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بهبود هزینه محاسباتی در سامانه‌های استخراج آزاد اطلاعات با استفاده از مدل لاگ لینیر A New Method for Improving Computational Cost of Open Information Extraction Systems Using Log-Linear Model 1 1 استخراج اطلاعات شامل توسعه الگوریتم‌­هایی است که به‌صورت خودکار متن غیرساخت‌­یافته را پردازش و پایگاه داده‌­ای از موجودیت­‌ها، روابط و وقایع را تولید می­‌کنند. یکی از مشکلات اساسی استخراج اطلاعات، هزینه بالای محاسباتی این روش‌­ها است. این موضوع در دامنه‌هایی با مقیاس بزرگ نظیر وب اهمیت زیادی دارد. در سال­‌های اخیر رو‌ش‌­های استخراج آزاد اطلاعات زیادی پیشنهاد شده است. این روش‌­ها محدوده وسیعی را از ابزارهای پردازش زبان طبیعی را اعم از سطحی (نظیر برچسب‌­زن اجزای کلام) تا عمیق (نظیر برچسب‌زن نقش معنایی) در برمی‌­گیرند. در این مقاله روشی بهینه­ برای استخراج آزاد اطلاعات نشان داده شده که بر پایه ترکیب مزایای استخراج‌­گرهای سطحی و عمیق و اجتناب از معایب آنها بنا شده است. استخراج‌گر که هسته اصلی روش پیشنهادی است، با استفاده از پارامترهای مؤثر، زیرمجموعه‌­ای را با کارایی بالا با استفاده از یک روش بهینه به کمک مدل لاگ لینیر به‌وجود می­‌آورد که قابل اجرا در مقیاس وب است. این روش با بررسی جمله ورودی و انتساب آن به مناسب­‌ترین استخراج­‌گر باعث استفاده بهینه از زمان و در‌نتیجه، کاهش هزینه محاسباتی شده و علاوه‌بر‌این به‌دقت قابل قبولی نیز دست می­‌یابد.  2 Information extraction (IE) is a process of automatically providing a structured representation from an unstructured or semi-structured text. It is a long-standing challenge in natural language processing (NLP) which has been intensified by the increased volume of information and heterogeneity, and non-structured form of it. One of the core information extraction tasks is relation extraction which aims at extracting semantic relations among entities from natural language text. Traditional relation extraction techniques were relation-specific, producing new instances of relations determined a priori. While effective, this model is not applicable in cases where the relations are not defined a priori or when the number of relations is high. Open Relation Extraction (ORE) methods were developed to elicit instances of arbitrary relations while requiring fewer training examples. Since ORE systems are employed by the applications depended on large-scale relation extraction, high performance and low computational cost are major requirements for ORE methods. This is particularly important in the large scales such as the Web. Many OIE systems have been proposed in recent years. These approaches range from shallow (such as part-of-speech tagging) to deep (such as semantic role labeling), therefore they differ in their performance level and computational cost. In this paper, we use the state-of-the-art shallow NLP tools to extract instances of relations. A supervised log-linear model for OIE is presented which is based on using advantages of shallow NLP tools, as they are fast and lead to a low computational time. Extractor which is the main core of proposed approach integrates a high performance subset of the shallow NLP tools with the strength of the deep NLP tools by using a supervised log linear model and produces a high performance method that is scalable. This causes efficient use of time and therefore reduces computational cost and increases precision. Proposed approach achieves higher precision and recall than ReVerb, one of the most successful shallow OIE system. 3 20 2017/11/9 1396/8/18 2019/01/26 1397/11/6 وحیده رشادت Vahideh Reshadat دانشکده فنی مهندسی میانه، دانشگاه تبریز ایران v.reshadat@gmail.com مریم حورعلی Maryam HoorAli دانشگاه صنعتی مالک اشتر ایران mhourali@mut.ac.ir هشام فیلی Heshaam Faili پردیس دانشکده‌های فنی دانشگاه تهران ایران hfailii@mut.ac.ir Information Extraction Open Information Extraction Relation Extraction Knowledge Discovery Fact Extraction پردازش زبان طبیعی استخراج اطلاعات استخراج آزاد اطلاعات استخراج رابطه [1] V. Reshadat, M. Hoorali, and H. Faili, "A Hybrid Method for Open Information Extraction Based on Shallow and Deep Linguistic Analysis," Inter-disciplinary Information Sciences, vol. 22, pp. 87-100, 2016.##[2] J. Piskorski and R. Yangarber, "Information extraction: Past, present and future," in Multi-source, Multilingual Information Extraction and Summarization, ed: Springer, 2013, pp. 23-49.##[3] N. mollaei, A. Abdolahzadeh, H. A. Shirazi, new approach to extract the required information from military documents. JSDP. 2012; 9 (1): pp.67-80##[4] L. Del Corro and R. Gemulla, "ClausIE: clause-based open information extraction," in Procee-dings of the 22nd international conference on World Wide Web, 2013, pp. 355-366.##[5] O. Etzioni, M. Banko, S. Soderland, and D. S. Weld, "Open information extraction from the web," Communications of the ACM, vol. 51, pp. 68-74, 2008.##[6] O. Etzioni, A. Fader, J. Christensen, S. Soderland, and M. Mausam, "Open Information Extraction: The Second Generation," in IJCAI, 2011, pp. 3-10.##[7] F. Wu and D. S. Weld, "Open information extraction using Wikipedia," in Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, 2010, pp. 118-127.##[8] A. Akbik and J. Broß, "Wanderlust: Extracting semantic relations from natural language text using dependency grammar patterns," in WWW Workshop, 2009.##[9] A. Akbik ,and A. Löser, "Kraken: N-ary facts in open information extraction," in Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction, 2012, pp. 52-56.##[10] P. Gamallo, M. Garcia, and S. Fernández-Lanza, "Dependency-based open information extraction," in Proceedings of the Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP, 2012, pp. 10-18.##[11] V. Tablan, K. Bontcheva, D. Maynard, and H. Cunningham, "Ollie: on-line learning for information extraction," in Proceedings of the HLT-NAACL 2003 workshop on Software engi-neering and architecture of language techno-logy systems-Volume 8, 2003, pp. 17-24.##[12] A. Fader, S. Soderland, and O. Etzioni, "Identify-ing relations for open information extraction," in Proceedings of the Conference on Empirical Methods in Natural Language Process-ing, 2011, pp. 1535-1545.##[13] F. Mesquita, J. Schmidek, and D. Barbosa, "Effectiveness and efficiency of open relation ex-traction," in Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, vol. 500, pp. 447-457, 2013.##[14] M. Banko, M. J. Cafarella, S. Soderland, M. Broadhead, and O. Etzioni, "Open information extraction for the web," in IJCAI, 2007, pp. 2670-2676.##[15] Y. Merhav, F. Mesquita, D. Barbosa, W. G. Yee, and O. Frieder, "Extracting information networks from the blogosphere," ACM Transactions on the Web (TWEB), vol. 6, p. 11, 2012.##[16] L. Qiu and Y. Zhang, "Zore: A syntax-based system for chinese open relation extraction," in Proceedings of EMNLP, 2014.##[17] Y.-H. Tseng, L.-H. Lee, S.-Y. Lin, B.-S. Liao, M.-J. Liu, H.-H. Chen, O. Etzioni, and A. Fader, "Chinese open relation extraction for knowledge acquisition," EACL 2014, p. 12, 2014.##[18] P. Gamallo and M. Garcia, "Multilingual open information extraction," in Portuguese Con-ference on Artificial Intelligence, 2015, pp. 711-722.##[19] C. Castella Xavier, S. de Lima, V. Lúcia, and M. Souza, "Open information extraction based on lexical-syntactic patterns," in Intelligent Systems (BRACIS), 2013 Brazilian Conference on, 2013, pp. 189-194.##[20] P. Cimiano ,and J. Wenderoth, "Automatically learning qualia structures from the web," in Proceedings of the ACL-SIGLEX workshop on deep lexical acquisition, 2005, pp. 28-37.##[21] M. Schmitz, R. Bart, S. Soderland, and O. Etzioni, "Open language learning for information extraction," in Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 2012, pp. 523-534.##[22] N. Nakashole, G. Weikum, and F. Suchanek, "PATTY: a taxonomy of relational patterns with semantic types," in Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 2012, pp. 1135-1145.##[23] H. Bast and E. Haussmann, "Open information extraction via contextual sentence decomposi-tion," in Semantic Computing (ICSC), 2013 IEEE Seventh International Conference on, 2013, pp. 154-159.##[24] H. Bast and E. Haussmann, "More informative open information extraction via simple inference," in Advances in information retrieval, ed: Springer, 2014, pp. 585-590.##[25] H. Lin, Y. Wang, P. Zhang, W. Wang, Y. Yue, and Z. Lin, "A Rule Based Open Information Extraction Method Using Cascaded Finite-State Transducer," in Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2016, pp. 325-337.##[26] Y. Xu, M.-Y. Kim, K. Quinn, R. Goebel, and D. Barbosa, "Open Information Extraction with Tree Kernels," in HLT-NAACL, 2013, pp. 868-877.##[27] J. Christensen, S. Soderland, and O. Etzioni, "An analysis of open information extraction based on semantic role labeling," in Proceedings of the sixth international conference on Knowledge capture, 2011, pp. 113-120.##[28] V. Punyakanok, D. Roth, and W.-t. Yih, "The importance of syntactic parsing and inference in semantic role labeling," Computational Linguistics, vol. 34, pp. 257-287, 2008.##[29]R. Johansson and P. Nugues, "The effect of syntactic representation on semantic role labeling," in Proceedings##[1] V. Reshadat, M. Hoorali, and H. Faili, "A Hybrid Method for Open Information Extraction Based on Shallow and Deep Linguistic Analysis," Inter-disciplinary Information Sciences, vol. 22, pp. 87-100, 2016.##[2] J. Piskorski and R. Yangarber, "Information extraction: Past, present and future," in Multi-source, Multilingual Information Extraction and Summarization, ed: Springer, 2013, pp. 23-49.##[3] نیما مولایی، حسین شیرازی. روش پیشنهادی برای استخراج اطلاعات مورد نیاز از متون نظامی. فصل‌نامه پردازش علائم و داده¬ها. ۱۳۹۱؛ ۹(۱): ۶۷-۸۰##[3] N. mollaei, A. Abdolahzadeh, H. A. Shirazi, new approach to extract the required information from military documents. JSDP. 2012; 9 (1): pp.67-80##[4] L. Del Corro and R. Gemulla, "ClausIE: clause-based open information extraction," in Procee-dings of the 22nd international conference on World Wide Web, 2013, pp. 355-366.##[5] O. Etzioni, M. Banko, S. Soderland, and D. S. Weld, "Open information extraction from the web," Communications of the ACM, vol. 51, pp. 68-74, 2008.##[6] O. Etzioni, A. Fader, J. Christensen, S. Soderland, and M. Mausam, "Open Information Extraction: The Second Generation," in IJCAI, 2011, pp. 3-10.##[7] F. Wu and D. S. Weld, "Open information extraction using Wikipedia," in Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, 2010, pp. 118-127.##[8] A. Akbik and J. Broß, "Wanderlust: Extracting semantic relations from natural language text using dependency grammar patterns," in WWW Workshop, 2009.##[9] A. Akbik ,and A. Löser, "Kraken: N-ary facts in open information extraction," in Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction, 2012, pp. 52-56.##[10] P. Gamallo, M. Garcia, and S. Fernández-Lanza, "Dependency-based open information extraction," in Proceedings of the Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP, 2012, pp. 10-18.##[11] V. Tablan, K. Bontcheva, D. Maynard, and H. Cunningham, "Ollie: on-line learning for information extraction," in Proceedings of the HLT-NAACL 2003 workshop on Software engi-neering and architecture of language techno-logy systems-Volume 8, 2003, pp. 17-24.##[12] A. Fader, S. Soderland, and O. Etzioni, "Identify-ing relations for open information extraction," in Proceedings of the Conference on Empirical Methods in Natural Language Process-ing, 2011, pp. 1535-1545.##[13] F. Mesquita, J. Schmidek, and D. Barbosa, "Effectiveness and efficiency of open relation ex-traction," in Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, vol. 500, pp. 447-457, 2013.##[14] M. Banko, M. J. Cafarella, S. Soderland, M. Broadhead, and O. Etzioni, "Open information extraction for the web," in IJCAI, 2007, pp. 2670-2676.##[15] Y. Merhav, F. Mesquita, D. Barbosa, W. G. Yee, and O. Frieder, "Extracting information networks from the blogosphere," ACM Transactions on the Web (TWEB), vol. 6, p. 11, 2012.##[16] L. Qiu and Y. Zhang, "Zore: A syntax-based system for chinese open relation extraction," in Proceedings of EMNLP, 2014.##[17] Y.-H. Tseng, L.-H. Lee, S.-Y. Lin, B.-S. Liao, M.-J. Liu, H.-H. Chen, O. Etzioni, and A. Fader, "Chinese open relation extraction for knowledge acquisition," EACL 2014, p. 12, 2014.##[18] P. Gamallo and M. Garcia, "Multilingual open information extraction," in Portuguese Con-ference on Artificial Intelligence, 2015, pp. 711-722.##[19] C. Castella Xavier, S. de Lima, V. Lúcia, and M. Souza, "Open information extraction based on lexical-syntactic patterns," in Intelligent Systems (BRACIS), 2013 Brazilian Conference on, 2013, pp. 189-194.##[20] P. Cimiano ,and J. Wenderoth, "Automatically learning qualia structures from the web," in Proceedings of the ACL-SIGLEX workshop on deep lexical acquisition, 2005, pp. 28-37.##[21] M. Schmitz, R. Bart, S. Soderland, and O. Etzioni, "Open language learning for information extraction," in Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 2012, pp. 523-534.##[22] N. Nakashole, G. Weikum, and F. Suchanek, "PATTY: a taxonomy of relational patterns with semantic types," in Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 2012, pp. 1135-1145.##[23] H. Bast and E. Haussmann, "Open information extraction via contextual sentence decomposi-tion," in Semantic Computing (ICSC), 2013 IEEE Seventh International Conference on, 2013, pp. 154-159.##[24] H. Bast and E. Haussmann, "More informative open information extraction via simple inference," in Advances in information retrieval, ed: Springer, 2014, pp. 585-590.##[25] H. Lin, Y. Wang, P. Zhang, W. Wang, Y. Yue, and Z. Lin, "A Rule Based Open Information Extraction Method Using Cascaded Finite-State Transducer," in Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2016, pp. 325-337.##[26] Y. Xu, M.-Y. Kim, K. Quinn, R. Goebel, and D. Barbosa, "Open Information Extraction with Tree Kernels," in HLT-NAACL, 2013, pp. 868-877.##[27] J. Christensen, S. Soderland, and O. Etzioni, "An analysis of open information extraction based on semantic role labeling," in Proceedings of the sixth international conference on Knowledge capture, 2011, pp. 113-120.##[28] V. Punyakanok, D. Roth, and W.-t. Yih, "The importance of syntactic parsing and inference in semantic role labeling," Computational Linguistics, vol. 34, pp. 257-287, 2008.##[29]R. Johansson and P. Nugues, "The effect of syntactic representation on semantic role labeling," in Proceedings## ##

یک روش جدید انتخاب ویژگی یک‌طرفه در دسته‌بندی داده‌های متنی نامتوازن A Novel One Sided Feature Selection Method for Imbalanced Text Classification 1 1 توزیع نامتوازن داده‌ها باعث افت کارایی دسته‌بندها می‌شود. راه‌حل‌های پیشنهاد‌شده برای حل این مشکل به چند دسته تقسیم می‌شوند، که روش‌های مبتنی بر نمونه‌گیری و روش‌های مبتنی بر الگوریتم از مهم‌ترین روش‌ها هستند. انتخاب ویژگی نیز به‌‌عنوان یکی از راه‌حل‌های افزایش کارایی دسته‌بندی داده‌های نامتوازن مورد توجه قرار گرفته است. در این مقاله یک روش جدید انتخاب ویژگی یک‌طرفه برای دسته‌بندی متون نامتوازن ارائه شده است. روش پیشنهادی با استفاده از توزیع ویژگی‌ها میزان نشان‌گر‌بودن ویژگی را محاسبه می‌کند. به‌منظور مقایسه عملکرد روش پیشنهادی، روش‌های انتخاب ویژگی مختلفی پیاده‌سازی و برای ارزیابی روش پیشنهادی از درخت تصمیم C4.5 و نایوبیز استفاده شد. نتایج آزمایش‌ها بر روی پیکره‌های Reuters-21875 و WebKB برحسب معیار Micro F ، Macro F و G-mean نشان می‌دهد که روش پیشنهادی نسبت به روش‌های دیگر، کارایی دسته‌بندها را به ‌اندازه قابل توجهی بهبود بخشیده است.  2 The imbalance data can be seen in various areas such as text classification, credit card fraud detection, risk management, web page classification, image classification, medical diagnosis/monitoring, and biological data analysis. The classification algorithms have more tendencies to the large class and might even deal with the minority class data as the outlier data. The text data is one of the areas where the imbalance occurs. The amount of text information is rapidly increasing in the form of books, reports, and papers. The fast and precise processing of this amount of information requires efficient automatic methods. One of the key processing tools is the text classification. Also, one of the problems with text classification is the high dimensional data that lead to the impractical learning algorithms. The problem becomes larger when the text data are also imbalance. The imbalance data distribution reduces the performance of classifiers. The various solutions proposed for this problem are divided into several categories, where the sampling-based methods and algorithm-based methods are among the most important methods. Feature selection is also considered as one of the solutions to the imbalance problem. In this research, a new method of one-way feature selection is presented for the imbalance data classification. The proposed method calculates the indicator rate of the feature using the feature distribution. In the proposed method, the one-figure documents are divided in different parts, based on whether they contain a feature or not, and also if they belong to the positive-class or not. According to this classification, a new method is suggested for feature selection. In the proposed method, the following items are used. If a feature is repeated in most positive-class documents, this feature is a good indicator for the positive-class; therefore, this feature should have a high score for this class. This point can be shown as a proportion of positive-class documents that contain this feature. Besides, if most of the documents containing this feature are belonged to the positive-class, a high score should be considered for this feature as the class indicator. This point can be shown by a proportion of documents containing feature that belong to the positive-class. If most of the documents that do not contain a feature are not in the positive-class, a high score should be considered for this feature as the representative of this class. Moreover, if most of the documents that are not in the positive class do not contain this feature, a high score should be considered for this feature. Using the proposed method, the score of features is specified. Finally, the features are sorted in descending order based on score, and the necessary number of required features is selected from the beginning of the feature list. In order to evaluate the performance of the proposed method, different feature selection methods such as the Gini, DFS, MI and FAST were implemented. To assess the proposed method, the decision tree C4.5 and Naive Bayes were used. The results of tests on Reuters-21875 and WebKB figures per Micro F , Macro F and G-mean criteria show that the proposed method has considerably improved the efficiency of the classifiers than other methods.   21 40 2017/11/92017/12/1 1396/9/10 2019/01/262019/02/24 1397/12/5 جعفر پورامینی Jafar Pouramini گروه مهندسی فناوری اطلاعات، دانشکده فنی و مهندسی، دانشگاه پیام نور تهران ایران j_pouramini@pnu.ac.ir بهروز مینایی بیدگلی Behrouze Minaei-Bidgoli دانشکده مهندسی کامپیوتر، دانشگاه علم و صنعت ایران ایران b_minaei@iust.ac.ir مهدی اسماعیلی Mahdi Esmaeili دانشکده مهندسی کامپیوتر، دانشگاه آزاد اسلامی واحد کاشان ایران m.esmaeili@iaukashan.ac.ir Feature selection Imbalanced class High dimensionality Text classification انتخاب ویژگی روش پالایه داده‌های نامتوازن دسته‌بندی متون [1] He, H. ,and E.A. Garcia, "Learning from Imbalanced Data," IEEE Transactions on Knowledge and Data Engineering, vol. 21(9),p p. 1263-1284, 2009.##[2] P.Yang, et al. , "Ensemble-based wrapper methods for feature," springer,Advances in Knowledge Discovery and Data Mining, vol. 7818, pp. 544-555,2013.##[3] M.Galar, et al., "A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches," IEEE Trans-actions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 42(4), pp. 463-484,2012.##[4] N.V. Chawla, N. Japkowicz, and A. Kotcz, Editorial: special issue on learning from im-balanced data sets. SIGKDD Explor. Newsl., 2004. ch,6(1), pp. 1-6.##[5] J.V.Hulse, T.M. Khoshgoftaar, and A. Napolitano, "Experimental perspectives on learn-ing from imbalanced data," in Proceedings of the 24th international conference on Machine learning, ACM: Corvalis, Oregon, USA, 2007. pp. 935-942.##[6] H. Ogura, , H. Amano, and M. Kondo, "Comparison of metrics for feature selection in imbalanced text classification," Expert Systems with Applications, vol. 38(5), pp. 4978-4989. 2011.##[7] S.Maldonadoa, R. Weberb, and F. Famili, "Feature selection for high-dimensional class-imbalanced data sets using Support Vector Machines," National Research Council of Canada, Ottawa, Canada Information Sciences, pp. 228-246, 2014.##[8] E.Chen, et al., "Exploiting probabilistic topic models to improve text categorization under class imbalance," Information Processing & Manage-ment, vol. 47(2), pp. 202-214, 2011.##[9] E.L. Iglesias, A. Seara Vieira, and L. Borrajo, "An HMM-based over-sampling technique to improve text classification," Expert Systems with Applications, vol. 40(18), pp. 7184-7192, 2013.##[10] R. Barandela , et al., "The imbalanced training sample problem: Under or over sampling?" in Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), Springer, 2004.##[11] N.V.Chawla, et al., "SMOTE: synthetic minority over-sampling technique," Journal of artificial intelligence research, pp. 321-357, 2002.##[12] S.Barua, et al., "MWMOTE--majority weighted minority oversampling technique for imbalanced data set learning," Knowledge and Data En-gineering, IEEE Transactions on, vol.26(2), pp. 405-425, 2014.##[13] A.Sun, E.-P. Lim, and Y. Liu, "On strategies for imbalanced text classification using SVM: A comparative study," Decision Support Systems, vol, 48(1), pp. 191-201, 2009.##[14] C.Sanchez-Hernandez, , D.S. Boyd, and G.M. Foody, "One-class classification for mapping a specific land-cover class: SVDD classification of fenland," IEEE Transactions on Geoscience and Remote Sensing, vol.45(4), pp. 1061-1073, 2007.##[15] S.S. Khan, and M.G. Madden, "A survey of recent trends in one class classification," in Irish con-ference on Artificial Intelligence and Cognitive Science, Springer, 2009.##[16] K.M. Ting, "A comparative study of cost-sensitive boosting algorithms," in Proceedings of the 17th International Conference on Machine Learning, Citeseer, 2000.##[17]Cheng, F., et al., Large cost-sensitive margin distribution machine for imbalanced data classi-fication. Neurocomputing, 2017. 224, pp. 45-57.##[18] X.-w. Chen, and M. Wasikowski, "FAST: a roc-based feature selection metric for small samples and imbalanced data classification problems," in Proceedings of the 14th ACM SIGKDD inter-national conference on Knowledge discovery and data mining, ACM: Las Vegas, Nevada, USA, 2008, pp. 124-132.##[19] Y. 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پیش‌بینی بار کوتاه‌مدت با انتخاب ورودی به روش LLE و موتور پیش‌بینی ترکیبی RBF-Fuzzy Short term load forecast by using Locally Linear Embedding manifold learning and a hybrid RBF-Fuzzy network 1 1 در این مقاله یک روش یادگیری خم تحت عنوان  Locally Linear Embedding (LLE) برای استخراج ورودی‌های دارای اطلاعات بیشتر و کاهش ابعاد فضای ورودی جهت پیش‌بینی بار کوتاه‌مدت، پیشنهاد شده است. روش LLE رابطه غیرخطی بین ویژگی‌ها را با تصویر‌کردن یک خم خطی محلی در فضای ویژگی، می‌یابد.  برای بررسی تأثیر روش پیشنهادی در خطای پیش‌بینی بار، یک سامانه پیش‌بینی ترکیبی، از شبکه‌ای  با یک تابع پایه رادیال  (RBF) و سامانه‌ای فازی، پیشنهاد شده است. شبکه RBF هسته موتور پیش‌بینی و ورودی آن تاریخچه بار است. سامانه استنتاج فازی جهت دخالت‌دادن اثر دما بر بار، با شبکه RBF ترکیب شده است. شبیه‌سازی با داده‌های واقعی بار منطقه مازندران، کارایی موتور پیش‌بینی پیشنهادی را در مقایسه با روش‌های شبکه عصبی مصنوعی، سری زمانی و شبکه نورو- فازی نشان می‌دهد؛ علاوه‌بر‌این، روش انتخاب ورودی (LLE) با روش تحلیل مؤلفه اصلی (PCA)  و انتخاب تجربی ورودی‌ها، مقایسه شده است. نتایج شبیه‌سازی با تجزیه و تحلیل آماری معنا‌دار، نشان می‌دهد که روش پیشنهادی، نسبت به سایر روش‌های انتخاب ورودی و موتورهای پیش‌بینی، دارای ابعاد ورودی کوچک‌تر و خطای پیش‌بینی کمتر است. 2 The aim of the short term load forecasting is to forecast the electric power load for unit commitment, evaluating the reliability of the system, economic dispatch, and so on. Short term load forecasting obviously plays an important role in traditional non-cooperative power systems. Moreover, in a restructured power system a generator company (GENCO) should predict the system demand and its corresponding price for efficient decision making. The task of a forecasting engine is to find the relation of the inputs and outputs of the system and also predicts the outputs for a given inputs. Therefore, the accuracy of forecasting is highly affected by the inputs of the forecasting engine. This effect can be studied from two points of view; First, extracting the more informative inputs and second, reducing the dimension of input space, both make it possible to learn the forecasting network via more simple models with more generalization. As a result, a reduced informative input space leads to lower prediction error. In many previous load forecasting methods, the inputs have been selected empirically. In this manner, the more correlative factors with the load in the forecasting day have been chosen as the inputs. They are generally a combination of load history and weather conditions. Several researches are focused on mathematical approaches of the input selection which are mainly based on principal component analysis (PCA) method as well as some intelligent algorithms. In this paper, a manifold learning method namely Locally Linear Embedding (LLE) is proposed, aiming to extract more informative inputs and to reduce the dimension of input space for short term load forecasting. Among all methods based on manifold learning, it can be seen that LLE performs very well in extracting the electric load curve features. The aim of this paper is to analyze the features of the load curve for estimating this curve in future. The extensive computational experiments show that the extracted features by LLE results in less prediction error than two other methods. Furthermore, LLE acts faster and makes input dimension lower than the two other methods. In the following section we will discuss the LLE method. The LLE method finds the nonlinear relationships among features by mapping a locally linear manifold in the feature space. Extracting the more informative inputs by extracting the combinational features by finding the nonlinear dependences of the features, results in reducing the dimension of input space. The resulted inputs from feature extraction and dimension reduction are utilized for load forecasting. To examine the effect of the proposed feature extraction method on load prediction error, a hybrid prediction system is proposed which is a combination of a radial basis function (RBF) network and a fuzzy system. The RBF network is the core of the prediction engine and works with historical load data as its inputs. The fuzzy inference system is combined with the RBF network to incorporate the impact of temperature on load. The case studies are carried out on the real data of electric power load of Mazandaran area in Iran. The efficiency of the proposed forecasting engine is compared with three benchmarks, the artificial neural network, time series and neuro-fuzzy methods. Furthermore, the proposed input selection method (LLE) is compared with principal component analysis (PCA) and empirical selection of inputs. Simulation results with statistical significance analysis show that the LLE method with the proposed forecasting engine is superior to other input selection methods and forecasting engines in sense of lower input dimension and lower prediction error. 41 56 2017/11/92017/12/12017/09/5 1396/6/14 2019/01/262019/02/242018/05/7 1397/2/17 حامد کبریائی Hamed Kebriaei پردیس دانشکده‌های فنی، دانشگاه تهران ایران kebriaei@ut.ac.ir حوراء کمالی نژاد Howra Kamalinejad شرکت برق منطقه‌ای تهران ایران howra.kamalinejad@gmail.com بابک نجار اعرابی Babak Nadjar Araabi پردیس دانشکده‌های فنی، دانشگاه تهران ایران araabi@ut.ac.ir Manifold learning input selection RBF network fuzzy system short term load forecasting. یادگیری خم به روش (LLE) انتخاب ورودی شبکه RBF سامانه فازی پیش‌بینی بار الکتریکی کوتاه‌مدت [1] D. Liang, M. Zhichun, "Short-term load forecasting based on fuzzy neural network," Journal of University of Science and Technology Beijing, 4, pp. 46-49, 1997.##[2] K.H Kim, H.S Youn, Y.C. Kang, "Short-term Load Forecasting for Special Days in anomalous Load Conditions Using Neural Network and Fuzzy Inference Method," IEEE Transactions on Power Systems, vol.15, pp. 559-569, 2000.##[3] W. Charytoniuk, M. S. Chen, "Neural Network design for Short-Term Load Forecasting," Proceedings of International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, London, pp. 4-7, 2000.##[4] Z. Tao, Z. Dengfu, Z. Lin, W. Xifan, X. Daozhi, "Short-Term Load Forecasting Using Radial Basis Function Networks and Expert system," Journal of XI'AN JIAOTONG University , vol.35, pp. 331-334, 2001.##[5] Z. Xin, C. Tian-Lun, "Nonlinear Time Series Forecast Using Radial Basis Function Neural Network," Commun. Theor. Phys, vol.40, pp.165-168, 2003.##[6] V. S. Kodogiannis, E. M. Anagnostakis, "Soft computing based techniques for short-term load forecasting," Fuzzy Sets and Systems 128(3), pp. 413-426, 2002.##[7] R.R.B. de Aquino, et.al, "Combined Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System for Improving a Short-Term Electric Load Forecasting," Lecture Notes in Computer Science, 4669, 779-788, 2007.##[8] Musa, Abdallah Bashir. "A comparison of ℓ1-regularizion, PCA, KPCA and ICA for dimensionality reduction in logistic regression." International Journal of Machine Learning and Cybernetics 5.6 , pp.861-873, 2014.##[9] L. Cayton, Algorithms for manifold learning, University of California, San Diego, Tech. Report, 2005.##[10] I. Borg and P. Groenen, Modern Multidimen-sional Scaling: Theory and Applications. New York: Springer-Verlag, 1997.##[11] J. B. Kruskal, "Multidimensal scaling by optimizing goodness of fit to a nonmetric hypothesis," Psychometrika, vol.29, pp. 1-27, 1964.##[12] Ji, Rongrong, et al, "Towards Optimal Manifold Hashing via Discrete Locally Linear Embedding," IEEE Transactions on Image Processing, 2017.##[13] Liu, Xin, et al, "Locally linear embedding (LLE) for MRI based Alzheimer's disease classification," Neuroimage, vol. 83, pp.148-157.2013.##[14] J. Yang, B. Ming Xiang, and Y. Zhang, "Multi-manifold Discriminant Isomap for visualization and classification," Pattern Recognition, vol. 55, pp. 215-230, 2016.##[15] M. Belkin ,and P. Niyogi, "Laplacian eigenmaps for dimensionality reduction and data representation," Neural Compute, vol. 15(6), pp. 1373-1396, 2003.##[16] Ye. Qiang, and W. 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رنگ‌آمیزی خودکار تصاویر خاکستری به‌کمک شبکه‌های زایای رقابتی Automatic Colorization of Grayscale Images Using Generative Adversarial Networks 1 1 رنگ‌آمیزی تصاویر خاکستری یکی از مسائل مهم در حوزه بازیابی اطلاعات تصویری محسوب می​شود. البته میزان موفقیت روش‌های خودکار در این حوزه در برابر عملکرد گرافیست‌ها و ویراستاران تصویر ناچیز بوده است. از‌بین‌رفتن اطلاعات رنگ‌ها و دست‌یابی به قسمت محدودی از اطلاعات اولیه تصاویر، این موضوع را به چالشی منحصربه‌فرد تبدیل می​کند؛ چون هدف اصلی در رنگ‌آمیزی تصاویر خاکستری، پیدا‌کردن رنگ اصلی و واقعی تصویر نیست؛ بلکه تلاش بر این است تا نوعی رنگ‌آمیزی که از نظر انسان‌ها «واقعی» به نظر می‌رسد، برای تصویر ارائه شود. در این مقاله سامانه‌ای برای رنگ‌آمیزی تصاویر خاکستری اشخاص و انسان‌ها ارائه شده است. در این سامانه که نیاز به پیش‌پردازش تصاویر یا مؤلفه‌های پردازشی مستقل ندارد، از یک شبکه عصبی عمیق برای رنگ‌آمیزی تصاویر افراد و از شبکه‌ای دیگر برای بررسی بازخورد عملکرد رنگ‌آمیزی استفاده می​شود. نتایج حاصل از پژوهش، عملکرد کیفی بهتری را در رنگ‌آمیزی تصاویر افراد در مقایسه با روش‌های مرسوم در این عرصه نشان می​دهد که در مدت‌زمان آموزش، به‌مراتب کمتری به‌دست آمده است. کاربرد این سامانه نه‌تنها در رنگ‌آمیزی تصاویر افراد، بلکه در رنگ‌آمیزی پُرتره‌های غیر انسانی و آثار هنری نیز کاربرد دارد و به‌کمک آن می​توان رنگ‌آمیزی مناسبی برای اثرهایی که ممکن است، ساعت‌ها زمان ببرند، در کسری از ثانیه انجام داد. 2 Automatic colorization of gray scale images poses a unique challenge in Information Retrieval. The goal of this field is to colorize images which have lost some color channels (such as the RGB channels or the AB channels in the LAB color space) while only having the brightness channel available, which is usually the case in a vast array of old photos and portraits. Having the ability to colorize such images would give us a multitude of possibilities ranging from colorizing old and historic images to providing alternate colorizations for real images or artistic creations. Be that as it may, the progress in this field is trivial compared to what the professionals are able to do using special-purpose applications such as Photoshop or GIMP. On the other hand, losing the information stored in color channels and having only access to the primary brightness channel, makes this problem a unique challenge, since the main aim of automatic colorization is not to find the image’s “real” color but to colorize it in such a way that makes it “seem real” as the original color information is lost forever and the only way to colorize it, is to provide a somewhat “proper” estimation. In this research we propose a model to automatically colorize gray human portraits. We start by reviewing the methods used for the task of image colorization and provide an explanation as to why most of them collapse to a situation known as “Averaging”. To counteract this effect, we design our end-to-end model with two separate deep neural networks forming a Generative Adversarial Network (GAN), one to colorize the images and the other to evaluate the colorization of the first network and guide it towards the proper distribution. The results show improvements over other proposed methods in this field especially in the case of colorizing human portraits along faster train times. This method not only works on real human portraits but also on non-human and artistic portraits that can be leveraged to colorize hand-drawn images some of which may take minutes up to hours by hand. 57 74 2017/11/92017/12/12017/09/52018/02/19 1396/11/30 2019/01/262019/02/242018/05/72019/01/9 1397/10/19 محمد مهدی حاجی اسمعیلی Mohammad Mahdi Haji-Esmaeili دانشگاه تربیت مدرس ایران mohammadhaji@modares.ac.ir غلامعلی منتظر Gholamali Montazer دانشگاه تربیت مدرس ایران montazer@modares.ac.ir colorization gray scale images deep neural networks generative adversarial networks image information retrieval رنگ‌آمیزی تصاویر خاکستری شبکه‌های عصبی عمیق شبکه‌های زایای رقابتی بازیابی اطلاعات تصویری [1] J. J. Lloyd, "The Complexity of Recolouring Photos," 2017. [Online]. Available: https-://www.fxguide.com/featured/the-complexity-of-re-colouring-photos/.##[2] "r/colorizationrequests." [Online]. Available: https://www.reddit.com/r/colorizationrequests.##[3] P. Whitt, Pro Photo Colorizing with GIMP. Apress, 2016.##[4] S. Koo, "Automatic Colorization with Deep Convolutional Generative Adversarial Networks," 2016. [Online]. Available: http://cs231n.stan-ford.edu/reports2016/224_Report.pdf.##[5] Aleju, "Aleju Torch Colorizer," 2016. [Online]. Available: https://github.com/aleju/colorizer.##[6] R. Zhang, P. Isola, and A. A. Efros, "Colorful Image Colorization," Eccv, pp. 1-25, 2016.##[7] R. Dahl, "Automatic Colorization," 2016. [Online]. Available: http://tinyclouds.org/colo-rize/.##[8] I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, "Generative Adversarial Networks," Jun. 2014.##[9] A. Radford, L. Metz, and S. Chintala, "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks," arXiv, pp. 1-15, 2015.##[10] S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee, "Generative Adversarial Text to Image Synthesis," Icml, pp. 1060-1069, 2016.##[11] M. Arjovsky, S. Chintala, and L. Bottou, "Wasserstein GAN," Jan. 2017.##[12] M. Mirza and S. Osindero, "Conditional Generative Adversarial Nets," CoRR, pp. 1-7, 2014.##[13] A. Levin, D. Lischinski, and Y. Weiss, "Colorization using optimization," ACM Trans. Graph., vol. 23, no. 3, p. 689, 2004.##[14] Y.-C. Huang, Y.-S. Tung, J.-C. Chen, S.-W. Wang, and J.-L. Wu, "An adaptive edge detection based colorization algorithm and its applications," Proc. 13th Annu. ACM Int. Conf. Multimed. - Multimed. '05, no. January, p. 351, 2005.##[15] L. Yatziv and G. Sapiro, "Fast image and video colorization using chrominance blending," IEEE Trans. Image Process., vol. 15, no. 5, pp. 1120-1129, 2006.##[16] T. Welsh, M. Ashikhmin, and K. Mueller, "Transferring color to greyscale images," ACM Trans. Graph., vol. 21, no. 3, pp. 277-280, 2002.##[17] R. Gupta, A. Chia, and D. Rajan, "Image colorization using similar images," Proc. 20th …, pp. 369-378, 2012.##[18] Z. Cheng, Q. Yang, and B. Sheng, "Deep colorization," Proc. IEEE Int. Conf. Comput. Vis., vol. 11-18-Dece, pp. 415-423, 2016.##[19] A. Deshpande, J. Rock, and D. Forsyth, "Learning large-scale automatic image colorization," Proc. IEEE Int. Conf. Comput. Vis., vol. 11-18-Dece, pp. 567-575, 2016.##[20] G. Charpiat, M. Hofmann, and B. Schölkopf, "Automatic image colorization via multimodal predictions," Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 5304 LNCS, no. PART 3, pp. 126-139, 2008.##[21] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, "ImageNet Large Scale Visual Recognition Challenge," Int. J. Comput. Vis., vol. 115, no. 3, pp. 211-252, 2015.##[22] K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," Iclr, vol. 96, no. 2, pp. 1-14, 2015.##[23] K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," Arxiv.Org, vol. 7, no. 3, pp. 171-180, 2015.##[24] S. Iizuka, Edgar Simo-Serra, and H. Ishikawa, "Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification," Siggraph '16, vol. 35, no. 4, pp. 1-11, 2016.##[25] A. Krizhevsky, Ii. Sulskever, and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," in Nips, 2012, pp. 1-9.##[26] A. Odena, V. Dumoulin, and C. Olah, "Deconvolution and Checkerboard Artifacts," Drill, pp. 1-14, 2016.##[27] Z. Liu, P. Luo, X. Wang, and X. Tang, "Deep learning face attributes in the wild," Proc. IEEE Int. Conf. Comput. Vis., vol. 11-18-Dece, pp. 3730-3738, 2016.##[28] Y. Guo, L. Zhang, Y. Hu, X. He, and J. Gao, "MS-Celeb-1M : Challenge of Recognizing One Million Celebrities in the Real World," Eur. Conf. Comput. Vis., pp. 87-102, 2016.##[29] M. D. Zeiler, "ADADELTA: An Adaptive Learning Rate Method," arXiv, p. 6, 2012.##[30] D. Kingma and J. Ba, "Adam: A Method for Stochastic Optimization," Int. Conf. Learn. Represent., 2014.##[1] J. J. Lloyd, "The Complexity of Recolouring Photos," 2017. [Online]. Available: https-://www.fxguide.com/featured/the-complexity-of-re-colouring-photos/.##[2] "r/colorizationrequests." [Online]. Available: https://www.reddit.com/r/colorizationrequests.##[3] P. Whitt, Pro Photo Colorizing with GIMP. Apress, 2016.##[4] S. Koo, "Automatic Colorization with Deep Convolutional Generative Adversarial Networks," 2016. [Online]. Available: http://cs231n.stan-ford.edu/reports2016/224_Report.pdf.##[5] Aleju, "Aleju Torch Colorizer," 2016. [Online]. Available: https://github.com/aleju/colorizer.##[6] R. Zhang, P. Isola, and A. A. Efros, "Colorful Image Colorization," Eccv, pp. 1-25, 2016.##[7] R. Dahl, "Automatic Colorization," 2016. [Online]. Available: http://tinyclouds.org/colo-rize/.##[8] I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, "Generative Adversarial Networks," Jun. 2014.##[9] A. Radford, L. Metz, and S. Chintala, "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks," arXiv, pp. 1-15, 2015.##[10] S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee, "Generative Adversarial Text to Image Synthesis," Icml, pp. 1060-1069, 2016.##[11] M. Arjovsky, S. Chintala, and L. Bottou, "Wasserstein GAN," Jan. 2017.##[12] M. Mirza and S. Osindero, "Conditional Generative Adversarial Nets," CoRR, pp. 1-7, 2014.##[13] A. Levin, D. Lischinski, and Y. Weiss, "Colorization using optimization," ACM Trans. Graph., vol. 23, no. 3, p. 689, 2004.##[14] Y.-C. Huang, Y.-S. Tung, J.-C. Chen, S.-W. Wang, and J.-L. Wu, "An adaptive edge detection based colorization algorithm and its applications," Proc. 13th Annu. ACM Int. Conf. Multimed. - Multimed. '05, no. January, p. 351, 2005.##[15] L. Yatziv and G. Sapiro, "Fast image and video colorization using chrominance blending," IEEE Trans. Image Process., vol. 15, no. 5, pp. 1120-1129, 2006.##[16] T. Welsh, M. Ashikhmin, and K. Mueller, "Transferring color to greyscale images," ACM Trans. Graph., vol. 21, no. 3, pp. 277-280, 2002.##[17] R. Gupta, A. Chia, and D. Rajan, "Image colorization using similar images," Proc. 20th …, pp. 369-378, 2012.##[18] Z. Cheng, Q. Yang, and B. Sheng, "Deep colorization," Proc. IEEE Int. Conf. Comput. Vis., vol. 11-18-Dece, pp. 415-423, 2016.##[19] A. Deshpande, J. Rock, and D. Forsyth, "Learning large-scale automatic image colorization," Proc. IEEE Int. Conf. Comput. Vis., vol. 11-18-Dece, pp. 567-575, 2016.##[20] G. Charpiat, M. Hofmann, and B. Schölkopf, "Automatic image colorization via multimodal predictions," Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 5304 LNCS, no. PART 3, pp. 126-139, 2008.##[21] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, "ImageNet Large Scale Visual Recognition Challenge," Int. J. Comput. Vis., vol. 115, no. 3, pp. 211-252, 2015.##[22] K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," Iclr, vol. 96, no. 2, pp. 1-14, 2015.##[23] K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," Arxiv.Org, vol. 7, no. 3, pp. 171-180, 2015.##[24] S. Iizuka, Edgar Simo-Serra, and H. Ishikawa, "Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification," Siggraph '16, vol. 35, no. 4, pp. 1-11, 2016.##[25] A. Krizhevsky, Ii. Sulskever, and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," in Nips, 2012, pp. 1-9.##[26] A. Odena, V. Dumoulin, and C. Olah, "Deconvolution and Checkerboard Artifacts," Drill, pp. 1-14, 2016.##[27] Z. Liu, P. Luo, X. Wang, and X. Tang, "Deep learning face attributes in the wild," Proc. IEEE Int. Conf. Comput. Vis., vol. 11-18-Dece, pp. 3730-3738, 2016.##[28] Y. Guo, L. Zhang, Y. Hu, X. He, and J. Gao, "MS-Celeb-1M : Challenge of Recognizing One Million Celebrities in the Real World," Eur. Conf. Comput. Vis., pp. 87-102, 2016.##[29] M. D. Zeiler, "ADADELTA: An Adaptive Learning Rate Method," arXiv, p. 6, 2012.##[30] D. Kingma and J. Ba, "Adam: A Method for Stochastic Optimization," Int. Conf. Learn. Represent., 2014.## ##

بهبود روش درهم‌تنیدگی تصویر‌ مبتنی بر یادگیری با درنظر‌گرفتن وزن‌های مختلف برای زمینه و پیش‌زمینه Enhancement of Learning Based Image Matting Method with Different Background/Foreground Weights 1 1 استخراج دقیق پیش زمینه در تصویر، درهم‌تنیدگی تصویر نامیده می‌شود. از جمله روش‌های معروف در این حوزه روش درهم‌تنیدگی مبتنی بر یادگیری است. در این شیوه داده‌های آموزشیِ دو طبقه در قالب یک نقشه سه‌گانه که شامل نقاط آموزشی برچسب‌دار (زمینه و پیش‌زمینه) و نقاط بدون برچسب هستند، به سامانه داده می‌شوند. میزان تعلق هر پیکسل به زمینه یا پیش‌زمینه بر اساس داده‌های آموزشی و کمینه‌سازی یک تابع هدف مشخص می‌شود. در تابع هدف  تمامی کارهای منتشر‌شده، میزان جریمه تخطی از مقادیر درست برای داده‌های آموزشی -خواه متعلق به زمینه باشند یا پیش‌زمینه- یکسان در نظر گرفته شده است. در این مقاله با درنظر‌گرفتن وزن متفاوت برای داده‌های آموزشی دو طبقه، این شیوه بهبود داده شده و کارایی آن در دو کاربرد متفاوت، نشان داده شده است. کاربرد نخست، دقیق‌تر‌کردن جداسازی متن از تصویر و کاربرد دوم، دقیق‌تر‌کردن خروجی روش‌های استخراج رگ‌های خونی شبکیه چشم در کناره‌های رگ‌های شناسایی‌شده است. در کاربرد نخست، متنی فارسی که بر روی زمینه دارای بافت ناهموار درج شده با یک روش معمول آستانه‌گذاری استخراج شده و سپس خروجی قطعه‌بندی‌شده با روش پیشنهادی دقیق‌تر شده است. در کاربرد دوم، ابتدا با یک روش موجود شناسایی رگ، قسمت‌هایی از تصویر که به‌احتمال زیاد متعلق به دو دسته رگ و غیر رگ هستند، برچسب‌گذاری می‌شوند. تعیین دقیق‌تر تعلق پیکسل‌های مرز رگ‌های استخراج‌شده به هر یک از دو دسته رگ یا غیر آن توسط روش‌ پیشنهادی انجام می‌شود. نتایج کمّی و دیداری، کارایی شیوه پیشنهادی را نشان داده است. 2 The problem of accurate foreground estimation in images is called Image Matting. In image matting methods, a map is used as learning data, which is produced by those pixels that are definitely foreground, definitely background ,and unknown. This three-level pixel map is often referred to as a trimap, which is produced manually in alpha matte datasets. The true class of unknown pixels will be estimated by minimizing of an objective function. Several methods for image matting has been proposed. The learning–based method is one the pioneering works which is the basis of many other approaches in the field of image matting.  In this method it is assumed that each pixel’s alpha value is a linear combination of its associated neighboring pixels. A Laplacian matrix in the objective function shows the similarity of the pixels. The coefficients of the linear combination are estimated with a local learning process by minimizing a quadratic cost function. The method of Lagrange multiplier and ridge regression technique are used for estimation of alpha values. In this objective function the violation of the predefined training pixels’ alpha values from their true values is controlled by a penalty term. Considering this coefficient as infinity, forces the matte (alpha) value to be 1 for the labeled foreground pixels and 0 for background. The weight of this penalty term still was taken equal for all training samples. In this paper the performance of the matting method is increased by considering different weights for different learning pixels. The good performance of the proposed method is demonstrated in two applications. The first application is improving the quality of a text extraction method and the second application is enhancement of an eye retinal segmentation system. In the first application, a Persian text which is fused onto a textured background is extracted by a thresholding method. After that the segmented output is enhanced by the proposed matting method. In the second application, segmentation is done with an existing vessel extraction method. The edges’ pixels of detected vessels that may be classified inaccurately are classified by the proposed image matting method. Subjective and objective comparisons show the better performance of the proposed method. 75 90 2017/11/92017/12/12017/09/52018/02/192018/01/4 1396/10/14 2019/01/262019/02/242018/05/72019/01/92019/01/26 1397/11/6 محمود امین طوسی Mahmood Amintoosi دانشگاه حکیم سبزواری ایران m.amintoosi@gmail.com Image Matting Image Segmentation Machine Learning Text Extraction Retinal Vessel Segmentation قطعه‌بندی تصویر درهم‌تنیدگی تصویر استخراج رگ یادگیری ماشین استخراج متن از تصویر [1] J. Wang and M. F. Cohen, "Image and video matting: A survey," Found. Trends. Comput. Graph. Vis., vol.3, pp.97-175, Jan. 2007.##[2] J. Johnson, D. Rajan, and H. Cholakkal, "Sparse codes as alpha matte," in BMVC, BMVA Press, 2014.##[3] Q. Chen, D. Li, and C.-K. Tang, "KNN mat-ting," Pattern Analysis and Machine Intelli-gence, IEEE Transactions on, vol.35, pp.2175-2188, Sept 2013.##[4] J. Gao, M. Paul, and J. Liu, "The image mat-ting method with regularized matte," IEEE Computer Society in ICME, , pp.550-555, 2012.##[5] P. G. Lee and Y. Wu, "L1 matting," IEEE in ICIP, pp.4665-4668, , 2010.##[6] I. Choi, S. Kim, M. S. Brown, and Y. W. Tai, "A learning-based approach to reduce jpeg ar-tifacts in image matting," in 2013 IEEE International Conference on Computer Vision, pp.2880-2887, Dec 2013.##[7] Y. Zheng and C. Kambhamettu, "Learning based digital matting.," in 12th International Conference on Computer Vision, (Kyoto), pp.889-896, 2009.##[8] A. Levin, D. Lischinski, and Y. Weiss, "A closed-form solution to natural image matting," IEEE Trans. Pattern Anal. Mach. Intell., vol.30, no.2, pp.228-242, 2008.##[9] J. Sun, J. Jia, C.-K. Tang, and H.-Y. Shum, "Poisson matting," ACM Trans. Graph., vol.23, pp.315-321, Aug. 2004.##[10] Z. Zhang, Q. Zhu, and Y. Xie, "Learning based alpha matting using support vector regression, " in 2012 ,19th IEEE International Conference on Image Processing, pp. 2109-2112, Sept 2012.##[11] X. Li and Q. Cui, Parallel Accelerated Matting Method Based on Local Learning, pp.152-162 Cham: Springer International Publishing, 2017.##[12] K. Jung, K.Kim, andA. Jain, "Text information extraction in images and video: a survey," Pattern Recognition, vol.37, pp.977-997, 5 2004.##[13] N. Otsu, "A Threshold Selection Method from Gray-level Histograms," IEEE Transactions on Systems, Man and Cybernetics, vol.9, no.1, pp.62-66, 1979.##[14] M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. Rudnicka, C. Owen, and S. Barman, "Blood vessel segmentation methodo-logies in retinal images - a survey," Comput. Methods Prog. Biomed., vol. 108, pp.407-433, Oct. 2012.##[15] M. T. Dehkordi, S. Sadri, and A. Doosthoseini, "A review of coronary vessel segmentation algo-rithms.," Journal of Medical Signals & Sensors, vol.1, no.1, pp.49-54, 2011.##[16] P. Talwar , M. D. Gupta, "Alpha-matting based retinal vessel extraction," United States Patent Application 20160163041, June 2016.##[17] P. Bankhead, C. N. Scholfield, J. G. McGeown, and T. M. Curtis, "Fast retinal vessel detection and measurement using wavelets and edge location refinement.," PloS one, vol.7, no.3, 2012.##[18] J. I. Orlando , M. Blaschko, "Learning fullyconnected CRFs for blood vessel segmentation in retinal images," in Medical Image Computing and Computer-Assisted Intervention -MIC-CAI 2014, vol. 8673 of Lecture Notes in Computer Science, pp.634-641, Springer, 2014.##[19] V. M. Saffarzadeh, A. Osareh, and B. Shadgar, "Vessel segmentation in retinal images using multi-scale line operator and k-means clustering," Journal of Medical Signals & Sensors, vol. 4, no.2, pp.122-129, 2014.##[20] G. Azzopardi, N. Strisciuglio, M. Vento, and N. Petkov, "Trainable COSFIRE filters for vessel delineation with application to retinal images," Medical Image Analysis, vol.19, no.1, pp.46-57, 2015.##[21] M. Zardadi ,and N. Mehrshad, "A New Approach to Retinal Vessel Segmentation by Using Computational Model of Simple Cells in Primary Visual Cortex". JSDP, vol. 13, no. 1, pp. 127-138, 2016.##[1] J. Wang and M. F. Cohen, "Image and video matting: A survey," Found. Trends. Comput. Graph. Vis., vol.3, pp.97-175, Jan. 2007.##[2] J. Johnson, D. Rajan, and H. Cholakkal, "Sparse codes as alpha matte," in BMVC, BMVA Press, 2014.##[3] Q. Chen, D. Li, and C.-K. Tang, "KNN mat-ting," Pattern Analysis and Machine Intelli-gence, IEEE Transactions on, vol.35, pp.2175-2188, Sept 2013.##[4] J. Gao, M. Paul, and J. Liu, "The image mat-ting method with regularized matte," IEEE Computer Society in ICME, , pp.550-555, 2012.##[5] P. G. Lee and Y. Wu, "L1 matting," IEEE in ICIP, pp.4665-4668, , 2010.##[6] I. Choi, S. Kim, M. S. Brown, and Y. W. Tai, "A learning-based approach to reduce jpeg ar-tifacts in image matting," in 2013 IEEE International Conference on Computer Vision, pp.2880-2887, Dec 2013.##[7] Y. Zheng and C. Kambhamettu, "Learning based digital matting.," in 12th International Conference on Computer Vision, (Kyoto), pp.889-896, 2009.##[8] A. Levin, D. Lischinski, and Y. Weiss, "A closed-form solution to natural image matting," IEEE Trans. Pattern Anal. Mach. Intell., vol.30, no.2, pp.228-242, 2008.##[9] J. Sun, J. Jia, C.-K. Tang, and H.-Y. Shum, "Poisson matting," ACM Trans. Graph., vol.23, pp.315-321, Aug. 2004.##[10] Z. Zhang, Q. Zhu, and Y. Xie, "Learning based alpha matting using support vector regression, " in 2012 ,19th IEEE International Conference on Image Processing, pp. 2109-2112, Sept 2012.##[11] X. Li and Q. Cui, Parallel Accelerated Matting Method Based on Local Learning, pp.152-162 Cham: Springer International Publishing, 2017.##[12] K. Jung, K.Kim, andA. Jain, "Text information extraction in images and video: a survey," Pattern Recognition, vol.37, pp.977-997, 5 2004.##[13] N. Otsu, "A Threshold Selection Method from Gray-level Histograms," IEEE Transactions on Systems, Man and Cybernetics, vol.9, no.1, pp.62-66, 1979.##[14] M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. Rudnicka, C. Owen, and S. Barman, "Blood vessel segmentation methodo-logies in retinal images - a survey," Comput. Methods Prog. Biomed., vol. 108, pp.407-433, Oct. 2012.##[15] M. T. Dehkordi, S. Sadri, and A. Doosthoseini, "A review of coronary vessel segmentation algo-rithms.," Journal of Medical Signals & Sensors, vol.1, no.1, pp.49-54, 2011.##[16] P. Talwar , M. D. Gupta, "Alpha-matting based retinal vessel extraction," United States Patent Application 20160163041, June 2016.##[17] P. Bankhead, C. N. Scholfield, J. G. McGeown, and T. M. Curtis, "Fast retinal vessel detection and measurement using wavelets and edge location refinement.," PloS one, vol.7, no.3, 2012.##[18] J. I. Orlando , M. Blaschko, "Learning fullyconnected CRFs for blood vessel segmentation in retinal images," in Medical Image Computing and Computer-Assisted Intervention -MIC-CAI 2014, vol. 8673 of Lecture Notes in Computer Science, pp.634-641, Springer, 2014.##[19] V. M. Saffarzadeh, A. Osareh, and B. Shadgar, "Vessel segmentation in retinal images using multi-scale line operator and k-means clustering," Journal of Medical Signals & Sensors, vol. 4, no.2, pp.122-129, 2014.##[20] G. Azzopardi, N. Strisciuglio, M. Vento, and N. Petkov, "Trainable COSFIRE filters for vessel delineation with application to retinal images," Medical Image Analysis, vol.19, no.1, pp.46-57, 2015.##[21]م، زردادی. ن، مهرشاد. " آشکارسازی عروق شبکیه چشم بر اساس مدل محاسباتی سلول ساده کورتکس اولیه بینایی". فصل‌نامه پردازش علائم و داده‌ها، دوره 13، شماره 1. ص 138-127، 1395.##[21] M. Zardadi ,and N. Mehrshad, "A New Approach to Retinal Vessel Segmentation by Using Computational Model of Simple Cells in Primary Visual Cortex". JSDP, vol. 13, no. 1, pp. 127-138, 2016.## ##

پیما: پیکره برچسب‌خورده موجودیت‌های اسمی زبان فارسی PAYMA: A Tagged Corpus of Persian Named Entities 1 1 هدف در مسأله تشخیص موجودیت‌های اسمی، رده­بندی اسامی خاص متن با برچسب‌هایی همچون شخص، مکان، و سازمان است. این مسأله به‌عنوان یکی از گام‌های پیش‌پردازشی بسیاری از مسائل پردازش زبان طبیعی مطرح است. اگر چه در زبان انگلیسی پژوهش‌های زیادی در این حوزه انجام شده و سامانه‌ها به کیفیت F1 بالای نود درصد دست یافته‌اند، در زبان فارسی به‌دلیل نبود یک مجموعه داده استاندارد، پژوهش‌های کمی در این زمینه انجام شده است. در این پژوهش به ساخت چنین مجموعه‌داده‌ای می‌پردازیم و آن را به‌صورت آزاد در اختیار پژوهش‌گران قرار می‌دهیم؛ سپس با استفاده از این مجموعه‌داده به طراحی سامانه آماری با استفاده از مدل میدان‌های تصادفی شرطی و نیز سامانه‌ای مبتنی بر شبکه‌های عصبی بازگشتی از نوع LSTM برای تشخیص موجودیت­های اسمی می‌پردازیم. در پیکره ایجاد‌شده هفت نوع موجودیت شخص، مکان، سازمان، زمان، تاریخ، درصد، و مقادیر پولی برچسب خورده­اند و در‌نتیجه تمام ارزیابی‌های سامانه طراحی‌شده بر روی این هفت برچسب انجام می‌گیرد. برای طراحی این سامانه، پس از آموزش یک سامانه آماری مبتنی بر الگوریتم CRF،  از خروجی این سامانه به‌عنوان یک ویژگی برای آموزش یک شبکه عصبی بازگشتی LSTM دوطرفه استفاده می‌کنیم. علاوه‌بر این ویژگی، از خوشه‌بندی واژگان به روش k- means نیز بهره می‌بریم. برای این کار، شماره خوشه واژگان را به‌عنوان یک ویژگی در اختیار شبکه عصبی LSTM قرار می‌دهیم و به این ترتیب سامانه ترکیبی نهایی ساخته می‌شود. این شیوه ترکیب مدل CRF با مدل شبکه عصبی و نیز استفاده از شماره خوشه برای هر واژه در روش خوشه‌بندی k-means نوآوری این پژوهش محسوب می‌شود. نتایج آزمایش‌ها نشان می‌دهد که با استفاده از مدل نهایی به F1 برابر با ۸۷ درصد در سطح واژه و هشتاد درصد در سطح عبارت موجودیت اسمی می‌رسیم. همچنین آزمایش‌ها نشان می‌دهد که روش پیشنهادی برای استفاده از خروجی مدل CRF به‌عنوان یک ویژگی در ورودی مدل شبکه عصبی باعث می‌شود که با در‌اختیار‌داشتن حجم کمتری از داده برچسب‌خورده به کیفیت قابل قبولی در تشخیص موجودیت‌های اسمی برسیم که این مسأله می‌تواند در زبان‌هایی که حجم داده برچسب‌خورده آن‌ها محدود است، مفید باشد. 2 The goal in the named entity recognition task is to classify proper nouns of a piece of text into classes such as person, location, and organization. Named entity recognition is an important preprocessing step in many natural language processing tasks such as question-answering and summarization. Although many research studies have been conducted in this area in English and the state-of-the-art NER systems have reached performances of higher than 90 percent in terms of F1 measure, there are very few research studies on this task in Persian. One of the main important reasons for this may be the lack of a standard Persian NER dataset to train and test the NER systems. In this research we create a standard tagged Persian NER dataset which will be distributed freely for research purposes. In order to construct this standard dataset, we studied the existing standard NER datasets in English and came to the conclusion that almost all of these datasets are constructed using news data. Thus we collected documents from ten news websites in Persian. In the next step, in order to provide the annotators with guidelines to tag these documents, we studied the guidelines used for constructing CoNLL and MUC English datasets and created our own guidelines considering the Persian linguistic rules. Using these guidelines, all words in documents can be labeled as person, location, organization, time, date, percent, currency, or other (words that are not in any of these 7 classes). We use IOB encoding for annotating named entities in documents, like most of the existing English NER datasets. Using this encoding, the first token of a named entity is labeled with B, and the next tokens (if exist) are labeled with I. The words that are not part of any named entity are labeled with O. The constructed corpus, named PAYMA, consists of 709 documents and includes 302530 tokens. 41148 tokens out of these tokens are labeled as named entities and the others are labeled as O. In order to determine the inter-annotator agreement, 160 documents were labeled by a second annotator. Kappa statistic was estimated as 95% using words that are labeled as named entities. After creating the dataset, we used the dataset to design a hybrid system for named entity recognition. We trained a statistical system based on the CRF algorithm, and used its output as a feature to train a bidirectional LSTM recurrent neural network. Moreover, we used the k-means word clustering method to cluster the words and fed the cluster number of each word to the LSTM neural network. This form of combining CRF with neural networks and using the cluster number for each word is the novelty of this research work. Experimental results show that the final model can reach an F1 score of 87% at word-level and 80% at phrase level. 91 110 2017/11/92017/12/12017/09/52018/02/192018/01/42017/12/16 1396/9/25 2019/01/262019/02/242018/05/72019/01/92019/01/262019/02/24 1397/12/5 مهسا‌سادات شهشهانی Mahsa Sadat Shahshahani دانشگاه تهران ایران ms.shahshahani@ut.ac.ir مهدی محسنی Mahdi Mohseni دانشگاه تهران ایران mahdi.mohseni@ut.ac.ir آزاده شاکری Azadeh Shakery دانشگاه تهران ایران shakery@ut.ac.ir هشام فیلی Heshaam Faili دانشگاه تهران ایران hfaili@ut.ac.ir Persian named entity corpus named entity recognition rule-based model deep-learning based model conditional random field’s method پیکره موجودیت‌های اسمی تشخیص موجودیت‌های اسمی روش قاعده‌محور روش مبتنی بر یادگیری عمیق روش میدان‌های تصادفی شرطی [1] S. A. Esfahani, S. Rahati Ghouchani, and N. Jahangiri, "Persian named entity recognition and classification", Journal of Signal and Data Processing, vol. 7, no. 1, 2010.##[2] M. Abdous, "Recognizing Persian Named Entities Using Persian Wikipedia Content", M.S Thesis, Iran University of Science and Technology, Tehran, Iran, 2015.##[3] M. Abdous and B. Minaei Bidgoli, "Improving Named Entity Recognition Using Izafe in Farsi", Journal of Signal and Data Processing, vol. 14, no. 4, 2017.##[4] P. S. Mortazavi, M. Shamsfard, "Named Entity Recognition in Persian Texts", in 15th National CSI Computer Conference, Tehran, Iran, 2009.##[5] F. Ahmadi and H. Moradi, "A Hybrid Method for Persian Named Entity Recognition," in 7th Internatonal Conference on Information Know-ledge Technology, 2015.##[6] D. M. Bikel, S. Miller, R. M. Schwartz, and R. Weischedel, "Nymble: A High-Performance Learning Name-Finder", in Proceedings of the fifth conference on Applied natural language process-ing, pp. 194-201, 1997.##[7] A. Borthwick and J. 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شناسایی گونه‌های گیاهی با استفاده از تصاویر برگ بر پایه ویژگی‌های بافت و شبکه عصبی On the use of Textural Features and Neural Networks for Leaf Recognition 1 1 برگ گیاهان منبع اطلاعاتی مهمی برای پژوهش و شناسایی گیاهان هستند. استخراج این اطلاعات به‌طورعمومی توسط کارشناسان خبره کشاورزی انجام می­‌گیرد. از آنجا که برگ­ها ویژگی­‌های مناسبی را برای تشخیص انواع گونه‌­های گیاهی در سامانه‌های هوشمند فراهم می‌کنند، لذا استفاده از سامانه‌های هوشمند می‌­تواند به تشخیص خودکار گونه‌­های گیاهی کمک کند. این مقاله روش جدیدی را برای شناسایی برگ‌­های گونه‌­های گیاهی با استفاده از الگوریتم استخراج ویژگی بافت GIST ارائه می‌­دهد که یک روش استخراج ویژگی عمومی برای طبقه‌بندی تصاویر است. این روش دارای دقت خوبی در تعیین شباهت­‌ها بین اشیای یکسان در تصاویر مختلف است. در مرحله طبقه‌­بندی داده‌­ها نیز، از شبکه عصبی Patternnet که برای استخراج الگو مناسب است استفاده می‌شود. برای ارزیابی روش پیشنهادی، الگوریتم حاصل بر روی داده‌­های دو پایگاه داده معتبر که تنوع گیاهی زیادی دارند، اعمال شده است. مقایسه نتایج با الگوریتم‌­های متداول استخراج ویژگی از تصاویر برگ‌­ها نشان می‌­دهد که الگوریتم GIST علاوه‌بر سرعت مناسب، دارای دقت طبقه­‌بندی قابل قبولی به‌ویژه در تصاویر هم‌راستا و حتی تصاویر از نوع شبه‌پویش است. 2 for recognizing various types of plants, so automatic image recognition algorithms can extract to classify plant species and apply these features. Fast and accurate recognition of plants can have a significant impact on biodiversity management and increasing the effectiveness of the studies in this regard. These automatic methods have involved the development of recognition techniques and digital image processing pattern.  Most of the previous studies on the classification and identification of plant species from leaf images are based on the shape, texture and color features. There were also different methods of data modeling which have been used to leave plant recognition. In this paper, we investigate a novel approach for the recognition of plant species using texture feature GIST to extract general features. In the classification step, Patternnet feed forward neural network algorithm has been applied. Essentially, the GIST feature has been designed to be employed for image classification. In this study, GIST feature vectors are considered as the basis of the leaves’ classification. The GIST descriptor of an image is computed by the first filtering of an image by a filter bank of Gabor filters, and then averaging the responses of filters in each block on a no overlapping grid. For evaluation of our approach, we have applied the algorithm on scan and pseudo-scan images of two famous different datasets Image CLEF2012 and Leaf snap with a high various. The results show that in comparison to some widely used algorithms, our approach outperforms in the case of time and also the accuracy of classification. Substantial results can be achieved when the image of the plants are aligned with one another and when we deal with pseudo scan images. The detection of combinations of leaves that have jagged edges is an important contribution of this study. In many of the previous algorithms, the computational complexity of this detection is high. While by using the GIST feature vector, these types of images are processed simply and precisely (above 90%).  precisely (above 90%). 111 124 2017/11/92017/12/12017/09/52018/02/192018/01/42017/12/162017/12/2 1396/9/11 2019/01/262019/02/242018/05/72019/01/92019/01/262019/02/242019/01/9 1397/10/19 فاطمه مستاجر خیرخواه Fateme Mostajer Kheirkhah پژوهشکده فناوری اطلاعات جهاد دانشگاهی ایران fkheirkhah@gmail.com حبیب الله اصغری Habibollah Asghari پژوهشکده فناوری اطلاعات جهاد دانشگاهی ایران asghari@ictrc.ac.ir داراب یزدانی Darab Yazdani مرکز تحقیقات گیاهان دارویی، پژوهشکده گیاهان دارویی، جهاد دانشگاهی ایران dayazdani@yahoo.com Species recognition GIST feature vector Neural networks Gabor filter شناسایی گونه‌های گیاهی بردار ویژگی GIST شبکه عصبی فیلتر گابور [1] S. L. Pimm, C. N. Jenkins, R. Abell, T. M. Brooks, J. L. Gittleman, L.N. Joppa, P.H. Raven, C.M. Roberts, and J.O. Sexton, "The biodiversity of species and their rates of extinction, distribution, and protection," Science, vol. 344(6187), 2014.##[2] A. Joly, H. Müller, H. Goëau, H. Glotin, C. Spampinato, A. Rauber, P. Bonnet, W. P. Vellinga, R. B. Fisher, and R. Planquè, "LifeCLEF: Multimedia life species identifica-tion," In EMR@ ICMR, pp. 7-13, April 2014.##[3] A. R. Backes, D. Casanova, and O. M. Bruno, "A complex network-based approach for boundary shape analysis," Pattern Recognition, vol. 42(1), pp. 54-67, 2009.##[4] C. Caballero and M. C. Aranda, "Plant species identification using leaf image retrieval," In Proceedings of the ACM International Con-ference on Image and Video Retrieval, July 2010. pp. 327-334.##[5] N. Kumar, P. N. Belhumeur, A. Biswas, D. W. Jacobs, W. J. Kress, I. C. Lopez, and J. V. Soares, "Leafsnap: A computer vision system for automatic plant species identification." In Computer Vision-ECCV 2012 Springer, Berlin, Heidelberg, pp. 502-516.##[6] N. Sakai, S. Yonekawa, A. Matsuzaki, and H. Morishima, "Two-dimensional image analysis of the shape of rice and its application to separating varieties," Journal of Food Engineering, vol. 27(4), pp. 397-407, 1996.##[7] J. X. Du, X. F. Wang, and G. J. Zhang, "Leaf shape based plant species recognition," Applied mathematics and computation," vol. 185 (2), pp.883-893, 2007.##[8] Z. Wang, Z. Chi, D. Feng, and Q. Wang, "Leaf image retrieval with shape features," In International Conference on Advances in Visual Information Systems, Springer, Berlin, Heidelberg, 2000. pp. 477-487.##[9] T. Beghin, J. S. Cope, P. Remagnino, and S. Barman, "Shape and texture based plant leaf classification," In International Conference on Advanced Concepts for Intelligent Vision Systems Springer, Berlin, Heidelberg, December 2010. pp. 345-353.##[10] B. S. Bama, S. M. Valli, S. Raju, and V. A. Kumar, "Content based leaf image retrieval (CBLIR) using shape, color and texture features," Indian Journal of Computer Science and Engineering, vol. 2(2), pp.202-211, 2011.##[11] H. Kebapci, B. Yanikoglu, and G. Unal, "Plant image retrieval using color, shape and texture features," The Computer Journal, vol. 54(9), pp.1475-1490, 2010.##[12] S. Abbasi, F. Mokhtarian, and J. Kittler, "Reliable classification of chrysanthemum leaves through curvature scale space," Scale-Space Theory in Computer Vision, pp. 284-295. 1997.##[13] Z. Wang, Z. Chi, and D. Feng, "Fuzzy integral for leaf image retrieval," In Fuzzy Systems, Proceedings of the 2002 IEEE International Conference, Vol. 1, 2002. pp. 372-377.##[14] J. X. Du, C. M. Zhai, and Q. P. Wang, "Recognition of plant leaf image based on fractal dimension features," Neurocomputing, vol. 116, pp.150-156, 2013.##[15] L. W. Yang and X. F. Wang, "Leaf image recognition using fourier transform based on ordered sequence," In International Conference on Intelligent Computing, Springer, Berlin, Heidelberg, 2012. pp. 393-400.##[16] Q. P. Wang, J. X. Du, and C. M. Zhai, "Recognition of leaf image based on ring projection wavelet fractal feature," In Advanced Intelligent Computing Theories and Appli-cations. With Aspects of Artificial Intelligence, Springer, Berlin, Heidelberg, 2010. pp. 240-246.##[17] S. Prasad, P. Kumar, and R. C. Tripathi, "Plant leaf species identification using curvelet transform," In IEEE Computer and Communi-cation Technology (ICCCT), 2011 2nd Inter-national Conference, September 2011. pp. 646-652.##[18] A. Kadir, L. E. Nugroho, A. Susanto, and P. I. Santosa, "Experiments of Zernike moments for leaf identification," Journal of Theoretical and Applied Information Technology (JATIT), vol. 41(1), pp.82-93, 2012.##[19] J. Pan and Y. He, "Recognition of plants by leaves digital image and neural network," In IEEE Computer Science and Software Engineering, 2008 International Conference on Vol. 4, December 2008. pp. 906-910.##[20] S. G. Wu, F. S. Bao, E. Y. Xu, Y. X. Wang, Y. F. Chang, and Q. L. Xiang, "A leaf recognition algorithm for plant classification using pro-babilistic neural network," In Signal Processing and Information Technology, 2007 IEEE Inter-national Symposium, December 2007. pp. 11-16.##[21] C. A. Priya, T. Balasaravanan, and A. S. Thanamani, "An efficient leaf recognition algorithm for plant classification using support vector machine," In Pattern Recognition, Informatics and Medical Engineering (PRIME), 2012 International Conference on IEEE, March 2012. pp. 428-432.##[22] N. Valliammal and S. N. Geethalakshmi, "A novel approach for plant leaf image segmentation using fuzzy clustering," Inter-national Journal of Computer Applications, vol. 44(13), pp. 10-20, 2012.##[23] H. Goëau, A. Joly, S. Selmi, P. Bonnet, E. Mouysset, L. Joyeux, J. F. Molino, P. Birnbaum, D. Bathelemy, and N. Boujemaa, "Visual-based plant species identification from crowdsourced data," In Proceedings of the 19th ACM inter-national conference on Multimedia, November 2011. pp. 813-814.##[24] J. Wu and J. M. Rehg, "CENTRIST: A visual descriptor for scene categorization," IEEE transactions on pattern analysis and machine intelligence, vol. 33(8), pp. 1489-1501, 2011.##[25] C. Li, A. Kowdle, A. Saxena, and T. Chen, "Towards holistic scene understanding: Feedback enabled cascaded classification models," In Advances in Neural Information Processing Systems, 2010. pp. 1351-1359.##[26] Z. Li and L. Itti, "Saliency and gist features for target detection in satellite images," IEEE Transactions on Image Processing, vol. 20(7), pp. 2017-2029, 2011.##[27] A. C. Murillo, G. Singh, J. Kosecka, and J. J. Guerrero, "Localization in urban environments using a panoramic gist descriptor," IEEE Trans-actions on Robotics, vol. 29(1), pp. 146-160, 2013.##[28] A. Farhadi, M. Hejrati, M. A. Sadeghi, P. Young, C. Rashtchian, J. Hockenmaier, and D. Forsyth, "Every picture tells a story: Generating sentences from images," In European con-ference on computer vision, Springer, Berlin, Heidelberg. Sep 2010. pp. 15-29.##[29] R. Bharath, "Computer-Assisted Algorithms for Ultrasound Imaging Systems," Doctoral dissertationIndian Institute of Technology Hyderabad. 2018.##[30] F. Alaei, A. Alaei, U. Pal, and M. Blumenstein, "Evaluation of Gist Operator for Document Image Retrieval," In 2018 13th IAPR Inter-national Workshop on Document Analysis Systems (DAS) Apr 2018. pp. 369-374.##[31] H. Yalcin, and S. Razavi, "Plant classification using convolutional neural networks," In Agro-Geoinformatics (Agro-Geoinformatics), 2016 Fifth Inter-national Conference, Jul 2016. pp. 1-5.##[32] A. Caglayan, O. 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Mohtar, "Plant leaf identification using moment invariants & general regression neural network," In Hybrid Intelligent Systems (HIS), 2011 11th Inter-national Conference, December 2011. pp. 430-435.##[37] A. H. Kulkarni, , H. M. Rai, , K. A. Jahagirdar, and P. S. Upparamani, "A leaf recognition technique for plant classification using RBPNN and Zernike moments," International Journal of Advanced Research in Computer and Communi-cation Engineering, vol. 2(1), pp.984-988, 2013.##[38] R. X. Hu, W. Jia, , H. Ling, and D. Huang, "Multiscale distance matrix for fast plant leaf recognition," IEEE Trans.Image Processing, vol. 21(11), pp.4667-4672, 2012.##[39] D. G. Tsolakidis, D. I. Kosmopoulos, and G. Papadourakis, "Plant leaf recognition using Zernike moments and histogram of oriented gradients," In Hellenic Conference on Artificial Intelligence, 2014, pp. 406-417.##[1] S. L. Pimm, C. N. Jenkins, R. Abell, T. M. Brooks, J. L. Gittleman, L.N. Joppa, P.H. Raven, C.M. Roberts, and J.O. Sexton, "The biodiversity of species and their rates of extinction, distribution, and protection," Science, vol. 344(6187), 2014.##[2] A. Joly, H. Müller, H. Goëau, H. Glotin, C. Spampinato, A. Rauber, P. Bonnet, W. P. Vellinga, R. B. Fisher, and R. Planquè, "LifeCLEF: Multimedia life species identifica-tion," In EMR@ ICMR, pp. 7-13, April 2014.##[3] A. R. Backes, D. Casanova, and O. M. Bruno, "A complex network-based approach for boundary shape analysis," Pattern Recognition, vol. 42(1), pp. 54-67, 2009.##[4] C. Caballero and M. C. Aranda, "Plant species identification using leaf image retrieval," In Proceedings of the ACM International Con-ference on Image and Video Retrieval, July 2010. pp. 327-334.##[5] N. Kumar, P. N. Belhumeur, A. Biswas, D. W. Jacobs, W. J. Kress, I. C. Lopez, and J. V. Soares, "Leafsnap: A computer vision system for automatic plant species identification." In Computer Vision-ECCV 2012 Springer, Berlin, Heidelberg, pp. 502-516.##[6] N. Sakai, S. Yonekawa, A. Matsuzaki, and H. Morishima, "Two-dimensional image analysis of the shape of rice and its application to separating varieties," Journal of Food Engineering, vol. 27(4), pp. 397-407, 1996.##[7] J. X. Du, X. F. Wang, and G. J. Zhang, "Leaf shape based plant species recognition," Applied mathematics and computation," vol. 185 (2), pp.883-893, 2007.##[8] Z. Wang, Z. Chi, D. Feng, and Q. Wang, "Leaf image retrieval with shape features," In International Conference on Advances in Visual Information Systems, Springer, Berlin, Heidelberg, 2000. pp. 477-487.##[9] T. Beghin, J. S. Cope, P. Remagnino, and S. Barman, "Shape and texture based plant leaf classification," In International Conference on Advanced Concepts for Intelligent Vision Systems Springer, Berlin, Heidelberg, December 2010. pp. 345-353.##[10] B. S. Bama, S. M. Valli, S. Raju, and V. A. Kumar, "Content based leaf image retrieval (CBLIR) using shape, color and texture features," Indian Journal of Computer Science and Engineering, vol. 2(2), pp.202-211, 2011.##[11] H. Kebapci, B. Yanikoglu, and G. Unal, "Plant image retrieval using color, shape and texture features," The Computer Journal, vol. 54(9), pp.1475-1490, 2010.##[12] S. Abbasi, F. Mokhtarian, and J. Kittler, "Reliable classification of chrysanthemum leaves through curvature scale space," Scale-Space Theory in Computer Vision, pp. 284-295. 1997.##[13] Z. Wang, Z. Chi, and D. Feng, "Fuzzy integral for leaf image retrieval," In Fuzzy Systems, Proceedings of the 2002 IEEE International Conference, Vol. 1, 2002. pp. 372-377.##[14] J. X. Du, C. M. Zhai, and Q. P. Wang, "Recognition of plant leaf image based on fractal dimension features," Neurocomputing, vol. 116, pp.150-156, 2013.##[15] L. W. Yang and X. F. Wang, "Leaf image recognition using fourier transform based on ordered sequence," In International Conference on Intelligent Computing, Springer, Berlin, Heidelberg, 2012. pp. 393-400.##[16] Q. P. Wang, J. X. Du, and C. M. Zhai, "Recognition of leaf image based on ring projection wavelet fractal feature," In Advanced Intelligent Computing Theories and Appli-cations. With Aspects of Artificial Intelligence, Springer, Berlin, Heidelberg, 2010. pp. 240-246.##[17] S. Prasad, P. Kumar, and R. C. Tripathi, "Plant leaf species identification using curvelet transform," In IEEE Computer and Communi-cation Technology (ICCCT), 2011 2nd Inter-national Conference, September 2011. pp. 646-652.##[18] A. Kadir, L. E. Nugroho, A. Susanto, and P. I. Santosa, "Experiments of Zernike moments for leaf identification," Journal of Theoretical and Applied Information Technology (JATIT), vol. 41(1), pp.82-93, 2012.##[19] J. Pan and Y. He, "Recognition of plants by leaves digital image and neural network," In IEEE Computer Science and Software Engineering, 2008 International Conference on Vol. 4, December 2008. pp. 906-910.##[20] S. G. Wu, F. S. Bao, E. Y. Xu, Y. X. Wang, Y. F. Chang, and Q. L. Xiang, "A leaf recognition algorithm for plant classification using pro-babilistic neural network," In Signal Processing and Information Technology, 2007 IEEE Inter-national Symposium, December 2007. pp. 11-16.##[21] C. A. Priya, T. Balasaravanan, and A. S. Thanamani, "An efficient leaf recognition algorithm for plant classification using support vector machine," In Pattern Recognition, Informatics and Medical Engineering (PRIME), 2012 International Conference on IEEE, March 2012. pp. 428-432.##[22] N. Valliammal and S. N. Geethalakshmi, "A novel approach for plant leaf image segmentation using fuzzy clustering," Inter-national Journal of Computer Applications, vol. 44(13), pp. 10-20, 2012.##[23] H. Goëau, A. Joly, S. Selmi, P. Bonnet, E. Mouysset, L. Joyeux, J. F. Molino, P. Birnbaum, D. Bathelemy, and N. Boujemaa, "Visual-based plant species identification from crowdsourced data," In Proceedings of the 19th ACM inter-national conference on Multimedia, November 2011. pp. 813-814.##[24] J. Wu and J. M. Rehg, "CENTRIST: A visual descriptor for scene categorization," IEEE transactions on pattern analysis and machine intelligence, vol. 33(8), pp. 1489-1501, 2011.##[25] C. Li, A. Kowdle, A. Saxena, and T. Chen, "Towards holistic scene understanding: Feedback enabled cascaded classification models," In Advances in Neural Information Processing Systems, 2010. pp. 1351-1359.##[26] Z. Li and L. Itti, "Saliency and gist features for target detection in satellite images," IEEE Transactions on Image Processing, vol. 20(7), pp. 2017-2029, 2011.##[27] A. C. Murillo, G. Singh, J. Kosecka, and J. J. 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انتخاب ویژگی پیشنهادی برای مدیریت دمای پویا در سیستم‌های چندهسته‌ای Proposed Feature Selection for Dynamic Thermal Management in Multicore Systems 1 1 افزایش تعداد هسته‌­ها، به‌منظور افزایش توان محاسباتی یک سیستم چندهسته‌­ای، منجر به افزایش دمای پردازنده می‌­شود. یکی از راه‌کارهای معمول برای کاهش دما، روش­‌های کنش‌­گراست. این روش‌­ها، با پیش‌­بینی دما پیش از رسیدن به دمای حدآستانه، مدیریت دما را انجام می‌دهند. در این مقاله، اثر استفاده از ویژگی­‌های مناسب برای مدیریت دمای پردازنده موردتوجه قرار گرفته است. برای مدیریت دما، سه مدل، به‌ترتیب برای پیش‌­بینی دما، پیش‌بینی پاسخ دمایی و کنترل دما پیشنهاد شده است. در این راستا، از شبکه عصبی پرسپترون چندلایه‌­ای برای پیش­‌بینی دما و پاسخ دمایی و از سامانه استنتاج عصبی-فازی وفقی به‌منظور مدیریت دما استفاده می­‌شود. برای آموزش هر یک از مدل­‌ها، مجموعه داده‌­ای با تنوع بالا از حالات مختلف دمایی پردازنده، ایجاد و تعدادی از ویژگی­‌های هر مجموعه، با نظارت حس‌گرها و شمارنده­‌های کارایی پردازنده ایجاد و همچنین، برای افزایش دقت هر یک از مدل­‌ها، تعدادی ویژگی با بهره­‌گیری از پردازش­‌های پیشنهادی فراهم و سپس، ویژگی­‌های مناسب برای هر یک از مدل­‌ها، با روش‌­های پیشنهادی در این مقاله انتخاب می‌­شود. ارزیابی مدل پیشنهادی برای پیش­‌بینی و کنترل دمای پردازنده برای فاصله­‌های زمانی مختلف، کمتر از 6/0 درجه سانتی‌­گراد خطا دارد.   2 Increasing the number of cores in order to the demand of more computing power has led to increasing the processor temperature of a multi-core system. One of the main approaches for reducing temperature is the dynamic thermal management techniques. These methods divided into two classes, reactive and proactive. Proactive methods manage the processor temperature, by forecasting the temperature before reaching the threshold temperature. In this paper, the effects of using proper features for processor thermal management have been considered. In this regard, three models have been proposed for temperature prediction, control response estimation, and thermal management, respectively. A multi-layered perceptron neural network is used to predict the temperature and to control the response. Also, an adaptive neuro-fuzzy inference system is utilized for controlling temperature. An appropriate data set, which includes a variety of processor temperature variations, has been created to train each model. Some features of the dataset are collected by monitoring the thermal sensors and performance counters. In addition, a number of features are created by proposing processes to increase the accuracy of each model . Then, the features of each model are selected by the proposed method. The evaluation of the proposed model for predicting and controlling the processor temperature for different time distances is below 0.6 ° C. 125 142 2017/11/92017/12/12017/09/52018/02/192018/01/42017/12/162017/12/22018/02/13 1396/11/24 2019/01/262019/02/242018/05/72019/01/92019/01/262019/02/242019/01/92019/01/26 1397/11/6 جواد محبی نجم‌آباد javad Mohebbi دانشگاه آزاد اسلامی واحد قوچان ایران javad.mohebi@shahroodut.ac.ir مرتضی مرادی Morteza Moradi دانشگاه فردوسی مشهد ایران moradi.edu@gmail.com باقر سلامی Bagher Salami دانشگاه فردوسی مشهد ایران bagher.salami@stu-mail.um.ac.ir thermal prediction control response feature selection multilayer perceptron adaptive neuro-fuzzy inference system پیش‌بینی دما پاسخ کنترلی انتخاب ویژگی پرسپترون چندلایه‌ای سیستم استنتاج عصبی-فازی وفقی [1] J. Kong, S. W. Chung, and K. Skadron, "Recent thermal management techniques for micro-processors," ACM Computing Surveys (CSUR), vol. 44, p. 13, 2012.##[2] A. K. Coskun, T. S. Rosing, and K. C. Gross, "Utilizing predictors for efficient thermal management in multiprocessor SoCs," IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, vol. 28, no. 10, pp. 1503-1516, 2009.##[3] A. K. Coskun, T. S. Rosing, and K. C. Gross, "Proactive temperature balancing for low cost thermal management in MPSoCs," Proc. IEEE/ACM International Conference on Computer-Aided Design, 2008, pp. 250-257.##[4] R. Cochran and S. Reda, "Thermal prediction and adaptive control through workload phase detection," ACM Trans. on Design Automation of Electronic Systems (TODAES), vol. 18, no. 1, p. 7, 2013.##[5] M. Chhablani, I. Koren, and C. M. Krishna, "Online Inertia-Based Temperature Estimation for Reliability Enhancement," Journal of Low Power Electronics, vol. 12, no. 3, pp. 159-171, 2016.##[6] M. Zaman, A. Ahmadi, and Y. Makris, "Workload characterization and prediction: A pathway to reliable multi-core systems," Proc. International On-Line Testing Symposium (IOLTS), pp. 116-121, 2015.##[7] M. Stockman, M. Awad, H. Akkary, and R. Khanna, "Thermal status and workload predict-tion using support vector regression," Proc. International Conference on Energy Aware Computing, 2012, pp. 1-5.##[8] Y. Ge, Q. Qiu, and Q. Wu, "A multi-agent framework for thermal aware task migration in many-core systems," IEEE Trans. on Very Large Scale Integration (VLSI) Systems, vol. 20, no. 10, pp. 1758-1771, 2012.##[9] P. Kumar and D. Atienza, "Neural network based on-chip thermal simulator," Proc. Circuits and Systems (ISCAS), pp. 1599-1602, 2010.##[10] A. Vincenzi, A. Sridhar, M. Ruggiero, and D. Atienza, "Fast thermal simulation of 2D/3D integrated circuits exploiting neural networks and GPUs," Proc. 17th IEEE/ACM international symposium on low-power electronics and design, pp. 151-156, 2011.##[11] A. Sridhar, A. Vincenzi, M. Ruggiero, and D. Atienza, "Neural network-based thermal simula-tion of integrated circuits on GPUs," IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, vol. 31, no. 1, pp. 23-35, 2012.##[12] D. Li, R. Ge, and K. Cameron, "System-level, Unified In-band and Out-of-band Dynamic Thermal Control, " In International Conference Parallel Processing (ICPP), 2010, pp. 131-140.##[13] V. Hanumaiah and S. Vrudhula, "Energy-efficient operation of multicore processors by DVFS, task migration, and active cooling, " IEEE Transactions on Computers, vol .63, no. 2, pp. 349-360, 2014.##[14] I. Yeo, C.C. Liu, and E.J. Kim, "Predictive dynamic thermal management for multicore systems," Proc. 45th annual Design Automation Conference, 2008, pp. 734-739.##[15] G. Liu, M. Fan, and G. Quan, "Neighbor-aware dynamic thermal management for multi-core platform," Proc. Design, Automation & Test in Europe Conference & Exhibition (DATE), 2012 pp. 187-192.##[16] A. Kumar, L. Shang, L.S. Peh, and N. K. Jha, "HybDTM: a coordinated hardware-software approach for dynamic thermal management," Proc. Design Automation Conference, 2006, pp. 548-553.##[17] K.J. Lee and K. Skadron, "Using performance counters for runtime temperature sensing in high-performance processors," IEEE Inter-national Parallel and Distributed Processing Symposium, 2005.##[18] S. J. Lu, R. Tessier, and W. Burleson, "Dynamic On-Chip Thermal Sensor Calibration Using Performance Counters," IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, vol. 33, no. 6, pp. 853-866, 2014.##[19] K. Skadron, M. R. Stan, W. Huang, S. Velusamy, K. Sankaran-Arayanan, and D. Tarjan, "Temperature aware microarchitecture: Exten-ded discussion and results," Technical Report CS-2003-08, University of Virginia, Dept. of Computer Science, 2003.##[20] K. Zhang, A. Guliani, S. Ogrenci-Memik, G. Memik, K. Yoshii, R. Sankaran, and P. Beckman, "Machine Learning-Based Tem-perature Prediction for Runtime Thermal Management Across System Components, " IEEE Trans. on Parallel and Distributed Systems, vol. 29, no. 2, pp. 405-419, 2018.##[21] J. M. N. Abad, B. Salami, H. Noori, A. Soleimani and F. Mehdipour, "A neuro-fuzzy fan speed controller for dynamic thermal management of multi-core processors," In Proceedings of the 11th ACM Conference on Computing Frontiers, 2014, p. 29.##[22] J. M. N. Abad and A. Soleimani, "A neuro-fuzzy fan speed controller for dynamic management of processor fan power consumption," In Swarm Intelligence and Evolutionary Computation (CSIEC), pp. 148-153, 2016.##[23] H. Peng, F. Long, and C. Ding, "Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy," IEEE Trans. on pattern analysis and machine intelligence, vol. 27, no. 8, pp. 1226-1238, 2005.##[24] C. Ding and H. Peng, "Minimum redundancy feature selection from microarray gene exp-ression data," Journal of bioinformatics and computational biology, vol. 3, no. 2, pp. 185-205, 2005.##[25] lm-sensors Linux hardware monitoring [Online]. Available: http://www.lm-sensors.org, Jan 2017.##[26] Linux cpufreq governors, LinuxKernel [Online]. Available:https://www.kernel.org/doc/Documentation/cpu-freq/governors.txt. Jan 2017.##[1] J. Kong, S. W. Chung, and K. Skadron, "Recent thermal management techniques for micro-processors," ACM Computing Surveys (CSUR), vol. 44, p. 13, 2012.##[2] A. K. Coskun, T. S. Rosing, and K. C. Gross, "Utilizing predictors for efficient thermal management in multiprocessor SoCs," IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, vol. 28, no. 10, pp. 1503-1516, 2009.##[3] A. K. Coskun, T. S. Rosing, and K. C. Gross, "Proactive temperature balancing for low cost thermal management in MPSoCs," Proc. IEEE/ACM International Conference on Computer-Aided Design, 2008, pp. 250-257.##[4] R. Cochran and S. Reda, "Thermal prediction and adaptive control through workload phase detection," ACM Trans. on Design Automation of Electronic Systems (TODAES), vol. 18, no. 1, p. 7, 2013.##[5] M. Chhablani, I. Koren, and C. M. Krishna, "Online Inertia-Based Temperature Estimation for Reliability Enhancement," Journal of Low Power Electronics, vol. 12, no. 3, pp. 159-171, 2016.##[6] M. Zaman, A. Ahmadi, and Y. Makris, "Workload characterization and prediction: A pathway to reliable multi-core systems," Proc. International On-Line Testing Symposium (IOLTS), pp. 116-121, 2015.##[7] M. Stockman, M. Awad, H. Akkary, and R. Khanna, "Thermal status and workload predict-tion using support vector regression," Proc. International Conference on Energy Aware Computing, 2012, pp. 1-5.##[8] Y. Ge, Q. Qiu, and Q. Wu, "A multi-agent framework for thermal aware task migration in many-core systems," IEEE Trans. on Very Large Scale Integration (VLSI) Systems, vol. 20, no. 10, pp. 1758-1771, 2012.##[9] P. Kumar and D. Atienza, "Neural network based on-chip thermal simulator," Proc. Circuits and Systems (ISCAS), pp. 1599-1602, 2010.##[10] A. Vincenzi, A. Sridhar, M. Ruggiero, and D. Atienza, "Fast thermal simulation of 2D/3D integrated circuits exploiting neural networks and GPUs," Proc. 17th IEEE/ACM international symposium on low-power electronics and design, pp. 151-156, 2011.##[11] A. Sridhar, A. Vincenzi, M. Ruggiero, and D. Atienza, "Neural network-based thermal simula-tion of integrated circuits on GPUs," IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, vol. 31, no. 1, pp. 23-35, 2012.##[12] D. Li, R. Ge, and K. Cameron, "System-level, Unified In-band and Out-of-band Dynamic Thermal Control, " In International Conference Parallel Processing (ICPP), 2010, pp. 131-140.##[13] V. Hanumaiah and S. Vrudhula, "Energy-efficient operation of multicore processors by DVFS, task migration, and active cooling, " IEEE Transactions on Computers, vol .63, no. 2, pp. 349-360, 2014.##[14] I. Yeo, C.C. Liu, and E.J. Kim, "Predictive dynamic thermal management for multicore systems," Proc. 45th annual Design Automation Conference, 2008, pp. 734-739.##[15] G. Liu, M. Fan, and G. Quan, "Neighbor-aware dynamic thermal management for multi-core platform," Proc. Design, Automation & Test in Europe Conference & Exhibition (DATE), 2012 pp. 187-192.##[16] A. Kumar, L. Shang, L.S. Peh, and N. K. Jha, "HybDTM: a coordinated hardware-software approach for dynamic thermal management," Proc. Design Automation Conference, 2006, pp. 548-553.##[17] K.J. Lee and K. Skadron, "Using performance counters for runtime temperature sensing in high-performance processors," IEEE Inter-national Parallel and Distributed Processing Symposium, 2005.##[18] S. J. Lu, R. Tessier, and W. Burleson, "Dynamic On-Chip Thermal Sensor Calibration Using Performance Counters," IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, vol. 33, no. 6, pp. 853-866, 2014.##[19] K. Skadron, M. R. Stan, W. Huang, S. Velusamy, K. Sankaran-Arayanan, and D. Tarjan, "Temperature aware microarchitecture: Exten-ded discussion and results," Technical Report CS-2003-08, University of Virginia, Dept. of Computer Science, 2003.##[20] K. Zhang, A. Guliani, S. Ogrenci-Memik, G. Memik, K. Yoshii, R. Sankaran, and P. Beckman, "Machine Learning-Based Tem-perature Prediction for Runtime Thermal Management Across System Components, " IEEE Trans. on Parallel and Distributed Systems, vol. 29, no. 2, pp. 405-419, 2018.##[21] J. M. N. Abad, B. Salami, H. Noori, A. Soleimani and F. Mehdipour, "A neuro-fuzzy fan speed controller for dynamic thermal management of multi-core processors," In Proceedings of the 11th ACM Conference on Computing Frontiers, 2014, p. 29.##[22] J. M. N. Abad and A. Soleimani, "A neuro-fuzzy fan speed controller for dynamic management of processor fan power consumption," In Swarm Intelligence and Evolutionary Computation (CSIEC), pp. 148-153, 2016.##[23] H. Peng, F. Long, and C. Ding, "Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy," IEEE Trans. on pattern analysis and machine intelligence, vol. 27, no. 8, pp. 1226-1238, 2005.##[24] C. Ding and H. Peng, "Minimum redundancy feature selection from microarray gene exp-ression data," Journal of bioinformatics and computational biology, vol. 3, no. 2, pp. 185-205, 2005.##[25] lm-sensors Linux hardware monitoring [Online]. Available: http://www.lm-sensors.org, Jan 2017.##[26] Linux cpufreq governors, LinuxKernel [Online]. Available:https://www.kernel.org/doc/Documentation/cpu-freq/governors.txt. Jan 2017.## ##

تشخیص جنسیت نویسندگان از روی متون با استفاده از جنگل تصادفی بیز Author gender identification from text using Bayesian Random Forest 1 1 امروزه استفاده زیاد کاربران از محیط‌های مجازی و ارتباط آنها از طریق شبکه‌های اجتماعی مانند فیسبوک و توییتر لزوم بررسی مطالب موجود را در فضای مجازی بیشتر از گذشته کرده است. از آنجا که بالاترین میزان تبادل اطلاعات در فضای مجازی از طریق متن صورت می‌گیرد؛ لذا تشخیص هویت کاربران از نظر سن، جنس، عقاید مذهبی و سیاسی از روی متن‌های اینترنت، پراهمیت خواهد بود. مسأله تشخیص جنسیت در حوزه‌های امنیت و بازاریابی، می‌تواند مؤثر واقع شود. در مقاله حاضر به تشخیص جنسیت نویسندگان مطالب بلاگ‌ها پرداخته می‌شود و جهت تشخیص جنسیت نویسنده، ویژگی‌های نحوی، مبتنی بر واژه، مبتنی بر حروف و واژگان گرامری مورد استفاده قرار می‌گیرند. به‌علاوه نتایج نشان می‌دهد که استفاده از ویژگی‌های -nگرمی حروف در بهبود عملکرد، بسیار مؤثر است. جهت انجام عمل دسته‌بندی روش جدیدی با عنوان جنگل تصادفی بیز ارائه می‌شود. نتایج آزمایش‌ها نشان می­دهد که این روش در مقایسه با الگوریتم‌هایی مانند الگوریتم بیز ساده، درخت بیز ساده و جنگل تصادفی، نتایج بهتری ارائه داده و دقت دسته‌بندی را تا 5/89 % افزایش داده است.   2 Nowadays high usage of users from virtual environments and their connection via social networks like Facebook, Instagram, and Twitter shows the necessity of finding out shared subjects in this environment more than before. There are several applications that benefit from reliable methods for inferring age and gender of users in social media. Such applications exist across a wide area of fields, from personalized advertising to law enforcement of reputation management. Text posts represent a large portion of user generated content, and contain information which can be relevant to discovering undisclosed user attributes, or investigating the honesty of self-reported age and gender. Because the highest rate of information exchanges is in text format, author identification from the aspects like age, gender, political and religious opinions from these contents will seem more considerable. Gender identification  that could be useful in security and marketing, also answers the following question: given a short text document, can we identify if the author is a male or a female?  This question is motivated by recent events where people faked their gender on the Internet. In this paper, author gender identification in blog’s data is investigated. In this regard, four groups of features include syntactic features, word-based features, character-based features, and function words are employed. In addition, character n-gram features is used for improving the accuracy of classification. For evaluation of the proposed method, 3212 texts were collected from Technorati.com and blogger.com. Experimental results demonstrate that these types of features are practical. furthermore, a new classification method called "Bayesian Random Forest" is introduced. Each tree in Bayesian Random Forest  is a Bayes tree. The results of experiment show that this method attains noticeable results in comparison with other classification algorithms such as Naïve Bayes, Naïve Bayes Tree, and Random Forest and it increases accuracy of gender identification to 89.5%.   143 157 2017/11/92017/12/12017/09/52018/02/192018/01/42017/12/162017/12/22018/02/132017/09/27 1396/7/5 2019/01/262019/02/242018/05/72019/01/92019/01/262019/02/242019/01/92019/01/262019/01/26 1397/11/6 هدیه ساجدی Hedieh Sajedi دانشگاه تهران ایران hhsajedi@ut.ac.ir مهناز تسلیمی Mahnaz Taslimi دانشگاه آزاد اسلامی، واحد قزوین ایران mahnaz_taslimi@yahoo.com Author gender identification Random Forest NBTree Text mining Classification تشخیص جنسیت نویسنده جنگل تصادفی درخت بیز ساده متن‌کاوی دسته‌بندی [1] N. Cheng, R. Chandramouli , and K.P. Subbalakshmi, "Author gender identification from text," Elsevier. Digital investigation, vol. 8, pp. 78-88, 2011.##[2] Z. Miller, B. Dickinson, and W. Hu, "Gender Prediction on Twitter Using Stream Algorithms with N-Gram Character Features," International Journal of Intelligence Science, 2012.##[3] K. Mita, A. Mukesh, "Automatic Classification of Unstructured Blog Text," Journal of Intelligent Learning Systems and Applications, vol. 5, pp. 108-114, 2013.##[4] S. Argamon, M. Koppel, J. Fine, and A. Shimoni, "Gender, Genre and Writing Style in Formal Written Texts," Dept of Computer Science. Illinois Institute of Technology, pp. 321-346, 2003.##[5] G. Murugaboopathy, S. Hariharasitaraman, and N. Sankarram, "Appropriate Gender Identification from the Text," International Journal of Emerg-ing Research in Management ;Technology, 2013.##[6] A. Narayanan, H. Paskov and N. Z. Gong, "On the Feasibility of Internet-Scale Author Identi-fication," IEEE Symposium on Security and Privacy, vol. 46, 2012.##[7] Y. Zhang, Y. Dang and H. Chen, "Gender classification for Web Forums," IEEE Trans. On Systems, vol. 41, no. 4, 2011.##[8] A. Mukherjee and B. Liu, "Improving Gender Classification of Blog Authors," Conference on Empirical Methods in Natural Language Processing, 2010, pp. 207-217.##[9] S. Nowson and J. Oberlander, "The identity of bloggers: Openness and gender in personal weblogs," in proc. AAAI Spring Symposia Com-put. Approaches Analyzing Weblogs, Stan-ford,CA, 2006.##[10] S. Hota, S. Argoman, M. Koppel, "performing gender Automatic stylistic analysis of shake-speare's characters," in Proc. Digital Humanit. Conf, 2006, pp. 100-106.##[11] R.S. Forsyth and D.I. Holmes, "Feature finding for text classification," Literary Linguistic Com-pute., vol. 11, No. 4, pp. 163-174, 1996.##[12] M. Koppel, "Automatically categorizing written texts by author gender," Literary and Linguistic Computing, 2002.##[13] N. Cheng, X. Chen, R. Chandramouli and K.P. Subbalakshmi, "Gender Identification from E-mails," computational intelligence and data min-ing, pp. 154-158, 2009.##[14] M. Corney, "Gender-preferential text mining of e-mail discourse," 18th Annual Computer Security applications Conference, 2002.##[15] R. Kohavi, "Scaling Up the Accuracy of NaiveBayes Classifers a Decision Tree Hybrid," Second International Conference on Knoledge Discovery and Data Mining, 1996, pp. 202-207.##[1] N. Cheng, R. Chandramouli , and K.P. Subbalakshmi, "Author gender identification from text," Elsevier. Digital investigation, vol. 8, pp. 78-88, 2011.##[2] Z. Miller, B. Dickinson, and W. Hu, "Gender Prediction on Twitter Using Stream Algorithms with N-Gram Character Features," International Journal of Intelligence Science, 2012.##[3] K. Mita, A. Mukesh, "Automatic Classification of Unstructured Blog Text," Journal of Intelligent Learning Systems and Applications, vol. 5, pp. 108-114, 2013.##[4] S. Argamon, M. Koppel, J. Fine, and A. Shimoni, "Gender, Genre and Writing Style in Formal Written Texts," Dept of Computer Science. Illinois Institute of Technology, pp. 321-346, 2003.##[5] G. Murugaboopathy, S. Hariharasitaraman, and N. Sankarram, "Appropriate Gender Identification from the Text," International Journal of Emerg-ing Research in Management ;Technology, 2013.##[6] A. Narayanan, H. Paskov and N. Z. Gong, "On the Feasibility of Internet-Scale Author Identi-fication," IEEE Symposium on Security and Privacy, vol. 46, 2012.##[7] Y. Zhang, Y. Dang and H. Chen, "Gender classification for Web Forums," IEEE Trans. On Systems, vol. 41, no. 4, 2011.##[8] A. Mukherjee and B. Liu, "Improving Gender Classification of Blog Authors," Conference on Empirical Methods in Natural Language Processing, 2010, pp. 207-217.##[9] S. Nowson and J. Oberlander, "The identity of bloggers: Openness and gender in personal weblogs," in proc. AAAI Spring Symposia Com-put. Approaches Analyzing Weblogs, Stan-ford,CA, 2006.##[10] S. Hota, S. Argoman, M. Koppel, "performing gender Automatic stylistic analysis of shake-speare's characters," in Proc. Digital Humanit. Conf, 2006, pp. 100-106.##[11] R.S. Forsyth and D.I. Holmes, "Feature finding for text classification," Literary Linguistic Com-pute., vol. 11, No. 4, pp. 163-174, 1996.##[12] M. Koppel, "Automatically categorizing written texts by author gender," Literary and Linguistic Computing, 2002.##[13] N. Cheng, X. Chen, R. Chandramouli and K.P. Subbalakshmi, "Gender Identification from E-mails," computational intelligence and data min-ing, pp. 154-158, 2009.##[14] M. Corney, "Gender-preferential text mining of e-mail discourse," 18th Annual Computer Security applications Conference, 2002.##[15] R. Kohavi, "Scaling Up the Accuracy of NaiveBayes Classifers a Decision Tree Hybrid," Second International Conference on Knoledge Discovery and Data Mining, 1996, pp. 202-207.## ##

استخراج ویژگی نظارت‌شده تصاویر چهره به‌منظور افزایش دقّت شناسایی Supervised Feature Extraction of Face Images for Improvement of Recognition Accuracy 1 1 استخراج ویژگی یک گام مهم برای پردازش و تحلیل داده‌های ابربٌعدی در مسائل شناسایی الگو است. تصاویر ابرطیفی اخذ‌شده از سنجنده‌های را ه دور و تصاویر چهره انسان از جمله داده‌های ابربعدی محسوب می‌شوند که با وجود تعداد نمونه آموزشی محدود، کاهش ویژگی یک گام پیش‌پردازش اساسی برای طبقه‌بندی این گونه داده‌ها محسوب می‌شود. در این مقاله، به بررسی و ارزیابی روش‌های نوین استخراج ویژگی‌ای می‌پردازیم که تا کنون برای داده چهره استفاده نشده و درهمین‌اواخر برای کاهش ابعاد تصاویر ابرطیفی سنجش از دور پیشنهاد شده‌اند. در این پژوهش، کارایی هفت روش نوین معرفی‌شده را برای داده ابرطیفی با چهار روش پرکاربرد استخراج ویژگی مورد ارزیابی و مقایسه قرار خواهیم داد.  نتایج آزمایش‌ها بر روی دو داده بانک Yale و ORL، برتری تعدادی از این روش‌های نوین را نسبت به روش‌های استخراج ویژگی LDA، NWFE، MMLDA و LPP نظارت‌شده، از نظر دقت شناسایی، نشان می‌دهند. 2 Dimensionality reduction methods transform or select a low dimensional feature space to efficiently represent the original high dimensional feature space of data. Feature reduction techniques are an important step in many pattern recognition problems in different fields especially in analyzing of high dimensional data. Hyperspectral images are acquired by remote sensors and human face images are one of the high dimensional data types. Because of limitation in the number of training samples, feature reduction is the important preprocessing step for classification of these types of data. Face recognition is one of the main interesting studies in human computer interaction applications. Face is among the most significant biometric characteristics which are used for identification of individuals. Before face recognition, feature reduction is an important processing step. In this paper, we apply the new feature extraction methods, which have been firstly proposed for feature reduction of hyperspectral imagery remote sensing, on the face databases for the first time. In this research, we compare the performance of seven new feature extraction methods with four state-of-the-art feature extraction methods. The proposed methods are Nonparametric Supervised Feature Extraction (NSFE), Clustering Based Feature Extraction (CBFE), Feature Extraction Using Attraction Points (FEUAP), Cluster Space Linear Discriminant Analysis (CSLDA), Feature Space Discriminant Analysis (FSDA), Feature Extraction using Weighted Training samples (FEWT), and Discriminant Analysis- Principal Component 1 (DA-PC1). The experimental results on two face databases, Yale and ORL, show the better performance of some new feature extraction methods, from the recognition accuracy point of view compared to methods such as linear discriminant analysis (LDA), non-parametric weighted feature extraction (NWFE), median-mean line discriminant analysis (MMLDA), and supervised locality preserving projection (LPP). 158 172 2017/11/92017/12/12017/09/52018/02/192018/01/42017/12/162017/12/22018/02/132017/09/272018/02/3 1396/11/14 2019/01/262019/02/242018/05/72019/01/92019/01/262019/02/242019/01/92019/01/262019/01/262019/01/9 1397/10/19 مریم ایمانی Maryam Imani دانشگاه تربیت مدرس ایران maryam.imani@modares.ac.ir محمد‌حسن قاسمیان یزدی Hassan Ghassemian دانشگاه تربیت مدرس ایران ghassemi@modares.ac.ir Face recognition Feature extraction Classification Hyperspectral ابرطیفی استخراج ویژگی شناسایی چهره طبقه‌بندی [1] P. Huang, C. Chen, Z. Tang, and Z. Yang, "Discriminant similarity and variance preserving projec-tion for feature extraction", Neurocomput-ing, vol. 139, pp. 180-188, 2014.##[2] S. Tan, X. Sun, W. Chan, L. Qu , and L. Shao, "Robust Face Recognition With Kernelized Locality-Sensitive Group Sparsity Representa-tion", IEEE Transactions on Image Processing, vol. 26, no. 10, pp. 4661-4668, Oct. 2017.##[3] Y. Shen, M. Yang, B. Wei, C. T. Chou and W. Hu, "Learn to Recognise: Exploring Priors of Sparse Face Recognition on Smartphones", IEEE Trans-actions on Mobile Computing, vol. 16, no. 6, pp. 1705-1717, June 2017.##[4] C. Guzel Turhan and H. S. Bilge, "Class-wise two-dimensional PCA method for face recognition", IET Computer Vision, vol. 11, no. 4, pp. 286-300, 2017.##[5] W. Wang, R. Wang, Z. Huang, S. Shan and X. Chen, "Discriminant Analysis on Riemannian Manifold of Gaussian Distributions for Face Recognition With Image Sets", IEEE Transactions on Image Processing, vol. 27, no. 1, pp. 151-163, Jan. 2018.##[6] Yang, W.-H., Dai, D.-Q., "Two-Dimensional Maximum Margin Feature Extraction for Face Recognition", IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 39, no. 4, pp. 1002-1012, 2009.##[7] S. Ahmadkhani, P. Adibi, "Supervised Probabilistic Principal Component Analysis Mixture Model in a Lossless Dimensionality Reduction Framework for Face Recognition", Quarterly Journal of Signal and Data Processing, vol. 12, no. 4, pp. 53-65, 2016.##[8] Yang, M., Zhang, L., Shiu, S. C.-K., and Zhang, D., "Monogenic Binary Coding: An Efficient Local Feature Extraction Approach to Face Recogni-tion", IEEE Transactions on Information Forensics and Security, vol. 7, no. 6, pp. 1738-1751, 2012.##[9] G. F. Hughes,"On the mean accuracy of statistical pattern recognition," IEEE Transactions on Information Theory, vol. IT-14, no. 1, pp. 55-63, 1968.##[10] K. Fukunaga, Introduction to Statistical Pattern Recognition. San Diego, CA, USA: Academic, 1990.##[11] B.C. Kuo, D.A. Landgrebe, "Nonparametric weighted feature extraction for classification", IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 5, pp. 1096-1105, 2004.##[12] J. Xu, J. Yang, Z. Gu, and N. Zhang, "Median-mean line based discriminant analysis", Neuro-computing, vol. 123, pp. 233-246, 2014.##[13] X.F.He, P.Niyogi, "Locality preserving project-tions", In: Advances in Neural Information Pro-cessing System, vol. 16, pp. 153-160, 2004.##[14] M. Imani, H. Ghassemian, "Nonparametric Supervis Feature Extraction for Classification of Hyperspectral Images Using Limited Training Samples", Electronics Industries Quarterly, vol. 4, no.3, Autumn 2013.##[15] M. Imani, H. Ghassemian, "Band Clustering-Based Feature Extraction for Classification of Hyperspectral Images Using Limited Training Samples", IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 8, pp. 1325-1329, 2014.##[16] M. Imani, H. Ghassemian, "Feature Extraction Using Attraction Points for Classification of Hyperspectral Images in a Small Sample Size Situation", IEEE Geoscience and Remote Sen-sing Letters, vol. 11, no. 11, pp. 1986-1990, 2014.##[17] M. Imani, H. Ghassemian, "Classification of Hyperspectral Images Using Cluster Space Linear Discriminant Analysis and Small Training Set", Iranian Journal of Electrical and Computer Engineering, vol. 14, no. 1, pp. 73-81, June 2016.##[18] M. Imani, H. Ghassemian, "Feature space discriminant analysis for hyperspectral data feature reduction", ISPRS Journal of Photo-grammetry and Remote Sensing, vol. 102, pp. 1-13, 2015.##[19] M. Imani, H. Ghassemian, "Feature Extraction Using Weighted Training Samples", IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 7, pp. 1387 - 1386, 2015.##[20] M. Imani, H. Ghassemian, "Feature reduction of hyperspectral images: discriminant analysis and the first principal component", Journal of AI and Data Mining, vol. 3, no. 1, pp.1-9, 2015.##[21] G. H. Golub, and C. F.van Loan, Matrix Computations, 3rd ed. Baltimore, MD, USA: The Johns Hopkins Univ. Press, 1996.##[22] M. Yang, N. Ahuja, and D. Kriegman, "Face recognition using kernel eigenfaces", Proc. International Conference on Image processing, 2000, pp. 37-40.##[23] V. D. M Nhat, and S. Lee, "Kernel-based 2DPCA for Face Recognition", Proc. IEEE International Symposium on Signal Processing and Infor-mation Technology, IEEE, December. 2007, pp. 35-39.##[1] P. Huang, C. Chen, Z. Tang, and Z. Yang, "Discriminant similarity and variance preserving projec-tion for feature extraction", Neurocomput-ing, vol. 139, pp. 180-188, 2014.##[2] S. Tan, X. Sun, W. Chan, L. Qu , and L. Shao, "Robust Face Recognition With Kernelized Locality-Sensitive Group Sparsity Representa-tion", IEEE Transactions on Image Processing, vol. 26, no. 10, pp. 4661-4668, Oct. 2017.##[3] Y. Shen, M. Yang, B. Wei, C. T. Chou and W. Hu, "Learn to Recognise: Exploring Priors of Sparse Face Recognition on Smartphones", IEEE Trans-actions on Mobile Computing, vol. 16, no. 6, pp. 1705-1717, June 2017.##[4] C. Guzel Turhan and H. S. Bilge, "Class-wise two-dimensional PCA method for face recognition", IET Computer Vision, vol. 11, no. 4, pp. 286-300, 2017.##[5] W. Wang, R. Wang, Z. Huang, S. Shan and X. Chen, "Discriminant Analysis on Riemannian Manifold of Gaussian Distributions for Face Recognition With Image Sets", IEEE Transactions on Image Processing, vol. 27, no. 1, pp. 151-163, Jan. 2018.##[6] Yang, W.-H., Dai, D.-Q., "Two-Dimensional Maximum Margin Feature Extraction for Face Recognition", IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 39, no. 4, pp. 1002-1012, 2009.##[7] احمدخانی سمیه، ادیبی پیمان،" مدل ترکیبی تحلیل مؤلفه اصلی احتمالاتی بانظارت در چارچوب کاهش بعد بدون اتلاف برای شناسایی چهره"، فصل‌نامه پردازش علائم و داده‌ها، دوره 12، شماره 4، صفحات 65-53، 1394.##[7] S. Ahmadkhani, P. Adibi, "Supervised Probabilistic Principal Component Analysis Mixture Model in a Lossless Dimensionality Reduction Framework for Face Recognition", Quarterly Journal of Signal and Data Processing, vol. 12, no. 4, pp. 53-65, 2016.##[8] Yang, M., Zhang, L., Shiu, S. C.-K., and Zhang, D., "Monogenic Binary Coding: An Efficient Local Feature Extraction Approach to Face Recogni-tion", IEEE Transactions on Information Forensics and Security, vol. 7, no. 6, pp. 1738-1751, 2012.##[9] G. F. Hughes,"On the mean accuracy of statistical pattern recognition," IEEE Transactions on Information Theory, vol. IT-14, no. 1, pp. 55-63, 1968.##[10] K. Fukunaga, Introduction to Statistical Pattern Recognition. San Diego, CA, USA: Academic, 1990.##[11] B.C. Kuo, D.A. Landgrebe, "Nonparametric weighted feature extraction for classification", IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 5, pp. 1096-1105, 2004.##[12] J. Xu, J. Yang, Z. Gu, and N. Zhang, "Median-mean line based discriminant analysis", Neuro-computing, vol. 123, pp. 233-246, 2014.##[13] X.F.He, P.Niyogi, "Locality preserving project-tions", In: Advances in Neural Information Pro-cessing System, vol. 16, pp. 153-160, 2004.##[14] ایمانی، مریم، قاسمیان، حسن،" استخراج ویژگی نظارت شده غیرپارامتریک برای طبقه‌بندی تصاویر ابرطیفی با نمونه آموزشی محدود"، فصل‌نامه صنایع الکترونیک، دوره 4، شماره 3، پاییز 1392.##[14] M. Imani, H. Ghassemian, "Nonparametric Supervis Feature Extraction for Classification of Hyperspectral Images Using Limited Training Samples", Electronics Industries Quarterly, vol. 4, no.3, Autumn 2013.##[15] M. Imani, H. Ghassemian, "Band Clustering-Based Feature Extraction for Classification of Hyperspectral Images Using Limited Training Samples", IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 8, pp. 1325-1329, 2014.##[16] M. Imani, H. Ghassemian, "Feature Extraction Using Attraction Points for Classification of Hyperspectral Images in a Small Sample Size Situation", IEEE Geoscience and Remote Sen-sing Letters, vol. 11, no. 11, pp. 1986-1990, 2014.##[17] ایمانی، مریم، قاسمیان، حسن،" طبقه‌بندی تصاویر ابرطیفی با استفاده از تحلیل ممیز خطی فضای خوشه و مجموعه نمونه‌های آموزشی کوچک"، مجله نشریه مهندسی برق و مهندسی کامپیوتر ایران، سال 14، شماره 1، صفحات 81-73، بهار 1395.##[17] M. Imani, H. Ghassemian, "Classification of Hyperspectral Images Using Cluster Space Linear Discriminant Analysis and Small Training Set", Iranian Journal of Electrical and Computer Engineering, vol. 14, no. 1, pp. 73-81, June 2016.##[18] M. Imani, H. Ghassemian, "Feature space discriminant analysis for hyperspectral data feature reduction", ISPRS Journal of Photo-grammetry and Remote Sensing, vol. 102, pp. 1-13, 2015.##[19] M. Imani, H. Ghassemian, "Feature Extraction Using Weighted Training Samples", IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 7, pp. 1387 - 1386, 2015.##[20] M. Imani, H. Ghassemian, "Feature reduction of hyperspectral images: discriminant analysis and the first principal component", Journal of AI and Data Mining, vol. 3, no. 1, pp.1-9, 2015.##[21] G. H. Golub, and C. F.van Loan, Matrix Computations, 3rd ed. Baltimore, MD, USA: The Johns Hopkins Univ. Press, 1996.##[22] M. Yang, N. Ahuja, and D. Kriegman, "Face recognition using kernel eigenfaces", Proc. International Conference on Image processing, 2000, pp. 37-40.##[23] V. D. M Nhat, and S. Lee, "Kernel-based 2DPCA for Face Recognition", Proc. IEEE International Symposium on Signal Processing and Infor-mation Technology, IEEE, December. 2007, pp. 35-39.## ##