Resolution Mechanism Combining SNM and Iteration''. in Web Information System and Application Conference (WISA), 2014 11th. 2014. IEEE.##[15] L. Alami, I. Hafidi, and A. Metrane, ''Entity Resolution in NoSQL Data Warehouse'', in International Conference on Information Technology and Communication Systems. 2017. Springer.##[16] M. Bilenko, and R.J. Mooney, ''Adaptive duplicate detection using learnable string similarity measures''. in Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. 2003. ACM.##[17] B. Kenig, and A. Gal, ''MFIBlocks: An effective blocking algorithm for entity resolution'', Information Systems, vol. 38(6), pp. 908-926, 2013.##[18] R. Agrawal, T. Imieliński, and A. Swami, ''Mining association rules between sets of items in large databases'', in Acm sigmod record. 1993. ACM.##[19] S. Chaudhuri, V. Ganti, and R. Motwani, ''Robust identification of fuzzy duplicates. in Data Engineering,'' 2005. ICDE 2005. Proceedings. 21st International Conference on. 2005. IEEE.##[20] A. Saeedi, E. Peukert, and E. Rahm, ''Comparative evaluation of distributed clustering schemes for multi-source entity resolution'', in Advances in Databases and Information Systems, 2017, Springer.##[21] P. Christen, ''Data matching: concepts and techniques for record linkage'', entity resolution, and duplicate detection. 2012: Springer Science & Business Media.##[22] T. Papenbrock, , A. Heise, and F. Naumann, ''Progressive duplicate detection,'' IEEE Transactions on knowledge and data engineering,vol. 27(5), p p. 1316-1329, 2015.##[23] S.E. Whang, D. Marmaros, and H. Garcia-Molina, ''Pay-as-you-go entity resolution'', IEEE Transactions on Knowledge and Data Engineering, vol.25(5), pp. 1111-1124, 2013.##[24] P. Indyk and R. Motwani, ''Approximate nearest neighbors: towards removing the curse of dimensionality'', in Proceedings of the thirtieth annual ACM symposium on Theory of computing, 1998, ACM.##[25] I. Van Dam, et al, ''Duplicate detection in web shops using LSH to reduce the number of computations'', in Proceedings of the 31st Annual ACM Symposium on Applied Computing, 2016, ACM.##[26] R. van Bezu, et al, ''Multi-component similarity method for web product duplicate detection,'' in Proceedings of the 30th annual ACM symposium on applied computing, 2015, ACM.##[27] D. Vatsalan, and P. Christen, ''Sorted nearest neighborhood clustering for efficient private blocking'', in Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2013, Springer.##[28] Z. Yuhang, W. Yue, and Y. Wei, ''Research on Data Cleaning in Text Clustering'', in Information Technology and Applications (IFITA), 2010 International Forum on, 2010, IEEE.##[29] S. Thampi, and D. Loganathan, Progressive of Duplicate Detection Using Adaptive Window Technique.##[30] M. Dash, and H. Liu, ''Feature selection for classification. Intelligent data analysis'',vol. 1(1-4), pp. 131-156. 1997.##[31] M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of machine learning. 2012, MIT press.##[32] P.E. Greenwood, and M.S. Nikulin, ''A guide to chi-squared testing,'' Vol. 280, 1996.##[33] https://www13.hpi.uni-potsdam.de/fileadmin/user_upload/fachgebiete/naumann/projekte/dude/restaurant.csv.##[34] https://www13.hpi.uni-potsdam.de/fileadmin/user_upload/fachgebiete/naumann/projekte/dude/cd.csv.##[35] https://www13.hpi.uni-potsdam.de/fileadmin/user_upload/fachgebiete/naumann/projekte/dude/CORA.xml.##[36] https://data.wa.gov/api/views/y3ds-rkew/rows.csv?accessType=DOWNLOAD.##[37] J.C. Dunn, Well-separated clusters and optimal fuzzy partitions. Journal of cybernetics, 1974. 4(1): p. 95-104.##[38] https://www.statisticshowto. datasciencecentral.com/probability-and-statistics/t-test/.##[39] M. keyvanpour, ''A Divisive Hierarchical Clustering-based Method for Indexing Image Information'' , JSDP, vol. 11 (2), pp. 91-109, 2015.##[1] E. Rahm, and H.H. Do, "Data cleaning: Problems and current approaches", IEEE Data Eng. Bull., 23(4), pp. 3-13, 2000.##[2] L. Bradji, and M. Boufaida, "Knowledge based data cleaning for data warehouse quality", in Digital Information Processing and Communications., Springer. pp. 373-384, 2011.##[3] D.K. Koshley, and R. Halder, "Data cleaning: An abstraction-based approach. in Advances in Computing, Communications and Informatics (ICACCI)," 2015 International Conference on. 2015. IEEE.##[4] M. Alian, , A. Awajan, and B. Ramadan, "Unsupervised learning blocking keys technique for indexing Arabic entity resolution", International Journal of Speech Technology, pp. 1-8, 2018.##[5] Y. Li, , H. Wang, and H. Gao, "Efficient entity resolution based on sequence rules", in Advanced Research on Computer Science and Information Engineering, Springer. pp. 381-388, 2011.##[6] https://www.reddit.com/r/datasets/ comments/3bxlg7/i_have_every_publicly_available_reddit_comment/.##[7] Y. Altowim, , D.V. Kalashnikov, and S. Mehrotra, "ProgressER: Adaptive Progressive Approach to Relational Entity Resolution", ACM Transactions on Knowledge Discovery from Data (TKDD),vol. 12(3), pp. 33, 2018.##[8] J.H. Martin, and D. Jurafsky, "Speech and language processing", International Edition, vol. 710: pp. 25, 2000.##[9] B. Hussain, et al., ''An evaluation of clustering algorithms in duplicate detection,'' Technical Report CSRG-620, University of Toronto, Department of Computer Science, 2013.##[10] M.A. Hernández, and S.J. Stolfo, ''Real-world data is dirty: Data cleansing and the merge/purge problem'', Data mining and knowledge discovery, vol. 2(1), pp. 9-37, 1998.##[11] L. He, et al, ''An efficient data cleaning algorithm based on attributes selection'', in Computer Sciences and Convergence Information Technology (ICCIT), 2011 6th International Conference on. 2011. IEEE.##[12] T. Smith, and M. Waterman, ªIdentification of Common Molecular Subsequences. º J. Molecular Biology, vol. 147, pp. 195-197, 1981.##[13] Li, M., Q. Xie, and Q. Ding, An Improved Data Cleaning Algorithm Based on SNM, in Cloud Computing and Security. 2015, Springer. p. 259-269.##[14] T. Wang, et al, ''SIER: An Efficient Entity Resolution Mechanism Combining SNM and Iteration''. in Web Information System and Application Conference (WISA), 2014 11th. 2014. IEEE.##[15] L. Alami, I. Hafidi, and A. Metrane, ''Entity Resolution in NoSQL Data Warehouse'', in International Conference on Information Technology and Communication Systems. 2017. Springer.##[16] M. Bilenko, and R.J. Mooney, ''Adaptive duplicate detection using learnable string similarity measures''. in Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. 2003. ACM.##[17] B. Kenig, and A. Gal, ''MFIBlocks: An effective blocking algorithm for entity resolution'', Information Systems, vol. 38(6), pp. 908-926, 2013.##[18] R. Agrawal, T. Imieliński, and A. Swami, ''Mining association rules between sets of items in large databases'', in Acm sigmod record. 1993. ACM.##[19] S. Chaudhuri, V. Ganti, and R. Motwani, ''Robust identification of fuzzy duplicates. in Data Engineering,'' 2005. ICDE 2005. Proceedings. 21st International Conference on. 2005. IEEE.##[20] A. Saeedi, E. Peukert, and E. Rahm, ''Comparative evaluation of distributed clustering schemes for multi-source entity resolution'', in Advances in Databases and Information Systems, 2017, Springer.##[21] P. Christen, ''Data matching: concepts and techniques for record linkage'', entity resolution, and duplicate detection. 2012: Springer Science & Business Media.##[22] T. Papenbrock, , A. Heise, and F. Naumann, ''Progressive duplicate detection,'' IEEE Transactions on knowledge and data engineering,vol. 27(5), p p. 1316-1329, 2015.##[23] S.E. Whang, D. Marmaros, and H. Garcia-Molina, ''Pay-as-you-go entity resolution'', IEEE Transactions on Knowledge and Data Engineering, vol.25(5), pp. 1111-1124, 2013.##[24] P. Indyk and R. Motwani, ''Approximate nearest neighbors: towards removing the curse of dimensionality'', in Proceedings of the thirtieth annual ACM symposium on Theory of computing, 1998, ACM.##[25] I. Van Dam, et al, ''Duplicate detection in web shops using LSH to reduce the number of computations'', in Proceedings of the 31st Annual ACM Symposium on Applied Computing, 2016, ACM.##[26] R. van Bezu, et al, ''Multi-component similarity method for web product duplicate detection,'' in Proceedings of the 30th annual ACM symposium on applied computing, 2015, ACM.##[27] D. Vatsalan, and P. Christen, ''Sorted nearest neighborhood clustering for efficient private blocking'', in Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2013, Springer.##[28] Z. Yuhang, W. Yue, and Y. Wei, ''Research on Data Cleaning in Text Clustering'', in Information Technology and Applications (IFITA), 2010 International Forum on, 2010, IEEE.##[29] S. Thampi, and D. Loganathan, Progressive of Duplicate Detection Using Adaptive Window Technique.##[30] M. Dash, and H. Liu, ''Feature selection for classification. Intelligent data analysis'',vol. 1(1-4), pp. 131-156. 1997.##[31] M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of machine learning. 2012, MIT press.##[32] P.E. Greenwood, and M.S. Nikulin, ''A guide to chi-squared testing,'' Vol. 280, 1996.##[33] https://www13.hpi.uni-potsdam.de/fileadmin/user_upload/fachgebiete/naumann/projekte/dude/restaurant.csv.##[34] https://www13.hpi.uni-potsdam.de/fileadmin/user_upload/fachgebiete/naumann/projekte/dude/cd.csv.##[35] https://www13.hpi.uni-potsdam.de/fileadmin/user_upload/fachgebiete/naumann/projekte/dude/CORA.xml.##[36] https://data.wa.gov/api/views/y3ds-rkew/rows.csv?accessType=DOWNLOAD.##[37] J.C. Dunn, Well-separated clusters and optimal fuzzy partitions. Journal of cybernetics, 1974. 4(1): p. 95-104.##[38] https://www.statisticshowto. datasciencecentral.com/probability-and-statistics/t-test/.##[39] M. keyvanpour, ''A Divisive Hierarchical Clustering-based Method for Indexing Image Information'' , JSDP, vol. 11 (2), pp. 91-109, 2015.##[39] ایزدپناه نجوا، کیوان پور محمدرضا، رنجبران سعیده. یک روش مبتنی بر خوشه‌بندی سلسله‌مراتبی تقسیم‌کننده جهت شاخص‌گذاری اطلاعات تصویری . پردازش علائم و داده‌ها. ۱۳۹۳; ۱۱ (۲) :۱۰۹-۹۱## ##

ارائه الگوریتم جست‌وجوی گرانشی مقید و حل مسأله مسیریابی وسایل نقلیه Proposing a Constrained-GSA for the Vehicle Routing Problem 1 1 امروزه مسأله مسیریابی وسایل نقلیه، یکی از موضوعات پرکاربرد در موضوعات صنعتی، نظامی و حتی امنیتی است و برای افزایش کارایی و بهره‌وری سامانه‌های حمل و نقل تعریف شده است. مسأله مسیریابی وسیله نقیله با شرایط برداشت و تحویل هم‌زمان محموله از جمله این مسائل است. این مسأله از نظر پیچیدگی محاسباتی در مجموعه مسائل سخت (NP-hard) قرار می‌گیرد؛ بنابراین محاسبه بهترین پاسخ برای این مسأله، در زمان محاسباتی نمایی انجام خواهد شد و در مسائل اجرایی قابل استفاده نخواهد بود. استفاده از الگوریتم‌های فراابتکاری یکی از روش‌هایی است که به‌وسیله آنها می‌توان جواب‌هایی مناسب و در زمان محاسباتی قابل قبول به‌دست آورد. در روش‌های موجود، قیود موجود در مسأله، با استفاده از روش جریمه به تابع هدف مسأله اضافه شده و مسأله بهینه‌سازی تک‌هدفه تعریف می‌شود. ضمن این‌که تعداد بهینه وسایل نقلیه مورد نیاز برای حل مسأله در نظر گرفته نمی‌شود. در این مقاله، الگوریتم جست‌وجوی گرانشی بهبود‌یافته برای حل مسائل مقید معرفی شده است. همچنین به‌منظور کنترل قابلیت‌های الگوریتم نظیر کاوش و بهره‌وری از یک کنترلر فازی برای تعیین پارامترهای موجود در الگوریتم استفاده شده، سپس، با استفاده از این الگوریتم، روشی برای حل مسأله مسیریابی وسایل نقلیه با شرایط برداشت و تحویل هم‌زمان ارائه‌ شده است. با استفاده از این روش، علاوه‌بر محاسبه مسیرهای مناسب برای انجام خدمات، تعداد بهینه وسایل نقلیه برای فرآیند خدماتی نیز تعیین می‌شود. برای ارزیابی کارایی روش پیشنهادی در این مقاله، روش پیشنهادی شبیه‌سازی شده و روی مجموعه‌داده استانداردی که برای این دسته از مسائل تعریف شده، اجرا شده است. نتایج تجربی و شبیه‌سازی نشان می‌دهد که این روش، با وجود سادگی در روش پیاده‌سازی و اجرا، دارای کارایی بهتری نسبت به الگوریتم‌ها و روش‌های بررسی شده است. 2 In the past decades, vehicle routing problem (VRP) has gained considerable attention for its applications in industry, military, and transportation applications. Vehicle routing problem with simultaneous pickup and delivery is an extension of the VRP. This problem is an NP-hard problem; hence finding the best solution for this problem which is using exact method, take inappropriate time, and these methods are not useful in real-world applications. Using meta-heuristic algorithms for calculating and computing the solutions for NP-hard problems is a common method to contrast this challenge. The objective function defined for this problem, is a constrained objective function. In previous algorithms, the penalty method was used as constraint handling technique to define the objective function. Determining the value of parameters and penalty coefficient is not easy in these methods. Moreover, the optimal number of vehicles was not considered in the previous algorithms. So, the user should guess number of vehicles and compare the result with other values for this variable. In this paper, a novel objective function is defined to solve the vehicle routing problem with simultaneous pickup and delivery. This method can find the vehicle routes such that increases the performance of the vehicles and decreases the processes’ costs of transportation. in addition, the optimal number of vehicle in this problem can be calculated using this objective function. Finding the best solution for this optimization problems is an NP-hard and meta-heuristic methods can be used to estimate good solutions for this problem. Then, a constrained version of gravitational search algorithm is proposed. In this method, a fuzzy logic controller is used to calculate the value of the parameters and control the abilities of the algorithm, automatically. Using this controller can balance the exploration and exploitation abilities in the gravitational search algorithm and improve the performance of the algorithm. This new version of gravitational search algorithm is used to find a good solution for the predefined objective function. The proposed method is evaluated on some standard benchmark test functions and problems. The experimental results show that the proposed method outperforms the state-of-the-art methods, despite the simplicity of implementation. 23 36 2019/06/182019/05/4 1398/2/14 2020/08/182020/08/18 1399/5/28 سپهر ابراهیمی مود Sepehr Ebrahimi Mood دانشگاه یزد ایران sepehr_ebrahimi@math.uk.ac.ir محمد مسعود جاویدی Mohammad Masoud Javidi دانشگاه شهیدباهنر کرمان ایران javidi@uk.ac.ir محمدرضا خسروی Mohammad Reza Khosravi دانشگاه عالی دفاع ملی و تحقیقات راهبردی ایران morekhosravi@gmail.com Vehicle Routing Problem Meta-heuristic algorithms Constrained Gravitational Search Algorithm مسیریابی وسایل نقلیه برداشت و تحویل هم‌زمان الگوریتم‌های فراابتکاری الگوریتم جست‌وجوی گرانشی مقید [1] N. Christofides, ''The vehicle routing problem'', Revue française d'automatique, informatique, recherche opérationnelle,vol. 10(V1), pp. 55-70, 1976.##[2] P. Toth, D. Vigo, The vehicle routing problem, SIAM, 2002.##[3] T.J. Ai, V. Kachitvichyanukul, ''A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery'', Computers & Operations Research, vol. 36(5), pp. 1693-1702, 2009.##[4] E. Rashedi, H. Nezamabadi-Pour, S. Saryazdi, ''GSA: a gravitational search algorithm'', Information sciences, vol.179(13), pp. 2232-2248, 2009.##[5] E. Rashedi, H. Nezamabadi-Pour, S. Saryazdi, ''BGSA: binary gravitational search algorithm'', Natural Computing, vol.9(3), pp. 727-74, 2010.##[6] S.E. Mood, M.M. Javidi, ''Energy-efficient clustering method for wireless sensor networks using modified gravitational search algorithm,'' Evolving Systems, pp. 1-13, 2019.##[7] B. González, P. Melin, F. Valdez, G. Prado-Arechiga, ''Ensemble Neural Network Optimization Using a Gravitational Search Algorithm with Interval Type-1 and Type-2 Fuzzy Parameter Adaptation in Pattern Recognition Applications, in: Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications, Springer, 2018, pp. 17-27.##[8] A. Karimi , L.S. Hoseini , ''An Optimal Algorithm for Dividing Microscopic Images of Blood for the Diagnosis of Acute Pulmonary Lymphoblastic Cell Using the FCM Algorithm and Genetic Optimization'', JSDP, vol.15 (2),pp. 45-54, 2018. URL: http://jsdp.rcisp.ac.ir/article-1-567-fa.html##[9] M. Vaghefi, F. Jamshidi , ''Features selection for cardiac arrhythmia diagnosis using multiple objective binary particle swarm optimization'', JSDP, vol. 18 (2), pp.163-176, 2021. URL: http://jsdp.rcisp.ac.ir/article-1-972-fa.html##[10] A. Karimi, L.S. Hoseini , ''An Optimal Algorithm for Dividing Microscopic Images of Blood for the Diagnosis of Acute Pulmonary Lymphoblastic Cell Using the FCM Algorithm and Genetic Optimization'', JSDP, vol. 15 (2), pp. 45-54, 2018.##URL: http://jsdp.rcisp.ac.ir/article-1-567-fa.html##[11] S. Mood, E. Rasshedi, M. Javidi, ''New functions for mass caculation in gravitational search algorithm,'' Journal of Computing and Security, 2(3), 2016.##[12] H.A. Kherabadi, S.E. Mood, M.M. Javidi, ''Mutation: a new operator in gravitational search algorithm using fuzzy controller'', Cybernetics and Information Technologies, vol. 17(1), pp. 72-86, 2017.##[13] M. Soleimanpour-Moghadam, H. Nezamabadi-Pour, M.M. Farsangi, ''A quantum inspired gravitational search algorithm for numerical function optimization,'' Information Sciences, pp. 83-100, 2014.##[14] A. Hatamlou, ''Black hole: A new heuristic optimization approach for data clustering'', Information sciences, vol. 222, pp. 175-184, 2013.##[15] M. Shams, E. Rashedi, A. Hakimi, ''Clustered-gravitational search algorithm and its application in parameter optimization of a low noise amplifier,'' Applied Mathematics and Computation, vol.258, pp. 436-453, 2015.##[16] E. Rashedi, E. Rashedi, H. Nezamabadi-pour, ''A comprehensive survey on gravitational search algorithm'', Swarm and evolutionary computation, vol. 41, pp.141-158, 2018.##[17] J.T. Zhang, L.X. Qiao, ''Optimization Mechanism Control Strategy of Vehicle Routing Problem Based on Improved PSO'', Advanced Materials Research, Trans Tech Publ, pp. 130-136, 2013.##[18] B. Yao, B. Yu, P. Hu, J. Gao, M. Zhang, ''An improved particle swarm optimization for carton heterogeneous vehicle routing problem with a collection depot'', Annals of Operations Research, vol.242(2), pp.303-320, 2016.##[19] M. Avci, S. Topaloglu, ''A hybrid metaheuristic algorithm for heterogeneous vehicle routing problem with simultaneous pickup and delivery'', Expert Systems with Applications, vol.53, pp. 160-17,12016.##[20] A. Gupta, S. Saini, ''On Solutions to Vehicle Routing Problems Using Swarm Optimization Techniques: A Review, '' in: Advances in Computer and Computational Sciences, Springer, pp. 345-354, 2017.##[21] E. Mezura-Montes, ''C.A.C. Coello, Constraint-handling in nature-inspired numerical optimization: past, present and future,'' Swarm and Evolutionary Computation, vol.1(4), pp. 173-194, 2011.##[22] D. Orvosh, L. Davis, ''Using a genetic algorithm to optimize problems with feasibility constraints,'' in: Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, IEEE, pp. 548-553, 1994.##[23] R. de Paula Garcia, B.S.L.P. de Lima, A.C. de Castro Lemonge, B.P. Jacob, ''A rank-based constraint handling technique for engineering design optimization problems solved by genetic algorithms,'' Computers & Structures, vol. 187, pp. 77-87, 2017.##[24] M. Dell'Amico, G. Righini, M. Salani, ''A branch-and-price approach to the vehicle routing problem with simultaneous distribution and collection,'' Transportation science, vol. 40(2), pp. 235-247, 2006.##[25] J. Dethloff, ''Vehicle routing and reverse logistics: the vehicle routing problem with simultaneous delivery and pick-up,'' OR-Spektrum, vol.23(1), pp. 79-96, 2001.##[26] F.A.T. Montané, R.D. Galvao, ''A tabu search algorithm for the vehicle routing problem with simultaneous pick-up and delivery service'', Computers & Operations Research, vol.33(3), pp.595-619, 2006.##[27] N. Bianchessi, G. Righini, ''Heuristic algorithms for the vehicle routing problem with simultaneous pick-up and delivery'', Computers & Operations Research, vol.34(2) pp.578-594, 2007.##[1] N. Christofides, ''The vehicle routing problem'', Revue française d'automatique, informatique, recherche opérationnelle,vol. 10(V1), pp. 55-70, 1976.##[2] P. Toth, D. Vigo, The vehicle routing problem, SIAM, 2002.##[3] T.J. Ai, V. Kachitvichyanukul, ''A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery'', Computers & Operations Research, vol. 36(5), pp. 1693-1702, 2009.##[4] E. Rashedi, H. Nezamabadi-Pour, S. Saryazdi, ''GSA: a gravitational search algorithm'', Information sciences, vol.179(13), pp. 2232-2248, 2009.##[5] E. Rashedi, H. Nezamabadi-Pour, S. Saryazdi, ''BGSA: binary gravitational search algorithm'', Natural Computing, vol.9(3), pp. 727-74, 2010.##[6] S.E. Mood, M.M. Javidi, ''Energy-efficient clustering method for wireless sensor networks using modified gravitational search algorithm,'' Evolving Systems, pp. 1-13, 2019.##[7] B. González, P. Melin, F. Valdez, G. Prado-Arechiga, ''Ensemble Neural Network Optimization Using a Gravitational Search Algorithm with Interval Type-1 and Type-2 Fuzzy Parameter Adaptation in Pattern Recognition Applications, in: Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications, Springer, 2018, pp. 17-27.##[8] کریمی عباس، حسینی لیلا سادات. الگوریتم بهینه تقسیم‌بندی تصاویر میکروسکوپی خون برای تشخیص سلول‌های لوسمی حاد لنفوبلاست با استفاده از الگوریتم FCM و بهینه‌سازی ژنتیک. پردازش علائم و داده‌ها. ۱۳۹۷; ۱۵ (۲) :۵۴-۴۵##[8] A. Karimi , L.S. Hoseini , ''An Optimal Algorithm for Dividing Microscopic Images of Blood for the Diagnosis of Acute Pulmonary Lymphoblastic Cell Using the FCM Algorithm and Genetic Optimization'', JSDP, vol.15 (2),pp. 45-54, 2018. URL: http://jsdp.rcisp.ac.ir/article-1-567-fa.html##[9] واقفی مهسا، جمشیدی فاطمه. انتخاب ویژگی برای تشخیص آریتمی های قلبی با استفاده از بهینه‌سازی ازدحام ذرات باینری چند هدفه. پردازش علائم و داده‌ها. ۱۴۰۰; ۱۸ (۲) :۱۷۶-۱۶۳##[9] M. Vaghefi, F. Jamshidi , ''Features selection for cardiac arrhythmia diagnosis using multiple objective binary particle swarm optimization'', JSDP, vol. 18 (2), pp.163-176, 2021. URL: http://jsdp.rcisp.ac.ir/article-1-972-fa.html##[10] کریمی عباس، حسینی لیلا سادات. الگوریتم بهینه تقسیم‌بندی تصاویر میکروسکوپی خون برای تشخیص سلول‌های لوسمی حاد لنفوبلاست با استفاده از الگوریتم FCM و بهینه‌سازی ژنتیک. پردازش علائم و داده‌ها. ۱۳۹۷; ۱۵ (۲) :۵۴-۴۵##[10] A. Karimi, L.S. Hoseini , ''An Optimal Algorithm for Dividing Microscopic Images of Blood for the Diagnosis of Acute Pulmonary Lymphoblastic Cell Using the FCM Algorithm and Genetic Optimization'', JSDP, vol. 15 (2), pp. 45-54, 2018.##URL: http://jsdp.rcisp.ac.ir/article-1-567-fa.html##[11] S. Mood, E. Rasshedi, M. Javidi, ''New functions for mass caculation in gravitational search algorithm,'' Journal of Computing and Security, 2(3), 2016.##[12] H.A. Kherabadi, S.E. Mood, M.M. Javidi, ''Mutation: a new operator in gravitational search algorithm using fuzzy controller'', Cybernetics and Information Technologies, vol. 17(1), pp. 72-86, 2017.##[13] M. Soleimanpour-Moghadam, H. Nezamabadi-Pour, M.M. Farsangi, ''A quantum inspired gravitational search algorithm for numerical function optimization,'' Information Sciences, pp. 83-100, 2014.##[14] A. Hatamlou, ''Black hole: A new heuristic optimization approach for data clustering'', Information sciences, vol. 222, pp. 175-184, 2013.##[15] M. Shams, E. Rashedi, A. Hakimi, ''Clustered-gravitational search algorithm and its application in parameter optimization of a low noise amplifier,'' Applied Mathematics and Computation, vol.258, pp. 436-453, 2015.##[16] E. Rashedi, E. Rashedi, H. Nezamabadi-pour, ''A comprehensive survey on gravitational search algorithm'', Swarm and evolutionary computation, vol. 41, pp.141-158, 2018.##[17] J.T. Zhang, L.X. Qiao, ''Optimization Mechanism Control Strategy of Vehicle Routing Problem Based on Improved PSO'', Advanced Materials Research, Trans Tech Publ, pp. 130-136, 2013.##[18] B. Yao, B. Yu, P. Hu, J. Gao, M. Zhang, ''An improved particle swarm optimization for carton heterogeneous vehicle routing problem with a collection depot'', Annals of Operations Research, vol.242(2), pp.303-320, 2016.##[19] M. Avci, S. Topaloglu, ''A hybrid metaheuristic algorithm for heterogeneous vehicle routing problem with simultaneous pickup and delivery'', Expert Systems with Applications, vol.53, pp. 160-17,12016.##[20] A. Gupta, S. Saini, ''On Solutions to Vehicle Routing Problems Using Swarm Optimization Techniques: A Review, '' in: Advances in Computer and Computational Sciences, Springer, pp. 345-354, 2017.##[21] E. Mezura-Montes, ''C.A.C. Coello, Constraint-handling in nature-inspired numerical optimization: past, present and future,'' Swarm and Evolutionary Computation, vol.1(4), pp. 173-194, 2011.##[22] D. Orvosh, L. Davis, ''Using a genetic algorithm to optimize problems with feasibility constraints,'' in: Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, IEEE, pp. 548-553, 1994.##[23] R. de Paula Garcia, B.S.L.P. de Lima, A.C. de Castro Lemonge, B.P. Jacob, ''A rank-based constraint handling technique for engineering design optimization problems solved by genetic algorithms,'' Computers & Structures, vol. 187, pp. 77-87, 2017.##[24] M. Dell'Amico, G. Righini, M. Salani, ''A branch-and-price approach to the vehicle routing problem with simultaneous distribution and collection,'' Transportation science, vol. 40(2), pp. 235-247, 2006.##[25] J. Dethloff, ''Vehicle routing and reverse logistics: the vehicle routing problem with simultaneous delivery and pick-up,'' OR-Spektrum, vol.23(1), pp. 79-96, 2001.##[26] F.A.T. Montané, R.D. Galvao, ''A tabu search algorithm for the vehicle routing problem with simultaneous pick-up and delivery service'', Computers & Operations Research, vol.33(3), pp.595-619, 2006.##[27] N. Bianchessi, G. Righini, ''Heuristic algorithms for the vehicle routing problem with simultaneous pick-up and delivery'', Computers & Operations Research, vol.34(2) pp.578-594, 2007.## ##

یک الگوریتم مبتنی بر افرازبندی گراف برای خوشه‌بندی سامانه‌‌های نرم‌افزاری با ابعاد بزرگ A partition-based algorithm for clustering large-scale software systems 1 1 از روش‌های خوشه‌بندی برای بازیابی ساختار نرم‌افزار جهت فهم درست آن و همچنین بازسازی نرم‌افزار استفاده می‌شود. در ادبیات موضوع، بیشتر الگوریتم‌های ارائه‌شده برای خوشه‌بندی سامانه‌های نرم‌افزاری به دو دسته الگوریتم‌های مبتنی بر جستجو و الگوریتم‌های سلسله‌­مراتبی طبقه‌بندی می‌شوند و الگوریتمی ‌از رده مبتنی بر افراز برای خوشه‌بندی یک سامانه نرم‌افزاری ارائه نشده ‌است. این روش‌ها سعی دارند که گراف وابستگی موجودیت به‌دست‌آمده  از کد منبع سامانه نرم‌افزاری را به چند مجموعه رأسی افراز کنند. در سامانه‌های نرم‌افزاری، موجودیت می‌تواند رده، تابع و یا یک فایل باشد. با توجه به چندجمله‌ای غیر قطعی، سخت‌بودن مسأله خوشه‌بندی، در سال‌های اخیر از روش‌های تکاملی و مبتنی بر جستجو مانند الگوریتم ژنتیک برای این حل این مسأله، زیاد استفاده شده است. هر چند این الگوریتم‌ها در برخی موارد می‌توانند ساختار مناسبی از نرم‌افزار را به‌دست آورند، اما برای نرم‌افزار‌های با ابعاد بزرگ، با توجه به زمان اجرا و حافظه مصرفی زیاد، قابل اجرا نیستند؛ همچنین، این روش‌ها از اطلاعات و دانش گرافی موجود در گراف وابستگی موجودیت استفاده‌ی چندانی نمی‌کنند. در این مقاله یک الگوریتم مبتنی بر افراز ارائه شده است که بتوان از آن در خوشه‌بندی نرم­افزار نیز استفاده کرد. همچنین، یک نوع فاصله جدید برای قیاس تشابه و عدم تشابه ارائه شده ‌است. انتظار می‌رود روش پیشنهادی بتواند در قیاس با سایر روش‌های موجود، خوشه‌بندی‌هایی با کیفیت بالاتر و نزدیک به خوشه‌بندی فرد خبره، تولید کند. برای بررسی صحت اجرای الگوریتم، آن را بر روی نرم‌افزار موزیلا فایرفاکس اجرا کرده و نتایج را با الگوریتم‌های مطرح این حوزه، مقایسه کرده‌ایم.   2 Clustering techniques are used to extract the structure of software for understanding, maintaining, and refactoring. In the literature, most of the proposed approaches for software clustering are divided into hierarchical algorithms and search-based techniques. In the former, clustering is a process of merging (splitting) similar (non-similar) clusters. These techniques suffered from the drawbacks such as finiteness criterion and arbitrary decisions occurred in the process. Because of the NP-hardness of clustering software systems, evolutionary and search-based algorithms are more commonly used algorithm than hierarchical ones. In evolutionary algorithms, the clustering of software systems is considered as a problem of searching over some possible clustering candidates. Although these algorithms are often able to achieve an appropriate structure of the software, they are not applicable in clustering large-scale software. Furthermore, these algorithms are unable to consider the knowledge in the artifact dependency graph, which extracted from the source code of the software. In software systems, an artifact can be everything like a class, a function, or a file. In this paper, a new partition-based clustering algorithm is presented. This algorithm attempts to partition the artifact dependency graph considering the knowledge therein. Moreover, a new distance criterion is presented to measure the similarity and dissimilarity of the artifacts. The proposed algorithm starts with the artifact dependency graph and creates the similarity matrices of the artifacts. So, it attempts to refine the partition candidate until a fixed point is reached. We expect that the proposed method compared with other methods could lead to achieve the clustering with high quality and similar to the expert's clustering based on MoJo-FM measure. To demonstrate the applicability and validity of the proposed algorithm, a large-scale case study, Mozilla Firefox, is employed. The results demonstrate that the proposed algorithm outperforms the commonly used evolutionary methods in the literature. 37 48 2019/06/182019/05/42019/07/10 1398/4/19 2020/08/182020/08/182020/08/18 1399/5/28 بابک پوراصغر Babak Pourasghar گروه علوم کامپیوتر، دانشکده علوم ریاضی، دانشگاه تبریز ایران b.pourasghar@tabrizu.ac.ir حبیب ایزدخواه Habib Izadkhah گروه علوم کامپیوتر، دانشکده علوم ریاضی، دانشگاه تبریز ایران izadkhah@tabrizu.ac.ir شهریار لطفی Shahriar Lotfi گروه علوم کامپیوتر، دانشکده علوم ریاضی، دانشگاه تبریز ایران shahriar_lotfi@tabrizu.ac.ir خیام صالحی Khayyam Salehi گروه علوم کامپیوتر، دانشکده علوم ریاضی، دانشگاه شهرکرد ایران khayyam.salehi@gmail.com Software Engineering Reverse Engineering Software Clustering K-means algorithm مهندسی نرم‌افزار مهندسی معکوس خوشه‌بندی نرم‌افزار الگوریتم K-means [1] A. Isazadeh, H. Izadkhah, and I. Elgedawy, ''Source code modularization: theory and techniques'', Springer, 2017.##[2] B.S. Mitchell, S. and Mancoridis, ''On the automatic modularization of software systems using the bunch tool'', IEEE Transactions on Software Engineering, vol. 32(3), pp.193-208 , 2006.##[3] I. Candela, G. Bavota, B. Russo, and R. Oliveto, ''Using cohesion and coupling for software remodularization: Is it enough?'', ACM Transactions on Software Engineering and Methodology (TOSEM), vol. 25(3), pp.1-28, 2016.##[4] G. Bavota, F. Carnevale, A. De Lucia, M. Di Penta, and R. Oliveto, ''Putting the developer in-the-loop: an interactive GA for software re-modularization'', In International Symposium on Search Based Software Engineering, 2012, pp. 75-89.##[5] W. Mkaouer, M. Kessentini, A. Shaout, P. Koligheu, S. Bechikh, K. Deb, andA. Ouni, ''Many-objective software remodularization using NSGA-III'', ACM Transactions on Software Engineering and Methodology (TOSEM), vol.24(3), pp.1-45, 2017.##[6] O. Maqbool, and H. Babri, ''Hierarchical clustering for software architecture recovery'', IEEE Transactions on Software Engineering, vol. 33(11), pp.759-780, 2007.##[7] P. Andritsos, and V. Tzerpos, ''Information-theoretic software clustering'', IEEE Transactions on Software Engineering, vol. 31(2), pp.150-165, 2007.##[8] M. Tajgardan, H. Izadkhah, and S. Lotfi, ''Software systems clustering using estimation of distribution approach,'' Journal of Applied Computer Science Methods, vol.8(2), pp.99-113, 2016.##[9] H. Izadkhah, I. Elgedawy, A. Isazadeh, ''E-cdgm: an evolutionary call-dependency graph modularization approach for software systems'', Cybernetics and Information Technologies, vol.16(3), pp.70-90, 2016.##[10] S. Parsa, and O. Bushehrian, '' The design and implementation of a framework for automatic modularization of software systems'', The Journal of Supercomputing, vol.32(1), pp.71-94, 2005.##[11] M. Kargar, A. Isazadeh, and H. Izadkhah, ''Semantic-based software clustering using hill climbing'', In 2017 International Symposium on Computer Science and Software Engineering Conference (CSSE) , pp. 55-60, IEEE, 2017, October.##[12] K. Praditwong, M. Harman, M. and X. Yao, ''Software module clustering as a multi-objective search problem'', IEEE Transactions on Software Engineering, vol. 37(2), pp.264-282, 2010.##[13] T.H. Cormen, C.E. Leiserson, R.L. Rivest, and C. Stein, ''Introduction to algorithms'', MIT press, 2009.##[14] Z. Wen, and V. Tzerpos, '' An effectiveness measure for software clustering algorithms'', In Proceedings. 12th IEEE International Workshop on Program Comprehension, 2004. pp. 194-203, IEEE.##[15] T. Lutellier, D. Chollak, J. Garcia, L. Tan, D. Rayside, N. Medvidović, and R. Kroeger, '' Measuring the impact of code dependencies on software architecture recovery techniques'', IEEE Transactions on Software Engineering, vol. 44(2), pp.159-181, 2017.##[16] N.S. Jalali, H. Izadkhah, and S. Lotfi, ''Multi-objective search-based software modularization: structural and non-structural features'', Soft Computing, vol23(21), pp.11141-11165. 2019##[17] R. Naseem, O. Maqbool, and S. Muhammad, ''Cooperative clustering for software modularization'', Journal of Systems and Software, vol. 86(8), pp.2045-2062, 2013.##[18] S. Mohammadi, and H. Izadkhah, ''A new algorithm for software clustering considering the knowledge of dependency between artifacts in the source code'', Information and Software Technology, vol.105, pp.252-256. 2019.##[19] H. Sözer, ''Evaluating the Effectiveness of Multi-level Greedy Modularity Clustering for Software Architecture Recovery'', In European Conference on Software Architecture, pp. 71-87, 2019.##[20] M. Kargar, A. Isazadeh, and H. Izadkhah, '' Multi-programming language software systems modularization'', Computers & Electrical Engineering, vol.80, pp.106-500, 2019.##[1] A. Isazadeh, H. Izadkhah, and I. Elgedawy, ''Source code modularization: theory and techniques'', Springer, 2017.##[2] B.S. Mitchell, S. and Mancoridis, ''On the automatic modularization of software systems using the bunch tool'', IEEE Transactions on Software Engineering, vol. 32(3), pp.193-208 , 2006.##[3] I. Candela, G. Bavota, B. Russo, and R. Oliveto, ''Using cohesion and coupling for software remodularization: Is it enough?'', ACM Transactions on Software Engineering and Methodology (TOSEM), vol. 25(3), pp.1-28, 2016.##[4] G. Bavota, F. Carnevale, A. De Lucia, M. Di Penta, and R. Oliveto, ''Putting the developer in-the-loop: an interactive GA for software re-modularization'', In International Symposium on Search Based Software Engineering, 2012, pp. 75-89.##[5] W. Mkaouer, M. Kessentini, A. Shaout, P. Koligheu, S. Bechikh, K. Deb, andA. Ouni, ''Many-objective software remodularization using NSGA-III'', ACM Transactions on Software Engineering and Methodology (TOSEM), vol.24(3), pp.1-45, 2017.##[6] O. Maqbool, and H. Babri, ''Hierarchical clustering for software architecture recovery'', IEEE Transactions on Software Engineering, vol. 33(11), pp.759-780, 2007.##[7] P. Andritsos, and V. Tzerpos, ''Information-theoretic software clustering'', IEEE Transactions on Software Engineering, vol. 31(2), pp.150-165, 2007.##[8] M. Tajgardan, H. Izadkhah, and S. Lotfi, ''Software systems clustering using estimation of distribution approach,'' Journal of Applied Computer Science Methods, vol.8(2), pp.99-113, 2016.##[9] H. Izadkhah, I. Elgedawy, A. Isazadeh, ''E-cdgm: an evolutionary call-dependency graph modularization approach for software systems'', Cybernetics and Information Technologies, vol.16(3), pp.70-90, 2016.##[10] S. Parsa, and O. Bushehrian, '' The design and implementation of a framework for automatic modularization of software systems'', The Journal of Supercomputing, vol.32(1), pp.71-94, 2005.##[11] M. Kargar, A. Isazadeh, and H. Izadkhah, ''Semantic-based software clustering using hill climbing'', In 2017 International Symposium on Computer Science and Software Engineering Conference (CSSE) , pp. 55-60, IEEE, 2017, October.##[12] K. Praditwong, M. Harman, M. and X. Yao, ''Software module clustering as a multi-objective search problem'', IEEE Transactions on Software Engineering, vol. 37(2), pp.264-282, 2010.##[13] T.H. Cormen, C.E. Leiserson, R.L. Rivest, and C. Stein, ''Introduction to algorithms'', MIT press, 2009.##[14] Z. Wen, and V. Tzerpos, '' An effectiveness measure for software clustering algorithms'', In Proceedings. 12th IEEE International Workshop on Program Comprehension, 2004. pp. 194-203, IEEE.##[15] T. Lutellier, D. Chollak, J. Garcia, L. Tan, D. Rayside, N. Medvidović, and R. Kroeger, '' Measuring the impact of code dependencies on software architecture recovery techniques'', IEEE Transactions on Software Engineering, vol. 44(2), pp.159-181, 2017.##[16] N.S. Jalali, H. Izadkhah, and S. Lotfi, ''Multi-objective search-based software modularization: structural and non-structural features'', Soft Computing, vol23(21), pp.11141-11165. 2019##[17] R. Naseem, O. Maqbool, and S. Muhammad, ''Cooperative clustering for software modularization'', Journal of Systems and Software, vol. 86(8), pp.2045-2062, 2013.##[18] S. Mohammadi, and H. Izadkhah, ''A new algorithm for software clustering considering the knowledge of dependency between artifacts in the source code'', Information and Software Technology, vol.105, pp.252-256. 2019.##[19] H. Sözer, ''Evaluating the Effectiveness of Multi-level Greedy Modularity Clustering for Software Architecture Recovery'', In European Conference on Software Architecture, pp. 71-87, 2019.##[20] M. Kargar, A. Isazadeh, and H. Izadkhah, '' Multi-programming language software systems modularization'', Computers & Electrical Engineering, vol.80, pp.106-500, 2019.## ##

برچسب‌زنی مقیاس‌پذیر تصاویر با خلاصه‌سازی نمونه‌ها به نماینده‌های برچسب‌دار Scalable Image Annotation by Summarizing Training Samples into Labeled Prototypes 1 1 با افزایش روز‌افزون تصاویر، اندیس‌گذاری و جستجوی سریع آنها در پایگاه داده‌های بزرگ، یک امر ضروری است. یکی از راه‌کارهای مؤثر، نسبت‌دادن یک یا چند برچسب به هر تصویر با هدف توصیف محتوای درون آن است. با وجود کارایی روش‌های خودکار برچسب‌زنی، یکی از چالش‌های اساسی آنها مقیاس‌پذیری با افزایش تصاویر پایگاه داده است. در این مقاله، با هدف حل این چالش، ابتدا براساس توصیف‌گر بصری تصاویر که از شبکه‌های یادگیری عمیق استخراج می‌شوند،‌ نمایندگان مناسبی به‌دست می‌آیند. سپس، با استفاده از رویه انتشار برچسب بر روی گراف، برچسب­‌های معنایی از تصاویر آموزشی به نمایندگان منتشر می‌شوند. با این راه‌کار، به یک مجموعه نمایندگان برچسب‎‌دار دست خواهیم یافت که می‌توان عمل برچسب‌زنی هر تصویر آزمون را بر اساس این نمایندگان انجام داد. برای برچسب‌زنی، یک رویکرد مبتنی بر آستانه‌گذاری وفقی پیشنهاد شده است. با روش پیشنهادی، می‌توان اندازه مجموعه‌داده آموزشی را به 6/22 درصد اندازه اولیه کاهش داد که منجر به تسریع حداقل 2/4 برابری زمان برچسب‌زنی خواهد شد. همچنین، کارایی برچسب­زنی بر روی مجموعه‌داده‌­های مختلف برحسب سه معیار دقت، یادآوری و F1 در حد مطلوبی حفظ شده است. 2 By increasing the number of images, it is essential to provide fast search methods and intelligent filtering of images. To handle images in large datasets, some relevant tags are assigned to each image to for describing its content. Automatic Image Annotation (AIA) aims to automatically assign a group of keywords to an image based on visual content of the image. AIA frameworks have two main stages; Feature Extraction and Tag Assignment which are both important in order to reach a proper performance. In the first stage of our proposed method, we utilize deep models to obtain a visual representation of images. We apply different pre-trained architectures of Convolutional Neural Networks (CNN) to the input image including Vgg16, Dense169, and ResNet 101. After passing the image through the layers of CNN, we obtain a single feature vector from the layer before the last layer, resulting into a rich representation for the visual content of the image. One advantage of deep feature extractor is that it substitutes a single feature vector instead of multiple feature vectors and thus, there is no need for combining multiple features. In the second stage, some tags are assigned from training images to a test image which is called “Tag Assignment”. Our approach for image annotation belongs to the search-based methods which have high performance in spite of simple structure. Although it is even more time-consuming due to its method of comparing the test image to every training in order to find similar images. Despite the efficiency of automatic Image annotation methods, it is challenging to provide a scalable method for large-scale datasets. In this paper, to solve this challenge, we propose a novel approach to summarize training database (images and their relevant tags) into a small number of prototypes. To this end, we apply a clustering algorithm on the visual descriptors of training images to extract the visual part of prototypes. Since the number of clusters is much smaller than the number of images, a good level of summarization will be achieved using our approach. In the next step, we extract the labels of prototypes based on the labels of input images in the dataset. because of this, semantic labels are propagated from training images to the prototypes using a label propagation process on a graph. In this graph, there is one node for each input image and one node for each prototypes. This means that we have a graph with union of input images and prototypes. Then, to extract the edges of graph, the visual feature of each node on graph is coded using other nodes to obtain its K-nearest neighbors. This goal is achieved by using Locality-constraints Linear Coding algorithm. After construction the above graph, a label propagation algorithm is applied on the graph to extract the labels of prototypes. Based on this approach, we achieve a set of labeled prototypes which can be used for annotating every test image. To assign tags for an input image, we propose an adaptive thresholding method that finds the labels of a new image using a linear interpolation from the labels of learned prototypes. The proposed method can reduce the size of a training dataset to 22.6% of its original size. This issue will considerably reduce the annotation time such that, compared to the state-of-the-art search-based methods such as 2PKNN,  the proposed method is at least 4.2 times faster than 2PKNN, while the performance of annotation process in terms of Precision, Recall and F1 will be maintained on different datasets. 49 68 2019/06/182019/05/42019/07/102019/07/14 1398/4/23 2020/08/182020/08/182020/08/182020/08/18 1399/5/28 محیا محمدی کاشانی Mahya Mohammadi Kashani دانشگاه تربیت دبیر شهید رجایی ایران mahya.mkashani@sru.ac.ir سید حمید امیری S. Hamid Amiri دانشگاه تربیت دبیر شهید رجایی ایران s.hamidamiri@sru.ac.ir Database Summarization Image Annotation Search-Based method Scalability خلاصه‌سازی پایگاه داده برچسب‌زنی تصویر روش مبتنی برجستجو مقیاس‌پذیری [1] V. N. Murthy, E. F. Can, and R. Manmatha, "A hybrid model for automatic image annotation," in Proceedings of International Conference on Multimedia Retrieval, pp. 355-369, 2014.##[2] S. Feng, R. Manmatha, and V. Lavrenko, "Multiple Bernoulli relevance models for image and video annotation," in Computer Vision and Pattern Recognition (CVPR), 2004.##[3] P. Ji, X. Gao, and X. Hu, "Automatic image annotation by combining generative and discriminant models," Neurocomputing, 2016.##[4] L. Ballan, T. Uricchio, L. Seidenari, and A. Del Bimbo, "A cross-media model for automatic image annotation," in Proceedings of International Conference on Multimedia Retrieval, 2014, pp. 73.##[5] J. Jeon, V. Lavrenko, and R. Manmatha, "Automatic image annotation and retrieval using cross-media relevance models," in Proceedings of the 26th annual international ACM SIGIR conference on Research and development in information retrieval, 2003, pp. 119-126.##[6] J. Li and J. Z. Wang, "Automatic linguistic indexing of pictures by a statistical modeling approach," IEEE Transactions on pattern analysis and machine intelligence, vol. 25, pp. 1075-1088, 2003.##[7] A. Makadia and V. Pavlovic, "Baselines for image annotation." International Journal of Computer Vision, pp. 88-105, 2010.##[8] Wang, J., Yang, J., Lv, F., Huang, T., "Locality-constrained linear coding for image classification," 2010.##[9] M. M. Kashani and S. H. Amiri, "Leveraging deep learning representation for search-based image annotation," in Artificial Intelligence and Signal Processing Conference (AISP), 2017, pp. 156-161.##[10] V. N. Murthy, S. Maji, and R. Manmatha, "Automatic image annotation using deep learning representations," in Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, 2015, pp. 603-606.##[11] X. Li, T. Uricchio, L. Ballan, M. Bertini, "Socializing the semantic gap: A comparative survey on image tag assignment, refinement, and retrieval." ACM Computing Surveys (CSUR), 2016, 49(1): 14.##[12] Q. Cheng, Q. Zhang, P. Fu, C. Tu, S. Li, "A survey and analysis on automatic image annotation," Pattern Recognition, pp. 242-259, 2018.##[13] D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation," Journal of machine Learning research, vol. 3, pp. 993-1022, 2003.##[14] F. Monay and D. Gatica-Perez, "PLSA-based image auto-annotation: constraining the latent space," in Proceedings of the 12th annual ACM international conference on Multimedia, 2004, pp. 348-351.##[15] A. Llorente, R. Manmatha, S. Ruger, Image retrieval using markov random Fields and global image features, in Proceedings of the ACM International,Conference on Image and Video Retrieval, ACM, 2010, pp. 243-250.##[16] Y. Xiang, X. Zhou, T.-S. Chua, C.-W. Ngo, A revisit of generative model for automatic image annotation using markov random _elds, in Computer Vision and Pattern Recognition, 2009. CVPR 2009, IEEE Conference on, IEEE, 2009, pp. 1153-1160.##[17] I. Dimitrovski, D. Kocev, S. Loskovska, S. D_zeroski, Hierarchical annotation of medical images, Pattern Recognition 44 (10-11), pp. 2436-2449, 2011.##[18] J. Wang and J. Hu, Multi-label image annotation via maximum consistency, in Image Processing (ICIP), 2010 17th IEEE International Conference on, IEEE, 2010, pp. 2337-2340.##[19] H.Wang, H. Huang, C. Ding, Image annotation using the bi-relational graph of images and semantic labels, in Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on IEEE, 2011, pp. 793-800.##[20] Z. Lin, G. Ding, M. Hu, J. Wang, X. Ye, Image tag completion via image-specific and tag-specific linear sparse reconstructions, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 1618-1625.##[21] L. Wu, R. Jin, A. K. Jain, Tag Completion for image retrieval, IEEE Trans. Pattern Anal. Mach. Intell. 35 (3), (2013), pp. 716-727.##[22] Z. Qin, C.-G. Li, H. Zhang, J. Guo, Improving tag matrix completion for image annotation and retrieval, in Visual Communications and Image Processing (VCIP), IEEE, 2015, pp. 1-4.##[23] X.-Y. Jing, F. Wu, Z. Li, R. Hu, D. Zhang, Multi-label dictionary learning for image annotation, IEEE Transactions on Image Processing 25 (6) (2016),2712-2725.##[24] Y. Hou, Z. Lin, Image tag completion and refinement by subspace clustering and matrix completion, in Visual Communications and Image Processing(VCIP), 2015, IEEE, 2015, pp. 1-4.##[25] Z. Lin, G. Ding, M. Hu, Y. Lin, S. S. Ge, Image tag completion via dual-view linear sparse reconstructions, Computer Vision and Image Understanding, 124 (2014) 42-60##[26] K. Q. Weinberger, L. K. Saul, Distance metric learning for large margin nearest neighbor classification, Journal of Machine Learning Research, 10, pp. 207-244, 2009.##[27] E. P. Xing, M. I. Jordan, S. J. Russell, A. Y. Ng, Distance metric learning with application to clustering with side-information, in Advances in neural information processing systems, pp. 521-528, 2003.##[28] S. C. Hoi, W. Liu, M. R. Lyu, W.-Y. Ma, Learning distance metrics with contextual constraints for image retrieval, in Computer vision and pattern recognition, IEEE computer society conference, Vol. 2, 2006, pp. 2072-2078.##[29] Y. Verma and & C. V. Jawahar, Image annotation by propagating labels from semantic neighbourhoods. International Journal of Computer Vision, 2017, 121. 1., pp. 126-148.##[30] M. Guillaumin, T. Mensink, J. Verbeek, and C. Schmid, "Tagprop: Discriminative metric learning in nearest neighbor models for image auto-annotation," in 2009 IEEE 12th international conference on computer vision, 2009, pp. 309-316.##[31] L. Wu, S. C. Hoi, R. Jin, J. Zhu, N. Yu, Distance metric learning from uncertain side information with application to automated photo tagging, in Proceedings of the 17th ACM international conference on Multimedia,, 2009, pp. 135-144.##[32] A. Bar-Hillel, T. Hertz, N. Shental, D. Weinshall, Learning a Mahalanobis metric from equivalence constraints, Journal of Machine Learning Research, 6, pp. 937-965, Jun 2005.##[33] F. Liu, T. Xiang, T. M. Hospedales, W. Yang, C. Sun, Semantic regularisation for recurrent image annotation, in Computer Vision and Pattern Recognition (CVPR), IEEE Conference, 2017, pp. 4160-4168.##[34] J. Johnson, L. Ballan, L. Fei-Fei, Love the neighbors: Image annotation by exploiting image metadata, in Proceedings of the IEEE international conference on computer vision, 2015, pp. 4624-4632.##[35] H.-F. Yu, P. Jain, P. Kar, I. Dhillon, Large-scale multi-label learning with missing labels, in International conference on machine learning, 2014, pp. 593-601.##[36] Y. Verma, C. Jawahar, Exploring svm for image annotation in presence of confusing labels, in BMVC, 2013, pp. 1-25.##[37] B. Hariharan, L. Zelnik-Manor, M. Varma, S. Vishwanathan, Large scale max-margin multi-label classification with priors, in Proceedings of the 27th International Conference on Machine Learning (ICML-10), Citeseer, 2010, pp. 423-430.##[38] Y. Li, Y. Song, J. Luo, Improving pairwise ranking for multi-label image classification, in the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 3617-3625.##[39] T. Lan, G. Mori, A max-margin riffled independence model for image tag ranking, in IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2013, pp. 3103-3110.##[40] Y. Yang, W. Zhang, and Y. Xie, "Image automatic annotation via multi-view deep representation," Journal of Visual Communication and Image Representation, vol. 33, 2015, pp. 368-377.##[41] H. K. Shooroki, M. A. Z. Chahooki, Selection of effective training instances for scalable automatic image annotation, Multimedia Tools and Applications, 2017, 76 (7) (2017), pp. 9643-9666.##[42] S. H. Amiri and M. Jamzad. "Leveraging multi-modal fusion for graph-based image annotation.", Journal of Visual Communication and Image Representation, 2018, 55, pp. 816-828.##[43] R. Rad and M. Jamzad. "Image annotation using multi-view non-negative matrix factorization with a different number of basis vectors." Journal of Visual Communication and Image Representation, 2017, 46: 1-12.##[44] M. M. Kalayeh, H. Idrees, and M. Shah, "NMF-KNN: Image annotation using weighted multi-view non-negative matrix factorization," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 184-191.##[45] Sun, Y., Liu, Q., Tang, J., Tao, D., "Learning discriminative dictionary for group sparse representation." IEEE transactions on image processing, 2014, 23(9): 3816-3828.##[46] XC. Deng, X. Liu, Y. Mu, J. Li, Large-scale multi-task image labeling with adaptive relevance discovery and feature hashing, Signal Processing 112 , 2015, pp. 137-145.##[47] J. Wang, G. Li, A multi-modal hashing learning framework for automatic image annotation, in IEEE Second International Conference on Data Science in Cyberspace (DSC), IEEE, 2017, pp. 14-21.##[48] Wang, Changhu, Shuicheng Yan, Lei Zhang, and Hong-Jiang Zhang. "Multi-label sparse coding for automatic image annotation." In 2009 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2009, pp. 1643-1650.##[49] Q. Zhang and B. Li, 2015. Dictionary learning in visual computing. Synthesis Lectures on Image, Video, & Multimedia Processing, 8(2), pp.1-151.##[50] F. Wang and C. Zhang, "Label propagation through linear neighborhoods," IEEE Transactions on Knowledge and Data Engineering, vol. 20, pp. 55-67, 2008.##[51] K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition,", arXiv preprint arXiv:1409.1556, 2014.##[52] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770-778.##[53] G. Huang and Z. Liu, "Densely connected convolutional networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 3.##[1] V. N. Murthy, E. F. Can, and R. Manmatha, "A hybrid model for automatic image annotation," in Proceedings of International Conference on Multimedia Retrieval, pp. 355-369, 2014.##[2] S. Feng, R. Manmatha, and V. Lavrenko, "Multiple Bernoulli relevance models for image and video annotation," in Computer Vision and Pattern Recognition (CVPR), 2004.##[3] P. Ji, X. Gao, and X. Hu, "Automatic image annotation by combining generative and discriminant models," Neurocomputing, 2016.##[4] L. Ballan, T. Uricchio, L. Seidenari, and A. Del Bimbo, "A cross-media model for automatic image annotation," in Proceedings of International Conference on Multimedia Retrieval, 2014, pp. 73.##[5] J. Jeon, V. Lavrenko, and R. Manmatha, "Automatic image annotation and retrieval using cross-media relevance models," in Proceedings of the 26th annual international ACM SIGIR conference on Research and development in information retrieval, 2003, pp. 119-126.##[6] J. Li and J. Z. Wang, "Automatic linguistic indexing of pictures by a statistical modeling approach," IEEE Transactions on pattern analysis and machine intelligence, vol. 25, pp. 1075-1088, 2003.##[7] A. Makadia and V. Pavlovic, "Baselines for image annotation." International Journal of Computer Vision, pp. 88-105, 2010.##[8] Wang, J., Yang, J., Lv, F., Huang, T., "Locality-constrained linear coding for image classification," 2010.##[9] M. M. Kashani and S. H. Amiri, "Leveraging deep learning representation for search-based image annotation," in Artificial Intelligence and Signal Processing Conference (AISP), 2017, pp. 156-161.##[10] V. N. Murthy, S. Maji, and R. Manmatha, "Automatic image annotation using deep learning representations," in Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, 2015, pp. 603-606.##[11] X. Li, T. Uricchio, L. Ballan, M. Bertini, "Socializing the semantic gap: A comparative survey on image tag assignment, refinement, and retrieval." ACM Computing Surveys (CSUR), 2016, 49(1): 14.##[12] Q. Cheng, Q. Zhang, P. Fu, C. Tu, S. Li, "A survey and analysis on automatic image annotation," Pattern Recognition, pp. 242-259, 2018.##[13] D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation," Journal of machine Learning research, vol. 3, pp. 993-1022, 2003.##[14] F. Monay and D. Gatica-Perez, "PLSA-based image auto-annotation: constraining the latent space," in Proceedings of the 12th annual ACM international conference on Multimedia, 2004, pp. 348-351.##[15] A. Llorente, R. Manmatha, S. Ruger, Image retrieval using markov random Fields and global image features, in Proceedings of the ACM International,Conference on Image and Video Retrieval, ACM, 2010, pp. 243-250.##[16] Y. Xiang, X. Zhou, T.-S. Chua, C.-W. Ngo, A revisit of generative model for automatic image annotation using markov random _elds, in Computer Vision and Pattern Recognition, 2009. CVPR 2009, IEEE Conference on, IEEE, 2009, pp. 1153-1160.##[17] I. Dimitrovski, D. Kocev, S. Loskovska, S. D_zeroski, Hierarchical annotation of medical images, Pattern Recognition 44 (10-11), pp. 2436-2449, 2011.##[18] J. Wang and J. Hu, Multi-label image annotation via maximum consistency, in Image Processing (ICIP), 2010 17th IEEE International Conference on, IEEE, 2010, pp. 2337-2340.##[19] H.Wang, H. Huang, C. Ding, Image annotation using the bi-relational graph of images and semantic labels, in Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on IEEE, 2011, pp. 793-800.##[20] Z. Lin, G. Ding, M. Hu, J. Wang, X. Ye, Image tag completion via image-specific and tag-specific linear sparse reconstructions, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 1618-1625.##[21] L. Wu, R. Jin, A. K. Jain, Tag Completion for image retrieval, IEEE Trans. Pattern Anal. Mach. Intell. 35 (3), (2013), pp. 716-727.##[22] Z. Qin, C.-G. Li, H. Zhang, J. Guo, Improving tag matrix completion for image annotation and retrieval, in Visual Communications and Image Processing (VCIP), IEEE, 2015, pp. 1-4.##[23] X.-Y. Jing, F. Wu, Z. Li, R. Hu, D. Zhang, Multi-label dictionary learning for image annotation, IEEE Transactions on Image Processing 25 (6) (2016),2712-2725.##[24] Y. Hou, Z. Lin, Image tag completion and refinement by subspace clustering and matrix completion, in Visual Communications and Image Processing(VCIP), 2015, IEEE, 2015, pp. 1-4.##[25] Z. Lin, G. Ding, M. Hu, Y. Lin, S. S. Ge, Image tag completion via dual-view linear sparse reconstructions, Computer Vision and Image Understanding, 124 (2014) 42-60##[26] K. Q. Weinberger, L. K. Saul, Distance metric learning for large margin nearest neighbor classification, Journal of Machine Learning Research, 10, pp. 207-244, 2009.##[27] E. P. Xing, M. I. Jordan, S. J. Russell, A. Y. Ng, Distance metric learning with application to clustering with side-information, in Advances in neural information processing systems, pp. 521-528, 2003.##[28] S. C. Hoi, W. Liu, M. R. Lyu, W.-Y. Ma, Learning distance metrics with contextual constraints for image retrieval, in Computer vision and pattern recognition, IEEE computer society conference, Vol. 2, 2006, pp. 2072-2078.##[29] Y. Verma and & C. V. Jawahar, Image annotation by propagating labels from semantic neighbourhoods. International Journal of Computer Vision, 2017, 121. 1., pp. 126-148.##[30] M. Guillaumin, T. Mensink, J. Verbeek, and C. Schmid, "Tagprop: Discriminative metric learning in nearest neighbor models for image auto-annotation," in 2009 IEEE 12th international conference on computer vision, 2009, pp. 309-316.##[31] L. Wu, S. C. Hoi, R. Jin, J. Zhu, N. Yu, Distance metric learning from uncertain side information with application to automated photo tagging, in Proceedings of the 17th ACM international conference on Multimedia,, 2009, pp. 135-144.##[32] A. Bar-Hillel, T. Hertz, N. Shental, D. Weinshall, Learning a Mahalanobis metric from equivalence constraints, Journal of Machine Learning Research, 6, pp. 937-965, Jun 2005.##[33] F. Liu, T. Xiang, T. M. Hospedales, W. Yang, C. Sun, Semantic regularisation for recurrent image annotation, in Computer Vision and Pattern Recognition (CVPR), IEEE Conference, 2017, pp. 4160-4168.##[34] J. Johnson, L. Ballan, L. Fei-Fei, Love the neighbors: Image annotation by exploiting image metadata, in Proceedings of the IEEE international conference on computer vision, 2015, pp. 4624-4632.##[35] H.-F. Yu, P. Jain, P. Kar, I. Dhillon, Large-scale multi-label learning with missing labels, in International conference on machine learning, 2014, pp. 593-601.##[36] Y. Verma, C. Jawahar, Exploring svm for image annotation in presence of confusing labels, in BMVC, 2013, pp. 1-25.##[37] B. Hariharan, L. Zelnik-Manor, M. Varma, S. Vishwanathan, Large scale max-margin multi-label classification with priors, in Proceedings of the 27th International Conference on Machine Learning (ICML-10), Citeseer, 2010, pp. 423-430.##[38] Y. Li, Y. Song, J. Luo, Improving pairwise ranking for multi-label image classification, in the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 3617-3625.##[39] T. Lan, G. Mori, A max-margin riffled independence model for image tag ranking, in IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2013, pp. 3103-3110.##[40] Y. Yang, W. Zhang, and Y. Xie, "Image automatic annotation via multi-view deep representation," Journal of Visual Communication and Image Representation, vol. 33, 2015, pp. 368-377.##[41] H. K. Shooroki, M. A. Z. Chahooki, Selection of effective training instances for scalable automatic image annotation, Multimedia Tools and Applications, 2017, 76 (7) (2017), pp. 9643-9666.##[42] S. H. Amiri and M. Jamzad. "Leveraging multi-modal fusion for graph-based image annotation.", Journal of Visual Communication and Image Representation, 2018, 55, pp. 816-828.##[43] R. Rad and M. Jamzad. "Image annotation using multi-view non-negative matrix factorization with a different number of basis vectors." Journal of Visual Communication and Image Representation, 2017, 46: 1-12.##[44] M. M. Kalayeh, H. Idrees, and M. Shah, "NMF-KNN: Image annotation using weighted multi-view non-negative matrix factorization," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 184-191.##[45] Sun, Y., Liu, Q., Tang, J., Tao, D., "Learning discriminative dictionary for group sparse representation." IEEE transactions on image processing, 2014, 23(9): 3816-3828.##[46] XC. Deng, X. Liu, Y. Mu, J. Li, Large-scale multi-task image labeling with adaptive relevance discovery and feature hashing, Signal Processing 112 , 2015, pp. 137-145.##[47] J. Wang, G. Li, A multi-modal hashing learning framework for automatic image annotation, in IEEE Second International Conference on Data Science in Cyberspace (DSC), IEEE, 2017, pp. 14-21.##[48] Wang, Changhu, Shuicheng Yan, Lei Zhang, and Hong-Jiang Zhang. "Multi-label sparse coding for automatic image annotation." In 2009 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2009, pp. 1643-1650.##[49] Q. Zhang and B. Li, 2015. Dictionary learning in visual computing. Synthesis Lectures on Image, Video, & Multimedia Processing, 8(2), pp.1-151.##[50] F. Wang and C. Zhang, "Label propagation through linear neighborhoods," IEEE Transactions on Knowledge and Data Engineering, vol. 20, pp. 55-67, 2008.##[51] K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition,", arXiv preprint arXiv:1409.1556, 2014.##[52] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770-778.##[53] G. Huang and Z. Liu, "Densely connected convolutional networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 3.## ##

ارائه راه‌کار برای مقابله با فریب ایجاد‌شده به‌وسیله ربات‌ها به‌‌منظور بهبود رتبه‌بندی ترافیکی تارنماها Representing a method to identify and contrast with the fraud which is created by robots for developing websites’ traffic ranking 1 1 با گسترش اینترنت و فضای وب، برقراری ارتباط و کسب اطلاعات در بین افراد از شکل سنتی و اولیه خود فاصله گرفته و به درون تارنماها کشیده شده است. همچنین فضای جهانی وب، فرصت بزرگی را برای کسب و کارها فراهم می‌کند تا ارتباط خود را با مشتری بهبود ببخشند و بازار خود را در دنیای برخط گسترش دهند. کسب و کارها برای بررسی میزان بازدید و محبوبیت سایت‌هایشان از معیاری به نام رتبه‌بندی ترافیکی استفاده می‌کنند. رتبه‌بندی ترافیکی میزان بازدیدکنندگان یک سایت را اندازه گرفته و براساس همین آمار، رتبه‌ای را به سایت اختصاص می‌دهد. یکی از مهم‌ترین چالش‌های موجود در رتبه‌بندی، ایجاد ترافیک جعلی تولید‌شده به‌وسیله برنامه‌های کاربردی به نام ربات است. ربات‌ها اجزای نرم‌افزاری مخرب مورد استفاده برای تولید هرزنامه‌ها، راه‌اندازی حملات مختل‌کننده سامانه، فیشینگ، سرقت هویت و خروج اطلاعات و دیگر فعالیت‌های غیر قانونی هستند تاکنون روش‌های مختلفی برای شناسایی و کشف ربات صورت گرفته است. در این پژوهش، شناسایی ربات‌ها از طریق تحلیل و پردازش لاگ دسترسی وب سرور و استفاده از روش‌های داده‌کاوی، انجام می‌شود. نتایج تجربی نشان می‌دهد که روش پیشنهادی در این پژوهش با کشف ویژگی‌های جدید و معرفی شرط جدید در برچسب‌گذاری نشست‌ها، باعث بهبود دقت در شناسایی ربات‌ها و در نتیجه ایجاد بهبود در رتبه‌بندی ترافیکی تارنماها نسبت به کارهای پیشین شده است. 2 With the expansion of the Internet and the Web, communication and information gathering between individual has distracted from its traditional form and into web sites. The World Wide Web also offers a great opportunity for businesses to improve their relationship with the client and expand their marketplace in online world. Businesses use a criterion called traffic ranking to determine their site's popularity and visibility. Traffic ranking measures the amount of visitors to a site and based on these statistics, allocates a ranking to the site. One of the most important challenges in the ranking is the creation of fake traffic that generated by applications called robots. Robots are malicious software components that used to generate spam, set up distributed denial of services attacks, fishing, identity theft, removal of information and other illegal activities .there are already several ways to identify and discover the robot. According to Doran et al., The identification methods are divided into two categories: offline and real-time. The offline detection method is divided into three categories: Syntactical Log Analysis, Traffic Pattern Analysis, and Analytical Learning Techniques. The real-time method is performed by the Turing test system. In this research, the identification of robots is done through the offline method by analysis and processing of access logs to the web server and the use of data mining techniques. In this method, first, the features of each session are extracted, then generally these sessions are labeled with three conditions into two categories of human and robot. Finally, by using data mining tool, web robots are detected. In all previous studies, the features are extracted from each sessions, for example in first studies, Tan&Kumar extracted 25 features of sessions. After that Bomhardt et al. used 34 features to identify the robots. In 2009 Stassopoulou et al. used 6 features that was extracted from sessions and so on. But in this research, features are extracted from sessions of a unique user. Experimental results show that the proposed method in this research, by discovering new features and introducing a new condition in session labeling, improves the accuracy of identifying robots and moreover, improves the ranking of web traffic from previous work. 69 80 2019/06/182019/05/42019/07/102019/07/142019/06/26 1398/4/5 2020/08/182020/08/182020/08/182020/08/182020/01/11 1398/10/21 زهرا عبدی zahra abdi دانشکده برق و کامپیوتر، دانشگاه آزاد اسلامی واحد علوم و تحقیقات ایران zahraabdi.ce@gmail.com مجتبی مازوچی mojtaba mazoochi پژوهشگاه ارتباطات و فناوری اطلاعات ایران mazoochi@itrc.ac.ir محمدعلی پورمینا mohammadali pourmina دانشکده برق و کامپیوتر، دانشگاه آزاد اسلامی واحد علوم و تحقیقات ایران pourmina@srbiau.ac.ir Traffic Ranking Robot Detection Session Labeling Web Server Access Log Data Mining رتبه‌بندی ترافیکی شناسایی ربات برچسب‌گذاری نشست لاگ دسترسی وب سرور داده‌کاوی [1] رجب‌نیا جواد، ذبیحی مهدیه، وفایی‌جهان مجید، "تشخیص روبات‌های وب با استفاده از سیستم استنتاج فازی مبتنی بر درخت تصمیم"، هفتمین کنفرانس داده‌کاوی ایران، 1392.##[1] J. Rajab Nia, M. Zabihi, M. VafahiJahan, "web robot detection with fuzzy inference system based on decision trees," The Seventh Iran Data Mining Conference, 2013.##[2] B. W.N.Lo, R.. SharmaSedhain, "How Reliable Are Website Rankings? Implications For E-Business Advertising And Internet Search," Issues in Information Systems, Volume VII, No. 2, pp. 233-238, 2006.##[3] What is fake traffic?, [Online], https://sedo-us1.custhelp.com/app/answers/detail/a_id/678/~/what-is-fake-traffic, February 2017.##[4] D.S. Sisodia, Sh. Verma, O.P. Vyas, "A Comparative Analysis of Browsing Behavior of Human Visitors and Automatic Software Agents," American Journal of Systems and Software, vol. 3, no. 2, pp. 31-35, 2015.##[5] A. Stassopoulou, M.D. Dikaiakos," Web robot detection: A probabilistic reasoning approach," Computer Networks, Vol. 53, pp. 265-278, 2009.##[6] D. Doran, S.S. Gokhale, "Web Robot Detection Techniques: Overview And Limitations," springer Data Mining and Knowledge Discovery, Vol. 22, pp. 183-210, 2010.##[7] P.N. TAN, V. KUMAR,"Discovery of Web Robot Sessions Based on their Navigational Patterns," Data Mining and Knowledge Discovery, vol. 6, pp. 9-35, 2002.##[8] CH. Bomhardt, W. Gaul, L. Schmidt-Thieme, "Web Robot Detection - Preprocessing Web Logfiles for Robot Detection," In Proceedings of SISCLADAG.Bologna, Ital, pp. 113-124, 2005.##[9] D. Stevanovic, A. An, N. Vlajic, "Feature evaluation for web crawler detection with data mining techniques," Elsevier, Expert Systems with Applications, Vol. 39, pp. 8707-8717, 2012.##[10] D. Stevanovic, N. Vlajic, A. An, "Detection of malicious and non-malicious website visitors using unsupervised neural network learning," Elsevier, Applied Soft Computing 13, pp. 698-708, 2012.##[11] M. Zabihimayvan, M. VafaeiJahan, J. Hamidzadeh,"A Density Based Clustering Approach for Web Robot Detection," IEEE, 4th International Conference On Computer And Knowledge Engineering (ICCKE), pp. 23-28, 2014.##[12] D.S. Sisodia, Sh. Verma, .O.P. Vyas, "Agglomerative Approach for Identification and Elimination of Web Robots from Web Server Logs to Extract Knowledge about Actual Visitors," Journal of Data Analysis and Information Processing, Vol. 3, pp. 1-10, 2015.##[13] J. Hamidzadeh, M. Zabihimayvan, R. Sadeghi, "Detection of Web site visitors based on fuzzy rough sets," Springer, pp. 2175-2188, 2017.##[14] user-agent-string. [online], http://user-agent-string.info/list-of-ua/bots-ip , ,(December 2017)##Bot vs.Browsers. [Online], http://www.botsvs-browsers.com, December 2017.##[15] User-Agents. [Online], http://www.user-agents.org, December 2017.##[16] S.S. Aksenova, "Machine Learning with WEKA :WEKA Explorer Tutorial for WEKA Version 3.4.3," 2004 .##[17]http://www.secrepo.com/maccdc2012/http.log.gz##[18] http://www.cs.waikato.ac.nz/ml/weka/##[1] رجب‌نیا جواد، ذبیحی مهدیه، وفایی‌جهان مجید، "تشخیص روبات‌های وب با استفاده از سیستم استنتاج فازی مبتنی بر درخت تصمیم"، هفتمین کنفرانس داده‌کاوی ایران، 1392.##[1] J. Rajab Nia, M. Zabihi, M. VafahiJahan, "web robot detection with fuzzy inference system based on decision trees," The Seventh Iran Data Mining Conference, 2013.##[2] B. W.N.Lo, R.. SharmaSedhain, "How Reliable Are Website Rankings? Implications For E-Business Advertising And Internet Search," Issues in Information Systems, Volume VII, No. 2, pp. 233-238, 2006.##[3] What is fake traffic?, [Online], https://sedo-us1.custhelp.com/app/answers/detail/a_id/678/~/what-is-fake-traffic, February 2017.##[4] D.S. Sisodia, Sh. Verma, O.P. Vyas, "A Comparative Analysis of Browsing Behavior of Human Visitors and Automatic Software Agents," American Journal of Systems and Software, vol. 3, no. 2, pp. 31-35, 2015.##[5] A. Stassopoulou, M.D. Dikaiakos," Web robot detection: A probabilistic reasoning approach," Computer Networks, Vol. 53, pp. 265-278, 2009.##[6] D. Doran, S.S. Gokhale, "Web Robot Detection Techniques: Overview And Limitations," springer Data Mining and Knowledge Discovery, Vol. 22, pp. 183-210, 2010.##[7] P.N. TAN, V. KUMAR,"Discovery of Web Robot Sessions Based on their Navigational Patterns," Data Mining and Knowledge Discovery, vol. 6, pp. 9-35, 2002.##[8] CH. Bomhardt, W. Gaul, L. Schmidt-Thieme, "Web Robot Detection - Preprocessing Web Logfiles for Robot Detection," In Proceedings of SISCLADAG.Bologna, Ital, pp. 113-124, 2005.##[9] D. Stevanovic, A. An, N. Vlajic, "Feature evaluation for web crawler detection with data mining techniques," Elsevier, Expert Systems with Applications, Vol. 39, pp. 8707-8717, 2012.##[10] D. Stevanovic, N. Vlajic, A. An, "Detection of malicious and non-malicious website visitors using unsupervised neural network learning," Elsevier, Applied Soft Computing 13, pp. 698-708, 2012.##[11] M. Zabihimayvan, M. VafaeiJahan, J. Hamidzadeh,"A Density Based Clustering Approach for Web Robot Detection," IEEE, 4th International Conference On Computer And Knowledge Engineering (ICCKE), pp. 23-28, 2014.##[12] D.S. Sisodia, Sh. Verma, .O.P. Vyas, "Agglomerative Approach for Identification and Elimination of Web Robots from Web Server Logs to Extract Knowledge about Actual Visitors," Journal of Data Analysis and Information Processing, Vol. 3, pp. 1-10, 2015.##[13] J. Hamidzadeh, M. Zabihimayvan, R. Sadeghi, "Detection of Web site visitors based on fuzzy rough sets," Springer, pp. 2175-2188, 2017.##[14] user-agent-string. [online], http://user-agent-string.info/list-of-ua/bots-ip , ,(December 2017)##Bot vs.Browsers. [Online], http://www.botsvs-browsers.com, December 2017.##[15] User-Agents. [Online], http://www.user-agents.org, December 2017.##[16] S.S. Aksenova, "Machine Learning with WEKA :WEKA Explorer Tutorial for WEKA Version 3.4.3," 2004 .##[17]http://www.secrepo.com/maccdc2012/http.log.gz##[18] http://www.cs.waikato.ac.nz/ml/weka/ ##

مدل‌سازی شبکه نورونی CA3-CA1 و مطالعه امواج تیز ریپل A neural mass model of CA1-CA3 neural network and studying sharp wave ripples 1 1 هر فرد حدود یک‌سوم عمر خود را در حالت خواب می‌گذراند. نکته جالب این است که مغز یک فرد خوابیده به‌هیچ‌عنوان در حالت غیر فعال و ساکت نیست و به‌خصوص در شبکه عصبی هیپوکمپ امواج تیز ریپل مشاهده می‌شوند. در اینجا یک مدل پدیده‌شناختی که در آن تطبیق‌پذیزی برای نورون‌های تحریکی در نظر گرفته شده است، برای شبکه CA1-CA3 هیپوکمپ ارائه می‌دهیم. این مدل ساده در غیاب محرک خارجی نوساناتی با خواص مشابه امواج تیز ریپل که در تجربه به‌دست آمده است، تولید می‌کند؛ به‌خصوص نشان می‌دهیم در اثر کاهش تحریک در شبکه، دامنه ریپل‌ها افزایش می‌یابد و فرکانس ریپل‌ها کم می‌شود؛ به‌علاوه احتمال تشکیل دوتایی‌های ریپل در اثر کاهش تحریک افزایش می‌یابد. این نتایج با نتایج تجربی هم‌خوانی بسیار خوبی دارد.  2 We spend one third of our life in sleep. The interesting point about the sleep is that the neurons are not quiescent during sleeping and they show synchronous oscillations at different regions. Especially sharp wave ripples are observed in the hippocampus. Here, we propose a simple phenomenological neural mass model for the CA1-CA3 network of the hippocampus considering the spike frequency adaptation for excitatory neurons. The model consists of one group of identical CA1 excitatory neurons, one group of identical CA1 inhibitory neurons, one group of identical CA3 excitatory neurons, and one group of identical CA3 inhibitory neurons. All the recurrent connections between the neurons of CA3 network are considered. For CA1 neurons the excitatory to inhibitory, inhibitory to excitatory and inhibitory to inhibitory connections are considered. CA1 and CA3 neurons are connected by long-range connections from CA3 excitatory neurons to both CA1 excitatory and inhibitory neurons. We show that this simple model can spontaneously generate the oscillations similar to the sharp waves in the CA3 network. The duration of the sharp waves is determined by the slow dynamic of the adaptation process. The excitatory inputs from CA3 network to the CA1 network during these sharp waves induce ripples in the CA1 network due to the interaction of excitatory and inhibitory neurons. We next show that contrary to intuition and in a very good agreement with the recent experimental findings, reduction of the excitation increases the amplitude of the ripples while decreases the frequency of them. This model can also spontaneously generate ripple doublets. The decrease in the excitation is associated with the increase in the probability of observing ripple doublets. Our results shed light on our understanding of the mechanism underlying the generation of sharp wave ripples. 81 88 2019/06/182019/05/42019/07/102019/07/142019/06/262019/06/28 1398/4/7 2020/08/182020/08/182020/08/182020/08/182020/01/112020/09/2 1399/6/12 مریم قربانی Maryam Ghorbani گروه مهندسی برق، دانشکده مهندسی، دانشگاه فردوسی مشهد و مرکز علوم اعصاب و رفتار رایان، دانشکده علوم، دانشگاه فردوسی مشهد ایران maryamgh@um.ac.ir Neural mass model Sharp wave ripples Spike frequency adaptation مدل جرم نورونی امواج تیز ریپل spike frequency adaptation [1] P. Anderson, et al., "Modeling sharp wave ripple complexes through a CA3-CA1 network model with chemical synapses",Hippo-campus,2012 May, vol. 22(5), pp.995-1017, 2011.##[2] D. Sullivan , J. Csicsvari, K. Mizuseki, S. Montgomery, K. Diba, G. Buzsáki, "Relationships between hippocampal sharp waves, ripples, and fast gamma oscillation: influence of dentate and entorhinal cortical activity.", J Neurosci, vol.31 (23), pp. 8605-16, 2011.##[3] G. Buzsa'ki," Hippocampal sharp waves: their origin and significance", Brain Res398, pp.242-252, 1986.##[4] G. Buzsa'ki," Rhythms of the Brain", Oxford University Press, 2006.##[5] G. Buzsa'ki, Z. Horvath, R. Urioste, J. Hetke, K. Wise , " High-frequency network oscillation in the hippocampus",Scien-ce vol. 256, pp. 1025-1027,1992.##[6] A. Draguhn, RD. Traub, D. Schmitz, JGR. Jefferys," Electrical couplingunderlies high-frequency oscillations in the hippocampus invitro," Nature vol. 394, pp.189-192, 1998.##[7] D. Schmitz, S. Schuchmann, A. Fisahn, A. Draguhn, EH. Buhl, E.Petrasch-Parwez, R. Dermietzel, U. Heinemann, RD. Traub,"Axo-axonalcoupling a novel mechanism for ultrafast neuronal communication," Neuron vol.31, pp. 31831-840, 2001.##[8] A. Ylinen, A. Bragin, Z. Nadasdy, G. Jando, I. Szabo, A. Sik, G. Buzsa'ki,"Sharp wave-associated high-frequency oscillation (200 Hz) inthe intact hippocampus: network and intracellular mechanisms.",J Neurosci, vol.15, pp. 30-46, 1995.##[9] N. Maier, V. Nimmrich, A. Draguhn," Cellular and network mechanisms underlying spon-taneous sharp waveripple complexes in mouse hippocampal slices," J Physiol, vol.550, pp. 873-887, 2003.##[10] M. Both, F. Ba¨hner, OB. Halbach, A. Draguhn," Propagation of specific network patterns through the mouse hippocampus," Hippocampus, vol.18, pp. 899-908, 2008.##[11] J. Csicsvari, H. Hirase, A. Czurko, A. Mamiya, G. Buzsa'ki, " Oscillatory coupling of hippocampal pyramidal cells and interneurons inthe behaving ra, ",J Neurosci. Vol. 19, pp. 274-287, 1999.##[12] J. Taxidis, S. Coombes , R. Mason , MR. Owen, "Modeling sharp wave-ripple complexes through a CA3-CA1 network model with chemical synapses," Hippo-campus, vol. 22(5), pp. 995-1017, 2012.##[13] M. Ghorbani, M. Mehta, R. Bruinsma, and J. A. Levine, "Nonlinear-dynamics theory of up-down transitions in neocortical neural networks",Phys. Rev. E, vol. 85, pp. 21-98, 2012.##[14] J. Gan, S. Weng, AJ. Pernía-Andrade, J. Csicsvari, P. Jonas, "Phase-locked inhibition, but not excitation, underlies hippocampal ripple oscillations in awake mice in vivo ,Neuron", vol. 93 (2), pp. 308-314, 2017.##[15] G. Buzsaki, "Hippocampal sharp wave‐ripple: A cognitive biomarker for episodic memory and planning, Hippocampus", vol. 25(10): pp.1073-1188, 2015.##[16] J. Sven, M. Timme, and R. Memmesheimer, "A unified dynamic model for learning, replay, and sharp-wave/ripples," Journal of Neuroscience, pp. 16236-16258, 2015.##[17] R. Memmesheimer, "Quantitative prediction of intermittent high-frequency oscillations in neural networks with supralinear dendritic interactions," Proceedings of the National Academy of Sciences , pp.11092-11097,2010.##[18] A. Amélie, et al. "A detailed anatomical and mathematical model of the hippocampal formation for the generation of sharp-wave ripples and theta-nested gamma oscillations," Journal of computational neuroscience , pp. 207-221, 2018.##[1] P. Anderson, et al., "Modeling sharp wave ripple complexes through a CA3-CA1 network model with chemical synapses",Hippo-campus,2012 May, vol. 22(5), pp.995-1017, 2011.##[2] D. Sullivan , J. Csicsvari, K. Mizuseki, S. Montgomery, K. Diba, G. Buzsáki, "Relationships between hippocampal sharp waves, ripples, and fast gamma oscillation: influence of dentate and entorhinal cortical activity.", J Neurosci, vol.31 (23), pp. 8605-16, 2011.##[3] G. Buzsa'ki," Hippocampal sharp waves: their origin and significance", Brain Res398, pp.242-252, 1986.##[4] G. Buzsa'ki," Rhythms of the Brain", Oxford University Press, 2006.##[5] G. Buzsa'ki, Z. Horvath, R. Urioste, J. Hetke, K. Wise , " High-frequency network oscillation in the hippocampus",Scien-ce vol. 256, pp. 1025-1027,1992.##[6] A. Draguhn, RD. Traub, D. Schmitz, JGR. Jefferys," Electrical couplingunderlies high-frequency oscillations in the hippocampus invitro," Nature vol. 394, pp.189-192, 1998.##[7] D. Schmitz, S. Schuchmann, A. Fisahn, A. Draguhn, EH. Buhl, E.Petrasch-Parwez, R. Dermietzel, U. Heinemann, RD. Traub,"Axo-axonalcoupling a novel mechanism for ultrafast neuronal communication," Neuron vol.31, pp. 31831-840, 2001.##[8] A. Ylinen, A. Bragin, Z. Nadasdy, G. Jando, I. Szabo, A. Sik, G. Buzsa'ki,"Sharp wave-associated high-frequency oscillation (200 Hz) inthe intact hippocampus: network and intracellular mechanisms.",J Neurosci, vol.15, pp. 30-46, 1995.##[9] N. Maier, V. Nimmrich, A. Draguhn," Cellular and network mechanisms underlying spon-taneous sharp waveripple complexes in mouse hippocampal slices," J Physiol, vol.550, pp. 873-887, 2003.##[10] M. Both, F. Ba¨hner, OB. Halbach, A. Draguhn," Propagation of specific network patterns through the mouse hippocampus," Hippocampus, vol.18, pp. 899-908, 2008.##[11] J. Csicsvari, H. Hirase, A. Czurko, A. Mamiya, G. Buzsa'ki, " Oscillatory coupling of hippocampal pyramidal cells and interneurons inthe behaving ra, ",J Neurosci. Vol. 19, pp. 274-287, 1999.##[12] J. Taxidis, S. Coombes , R. Mason , MR. Owen, "Modeling sharp wave-ripple complexes through a CA3-CA1 network model with chemical synapses," Hippo-campus, vol. 22(5), pp. 995-1017, 2012.##[13] M. Ghorbani, M. Mehta, R. Bruinsma, and J. A. Levine, "Nonlinear-dynamics theory of up-down transitions in neocortical neural networks",Phys. Rev. E, vol. 85, pp. 21-98, 2012.##[14] J. Gan, S. Weng, AJ. Pernía-Andrade, J. Csicsvari, P. Jonas, "Phase-locked inhibition, but not excitation, underlies hippocampal ripple oscillations in awake mice in vivo ,Neuron", vol. 93 (2), pp. 308-314, 2017.##[15] G. Buzsaki, "Hippocampal sharp wave‐ripple: A cognitive biomarker for episodic memory and planning, Hippocampus", vol. 25(10): pp.1073-1188, 2015.##[16] J. Sven, M. Timme, and R. Memmesheimer, "A unified dynamic model for learning, replay, and sharp-wave/ripples," Journal of Neuroscience, pp. 16236-16258, 2015.##[17] R. Memmesheimer, "Quantitative prediction of intermittent high-frequency oscillations in neural networks with supralinear dendritic interactions," Proceedings of the National Academy of Sciences , pp.11092-11097,2010.##[18] A. Amélie, et al. "A detailed anatomical and mathematical model of the hippocampal formation for the generation of sharp-wave ripples and theta-nested gamma oscillations," Journal of computational neuroscience , pp. 207-221, 2018.## ##

ارائه یک سامانه پیشنهادگر حافظه پایه ترکیبی با استفاده از هستان‌شناسی و محتوا A New WordNet Enriched Content-Collaborative Recommender System 1 1 سامانه­‌های پیشنهادگر در زمینه تجارت الکترونیک شناخته شده هستند. از این­‌گونه سیستم‌ها انتظار می‌­رود که کالاها و اقلام مهمی (از جمله موسیقی و فیلم) را به مشتریان پیشنهاد دهند. در سامانه‌های پیشنهادگر سنتی از جمله روش­های پالایش محتوا پایه و پالایش مشارکتی، چالش‌­ها و مشکلات مهمی از جمله شروع سرد، مقیاس‌­پذیری و پراکندگی داده‌­ها وجود دارد. اخیراً به‌­کارگیری روش‌­های ترکیبی توانسته با بهره‌­گیری از مزایای این روش‌­ها با هم، برخی از این چالش‌­ها را تا حد قابل قبولی حل نمایند. در این مقاله سعی می­‌شود روشی برای پیشنهاد ارائه شود که ترکیبی از دو روش پالایش محتوا پایه و پالایش مشارکتی (شامل دو رویکرد حافظه پایه و مدل پایه) باشد. روش پالایش مشارکتی حافظه پایه، دقت بالایی دارد، اما از مقیاس‌­پذیری کمی برخوردار است. در مقابل، رویکرد مدل پایه دارای دقت کمی در ارائه پیشنهاد به کاربران بوده اما مقیاس‌­پذیری بالایی از خود نشان می­‌دهد. در این مقاله سامانه پیشنهادگر ترکیبی مبتنی بر هستان­‌شناسی ارائه شده که از مزایای هر دو روش بهره برده و  براساس رتبه‌­بندی­‌های واقعی، مورد ارزیابی قرار می­‌گیرد. هستان‌شناسی، توصیفی واضح و رسمی برای تعریف یک پایگاه دانش شامل مفاهیم (کلاس‌­ها) در حوزه موضوعی، نقش‌­ها (رابط­‌ها) بین نمونه‌­های مفاهیم، محدودیت­‌های مربوط به رابطه‌­ها، همراه با یک مجموعه از عناصر و اعضا (یا نمونه­‌ها) است که یک پایگاه دانش را تعریف می­‌کند. هستان­‌شناسی در بخش پالایش محتوا پایه مورد استفاده قرار می­‌گیرد و ساختار هستان‌شناسی توسط تکنیک­‌های پالایش مشارکتی بهبود می­‌یابد. در روش ارائه‌شده در این پژوهش، عملکرد سیستم پیشنهادی بهتر از عملکرد پالایش محتوا پایه و مشارکتی است. روش پیشنهادی با استفاده از یک مجموعه‌داده­ واقعی ارزیابی شده است و نتایج آزمایش­ها نشان می‌دهد روش مذکور کارایی بهتری دارد. همچنین با توجه به راه‌کارهای ارائه‌شده در مقاله حاضر، مشخص شد، روش پیشنهادی دقت و مقیاس‌­پذیری مناسبی نسبت به سامانه‌های پیشنهادگری دارد که صرفاً حافظه پایه (KNN) و یا مدل پایه هستند. 2 The recommender systems are models that are to predict the potential interests of users among a number of items. These systems are widespread and they have many applications in real-world. These systems are generally based on one of two structural types: collaborative filtering and content filtering. There are some systems which are based on both of them. These systems are named hybrid recommender systems. Recently, many researchers have proved that using content models along with these systems can improve the efficacy of hybrid recommender systems. In this paper, we propose to use a new hybrid recommender system where we use a WordNet to improve its performance. This WordNet is also automatically generated and improved during its generation. Our ontology creates a knowledge base of concepts and their relations. This WordNet is used in the content collaborator section in our hybrid recommender system. We improve our ontological structure via a content filtering technique. Our method also benefits from a clustering task in its collaborative section. Indeed, we use a passive clustering task to improve the time complexity of our hybrid recommender system. Although this is a hybrid method, it consists of two separate sections. These two sections work together during learning. Our hybrid recommender system incorporates a basic memory-based approach and a basic model-based approach in such a way that it is as accurate as a memory-based approach and as scalable as a model-based approach. Our hybrid recommender system is assessed by a well-known data set. The empirical results indicate that our hybrid recommender system is superior to the state of the art methods. Also, our hybrid recommender system is more accurate and scalable compared to the recommender systems, which are simply memory-based (KNN) or basic model-based. The empirical results also confirm that our hybrid recommender system is superior to the state of the art methods in terms of the consumed time. While this method is more accurate than model-based methods, it is also faster than memory-based methods. However, this method is not much weaker in terms of accuracy than memory-based methods, and not much weaker in terms of speed than model-based methods. 89 124 2019/06/182019/05/42019/07/102019/07/142019/06/262019/06/282020/10/25 1399/8/4 2020/08/182020/08/182020/08/182020/08/182020/01/112020/09/22021/03/8 1399/12/18 پیام بحرانی Payam Bahrani گروه مهندسی کامپیوتر، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی ایران p.bahrani@gmail.com بهروز مینایی بیدگلی Behrouz Minaei2 دانشکده مهندسی کامپیوتر، دانشگاه علم و صنعت ایران ایران b_minaei-at@ac.ir حمید پروین Hamid Parvin گروه مهندسی کامپیوتر، واحد نورآباد ممسنی، دانشگاه آزاد اسلامی ایران parvin@iust.ac.ir میترا میرزارضایی Mitra Mirzarezaee گروه مهندسی کامپیوتر، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی ایران mitra_mirzaee@gmail.com احمد کشاورز Ahmad Keshavarz گروه مهندسی برق، دانشکده مهندسی سیستم‌های هوشمند و علوم داده، دانشگاه خلیج فارس ایران ahmad_keshavarz@gmail.com Recommender System Ontology Memory-based Filtering Model-based Filtering Clustering KNN سامانه پیشنهادگر هستان‌شناسی پالایش حافظه پایه پالایش مدل پایه خوشه‌بندی KNN [1] E. 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پیش‌بینی قیمت سهام در بورس اوراق بهادار تهران توسط ترکیب دوگانه سامانه استنتاج فازی و الگوریتم رقابت استعماری فازی Predicting stock prices on the Tehran Stock Exchange by a new hybridization of Fuzzy Inference System and Fuzzy Imperialist Competitive Algorithm 1 1 پیش‌بینی قیمت سهام در بورس اوراق بهادار از جمله چالش برانگیزترین مباحث در مقوله پیش‌بینی است که توجهات بسیاری از جمله محققان را به خود جلب کرده است. عوامل مختلف درگیر در بورس اوراق بهادار سبب شده است تا بازار بورس همیشه از خود فرآیندی پویا و پیچیده داشته باشند. لذا پژوهش‌گران بر آن شده‌اند تا در پیش‌بینی رفتار بورس، به دنبال روش‌های نوینی باشند که دربرابر عدم ایستایی و پیچیده بودن مقاوم باشند. در این پژوهش یک مدل ترکیبی دوگانه متشکل از دو سامانه استنتاج فازی و یک الگوریتم رقابت استعماری به‌صورت ترکیبی استفاده شده است که یک سامانه فازی برای ایجاد مدلی برای پیش‌بینی قیمت سهام براساس 10 متغیر تأثیرگذار بر قیمت سهام استفاده می‌شود که قوانین فازی موتور استنتاج این سامانه فازی توسط نسخه بهبود یافته فازی جدید الگوریتم رقابت استعماری به‌دست می‌آید و پارامترهای الگوریتم رقابت استعماری نیز توسط یک سامانه فازی دیگر به نام تنظیم‌کننده پارامترها ، تعیین می‌شوند. به‌منظور ارزیابی عملکرد مدل پیشنهادی اطلاعات مرتبط با قیمت سهام شش شرکت فعال در بورس اوراق بهادار تهران در نظر گرفته شده و هشت مدل پیش‌بینی قیمت سهام در دو گروه الگوریتم به همراه مدل پیشنهادی پیاده‌سازی شدند. نتایج به‌دست‌آمده نشان از عملکرد بهتر مدل پیشنهادی از جهت کیفیت نتایج پیش‌بینی شده و انحراف کم نتایج فاز آزمون از فاز آموزش دارد.   2 Investing on the stock exchange, as one of the financial resources, has always been a favorite among many investors. Today, one of the areas, where the prediction is its particular importance issue, is financial area, especially stock exchanges. The main objective of the markets is the future trend prices prediction in order to adopt a suitable strategy for buying or selling. In general, an investor should be predicted the future status of the time, the amount and location of his assets in a way that increases the return on his assets. Stock price prediction is one of the most challenging topics in the field of forecasting, which has attracted many attentions from researchers. The various factors of the markets have caused the situation that they always have a dynamic and complex process. Therefore, researchers have been determined to look for new prediction methods of stock price, which will reduce the instability and complexity of the markets. In fact, the most of recent studies have shown that the stock market is a nonlinear, dynamic, and non-parametric system that is affected by various economic factors. The applications of artificial intelligence and machine learning techniques to identify the relationship between the factors and stock price exchanges can be organized in seven major groups such as neural networks and deep learning, support vector machine, decision tree and random forest, k nearest neighbor, regression, Bayesian networks and fuzzy inference-base methods. Due to the mentioned prediction methods have their own challenges, hydridizations of the meta-heuristic algorithms and the methods were applied to stock price prediction. In this paper, a new hybridization of Fuzzy Inference System and a novel modified Fuzzy Imperialist Competitive Algorithm (FICA+FIS) are proposed to stock price prediction. To achieve this aim, two Fuzzy Inference Systems are designed to tuing the ICA’s parameters based on three effective factors in search strategy and to predict stock price based on 10 effective economic factors. The candidate fuzzy rules set of the inference engine is obtained by the FICA for the second FIS and six fuzzy rules of the first FIS are designed based on the ICA’s behaviour. The FICA+FIS has 10 inputs of the stock price variables including the lowest stock price, the highest stock price, the initial stock price, the trading volume, the trading value, the first market index of the trading floor, the total market price index, the dollar exchange rate, the global price per ounce of gold, the global oil price, and its output is also the stock price. The inputs and output variables consist of three linguistic vairables such as Low, Medium, and High with triangular membership functions. Each country (search agent) of the FICA contains information on all the fuzzy rules of the inference engine attributed to the country and has r×12 elements, where r is the number of fuzzy rules. The FICA’s objective function is the mean square error (MSE) to evaluate the power of each country. A challenge of the ICA is the proper tuning paprameters such as the Revolution Probability (Prevolve), Assimilation Coefficient (Beta) and the Colonies Mean Cost Coefficient (zeta), which has a great impact on the efficiency of the algorithm (precision and time of access to solution). These parameters are usually constant and according to different problems, they have different values and are given experimentally. In this paper, the parameters are tuned based on the number of iterations that the best objective function value has not improved (UN), the number of imperialist (Ni) and the current number iteration (Iter). To this aim, a FIS is designed based on six fuzzy rules that UN, Ni and Iter are its input variables and Prevolve, Beta and zeta are its output variables. To analyze the efficiency of the FICA+FIS as a case study, six datasets are collocted from six companies which were active between 1389 to 1394 in Tehran Stock Exchange such as Pars Oil, Iran Khodro, Motogen, Ghadir, Tidewater and Mobarakeh. The information of around 2000 days are collected for each company and the data are divided to train and test data based on cross validation 10-fold. To compare the performance of the FICA+FIS, two groups of stock price prediction methods were implemented. In the first group, the fuzzy rules of the FIS’s engine to stock price prediction are obtained by the classic draft of the Imperialist Competitive Algorithm (ICA+FIS), the Genetic Algorithm (GA+FIS) and the Whale Optimization Algorithm (WOA+FIS), which are used to compare with the FICA. The second group includes classic stock price prediction methods such as multi-layered neural network (NN), support vector machine (SVM), CART decision tree (DT-CART), random forest (RF) and Gaussian process regression (GPR), which are used to compare with the FICA+FIS. The experimental results show that first, the improved fuzzy draft of the ICA performed better than its classic draft, the GA and the WOA, and second, the performance of the FICA FIS is better than other investigated algorithms in both training and testing phases, although the DT is a competitor in the training phase and the RF is a competitor in the test phase on some datasets. 125 152 2019/06/182019/05/42019/07/102019/07/142019/06/262019/06/282020/10/252019/06/26 1398/4/5 2020/08/182020/08/182020/08/182020/08/182020/01/112020/09/22021/03/82021/02/2 1399/11/14 مجید عبدالرزاق نژاد Majid Abdolrazzagh-Nezhad گروه مهندسی کامپیوتر، دانشکده مهندسی، دانشگاه بزرگمهر قائنات ایران abdolrazzagh@buqaen.ac.ir مهدی خرد Mehdi Kherad گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه قم ایران m.kherad@stu.qom.ac.ir Stock Price Prediction Fuzzy Inference Systems Neural Networks Decision Tree Random Forest Support Vector Machine Imperialist Competitive Algorithm پیش‌بینی قیمت سهام سامانه استنتاج فازی ممدانی شبک عصبی درخت تصمیم جنگل تصادفی ماشین بردار پشتیبان الگوریتم رقابت استعماری [1] E. 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Yang, "Hybrid methods for stock index modeling," Fuzzy Systems and Knowledge Discovery, pp. 490-490, 2005.##[11] S. Wang, L. Wang, S. Gao, and Z. Bai, "Stock price prediction based on chaotic hybrid particle swarm optimisation-RBF neural network," International Journal of Applied Decision Sciences, vol. 10, no. 2, pp. 89-100, 2017.##[12] T. T. Khuat and M. H. Le, "An Application of Artificial Neural Networks and Fuzzy Logic on the Stock Price Prediction Problem," International Journal on Informatics Visualization, vol. 1, no. 2, pp. 40-49, 2017.##[13] R. Ghasemiyeh, R. Moghdani, and S. S. Sana, "A Hybrid Artificial Neural Network with Metaheuristic Algorithms for Predicting Stock Price," Cybernetics and Systems, vol. 48, no. 4, pp. 365-392, 2017.##[14] Y. Rajihy, K. Nermend, and A. Alsakaa, "Back-propagation artificial neural networks in stock market forecasting. An application to the Warsaw Stock Exchange WIG20," Aestimatio, no. 15, p. 88, 2017.##[15] S. A. Mousavi, and Gholami, "Using Hybrid Firefly Neural Algorithm and Bayesian Regulation Method to Predict Stock Prices," Financial Engineering and Securities Management, vol. 9, no. 36, pp. 295-321, 1397.##[16] T. Fischer and C. Krauss, "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, vol. 270, no. 2, pp. 654-669, 2018.##[17] W. Long, Z. Lu, and L. Cui, "Deep learning-based feature engineering for stock price movement prediction," Knowledge-Based Systems, vol. 164, pp. 163-173, 2019.##[18] A. Kelotra and P. Pandey, "Stock market prediction using optimized deep-convlstm model," Big Data, vol. 8, no. 1, pp. 5-24, 2020.##[19] C. Xiao, W. Xia, and J. Jiang, "Stock price forecast based on combined model of ARI-MA-LS-SVM," Neural Computing and Applications, pp. 1-10, 2020.##[20] M.-C. Lee, "Using support vector machine with a hybrid feature selection method to the stock trend prediction," Expert Systems with Applications, vol. 36, no. 8, pp. 10896-10904, 2009.##[21] Y. Chen and Y. Hao, "A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction," Expert Systems with Applications, vol. 80, pp. 340-355, 2017.##[22] B. B. Nair, V. Mohandas, and N. Sakthivel, "A decision tree-rough set hybrid system for stock market trend prediction," International Journal of Computer Applications, vol. 6, no. 9, pp. 1-6, 2010.##[23] W. Qiu, X. Liu, and L. Wang, "Forecasting shanghai composite index based on fuzzy time series and improved C-fuzzy decision trees," Expert Systems with Applications, vol. 39, no. 9, pp. 7680-7689, 2012.##[24] S. Basak, S. Kar, S. Saha, L. Khaidem, and S. R. Dey, "Predicting the direction of stock market prices using tree-based classifiers," The North American Journal of Economics and Finance, vol. 47, pp. 552-567, 2019.##[25] L. Khaidem, S. Saha, and S. R. Dey, "Predicting the direction of stock market prices using random forest," arXiv preprint arXiv:1605.00003, 2016.##[26] N. Sharma and A. Juneja, "Combining of random forest estimates using LSboost for stock market index prediction," in 2017 2nd International Conference for Convergence in Technology (I2CT), 2017: IEEE, pp. 1199-1202.##[27] Gh. Elham and d. Seyed Mohammad Reza, "Predicting price trends in the stock market using a random forest algorithm," Quarterly Journal of Financial Engineering and Securities Management, vol. 9, no. 35, pp. 301-322, 1397.##[28] K. Alkhatib, H. Najadat, I. Hmeidi, and M. K. A. Shatnawi, "Stock price prediction using k-nearest neighbor (kNN) algorithm," International Journal of Business, Humanities and Technology, vol. 3, no. 3, pp. 32-44, 2013.##[29] M. Zadeh, H. Gholipoor, and Gh. Vahid, "Stock Price Forecasting Using Distributed Intermediate Regression (ARDL) Method," Financial Research, vol. 9, no. 23, pp. 49-60, 1386.##[30] M. Zadeh, H. Gholipoor, and Gh. Vahid, "Stock price forecasting by fuzzy regression method," Journal of Macroeconomics, vol. 6, no. 12, pp. 107-128, 1390.##[31] E. Kita, M. Harada, and T. Mizuno, "Application of Bayesian Network to stock price prediction," Artif. Intell. Research, vol. 1, no. 2, pp. 171-184, 2012.##[32] Q. Sun, W.-G. Che, and H.-L. Wang, "Bayesian regularization BP neural network model for the stock price prediction," in Foundations and applications of intelligent systems: Springer, 2014, pp. 521-531.##[33] L. Wang, Z. Wang, S. Zhao, and S. Tan, "Stock market trend prediction using dynamical Bayesian factor graph," Expert Systems with Applications, vol. 42, no. 15-16, pp. 6267-6275, 2015.##[34] M. R. Hassan, K. Ramamohanarao, J. Kamruzzaman, M. Rahman, and M. M. Hossain, "A HMM-based adaptive fuzzy inference system for stock market forecasting," Neurocomputing, vol. 104, pp. 10-25, 2013.##[35] P.-C. Chang and C.-H. Liu, "A TSK type fuzzy rule based system for stock price prediction," Expert Systems with applications, vol. 34, no. 1, pp. 135-144, 2008.##[36] G. R. M. Lincy and C. J. John, "A multiple fuzzy inference systems framework for daily stock trading with application to NASDAQ stock exchange," Expert Systems with Applications: An International Journal, vol. 44, no. C, pp. 13-21, 2016.##[37] S. K. Chandar, "Fusion model of wavelet transform and adaptive neuro fuzzy inference system for stock market prediction," Journal of Ambient Intelligence and Humanized Computing, pp. 1-9, 2019.##[38] M. R. Feylizadeh, M. H. Keshavarz, and A. Hendalianpour, "Presenting a model for predicting the Tehran Stock Exchange Index using ANFIS and fuzzy regression," Journal of New Researches in Mathematics, 2019.##[39] Ramezani and Ameli, "Stock Price Prediction Using Fuzzy Neural Network Based on Genetic Algorithm and Comparison with Fuzzy Neural Network," Economic Modeling Research, vol. 6, no. 22, pp. 61-91, 2016.##[40] J. Babajani, T. Bolo, Gh. Abdollahi, "Predicting stock prices on the Tehran Stock Exchange using a recursive neural network optimized by the artificial bee colony algorithm," Financial Management Strategy, vol. 7, no. 2, pp. 195-228, 2019.##[41] E. Giovanis, "Application of ARCH-GARCH models and feed-forward neural networks with Bayesian regularization in Capital Asset Pricing Model: The case of two stocks in Athens exchange stock market," 2009.##[42] S. moshiri and H. morevat, "Forecasts Tehran Stock Exchange general index returns using linear and nonlinear models," Quarterly Journal of Business Research, vol. 41, no. 84.##[43] O. Cordón, E. Herrera, E. Gomide, E. Hoffman, and L. Magdalena, "Ten years of genetic fuzzy systems: current framework and new trends," in IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th, 2001, vol. 3: IEEE, pp. 1241-1246.##[44] H. N. Nhu, S. Nitsuwat, and M. Sodanil, "Prediction of stock price using an adaptive Neuro-Fuzzy Inference System trained by Firefly Algorithm," in 2013 International Computer Science and Engineering Conference (ICSEC) , 2013 ,IEEE, pp. 302-307.##[45] R. Dash and P. Dash, "Efficient stock price prediction using a self evolving recurrent neuro-fuzzy inference system optimized through a modified differential harmony search technique," Expert Systems with Applications, vol. 52, pp. 75-90, 2016.##[46] L.-Y. Wei, "A hybrid model based on ANFIS and adaptive expectation genetic algorithm to forecast TAIEX," Economic Modelling, vol. 33, pp. 893-899, 2013.##[47] A. Bagheri, H. M. Peyhani, and M. Akbari, "Financial forecasting using ANFIS networks with quantum-behaved particle swarm optimization," Expert Systems with Applications, vol. 41, no. 14, pp. 6235-6250, 2014.##[48] J. Han, J. Pei, and M. Kamber, Data mining: concepts and techniques. Elsevier, 2011.##[49] S. Hosseini and A. Al Khaled, "A survey on the imperialist competitive algorithm metaheuristic: implementation in engineering domain and directions for future research," Applied Soft Computing, vol. 24, pp. 1078-1094, 2014.##[50] A. P. Engelbrecht, Computational intelligence: an introduction., 2 ed. England: John Wiley & Sons, 2007, p. 597.##[51] P. J. Werbos, "Beyond Regression: New Tools for Prediction and Analysis in the Behavioural Sciences," PhD thesis, Harvard University, Boston, USA, 1974.##[52] Z. Pashaei, R. Dehkharghani, "Stock Market Modeling Using Artificial Neural Network and Comparison with Classical Linear Models", JSDP, 2021, vol.17, no. 4, pp. 89-102.##[1] E. Stringham, Private governance: Creating order in economic and social life. Oxford University Press, USA, 2015.##[2] H. Vachhani et al., "Machine learning based stock market analysis: A short survey," in International Conference on Innovative Data Communication Technologies and Application, 2019: Springer, pp. 12-26.##[3] V. R. Jain, M. Gupta, and R. M. Singh, "Analysis and Prediction of Individual Stock Prices of Financial Sector Companies in NIFTY50," International Journal of Information Engineering and Electronic Business, vol. 11, no. 2, p. 33, 2018.##[4] T. Kim and H. Y. Kim, "Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data," PloS one, vol. 14, no. 2, 2019.##[5] ف. جهانتیغ, د. پ. تلگردویی, and صفورا, "وقفه‌های زمانی بهینه در پیش‌بینی قیمت نفت توسط شبکه عصبی پویا اصلاح‌شده با الگوریتم ژنتیک," فصل‌نامه مطالعات اقتصاد انرژی, vol. 14, no. 56, pp. 115-143, 1397.##[5] F. Jahantegh, d. P. Telegraph, and Safoura, "Optimal time intervals in oil price forecasting by a dynamic neural network modified by genetic algorithm," Quarterly Journal of Energy Economics Studies, vol. 14, no. 56, pp. 115-143, 1397.##[6] قربانی, ی. ز. فر, محمود, و ن. چاشمی, "پیش‌بینی سیگنال معاملات سهام با استفاده از شبکه‌های پتری رنگی و الگوریتم ژنتیک (مطالعه موردی: بازار بورس تهران)," پژوهشن‌امه مدیریت اجرایی, vol. 11, no. 21, pp. 205-227, 2019.##[6] Y. Rabbani, M. and N. Chashmi, "Stock Trading Signal Prediction Using Color Petroleum Networks and Genetic Algorithm (Case Study: Tehran Stock Exchange)," Journal of Executive Management, vol. 11, no. 21, pp. 205-227, 2019.##[7] monajemi, abzari, and rayati, "Stock price prediction in stock exchange stock exchange using fuzzy neural network and genetic algorithm and comparing it with artificial neural network," Quarterly Journal of Economics, vol. 3, no. 6, pp. 1-26, 2010.##[8] P. Hájek, V. Olej, and R. Myskova, "Forecasting stock prices using sentiment information in annual reports: A neural network and support vector regression approach," WSEAS Transactions on Business and Economics, vol. 10, no. 4, pp. 293-305, 2013.##[9] E. Hadavandi, H. Shavandi, and A. Ghanbari, "Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting," Knowledge-Based Systems, vol. 23, no. 8, pp. 800-808, 2010.##[10] Y. Chen, A. Abraham, J. Yang, and B. Yang, "Hybrid methods for stock index modeling," Fuzzy Systems and Knowledge Discovery, pp. 490-490, 2005.##[11] S. Wang, L. Wang, S. Gao, and Z. Bai, "Stock price prediction based on chaotic hybrid particle swarm optimisation-RBF neural network," International Journal of Applied Decision Sciences, vol. 10, no. 2, pp. 89-100, 2017.##[12] T. T. Khuat and M. H. 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Mousavi, and Gholami, "Using Hybrid Firefly Neural Algorithm and Bayesian Regulation Method to Predict Stock Prices," Financial Engineering and Securities Management, vol. 9, no. 36, pp. 295-321, 1397.##[16] T. Fischer and C. Krauss, "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, vol. 270, no. 2, pp. 654-669, 2018.##[17] W. Long, Z. Lu, and L. Cui, "Deep learning-based feature engineering for stock price movement prediction," Knowledge-Based Systems, vol. 164, pp. 163-173, 2019.##[18] A. Kelotra and P. Pandey, "Stock market prediction using optimized deep-convlstm model," Big Data, vol. 8, no. 1, pp. 5-24, 2020.##[19] C. Xiao, W. Xia, and J. Jiang, "Stock price forecast based on combined model of ARI-MA-LS-SVM," Neural Computing and Applications, pp. 1-10, 2020.##[20] M.-C. 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Sun, W.-G. Che, and H.-L. Wang, "Bayesian regularization BP neural network model for the stock price prediction," in Foundations and applications of intelligent systems: Springer, 2014, pp. 521-531.##[33] L. Wang, Z. Wang, S. Zhao, and S. Tan, "Stock market trend prediction using dynamical Bayesian factor graph," Expert Systems with Applications, vol. 42, no. 15-16, pp. 6267-6275, 2015.##[34] M. R. Hassan, K. Ramamohanarao, J. Kamruzzaman, M. Rahman, and M. M. Hossain, "A HMM-based adaptive fuzzy inference system for stock market forecasting," Neurocomputing, vol. 104, pp. 10-25, 2013.##[35] P.-C. Chang and C.-H. Liu, "A TSK type fuzzy rule based system for stock price prediction," Expert Systems with applications, vol. 34, no. 1, pp. 135-144, 2008.##[36] G. R. M. Lincy and C. J. John, "A multiple fuzzy inference systems framework for daily stock trading with application to NASDAQ stock exchange," Expert Systems with Applications: An International Journal, vol. 44, no. 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تشخیص تغییر مفهوم در جریان داده با کمک رده‌بند نیمه‌نظارتی Detecting Concept Drift in Data Stream Using Semi-Supervised Classification 1 1 جریان داده به دنباله‌ای از داده‌ها گفته می‌شود که از منابع اطلاعاتی مختلف با سرعت زیاد و حجم بالا تولید می‌شوند. از مهم‌ترین چالش‌های موجود در تحلیل جریان‌ داده وجود تغییر مفهوم در آن‌ها است. تغییر مفهوم به معنای تغییر ویژگی‌های آماری داده‌هاست. در بسیاری از پژوهش‌های موجود برای مقابله با چالش نامحدود‌بودن طول جریان داده و یا چالش تغییر مفهوم، از رویکردهایی با فرض موجود‌بودن برچسب درست برای همه داده‌ها استفاده می‌کنند؛ در‌حالی‌که با توجه به هزینه‌بر‌بودن فرآیند برچسب‌دهی جریان داده، به‌طورعمومی فرض می‌شود تنها بخشی از داده‌ها دارای برچسب هستند. در این مقاله یک روش یادگیری گروهی نیمه‌نظارتی ارائه شده که از تغییر آنتروپی برای تشخیص تغییر مفاهیم در رده‌بندی جریان داده استفاده می‌کند. مدل یادگیری گروهی پیشنهادی با تعداد محدودی داده برچسب‌دار اولیه آموزش می‌بیند؛ سپس در صورت مشاهده تغییر مفهوم، از داده‌های بدون برچسب برای به‌روزرسانی مدل رده‌بند گروهی استفاده می‌کند. روش پیشنهادی قادر است تغییرات موجود در مجموعه‌داده را تشخیص داده و با به‌روزرسانی مدل یادگیری، در بهبود دقت الگوریتم مؤثر باشد. نتایج آزمایش‌ها نشان می‌دهد که روش پیشنهادی از جنبه‌های مختلف نسبت به سایر روش‌ها کارایی بالاتری دارد.    2 Data stream is a sequence of data generated from various information sources at a high speed and high volume. Classifying data streams faces the three challenges of unlimited length, online processing, and concept drift. In related research, to meet the challenge of unlimited stream length, commonly the stream is divided into fixed size windows or gradual forgetting is used. Concept drift refers to changes in the statistical properties of data, and is divided into four categories: sudden, gradual, incremental, and recurring. Concept drift is generally dealt with by periodically updating the classifier, or employing an explicit change detector to determine the update time. These approaches are based on the assumption that the true labels are available for all data samples. Nevertheless, due to the cost of labeling instances, access to a partial labeling is more realistic. In a number of studies that have used semi-supervisory learning, the labels are received from the user to update the models in form of active learning. The purpose of this study is to classify samples in an unlimited data stream in presence of concept drift, using only a limited set of initial labeled data. To this end, a semi-supervised ensemble learning algorithm for data stream is proposed, which uses entropy variation to detect concept drift and is applicable for sudden and gradual drifts. The proposed model is trained with a limited initial labeled set. In occurrence of concept drift, the unlabeled data is used to update the ensemble model. It does not require receiving the labels from the user. In contrast to many of the current studies, the proposed algorithm uses an ensemble of K-NN classifiers. It constructs a group of clustering-based classification models, each of which is trained on a batch of data. On receiving each new sample, first it is determined whether the data sample is an outlier or not. If the data is included in a cluster, the sample class is determined by majority voting. When a window of the stream is received, the possibility of concept drift is examined based on entropy variation, and the classifier is updated by a semi-supervised approach if necessary. The model itself determines the required data labels. The proposed method is capable of detecting concept drift in data, and improving its accuracy via updating the learning model with appropriate samples received from the stream. Therefore, the proposed method only requires a small initial labeled data. Experiments are performed using five real and synthetic datasets, and the model performance is compared to three other approaches. The results show that the proposed method is superior in terms of precision, recall and F1 score compared to other studies. 153 164 2019/06/182019/05/42019/07/102019/07/142019/06/262019/06/282020/10/252019/06/262019/06/8 1398/3/18 2020/08/182020/08/182020/08/182020/08/182020/01/112020/09/22021/03/82021/02/22021/03/1 1399/12/11 حسین حسن نژاد نامقی Hossein Hasan Nezhad Namaghi دانشکده مهندسی کامپیوتر، دانشگاه صنعتی شاهرود ایران h_hasannezhad@shahroodut.ac.ir هدی مشایخی Hoda Mashayekhi دانشکده مهندسی کامپیوتر، دانشگاه صنعتی شاهرود ایران hmashayekhi@shahroodut.ac.ir مرتضی زاهدی Morteza Zahedi دانشکده مهندسی کامپیوتر، دانشگاه صنعتی شاهرود ایران zahedi@suigle.com data stream ensemble learning concept drift entropy semi-supervised classification جریان داده یادگیری گروهی تغییر مفهوم آنتروپی رده‌بند نیمه‌نظارتی [1] M. Masud, J. Gao, L. Khan, J. Han and B. M. Thuraisingham, "Classification and novel class detection in concept-drifting data streams under time constraints," IEEE Transactions on knowledge and data engineering, vol. 23, no. 6, pp. 859-874, 2010.##[2] M. M. Masud, Q. Chen, L. Khan, C. Aggarwal, J. Gao, J. Han and B. Thuraisingham, "Addressing concept-evolution in concept-drifting data streams," in 2010 IEEE International Conference on Data Mining, IEEE, 2010, pp. 929-934.##[3] B. S. Parker and L. Khan, "Detecting and tracking concept class drift and emergence in non-stationary fast data streams," in Twenty-ninth AAAI conference on artificial intelligence, 2015.##[4] R. Klinkenberg, "Learning drifting concepts: Example selection vs. example weighting," Intelligent data analysis, vol. 8, no. 3, pp. 281-300, 2004.##[5] A. Bifet and R. Gavalda, "Learning from time-changing data with adaptive windowing," in Proceedings of the 2007 SIAM international conference on data mining, SIAM, 2007, pp. 443-448.##[6] A. Haque, L. Khan and M. Baron, "Sand: Semi-supervised adaptive novel class detection and classification over data stream," in THIRTIETH AAAI Conference on Artificial Intelligence, 2016.##[7] L. I. Kuncheva and W. J. Faithfull, "PCA feature extraction for change detection in multidimensional unlabeled data," IEEE transactions on neural networks and learning systems, vol. 25, no. 1, pp. 69-80, 2013.##[8] P. Sidhu and M. Bhatia, "A novel online ensemble approach to handle concept drifting data streams: diversified dynamic weighted majority," International Journal of Machine Learning and Cybernetics, vol. 9, no. 1, pp. 37-61, 2018.##[9] O. A. Mahdi, E. Pardede and J. Cao, "Combination of information entropy and ensemble classification for detecting concept drift in data stream," in Proceedings of the Australasian Computer Science Week Multiconference, ACM, 2018, p. 13.##[10] M. Ester, H.-P. Kriegel, J. Sander and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise," in Kdd, 1996, pp. 226-231.##[11] X. Zhu and A. B. Goldberg, "Introduction to semi-supervised learning," Synthesis lectures on artificial intelligence and machine learning, vol. 3, no. 1, pp. 1-130, 2009.##[12] A. Tsymbal, "The problem of concept drift: definitions and related work," Computer Science Department, Trinity College Dublin, vol. 106, no. 2, p. 58, 2004.##[13] I. Žliobaitė, "Learning under concept drift: an overview," in arXiv preprint arXiv:1010.4784, 2010.##[14] A. Bifet, G. Holmes, B. Pfahringer, R. Kirkby and R. Gavaldà, "New ensemble methods for evolving data streams," in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2009, pp. 139-148.##[15] S. J. Morshed, J. Rana and M. Milrad, "Real-time Data analytics: An algorithmic perspective," in International Conference on Data Mining and Big Data, Springer, 2016, pp. 311-320.##[16] A. Bifet, G. Holmes, R. Kirkby and B. Pfahringer, "Moa: Massive online analysis," Journal of Machine Learning Research, vol. 11, no. May, pp. 1601-1604, 2010.##[17] B. Pfahringer, G. Holmes and R. Kirkby, "Handling numeric attributes in hoeffding trees," in Pacific-Asia Conference on Knowledge Discovery and Data Mining, Berlin, Heidelberg, Springer, 2008, pp. 296-307.##[18] D.L. Cabral, D. Rafael, and R.S.M. de Barros. "Concept drift detection based on Fisher's Exact test." Information Sciences, vol. 442, pp. 220-234, 2018.##[19] R.F. de Mello, Y. Vaz, C.H. Grossi, and A. Bifet. "On learning guarantees to unsupervised concept drift detection on data streams." Expert Systems with Applications. Vol. 117, pp. 90-102, 2019.##[20] X. Wang, Q. Kang, M. Zhou, L. Pan, and A. Abusorrah. "Multiscale Drift Detection Test to Enable Fast Learning in Nonstationary Environments." IEEE Transactions on Cybernetics, pp. 1-13, 2020.##[21] Y. Song, J. Lu, H. Lu, and G. Zhang. "Fuzzy clustering-based adaptive regression for drifting data streams." IEEE Transactions on Fuzzy Systems, vol. 28, no. 3, pp. 544-557, 2019.##[22] Y. Li, Y. Wang, Q. Liu, C. Bi, X. Jiang, and S. Sun. "Incremental semi-supervised learning on streaming data." Pattern Recognition, vol. 88 pp. 383-396, 2019.##[23] X. Mu, F. Zhu, J. Du, E.P. Lim, & Z.H. Zhou, "Streaming classification with emerging new class by class matrix sketching" In Thirty-First AAAI Conference on Artificial Intelligence, pp. 2373-2379, 2017.##[24] P. Vorburger, A. Bernstein. "Entropy-based concept shift detection" In Sixth IEEE International Conference on Data Mining, ICDM'06, pp. 1113-1118, 2006.##[25] L. Du, Q. Song, and X. Jia. "Detecting concept drift: an information entropy based method using an adaptive sliding window." Intelligent Data Analysis vol. 18, no. 3, pp. 337-364, 2014.##[26] J. Haug, G. Kasneci. "Learning Parameter Distributions to Detect Concept Drift in Data Streams". arXiv preprint arXiv:2010.09388. 2020.##[27] H. Hanqing, M. Kantardzic, T. S. Sethi. "No Free Lunch Theorem for concept drift detection in streaming data classification: A review." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 10, no. 2, e1327, 2020.##[28] M. Mosaferi, A. Safaei, "Providing a Dynamic Technique for Answering Ad-hoc Continuous Aggregate". Journal of Signal and Data Processing. Vol. 14, No. 3, pp. 3-22, 2017.##[1] M. Masud, J. Gao, L. Khan, J. Han and B. M. Thuraisingham, "Classification and novel class detection in concept-drifting data streams under time constraints," IEEE Transactions on knowledge and data engineering, vol. 23, no. 6, pp. 859-874, 2010.##[2] M. M. Masud, Q. Chen, L. Khan, C. Aggarwal, J. Gao, J. Han and B. Thuraisingham, "Addressing concept-evolution in concept-drifting data streams," in 2010 IEEE International Conference on Data Mining, IEEE, 2010, pp. 929-934.##[3] B. S. Parker and L. Khan, "Detecting and tracking concept class drift and emergence in non-stationary fast data streams," in Twenty-ninth AAAI conference on artificial intelligence, 2015.##[4] R. Klinkenberg, "Learning drifting concepts: Example selection vs. example weighting," Intelligent data analysis, vol. 8, no. 3, pp. 281-300, 2004.##[5] A. Bifet and R. Gavalda, "Learning from time-changing data with adaptive windowing," in Proceedings of the 2007 SIAM international conference on data mining, SIAM, 2007, pp. 443-448.##[6] A. Haque, L. Khan and M. Baron, "Sand: Semi-supervised adaptive novel class detection and classification over data stream," in THIRTIETH AAAI Conference on Artificial Intelligence, 2016.##[7] L. I. Kuncheva and W. J. Faithfull, "PCA feature extraction for change detection in multidimensional unlabeled data," IEEE transactions on neural networks and learning systems, vol. 25, no. 1, pp. 69-80, 2013.##[8] P. Sidhu and M. Bhatia, "A novel online ensemble approach to handle concept drifting data streams: diversified dynamic weighted majority," International Journal of Machine Learning and Cybernetics, vol. 9, no. 1, pp. 37-61, 2018.##[9] O. A. Mahdi, E. Pardede and J. Cao, "Combination of information entropy and ensemble classification for detecting concept drift in data stream," in Proceedings of the Australasian Computer Science Week Multiconference, ACM, 2018, p. 13.##[10] M. Ester, H.-P. Kriegel, J. Sander and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise," in Kdd, 1996, pp. 226-231.##[11] X. Zhu and A. B. Goldberg, "Introduction to semi-supervised learning," Synthesis lectures on artificial intelligence and machine learning, vol. 3, no. 1, pp. 1-130, 2009.##[12] A. Tsymbal, "The problem of concept drift: definitions and related work," Computer Science Department, Trinity College Dublin, vol. 106, no. 2, p. 58, 2004.##[13] I. Žliobaitė, "Learning under concept drift: an overview," in arXiv preprint arXiv:1010.4784, 2010.##[14] A. Bifet, G. Holmes, B. Pfahringer, R. Kirkby and R. Gavaldà, "New ensemble methods for evolving data streams," in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2009, pp. 139-148.##[15] S. J. Morshed, J. Rana and M. Milrad, "Real-time Data analytics: An algorithmic perspective," in International Conference on Data Mining and Big Data, Springer, 2016, pp. 311-320.##[16] A. Bifet, G. Holmes, R. Kirkby and B. Pfahringer, "Moa: Massive online analysis," Journal of Machine Learning Research, vol. 11, no. May, pp. 1601-1604, 2010.##[17] B. Pfahringer, G. Holmes and R. Kirkby, "Handling numeric attributes in hoeffding trees," in Pacific-Asia Conference on Knowledge Discovery and Data Mining, Berlin, Heidelberg, Springer, 2008, pp. 296-307.##[18] D.L. Cabral, D. Rafael, and R.S.M. de Barros. "Concept drift detection based on Fisher's Exact test." Information Sciences, vol. 442, pp. 220-234, 2018.##[19] R.F. de Mello, Y. Vaz, C.H. Grossi, and A. Bifet. "On learning guarantees to unsupervised concept drift detection on data streams." Expert Systems with Applications. Vol. 117, pp. 90-102, 2019.##[20] X. Wang, Q. Kang, M. Zhou, L. Pan, and A. Abusorrah. "Multiscale Drift Detection Test to Enable Fast Learning in Nonstationary Environments." IEEE Transactions on Cybernetics, pp. 1-13, 2020.##[21] Y. Song, J. Lu, H. Lu, and G. Zhang. "Fuzzy clustering-based adaptive regression for drifting data streams." IEEE Transactions on Fuzzy Systems, vol. 28, no. 3, pp. 544-557, 2019.##[22] Y. Li, Y. Wang, Q. Liu, C. Bi, X. Jiang, and S. Sun. "Incremental semi-supervised learning on streaming data." Pattern Recognition, vol. 88 pp. 383-396, 2019.##[23] X. Mu, F. Zhu, J. Du, E.P. Lim, & Z.H. Zhou, "Streaming classification with emerging new class by class matrix sketching" In Thirty-First AAAI Conference on Artificial Intelligence, pp. 2373-2379, 2017.##[24] P. Vorburger, A. Bernstein. "Entropy-based concept shift detection" In Sixth IEEE International Conference on Data Mining, ICDM'06, pp. 1113-1118, 2006.##[25] L. Du, Q. Song, and X. Jia. "Detecting concept drift: an information entropy based method using an adaptive sliding window." Intelligent Data Analysis vol. 18, no. 3, pp. 337-364, 2014.##[26] J. Haug, G. Kasneci. "Learning Parameter Distributions to Detect Concept Drift in Data Streams". arXiv preprint arXiv:2010.09388. 2020.##[27] H. Hanqing, M. Kantardzic, T. S. Sethi. "No Free Lunch Theorem for concept drift detection in streaming data classification: A review." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 10, no. 2, e1327, 2020.##[28] م. مسافری، ع. صفائی. ارائه روشی پویا جهت پاسخ به پرس‌وجوهای پیوسته تجمّعی اقتضایی. پردازش علائم و داده‌ها. جلد ۱۴، شماره ۳، ص۲۲-۳، ۱۳۹۶.##[28] M. Mosaferi, A. Safaei, "Providing a Dynamic Technique for Answering Ad-hoc Continuous Aggregate". Journal of Signal and Data Processing. Vol. 14, No. 3, pp. 3-22, 2017.## ##

روشی نوین در طبقه‌بندی مقاوم به نوفه تصاویر بافتی با استفاده از توصیف چند‌مقیاسه توأمان الگوی باینری محلی A Novel Noise-Robust Texture Classification Method Using Joint Multiscale LBP 1 1 نخستین گام در طبقه‌بندی تصاویر بافتی، توصیف بافت با استفاده از استخراج ویژگی‌های تصویری مختلف از آن است. تاکنون روش‌های متعددی برای این موضوع توسعه یافته‌اند که از جمله مشهورترین آن‌ها می‌توان به روش الگوی دودویی محلی اشاره کرد. به‌منظور استخراج اطلاعات بافتی در مقیاس‌های مختلف، روش الگوی باینری محلی را می‌توان در یک چهارچوب چندمقیاسه پیاده‌سازی کرد. در این حالت، بردارهای ویژگی به‌دست‌آمده در سطوح مقیاس مختلف به یکدیگر پیوست می‌شوند تا یک بردار ویژگی برآیند با طول بیشتر را تولید کند؛ اما چنین روشی دو عیب مهم دارد؛ نخست‌این‌که، روش الگوی دودویی محلی به‌شدت نسبت به نوفه حساس و با افزودن نوفه به تصویر بافتی، بردارهای ویژگی به‌دست‌آمده ممکن است به‌شدت تغییر کنند. دوم‌این‌که، با افزایش تعداد مقیاس‌ها، طول بردار ویژگی به‌دست‌آمده نیز افزایش می‌یابد که این امر ضمن کاهش سرعت فرآیند طبقه‌بندی بافت، ممکن است دقت طبقه‌بندی را نیز کاهش دهد. برای رفع و یا کاهش این دو عیب، در این مقاله، روشی مبتنی بر الگوی دودویی محلی چندمقیاسه پیشنهاد می‌شود که از مقاومت بهتری در مقابل نوفه سفید گوسی برخوردار و در عین حال، طول بردار ویژگی تولیدی به‌وسیله آن به‌طوردقیق برابر با طول بردار ویژگی تولیدی به‌وسیله روش اصلی الگوی دودویی محلی در حالت تک‌مقیاسه است. آزمایش‌ها بر روی چهار گروه از پایگاه داده Outex انجام شده که آزمایش‌های انجام‌گرفته نشان‌دهنده برتری روش پیشنهادی نسبت به روش‌های موجود مشابه است. 2 In this paper we describe a novel noise-robust texture classification method using joint multiscale local binary pattern. The first step in texture classification is to describe the texture by extracting different features. So far, several methods have been developed for this topic, one of the most popular ones is Local Binary Pattern (LBP) method and its variants such as Completed Local Binary Pattern, Extended Local Binary Pattern, Local Temporary Pattern, Local Contrast Pattern, etc. In order to extract the features of a texture in different scales, the LBP method can be implemented in a multi-scale framework. For this purpose, the extracted feature vectors at different scales are usually concatenated together to produce the final feature vector with a longer length. But such a scheme has two main shortcomings. First, the LBP method is very sensitive to noise, hence by adding noise to a texture image, its feature vectors may change significantly. Second, by increasing the number of the scales, the length of the final feature vector is increased accordingly. This action increases the classification process time, and it may reduce the classification accuracy. To mitigate these shortcomings, this paper presents a method based on multiscale LBP, which has a better resistance against white Gaussian noise, while the length of its final feature vector is equal to the length of the final feature vector produced by the original LBP method. To implement the proposed method, we used 17 circular binary masks that contain 8 directed first-order masks, 8 directed second-order masks and 1 undirected mask. These masks have positive and negative weightes and each group of these masks have different radius which after convolution with input image extract features in different scales. Experiments were performed on four test groups of Outex database. Experimental results show that the proposed method is superior to the existing state-of-the-art methods. The complexity of proposed method is also analyzed. The results show that in this method, despite obtaining excellent classification accuracy, the complexity of the method has not changed much and even its complexity is less than some of the existing state-of-the-art methods. 165 180 2019/06/182019/05/42019/07/102019/07/142019/06/262019/06/282020/10/252019/06/262019/06/82018/11/6 1397/8/15 2020/08/182020/08/182020/08/182020/08/182020/01/112020/09/22021/03/82021/02/22021/03/12022/01/8 1400/10/18 محمد رضا جلالیان شهری Mohammad Reza Jalalian Shahri گروه مهندسی برق، دانشکده مهندسی، دانشگاه فردوسی مشهد ایران jalalianshahri@mail.um.ac.ir هادی هادی‌زاده Hadi Hadizadeh گروه مهندسی برق، دانشکده مهندسی، دانشگاه صنعتی قوچان ایران h.hadizadeh@qiet.ac.ir مرتضی خادمی درح Morteza Khademi Darah گروه مهندسی برق، دانشکده مهندسی، دانشگاه فردوسی مشهد ایران khademi@um.ac.ir عباس ابراهیمی‌مقدم Abbas Ebrahimi Moghadam گروه مهندسی برق، دانشکده مهندسی، دانشگاه فردوسی مشهد ایران a.ebrahimi@um.ac.ir feature extraction Local Binary Pattern texture texture classification white gaussian noise استخراج ویژگی الگوی دودویی محلی بافت طبقه‌بندی بافت نوفه سفید گوسی [1] M. Pietikäinen, A. Hadid, G. Zhao, and T. Ahonen, Computer vision using local binary patterns vol. 40: Springer Science & Business Media, 2011.##[2] Y. Dong, J. Feng, L. Liang, L. Zheng, and Q. Wu, "Multiscale sampling based texture image classification," IEEE Signal Processing Letters, vol. 24, pp. 614-618, 2017.##[3] V.-L. Nguyen, N.-S. Vu, and P.-H. Gosselin, "A scattering transform combination with local binary pattern for texture classification," in International Workshop on Content-based Multimedia Indexing, 2016.##[4] F. Bianconi and A. Fernández, "Evaluation of the effects of Gabor filter parameters on texture classification," Pattern Recognition, vol. 40, pp. 3325-3335, 2007.##[5] J. Oh, S.-I. Choi, C. Kim, J. Cho, and C.-H. Choi, "Selective generation of Gabor features for fast face recognition on mobile devices," Pattern Recognition Letters, vol. 34, pp. 1540-1547, 2013.##[6] P. Cavalin, L. Oliveira, A. Koerich, and A. Britto, "Wood defect detection using grayscale images and an optimized feature set," in IEEE Industrial Electronics, IECON 2006-32nd Annual Conference on, 2006, pp. 3408-3412.##[7] P. R. Cavalin, M. N. Kapp, J. Martins, and L. E. Oliveira, "A multiple feature vector framework for forest species recognition," in Proceedings of the 28th Annual ACM Symposium on Applied Computing, 2013, pp. 16-20.##[8] R. M. Haralick and K. Shanmugam, "Textural features for image classification," IEEE Transactions on systems, man, and cybernetics, pp. 610-621, 1973.##[9] Z. Guo, L. Zhang, and D. Zhang, "A completed modeling of local binary pattern operator for texture classification," IEEE Transactions on Image Processing, vol. 19, pp. 1657-1663, 2010.##[10] L. Liu, S. Lao, P. W. Fieguth, Y. Guo, X. Wang, and M. Pietikäinen, "Median robust extended local binary pattern for texture classification," IEEE Transactions on Image Processing, vol. 25, pp. 1368-1381, 2016.##[11] L. Liu, L. Zhao, Y. Long, G. Kuang, and P. Fieguth, "Extended local binary patterns for texture classification," Image and Vision Computing, vol. 30, pp. 86-99, 2012.##[12] T. Ojala, M. Pietikäinen, and D. Harwood, "A comparative study of texture measures with classification based on featured distributions," Pattern recognition, vol. 29, pp. 51-59, 1996.##[13] X. Tan and B. Triggs, "Enhanced local texture feature sets for face recognition under difficult lighting conditions," IEEE transactions on image processing, vol. 19, pp. 1635-1650, 2010.##[14] T. Song, H. Li, F. Meng, Q. Wu, B. Luo, B. Zeng, et al., "Noise-robust texture description using local contrast patterns via global measures," IEEE Signal Processing Letters, vol. 21, pp. 93-96, 2014.##[15] T. Ojala, M. Pietikainen, and T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," IEEE Transactions on pattern analysis and machine intelligence, vol. 24, pp. 971-987, 2002.##[16] T. Ojala, T. Maenpaa, M. Pietikainen, J. Viertola, J. Kyllonen, and S. Huovinen, "Outex-new framework for empirical evaluation of texture analysis algorithms," in Pattern Recognition, 2002. Proceedings. 16th International Conference on, 2002, pp. 701-706.##[17] J. He, H. Ji, and X. Yang, "Rotation invariant texture descriptor using local shearlet-based energy histograms," IEEE Signal Processing Letters, vol. 20, pp. 905-908, 2013.##[18] I. El khadiri, A. Chahi, Y. El-Merabet, Y. Ruichek and R. Touahni, "Image classification with Local Directional Decoded Ternary Pattern," 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), 2019, pp. 812-817,##[19] S. R. Barburiceanu, S. Meza, C. Germain and R. Terebes, "An Improved Feature Extraction Method for Texture Classification with Increased Noise Robustness," 2019 27th European Signal Processing Conference (EUSIPCO), 2019, pp. 1-5##[1] M. Pietikäinen, A. Hadid, G. Zhao, and T. Ahonen, Computer vision using local binary patterns vol. 40: Springer Science & Business Media, 2011.##[2] Y. Dong, J. Feng, L. Liang, L. Zheng, and Q. Wu, "Multiscale sampling based texture image classification," IEEE Signal Processing Letters, vol. 24, pp. 614-618, 2017.##[3] V.-L. Nguyen, N.-S. Vu, and P.-H. Gosselin, "A scattering transform combination with local binary pattern for texture classification," in International Workshop on Content-based Multimedia Indexing, 2016.##[4] F. Bianconi and A. Fernández, "Evaluation of the effects of Gabor filter parameters on texture classification," Pattern Recognition, vol. 40, pp. 3325-3335, 2007.##[5] J. Oh, S.-I. Choi, C. Kim, J. Cho, and C.-H. Choi, "Selective generation of Gabor features for fast face recognition on mobile devices," Pattern Recognition Letters, vol. 34, pp. 1540-1547, 2013.##[6] P. Cavalin, L. Oliveira, A. Koerich, and A. Britto, "Wood defect detection using grayscale images and an optimized feature set," in IEEE Industrial Electronics, IECON 2006-32nd Annual Conference on, 2006, pp. 3408-3412.##[7] P. R. Cavalin, M. N. Kapp, J. Martins, and L. E. Oliveira, "A multiple feature vector framework for forest species recognition," in Proceedings of the 28th Annual ACM Symposium on Applied Computing, 2013, pp. 16-20.##[8] R. M. Haralick and K. Shanmugam, "Textural features for image classification," IEEE Transactions on systems, man, and cybernetics, pp. 610-621, 1973.##[9] Z. Guo, L. Zhang, and D. Zhang, "A completed modeling of local binary pattern operator for texture classification," IEEE Transactions on Image Processing, vol. 19, pp. 1657-1663, 2010.##[10] L. Liu, S. Lao, P. W. Fieguth, Y. Guo, X. Wang, and M. Pietikäinen, "Median robust extended local binary pattern for texture classification," IEEE Transactions on Image Processing, vol. 25, pp. 1368-1381, 2016.##[11] L. Liu, L. Zhao, Y. Long, G. Kuang, and P. Fieguth, "Extended local binary patterns for texture classification," Image and Vision Computing, vol. 30, pp. 86-99, 2012.##[12] T. Ojala, M. Pietikäinen, and D. Harwood, "A comparative study of texture measures with classification based on featured distributions," Pattern recognition, vol. 29, pp. 51-59, 1996.##[13] X. Tan and B. Triggs, "Enhanced local texture feature sets for face recognition under difficult lighting conditions," IEEE transactions on image processing, vol. 19, pp. 1635-1650, 2010.##[14] T. Song, H. Li, F. Meng, Q. Wu, B. Luo, B. Zeng, et al., "Noise-robust texture description using local contrast patterns via global measures," IEEE Signal Processing Letters, vol. 21, pp. 93-96, 2014.##[15] T. Ojala, M. Pietikainen, and T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," IEEE Transactions on pattern analysis and machine intelligence, vol. 24, pp. 971-987, 2002.##[16] T. Ojala, T. Maenpaa, M. Pietikainen, J. Viertola, J. Kyllonen, and S. Huovinen, "Outex-new framework for empirical evaluation of texture analysis algorithms," in Pattern Recognition, 2002. Proceedings. 16th International Conference on, 2002, pp. 701-706.##[17] J. He, H. Ji, and X. Yang, "Rotation invariant texture descriptor using local shearlet-based energy histograms," IEEE Signal Processing Letters, vol. 20, pp. 905-908, 2013.##[18] I. El khadiri, A. Chahi, Y. El-Merabet, Y. Ruichek and R. Touahni, "Image classification with Local Directional Decoded Ternary Pattern," 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), 2019, pp. 812-817,##[19] S. R. Barburiceanu, S. Meza, C. Germain and R. Terebes, "An Improved Feature Extraction Method for Texture Classification with Increased Noise Robustness," 2019 27th European Signal Processing Conference (EUSIPCO), 2019, pp. 1-5## ##