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آشکارسازی سیگنال های مخابراتی بکمک برازش خم Signal detection Using Rational Function Curve Fitting 1 1 در این مقاله روش جدیدی برای آشکارسازی سیگنال های مخابراتی پیشنهاد شده است که بر مبنای استخراج ویژگی های سیگنال مخابراتی بکمک برازش خم  عمل می کند. در هریک از سمبل های سیگنال  مخابراتی  یک تابع تقریب کسر گویا  بوسیله  برازش بر منحنی سیگنال آن ایجاد می شود. ویژگی های جدید توسط ضرایب چند جمله ای صورت و مخرج  این تابع تقریب کسر گویا  تعیین می شوند. در روش پیشنهادی دو فاز آموزش و آزمون در نظر گرفته شده است. ابتدا در فاز آموزش الگوریتم، تعداد مشخصی سمبل های تصادفی تولید می شود و توسط مدولاسیون دودویی ASK و FSK مدوله می شوند، درادامه سیگنال  مدوله شده هریک از سمبل ها در کانال به نویز جمع شونده گوسی آغشته  می شود و توسط آنتن گیرنده دریافت می شود. سپس  نمونه های مشخصی  از شکل موج سیگنال دریافت شده  با نرخ نمونه برداری مشخص استخراج می شود. به ازای هر N=1500,12500  نمونه از  سیگنال یک منحنی کسر گویا با درجه L و M مشخص برازش می شود. سپس تمامی ضرایب  صورت و مخرج تابع کسر گویا  برازش شده با درجات L وM   مختلف ذخیره شده و هیستوگرام ضرایبی که قابلیت تفکیک کامل دو کلاس 0 و 1  را دارند بدست می آیند. لذا تمامی ضرایب با درجات L و M مختلفی که  امکان تفکیک کامل را دارند همراه  با  مرز تصمیم گیری شان در یک جدول ذخیره می شوند. شایان ذکر است که داده های مورد  استفاده جهت  استخراج و شناسایی ضرایب تفکیک کننده، داده های  آموزشی هستند. سرانجام  جهت آنالیز کارایی روش پیشنهادی، تعداد مشخصی از داده های آزمون با مدولاسیون مشخص ارسال می گردد و با مقایسه با مرزهای تصمیم گیری بدست آمده در فاز آموزش نسبت به طبقه بندی آن در کلاس مورد نظر تصمیم گیری می شود. نتایج طبقه بندی روش پیشنهادی بیانگر برتری روش پیشنهادی در مقایسه با روش احتمال خطا تئوری  می باشد.   2 In this manuscript, we proposed a new scheme in communication signal detection which is respect to the curve shape of received signal and based on the extraction of curve fitting (CF) features. This feature extraction technique is proposed for signal data classification in receiver. The proposed scheme is based on curve fitting and approximation of rational fraction coefficients. For each symbol of received signal, a specific rational function approximation is developed to fit with received signal curve and the coefficients of the numerator and denominator polynomials of this function are considered as new extracted features. Then  it will be shown that the coefficients of this polynomials have the potential for using as new features in a statistical classifier and have better performance in competition with other solutions such as linear and even nonlinear feature extraction methods in  classification. The criteria used in performance evaluation are  probability of error and signal to noise ratio in FSK and ASK modulations. For each symbol of received signal, a specific rational function approximation is developed to fit with received signal curve and the coefficients of the numerator and denominator polynomials of this function are considered as new extracted features. In the proposed method, there are two phases train and test, which are described in the following two steps. First, in the train phase, the algorithm starts by using binary FSK and ASK modulations, so first, a system generate a number of random symbols then signal is modulated by binary ASK and FSK. The Modulated FSK and ASK signals are corrupted in the channel with noise. The noise-corrupted signal enters the receiver at the corresponding transmitted interval. Then, the samples are extracted from the modulated signals based on predetermined sample rates. Then, we fit a rational fraction curve with degrees of L and M to each set of N samples. Afterward, we apply all the numerator (L+1) and denominator (M) coefficients to 0 and 1 classes  in the binary FSK and ASK modulations. We store all the specific coefficients of the deterministic symbols at different M and L values to create the corresponding histogram in each class. In each histogram (i.e., the coefficients of a class), we extract and store specific coefficients that completely discriminate between the two classes. Therefore, we determine all the histograms where there is a good approximation of discrimination and create the related table. Note that the data used in histograms are the training data. Then, in order to analyze and evaluate the performance of the proposed curve fitting method, we send the testing data through the channel corresponding to the related modulator. The results of the proposed classification method show that it provides smaller error rate regarding to the theoretical error rate probability in AWGN channel.   3 18 2020/10/5 1399/7/14 2021/12/6 1400/9/15 حمید نوراللهی Hamid Nourollahi گروه آموزشی مخابرات دانشکده برق و کامپیوتر واحد یادگار امام خمینی (ره) شهرری دانشگاه آزاد اسلامی تهران ایران ایران h_nourollahi@yahoo.com سید ابوالفضل حسینی S. Abolfazl Hosseini گروه آموزشی مخابرات دانشکده برق و کامپیوتر واحد یادگار امام خمینی (ره) شهرری دانشگاه آزاد اسلامی تهران ایران ایران universizen@yahoo.com علی شهزادی Ali Shahzadi گروه آموزشی مخابرات دانشکده مهندسی برق و کامپیوتر دانشگاه سمنان ایران ایران shahzadi@semnan.ac.ir رامین شقاقی کندوان Ramin Shaghaghi Kandovan گروه آموزشی مخابرات دانشکده برق و کامپیوتر واحد یادگار امام خمینی (ره) شهرری دانشگاه آزاد اسلامی تهران ایران ایران ramin.shaghaghi@gmail.com detection feature extraction curve fitting classification آشکارسازی استخراج ویژگی برازش منحنی طبقه بندی [1] T. S. Rappaport, Wireless communications: Principles and practice,2nd ed. Prentice Hall, 2002.##[2] R. M. Gagliardi and S. Karp, Optical communications, 2nd ed. Wiley,1995.##[3] H. Meyr, M. Moeneclaey, and S. A. Fechtel, Digital communication receivers: Synchronization, channel estimation, and signal processing. John Wiley & Sons, Inc., 1998.##[4] T. Schenk, RF imperfections in high-rate wireless systems: Impact and digital compensation. Springer Science & Business Media, 2008.##[5] J. Proakis and M. Salehi, Digital Communications, 5th ed. McGraw-Hill Education, 2007.##[6] A. Goldsmith, Joint source/channel coding for wireless channels, in Proc. IEEE Vehicular Technol. Conf., vol. 2, 1995, pp. 614-618.##[7] E. Zehavi, 8-PSK trellis codes for a Rayleigh channel, IEEE Trans.Commun., vol. 40, no. 5, pp. 873-884, 1992.##[8] H. Wymeersch, Iterative receiver design. Cambridge University Press,2007, vol. 234.##[9] K. Hornik, M. Stinchcombe, and H. White, Multilayer feedforward networks are universal approximators, Neural networks, vol. 2, no. 5, 1989,pp. 359-366.##[10] S. Reed and N. de Freitas, Neural programmer-interpreters, arXiv preprint, 2015, arXiv: 1511.06279.##[11] H. T. Siegelmann and E. D. Sontag, On the computational power of neural nets, in Proc. 5th Annu. Workshop Computational Learning Theory. ACM, 1992, pp. 440-449.##[12] V. Vanhoucke, A. Senior, and M. Z. Mao, Improving the speed of neural networks on CPUs, in Proc. Deep Learning and Unsupervised Feature Learning NIPS Workshop, 2011.##[13] Y.-H. Chen, T. Krishna, J. S. Emer, and V. Sze, Eyeriss: An energyefficient reconfigurable accelerator for deep convolutional neural networks, IEEE J. Solid-State Circuits, vol. 52, no. 1, 2017 pp. 127-138,.##[14] R. Raina, A. Madhavan, and A. Y. Ng, Large-scale deep unsupervised learning using graphics processors, in Proc. Int. Conf. Mach. Learn.(ICML). ACM, 2009, pp. 873-880.##[15] A. Atieg, G.A. Watson, " A class of methods for fitting a curve or surface to data by minimizing the sum of squares of orthogonal distances", Journal of Computational and Applied Mathematics 158 2003 277-296, doi:10.1016/S0377-0427(03)00448-5##[16] Mostafa Ghazizadeh Ahsaee, Hadi Sadoghi Yazdi, Mahmoud Naghibzadeh, " Curve fitting space for classification", Neural Comput & Applic 2011 20:273-285 DOI 10.1007 / s00521-010-0383-7##[17] Maryam Haddadi, Maliheh Ahmadi, Mohammad Reza Keyvanpour, and Noushin Riahi" Using Curve Fitting in Error Correcting Output Codes" Journal of Soft Computing and Information Technology (JSCIT), 2016, Vol. 5, No. 1##[18] Seyed Abolfazl Hosseini, Hassan Ghassemian, " Rational function approximation for feature reduction in hyperspectral data " Taylor & Francis, Remote Sensing Letters, 2016 ,Volume 7, Issue 2, Pages 101-110.##[19] Mersedeh Beitollahi, S Abolfazl Hosseini, " Using Savitsky-Golay filter and interval curve fitting in order to hyperspectral data compression ", IEEE, Iranian Conference on Electrical Engineering (ICEE), Pages 1967-1972 , 2017##[20] Maryam Hamidi, Hassan Ghassemian∗, Maryam Imani " Classification of heart sound signal using curve fitting and fractal dimension" Elsevier, Biomedical Signal Processing and Control 39, 2018 ,351-359##[21] Yazan A. Alqudah, " Path Loss Modeling Based on Field Measurements Using Deployed 3.5GHzWiMAX Network " Springer Science+Business Media, LC, Wireless Pers Commun , 2012,DOI 10.1007/s11277-012-0612-8 Path.##[22] HARRY L. VAN TREES, KRISTINE L. BELL, with ZHI TIAN, " Detection, Estimation, and Modulation Theory Part I: Detection, Estimation, and Filtering Theory Second Edition ", John Wiley & Sons, Inc. 2013##[1] T. S. Rappaport, Wireless communications: Principles and practice,2nd ed. Prentice Hall, 2002.##[2] R. M. Gagliardi and S. Karp, Optical communications, 2nd ed. Wiley,1995.##[3] H. Meyr, M. Moeneclaey, and S. A. Fechtel, Digital communication receivers: Synchronization, channel estimation, and signal processing. John Wiley & Sons, Inc., 1998.##[4] T. Schenk, RF imperfections in high-rate wireless systems: Impact and digital compensation. Springer Science & Business Media, 2008.##[5] J. Proakis and M. Salehi, Digital Communications, 5th ed. McGraw-Hill Education, 2007.##[6] A. Goldsmith, Joint source/channel coding for wireless channels, in Proc. IEEE Vehicular Technol. Conf., vol. 2, 1995, pp. 614-618.##[7] E. Zehavi, 8-PSK trellis codes for a Rayleigh channel, IEEE Trans.Commun., vol. 40, no. 5, pp. 873-884, 1992.##[8] H. Wymeersch, Iterative receiver design. Cambridge University Press,2007, vol. 234.##[9] K. Hornik, M. Stinchcombe, and H. White, Multilayer feedforward networks are universal approximators, Neural networks, vol. 2, no. 5, 1989,pp. 359-366.##[10] S. Reed and N. de Freitas, Neural programmer-interpreters, arXiv preprint, 2015, arXiv: 1511.06279.##[11] H. T. Siegelmann and E. D. Sontag, On the computational power of neural nets, in Proc. 5th Annu. Workshop Computational Learning Theory. ACM, 1992, pp. 440-449.##[12] V. Vanhoucke, A. Senior, and M. Z. Mao, Improving the speed of neural networks on CPUs, in Proc. Deep Learning and Unsupervised Feature Learning NIPS Workshop, 2011.##[13] Y.-H. Chen, T. Krishna, J. S. Emer, and V. Sze, Eyeriss: An energyefficient reconfigurable accelerator for deep convolutional neural networks, IEEE J. Solid-State Circuits, vol. 52, no. 1, 2017 pp. 127-138,.##[14] R. Raina, A. Madhavan, and A. Y. Ng, Large-scale deep unsupervised learning using graphics processors, in Proc. Int. Conf. Mach. Learn.(ICML). ACM, 2009, pp. 873-880.##[15] A. Atieg, G.A. Watson, " A class of methods for fitting a curve or surface to data by minimizing the sum of squares of orthogonal distances", Journal of Computational and Applied Mathematics 158 2003 277-296, doi:10.1016/S0377-0427(03)00448-5##[16] Mostafa Ghazizadeh Ahsaee, Hadi Sadoghi Yazdi, Mahmoud Naghibzadeh, " Curve fitting space for classification", Neural Comput & Applic 2011 20:273-285 DOI 10.1007 / s00521-010-0383-7##[17] Maryam Haddadi, Maliheh Ahmadi, Mohammad Reza Keyvanpour, and Noushin Riahi" Using Curve Fitting in Error Correcting Output Codes" Journal of Soft Computing and Information Technology (JSCIT), 2016, Vol. 5, No. 1##[18] Seyed Abolfazl Hosseini, Hassan Ghassemian, " Rational function approximation for feature reduction in hyperspectral data " Taylor & Francis, Remote Sensing Letters, 2016 ,Volume 7, Issue 2, Pages 101-110.##[19] Mersedeh Beitollahi, S Abolfazl Hosseini, " Using Savitsky-Golay filter and interval curve fitting in order to hyperspectral data compression ", IEEE, Iranian Conference on Electrical Engineering (ICEE), Pages 1967-1972 , 2017##[20] Maryam Hamidi, Hassan Ghassemian∗, Maryam Imani " Classification of heart sound signal using curve fitting and fractal dimension" Elsevier, Biomedical Signal Processing and Control 39, 2018 ,351-359##[21] Yazan A. Alqudah, " Path Loss Modeling Based on Field Measurements Using Deployed 3.5GHzWiMAX Network " Springer Science+Business Media, LC, Wireless Pers Commun , 2012,DOI 10.1007/s11277-012-0612-8 Path.##[22] HARRY L. VAN TREES, KRISTINE L. BELL, with ZHI TIAN, " Detection, Estimation, and Modulation Theory Part I: Detection, Estimation, and Filtering Theory Second Edition ", John Wiley & Sons, Inc. 2013## ##

ارائه یک چارچوب توزیع شده برای انتخاب ویژگی چندمتغیره A New Framework for Distributed Multivariate Feature Selection 1 1 در بسیاری از مسائل یادگیری ماشین، انتخابِ ویژگی­های مرتبط و اجتناب از ویژگی­های افزونه، برای بهبود کارایی انتخاب ویژگی ضروری است. در اکثر رویکردهای موجود، از الگوریتم­های فیلتر چندمتغیره برای این منظور استفاده می­شود که در آن­ها تعامل با طبقه­بند نادیده گرفته می­شود. این مقاله با ارائه یک چارچوب، ترکیب روش­های نهفته با روش­های  فیلتر چندمتغیره را پیشنهاد می­دهد تا با درنظر گرفتن تعامل با طبقه­بند در انتخاب ویژگی­ها، این مشکل را برطرف نماید. در چارچوب پیشنهاد شده، ارتباط بین هر ویژگی و برچسب­های کلاس توسط الگوریتم­های نهفته محاسبه می­شود و افزونگی بین ویژگی­ها از طریق الگوریتم­های فیلتر چندمتغیره بررسی می­شود. این چارچوب پیشنهادی، دقت طبقه­بندی را روی چندین مجموعه داده­ بهبود داده­ است. به­علاوه در فرایند انتخاب ویژگی پیشنهاد شده، بجای استفاده یکدفعه  از همه مجموعه داده­ها، از توزیع افقی آن­ها استفاده شده است. این خصوصیت برای مجموعه داده­هایی که دارای نمونه­های زیادی هستند و نیز در محیط ­هایی که داده ها  متمرکز نیستند، باعث کاهش زمان اجرای فرایند انتخاب ویژگی شده است. کیفیت روش ما با استفاده از شش مجموعه داده ارزیابی شده است. نتایج ثابت می­کنند که چارچوب پیشنهاد شده، می­تواند دقت طبقه­بندی را در مقایسه با روش­های صرفا مبتنی بر فیلتر چندمتغیره بهبود دهد. همچنین سرعت اجرا می­تواند در مقایسه با روش­های متمرکز بهبود یابد. 2 Feature selection is considered as an important issue in classification domain. Selecting a good feature through maximum relevance criterion to class label and minimum redundancy among features affect improving the classification accuracy. However, most current feature selection algorithms just work with the centralized methods. In this paper, we suggest a distributed version of the mRMR feature selection approach. In mRMR, feature selection is performed based on maximum relevance to class and minimum redundancy among the features. The suggested method include six stages: in the first stage, after determining training and test data, training data are distributed horizontally. All subsets have same number of features. In the second stage, each subset of features is scored using mRMR feature selection. Features with higher ranks are selected and others are eliminated. In the fourth stage, features which were omitted are voted. In the fifth stage, the selected features are merged to determine the final set. In the final stage, classification accuracy is evaluated using final training data and test data. Our method quality has been evaluated by six datasets. The results prove that the suggested method can improve classification accuracy compared to methods just based on maximum relevance to class label in addition to runtime reduction. 19 32 2020/10/52020/07/23 1399/5/2 2021/12/62022/05/11 1401/2/21 منا شریف نژاد Mona Sharifnezhad دانشگاه اراک ایران m.sharifnezhad98@gmail.com محسن رحمانی Mohsen Rahmani دانشگاه اراک ایران m-rahmani@araku.ac.ir حسین غفاریان Hosein Ghafarian دانشگاه اراک ایران h-ghaffarian@araku.ac.ir Multivariate filter feature selection Embedded feature selection Classification Distribution انتخاب ویژگی فیلتر چندمتغیره انتخاب ویژگی نهفته طبقه‌بندی توزیع شدگی [1] I. Guyon, A. Elisseeff, (2003)," An introduction to variable and feature selection", Journal of Machine Learning Research, Vol.3, pp.1157-1182.##[2] I.Guyon, S.Gunn, M.Nikravesh and L.A.Zadeh, (2006), "Feature Extraction: Foundations and Applications", vol. 207, Springer, ISBN-10: 9783540354871.##[3] V. Bolón-Canedo, N. Sánchez-Maroño, and A. Alonso-Betanzos (2013), "A Distributed Wrapper Approach for Feature Selection", ESANN proceedings, Computational Intelligence and Machine Learning, ISBN 978-2-87419-081-0.##[4] V. Bolón-Canedo, N. Sánchez-Maroño, and A. Alonso-Betanzos(2015), "A Distributed Feature Selecion Approach Based on a Complexity Measure", Advances in Computational Intelligence, pp. 15-28.##[5] G. Chandrashekar and F. Sahin (2014), "A survey on feature selection methods", journal of Computers and Electrical Engineering vol. 40, pp.16-28.##[6] V. Bolón-Canedo, N. Sánchez-Maroño, and A. Alonso-Betanzos (2015), "Distributed feature selection: An application to microarray data classification", Applied Soft Computing, vol. 30, pp. 136-150.##[7] V. Bolón-Canedo, N. Sánchez-Maroño, and J. Cerviño-Rabuñal(2013), "Scaling up feature selection: a distributed filter approach", Advances in Artificial Intelligence, pp. 121-130.##[8] L. Morán-Fernández, V. Bolón-Canedo, and A. Alonso-Betanzos (2016), "Centralized vs. distributed feature selection methods based on data complexity measures", Journal of Knowledge-Based Systems, vol. 117 , pp.27-45.##[9] L. Mor'an-Fern'andez, V. Bol'on-Canedo, and A. Alonso-Betanzos(2015), "A Time Efficient Approach for Distributed Feature Selection Partitioning by Features", Lecture Notes in Computer Science book series (LNCS), vol. 9422, pp.245-254.##[10] L. Yu, H. Liu, (2004)," Efficient feature selection via analysis of relevance and redundancy", J. Mach. Learn. Res. 5 , 1205-1224.##[11] C. Ding, H. Peng, (2005) "Minimum redundancy feature selection from microarray gene expression data", Journal of Bioinformatics and computational Biology, Vol.03, No.02, pp.185-205.##[12] R. Kohavi, GH. John (1997), "Wrappers for feature subset selection", Artificial Intelligence, Vol. 97, Issues 1-2, pp.273-324.##[13] J.Li, K.Cheng, S.Wang, F. Morstatter, and R. P. Trevino(2018)," Feature Selection: A Data Perspective", Journal of ACM Computing Surveys (CSUR), Vol. 50 ,Issue 6.##[14] A.De Haro Garc'ıa, (2011), "Scaling data mining algorithms. Application to instance and feature selection", Ph.D. Thesis, University of Granada.##[15] H. Djellali, N. Ghoualmi Zine and N. Azizi (2016), "Two Stages Feature Selection Based on Filter Ranking Methods and SVMRFE on Medical Applications", Modelling and Implementation of Complex Systems. Lecture Notes in Networks and Systems, Springer, Cham,, vol. 1, pp. 281-293.##[16] H. Min and W. Fangfang (2010), "Filter-Wrapper Hybrid Method on Feature Selection ", Second WRI Global Congress on Intelligent Systems (GCIS), pp.98-101.##[17] I. Guyon, J. Weston, S. Barnhill, and V. Vapnik (2002), "Gene selection for cancer classification using support vector machines", Journal of Machine Learning, vol. 46, Issue 1-3, pp. 389-422.##[18] D. Boughaci and A.A Alkhawaldeh (2018), "Three local search-based methods for feature selection in credit scoring", Vietnam Journal of Computer Science, May 2018, Vol. 5, Issue 2, pp. 107-121.##[19] Q.Wang , J. Wan, F. Nie , B. Liu , C.Yan , and X. Li (2019), "Hierarchical Feature Selection for Random Projection", IEEE Transactions on Neural Networks and Learning Systems, Vol. 30 , Issue 5, pp. 1581 - 1586.##[20] http://archive.ics.uci.edu/ml/datasets/##[21] I.Guyon, J.Weston, S.Barnhill and V.Vapnik,(2002), "Gene selection for cancer classificationusing support vector machines, " Jornal of Machine Learning, vol.46, pp.389-422.##[22] H. Peng, F. Long, and C. Ding, (2005), "Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and minredundancy, "IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 8, pp. 1226-1238, Aug..##[23] M.A. Hall, L.A. Smith, (1998), "Practical feature subset selection for machine learning", Comput. Sci.98,181-191##[24] I. Kononenko,(1994)," Estimating attributes: analysis and extensions of RELIEF", Machine Learning: ECML-94, vol. 784, pp 171-182##[25] M. Robnik-Šikonja and I. Kononenko, (2003), "Theoretical and empirical analysis of ReliefF and RReliefF", Machine learning, vol. 53,Issue:1-2, pp. 23-69.##[1] I. Guyon, A. Elisseeff, (2003)," An introduction to variable and feature selection", Journal of Machine Learning Research, Vol.3, pp.1157-1182.##[2] I.Guyon, S.Gunn, M.Nikravesh and L.A.Zadeh, (2006), "Feature Extraction: Foundations and Applications", vol. 207, Springer, ISBN-10: 9783540354871.##[3] V. Bolón-Canedo, N. Sánchez-Maroño, and A. Alonso-Betanzos (2013), "A Distributed Wrapper Approach for Feature Selection", ESANN proceedings, Computational Intelligence and Machine Learning, ISBN 978-2-87419-081-0.##[4] V. Bolón-Canedo, N. Sánchez-Maroño, and A. Alonso-Betanzos(2015), "A Distributed Feature Selecion Approach Based on a Complexity Measure", Advances in Computational Intelligence, pp. 15-28.##[5] G. Chandrashekar and F. Sahin (2014), "A survey on feature selection methods", journal of Computers and Electrical Engineering vol. 40, pp.16-28.##[6] V. Bolón-Canedo, N. Sánchez-Maroño, and A. Alonso-Betanzos (2015), "Distributed feature selection: An application to microarray data classification", Applied Soft Computing, vol. 30, pp. 136-150.##[7] V. Bolón-Canedo, N. Sánchez-Maroño, and J. Cerviño-Rabuñal(2013), "Scaling up feature selection: a distributed filter approach", Advances in Artificial Intelligence, pp. 121-130.##[8] L. Morán-Fernández, V. Bolón-Canedo, and A. Alonso-Betanzos (2016), "Centralized vs. distributed feature selection methods based on data complexity measures", Journal of Knowledge-Based Systems, vol. 117 , pp.27-45.##[9] L. Mor'an-Fern'andez, V. Bol'on-Canedo, and A. Alonso-Betanzos(2015), "A Time Efficient Approach for Distributed Feature Selection Partitioning by Features", Lecture Notes in Computer Science book series (LNCS), vol. 9422, pp.245-254.##[10] L. Yu, H. Liu, (2004)," Efficient feature selection via analysis of relevance and redundancy", J. Mach. Learn. Res. 5 , 1205-1224.##[11] C. Ding, H. Peng, (2005) "Minimum redundancy feature selection from microarray gene expression data", Journal of Bioinformatics and computational Biology, Vol.03, No.02, pp.185-205.##[12] R. Kohavi, GH. John (1997), "Wrappers for feature subset selection", Artificial Intelligence, Vol. 97, Issues 1-2, pp.273-324.##[13] J.Li, K.Cheng, S.Wang, F. Morstatter, and R. P. Trevino(2018)," Feature Selection: A Data Perspective", Journal of ACM Computing Surveys (CSUR), Vol. 50 ,Issue 6.##[14] A.De Haro Garc'ıa, (2011), "Scaling data mining algorithms. Application to instance and feature selection", Ph.D. Thesis, University of Granada.##[15] H. Djellali, N. Ghoualmi Zine and N. Azizi (2016), "Two Stages Feature Selection Based on Filter Ranking Methods and SVMRFE on Medical Applications", Modelling and Implementation of Complex Systems. Lecture Notes in Networks and Systems, Springer, Cham,, vol. 1, pp. 281-293.##[16] H. Min and W. Fangfang (2010), "Filter-Wrapper Hybrid Method on Feature Selection ", Second WRI Global Congress on Intelligent Systems (GCIS), pp.98-101.##[17] I. Guyon, J. Weston, S. Barnhill, and V. Vapnik (2002), "Gene selection for cancer classification using support vector machines", Journal of Machine Learning, vol. 46, Issue 1-3, pp. 389-422.##[18] D. 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بهبود شبکه های رقابتی مولد برای تولید خودکار تصویر از روی متن Improvement of generative adversarial networks for automatic text-to-image generation 1 1 این پژوهش در رابطه با به‌کارگیری ابزارهای یادگیری عمیق و فناوری پردازش تصویر در تولید خودکار تصویر از روی متن می‌باشد. پژوهش‌های پیشین از یک جمله برای تولید تصاویر بهره می‌برند. در این پژوهش یک مدل سلسله‌مراتبی مبتنی بر حافظه ارائه شده است که از سه توصیف مختلف که در قالب جمله ارائه می‌شوند، برای تولید و بهبود تصویر بهره می‌برد. طرح پیشنهادی با بهره‌گیری از شبکه‌های رقابتی مولد، بر به‌کارگیری اطلاعات بیشتر جهت تولید تصاویر با وضوح بالا تمرکز دارد.  پیاده‌سازی و اجرای برنامه‌های مربوط به این حوزه نیاز به منابع پردازشی بالا دارند. لذا طرح پیشنهادی با بهره‌گیری از بستره سخت‌افزاری دانشگاه کپنهاگ بر روی یک کلاستر با 25 واحد پردازش گرافیکی پیاده‌سازی و تحت آزمون قرار گرفت. آزمایش‌ها روی مجموعه دادگان CUB-200 و ids-ade انجام شدند. نتایج آزمایش‌ها نشان می‌دهند که مدل ارائه شده می‌تواند تصاویر با کیفیت بالاتری نسبت به دو مدل پایه StackGAN و AttGAN تولید کند. 2 This research is related to the use of deep learning tools and image processing technology in the automatic generation of images from text. Previous researches have used one sentence to produce images. In this research, a memory-based hierarchical model is presented that uses three different descriptions that are presented in the form of sentences to produce and improve the image. The proposed scheme focuses on using more information to produce high-resolution images, using competitive productive networks. Implementing programs related to this field require massive processing resources. Therefore, the proposed method was implemented and tested on a cluster with 25 GPUs using the hardware platform of the University of Copenhagen. The experiments were performed on CUB-200 and ids-ade datasets. The experimental results show that the proposed model can produce higher quality images than the two basic models StackGAN and AttGAN. 33 44 2020/10/52020/07/232020/08/21 1399/5/31 2021/12/62022/05/112021/05/24 1400/3/3 الهام پژهان Elham Pejhan دانشگاه یزد ایران e.pejhan@stu.yazd.ac.ir محمد قاسم زاده Mohammad Ghasemzadeh دانشگاه یزد ایران m.ghasemzadeh@yazd.ac.ir Generative Adversarial Network Deep Learning Hierarchical Model Natural Language Processing شبکه رقابتی مولد یادگیری عمیق مدل سلسله مراتبی پردازش زبان طبیعی. [1] M. M. Haji-Esmaeili, and G. Montazer, "Automatic Coloring of Grayscale Images Using Generative Adversarial Networks, ", Journal of Signal and Data Processing (JSDP), vol. 16 (1), pp. 57-74, 2019.##[2] T. Baltrusaitis, C. Ahuja, and L. P. Morency, "Multimodal machine learning: A survey and taxonomy, " in IEEE Transactions on Pattern Analysis, 2017.##[3] A. Dash, J. C. B. Gamboa, S. Ahmed, M. Liwicki, and M. Z. Afzal, "Tac-gan-text conditioned auxiliary classifier generative adversarial network, " arXiv preprint arXiv:1703.06412, 2017.##[4] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, "Generative adversarial nets, " in Advances in neural information processing systems, 2014.##[5] C. Gulcehre, S. Chandar, K. Cho, and Y. Bengio, "Dynamic neural turing machine with continuous and discrete addressing schemes, " Neural computation, vol. 30, no. 4, pp. 857-884, 2018.##[6] N. Ilinykh, S. Zarrieß, and D. Schlangen, "Tell Me More: A Dataset of Visual Scene Description Sequences, " in Proceedings of the 12th International Conference on Natural Language Generation, 2019.##[7] K. J. Joseph, A. Pal, S. Rajanala, and V. N. Balasubramanian, "C4synth: Cross-caption cycle-consistent text-to-image synthesis, " in IEEE Winter Conference on Applications of Computer Vision (WACV), 2019.##[8] W. Li, P. Zhang, L. Zhang, Q. Huang, X. He, S. Lyu, and J. Gao, "Object-driven text-to-image synthesis via adversarial training, " in Proc. of the IEEE Conf.e on Computer Vision and Pattern Recognition, 2019.##[9] A. Miller, A. Fisch, J. Dodge, A. H. Karimi, A. Bordes, and J. Weston, "Key-value memory networks for directly reading documents, " in Proceeding of Empirical Methods in Natural Language Processing (EMNLP), 2016.##[10] S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee, "Generative adversarial text to image synthesis, " arXiv preprint arXiv:1605.05396, 2016.##[11] T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, "Improved techniques for training gans, " in Advances in neural information processing systems (NIPS), 2016.##[12] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the inception architecture for computer vision, " in Proc. of the IEEE conf. on computer vision and pattern recognition, 2016.##[13] C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie, The caltech-ucsd birds-200-2011 dataset, 2011.##[14] T. Xu, P. Zhang, Q. Huang, H. Zhang, Z. Gan, X. Huang, and X. He, "Attngan: Fine-grained text to image generation with attentional generative adversarial networks, " in Proc. of the IEEE conf. on computer vision and pattern recognition, 2018.##[15] X. Yan, J. Yang, K. Sohn, and H. Lee, "Attribute2image: Conditional image generation from visual attributes, " in European Conf. on Computer Vision, 2016.##[16] G. Yin, B. Liu, L. Sheng, N. Yu, X. Wang, and J. Shao, "Semantics disentangling for text-to-image generation, " in Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2019.##[17] H. Zhang, T. Xu, H. Li, S. Zhang, X. Huang, X. Wang, and D. Metaxas, "Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks, " in Proc.of the IEEE int. conference on computer vision, 2017.##[18] H. Zhang, T. Xu, H. Li, S. Zhang, X. Wang, X. Huang, and D. N. Metaxas, "Stackgan++: Realistic image synthesis with stacked generative adversarial networks, " in IEEE transactions on pattern analysis and machine intelligence, 2017.##[19] Z. Zhang, Y. Xie, and L. Yang, " Photo-graphic Text-to-Image Synthesis with a Hierarchically-nested Adversarial Network" in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2018.##[20] P. Zhou, W. Shi, J. Tian, Z. Qi, B. Li, H. Hao, and B. Xu, "Attention-based bidirectional long short-term memory networks for relation classification, " in Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2016.##[21] M. Zhu, P. Pan, W. Chen, and Y. Yang, "dm-gan: Dynamic memory generative adversarial net. for text-to-image synthesis, " in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2019.##[22] X. Zhu, A. B. Goldberg, M. Eldawy, C. R. Dyer, and B. Strock, "A text-to-picture synthesis system for augmenting communication, " in proceeding of Association for the Advanced##[1]حاجی اسمعیلی، محمد مهدی و غلامعلی، منتظر، "رنگآمیزی خودکار تصاویر خاکستری بهکمک شبکههای زایای رقابتی"، مجله پردازش علائم و دادهها، دوره 16، شماره 1، صفحات 74-57، 1398.##[1] M. M. Haji-Esmaeili, and G. Montazer, "Automatic Coloring of Grayscale Images Using Generative Adversarial Networks, ", Journal of Signal and Data Processing (JSDP), vol. 16 (1), pp. 57-74, 2019.##[2] T. Baltrusaitis, C. Ahuja, and L. P. Morency, "Multimodal machine learning: A survey and taxonomy, " in IEEE Transactions on Pattern Analysis, 2017.##[3] A. Dash, J. C. B. Gamboa, S. Ahmed, M. Liwicki, and M. Z. Afzal, "Tac-gan-text conditioned auxiliary classifier generative adversarial network, " arXiv preprint arXiv:1703.06412, 2017.##[4] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, "Generative adversarial nets, " in Advances in neural information processing systems, 2014.##[5] C. Gulcehre, S. Chandar, K. Cho, and Y. Bengio, "Dynamic neural turing machine with continuous and discrete addressing schemes, " Neural computation, vol. 30, no. 4, pp. 857-884, 2018.##[6] N. Ilinykh, S. Zarrieß, and D. Schlangen, "Tell Me More: A Dataset of Visual Scene Description Sequences, " in Proceedings of the 12th International Conference on Natural Language Generation, 2019.##[7] K. J. Joseph, A. Pal, S. Rajanala, and V. N. Balasubramanian, "C4synth: Cross-caption cycle-consistent text-to-image synthesis, " in IEEE Winter Conference on Applications of Computer Vision (WACV), 2019.##[8] W. Li, P. Zhang, L. Zhang, Q. Huang, X. He, S. Lyu, and J. Gao, "Object-driven text-to-image synthesis via adversarial training, " in Proc. of the IEEE Conf.e on Computer Vision and Pattern Recognition, 2019.##[9] A. Miller, A. Fisch, J. Dodge, A. H. Karimi, A. Bordes, and J. Weston, "Key-value memory networks for directly reading documents, " in Proceeding of Empirical Methods in Natural Language Processing (EMNLP), 2016.##[10] S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee, "Generative adversarial text to image synthesis, " arXiv preprint arXiv:1605.05396, 2016.##[11] T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, "Improved techniques for training gans, " in Advances in neural information processing systems (NIPS), 2016.##[12] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the inception architecture for computer vision, " in Proc. of the IEEE conf. on computer vision and pattern recognition, 2016.##[13] C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie, The caltech-ucsd birds-200-2011 dataset, 2011.##[14] T. Xu, P. Zhang, Q. Huang, H. Zhang, Z. Gan, X. Huang, and X. He, "Attngan: Fine-grained text to image generation with attentional generative adversarial networks, " in Proc. of the IEEE conf. on computer vision and pattern recognition, 2018.##[15] X. Yan, J. Yang, K. Sohn, and H. Lee, "Attribute2image: Conditional image generation from visual attributes, " in European Conf. on Computer Vision, 2016.##[16] G. Yin, B. Liu, L. Sheng, N. Yu, X. Wang, and J. Shao, "Semantics disentangling for text-to-image generation, " in Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2019.##[17] H. Zhang, T. Xu, H. Li, S. Zhang, X. Huang, X. Wang, and D. Metaxas, "Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks, " in Proc.of the IEEE int. conference on computer vision, 2017.##[18] H. Zhang, T. Xu, H. Li, S. Zhang, X. Wang, X. Huang, and D. N. Metaxas, "Stackgan++: Realistic image synthesis with stacked generative adversarial networks, " in IEEE transactions on pattern analysis and machine intelligence, 2017.##[19] Z. Zhang, Y. Xie, and L. Yang, " Photo-graphic Text-to-Image Synthesis with a Hierarchically-nested Adversarial Network" in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2018.##[20] P. Zhou, W. Shi, J. Tian, Z. Qi, B. Li, H. Hao, and B. Xu, "Attention-based bidirectional long short-term memory networks for relation classification, " in Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2016.##[21] M. Zhu, P. Pan, W. Chen, and Y. Yang, "dm-gan: Dynamic memory generative adversarial net. for text-to-image synthesis, " in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2019.##[22] X. Zhu, A. B. Goldberg, M. Eldawy, C. R. Dyer, and B. Strock, "A text-to-picture synthesis system for augmenting communication, " in proceeding of Association for the Advanced## ##

اعتبارسنجی ادعای بیمه بیکاری با استفاده از روش ترکیب رده‌بندها Verification of unemployment benefits’ claims using Classifier Combination method 1 1 بیمه بیکاری یکی از مهم‌ترین و پرطرفدارترین انواع بیمه در دنیای امروزی محسوب می‌شود. سازمان تأمین اجتماعی در مقابل ادعای بیکاری افراد تحت پوشش این سازمان، وظیفه بررسی صحت این موضوع را دارد. بررسی دستیِ ادعای افراد بیکار نیازمند صرف زمان و هزینه زیادی است. روش‌های داده‌کاوی و یادگیری ماشین به‌عنوان ابزارهای کارآمدِ تحلیل داده‌ها می‌تواند در خودکارسازی این فرآیند به سازمان تأمین اجتماعی کمک کنند. در این پژوهش، روشی مبتنی بر یادگیری نظارتی برای بررسی صحت ادعای بیکاری افراد متقاضی ارائه شده است. روش پیشنهادی، اطلاعات بیمه‌شدگان را به‌عنوان ورودی دریافت کرده و پس از تحلیل داده‌ها به هر فرد متقاضی امتیازی تخصیص می‌دهد. سپس بر اساس مقدار این امتیاز، مدعیان بیمه بیکاری را به دو گروه "شایسته دریافت بیمه بیکاری" و "فاقد کفایت برای دریافت بیمه بیکاری" دسته‌بندی می‌کند. روش پیشنهادی از دو ترکیب مختلف برای دسته‌بندی ادعای متقاضیان استفاده می‌کند: روش BSA-SVM و روش ترکیب  ضرایب اطمینان طبقه‌بندها. در روش BSA-SVM برای بهبود کارایی و تخمین پارامترهای کنترلی SVM، از الگوریتم بهینه‌سازی جستجوی عقبگرد (BSA) استفاده شده‌است. در روش ترکیب  ضرایب اطمینان طبقه‌بندها، تعدادی طبقه‌بند، از جمله شبکه‌های عصبی مصنوعی، درخت تصمیم و رگرسیون لجستیک داده‌ها را طبقه‌بندی کرده و ضرایب اطمینان این طبقه‌بندها با دو روش مختلف با همدیگر ترکیب می‌شوند. نتایج آزمایش‌ها نشان می‌دهد که روش پیشنهادی BSA-SVM با کسب 87% و روش ترکیب طبقه‌بندها با ضرایب اطمینان با کسب دقت 86%، کارایی بهتری در قیاس با سایر روش‌های موجود کسب کرده اند. 2 Unemployment insurance is one of the most popular insurance types in the modern world. The Social Security Organization is responsible for checking the unemployment benefits of individuals supported by unemployment insurance. Hand-crafted evaluation of unemployment claims requires a big deal of time and money. Data mining and machine learning as two efficient tools for data analysis can assist Social Security Organization in automating this process. In this research work, a hybrid supervised learning method is proposed to verify the eligibility of applicants for unemployment. The proposed method takes as input the information of insured individuals, and assigns a numeric score to each applicant through analyzing the input data. Then, claimants are classified into two groups according to those scores: "Qualified” and "Unqualified". The proposed method includes two hybrid strategies: BSA-SVM and combination of confidence values. In BSA-SVM method, backtracking search algorithm (BSA) is used to estimate the prameters of support vector machines (SVM) and improves the classification performance. In the second approach, confidence values extracted from individual classofiers are combined to better classify the input data. Empirical evaluation shows an accuracy of 87% for BSA-SVM and 86% for the second approach. 45 60 2020/10/52020/07/232020/08/212019/05/3 1398/2/13 2021/12/62022/05/112021/05/242020/05/13 1399/2/24 رحیم دهخوارقانی Rahim Dehklharghani دانشگاه بناب ایران rdehkharghani@bonabu.ac.ir حجت امامی Hojat Emami دانشگاه بناب ایران emami@bonabu.ac.ir Unemployment benefits data mining machine learning supervised learning BSA-SVM classifier combination. بیمه بیکاری داده‌کاوی یادگیری ماشین یادگیری نظارتی BSA-SVM ترکیب طبقه‌بندها. [1] E.W.T.Ngai, Y. Hu, Y.H.Wong, Y.Chen, and X. Sun, "The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature," Decis. Support Syst., vol. 50, no. 3, pp. 559-569, 2011.##[2] A. Hosseini, A. Rezaei, " Fraud detection and solutions to deal with it in insurance organizations using data mining (case study: Social Security Organization) ", Social Security Quarterly, vol. 14, no. 1, pp. 111-136.##[3] S. M. Taqwa Fard, z. Jafari, "Detecting Fraud in Car Insurance Using Fuzzy Expert System", Information Technology Management, vol. 7, no. 2, pp. 239-258, 2014.##[4] A. Ghorbani and S. Farzai, "Fraud Detection in Automobile Insurance using a Data Mining Based Approach, " Int. J. Mechatronics, Electr. Comput. Technol., vol. 8, no. 27, pp. 3764-3771, 2018, doi: IJMEC/10.225163.##[5] M. Firouzi, M. Shakuri, L. Kazemi, S. ascetic; "Identifying fraud in car insurance using data mining methods", Insurance Research Journal, vol. 26, no. 3, p. 103-128, 1390.##[6] "Viaene, S. and Dedene, G., 2004. Insurance fraud: issues and challenges. Geneva Papers on Risk and Insurance and Practice, 29, pp.313-33.".##[7] N. Haji Heydari, S. Khalaha and A. Farahi; "Classification of the risk level of car insurance policyholders using data mining algorithms (case study: an insurance company), " Insurance Research Journal, vol. 26, no. 4, p. 107-129, 1390.##[8] L. Hosseinzadeh, "Categories of target customers in the insurance industry using data mining", master's thesis, Tarbiat Modares University, 2016.##[9] J. Aghabeigi and S. Rezaei, "Validation of credit customers of Melli Bank based on data mining techniques (logistic regression) ", 2016.##[10] R. Tehrani and M. F. Shams, "Designing and explaining the credit risk model in the country's banking system, " Journal of Social Sciences and Humanities of Shiraz University##[11] M. Mohammad Khan, M. Ismaili, and M. Yarahamdi, "Designing a credit risk assessment model for bank customers using a logistic regression model," 2017.##[12] M. Galar, A. Fernández, E. Barrenechea, H. Bustince, and F. Herrera, "An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes, " Pattern Recognit., vol. 44, no. 8, pp. 1761-1776, 2011.##[13] Z.-G. Liu, Q. Pan, J. Dezert, and A. Martin, "Combination of classifiers with optimal weight based on evidential reasoning," IEEE Trans. Fuzzy Syst., vol. 26, no. 3, pp. 1217-1230, 2017.##[14] M. A. Duval-Poo, J. Sosa-Garcia, A. Guerra-Gandón, S. Vega-Pons, and J. Ruiz-Shulcloper, "A new classifier combination scheme using clustering ensemble, " in Iberoamerican Congress on Pattern Recognition, 2012, pp. 154-161.##[15]B. Krawczyk and M. Woźniak, "Untrained weighted classifier combination with embedded ensemble pruning, " Neurocomputing, vol. 196, pp. 14-22, 2016.##[16]R. Pan, T. Yang, J. Cao, K. Lu, and Z. Zhang, "Missing data imputation by K nearest neighbours based on grey relational structure and mutual information, " Appl. Intell., vol. 43, no. 3, pp. 614-632, 2015, doi: 10.1007/s10489-015-0666-x.##[17]D. E. N. Frossard, I. O. Nunes, and R. A. Krohling, "An approach to dealing with missing values in heterogeneous data using k-nearest neighbors, " arXiv Prepr. arXiv1608.04037, 2016, [Online]. Available: http://arxiv.org/abs/1608.04037##[18]V. Kumar and S. Minz, "Feature selection: a literature review, " Smart Comput. Rev., vol. 4, no. 3, pp. 211-229, 2014, doi: 10.1504/ijise.2013.052279.##[19] M. V. Erp, L. G. Vuurpijl, and L.. Schomaker, "An Overview and Comparison of Voting Methods for Pattern Recognition, " in Proc. of the 8th International Workshop on Frontiers in Handwriting Recognition (IWFHR-8), Niagara-onthe-Lake, Canada, 2002, pp. 195-200.##[20] L. A. Alexandre, A. C. Campilho, and M. Kamel, "Combining independent and unbiased classifiers using weighted average, " in Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2000, vol. 2, pp. 495-498.##[21]R. Yager and L. Liu, Classic Works of the Dempster-Shafer Theory of Belief Functions, vol. 219. 2008. doi: 10.1007/978-3-540-44792-4.##[22]P. Civicioglu, "Backtracking Search Optimization Algorithm for numerical optimization problems, " Appl. Math. Comput., vol. 219, no. 15, pp. 8121-8144, 2013.##[23]D. M. W. Powers, "Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation, " J. Mach. Learn. Technol., vol. 2, no. 1, pp. 37-63, 2011.##[24] M. T. Fard, F. s. Hosseini, and M. Kh. Babaei, "Hybrid Credit Rating Model Using Genetic Algorithms and Fuzzy Expert Systems (Case Study: Qavamin Financial and Credit Institute), " Information Technology Management, vol. 6, no. 1, pp. 31-46, 2013.##[25] M. Salehi and A. Katoli, "Choosing the optimal features in order to determine the credit risk of bank customers, " Smart Business Management Studies Quarterly, vol. 6, no. 2, pp. 129-154, 2016.##[1] E.W.T.Ngai, Y. Hu, Y.H.Wong, Y.Chen, and X. Sun, "The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature," Decis. Support Syst., vol. 50, no. 3, pp. 559-569, 2011.##[2] ع. حسینی و ع. رضائی، "کشف تقلب و راهکارهای مقابله با آن در سازمانهای بیمهای با استفاده از دادهکاوی (مطالعه موردی: سازمان تأمین اجتماعی), " فصلنامه تأمین اجتماعی، دوره 14، شماره 1، ص 111-136، 1397.##[2] A. Hosseini, A. Rezaei, " Fraud detection and solutions to deal with it in insurance organizations using data mining (case study: Social Security Organization) ", Social Security Quarterly, vol. 14, no. 1, pp. 111-136.##[3] س. م. تقوی فرد، ز. جعفری، "کشف تقلب در بیمه بنده خودرو با بهره مندی از سامانه خبره فازی"، مدیریت فناوری اطلاعات، دوره 7، شماره 2، ص 239-258، 1394.##[3] S. M. Taqwa Fard, z. Jafari, "Detecting Fraud in Car Insurance Using Fuzzy Expert System", Information Technology Management, vol. 7, no. 2, pp. 239-258, 2014.##[4] A. Ghorbani and S. Farzai, "Fraud Detection in Automobile Insurance using a Data Mining Based Approach, " Int. J. Mechatronics, Electr. Comput. Technol., vol. 8, no. 27, pp. 3764-3771, 2018, doi: IJMEC/10.225163.##[5] م. فیروزی، م. شکوری، ل. کاظمی، س. زاهدی؛ "شناسایی تقلب در بیمه اتومبیل با استفاده از روشهای دادهکاوی"، پژوهشنامه بیمه، دوره 26، شماره 3، ص. 103-128، 1390.##[5] M. Firouzi, M. Shakuri, L. Kazemi, S. ascetic; "Identifying fraud in car insurance using data mining methods", Insurance Research Journal, vol. 26, no. 3, p. 103-128, 1390.##[6] "Viaene, S. and Dedene, G., 2004. Insurance fraud: issues and challenges. Geneva Papers on Risk and Insurance and Practice, 29, pp.313-33.".##[7] ن. حاجی حیدری، س. خالهء و ا. فراهی؛ "ردهبندی میزان ریسک بیمهگذاران بیمه بنده خودرو با استفاده از الگوریتمهای دادهکاوی (مورد مطالعه: یک شرکت بیمه) "، پژوهشنامه بیمه، دوره 26، شماره 4، ص. 107-129، 1390.##[7] N. Haji Heydari, S. Khalaha and A. Farahi; "Classification of the risk level of car insurance policyholders using data mining algorithms (case study: an insurance company), " Insurance Research Journal, vol. 26, no. 4, p. 107-129, 1390.##[8] ل. حسینزاده، "دسته بندی مشتریان هدف در صنعت بیمه با استفاده از دادهکاوی"، پایان نامه کارشناسیارشد، دانشگاه تربیتمدرس، سال 1386.##[8] L. Hosseinzadeh, "Categories of target customers in the insurance industry using data mining", master's thesis, Tarbiat Modares University, 2016.##[9] ژ. آقابیگی و س. رضائی, "اعتبار سنجی مشتریان اعتباری بانک ملی بر اساس تکنیکهای دادهکاوی (رگرسیون لجستیک) " اولین کنفرانس دادهکاوی ایران، 1386.##[9] J. Aghabeigi and S. Rezaei, "Validation of credit customers of Melli Bank based on data mining techniques (logistic regression) ", 2016.##[10] ر. تهرانی و م. ف. شمس, "طراحی و تبیین مدل ریسک اعتباری در نظام بانکی کشور, " مجله علوم اجتماعی و انسانی دانشگاه شیراز، دوره 43، ص. 45-60، 1384.##[10] R. Tehrani and M. F. Shams, "Designing and explaining the credit risk model in the country's banking system, " Journal of Social Sciences and Humanities of Shiraz University##[11]م. محمدخان, م. اسماعیلی و م. یاراحمدی، "طراحی مدل ارزیابی ریسک اعتباری مشتریان بانک با استفاده از مدل رگرسیون نجستیک" ششمین کنفرانس بین المللی مهندسی صنایع، 1387.##[11] M. Mohammad Khan, M. Ismaili, and M. Yarahamdi, "Designing a credit risk assessment model for bank customers using a logistic regression model," 2017.##[12] M. Galar, A. Fernández, E. Barrenechea, H. Bustince, and F. Herrera, "An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes, " Pattern Recognit., vol. 44, no. 8, pp. 1761-1776, 2011.##[13] Z.-G. Liu, Q. Pan, J. Dezert, and A. Martin, "Combination of classifiers with optimal weight based on evidential reasoning," IEEE Trans. Fuzzy Syst., vol. 26, no. 3, pp. 1217-1230, 2017.##[14] M. A. Duval-Poo, J. Sosa-Garcia, A. Guerra-Gandón, S. Vega-Pons, and J. Ruiz-Shulcloper, "A new classifier combination scheme using clustering ensemble, " in Iberoamerican Congress on Pattern Recognition, 2012, pp. 154-161.##[15]B. Krawczyk and M. Woźniak, "Untrained weighted classifier combination with embedded ensemble pruning, " Neurocomputing, vol. 196, pp. 14-22, 2016.##[16]R. Pan, T. Yang, J. Cao, K. Lu, and Z. Zhang, "Missing data imputation by K nearest neighbours based on grey relational structure and mutual information, " Appl. Intell., vol. 43, no. 3, pp. 614-632, 2015, doi: 10.1007/s10489-015-0666-x.##[17]D. E. N. Frossard, I. O. Nunes, and R. A. Krohling, "An approach to dealing with missing values in heterogeneous data using k-nearest neighbors, " arXiv Prepr. arXiv1608.04037, 2016, [Online]. Available: http://arxiv.org/abs/1608.04037##[18]V. Kumar and S. Minz, "Feature selection: a literature review, " Smart Comput. Rev., vol. 4, no. 3, pp. 211-229, 2014, doi: 10.1504/ijise.2013.052279.##[19] M. V. Erp, L. G. Vuurpijl, and L.. Schomaker, "An Overview and Comparison of Voting Methods for Pattern Recognition, " in Proc. of the 8th International Workshop on Frontiers in Handwriting Recognition (IWFHR-8), Niagara-onthe-Lake, Canada, 2002, pp. 195-200.##[20] L. A. Alexandre, A. C. Campilho, and M. Kamel, "Combining independent and unbiased classifiers using weighted average, " in Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2000, vol. 2, pp. 495-498.##[21]R. Yager and L. Liu, Classic Works of the Dempster-Shafer Theory of Belief Functions, vol. 219. 2008. doi: 10.1007/978-3-540-44792-4.##[22]P. Civicioglu, "Backtracking Search Optimization Algorithm for numerical optimization problems, " Appl. Math. Comput., vol. 219, no. 15, pp. 8121-8144, 2013.##[23]D. M. W. Powers, "Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation, " J. Mach. Learn. Technol., vol. 2, no. 1, pp. 37-63, 2011.##[24]م. ت. فرد، ف. س. حسینی، و م. خ. بابایی، "مدل رتبه بندی اعتباری هیبریدی با استفاده از الگوریتمهای ژنتیک و سیستمهای خبره فازی (مطالعه موردی: مؤسسه مالی و اعتباری قوامین)، " مدیریت فناوری اطلاعات, دوره 6، شماره 1، ص. 31-46، 1393.##[24] M. T. Fard, F. s. Hosseini, and M. Kh. Babaei, "Hybrid Credit Rating Model Using Genetic Algorithms and Fuzzy Expert Systems (Case Study: Qavamin Financial and Credit Institute), " Information Technology Management, vol. 6, no. 1, pp. 31-46, 2013.##[25]م. صالحی و ع. کتولی، "انتخاب ویژگیهای بهینه بهمنظور تعیین ریسک اعتباری مشتریان بانکی, " فصلنامه مطالعات مدیریت کسب و کار هوشمند، دوره 6، شماره 2، ص. 129-154، 1396.##[25] M. Salehi and A. Katoli, "Choosing the optimal features in order to determine the credit risk of bank customers, " Smart Business Management Studies Quarterly, vol. 6, no. 2, pp. 129-154, 2016.## ##

یک چارچوب کنترل دسترسی برای سامانه‌های مبتنی بر پایگاه داده An Authorization Framework for Database Systems 1 1 حمله به پایگاه داده در یک سامانه نرم افزاری می تواند آسیب‌های جبران‌ناپذیری به همراه داشته باشد. این حمله ممکن است در اشکال متفاوتی مانند سرقت داده، جعل داده و یا نقض حریم خصوصی نمایان شود. گستردگی این حمله، با توجه به کاربرد داده ی ذخیره شده، می تواند منجر به  ایجاد خسارت های جانی و مالی فراوانی حتی در سطح ملی گردد. از آنجایی که کاربران قانونی نقش کلیدی در تأمین امنیت پایگاه داده دارند، یکی از تهدیهای خطرناک پایگاه داده حمله کاربران قانونی است. این حمله هنگامی بوجود می آید که کاربر خودی با سوءاستفاده از مجوزهای قانونی تلاش برای استفاده غیرمجاز از داده ها داشته باشد. در این مقاله یک چارچوب مجوزدهی مبتنی بر کارایی برای کاهش تهدید کاربران خودی ارائه‌شده است. در این چارچوب سطح دسترسی کاربر به جدول پایگاه داده با توجه به مقدار کارایی وی و سطح حساسیت جدول تعیین می‌گردد. مقدار کارایی کاربر در فواصل زمانی معین و یا هنگام تشخیص سوءاستفاده به‌روزرسانی می‌شود. نتایج شبیه‌سازی با استفاده از داده‌های واقعی از یک سیستم اطلاعات بیمارستانی نشان می‌دهد که چارچوب پیشنهادی از کارایی مناسبی برخوردار است. 2 Today, data plays an essential role in all levels of human life, from personal cell phones to medical, educational, military and government agencies. In such circumstances, the rate of cyber-attacks is also increasing. According to official reports, data breaches exposed 4.1 billion records in the first half of 2019. An information system consists of several components, which one of the most important them is the database. A database in addition to being a repository of data, acts as a common information bus between system components. For this reason, any attack on the database may disrupt the operation of other components of the system. In fact, database security is shared throughout the whole information system. The attack may carried out in various ways, such as data theft, damaging data, and privacy breach. According to the sensitivity of the stored data, database attack could lead to significant human and financial losses even at the national level. Among the different types of threats, since legitimate operator plays a key role in an information system, his/her threat is one of the most dangerous threats to the security and integrity of a database system. This type of cyber-attack occurs when an insider operator abuses his/her legal permissions in order to access unauthorized data. In this paper, a new performance-based authorization framework has been presented which is able to reduce the potential of insider threat in the database system. The proposed method insure that only authenticated operator performs authorized activities on the database objects. In the proposed framework, the access permission of the operator to a database table is determined using his/her performance and the level of sensitivity of the table. The value of the operator performance is updated periodically or when an abuse is detected, in order to protect access to the contents of a database as well as preserve the consistency, integrity, and overall quality of the data. Simulation results, using real dataset from a hospital information system, indicate that the proposed framework has effective performance for mitigating insider threats. 61 70 2020/10/52020/07/232020/08/212019/05/32020/08/13 1399/5/23 2021/12/62022/05/112021/05/242020/05/132021/12/11 1400/9/20 پیام محمودی نصر Payam Mahmoudi-Nasr دانشگاه مازندران ایران P.Mahmoudi@umz.ac.ir Access control Authorization cyber security database insider threat امنیت سایبری پایگاه داده تهدید خودی کنترل دسترسی مجوزدهی [1] S. Dhal and V. Bhuwan, "Cryptanalysis and improvement of a cloud based login and authentication protocol, " in 2018 4th International Conference on Recent Advances in Information Technology (RAIT), 2018: IEEE, pp. 1-6.##[2] H. Bao, R. Lu, B. Li, and R. Deng, "BLITHE: Behavior rule-based insider threat detection for smart grid, " IEEE Internet of Things Journal, vol. 3, no. 2, pp. 190-205, 2016.##[3] C.-C. Sun, A. Hahn, and C.-C. Liu, "Cyber security of a power grid: State-of-the-art," International Journal of Electrical Power & Energy Systems, vol. 99, pp. 45-56, 2018.##[4] P. A. Legg, O. Buckley, M. Goldsmith, and S. Creese, "Automated insider threat detection system using user and role-based profile assessment, " IEEE Systems Journal, vol. 11, no. 2, pp. 503-512, 2017.##[5] I. Agrafiotis, P. A. Legg, M. Goldsmith, and S. Creese, "Towards a User and Role-based Sequential Behavioural Analysis Tool for Insider Threat Detection, " J. Internet Serv. Inf. Secur., vol. 4, no. 4, pp. 127-137, 2014.##[6] I. Homoliak, F. Toffalini, J. Guarnizo, Y. Elovici, and M. Ochoa, "Insight into Insiders: A Survey of Insider Threat Taxonomies, Analysis, Modeling, and Countermeasures, " arXiv preprint arXiv:1805.01612, 2018.##[7] L. Liu, O. De Vel, Q.-L. Han, J. Zhang, and Y. Xiang, "Detecting and Preventing Cyber Insider Threats: A Survey," IEEE Communications Surveys & Tutorials, vol. 20, no. 2, pp. 1397-1417, 2018.##[8] Q. Lv, Y. Wang, L. Wang, and D. Wang, "Towards a User and Role-Based Behavior Analysis Method for Insider Threat Detection, " in 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC), 2018: IEEE, pp. 6-10.##[9] P. Chattopadhyay, L. Wang, and Y.-P. Tan, "Scenario-Based Insider Threat Detection From Cyber Activities," IEEE Transactions on Computational Social Systems, vol. 5, no. 3, pp. 660-675, 2018.##[10] M. S. Islam, M. Kuzu, and M. Kantarcioglu, "A dynamic approach to detect anomalous queries on relational databases, " in Proceedings of the 5th ACM Conference on Data and Application Security and Privacy, 2015: ACM, pp. 245-252.##[11] A. Almehmadi and K. El-Khatib, "On the possibility of insider threat prevention using intent-based access control (IBAC), " IEEE Systems Journal, vol. 11, no. 2, pp. 373-384, 2017.##[12] L. Argento, A. Margheri, F. Paci, V. Sassone, and N. Zannone, "Towards adaptive access control," 2018.##[13] F. Ghofrani and M. Amini, "Privacy Preserving Dynamic Access Control Model with Access Delegation for eHealth, " Signal and Data Processing, vol. 17, no. 3, pp. 109-140, 2020.##[14] P. Mahmoudi Nasr and A. Yazdian Varjani, "An Access Management System to Mitigate Operational Threats in SCADA System, " Signal and Data Processing, vol. 14, no. 4, pp. 3-18, 2018.##[15] M. Toahchoodee, R. Abdunabi, I. Ray, and I. Ray, "A trust-based access control model for pervasive computing applications, " in IFIP Annual Conference on Data and Applications Security and Privacy, 2009: Springer, pp. 307-314.##[16] N. Baracaldo and J. Joshi, "An adaptive risk management and access control framework to mitigate insider threats, " Computers & Security, vol. 39, pp. 237-254, 2013.##[17] R. S. Sandhu, E. J. Coyne, H. L. Feinstein, and C. E. Youman, "Role-based access control models, " Computer, vol. 29, no. 2, pp. 38-47, 1996.##[18] M. Collins, "Common sense guide to mitigating insider threats, " CERT Division, Technical Note, 2016.##[19] P. Mahmoudi-Nasr, A. Yazdian Varjani, "An Access Management System to Mitigate Operational Threats in SCADA System, " JSDP 2018; 14 (4) :3-18.##[20] C. Y. Chung, M. Gertz, and K. Levitt, "Demids: A misuse detection system for database systems, " in Integrity and Internal Control in Information Systems: Springer, 2000, pp. 159-178.##[21] E. Bertino, E. Terzi, A. Kamra, and A. Vakali, "Intrusion detection in RBAC-administered databases, " in Computer security applications conference, 21st annual, 2005: IEEE, pp. 10 pp.-182.##[22] D. C. Montgomery, Introduction to statistical quality control. John Wiley & Sons (New York), 2009.##[1] S. Dhal and V. Bhuwan, "Cryptanalysis and improvement of a cloud based login and authentication protocol, " in 2018 4th International Conference on Recent Advances in Information Technology (RAIT), 2018: IEEE, pp. 1-6.##[2] H. Bao, R. Lu, B. Li, and R. Deng, "BLITHE: Behavior rule-based insider threat detection for smart grid, " IEEE Internet of Things Journal, vol. 3, no. 2, pp. 190-205, 2016.##[3] C.-C. Sun, A. Hahn, and C.-C. Liu, "Cyber security of a power grid: State-of-the-art," International Journal of Electrical Power & Energy Systems, vol. 99, pp. 45-56, 2018.##[4] P. A. Legg, O. Buckley, M. Goldsmith, and S. Creese, "Automated insider threat detection system using user and role-based profile assessment, " IEEE Systems Journal, vol. 11, no. 2, pp. 503-512, 2017.##[5] I. Agrafiotis, P. A. Legg, M. Goldsmith, and S. Creese, "Towards a User and Role-based Sequential Behavioural Analysis Tool for Insider Threat Detection, " J. Internet Serv. Inf. Secur., vol. 4, no. 4, pp. 127-137, 2014.##[6] I. Homoliak, F. Toffalini, J. Guarnizo, Y. Elovici, and M. Ochoa, "Insight into Insiders: A Survey of Insider Threat Taxonomies, Analysis, Modeling, and Countermeasures, " arXiv preprint arXiv:1805.01612, 2018.##[7] L. Liu, O. De Vel, Q.-L. Han, J. Zhang, and Y. Xiang, "Detecting and Preventing Cyber Insider Threats: A Survey," IEEE Communications Surveys & Tutorials, vol. 20, no. 2, pp. 1397-1417, 2018.##[8] Q. Lv, Y. Wang, L. Wang, and D. Wang, "Towards a User and Role-Based Behavior Analysis Method for Insider Threat Detection, " in 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC), 2018: IEEE, pp. 6-10.##[9] P. Chattopadhyay, L. Wang, and Y.-P. Tan, "Scenario-Based Insider Threat Detection From Cyber Activities," IEEE Transactions on Computational Social Systems, vol. 5, no. 3, pp. 660-675, 2018.##[10] M. S. Islam, M. Kuzu, and M. Kantarcioglu, "A dynamic approach to detect anomalous queries on relational databases, " in Proceedings of the 5th ACM Conference on Data and Application Security and Privacy, 2015: ACM, pp. 245-252.##[11] A. Almehmadi and K. El-Khatib, "On the possibility of insider threat prevention using intent-based access control (IBAC), " IEEE Systems Journal, vol. 11, no. 2, pp. 373-384, 2017.##[12] L. Argento, A. Margheri, F. Paci, V. Sassone, and N. Zannone, "Towards adaptive access control," 2018.##[13] F. Ghofrani and M. Amini, "Privacy Preserving Dynamic Access Control Model with Access Delegation for eHealth, " Signal and Data Processing, vol. 17, no. 3, pp. 109-140, 2020.##[14] P. Mahmoudi Nasr and A. Yazdian Varjani, "An Access Management System to Mitigate Operational Threats in SCADA System, " Signal and Data Processing, vol. 14, no. 4, pp. 3-18, 2018.##[15] M. Toahchoodee, R. Abdunabi, I. Ray, and I. Ray, "A trust-based access control model for pervasive computing applications, " in IFIP Annual Conference on Data and Applications Security and Privacy, 2009: Springer, pp. 307-314.##[16] N. Baracaldo and J. Joshi, "An adaptive risk management and access control framework to mitigate insider threats, " Computers & Security, vol. 39, pp. 237-254, 2013.##[17] R. S. Sandhu, E. J. Coyne, H. L. Feinstein, and C. E. Youman, "Role-based access control models, " Computer, vol. 29, no. 2, pp. 38-47, 1996.##[18] M. Collins, "Common sense guide to mitigating insider threats, " CERT Division, Technical Note, 2016.##[19] P. Mahmoudi-Nasr, A. Yazdian Varjani, "An Access Management System to Mitigate Operational Threats in SCADA System, " JSDP 2018; 14 (4) :3-18.##[19] محمودی نصر پیام، یزدیان ورجانی علی. یک سامانه مدیریت دسترسی برای کاهش تهدیدهای عملیاتی در سامانه اسکادا. پردازش علائم و داده‌ها ۱۳۹۶; ۱۴ (۴):۱۸-۳##[20] C. Y. Chung, M. Gertz, and K. Levitt, "Demids: A misuse detection system for database systems, " in Integrity and Internal Control in Information Systems: Springer, 2000, pp. 159-178.##[21] E. Bertino, E. Terzi, A. Kamra, and A. Vakali, "Intrusion detection in RBAC-administered databases, " in Computer security applications conference, 21st annual, 2005: IEEE, pp. 10 pp.-182.##[22] D. C. Montgomery, Introduction to statistical quality control. John Wiley & Sons (New York), 2009.## ##

طراحی مدل سایبرنتیک الگوریتمهای رمزنگاری و رتبه بندی مولفه های پشتیبان آن با استفاده از روش ELECTRE III Design of cybernetic metamodel of cryptographic algorithms and ranking of its supporting components using ELECTRE III method 1 1 تحقق امنیت مطلوب و پایدار در شبکه‌های برخوردار از گستره ملی، سازمانی و حتی در سامانه‌های اطلاعاتی دارای حساسیت، باید مبتنی بر یک روش نظام‌مند و همه جانبه‌نگر بوده و به صورت گام به گام انجام گیرد. رمزنگاری مهمترین سازوکار برای تأمین امنیت اطلاعات بوده که عمدتا مبتنی بر الگوریتم­های رمزنگاری است. در طراحی یک الگوریتم همه مؤلفه­های لازم امنیت را باید در یک الگوی تعالی از جنبه­های فنی، سازمانی، رویه­ای و انسانی در نظر گرفت. برای پاسخگویی به این نیازها، ابتدا باید بر اساس یک مدل، مولفه­های موثر را استخراج و سپس میزان تاثیر مولفه­ها را تعیین نمود. در این مقاله از روش­شناسی سایبرنتیک برای تهیه یک اَبَرمدل استفاده می­کنیم. فعل و انفعالات مولفه­های این ابرمدل یک گراف پیچیده تشکیل می­دهند. برای غلبه بر این پیچیدگی برای تعیین اولویت مولفه­های آن از ابزار ELECTRE III  استفاده می­کنیم. نتایج حاصل  از آن با درصد بالایی منطبق بر گزارش­های منتشر شده توسطITU در سال­های 2015، 2017 و 2018  است. 2 Nowadays, achieving desirable and stable security in networks with national and organizational scope and even in sensitive information systems, should be based on a systematic and comprehensive method and should be done step by step. Cryptography is the most important mechanism for securing information. a cryptographic system consists of three main components: cryptographic algorithms, cryptographic keys, and security protocols, which are mainly based on cryptographic algorithms. In designing a cryptographic algorithm, all the necessary components of information security must be considered in a model of excellence in technical, organizational, procedural and human aspects. To meet these needs, we must first extract the effective components in the design and implementation of cryptographic algorithms based on a model and then determine the impact of the components. In this paper, we use cybernetic methodology to prepare a   metamodel.   The cryptographic cybernetics metamodel has four components: " strategy / policy ", "main process", "support process" and "control process". The "main process" has four stages and also, the "suport process" includes 13 components of hardware and software. The interactions of these two processes shape its structure, leading to a complex graph. To prioritize suport components for resource allocation and cryptography strategy, it is necessary to rank these components in the designed metamodel. To overcome this complexity in order to rank the support components, we use the ELECTRE III method, which is a multi-criteria decision-making method. The results show that the components with high priority for the development of the cryptographic system are: Research and Development, Human Resources, Management, Organizational, Information and Communication Technology, Rrules and Regulations and standards. These results are consistent with reports published by the ITU in 2015, 2017 and 2018. 71 84 2020/10/52020/07/232020/08/212019/05/32020/08/132020/09/4 1399/6/14 2021/12/62022/05/112021/05/242020/05/132021/12/112020/10/21 1399/7/30 علی محمد نوروززاده گیل ملک Ali Mohammad Norouzzadeh Gilmolk پارک علم و فناوری ایران a_norouzzadeh@iau-tnb.ac.ir محمد رضا عارف Mohammad Reza Aref دانشگاه شریف ایران aref@sharif.edu رضا رمضانی خورسید دوست Reza Ramazani Khorshidoust دانشگاه صنعتی امیرکبیر ایران ramazani@aut.ac.ir Cryptographic algorithms Metamodel Cybernetics MCDM ELECTRE III. الگوریتم های رمزنگاری ابرمدل سایبرنتیک MCDM ELECTRE III . [1] L. J. Fennelly, M. Beaudry, and M. A. Perry, "Security in 2025."##[2] Thales, "Global encryption trends study, " Ponemon Institute Research, 2018. https://www.ncipher.com/2018/global-encryption-trendsstudy.##[3] P. Kuppuswamy and S. Q. Y. Al-Khalidi, "Hybrid encryption/decryption technique using new public key and symmetric key algorithm," Int. J. Inf. Comput. 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Al-Khalidi, "Hybrid encryption/decryption technique using new public key and symmetric key algorithm," Int. J. Inf. Comput. Secur., vol. 6, no. 4, pp. 372-382, 2014, doi: 10.1504/IJICS.2014.068103.##[4] A. Vassilev, L. Feldman, and G. Witte, "ITL Bulletin for September 2018 AUTOMATED CRYPTOGRAPHIC VALIDATION (ACV) testing" no. September, pp. 1-4, 2018.##[5] NIST, "NIST Cryptographic Standards and Guidelines Development Process, " Nist, p. 27, 2016, doi: 10.6028/NIST.IR.7977.##[6] NIST, "Report on Lightweight Cryptography March 2017 • Final Publication: 10.6028/NIST.IR.8114 (which links to • Information on other NIST cybersecurity publications a, " Nist, vol. 8114, no. March, 2017.##[7] G. Alagic et al., "Status Report on the Second Round of the NIST Post-Quantum Cryptography Standardization Process, " pp. 1-39, 2020, doi: 10.6028/NIST.IR.8240.##[8] V. Cerf, E. Felten, S. Lipner, B. Preneel, and E. Richey, "NIST cryptographic standards and guidelines development process: Report and recommendations of the Visiting Committee on Advanced Technology of," 2014.##[9] I. Damaj and S. Kasbah, "An Analysis Framework for Hardware and Software Implementations with Applications from Cryptography," 2019, doi: 10.1016/j.compeleceng.2017.06.008.##[10] S. Keller, "The Cryptographic Algorithm Validation Program, " no. September, 2004.##[11] S. Bhat and V. Kapoor, "Secure and Efficient Data Privacy, Authentication and Integrity Schemes Using Hybrid Cryptography, " in Advances in Intelligent Systems and Computing, 2019, vol. 870, pp. 279-285, doi: 10.1007/978-981-13-2673-8_30.##[12] P. Patil and P. Narayankar, "A Comprehensive Evaluation of Cryptographic Algorithms: DES, 3DES, AES, RSA and Blowfish, " Procedia - Procedia Comput. Sci., vol. 78, pp. 617-624, 2016, doi: 10.1016/j.procs.2016.02.108.##[13] A. A. Soofi, I. Riaz, and U. Rasheed, "An Enhanced Vigenere Cipher For Data Security, " Int. J. Sci. Technol. Res., vol. 4, no. 8, pp. 141-145, 2015.##[14] D. Karaoğlan Altop, A. Levi, and V. Tuzcu, "Deriving cryptographic keys from physiological signals, " Pervasive Mob. Comput., vol. 39, pp. 65-79, 2017, doi: 10.1016/j.pmcj.2016.08.004.##[15] O. Uzunkol and M. S. Kiraz, "Still wrong use of pairings in cryptography, " Appl. Math. Comput., vol. 333, pp. 467-479, 2018, doi: 10.1016/j.amc.2018.03.062.##[16] P. Pawlak and P.-N. Barmpaliou, "Politics of cybersecurity capacity building: conundrum and opportunity," J. Cyber Policy, vol. 2, no. 1, pp. 123-144, Jan. 2017, doi: 10.1080/23738871.2017.1294610.##[17] I. Mohammed and A. Musa Bade, "CYBERSECURITY CAPABILITY MATURITY MODEL FOR NETWORK SYSTEM Cyber Security Capability Maturity Model for Critical IT Infrastructure among Financial Organizations View project CYBERSECURITY CAPABILITY MATURITY MODEL FOR NETWORK SYSTEM," 2019. Accessed: Aug. 27, 2020. [Online]. Available: http://www.journalijdr.com.##[18] T. Roberts, "Cyber Security Capability Maturity Model (CMM) -Pilot. Retrieved February 18, 2016," 2014.##[19] F. Heylighen, "04. Cybernetics and second order cybernetics, " Encycl. Phys. Sci. Technol., pp. 1-24, 2001, [Online]. Available: http://www.nomads.usp.br/pesquisas/design/objetos_interativos/arquivos/restrito/heylighen_Cybernetics and Second-Order Cybernetics.pdf.##[20] A. Kappos and W. Pohlit, "A cybernetic model for radiation reactions in living cells: I. Sparsely-ionizing radiations; stationary cells, " Int. J. Radiat. Biol., vol. 22, no. 1, pp. 51-65, 1972, doi: 10.1080/09553007214550781.##[21] K. D. Jones and D. S. Kompala, "Cybernetic model of the growth dynamics of Saccharomyces cerevisiae in batch and continuous cultures, " J. Biotechnol., vol. 71, no. 1-3, pp. 105-131, 1999, doi: 10.1016/S0168-1656(99)00017-6.##[22] R. K. Pitman, "A cybernetic model of obsessive-compulsive psychopathology, " Compr. 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مشاهداتی روی یک طرح احراز اصالت سبک‌وزن با قابلیت گمنامی و اعتماد در اینترنت اشیا Some observations on a lightweight authentication scheme with capabilities of anonymity and trust in Internet of Things (IoT) 1 1 پروتکل‌های احراز اصالت و توافق کلید (AKA)  نقش مهمی در امنیت اینترنت اشیا (IoT) دارند. شبکه‌های حسگر بی‌سیم (WSN) یک مولفه مهم در برخی کاربردهای IoT هستند. در سال 2019، جانبابائی و همکاران یک پروتکل AKA سبک‌وزن برای WSN ارائه و ادعا کردند ویژگی‌های امنیتی مانند گمنامی و محرمانگی را تامین می‌کند. در این مقاله، چند آسیب‌پذیری مهم و غیر بدیهی از این طرح ارائه می‌شود. دقیق‌تر اینکه نشان داده می‌شود هنگام برقراری نشست با استفاده از این پروتکل، یک حسگر بدخواه می‌تواند پارامترهای محرمانه یک حسگر دیگر را به دست آورد. علاوه بر این نشان داده می­شود یک مهاجم با داشتن تنها یک کلید نشست شناخته شده، می­تواند هر کلید نشست دیگر توافق شده میان حسگرها را به دست آورد. با توجه به این ضعف­ها، حملاتی مانند حمله جعل گره حسگر و مردی در میانه روی پروتکل جانبابائی و همکاران عملی است و می­توان نشان داد این طرح، بر خلاف ادعای مولفان، نمی­تواند ویژگی گمنامی گره­های حسگر را تامین کند. ضعف مهم این طرح مربوط به انتقال کلید نشست بدون استفاده از تابع چکیده‌ساز روی آن است که برای رفع آن یک پیشنهاد ساده ارائه می­شود. 2 Over the last years‎, ‎the concept of Internet of Things (IoT) leads to a revolution in the communications of humans and things. ‎Security and efficiency could be the main challenges of that communication‎‎. ‎‎On the other hand, ‎ authenticity and confidentiality are two important goals to provide desired security in an information system‎, including IoT-based applications. An Authentication and Key Agreement (AKA) protocol is a tool to ‎achieve authenticity and agree on a secret key to reach confidentiality. Therefor using a secure AKA protocol, one can establish the mentioned security. ‎In the last years‎, ‎several articles have discussed AKA protocols in the WSN‎. ‎For example‎, ‎in 2014‎, ‎Turkanovic et al‎. proposed a new AKA scheme for the heterogeneous ad-hoc WSN. ‎In 2016‎, ‎Sabzinejad et al‎. presented an improved one. ‎In 2017‎, ‎Jiang et al‎. introduced a secure AKA protocol‎. ‎Some other AKA protocols have presented in the last three years. ‎All the mentioned protocols are lightweight ones and need minimum resources and try to decrease the computation and communication costs in the WSN context‎. ‎In 2019‎, ‎Janababaei et al. proposed an AKA scheme in the WSN for the IoT applications, in the journal of Signal and Data Processing (JSDP)‎. ‎In the context of efficiency‎, ‎the protocol only uses a hash function‎, ‎bitwise XOR‎, ‎and concatenation operation‎. ‎Hence‎, ‎it can be  considered as a lightweight protocol‎. ‎The authors also discussed the security of their scheme and claimed that the proposed protocol has the capability  to offer anonymity and trust and is secure against traceability‎, ‎impersonation‎, ‎reply and man in the middle attacks‎. ‎However, despite their claims‎, ‎this research highlights some vulnerabilities in that protocol, for the first time to the best of our knowledge‎. More precisely, we showe that a malicious sensor node can find the secret parameters of another sensor node when it establishes a session with the victimized sensor. Besides, an adversary can determine any session key of two sensor nodes, given only a known session key of them. We also show that the protocol could not satisfy the anonymity of the sensor nodes. Other attacks which influence the Janababaei et al.’s scheme, are impersonation attack on the sensor nodes and cluster heads and also the man in the middle attack. In this paper we find that the main weaknesses of the Janababaei et al.’s protocol are related to computation of the session key, . We also propose a simple remedy to enhance the security of the Janababaei et al.’s protocol. ‎An initial attempt to improve the protocol is using a hash function on the calculated key, . This suggestion is presented to enhance the security of the protocol against the observed weaknesses in this paper; but it does not mean that there are no other security issues in the protocol. Therefore, modification and improvement of the Janababaei et al.’s protocol such that it provides other security features can be considered in the future research of this paper. Besides, since in this paper we focus on the security of the protocol, then the efficiency of it was not discussed. Therefore one can consider the modification of the message structure of the protocol to reduce the computational and telecommunication costs of it as another future work in the context of this paper. 85 94 2020/10/52020/07/232020/08/212019/05/32020/08/132020/09/42020/09/14 1399/6/24 2021/12/62022/05/112021/05/242020/05/132021/12/112020/10/212021/12/11 1400/9/20 جواد علیزاده Javad Alizadeh دانشگاه جامع امام حسین ع ایران alizadja@gmail.com نصور باقری Nasour Bagheri دانشگاه تربیت دبیر شهید رجایی ایران na.bagheri@gmail.com Internet of Things Wireless Sensor Network Authentication and Key Agreement Anonymity اینترنت اشیا شبکه حسگر بی‌سیم احراز اصالت و توافق کلید گمنامی [1] Sh. Janbabaei, H. Gharaee, and N. Mohammadzadeh, "The lightweight authentication scheme with capabilities of anonymity and trust in internet of things (IoT)," SIGNAL AND DATA PROCESSING, vol. 15, no. 4 (38), 2019, (In Persian).##[2] M. A. Ferrag, L. A. Maglaras, H. Janicke, J. Jiang, and L. Shu, "Authentication protocols for internet of things: a comprehensive survey," Security and Communication Networks, 2017.##[3] J. Andress, The basics of information security: understanding the fundamentals of InfoSec in theory and practice. Syngress, 2014.##[4] M. Turkanovi'c, B. Brumen, and M. H¨olbl, "A novel user authentication and key agreement scheme for heterogeneous ad hoc wireless sensor networks, based on the internet of things notion," Ad Hoc Networks, vol. 20, pp. 96-112, 2014.##[5] M. S. Farash, M. Turkanovi'c, S. Kumari, and M. H¨olbl, "An efficient user authentication and key agreement scheme for heterogeneous wireless sensor network tailored for the internet of things environment," Ad Hoc Networks, vol. 36, pp. 152-176, 2016.##[6] R. Amin and G. Biswas, "A secure light weight scheme for user authentication and key agreement in multigateway based wireless sensor networks," Ad Hoc Networks, vol. 36, pp. 58-80, 2016.##[7] R. Amin, S. H. Islam, G. Biswas, M. K. Khan, L. Leng, and N. Kumar, "Design of an anonymity-preserving three-factor authenticated key exchange protocol for wireless sensor networks," Computer Networks, vol. 101, pp. 42-62, 2016.##[8]Y. Lu, L. Li, H. Peng, and Y. Yang, "An energy efficient mutual authentication and key agreement scheme preserving anonymity for wireless sensor networks," Sensors, vol. 16, no. 6, p. 837, 2016.##[9]Q. Jiang, S. Zeadally, J. Ma, and D. He, "Lightweight three-factor authentication and key agreement protocol for internet-integrated wireless sensor networks," IEEE Access, vol. 5, pp. 3376-3392, 2017.##[10]R. Ali, A. K. Pal, S. Kumari, M. Karuppiah, and M. Conti, "A secure user authentication and keyagreement scheme using wireless sensor networks for agriculture monitoring," Future Generation Computer Systems, vol. 84, pp. 200-215, 2018.##[11]Y. Lu, G. Xu, L. Li, and Y. Yang, "Anonymous threefactor authenticated key agreement for wireless sensor networks," Wireless Networks, vol. 25, no. 4, pp. 1461-1475, 2019.##[12]S. Athmani, A. Bilami, and D. E. Boubiche, "Edak: An efficient dynamic authentication and key management mechanism for heterogeneous wsns," Future Generation Computer Systems, vol. 92, pp. 789-799, 2019.##[13]M. Nikravan and A. Reza, "A multi-factor user authentication and key agreement protocol based on bilinear pairing for the internet of things," Wireless Personal Communications, vol. 111, no. 1, pp. 463-494, 2020.##[14]Y. Yu, L. Hu, and J. Chu, "A secure authentication and key agreement scheme for iot-based cloud computing environment," Symmetry, vol. 12, no. 1, p. 150, 2020.##[1] جانبابائی شادی، قرائی حسین، محمد زاده ناصر. ارائه طرح احراز اصالت سبک با قابلیت گمنامی و اعتماد در اینترنت اشیا. پردازش علائم و داده‌ها. ۱۳۹۷; ۱۵ (۴) :۱۱۱-۱۲۲##[1] Sh. Janbabaei, H. Gharaee, and N. Mohammadzadeh, "The lightweight authentication scheme with capabilities of anonymity and trust in internet of things (IoT)," SIGNAL AND DATA PROCESSING, vol. 15, no. 4 (38), 2019, (In Persian).##[2] M. A. Ferrag, L. A. Maglaras, H. Janicke, J. Jiang, and L. Shu, "Authentication protocols for internet of things: a comprehensive survey," Security and Communication Networks, 2017.##[3] J. Andress, The basics of information security: understanding the fundamentals of InfoSec in theory and practice. Syngress, 2014.##[4] M. Turkanovi'c, B. Brumen, and M. H¨olbl, "A novel user authentication and key agreement scheme for heterogeneous ad hoc wireless sensor networks, based on the internet of things notion," Ad Hoc Networks, vol. 20, pp. 96-112, 2014.##[5] M. S. Farash, M. Turkanovi'c, S. Kumari, and M. H¨olbl, "An efficient user authentication and key agreement scheme for heterogeneous wireless sensor network tailored for the internet of things environment," Ad Hoc Networks, vol. 36, pp. 152-176, 2016.##[6] R. Amin and G. Biswas, "A secure light weight scheme for user authentication and key agreement in multigateway based wireless sensor networks," Ad Hoc Networks, vol. 36, pp. 58-80, 2016.##[7] R. Amin, S. H. Islam, G. Biswas, M. K. Khan, L. Leng, and N. Kumar, "Design of an anonymity-preserving three-factor authenticated key exchange protocol for wireless sensor networks," Computer Networks, vol. 101, pp. 42-62, 2016.##[8]Y. Lu, L. Li, H. Peng, and Y. Yang, "An energy efficient mutual authentication and key agreement scheme preserving anonymity for wireless sensor networks," Sensors, vol. 16, no. 6, p. 837, 2016.##[9]Q. Jiang, S. Zeadally, J. Ma, and D. He, "Lightweight three-factor authentication and key agreement protocol for internet-integrated wireless sensor networks," IEEE Access, vol. 5, pp. 3376-3392, 2017.##[10]R. Ali, A. K. Pal, S. Kumari, M. Karuppiah, and M. Conti, "A secure user authentication and keyagreement scheme using wireless sensor networks for agriculture monitoring," Future Generation Computer Systems, vol. 84, pp. 200-215, 2018.##[11]Y. Lu, G. Xu, L. Li, and Y. Yang, "Anonymous threefactor authenticated key agreement for wireless sensor networks," Wireless Networks, vol. 25, no. 4, pp. 1461-1475, 2019.##[12]S. Athmani, A. Bilami, and D. E. Boubiche, "Edak: An efficient dynamic authentication and key management mechanism for heterogeneous wsns," Future Generation Computer Systems, vol. 92, pp. 789-799, 2019.##[13]M. Nikravan and A. Reza, "A multi-factor user authentication and key agreement protocol based on bilinear pairing for the internet of things," Wireless Personal Communications, vol. 111, no. 1, pp. 463-494, 2020.##[14]Y. Yu, L. Hu, and J. Chu, "A secure authentication and key agreement scheme for iot-based cloud computing environment," Symmetry, vol. 12, no. 1, p. 150, 2020.## ##

خوشه‌بندی ترکیبی با بیشینه‌سازی تنوع با به-کارگیری الگوریتم‌های بهینه‌سازی تکاملی The ensemble clustering with maximize diversity using evolutionary optimization algorithms 1 1 خوشه‌بندی داده‌ها یکی از مراحل اصلی در داده‌کاوی است که وظیفه کاوش الگوهای پنهان در داده‌های بدون برچسب را بر عهده دارد. به خاطر پیچیدگی مسئله و ضعف روش‌های خوشه‌بندی پایه، امروزه اکثر مطالعات به­سمت روش‌های خوشه‌بندی ترکیبی هدایت شده است. پراکندگی در نتایج اولیه یکی از مهم‌ترین عواملی است که می‌تواند در کیفیت نتایج نهایی اثرگذار باشد. همچنین، کیفیت نتایج اولیه نیز عامل دیگری است که در کیفیت نتایج حاصل از ترکیب موثر است. هر دو عامل در تحقیقات اخیر خوشه‌بندی ترکیبی مورد توجه قرار گرفته‌اند. در این­جا یک چارچوب جدید برای بهبود کارایی خوشه‌بندی ترکیبی پیشنهاد شده است که مبتنی بر استفاده از زیرمجموعه‌ای از خوشه‌های اولیه می‌باشند روش ارائه شده نشان می­دهد که  استفاده از زیرمجموعه‌ای از نتایج خوشه‌بندی‌های اولیه می‌تواند بهتر از استفاده از کل نتایج باشد همچنین معیاری را پشنهاد می­دهد  که چگونه  نتایج اولیه نسبت به هم ارزیابی شوند. این تحقیق معیاری ارایه می­دهد که به وسیله آن میتوان تشخیص داد کدام زیرمجموعه از نتایج اولیه می‌تواند منجر به بهبود عملکرد خوشه‌بندی ترکیبی شود.  از آن­جایی که الگوریتم­های هوشمند تکاملی توانسته­اند اکثریت مسائل پیچیده مهندسی را حل نمایند، در این مقاله نیز از این روش­های هوشمند برای انتخاب زیرمجموعه­ای از خوشه­های اولیه استفاده شده است.  این انتخاب به کمک سه روش هوشمند (الگوریتم ژنتیک، شبیه­سازی تبرید و الگوریتم ازدحام ذرات) انجام می­گیرد. ایده‌های اصلی در روش‌های پیشنهادی برای انتخاب زیرمجموعه‌ای از خوشه‌ها، استفاده از خوشه‌های پایدار به کمک الگوریتم­های جستجوی هوشمند (الگوریتم­های تکاملی) می‌باشند. برای ارزیابی خوشه‌ها، از معیار پایداری مبتنی بر اطلاعات متقابل استفاده شده است. در آخر نیز خوشه­های انتخاب شده را به کمک چندین روش ترکیب نهایی با هم جمع می­­کنیم. نتایج تجربی روی چندین مجموعه داده استاندارد و با معیارهای ارزیابی اطلاعات متقابل نرمال شده، فیشر و دقت در مقایسه با روش­­های علیزاده، عظیمی، Berikov ، CLWGC، RCESCC، KME، CFSFDP،DBSCAB، NSC و Chenنشان می‌دهد که روش­های‌ پیشنهادی می‌تواند به طور موثری روش ترکیب کامل  را بهبود دهد.     کلیدواژه‌ها: بهینه ­سازی محلی،  پراکندگی، الگوریتم­های تکاملی، ماتریس همبستگی،  پراکندگی. 2 Data clustering is one of the main steps in data mining, which is responsible for exploring hidden patterns in non-tagged data. Due to the complexity of the problem and the weakness of the basic clustering methods, most studies today are guided by clustering ensemble methods. Diversity in primary results is one of the most important factors that can affect the quality of the final results. Also, the quality of the initial results is another factor that affects the quality of the results of the ensemble. Both factors have been considered in recent research on ensemble clustering. Here, a new framework for improving the efficiency of clustering has been proposed, which is based on the use of a subset of primary clusters, and the proposed method answers the above questions and ambiguities. The selection of this subset plays a vital role in the efficiency of the assembly. Since evolutionary intelligent algorithms have been able to solve the majority of complex engineering problems, this paper also uses these intelligent methods to select subsets of primary clusters. This selection is done using three intelligent methods (genetic algorithm, simulation annealing and particle swarm optimization). In this paper a clustering ensemble method is proposed which is based on a subset of primary clusters. The main idea behind this method is using more stable clusters in the ensemble. The stability is applied as a goodness measure of the clusters. The clusters which satisfy a threshold of this measure are selected to participate in the ensemble. For combining the chosen clusters, a co-association based consensus function is applied. A new EAC based method which is called Extended Evidence Accumulation Clustering, EEAC, is proposed for constructing the Co-association Matrix from the subset of clusters. Experimental results on several standard datasets with normalized mutual information evaluation, Fisher and accuracy criteria compared to Alizadeh, Azimi, Berikov, CLWGC, RCESCC, KME, CFSFDP, DBSCAB, NSC and Chen methods show the significant improvement of the proposed method in comparison with other ones.  Keywords: Clustering Ensemble, local optimization, evolutionary algorithm, correlation matrix, diversity. 95 120 2020/10/52020/07/232020/08/212019/05/32020/08/132020/09/42020/09/142020/05/17 1399/2/28 2021/12/62022/05/112021/05/242020/05/132021/12/112020/10/212021/12/112022/05/11 1401/2/21 صدراله عباسی sadrollah Abbasi دانشکده مهندسی کامپیوتر، واحد یاسوج، دانشگاه آزاد اسلامی، یاسوج، ایران ایران s.abbasi680@gmail.com صمد نجاتیان Samad Nejatian دانشکده مهندسی برق، واحد یاسوج، دانشگاه آزاد اسلامی، کهگیلویه و بویراحمد، ایران ایران samad.nej.2007@gmail.com حمید پروین Hamid Parvin دانشگاه آزاد اسلامی واحد نورآباد ممسنی، فارس، ایران ایران parvin@iust.ac.ir وحیده رضایی Vahideh Rezaei دانشکده ریاضی، واحد یاسوج، دانشگاه آزاد اسلامی، کهگیلویه و بویراحمد، ایران ایران vahidehrezaie@gmail.com کرم اله باقری فرد Karamollah Bagheri Fard دانشکده مهندسی کامپیوتر، واحد یاسوج، دانشگاه آزاد اسلامی، یاسوج، ایران ایران k.bagherifard@iauyasooj.ac.ir Clustering Ensemble local optimization evolutionary algorithm correlation matrix diversity. بهینه‌سازی محلی تنوع الگوریتم‌های تکاملی ماتریس همبستگی تنوع. [1] Azimi J., The investigation of the Ensemble Clustering Diversity. MSc Thesis. Iran University of Science and Technology, 2006.##[2] Alizadeh A., Minaei-Bidgoli B., Parvin H. Cluster ensemble selection based on a new cluster stability measure. Intell. Data Anal. 18(3): 389-408, 2014.##[3] Jain A., Murty M. N., and Flynn P. (1999), Data clustering: A review. ACM Computing Surveys, 31(3):264-323.##[4] Faceli K., Marcilio C.P. Souto d., Multi-objective Clustering Ensemble, Proceedings of the Sixth International Conference on Hybrid Intelligent Systems (HIS'06), 2006.##[5] Strehl A. and Ghosh J., "Cluster ensembles - a knowledge reuse framework for combining multiple partitions". Journal of Machine Learning Research, 3(Dec):583-617, 2002.##[6] Fred, A. and Jain, A.K. "Data Clustering Using Evidence Accumulation", Proc. of the 16th Intl. Conf. on Pattern Recognition, ICPR02, Quebec City, pp. 276 - 280, 2002.##[7] Topchy, A., Jain, A.K. and Punch, W.F., "Combining Multiple Weak Clusterings", Proc. 3d IEEE Intl. Conf. on Data Mining, pp. 331-338, 2003.##[8] Fred A. and Lourenco A. (2008), "Cluster Ensemble Methods: from Single Clusterings to Combined Solutions", Studies in Computational Intelligence (SCI), 126, 3-30.##[9] Ayad H.G. and Kamel M.S., Cumulative Voting Consensus Method for Partitions with a Variable Number of Clusters, IEEE Trans. on Pattern Analysis and Machine Intelligence, VOL. 30, NO. 1, 160-173, 2008.##[10] Minaei-Bidgoli B., Topchy A. and Punch W.F., "Ensembles of Partitions via Data Resampling", in Proc. Intl. Conf. on Information Technology, ITCC 04, Las Vegas, 2004.##[11] Parvin H., Minaei-Bidgoli B. "A clustering ensemble framework based on selection of fuzzy weighted clusters in a locally adaptive clustering algorithm". Pattern Anal. Appl. 18(1): 87-112, 2015.##[12] Alizadeh H., Minaei-Bidgoli B., Parvin H. Optimizing Fuzzy Cluster Ensemble in String Representation. IJPRAI 27(2), 2013.##[13] Parvin H., Minaei-Bidgoli B., Alinejad-Rokny H., Punch W.F. "Data weighing mechanisms for clustering ensembles". Computers & Electrical Engineering 39(5): 1433-1450, 2013.##[14] Barthelemy J.P. and Leclerc B., The median procedure for partition, In Partitioning Data Sets, AMS DIMACS Series in Discrete Mathematics, Cox, I. J. et al eds., 19, pp. 3-34, 1995.##[15] Fern X.Z., and Lin W., "Cluster Ensemble Selection". Statistical Analysis and Data Mining 1(3): 128-141, 2008.##[16] Parvin H., Mirnabibaboli M., Alinejad-Rokny H. "Proposing a classifier ensemble framework based on classifier selection and decision tree". Eng. Appl. of AI 37: 34-42, 2015.##[17] Dudoit S. and Fridlyand, J., Bagging to improve the accuracy of a clustering procedure, Bioinformatics, 19 (9), pp. 1090-1099, 2003.##[18] Fischer B. and Buhmann J.M., "Bagging for path-based clustering", IEEE Transactions on Pattern Analysis and Machine Intelligence, pp.1411-1415, 2003.##[19] Fred A. and Jain A.K., "Robust data clustering", in: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR ,USA, vol. II, pp. 128-136, 2003.##[20] Fred A.L. and Jain A.K. "Combining Multiple Clusterings Using Evidence Accumulation". IEEE Trans. on Pattern Analysis and Machine Intelligence, 27(6):835-850, 2005.##[21] Kuncheva L.I. and Hadjitodorov S. "Using diversity in cluster ensembles". In Proc. of IEEE Intl. Conference on Systems, Man and Cybernetics, pages 1214-1219, 2004.##[22] Kuncheva L.I. and Whitaker C. J., "Measures of diversity in classifier ensembles", Machine Learning, 2003.##[23] Baumgartner R., Somorjai R., Summers R., Richter W., Ryner L., and Jarmasz M., Resampling as a Cluster Validation Technique in fMRI, JOURNAL OF MAGNETIC RESONANCE IMAGING 11: pp. 228-231, 2000.##https://doi.org/10.1002/(SICI)1522-2586(200002)11:23.0.CO;2-Z##[24] Breckenridge J., Replicating cluster analysis: Method, consistency and validity, Multivariate Behavioral research, 1989.##[25] Shamiry O., Tishby N., "Cluster Stability for Finite Samples", 21st Annual Conference on Neural Information Processing Systems (NIPS07), 2007.##[26] Roth V., Braun M.L., Lange T., and Buhmann J.M., "Stability-Based Model Order Selection in Clustering with Applications to Gene Expression Data", ICANN 2002, LNCS 2415, pp. 607-612, 2002a.##[27] Roth V., Lange T., Braun M., and Buhmann J., A "Resampling Approach to Cluster Validation", Intl. Conf. on Computational Statistics, COMPSTAT, 2002b.##[28] Saha A., Das S. "Categorical fuzzy k-modes clustering with automated feature weight learning". Neurocomputing 166: 422-435, 2015.##[29] Law M.H.C., Topchy A.P., and Jain A.K. "Multiobjective data clustering". In Proc. of IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 424-430, Washington D.C, 2004.##[30] Akbari E., Dahlan H.M., Ibrahim R., Alizadeh H.: Hierarchical cluster ensemble selection. Eng. Appl. of AI 39: 146-156 2015.##[31] Iam-On, N. and T. Boongoen, "Diversity-driven generation of link-based cluster ensemble and application to data classification", Expert Systems with Applications, 42(21): p. 8259-8273, 2015.##[32] Melanie M., "An Introduction to Genetic Algorithms", A Bradford Book The MIT Press, Cambridge, Massachusetts. London, England, Fifth printing, 1999.##[33] Aarts E. H. L. and Korst J. Simulated Annealing and Boltzmann Machines, John Wiley & Sons, Essex, U.K, 1989.##[34] Kennedy J and Eberhart R.C., "Particle Swarm Optimization", Proceedings of IEEE International Conference on Neural Networks", Piscataway, NJ, pp. 1942-1948, 1995.##[35] Fred A. and Jain A.K., "Learning Pairwise Similarity for Data Clustering", In Proc. of the 18th Int. Conf. on Pattern Recognition (ICPR'06), 2006.##[36] Fridlyand J. and Dudoit S. "Applications of resampling methods to estimate the number of clusters and to improve the accuracy of a clustering method". Stat. Berkeley Tech Report. No. 600, 2001.##[37] X. Fern, C. Brodley, "Solving cluster ensemble problems by bipartite graph partitioning", Proc. of the 21st International Conference on Machine Learning, 2004.##[38] D. Huang, J. Lai, C. D. Wang, "Ensemble clustering using factor graph", Pattern Recognition, vol. 50, pp. 131-142, 2016.##[39] M. Selim, E. Ertunc, "Combining multiple clusterings using similarity graph", Pattern Recognition, vol. 44, no. 3, 694-703, 2011.##[40] C. Boulis, M. Ostendorf, "Combining multiple clustering systems", Proc. European Conf. Principles and Practice of Knowledge Discovery in Databases, 2004.##[41] A. Topchy, B. Minaei-Bidgoli, A. Jain, "Adaptive clustering ensembles", Proc. the 17th International Conference on Pattern Recognition, 2004.##[42] P. Hore, L. O. Hall, B. Goldgo, "A scalable framework for cluster ensembles", Pattern Recognition, vol. 42, no. 5, 676-688, 2009.##[43] B. Long, Z. Zhang, P. S. Yu, "Combining multiple clusterings by soft correspondence", Proc. the 4th IEEE International Conference on Data Mining, 2005.##[44] D. Cristofor, D. Simovici, "Finding median partitions using information theoretical based genetic algorithms", J. Universal Computer Science, vol. 8, no. 2, pp. 153-172, 2002.##[45] A. Topchy, A. Jain, W. Punch, "Clustering ensembles: Models of consensus and weak partitions", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 12, 1866-1881, 2005.##[46] H. Wang, H. Shan, A. Banerjee, "Bayesian cluster ensembles", Statistical Analysis and Data Mining, vol. 4, no. 1, pp. 54-70, 2011.##[47] Z. He, X. Xu, S. Deng, "A cluster ensemble method for clustering categorical data", Information Fusion, vol. 6, no. 2, pp. 143C151, 2005.##[48] N. Nguyen, R. Caruana, "Consensus Clusterings", Proc. IEEE Intl Conf. Data Mining, pp. 607-612, 2007.##[49] Z. Huang, "Extensions to the kmeans algorithm for clustering large data sets with categorical values", Data Mining and Knowledge Discovery, vol. 2, no. 3, pp. 283-304, 1998.##[50] S. Abbasi, S. Nejatian, H. Parvin, V. Rezaie ;K. Bagherifard, "Clustering ensemble selection considering quality and diversity, " Artificial Intelligence Review, vol. 52, PP. 1311-1340, Springer Nature B.V. 2018, ##https://doi.org/10.1007/s10462-018-9642-2##[51] A. Bagherinia, B. Minaei-Bidgoli, M. Hossinzadeh, H. Parvin, "Elite fuzzy clustering ensemble based on clustering diversity and quality measures, " Springer Science+Business Media, LLC, part of Springer Nature, Applied Intelligence, 49, PP. 1724-1747, 2019. ##https://doi.org/10.1007/s10489-018-1332-x##[52] A. Nazari, A. Dehghan, S Nejatian, V. Rezaie, H. Parvin, "A comprehensive study of clustering ensemble weighting based on cluster quality and diversity, " Pattern Analysis and Applications, vol. 22, pp.133-145, 2019.##[53] M. Mojarad, S. Nejatian, H. Parvin, M. Mohammadpoor, "A fuzzy clustering ensemble based on cluster clustering and iterative Fusion of base clusters, " The International Journal of Research on Systems for Real Life Complex Problems, Applied Intelligence vol. 49, pp. 2567-2581, 2019.##[54] Z. Chen, A. Bagherinia B. Minaei-Bidgoli, H. Parvin, Pho KH. Fuzzy Clustering Ensemble Considering Cluster Dependability. International Journal on Artificial Intelligence Tools. 2021 Mar 26;30(02):2150007##[55] V. Berikov, "A probabilistic model of fuzzy clustering ensemble." Pattern Recognition and Image Analysis 28, no. 1 (2018): 1-10.##[56] moradi M, nejatian S, parvin H, bagherifard K, rezaei V. Clustering and Memory-based Parent-Child Swarm Meta-heuristic Algorithm for Dynamic Optimization. JSDP 2021; 18 (3) :127-146##[57] Omidvar M, Nejatian S, Parvin H, Bagherifard K, Rezaie V. Providing an algorithm for solving general optimization problems based on Domino theory. JSDP 2022; 19 (2) :87-106##[1] Azimi J., The investigation of the Ensemble Clustering Diversity. MSc Thesis. Iran University of Science and Technology, 2006.##[1] عظیمی ج، " بررسی پراکندگی در خوشه‌بندی ترکیبی"، پایان‌نامه کارشناسیارشد، دانشگاه علم و صنعت ایران، خرداد 1386.##[2] Alizadeh A., Minaei-Bidgoli B., Parvin H. Cluster ensemble selection based on a new cluster stability measure. Intell. Data Anal. 18(3): 389-408, 2014.##[3] Jain A., Murty M. N., and Flynn P. (1999), Data clustering: A review. ACM Computing Surveys, 31(3):264-323.##[4] Faceli K., Marcilio C.P. Souto d., Multi-objective Clustering Ensemble, Proceedings of the Sixth International Conference on Hybrid Intelligent Systems (HIS'06), 2006.##[5] Strehl A. and Ghosh J., "Cluster ensembles - a knowledge reuse framework for combining multiple partitions". Journal of Machine Learning Research, 3(Dec):583-617, 2002.##[6] Fred, A. and Jain, A.K. "Data Clustering Using Evidence Accumulation", Proc. of the 16th Intl. Conf. on Pattern Recognition, ICPR02, Quebec City, pp. 276 - 280, 2002.##[7] Topchy, A., Jain, A.K. and Punch, W.F., "Combining Multiple Weak Clusterings", Proc. 3d IEEE Intl. Conf. on Data Mining, pp. 331-338, 2003.##[8] Fred A. and Lourenco A. (2008), "Cluster Ensemble Methods: from Single Clusterings to Combined Solutions", Studies in Computational Intelligence (SCI), 126, 3-30.##[9] Ayad H.G. and Kamel M.S., Cumulative Voting Consensus Method for Partitions with a Variable Number of Clusters, IEEE Trans. on Pattern Analysis and Machine Intelligence, VOL. 30, NO. 1, 160-173, 2008.##[10] Minaei-Bidgoli B., Topchy A. and Punch W.F., "Ensembles of Partitions via Data Resampling", in Proc. Intl. Conf. on Information Technology, ITCC 04, Las Vegas, 2004.##[11] Parvin H., Minaei-Bidgoli B. "A clustering ensemble framework based on selection of fuzzy weighted clusters in a locally adaptive clustering algorithm". Pattern Anal. Appl. 18(1): 87-112, 2015.##[12] Alizadeh H., Minaei-Bidgoli B., Parvin H. Optimizing Fuzzy Cluster Ensemble in String Representation. IJPRAI 27(2), 2013.##[13] Parvin H., Minaei-Bidgoli B., Alinejad-Rokny H., Punch W.F. "Data weighing mechanisms for clustering ensembles". Computers & Electrical Engineering 39(5): 1433-1450, 2013.##[14] Barthelemy J.P. and Leclerc B., The median procedure for partition, In Partitioning Data Sets, AMS DIMACS Series in Discrete Mathematics, Cox, I. J. et al eds., 19, pp. 3-34, 1995.##[15] Fern X.Z., and Lin W., "Cluster Ensemble Selection". Statistical Analysis and Data Mining 1(3): 128-141, 2008.##[16] Parvin H., Mirnabibaboli M., Alinejad-Rokny H. "Proposing a classifier ensemble framework based on classifier selection and decision tree". Eng. Appl. of AI 37: 34-42, 2015.##[17] Dudoit S. and Fridlyand, J., Bagging to improve the accuracy of a clustering procedure, Bioinformatics, 19 (9), pp. 1090-1099, 2003.##[18] Fischer B. and Buhmann J.M., "Bagging for path-based clustering", IEEE Transactions on Pattern Analysis and Machine Intelligence, pp.1411-1415, 2003.##[19] Fred A. and Jain A.K., "Robust data clustering", in: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR ,USA, vol. II, pp. 128-136, 2003.##[20] Fred A.L. and Jain A.K. "Combining Multiple Clusterings Using Evidence Accumulation". IEEE Trans. on Pattern Analysis and Machine Intelligence, 27(6):835-850, 2005.##[21] Kuncheva L.I. and Hadjitodorov S. "Using diversity in cluster ensembles". In Proc. of IEEE Intl. Conference on Systems, Man and Cybernetics, pages 1214-1219, 2004.##[22] Kuncheva L.I. and Whitaker C. J., "Measures of diversity in classifier ensembles", Machine Learning, 2003.##[23] Baumgartner R., Somorjai R., Summers R., Richter W., Ryner L., and Jarmasz M., Resampling as a Cluster Validation Technique in fMRI, JOURNAL OF MAGNETIC RESONANCE IMAGING 11: pp. 228-231, 2000.##https://doi.org/10.1002/(SICI)1522-2586(200002)11:23.0.CO;2-Z##[24] Breckenridge J., Replicating cluster analysis: Method, consistency and validity, Multivariate Behavioral research, 1989.##[25] Shamiry O., Tishby N., "Cluster Stability for Finite Samples", 21st Annual Conference on Neural Information Processing Systems (NIPS07), 2007.##[26] Roth V., Braun M.L., Lange T., and Buhmann J.M., "Stability-Based Model Order Selection in Clustering with Applications to Gene Expression Data", ICANN 2002, LNCS 2415, pp. 607-612, 2002a.##[27] Roth V., Lange T., Braun M., and Buhmann J., A "Resampling Approach to Cluster Validation", Intl. Conf. on Computational Statistics, COMPSTAT, 2002b.##[28] Saha A., Das S. "Categorical fuzzy k-modes clustering with automated feature weight learning". Neurocomputing 166: 422-435, 2015.##[29] Law M.H.C., Topchy A.P., and Jain A.K. "Multiobjective data clustering". In Proc. of IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 424-430, Washington D.C, 2004.##[30] Akbari E., Dahlan H.M., Ibrahim R., Alizadeh H.: Hierarchical cluster ensemble selection. Eng. Appl. of AI 39: 146-156 2015.##[31] Iam-On, N. and T. Boongoen, "Diversity-driven generation of link-based cluster ensemble and application to data classification", Expert Systems with Applications, 42(21): p. 8259-8273, 2015.##[32] Melanie M., "An Introduction to Genetic Algorithms", A Bradford Book The MIT Press, Cambridge, Massachusetts. London, England, Fifth printing, 1999.##[33] Aarts E. H. L. and Korst J. Simulated Annealing and Boltzmann Machines, John Wiley & Sons, Essex, U.K, 1989.##[34] Kennedy J and Eberhart R.C., "Particle Swarm Optimization", Proceedings of IEEE International Conference on Neural Networks", Piscataway, NJ, pp. 1942-1948, 1995.##[35] Fred A. and Jain A.K., "Learning Pairwise Similarity for Data Clustering", In Proc. of the 18th Int. Conf. on Pattern Recognition (ICPR'06), 2006.##[36] Fridlyand J. and Dudoit S. "Applications of resampling methods to estimate the number of clusters and to improve the accuracy of a clustering method". Stat. Berkeley Tech Report. No. 600, 2001.##[37] X. Fern, C. Brodley, "Solving cluster ensemble problems by bipartite graph partitioning", Proc. of the 21st International Conference on Machine Learning, 2004.##[38] D. Huang, J. Lai, C. D. Wang, "Ensemble clustering using factor graph", Pattern Recognition, vol. 50, pp. 131-142, 2016.##[39] M. Selim, E. Ertunc, "Combining multiple clusterings using similarity graph", Pattern Recognition, vol. 44, no. 3, 694-703, 2011.##[40] C. Boulis, M. Ostendorf, "Combining multiple clustering systems", Proc. European Conf. Principles and Practice of Knowledge Discovery in Databases, 2004.##[41] A. Topchy, B. Minaei-Bidgoli, A. Jain, "Adaptive clustering ensembles", Proc. the 17th International Conference on Pattern Recognition, 2004.##[42] P. Hore, L. O. Hall, B. Goldgo, "A scalable framework for cluster ensembles", Pattern Recognition, vol. 42, no. 5, 676-688, 2009.##[43] B. Long, Z. Zhang, P. S. Yu, "Combining multiple clusterings by soft correspondence", Proc. the 4th IEEE International Conference on Data Mining, 2005.##[44] D. Cristofor, D. Simovici, "Finding median partitions using information theoretical based genetic algorithms", J. Universal Computer Science, vol. 8, no. 2, pp. 153-172, 2002.##[45] A. Topchy, A. Jain, W. Punch, "Clustering ensembles: Models of consensus and weak partitions", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 12, 1866-1881, 2005.##[46] H. Wang, H. Shan, A. Banerjee, "Bayesian cluster ensembles", Statistical Analysis and Data Mining, vol. 4, no. 1, pp. 54-70, 2011.##[47] Z. He, X. Xu, S. Deng, "A cluster ensemble method for clustering categorical data", Information Fusion, vol. 6, no. 2, pp. 143C151, 2005.##[48] N. Nguyen, R. Caruana, "Consensus Clusterings", Proc. IEEE Intl Conf. Data Mining, pp. 607-612, 2007.##[49] Z. Huang, "Extensions to the kmeans algorithm for clustering large data sets with categorical values", Data Mining and Knowledge Discovery, vol. 2, no. 3, pp. 283-304, 1998.##[50] S. Abbasi, S. Nejatian, H. Parvin, V. Rezaie ;K. Bagherifard, "Clustering ensemble selection considering quality and diversity, " Artificial Intelligence Review, vol. 52, PP. 1311-1340, Springer Nature B.V. 2018, ##https://doi.org/10.1007/s10462-018-9642-2##[51] A. Bagherinia, B. Minaei-Bidgoli, M. Hossinzadeh, H. Parvin, "Elite fuzzy clustering ensemble based on clustering diversity and quality measures, " Springer Science+Business Media, LLC, part of Springer Nature, Applied Intelligence, 49, PP. 1724-1747, 2019. ##https://doi.org/10.1007/s10489-018-1332-x##[52] A. Nazari, A. Dehghan, S Nejatian, V. Rezaie, H. Parvin, "A comprehensive study of clustering ensemble weighting based on cluster quality and diversity, " Pattern Analysis and Applications, vol. 22, pp.133-145, 2019.##[53] M. Mojarad, S. Nejatian, H. Parvin, M. Mohammadpoor, "A fuzzy clustering ensemble based on cluster clustering and iterative Fusion of base clusters, " The International Journal of Research on Systems for Real Life Complex Problems, Applied Intelligence vol. 49, pp. 2567-2581, 2019.##[54] Z. Chen, A. Bagherinia B. Minaei-Bidgoli, H. Parvin, Pho KH. Fuzzy Clustering Ensemble Considering Cluster Dependability. International Journal on Artificial Intelligence Tools. 2021 Mar 26;30(02):2150007##[55] V. Berikov, "A probabilistic model of fuzzy clustering ensemble." Pattern Recognition and Image Analysis 28, no. 1 (2018): 1-10.##[56] moradi M, nejatian S, parvin H, bagherifard K, rezaei V. Clustering and Memory-based Parent-Child Swarm Meta-heuristic Algorithm for Dynamic Optimization. JSDP 2021; 18 (3) :127-146##[57] Omidvar M, Nejatian S, Parvin H, Bagherifard K, Rezaie V. Providing an algorithm for solving general optimization problems based on Domino theory. JSDP 2022; 19 (2) :87-106## ##

ترکیب روش های تجمیعی داده کاوی برای کشف تراکنش های تقلب در کارت های اعتباری Combination of Ensemble Data Mining Methods for Detecting Credit Card Fraud Transactions 1 1 کارت­های اعتباری باعث سرعت بخشیدن و سهولت زندگی تمامی شهروندان و مشتریان بانک­ها می­شود. این امر موجب استفاده گسترده و روزافزون جهت پرداخت آسان پول از طریق تلفن همراه، اینترنت، دستگاههای خودپرداز و غیره می­باشد. با وجود محبوبیت کارت­های اعتباری، مشکلات امنیتی مختلف مانند تقلب برای آن وجود دارد. همان­طور که روش­های امنیتی بروز می­شوند، متقلبان نیز روش­های خود را بروز می­کنند که این امر موجب نگرانی بانک­ها و مشتریان آنها می­شود. به همین دلیل محققان سعی کردند راه حل های مختلفی جهت تشخیص، پیش­بینی و پیشگیری از تقلب در کارت های اعتباری ارائه دهند. یکی از روش­ها روش داده­کاوی و یادگیری ماشین است. یکی از با اهمیت ترین مسائل در این زمینه، دقت و کارایی است. در این پژوهش روش­های Gradient Boosting که زیر مجموعه روش­های تجمیعی و یادگیری ماشین هستند را بررسی کرده و با ترکیب روش­ها نرخ خطا را کاهش و دقت تشخیص را بهبود می­دهیم. بنابراین دو الگوریتم LightGBM و XGBoost را مقایسه کرده و سپس آنها را با استفاده از روش­های تجمیعی میانگین­گیری ساده و وزن­دار ترکیب نمودیم و در نهایت مدل­ها را بوسیله AUC و Recall وscore - F1 و  Precisionو Accuracy ارزیابی کردیم. مدل پیشنهادی پس از اعمال مهندسی ویژگی با استفاده از روش میانگین­گیری وزن­دار به ترتیب برای روش­های ارزیابی مذکور به اعدادی معادل 08/95، 57/90، 35/89، 28/88 و 27/99 رسیده است. بر این اساس مهندسی ویژگی و میانگین­گیری وزن­دار تاثیر به سزایی در بهبود دقت پیش­بینی و شناسایی داشتند. 2 As we know, credit cards speed up and make life easier for all citizens and bank customers. They can use it anytime and anyplace according to their personal needs, instantly and quickly and without hassle, without worrying about carrying a lot of cash and more security than having liquidity. Together, these factors make credit cards one of the most popular forms of online banking. This has led to widespread and increasing use for easy payment for purchases made through mobile phones, the Internet, ATMs, and so on. Despite the popularity and ease of payment with credit cards, there are various security problems, increasing day by day. One of the most important and constant challenges in this field is credit card fraud all around the world. Due to the increasing security issues in credit cards, fraudsters are also updating themselves. In general, as a field grows in popularity, more fraudsters are attracted to it, and this is where credit card security comes into play. So naturally, this worries banks and their customers around the world. Meanwhile, financial information acts as the main factor in market financial transactions. For this reason, many researchers have tried to prioritize various solutions for detecting, predicting, and preventing credit card fraud in their research work and provide essential suggestions that have been associated with significant success. One of the practical and successful methods is data mining and machine learning. In these methods, one of the most critical parameters in fraud prediction and detection is the accuracy of fraud transaction detection. This research intends to examine the Gradient Boosting methods, which are a subset of Ensemble Learning and machine learning methods. By combining these methods, we can identify credit card fraud, reduce error rates, and improve the detection process, which in turn increases efficiency and accuracy. This study compared the two algorithms LightGBM and XGBoost, merged them using simple and weighted averaging techniques, and then evaluate the models using AUC, Recall, F1-score, Precision, and Accuracy. The proposed model provided 95.08, 90.57, 89.35, 88.28, and 99.27, respectively, after applying feature engineering and using the weighted average approach for the mentioned validation parameters. As a result, function engineering and weighted averaging significantly improved prediction and detection accuracy. 121 136 2020/10/52020/07/232020/08/212019/05/32020/08/132020/09/42020/09/142020/05/172021/05/26 1400/3/5 2021/12/62022/05/112021/05/242020/05/132021/12/112020/10/212021/12/112022/05/112022/05/11 1401/2/21 سعید بختیاری Saeid Bakhtiari دانشگاه امین ایران Saeid_bakhtiarii@yahoo.com زهرا نصیری Zahra Nasiri موسسه آموزش عالی آل طه ایران Lnasiri007@gmail.com سید محمد صادق حجازی Seyed Mohammad Sadegh Hejazi موسسه آموزش عالی پردیسان ایران Sadegh.hejazi@hotmail.com Fraud Detection Credit Card Ensemble Learning Data Mining تشخیص تقلب کارت اعتباری یادگیری تجمیعی داده کاوی [1] t. o. c. cart. [Online]. Available: [https://www.thebalance.com/key-differences-between-visa-mastercard-discover-anmerican-express-4588450#citation-4.##[2] "Performance Evaluation of Credit Card Fraud Transactions using Boosting Algorithms, " International Journal of Electronics Communication and Computer Engineering, vol. 10, no. 6, pp. 262-270, 2019.##[3] J. Huang, "Credit Card Transaction Fraud Using Machine Learning Algorithms, " in 2019 International Conference on Education Science and Economic Development (ICESED 2019), 2020.##[4] Y. Ganin, E. Ustinova, H. Ajakan and P. Germain, "Domain-Adversarial Training of Neural Networks, " The Journal of Machine Learning Research, vol. 17, no. 1, pp. 2030-2096, 2016.##[5] M. Raza and U. Qayyum, "Classical and deep learning classifiers for anomaly detection," 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST), pp. 614-618, 2019.##[6] B. Lebichot, Y.-A. L. Borgne, L. He-Guelton, F. Oblé and G. Bontempi, "Deep-learning domain adaptation techniques for credit cards fraud detection," in NNS Big Data and Deep Learning conference, Cham, 2019.##[7] A. A. Abdulrazaq, M. B. Abdulrazaq, I. J. Umoh and E. A. Adedokun, "Fraud Detection in Credit Card and Application of VAT Clustering Algorithm: A Review, " in 2019 2nd International Conference of the IEEE Nigeria Computer Chapter (NigeriaComputConf), 2019, October.##[8] Y. Lucas, P.-E. Portier, L. Laporte, L. He-Guelton, O. Caelen, M. Granitzer and S. Calabretto, "Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs," Future Generation Computer Systems, vol. 102, pp. 393-402, 2020.##[9] I. Sadgali, N. Sael and F. Benabbou, "Comparative Study Using Neural Networks Techniques for Credit Card Fraud Detection, " The Proceedings of the Third International Conference on Smart City Applications, 2019.##[10] R. Saia and S. Carta, "Evaluating the benefits of using proactive transformed-domain-based techniques in fraud detection tasks, " uture Generation Computer Systems, vol. 93, 2019.##[11] E. Kim, J. Lee, H. Shin, H. Yang, S. Cho, S.-k. Nam and e. al, "Champion-challenger analysis for credit card fraud detection: Hybrid ensemble and deep learning," Expert Systems with Applications, vol. 128, pp. 214-224, 2019.##[12] C.-H. Su, F. Tu, X. Zhang, B.-C. Shia and T.-S. Lee, "A ENSEMBLE MACHINE LEARNING BASED SYSTEM FOR MERCHANT CREDIT RISK DETECTION IN MERCHANT MCC MISUSE," Journal of Data Science, vol. 17, no. 1, pp. 81-106, 2019.##[13] G. M. C. A. R. Hajela, "A Clustering Based Hotspot Identification Approach For Crime Prediction, " Procedia Computer Science, vol. 167, pp. 1462-1470, 2020.##[14] R. Md and A. Rab, "A Comparative Study on Crime in Denver City Based on Machine Learning and Data Mining., " arXiv preprint arXiv:2001.02802, 2020 Jan 9.##[15] R. Polikar, Ensemble Learning, M. Y. Zhang C., Ed., Boston, Massachusetts: Springer, 19 January 2012.##[16] L. F. A. A. S. N. K. S. J. D. R. S. Gutierrez-Espinoza, "Fake Reviews Detection through Ensemble Learning., " arXiv preprint arXiv:2006.07912, 2020 Jun 14.##[17] A. A. a. S. J. M. Taha, "An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine., " IEEE Access 8, vol. 8, pp. 25579-25587, 2020 Feb 3.##[18] M. H. S. G. Arya, "DEAL-'Deep Ensemble ALgorithm'Framework for Credit Card Fraud Detection in Real-Time Data Stream with Google TensorFlow., " Smart Science, vol. 8, no. 2, pp. 71-83, 2020 Apr 2.##[19] S. A. G. N. G. A. G. Bagga, "Credit Card Fraud Detection using Pipeling and Ensemble Learning, " Procedia Computer Science, vol. 173, pp. 104-112, 2020 Jan 1.##[20] P. Kumari and S. P. Mishra, "Analysis of credit card fraud detection using fusion classifiers, " Computational Intelligence in Data Mining, pp. 111-122, 2019.##[21] H. Najadat, O. Altiti, A. A. Aqouleh and M. Younes, "Credit Card Fraud Detection Based on Machine and Deep Learning, " in 11th International Conference on Information and Communication Systems (ICICS), IEEE, 2020.##[22] G. Alicja, M. Bakala, K. Woznica, M. Zwolinski and P. Biecek, "EPP: interpretable score of model predictive power., " arXiv, p. preprint arXiv:1908.09213, 2019 Aug 24.##[23] Z. Yixuan, J. Tong, Z. Wang and F. Gao, "Customer Transaction Fraud Detection Using Xgboost Model, " in International Conference on Computer Engineering and Application (ICCEA), IEEE, 2020 Mar 18.##[24] D. J. G. S. C. a. J. C. Ge, "Credit Card Fraud Detection Using Lightgbm Model., " in International Conference on E-Commerce and Internet Technology (ECIT), IEEE, 2020 Apr 22.##[25] J. Choi, B. Jeong, Y. Park, J. Seo and C. Min, "AN OPTIMAL BOOSTING ALGORITHM BASED ON NONLINEAR CONJUGATE GRADIENT METHOD, " Journal of the Korean Society for Industrial and Applied Mathematics, vol. 22, no. 1, pp. 1-13, 2018.##[26] D. Kavya and K. Chitharanjan, "Performance Evaluation of Credit Card Fraud Transactions using Boosting Algorithms, " International Journal of Electronics Communication and Computer Engineering, vol. 10, no. 6, pp. 262-270, 2019.##[27] Y. Liang, W. Jiyu, W. Wei, C. Yujun, Z. Biliang, C. Zhenkun and L. Zhenzhang, "Product marketing prediction based on XGboost and LightGBM algorithm, " the 2nd International Conference on Artificial Intelligence and Pattern Recognition, pp. 150-153, 2019.##[28] V. K. Ayyadevara, "Gradient Boosting Machine, " Pro Machine Learning Algorithms, pp. 117-134, 01 July 2018.##[29] P. KHANDELWAL, "Which algorithm takes the crown: Light GBM vs XGBOOST?," 12 June 2017. [Online]. Available: https://www.analyticsvidhya.com/blog/2017/06/which-algorithm-takes-the-crown-light-gbm-vs-xgboost/.##[30] S. Mittal and S. Tyagi, "Computational Techniques for Real-Time Credit Card Fraud Detection., " Handbook of Computer Networks and Cyber Security, pp. 653-681, 2020.##[1] t. o. c. cart. [Online]. Available: [https://www.thebalance.com/key-differences-between-visa-mastercard-discover-anmerican-express-4588450#citation-4.##[2] "Performance Evaluation of Credit Card Fraud Transactions using Boosting Algorithms, " International Journal of Electronics Communication and Computer Engineering, vol. 10, no. 6, pp. 262-270, 2019.##[3] J. Huang, "Credit Card Transaction Fraud Using Machine Learning Algorithms, " in 2019 International Conference on Education Science and Economic Development (ICESED 2019), 2020.##[4] Y. Ganin, E. Ustinova, H. Ajakan and P. Germain, "Domain-Adversarial Training of Neural Networks, " The Journal of Machine Learning Research, vol. 17, no. 1, pp. 2030-2096, 2016.##[5] M. Raza and U. Qayyum, "Classical and deep learning classifiers for anomaly detection," 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST), pp. 614-618, 2019.##[6] B. Lebichot, Y.-A. L. Borgne, L. He-Guelton, F. Oblé and G. Bontempi, "Deep-learning domain adaptation techniques for credit cards fraud detection," in NNS Big Data and Deep Learning conference, Cham, 2019.##[7] A. A. Abdulrazaq, M. B. Abdulrazaq, I. J. Umoh and E. A. Adedokun, "Fraud Detection in Credit Card and Application of VAT Clustering Algorithm: A Review, " in 2019 2nd International Conference of the IEEE Nigeria Computer Chapter (NigeriaComputConf), 2019, October.##[8] Y. Lucas, P.-E. Portier, L. Laporte, L. He-Guelton, O. Caelen, M. Granitzer and S. Calabretto, "Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs," Future Generation Computer Systems, vol. 102, pp. 393-402, 2020.##[9] I. Sadgali, N. Sael and F. Benabbou, "Comparative Study Using Neural Networks Techniques for Credit Card Fraud Detection, " The Proceedings of the Third International Conference on Smart City Applications, 2019.##[10] R. Saia and S. Carta, "Evaluating the benefits of using proactive transformed-domain-based techniques in fraud detection tasks, " uture Generation Computer Systems, vol. 93, 2019.##[11] E. Kim, J. Lee, H. Shin, H. Yang, S. Cho, S.-k. Nam and e. al, "Champion-challenger analysis for credit card fraud detection: Hybrid ensemble and deep learning," Expert Systems with Applications, vol. 128, pp. 214-224, 2019.##[12] C.-H. Su, F. Tu, X. Zhang, B.-C. Shia and T.-S. Lee, "A ENSEMBLE MACHINE LEARNING BASED SYSTEM FOR MERCHANT CREDIT RISK DETECTION IN MERCHANT MCC MISUSE," Journal of Data Science, vol. 17, no. 1, pp. 81-106, 2019.##[13] G. M. C. A. R. Hajela, "A Clustering Based Hotspot Identification Approach For Crime Prediction, " Procedia Computer Science, vol. 167, pp. 1462-1470, 2020.##[14] R. Md and A. Rab, "A Comparative Study on Crime in Denver City Based on Machine Learning and Data Mining., " arXiv preprint arXiv:2001.02802, 2020 Jan 9.##[15] R. Polikar, Ensemble Learning, M. Y. Zhang C., Ed., Boston, Massachusetts: Springer, 19 January 2012.##[16] L. F. A. A. S. N. K. S. J. D. R. S. Gutierrez-Espinoza, "Fake Reviews Detection through Ensemble Learning., " arXiv preprint arXiv:2006.07912, 2020 Jun 14.##[17] A. A. a. S. J. M. Taha, "An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine., " IEEE Access 8, vol. 8, pp. 25579-25587, 2020 Feb 3.##[18] M. H. S. G. Arya, "DEAL-'Deep Ensemble ALgorithm'Framework for Credit Card Fraud Detection in Real-Time Data Stream with Google TensorFlow., " Smart Science, vol. 8, no. 2, pp. 71-83, 2020 Apr 2.##[19] S. A. G. N. G. A. G. Bagga, "Credit Card Fraud Detection using Pipeling and Ensemble Learning, " Procedia Computer Science, vol. 173, pp. 104-112, 2020 Jan 1.##[20] P. Kumari and S. P. Mishra, "Analysis of credit card fraud detection using fusion classifiers, " Computational Intelligence in Data Mining, pp. 111-122, 2019.##[21] H. Najadat, O. Altiti, A. A. Aqouleh and M. Younes, "Credit Card Fraud Detection Based on Machine and Deep Learning, " in 11th International Conference on Information and Communication Systems (ICICS), IEEE, 2020.##[22] G. Alicja, M. Bakala, K. Woznica, M. Zwolinski and P. Biecek, "EPP: interpretable score of model predictive power., " arXiv, p. preprint arXiv:1908.09213, 2019 Aug 24.##[23] Z. Yixuan, J. Tong, Z. Wang and F. Gao, "Customer Transaction Fraud Detection Using Xgboost Model, " in International Conference on Computer Engineering and Application (ICCEA), IEEE, 2020 Mar 18.##[24] D. J. G. S. C. a. J. C. Ge, "Credit Card Fraud Detection Using Lightgbm Model., " in International Conference on E-Commerce and Internet Technology (ECIT), IEEE, 2020 Apr 22.##[25] J. Choi, B. Jeong, Y. Park, J. Seo and C. Min, "AN OPTIMAL BOOSTING ALGORITHM BASED ON NONLINEAR CONJUGATE GRADIENT METHOD, " Journal of the Korean Society for Industrial and Applied Mathematics, vol. 22, no. 1, pp. 1-13, 2018.##[26] D. Kavya and K. Chitharanjan, "Performance Evaluation of Credit Card Fraud Transactions using Boosting Algorithms, " International Journal of Electronics Communication and Computer Engineering, vol. 10, no. 6, pp. 262-270, 2019.##[27] Y. Liang, W. Jiyu, W. Wei, C. Yujun, Z. Biliang, C. Zhenkun and L. Zhenzhang, "Product marketing prediction based on XGboost and LightGBM algorithm, " the 2nd International Conference on Artificial Intelligence and Pattern Recognition, pp. 150-153, 2019.##[28] V. K. Ayyadevara, "Gradient Boosting Machine, " Pro Machine Learning Algorithms, pp. 117-134, 01 July 2018.##[29] P. KHANDELWAL, "Which algorithm takes the crown: Light GBM vs XGBOOST?," 12 June 2017. [Online]. Available: https://www.analyticsvidhya.com/blog/2017/06/which-algorithm-takes-the-crown-light-gbm-vs-xgboost/.##[30] S. Mittal and S. Tyagi, "Computational Techniques for Real-Time Credit Card Fraud Detection., " Handbook of Computer Networks and Cyber Security, pp. 653-681, 2020.## ##

ارائه روشی جدید برای تعبیه اسناد جهت دسته‌بندی متون خبری A New Document Embedding Method for News Classification 1 1 یکی از کاربردهای مهم در پردازش زبان طبیعی، دسته‌بندی متون است. برای دسته­بندی متون خبری باید ابتدا آنها را به شیوه مناسبی بازنمایی کرد. روش­های مختلفی برای بازنمایی متن وجود دارد ولی بیشتر آنها روش­هایی همه منظوره هستند و  فقط از اطلاعات هم‌رخدادی محلی و مرتبه اول کلمات برای بازنمایی استفاده می­نمایند. در این مقاله روشی  بی­ناظر برای بازنمایی متون خبری ارائه شده است که از اطلاعات هم‌رخدادی سراسری و اطلاعات موضوعی  برای بازنمایی اسناد استفاده می­نماید. اطلاعات موضوعی علاوه بر اینکه بازنمایی انتزاعی­تری از متن ارائه می­دهد حاوی اطلاعات هم‌رخدادی­های مراتب بالاتر نیز هست. اطلاعات هم‌رخدادی سراسری و موضوعی مکمل یکدیگرند. بنابراین در این مقاله به‌منظور تولید بازنمایی غنی­تری برای دسته­بندی متن، هر دو بکارگرفته شده­اند. روش پیشنهادی بر روی پیکره­های R8  و 20-Newsgruops که از پیکره­های شناخته­شده برای دسته­بندی متون هستند آزمایش شده و با روش­های مختلفی مقایسه گردید. در مقایسه با روش پیشنهادی با سایر روش‌ها افزایش دقتی به میزان افزایش 3%  مشاهده گردید. 2 Abstract- Text classification is one of the main tasks of natural language processing (NLP). In this task, documents are classified into pre-defined categories. There is lots of news spreading on the web. A text classifier can categorize news automatically and this facilitates and accelerates access to the news. The first step in text classification is to represent documents in a suitable way that can be distinguishable by a classifier. There is an abundance of methods in the literature for document representation which can be divided into a bag of words model, graph-based methods, word embedding pooling, neural network-based, and topic modeling based methods. Most of these methods only use local word co-occurrences to generate document embeddings. Local word co-occurrences miss the overall view of a document and topical information which can be very useful for classifying news articles.  In this paper, we propose a method that utilizes term-document and document-topic matrix to generate richer representations for documents.  Term-document matrix represents a document in a specific way where each word plays a role in representing a document. The generalization power of this type of representation for text classification and information retrieval is not very well. This matrix is created based on global co-occurrences (in document-level). These types of co-occurrences are more suitable for text classification than local co-occurrences. Document-topic matrix represents a document in an abstract way and the higher level co-occurrences are used to generate this matrix. So this type of representation has a good generalization power for text classification but it is so high-level and misses the rare words as features which can be very useful for text classification. The proposed approach is an unsupervised document-embedding model that utilizes the benefit of both document-topic and term-document matrices to generate a richer representation for documents. This method constructs a tensor with the help of these two matrices and applied tensor factorization to reveal the hidden aspects of data. The proposed method is evaluated on the task of text classification on 20-Newsgroups and R8 datasets which are benchmark datasets in the news classification area. The results show the superiority of the proposed model with respect to baseline methods. The accuracy of text classification is improved by 3%. 137 148 2020/10/52020/07/232020/08/212019/05/32020/08/132020/09/42020/09/142020/05/172021/05/262020/08/1 1399/5/11 2021/12/62022/05/112021/05/242020/05/132021/12/112020/10/212021/12/112022/05/112022/05/112021/03/8 1399/12/18 زهرا رحیمی Zahra Rahimi دانشگاه صنعتی امیرکبیر ایران zah-ra@aut.ac.ir محمدمهدی همایونپور Mohammad Mehdi Homayounpour دانشگاه صنعتی امیرکبیر ایران homayoun@aut.ac.ir Text classification Document representation Document Embedding Topic modeling word co-occurrences بازنمایی سند تعبیه سند تعبیه کلمه همرخدادی کلمات اطلاعات موضوعی دسته‌بندی متن [1] M. Fu, H. Qu, L. Huang, and L. Lu, "Bag of meta-words: A novel method to represent document for the sentiment classification, " Expert Syst. Appl., vol. 113, pp. 33-43, 2018.##[2] R. Zhao and K. Mao, "Fuzzy Bag-of-Words Model for Document Representation, " IEEE Trans. Fuzzy Syst., vol. 26, no. 2, pp. 794-804, 2018.##[3] G. Salton and M. J. McGill, Introduction to Modern Information Retrieval. 1987.##[4] M. A. M. Garcia, R. P. Rodriguez, M. V. Ferro, and L. A. Rifon, "Wikipedia-Based Hybrid Document Representation for Textual News Classification," 2016 3rd Int. Conf. Soft Comput. Mach. Intell., no. November, pp. 148-153, 2016.##[5] P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, "Enriching Word Vectors with Subword Information, " Trans. Assoc. Comput. Linguist., vol. 5, pp. 135-146, 2016.##[6] T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Efficient Estimation of Word Representations in Vector Space, " in International estimation on learning representations: Workshop Track, 2013, pp. 1-12.##[7] R. Collobert and J. Weston, "A unified architecture for natural language processing, " pp. 160-167, 2008.##[8] P. Li, K. Mao, Y. Xu, Q. Li, and J. 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پیش گویی حملات صرعی در بیماران با صرع لوب تمپورال (TLE) بر اساس آنالیز کپستروم و مدل AR تعمیم یافته سیگنال EEG Prediction of Epileptic Seizures in Patients with Temporal Lobe Epilepsy (TLE) based on Cepstrum analysis and AR model of EEG signal 1 1 با توجه به اینکه تشنج‏ها موجب اختلال در هوشیاری بدون پیش آگاهی می‏شود، پیش­بینی آن‏ها می‏تواند باعث کاهش فشار روانی و بهبود کیفیت زندگی شود. در این مقاله، امکان پیش­بینی کوتاه مدت حمله صرع بدون حذف مصنوعات ، با زمان و دقت مناسب با استفاده از مدل AR و کپستروم بهبود یافته بررسی شده است. ابتدا سیگنال EEG با تبدیل موجک، به دلیل تفاوت فرکانس حمله ‏ها و مصنوعات هر بیمار با بیمار دیگر تفکیک می‏شود. سپس تشخیص تغییرات دوره حمله با استفاده از مدل‏سازی AR و روش کپستروم به دلیل متناوب بودن دامنه و فرکانس این دوره، انجام می‏پذیرد. در مرحله بعد با مقایسه دوره جاری با دوره پس‏زمینه و اعمال فیلتر میانه، خطای ناشی از مصنوعات (Artifact) و تخلیه‏های میان حمله‏ای کاهش داده می‏شود. در نهایت سیگنال با روش پنجره پیشرو متوسط‏گیری شده و منحنی پوش بالای نمودار محاسبه می‏شود. روش پیشنهادی روی مدل پیشنهادی صرعی بزرگسال و همچنین 10 بیمار با داده‏های EEG طولانی مدت بدون حذف مصنوعات بررسی شده است. دقت و مقدار متوسط زمان پیش­بینی، به ترتیب 92% ، 5/18 ثانیه بدست آمده است. 2 Epilepsy is a chronic disorder of brain function caused by abnormal and excessive electrical neurons discharge in the brain. Seizures cause disturbances in consciousness that occur without prior notice, so their prediction ability, based on EEG data, can reduce stress and improve quality of life. An epileptic patient EEG data consists of five parts: Ictal, Inter-Ictal, pre-Ictal, Post-Ictal, and IT (seconds before Ictal onset). The purpose of predicting an attack is to detect the period of pre-ictal or IT to create warnings for medical procedures that are actually determined hours or minutes before ictal and do not necessarily mean the exact time of ictal [4]. The aim of many studies has been to identify the pre-ictal period based on EEG data. However, the problem of reliable prediction of epileptic seizures remains largely unsolved [5].  EEG and IEEG data types are used in detection and predicting methods. Due to the fact that artifacts and noises have a greater effect on EEG than IEEG, if there is IEEG, it has been tried to use it [6, 7]. Seizure warning methods that have a clinical application are generally based on the use on EEG [8]. Numerous studies have been performed to detect and predict seizures. The methods of signal processing and feature extraction are same in detection and prediction, but the difference is that, in detection, ictal and inter-ictal periods are compared, while in prediction, pre-ictal or IT and inter-ictal periods are being compared. Some algorithms use data modeling to extract features. References [13, 14], the coefficients AR model for the EEG data is obtained with least squares estimator, then the model coefficients are classified by SVM binary classification. In the article [15] the non-Gaussian EEG is considered using the ARIMA model (Autoregressive integrated moving average). In references [16, 17], predictions are performed based on the dynamic model with hidden variable and the sparse LVAR model, respectively. Also other features such as Mean Phase Coherency [18-20], Lag Synchronization Index to compare phase Synchronization between irregular oscillations [8,21], eigenspectra of space-delay correlation and covariance matrices [22], Largest Lyapunov Exponent [23, 25], decorrelation time, Hjorth parameters such as mobility and complexity, power spectrum in frequency bands, spectral edge frequency, the four statistical moments: mean, variance, kurtosis, skewness and there are features based on entropy and probability [6, 26-29]. Empirical mode decomposition (EMD) and wavelet transform methods have also been used to extract the feature [2, 30, 31, 37]. In articles [32, 33], the Cepstrum method has been used on short time multi channels EEG and IEEG in different patient states. Cepstrum is used to extract slow and periodic changes in speech that can be used to detect the ictal period from the inter-ictal, and has also been used to linearize the EEG [34]. In the paper [33], Cepstrum coefficients of multi-channel EEG are calculated and the 9 first coefficients are considered, then calculates the velocity and acceleration of the desired coefficients and uses a neural network to detect an epileptic seizure. The method of this paper was improved in 2014. In this way, first the signal energy and coefficients of Cepstrum are calculated and then the same process is followed. The accuracy values ​​of velocity and acceleration coefficients in this study were 89.7% - 98.7% and 98.9% - 99.9%, respectively [32].  In this study, the period of IT was detected in patients with temporal lobe epilepsy (TLE), which is the most common type of epilepsy [38]. For this purpose, two long term EEG channels LTM (long term monitoring) with a sampling rate 256, which are facing each other have been used. First, the desired signal is considered by the moving window with a length if 5 seconds and 80% overlap. The desired signal is normalized and its linear trend is removed and band-pass filtered (220 order FIR filter, cutoff at 6-20 Hz). Then the filtered date will de decomposed using discrete wavelet transform with 6-levels and Daubechies4 mother wavelet. In this step we will have 12 outputs. Next, by windowing of 500 samples and 75% overlap, the AR model with 8 order is applied to outputs. Cepstrum method can be used to detect regular and periodic changes in the ictal period of the EEG signal. According to this feature, the Cepstrum coefficients of the data window are calculated and the first coefficient of each window is considered. By applying a median filter to the 12 outputs of the previous stage, the current period of the first channel is compared to the background period of the same channel and the second channel, and the same is done for the second channel. This method reduces artifact error and inter-attack discharges. Finally, the signal is averaged by the moving window and the positive envelope of the curve is calculated. Given that we will eventually have 12 outputs, 12 threshold values are obtained for a patient’s training data, then these values are checked on the test data.  The proposed method was reviewed on a proposed model of adult epilepsy as well as 10 patients with long-term EEG data without artifact removal. Accuracy and average prediction time were 92% and 18.5 seconds, respectively. The algorithm performed better than other methods. Another advantage of the algorithm is the ability to reduce artifacts, while many studies have used short-term data without artifacts. Artifacts are located at different frequencies, which frequency analysis is performed by wavelet transform. Because two channel artifacts are unequal at the same time and in the same channel at different times, the artifacts are reduced by comparing the channels to each other. Algorithm testing on more patients is recommended to confirm the performance of the algorithm clinically.     149 172 2020/10/52020/07/232020/08/212019/05/32020/08/132020/09/42020/09/142020/05/172021/05/262020/08/12020/09/19 1399/6/29 2021/12/62022/05/112021/05/242020/05/132021/12/112020/10/212021/12/112022/05/112022/05/112021/03/82022/09/24 1401/7/2 بهار تاج الدینی Bahar Tajadini دانشگاه شهید باهنر کرمان ایران tajadinibahar@yahoo.com سعیدرضا صیدنژاد SaeidReza Seydnejad دانشگاه شهید باهنر کرمان ایران sseydnejad@uk.ac.ir سهیلا رضاخانی Soheila Rezakhani دانشگاه علوم پزشکی کرمان ایران drrezakhani@gmail.com Epileptic Seizure Prediction Temporal Lobe Epilepsy Wavelet Transform AR Model Cepstrum Median Filter Positive Envelope of the Curve Adult Epileptic Model. پیش‌گویی حمله صرع صرع لوب تمپورال تبدیل موجک مدل AR کپستروم فیلتر میانه منحنی پوش مدل صرعی بزرگسال [1] S. Engelborghs, R. D'hooge, and P. 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Amindavar, and S. A. T. Hosseini, "ARIMA-GARCH modeling for epileptic seizure prediction," in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017, pp. .994-998##[16] M. Bozek-Juzmicki, D. Colella, and G. M. Jacyna, "Feature-based epileptic seizure detection and prediction from ECoG recordings," in Proceedings of IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis, 1994, pp. 564-567##[17] P.-N. Yu, S. A. Naiini, C. N. Heck, C. Y. Liu, D. Song, and T. W. Berger, "A sparse Laguerre-Volterra autoregressive model for seizure prediction in temporal lobe epilepsy," in 2016 38 th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. .1664-1667, 2016##[18] S. Nesaei, AR. Sharafat, " Epileptic seizure prediction based on phase synchronization analysis in time-frequency domain," in 2011 19th Iranian Conference on Electric Engineering (ICEE), pp. 3364-3369, 2011##[19] F. Mormann, K. Lehnertz, P. David, and C. E. Elger, "Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients," Physica D: Nonlinear Phenomena, vol. 144, pp. 358-369, .2000##[20] M. Le Van Quyen, J. Soss, V. Navarro, R. Robertson, M. Chavez, M. Baulac, et al., "Preictal state identification by synchronization changes in long-term intracranial EEG recordings," Clinical Neurophysiology, vol. 116, pp. 559-568, .2005##[21] B. Schelter, M. Winterhalder, T. Maiwald, A. Brandt, A. Schad, A. Schulze-Bonhage, et al., "Testing statistical significance of multivariate time series analysis techniques for epileptic seizure prediction," Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 16, p. 013108, .2006##[22] J. R. Williamson, D. W. Bliss, D. W. Browne, and J. T. Narayanan, "Seizure prediction using EEG spatiotemporal correlation structure," Epilepsy & Behavior, vol. 25, pp. 230-238, .2012##[23] X. Huang, W. Wang, X. Sun, Y. Chen, L. Li, Y. Deng, et al., "Model research for epileptic prediction based on improved chaos operator of Lyapunov," in 2009 3rd International Conference on Bioinformatics and Biomedical Engineering, pp. .1-4, 2009##[24] L. Tong, W. Wang, N. Zhao, and X. Huang, "The method evaluation for preictal prediction of epilepsy with strong-noise EEG and simulation of automatic drug release system," in 2010 3rd International Conference on Biomedical Engineering and Informatics, pp. .1054-1058, 2010##[25] S. Wang, W. A. Chaovalitwongse, and S. Wong, "A novel reinforcement learning framework for online adaptive seizure prediction," in 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 499, 2010##[26] C. A. Teixeira, B. Direito, M. Bandarabadi, M. Le Van Quyen, M. Valderrama, B. Schelter, et al., "Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients," Computer methods and programs in biomedicine, vol. 114, pp. 324-336 , 2014##[27] B. Hjorth, "EEG analysis based on time domain properties," Electroencephalography and clinical neurophysiology, vol. 29, pp. 306-310, .504, 1970##[28] C. Sudalaimani, S. Asha, K. Parvathy, T. E. Thomas, P. Devanand, P. Sasi, et al., "Use of electrographic seizures and interictal epileptiform discharges for improving performance in seizure prediction," in 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS), 2015, pp. .229-234##[29] M. A. F. Harrison, I. Osorio, M. G. Frei, S. Asuri, and Y.-C. Lai, "Correlation dimension and integral do not predict epileptic seizures," Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 15, p. 033106, .2005##[30] L. Boubchir and B. Boashash, "Wavelet denoising based on the MAP estimation using the BKF prior with application to images and EEG signals," IEEE Transactions on signal processing, vol. 61, pp. 1880-1894, .2013##[31] A. K. Tafreshi, A. M. Nasrabadi, and A. H. Omidvarnia, "Empirical mode decomposition in epileptic seizure prediction," in 2008 IEEE International Symposium on Signal Processing and Information Technology, pp. .275-280, 2008##[32] C. Kamath, "Automatic seizure detection based on Teager Energy Cepstrum and pattern recognition neural networks," QScience Connect, vol. 2014, p. 1, .2014##[33] C. Kamath, "Comparison of baseline cepstral vector and composite vectors in the automatic seizure detection using probabilistic neural networks," ISRN Biomedical engineering, vol. 2013, .2013##[34] H. Ren, J. Qu, Y. Chai, L. Huang, and Q. Tang, "Cepstrum Coefficient Analysis from Low-Frequency to High-Frequency Applied to Automatic Epileptic Seizure Detection with Bio-Electrical Signals," Applied Sciences, vol. 8, p. 1528, .2018##[35] I. Osorio, M. G. Frei, and S. B. Wilkinson, "Real‐time automated detection and quantitative analysis of seizures and short‐term prediction of clinical onset," Epilepsia, vol. 39, pp. 615-627, .1998##[36] P. Detti, G. Z. M. de Lara, R. Bruni, M. Pranzo, F. Sarnari, and G. Vatti, "A patient-specific approach for short-term epileptic seizures prediction through the analysis of EEG synchronization," IEEE Transactions on Biomedical Engineering, vol. 66, pp. 1494-1504, 2018.##[37] H. Daoud and M. A. Bayoumi, "Efficient epileptic seizure prediction based on deep learning," IEEE transactions on biomedical circuits and systems, vol. 13, pp. 804-813, 2019.##[38] C. Panayiotopoulos, "Epileptic syndromes and their treatment," Neonatal Seizures, pp. 185-206, .2007##[39] F. Shayegh, F. Ghasemi, R. Amirfattahi, S. Sadri, K. Ansariasl, " Online single-channel seizure prediction, based on seizure gensis model of depth-EEG signals using extended Kalman filter," JSDP, pp. 3-27, 2019##[40] L. Hao, R. Ghodadra, and N. V. Thakor, "Quantification of brain injury by EEG cepstral distance during transient global ischemia," in Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.'Magnificent Milestones and Emerging Opportunities in Medical Engineering'(Cat. No. 97CH36136), pp. .1205-1206, 1997##[41] M. Le Van Quyen, "Anticipating epileptic seizures: from mathematics to clinical applications," Comptes rendus biologies, vol. 328, pp. 187-198, .2005##[42] P. Ong, Z. Zainuddin, and K. H. Lai, "A novel selection of optimal statistical features in the DWPT domain for discrimination of ictal and seizure-free electroencephalography signals," Pattern Analysis and Applications, vol. 21, pp. 515-527, .2018##[43] S. 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بازشناسی آوای فارسی با استفاده از شاخص‌های صوتی و روش‌های جبران‌سازی تنوعاتِ مبتنی بر شبکه‌های عصبی Persian Phone Recognition Using Acoustic Landmarks and Neural Network-based variability compensation methods 1 1 شواهد و آزمایشات گفتاری نشان می‌دهد که اطلاعات در سیگنال گفتار به صورت غیر یکنواخت توزیع شده و انسان با تمرکز به نواحی پُر اطلاعات آن قادر است به صورت مقاوم گفتار را بازشناسی کند. در این راستا در این تحقیق، یک سامانه‌‌ی بازشناسی آوای فارسی مبتنی بر تمرکز روی بازشناسی مقاوم نواحی پُراطلاعات و مجزای صوتی ارائه شده است. این نواحی شاخص‌های صوتی نامیده می‌شوند. بدین منظور ابتدا برای سیگنال گفتارِ زبان فارسی یک مجموعه از شاخص‌های مناسب صوتی انتخاب شده و به یک شبکه‌ی عصبی عمیق آموزش داده شده‌اند. سپس، به منظور حذف تنوعات شاخص‌های صوتی، تغییراتی در ساختار مدل و شیوه‌ی آموزش آن در چهار طرح مختلف انجام شده است. در طرح اول، از یک شبکه‌ی عصبی جداگانه و در طرح دوم از یک ساختار یادگیری چند تکلیفی برای جبران­سازی غیرخطی تنوعات شاخص­های صوتی استفاده شده است. در طرح سوم نیز از یک اتصال بازگشتی در لایه­ی پنهان شبکه برای بازسازی ورودی و در طرح چهارم از یک ساختار مبتنی بر شبکه­های جاذب­دار عمیق برای کاهش تنوعات ناخواسته استفاده شده است. در این مقاله آزمایش‌ها روی مجموعه دادگانِ گفتاری فارسی "فارس‌دات" انجام شده است و نتایج بازشناسی به صورت خطای بازشناسی آوا گزارش شده است. بهترین مدل آموزش یافته، یک شبکه‌‌ی عصبی جلوسو با پنج لایه‌‌ی پنهان است. خطای بازشناسی آوای این ساختار روی دادگان آزمون برابر 74/21 درصد به دست آمد. همچنین استفاده از چهارطرحِ پالایش تنوعات به ترتیب خطای بازشناسی آوا را به طور مطلق 39/0، 58/0، 43/0 و 3/1 درصد کاهش داده است. 2 Speech recognition is a subfield of artificial intelligence that develops technologies to convert speech utterance into transcription. So far, various methods such as hidden Markov models and artificial neural networks have been used to develop speech recognition systems. In most of these systems, the speech signal frames are processed uniformly, while the information is not evenly distributed in all of them. Auditory experiments have also shown that the human brain pays more attention to information-rich areas. By focusing on these areas instead of uniform processing, the brain can more robustly recognize speech in intrinsic and environmental speech variations such as speaker and noise. In contrast, the performance of most speech recognition systems degrades dramatically in these conditions. Therefore, to boost speech recognition systems' robustness, some researchers have focused on developing speech recognition systems by modeling these informative parts of the speech signal named landmarks. Similarly, in this article, we implemented a landmark-based system to obtain a robust Persian speech recognition system inspired by human brain perception. We also conducted neural networks-based variation compensation methods to boost its performance. In this article, acoustic landmarks are classified into two categories of events and states with the following definitions. Events are defined as areas of the speech signal in which the spectral characteristics change drastically while their length does not change a lot. The transition areas between some adjacent pairs of phones (phones' borders) are primarily selected as events. States are also defined as areas of the speech signal that spectral characteristics do not change significantly. Here the nuclei of phones are considered as the states. Previous research, linguistic sources, and implementation results have been used to determine the Persian language's appropriate landmarks. Finally, a set of 313 landmarks was selected and used in our acoustic landmarks-based phone recognition system.  The neural network structure used to recognize acoustic landmarks is a feed-forward fully connected structure with ReLU function in its hidden layers and a linear function in its final layer. The number of layers and neurons of this structure has been determined experimentally. The best structure is composed of 5 fully connected layers with 1000 neurons per layer. In this study, instead of considering 313 neurons to express each of the 313 landmarks, a heuristic labeling method is used to reduce the number of output neurons and utilize the shared information between the landmarks. The landmark recognition model slides on the speech feature sequence in the test phase to produce the output landmark sequence. Finally, to convert the obtained landmark sequence to a phone sequence, three rule-based post-processing steps are performed.  Variabilities are among the essential quality degradation sources in speech recognition; therefore, we proposed two approaches to reduce them and boost phone recognition quality in our landmark-based system. To this aim, we have utilized the nonlinear filtering characteristic of neural networks by implementing four neural network schemes. In scheme 1, a feed-forward neural network is first trained to map training landmarks to their corresponding well-recognized samples. Then this structure can act as a nonlinear filter before the landmark recognition block. In scheme 2, a unified structure is simultaneously trained to learn landmark labels and the filtering part. In both of these schemes, we used a recursive loop to increase the chance of attractor manipulation in the structures. In scheme 3, a recursive loop is added to one hidden layer. This loop acts as an input variability simulator and forces the network to recognize the input data and its variations correctly. Finally, in scheme four, a deep attractor neural network-based structure is proposed to shape the structure’s hidden layer components so that it can compensate for variabilities. The experiments are implemented on a Persian database named Farsdat, and the results are reported using phone error rate (PER) criteria. From every 25-millisecond speech frame, an acoustic feature called LHCB is extracted and combined with delta and delta-delta features of that frame. Every frame's features are concatenated with fourteen adjacent frames and are finally fed to our neural network-based landmark extraction model. The best-trained model obtained the PER of 21.74% on test data. Using scheme one to four, we achieved an absolute PER decrease by 0.39, 0.58, 0.43 and 1.30 percent, respectively. Comparing our landmark-based system's performance with other Persian phone recognition systems shows that this method could perform efficiently as a Persian phone recognition system.  In our future works, we intend to compare our acoustic-based phone recognition system's performance with conventional methods such as CTC in noisy conditions. Besides, it seems that acoustic landmarks can be used to create an alignment of the input speech sequence and the output transcription. Therefore, we will present a combination of CTC-based methods and acoustic landmarks to utilize acoustic landmarks' complementary information. This information might boost the performance and speed of CTC-based speech recognition methods, particularly in low resource languages. 173 196 2020/10/52020/07/232020/08/212019/05/32020/08/132020/09/42020/09/142020/05/172021/05/262020/08/12020/09/192020/09/7 1399/6/17 2021/12/62022/05/112021/05/242020/05/132021/12/112020/10/212021/12/112022/05/112022/05/112021/03/82022/09/242021/08/25 1400/6/3 شقایق رضا Shaghayegh Reza ایران sh.reza@aut.ac.ir علی سید صالحی Ali Seyyedsalehi ایران ssalehi@aut.ac.ir زهره سید صالحی Zohreh Seyyedsalehi دانشکده بهداشت علوم پزشکی تهران، دانشگاه آزاد اسلامی تهران ایران z.seyyedsalehi@aut.ac.ir Phone Recognition Acoustic Landmarks Deep Learning Robust Recognition Nonlinear Filtering بازشناسی آوا شاخص‌های صوتی یادگیری عمیق بازشناسی مقاوم پالایش غیر‌خطی [1] B. Babaali, A state-of-the-art and effitient framework for persian speech recognition, Signal and Data Processing, Vol. 13, pp. 51-62, 2016.##[2] Y. Samareh, Persian language phonology, Tehran, university publishing center, 1985.##[3] M. Rahiminezhad, S. A. Seyyedsalehi, Comparision and assessment of different feature extraction and normalization methods in speaker independent speech recognition, Amirkabir journal of science and research, 2000.##[4] S. A. Seyyedsalehi, I. Nejadgholi, F. Tohidkhah, Boostingt pattern recognition performance of neural networks with deleoping bidirectional methods, independent research report, 2004.##[5] S. Karami, Speaker independent persian phone recognition using a neural network model with a combination of steady and transition parts of phones, M.Sc. thesis, Biomedical engineering faculty, Amirkabir University, 2000.##[6] M. Yazdiyan, Persian continous speech recognition based on discrete acoustic events modeling, M.Sc. thesis, Biomedical engineering faculty, Amirkabir University, 2001.##[7] S. Alisamir, S. M. Ahadi, and S. Seyedin, An end-to-end deep learning model to recognize Farsi speech from raw input, 4th Iranian Conference on Signal Processing and Intelligent Systems, pp. 1-5, 2018.##[8] N. Amini, S. A. Seyyedsalehi, Manipulation of attractors in feed-forward autoassociative neural networks for robust learning, Iranian Conference on Electrical Engineering (ICEE), 2017.##[9] Z. Ansari and S. A. Seyyedsalehi, Toward growing modular deep neural networks for continuous speech recognition, Neural Computing and Applications, pp.1177-1196, 2017.##[10] S. Babaei, , A. Geranmayeh, and S. A. Seyyedsalehi, Protein secondary structure prediction using modular reciprocal bidirectional recurrent neural networks, Computer methods and programs in biomedicine, 100(3), pp.237-247, 2010.##[11] M. Bijankhan, J. Sheikhzadegan, M. R. Roohani, FARSDAT-the speech database of Farsi spoken language, proccedings australian conference on speech science and technology, 1994.##[12] S. Borysand M. Hasegawa-Johnson, SVM-HMM landmark based speech recognition, 2009.##[13] Z. Chen, Y., Luo and N. Mesgarani, Deep attractor network for single-microphone speaker separation, In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 246-250, 2017.##[14] J. Chorowski, D. Bahdanau, K. Cho and Y. Bengio, End-to-end continuous speech recognition using attention-based recurrent NN: first results, arXiv, pp.1412.1602, 2014.##[15] G. Dahl, M. A. Ranzato, A. R. Mohamed and G. E. Hinton, Phone recognition with the mean-covariance restricted Boltzmann machine, Advances in neural information processing systems, pp. 469-477, 2010.##[16] Z. D. Doolab, S. A. Seyyedsalehi, and N. S. Dehaghani, , Nonlinear Normalization of Input Patterns to Handwritten Character Variability in Handwriting Recognition Neural Network, International Conference on Biomedical Engineering and Biotechnology, pp. 848-851, 2012.##[17] L. Dehyadegary, S. A. Seyyedsalehi and I. Nejadgholi, Nonlinear enhancement of noisy speech using continuous attractor dynamics formed in recurrent neural networks, Neurocomputing. 2011.##[18] B. Delgutte and N. Y. Kiang, Speech coding in the auditory nerve: IV. Sounds with consonant‐like dynamic characteristics, The Journal of the Acoustical Society of America, pp.897-907, 1984.##[19] S. Firooz, F. Almasganj, and Y. Shekofteh, Improvement of automatic speech recognition systems via nonlinear dynamical features evaluated from the recurrence plot of speech signals, Computers & Electrical Engineering, pp. 215-226, 2017.##[20] D. Gillick, S. Wegmann and L. Gillick, Discriminative training for speech recognition is compensating for statistical dependence in the HMM framework, international conference on acoustics, speech and signal processing (ICASSP), pp. 4745-4748, 2012.##[21] A.H. Hadjahmadi, and M. M. Homayounpour, Robust feature extraction and uncertainty estimation based on attractor dynamics in cyclic deep denoising autoencoders, Neural Computing and Applications, 31(11), pp.7989-8002, 2019.##[22] M. Hasegawa-Johnson, J. Baker, S. Borys, K. Chen, E. Coogan, S. Greenberg, A. Juneja, K. Kirchhoff, K. Livescu, S. Mohan and J. Muller, Landmark-based speech recognition, Report of the 2004 Johns Hopkins summer workshop, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2005.##[23] D. He, B. P. Lim, X. Yang, M. Hasegawa-Johnson and D. Chen, Acoustic landmarks contain more information about the phone string than other frames for automatic speech recognition with deep neural network acoustic model, The Journal of the Acoustical Society of America, pp. 3207-3219, 2018.##[24] D. He, X. Yang, B. P. Lim, Y. Liang, M. Hasegawa-Johnson and D. Chen, When CTC training meets acoustic landmarks., International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5996-6000, 2019.##[25] G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. R. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath and B. Kingsbury, Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups, IEEE signal processing magazine, pp. 82-97, 2012.##[26] A. Juneja and C. Espy-Wilson, A probabilistic framework for landmark detection based on phonetic features for automatic speech recognition, journal of the acoustical society of America, pp. 1154-1168, 2008.##[27] J. Kahn, A. Lee and A. Hannun, Self-training for end-to-end speech recognition, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7084-7088, 2020.##[28] M. A. Kermanshahi and M. M. Homayounpour, Improving Phoneme Sequence Recognition using Phoneme Duration Information in DNN-HSMM, Journal of AI and Data Mining, pp.137-147, 2019.##[29] R., Kumar, Y. Luo, and N. Mesgarani, Music Source Activity Detection and Separation Using Deep Attractor Network, In INTERSPEECH, pp. 347-351, 2018.##[30] J. W. Lee, J. Y. Choi and H. G. Kang, Classifcation of stop place in consonant-vowel contexts using feature extrapolation of acoustic-phonetic features in telephone speech, The Journal of the Acoustical Society of America, Vol. 131, 2012.##[31] Y. Luo, Z. Chen and N. Mesgarani, Speaker-independent speech separation with deep attractor network, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 26(4), 787-796, 2018.##[32] M. Meister and M. J. Berry, The neural code of the retina, Neuron, pp.435-450, 1999.##[33] N. Morgan, J. Cohen, S. H. Krishnan, S. Changand S. Wegmann, Final Report: OUCH Project (Outing Unfortunate Characteristics of HMMs), 2013.##[34] T.S. Nguyen, S. Stüker, J. Niehues, and A. Waibel, Improving sequence-to-sequence speech recognition training with on-the-fly data augmentation, IEEE International Conference on Acoustics, Speech and Signal Processing, 2020.##[35] C. Niu, J. Zhang, X. Yang and Y. Xie, A study on landmark detection based on CTC and its application to pronunciation error detection, Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, pp. 636-640, 2017.##[36] S. Parveen, and P. Green, Speech enhancement with missing data techniques using recurrent neural networks, IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 1, pp. I-733, 2004.##[37] M. Ravanelli, T. Parcollet and Y. Bengio, The pytorch-kaldi speech recognition toolkit, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6465-6469, 2019.##[38] S. Reza, S. A. Seyyedsalehi, S. Z. Seyyedsalehi, A Persian Language Phone Recognition Based on Robust Extraction of Acoustic Landmarks, 27th Iranian Conference on Biomedical Engineering, 2020.##[39] S. Reza, S. A. Seyyedsalehi, S. Z. Seyyedsalehi, Attractor Manipulation in Denoising Autoencoders for Robust Phone Recognition, 29th Iranian Conference on Electrical Engineering, 2021.##[40] T. N. Sainath, Island-driven search using broad phonetic classes, automatic speech recognition & understanding, pp. 287-292, 2009.##[41] T. N. Sainath, B. Kingsbury and B. Ramabhadran, Auto-encoder bottleneck features using deep belief networks, IEEE international conference on acoustics, speech and signal processing (ICASSP), pp. 4153-4156, 2012.##[42] L. San, N. Moritz, T. Hori, and J. L. Roux, Unsupervised Speaker Adaptation Using Attention-Based Speaker Memory for End-to-End ASR, International Conference on Acoustics, Speech and Signal Processing, 2020.##[43] S. A. Seyyedsalehi, A modular neural network speech recognizer based on the both acoustic steady portions and transitions, international conference of spoken language processing (ICSLP), 2000.##[44] S. Z. Seyyedsalehi, and S. A. Seyyedsalehi, Attractor analysis in associative neural networks and its application to facial image analysis, Computational Intelligence in Electrical Engineering, Vol. 9, No. 1, 2018##[45] K. N. Stevens, S. J. Keyser, and H. Kawasaki, Toward a phonetic and phonological theory of redundant features , Ph.D. thesis. MIT, camberidge, 1986.##[46] K. N. Stevens, From acoustic cues to segments, features and words, international conference on Spoken Language Processing (ICSLP), pp. A1-A8, 2000.##[47] J. Vaněk, J. Michálek and J. Psutka, Recurrent DNNs and Its Ensembles on the TIMIT Phone Recognition Task, International Conference on Speech and Computer, pp. 728-736, 2018.##[48] H. Veisi, and A. H. Mani, Persian speech recognition using deep learning, International Journal of Speech Technology, 23(4), pp. 893-905, 2020.##[49] P. Vincent, H. Larochelle, Y. Bengio and P. A. Manzagol, Extracting and composing robust features with denoising autoencoders, Proceedings of the 25th international conference on Machine learning, pp. 1096-1103), 2008.##[50] P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. A. Manzagol, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, Journal of machine learning research, pp. 3371-3408, 2010.##[51] M. Zapotoczny, P. Pietrzak, A. Lancucki and J. Chorowski, 2019. Lattice generation in attention-based speech recognition models, pp.2225-2229, 2019.##[52] T. Yoshimura, T. Hayashi, K. Takeda and S. Watanabe, End-to-end automatic speech recognition integrated with CTC-based voice activity detection, arXiv, 2020.##[53] N. Zeghidour, N. Usunier, I. Kokkinos, T. Schaiz, G. Synnaeve and E. Dupoux, Learning filterbanks from raw speech for phone recognition, International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5509-5513, 2018.##[1] ب. باباعلی، پایه‌گذاری بستری نو و کارآمد در حوزه بازشناسی گفتار فارسی، مجله پردازش علائم و داده‌ها، جلد 13، صفحات 62-51، 1395.##B. Babaali, A state-of-the-art and effitient framework for persian speech recognition, Signal and Data Processing, Vol. 13, pp. 51-62, 2016.##[2] ی.ثمره، آواشناسی زبان فارسی، تهران، مرکز نشر دانشگاهی، 1364.##Y. Samareh, Persian language phonology, Tehran, university publishing center, 1985.##[3] م. رحیمی‌نژاد و س. ع. سیدصالحی، مقایسه و ارزیابی کارایی انواع روش‌های استخراج پارامترهای بازنمایی و هنجارسازی در بازشناسی مستقل از گوینده گفتار، نشریه علمی پژوهشی امیرکبیر، 1382.##M. Rahiminezhad, S. A. Seyyedsalehi, Comparision and assessment of different feature extraction and normalization methods in speaker independent speech recognition, Amirkabir journal of science and research, 2000.##[4] س. ع. سید صالحی، ا. نژادقلی، ف. توحیدخواه، افزایش کارایی بازشناخت الگوی شبکه‌های عصبی جلوسو از طریق توسعه روش‌هایی برای دوسویه کردن عملکرد آنها، گزارش طرح مستقل پژوهشی، 1383.##S. A. Seyyedsalehi, I. Nejadgholi, F. Tohidkhah, Boostingt pattern recognition performance of neural networks with deleoping bidirectional methods, independent research report, 2004.##[5] ش. کرمی، بازشناسی واج‌های گفتار پیوسته فارسی به‌وسیله شبکه‌های عصبی به‌صورت مستقل از گوینده با ترکیب اطلاعات نواحی گذرا و یکنواخت واج‌ها، پایان‌نامه کارشناسی ارشد مهندسی پزشکی، دانشگاه صنعتی امیرکبیر، 1379.##S. Karami, Speaker independent persian phone recognition using a neural network model with a combination of steady and transition parts of phones, M.Sc. thesis, Biomedical engineering faculty, Amirkabir University, 2000.##[6] م. یزدیان، بازشناسی گفتار پیوسته فارسی بر مبنای مدل‌سازی وقایع گسسته صوتی، پایان‌نامه کارشناسی ارشد مهندسی پزشکی، دانشگاه صنعتی امیرکبیر، 1380.##M. Yazdiyan, Persian continous speech recognition based on discrete acoustic events modeling, M.Sc. thesis, Biomedical engineering faculty, Amirkabir University, 2001.##[7] S. Alisamir, S. M. Ahadi, and S. Seyedin, An end-to-end deep learning model to recognize Farsi speech from raw input, 4th Iranian Conference on Signal Processing and Intelligent Systems, pp. 1-5, 2018.##[8] N. Amini, S. A. Seyyedsalehi, Manipulation of attractors in feed-forward autoassociative neural networks for robust learning, Iranian Conference on Electrical Engineering (ICEE), 2017.##[9] Z. Ansari and S. A. Seyyedsalehi, Toward growing modular deep neural networks for continuous speech recognition, Neural Computing and Applications, pp.1177-1196, 2017.##[10] S. Babaei, , A. Geranmayeh, and S. A. Seyyedsalehi, Protein secondary structure prediction using modular reciprocal bidirectional recurrent neural networks, Computer methods and programs in biomedicine, 100(3), pp.237-247, 2010.##[11] M. Bijankhan, J. Sheikhzadegan, M. R. Roohani, FARSDAT-the speech database of Farsi spoken language, proccedings australian conference on speech science and technology, 1994.##[12] S. Borysand M. Hasegawa-Johnson, SVM-HMM landmark based speech recognition, 2009.##[13] Z. Chen, Y., Luo and N. Mesgarani, Deep attractor network for single-microphone speaker separation, In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 246-250, 2017.##[14] J. Chorowski, D. Bahdanau, K. Cho and Y. Bengio, End-to-end continuous speech recognition using attention-based recurrent NN: first results, arXiv, pp.1412.1602, 2014.##[15] G. Dahl, M. A. Ranzato, A. R. Mohamed and G. E. Hinton, Phone recognition with the mean-covariance restricted Boltzmann machine, Advances in neural information processing systems, pp. 469-477, 2010.##[16] Z. D. Doolab, S. A. Seyyedsalehi, and N. S. Dehaghani, , Nonlinear Normalization of Input Patterns to Handwritten Character Variability in Handwriting Recognition Neural Network, International Conference on Biomedical Engineering and Biotechnology, pp. 848-851, 2012.##[17] L. Dehyadegary, S. A. Seyyedsalehi and I. Nejadgholi, Nonlinear enhancement of noisy speech using continuous attractor dynamics formed in recurrent neural networks, Neurocomputing. 2011.##[18] B. Delgutte and N. Y. Kiang, Speech coding in the auditory nerve: IV. Sounds with consonant‐like dynamic characteristics, The Journal of the Acoustical Society of America, pp.897-907, 1984.##[19] S. Firooz, F. Almasganj, and Y. Shekofteh, Improvement of automatic speech recognition systems via nonlinear dynamical features evaluated from the recurrence plot of speech signals, Computers & Electrical Engineering, pp. 215-226, 2017.##[20] D. Gillick, S. Wegmann and L. Gillick, Discriminative training for speech recognition is compensating for statistical dependence in the HMM framework, international conference on acoustics, speech and signal processing (ICASSP), pp. 4745-4748, 2012.##[21] A.H. Hadjahmadi, and M. M. Homayounpour, Robust feature extraction and uncertainty estimation based on attractor dynamics in cyclic deep denoising autoencoders, Neural Computing and Applications, 31(11), pp.7989-8002, 2019.##[22] M. Hasegawa-Johnson, J. Baker, S. Borys, K. Chen, E. Coogan, S. Greenberg, A. Juneja, K. Kirchhoff, K. Livescu, S. Mohan and J. Muller, Landmark-based speech recognition, Report of the 2004 Johns Hopkins summer workshop, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2005.##[23] D. He, B. P. Lim, X. Yang, M. Hasegawa-Johnson and D. Chen, Acoustic landmarks contain more information about the phone string than other frames for automatic speech recognition with deep neural network acoustic model, The Journal of the Acoustical Society of America, pp. 3207-3219, 2018.##[24] D. He, X. Yang, B. P. Lim, Y. Liang, M. Hasegawa-Johnson and D. Chen, When CTC training meets acoustic landmarks., International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5996-6000, 2019.##[25] G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. R. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath and B. Kingsbury, Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups, IEEE signal processing magazine, pp. 82-97, 2012.##[26] A. Juneja and C. Espy-Wilson, A probabilistic framework for landmark detection based on phonetic features for automatic speech recognition, journal of the acoustical society of America, pp. 1154-1168, 2008.##[27] J. Kahn, A. Lee and A. Hannun, Self-training for end-to-end speech recognition, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7084-7088, 2020.##[28] M. A. Kermanshahi and M. M. Homayounpour, Improving Phoneme Sequence Recognition using Phoneme Duration Information in DNN-HSMM, Journal of AI and Data Mining, pp.137-147, 2019.##[29] R., Kumar, Y. Luo, and N. Mesgarani, Music Source Activity Detection and Separation Using Deep Attractor Network, In INTERSPEECH, pp. 347-351, 2018.##[30] J. W. Lee, J. Y. Choi and H. G. Kang, Classifcation of stop place in consonant-vowel contexts using feature extrapolation of acoustic-phonetic features in telephone speech, The Journal of the Acoustical Society of America, Vol. 131, 2012.##[31] Y. Luo, Z. Chen and N. Mesgarani, Speaker-independent speech separation with deep attractor network, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 26(4), 787-796, 2018.##[32] M. Meister and M. J. Berry, The neural code of the retina, Neuron, pp.435-450, 1999.##[33] N. Morgan, J. Cohen, S. H. Krishnan, S. Changand S. Wegmann, Final Report: OUCH Project (Outing Unfortunate Characteristics of HMMs), 2013.##[34] T.S. Nguyen, S. Stüker, J. Niehues, and A. Waibel, Improving sequence-to-sequence speech recognition training with on-the-fly data augmentation, IEEE International Conference on Acoustics, Speech and Signal Processing, 2020.##[35] C. Niu, J. Zhang, X. Yang and Y. Xie, A study on landmark detection based on CTC and its application to pronunciation error detection, Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, pp. 636-640, 2017.##[36] S. Parveen, and P. Green, Speech enhancement with missing data techniques using recurrent neural networks, IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 1, pp. I-733, 2004.##[37] M. Ravanelli, T. Parcollet and Y. Bengio, The pytorch-kaldi speech recognition toolkit, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6465-6469, 2019.##[38] S. Reza, S. A. Seyyedsalehi, S. Z. Seyyedsalehi, A Persian Language Phone Recognition Based on Robust Extraction of Acoustic Landmarks, 27th Iranian Conference on Biomedical Engineering, 2020.##[39] S. Reza, S. A. Seyyedsalehi, S. Z. Seyyedsalehi, Attractor Manipulation in Denoising Autoencoders for Robust Phone Recognition, 29th Iranian Conference on Electrical Engineering, 2021.##[40] T. N. Sainath, Island-driven search using broad phonetic classes, automatic speech recognition & understanding, pp. 287-292, 2009.##[41] T. N. Sainath, B. Kingsbury and B. Ramabhadran, Auto-encoder bottleneck features using deep belief networks, IEEE international conference on acoustics, speech and signal processing (ICASSP), pp. 4153-4156, 2012.##[42] L. San, N. Moritz, T. Hori, and J. L. Roux, Unsupervised Speaker Adaptation Using Attention-Based Speaker Memory for End-to-End ASR, International Conference on Acoustics, Speech and Signal Processing, 2020.##[43] S. A. Seyyedsalehi, A modular neural network speech recognizer based on the both acoustic steady portions and transitions, international conference of spoken language processing (ICSLP), 2000.##[44] S. Z. Seyyedsalehi, and S. A. Seyyedsalehi, Attractor analysis in associative neural networks and its application to facial image analysis, Computational Intelligence in Electrical Engineering, Vol. 9, No. 1, 2018##[45] K. N. Stevens, S. J. Keyser, and H. Kawasaki, Toward a phonetic and phonological theory of redundant features , Ph.D. thesis. MIT, camberidge, 1986.##[46] K. N. Stevens, From acoustic cues to segments, features and words, international conference on Spoken Language Processing (ICSLP), pp. A1-A8, 2000.##[47] J. Vaněk, J. Michálek and J. Psutka, Recurrent DNNs and Its Ensembles on the TIMIT Phone Recognition Task, International Conference on Speech and Computer, pp. 728-736, 2018.##[48] H. Veisi, and A. H. Mani, Persian speech recognition using deep learning, International Journal of Speech Technology, 23(4), pp. 893-905, 2020.##[49] P. Vincent, H. Larochelle, Y. Bengio and P. A. Manzagol, Extracting and composing robust features with denoising autoencoders, Proceedings of the 25th international conference on Machine learning, pp. 1096-1103), 2008.##[50] P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. A. Manzagol, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, Journal of machine learning research, pp. 3371-3408, 2010.##[51] M. Zapotoczny, P. Pietrzak, A. Lancucki and J. Chorowski, 2019. Lattice generation in attention-based speech recognition models, pp.2225-2229, 2019.##[52] T. Yoshimura, T. Hayashi, K. Takeda and S. Watanabe, End-to-end automatic speech recognition integrated with CTC-based voice activity detection, arXiv, 2020.##[53] N. Zeghidour, N. Usunier, I. Kokkinos, T. Schaiz, G. Synnaeve and E. Dupoux, Learning filterbanks from raw speech for phone recognition, International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5509-5513, 2018.## ##