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تشخیص دست‌نوشتۀ‌ برخط فارسی با استفاده از مدل زبانی و کاهش قوانین نگارش کاربر Online Persian Hand Writing Recognition Using Language Model and Reduction of User Writing Rules 1 1 پیوسته‌بودن کلمات فارسی و وجود تنوع بسیار زیاد رسم‌الخط این زبان و همچنین شکل‌های متنوع حروف فارسی بسته به محل قرارگیری‌شان در کلمه، تشخیص دست‌نوشته‌های فارسی را به چالش کشانده‌اند. مهم‌ترین اشکال در اغلب روش‌های بازشناسی بی‌توجهی به بافت جمله است که باعث می‌شود در مواردی که کلمه ورودی اشتباه بازشناسی می‌شود، واژه‌ای با ظاهر درست در جمله‌ای نابه‌جا به کار رود. طراحی مدلی که بتواند بافت جمله را به‌خوبی تحلیل کند، مستلزم در‌اختیار‌داشتن منابع زبانی حجیمی است که نمایندۀ خوبی از زبان مورد بازشناسی باشند. در این مقاله روش جدیدی برای بازشناسی کلمات برخط فارسی ارائه شده است که با استفاده از بافت جمله سعی در بهبود بازشناسی دارد. فرآیند بازشناسی معرفی‌شده در این نوشتار به این صورت است که ابتدا علائم و بدنه زیرکلمات دست‌نوشته ورودی تفکیک شده و بدنه هر زیرکلمه و علائم آن مشخص می‌شود؛ سپس علائم زیرکلمات تشخیص داده‌شده و بر اساس آن مجموعه‌ای از واژگان به‌عنوان فرضیه در نظر گرفته می‌شوند؛ به هر فرضیه بر اساس میزان شباهت آن به دست‌نوشته ورودی امتیازی تعلق می‌گیرد و بر اساس امتیاز حاصله محتمل‌ترین فرضیات مشخص می‌شوند. سپس این رویه توسط مدل زبانی برای یافتن فرضیات محتمل‌تر، هدایت می‌شود. نتایج آزمایش‌های به‌عمل‌آمده نشان می‌دهد که کاهش قابل توجهی در نرخ خطای بازشناسی کلمات حاصل شده و کاربر در نگارش ملزم به رعایت محدودیت‌های کمتری است. از طرفی روش پیشنهادی می‌تواند نسبت به روش‌های قبلی با در‌اختیار‌داشتن یک پایگاه داده دست‌نویس محدود، صحت مطلوب‌تری ارائه کند. با به‌کارگیری روش ارائه‌شده، دقت بازشناسی در مرحلۀ‌ اولیه در سطح حروف 9/95% و پس از بازشناسی به‌کمک مدل زبانی دقت بازشناسی به 3/99% ارتقا یافت. برای بهبود عملکرد الگوریتم، استفاده از الگوریتم یادگیری تقویتی برای تطبیق پذیری الگوریتم با نویسنده به‌عنوان کار آینده پیشنهاد می‌شود.   2 The Joint-up, cursive form of Persian words and immense variety of its scripts, also different figures of Persian letters depending on their sitting positions in the words, have turned the Persian handwritings recognition to an intense challenge. The major obstacle of the most often recognition ways, is their inattention to sentence contexture which causes utilizing of a word with correct appearance within an incorrect sentence, when an input word is misrecognized. Sketching a solution that provides suitable analysis of sentence contexture, requires huge linguistic resources to take place as a fine representative for the chosen language to be recognized. In this article, a new method for online recognition of Persian words is presented which tries to improve recognition process by using the term contexture. In this article, the vocabularies collection of Persian language is divided into two groups. The first category is the vocabulary with all of their sub-words being supported by the database of handwritten subclasses, while these vocabulary form 68.2% of the total vocabulary, and the assumptions being scored at the recognition stage, are members of these vocabularies. The second category is the vocabulary that is not supported by the database. Obviously, if the recognition system does not support this vocabulary, it cannot recognize more than 30 percentages of the language's words. At the recognition stage, the symptoms are detected and a symptom tag is produced. Also, at this stage, using the same label, the vocabulary is also selected as the sign with the input word. (These vocabularies are chosen from those were not supported at the recognition stage). Scoring for hypotheses was done by combining recognition scores and linguistic models. The certain fact in this section is that it is impossible to calculate recognition scores due to the absence of hypothetical subheadings. Therefore, the vocabulary score being recognized in the previous steps, is used. According to the studies, it was concluded that if the word is equivalent to a member's input from a supported vocabulary, even if the result of the recognition is incorrect, in most cases the correct term is in the first four hypotheses. Usually, scores of the first few hypotheses are close to each other, and the other assumptions are far from the correct hypothesis. Since the system operates online, unnecessary computations should be avoided. Therefore, if the number of hypotheses in the recognition section are more than four hypotheses, only the first four hypotheses are calculated for the language model. To calculate the recognition score for new hypotheses, if there are fewer than four hypotheses in the recognition section, the lowest hypothesis score and otherwise the hypothesis score are considered for the recognition score of the new hypotheses. Then, as with previous assumptions, for the new hypotheses, the linguistic score is calculated, and then the final score is obtained for each hypothesis. Finally, the assumption with the highest score is considered as the system output, and the rest of the assumptions are displayed in the output to the user. Experiments show that even in the event of a mistake, the correct word is often presented as a second hypothesis in most cases, and in some cases as a third hypothesis. Also, to reduce the limits and rules that gainers compel to submit. The method demonstrated in this article includes the symptoms and morphemes framework of input handwritten are segregated and the framework of each morpheme with its symptoms is specified at first, then the symptoms of morphemes are specified and based on them a collection of words is being considered as a hypothesis. Each hypothesis is given a score by measuring the similarity to input handwritten and according to taken scores, the likely hypotheses are indicated. Then, this procedure is led to achieve hypotheses more likely by lingual models. To totalize the scores of a hypothesis, for the differences in scale of taken scores, a method of score normalization is being offered. The results demonstrate that by utilizing of a language model with an online system of handwriting recognition, a significant reduction of words recognition error rate is being achieved. In addition to error rate reduction, by taking advantages of this language model, a technique is being offered that can handle the Persian vocabulary recognition entirely. By availing the offered manner, the recognition precision at initial stage of letters level up to 95.9% and so the language model recognition up to 99.3% improved. So, using huge linguistic resources for Persian language and utilizing a language model, can improve the accuracy of recognition. For further work, reinforcement learning algorithm is suggested to adapt the algorithm for users.   3 24 2015/09/26 1394/7/4 2016/11/6 1395/8/16 سلمان مسکنتی Salman Maskanati دانشگاه خلیج فارس بوشهر ایران Maskanati@gmail.com احمد کشاورز Ahmad Keshavarz دانشگاه خلیج فارس بوشهر ایران a.keshavarz@pgu.ac.ir Online Recognition Persian Handwriting k-nearest Neighbor Language Model User Limitation بازشناسی برخط دست‌نوشته فارسی نزدیک‌ترین همسایه مدل زبانی محدودیت کاربر [1] T. Ebrahimi and B. Z. Dehkordi," Using of factor oriented language model for increase of speech recognition rate," 1st conference on new ideas in computer engineering, Sharekord,2015.##[2] F. Nooshabadi, A. Ahmadifard, H. Khosravi," Online persian hand writing recognition using Analytical approach", M.S. Thesis, Shahrood university of technology, 2014.##[3] M. Mehralian and K. Fooladi," Online persian hand writing discrete letter recognition based on group and main body detection using SVM", 7th Iranian conference on machine vision and image processing, Tehran, 2011.##[4] N. Esmailpour and N. Broomandnia, "Recognition of Persian online sub-words based on fuzzy and structural approach using link list structure", 11th Iranian conference on intelligent systems, 2012.##[5] Z. K. Mohassesi, S.A. Ebrahimi and A. Farhoodinezhad," The design of a neuro-fuzzy system with simultaneous training for on line recognizing the Persian sub-words", 8th Symposium on advances in science and technology (computer networks, modelling and system security), Mashahd, 2013.##[6] M. Imanian, "Semantic clustering of verbs in Persian language", M.S. thesis, Sharif university of technology, 2012.##[7] M. Bahrani, H. Sameti, N. Hafezi, S. Momtazi and H. Movasegh, "The use of the Persian text framework in the production of statistical language models for Persian continuous speech recognition systems", 2nd workshop of Persian language and computer, PP. 92-109, 2006.##[8] Sh. P. Naeini and M. Khademi, "Persian online handwriting fragmentation using feature extraction", 3rd national conference on computer engineering and information technology, P.P. 266-271, Hamedan, Iran, 2010.##[9] S. M. Razavi and E. Kabir, "Online Persian hand writing discrete letter recognition using neural network", 3rd Iranian conference on machine vision and image processing, P.P. 83-89, Tehran, Iran, 2004.##[10] S. M. Razavi and E. Kabir, "Online Persian hand writing words recognition by Extensive vocabulary", 5th Iranian conference on machine vision and image processing, Iran, 2008.##[11] S. M. Razavi and E. Kabir, "A simple way to recognize online Persian sub-words", Iranian journal of electrical and computer engineering, vol. 2, P.P. 63-72, 2005.##[12] S. M. Razavi and E. Kabir, "A database for recognizing Persian online hand writing", Iranian journal of electrical and computer engineering, 6th Iranian conference on intelligent systems, Kerman, Iran, 2004.##[13] H. Sajedi, M. Jamzadeh, H. Sameti, B. Babaali "Presentation of a Grouping-Based Approach to Recognition of Persian Separated Letters Using the Hidden Markov Model", 12th International conference of Iranian computer society, Tehran, Iran, 2006.##[14] V. Ghods and E. Kabir, "The study of common ways of Persian online hand writing for use in their recognition", Tabriz journal of electrical engineering, Vol. 41, No. 1, P.P. 22-32, 2012.##[15] "Persian Language and Literature Academy (Publishing Works)", Persian writing order, 9th edition, 2010.##[16] J. Kaboodian, H. S. Moadab and J. Shaikhzadegan, "A word search engine based on the hidden Markov model with unlimited vocabulary to search for spoken documentation in real-world environments", 10th national conference of Iranian computer society, 2004.##[17] F. Mirzadeh, "Fuzzy based recognition of words in the Persian hand writing", M.S. Thesis, Sharif university of technology, 2007.##[18] "Research papers of Persian OCR", The OCR Research Council of the Persian writing and Language Teams, 2007.##[19] M. M. Homayoonpor and A. Salimi Badr, "Determining the boundary and type of syntactic expressions in Persian texts", Signal and data processing, Vol. 10, No. 2, P.P. 69-86, 2013.##[20] E. B. Tashk, A. Ahmadifard and H. Khosravi, "A two-step method for recognizing Persian handwritten words using the adaptive blocking of image gradients", Signal and data processing, Vol. 12, No. 3, P.P. 15-29, 2015.##[21] R. Deinat, M. Aliahmadi, M. Y. Akhlaghipour and B. Babaali, "Introducing a new information retrieval method applicable for speech recognized texts", Signal and data processing, Vol. 13, No. 4, P.P. 93-108, 2016.##[22] C. L. Liu, S. Jaeger, and M. Nakagawa, "Online recognition of Chinese characters: the state-of-the-art", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 2, pp. 198–213, 2004.##[23] D. Jurafsky and J. H. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Pearson Prentice Hall, 2009.##[24] H. N. Eseen and R. Kneser, "On Structuring Probabilistic Dependencies in Stochastic Language Modeling", Computer, Speech, and Language, vol. 8, pp. 1–38, 1994.##[25] H. Witten and T. C. Bell, "The zero-frequency problem: Estimating the probabilities of novel events in adaptive text compression", IEEE Transactions on Information Theory, vol. 37, no. 4, pp. 1085–1094, 1991.##[26] M. Bijankhan, "The role of the corpus in writing a grammar: An introduction to a software", Iranian Journal of Linguistics, vol. 19, no. 2, 2004.##[27] M. P. Harper, L. H. Jamieson, C. D. Mitchell, G. Ying, S. Potisuk, P. N. Srinivasan, R. Chen, C. B. Zoltowski, L. L. McPheters, and B. Pellom, "Integrating language models with speech recognition". AAAI-94 Workshop on the Integration of Natural Language and Speech Processing, Seattle, Washington, pp. 139-146, 1994.##[28] R. Plamondon and S. Srihari, "Online and off-line handwriting recognition: a comprehensive survey", Pattern Analysis and Machine, vol. 22, no. 1, pp. 63–84, 2000.##[29] S. Al-Emami and M. Usher, "On-line recognition of handwritten Arabic characters", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 704–710, 1990.##[30] S. Atkins, J. Clear, and N. Ostler, "Corpus design criteria", Literary and linguistic computing, vol. 7, no. 1, pp. 1–16, 1992.##[31] S. Connell and A. Jain, "Online handwriting recognition using multiple pattern class models", Michigan State University, 2000.##[32] S. Jaeger, C. L. Liu, and M. Nakagawa, "The state of the art in Japanese online handwriting recognition compared to techniques in western handwriting recognition", International Journal on Document Analysis and Recognition, vol. 6, no. 2, pp. 75–88, 2003.##[33] S. Katz, "Estimation of probabilities from sparse data for the language model component of a speech recognizer", IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 35, no. 3, pp. 400–401, 1987.##[1] ابراهیمی طاهره و زمانی دهکردی، بهزاد، "استفاده از مدل زبانی فاکتورگرا در افزایش نرخ صحیح بازشناسی گفتار"، اولین کنفرانس ملی ایده‌های نو در مهندسی کامپیوتر، شهرکرد، دانشگاه آزاد اسلامی واحد شهرکرد، 1394.##[2] نوش‌آبادی فخری،احمدی فرد علیرضا، خسروی حسین، "تشخیص دست‌نوشته برخط فارسی با رویکرد تجزیه‌ای"، پایان‌نامه‌ کارشناسی ارشد؛ دانشگاه صنعتی شاهرود؛ 1393.##[3] مهرعلیان محمدامین و فولادی کاظم، "بازشناسی برخط حروف مجزای دست‌نویس فارسی بر اساس تشخیص گروه و بدنه اصلی با استفاده از ماشین بردار پشتیبان"، هفتمین کنفرانس ماشین بینایی و پردازش تصویر، تهران، دانشگاه علم و صنعت، 1390.##[4] اسمعیل‌پور ندا، برومندنیا ندا، "بازشناسی زیر-کلمات برخط فارسی بر اساس رویکرد فازی و ساختاری با استفاده از ساختار لیست‌های پیوندی"، یازدهمین کنفرانس سراسری سیستم‌های هوشمند. انجمن سیستم‌های هوشمند ایران، 1391.##[5] خوش کلام محصصی زهرا، رضوی ابراهیمی سید علی و فرهودی نژاد اکبر، «طراحی یک سیستم عصبی - فازی با قابلیت آموزش هم‌زمان برای بازشناسی بر خط زیر - کلمات فارسی»، همایش ملی مهندسی کامپیوتر و توسعه پایدار با محوریت شبکه‌های کامپیوتری، مدل‌سازی و امنیت سیستم‌ها، مشهد، موسسه آموزش عالی خاوران، ۱۳۹۲.##[6] امینیان، مریم، "خوشه‌بندی معنایی افعال زبان فارسی"، پایان‌نامه‌ کارشناسی ارشد؛ دانشگاه صنعتی شریف؛ 1391.##[7] بحرانی محمد، ثامتی حسین، حافظی نازیلا، ممتازی سعیده، موثق حامد، "به‌کارگیری پیکره متنی زبان فارسی در ساخت مدل‌های زبانی آماری برای سیستم‌های بازشناسی گفتار پیوسته فارسی"، دومین کارگاه پژوهشی زبان فارسی و رایانه، صص 92-109، 1385.##[8] پیرنیا نائینی، شهریار و خادمی، مریم، "قطعه‌بندی برخط دست‌نویس فارسی با استفاده از استخراج ویژگی‌ها" ، سومین همایش ملی کامپیوتر و فناوری اطلاعات مهندسی سما، صص 266-271، همدان، ایران، 1389.##[9] رضوی، سید محمد و کبیر، احسان‌الله، "بازشناسی برخط حروف مجزای فارسی با شبکه‌ عصبی"، سومین کنفرانس ماشین بینایی و پردازش تصویر، جلد 41 شماره 1، صص 83-89، دانشگاه تهران،1383.##[10] رضوی، سید محمد و کبیر، احسان‌الله، "بازشناسی برخط کلمات دست‌نویس فارسی با واژگانی گسترده"، 1387، پنجمین کنفرانس ماشین بینایی و پردازش تصویر.##[11] رضوی، سید محمد و کبیر، احسان‌الله، "روشی ساده برای بازشناسی برخط زیر-کلمات فارسی"، نشریه مهندسی برق و مهندسی کامپیوتر ایران، شماره 2، صص 63- 72، 1384.##[12] رضوی، سید محمد و کبیر، احسان‌الله، "یک پایگاه داده برای بازشناسی دست‌نوشته‌های برخط فارسی"، ششمین کنفرانس سیستم‌های هوشمند، کرمان، 1383.##[13] ساجدی، هدیه و جم‌زاده، منصور و ثامتی، حسین و باباعلی، باقر، "ارائه‌ی یک روش مبتنی بر گروه‌بندی برای بازشناسی حروف مجزای برخط فارسی به کمک مدل مخفی مارکوف "، دوازدهمین کنفرانس بین‌المللی انجمن کامپیوتر ایران، صص 419-425، دانشگاه تهران، 1385.##[14] قدس، وحید و کبیر، احسان‌الله، "بررسی شیوه‌های متداول نگارش دست‌نوشته‌های برخط فارسی به‌منظور استفاده در بازشناسی آن‌ها"، مجله مهندسی برق دانشگاه تبریز، جلد 41 شماره 1، صص 22-32، 1391.##[15] فرهنگستان زبان و ادب فارسی (نشر آثار) ، دستور خط فارسی، چاپ نهم، 1389.##[16] کبودیان جهانشاه، شجاع مودب حمیدرضا، شیخ زادگان جواد، "یک سیستم جستجوگر کلمات مبتنی بر مدل پنهان مارکوف با دایره لغات نامحدود برای جستجوی مستندات گفتاری در محیط‌های واقعی و عملیاتی"، دهمین کنفرانس سالانه انجمن کامپیوتر ایران، 1383.##[17] میرزازاده، فرزانه، "بازشناسی کلمات در دست‌نوشته بر خط فارسی به روش فازی"، پایان‌نامه‌ کارشناسی ارشد، دانشگاه صنعتی شریف، تهران، 1386.##[18] پژوهشنامه نویسه‌خوان نوری OCR فارسی، شورای پژوهشی OCR کارگروه خط و زبان فارسی شورای عالی اطلاع‌رسانی, پائیز 1386.##[19] همایون پور محمد مهدی، سلیمی بدر آرمین، "تعیین مرز و نوع عبارات نحوی در متون فارسی"، فصلنامه علمی-پژوهشی پردازش علائم و داده ها، جلد 10، شماره 2، صفحه 69-86، 1392.##[20] بایسته تاشک الهام، احمدی فرد علیرضا، خسروی حسین، " روشی دو مرحله ای برای بازشناسی کلمات دست نوشته فارسی به کمک بلوک بندی تطبیقی گرادیان تصویر"، فصلنامه علمی-پژوهشی پردازش علائم و داده ها، جلد 12، شماره 3، صفحه 15-29، 1394.##[21] دیانت روح الله، علی احمدی مرتضی، اخلاقی محمد یحیی، باباعلی باقر، " ارایه یک روش جدید بازیابی اطلاعات مناسب برای متون حاصل از بازشناسی گفتار"، فصلنامه علمی-پژوهشی پردازش علائم و داده ها، جلد 13، شماره 4، صفحه 93-108، 1395##[1] T. Ebrahimi and B. Z. Dehkordi," Using of factor oriented language model for increase of speech recognition rate," 1st conference on new ideas in computer engineering, Sharekord,2015.##[2] F. Nooshabadi, A. Ahmadifard, H. Khosravi," Online persian hand writing recognition using Analytical approach", M.S. Thesis, Shahrood university of technology, 2014.##[3] M. Mehralian and K. Fooladi," Online persian hand writing discrete letter recognition based on group and main body detection using SVM", 7th Iranian conference on machine vision and image processing, Tehran, 2011.##[4] N. Esmailpour and N. Broomandnia, "Recognition of Persian online sub-words based on fuzzy and structural approach using link list structure", 11th Iranian conference on intelligent systems, 2012.##[5] Z. K. Mohassesi, S.A. Ebrahimi and A. Farhoodinezhad," The design of a neuro-fuzzy system with simultaneous training for on line recognizing the Persian sub-words", 8th Symposium on advances in science and technology (computer networks, modelling and system security), Mashahd, 2013.##[6] M. Imanian, "Semantic clustering of verbs in Persian language", M.S. thesis, Sharif university of technology, 2012.##[7] M. Bahrani, H. Sameti, N. Hafezi, S. Momtazi and H. Movasegh, "The use of the Persian text framework in the production of statistical language models for Persian continuous speech recognition systems", 2nd workshop of Persian language and computer, PP. 92-109, 2006.##[8] Sh. P. Naeini and M. Khademi, "Persian online handwriting fragmentation using feature extraction", 3rd national conference on computer engineering and information technology, P.P. 266-271, Hamedan, Iran, 2010.##[9] S. M. Razavi and E. Kabir, "Online Persian hand writing discrete letter recognition using neural network", 3rd Iranian conference on machine vision and image processing, P.P. 83-89, Tehran, Iran, 2004.##[10] S. M. Razavi and E. Kabir, "Online Persian hand writing words recognition by Extensive vocabulary", 5th Iranian conference on machine vision and image processing, Iran, 2008.##[11] S. M. Razavi and E. Kabir, "A simple way to recognize online Persian sub-words", Iranian journal of electrical and computer engineering, vol. 2, P.P. 63-72, 2005.##[12] S. M. Razavi and E. Kabir, "A database for recognizing Persian online hand writing", Iranian journal of electrical and computer engineering, 6th Iranian conference on intelligent systems, Kerman, Iran, 2004.##[13] H. Sajedi, M. Jamzadeh, H. Sameti, B. Babaali "Presentation of a Grouping-Based Approach to Recognition of Persian Separated Letters Using the Hidden Markov Model", 12th International conference of Iranian computer society, Tehran, Iran, 2006.##[14] V. Ghods and E. Kabir, "The study of common ways of Persian online hand writing for use in their recognition", Tabriz journal of electrical engineering, Vol. 41, No. 1, P.P. 22-32, 2012.##[15] "Persian Language and Literature Academy (Publishing Works)", Persian writing order, 9th edition, 2010.##[16] J. Kaboodian, H. S. Moadab and J. Shaikhzadegan, "A word search engine based on the hidden Markov model with unlimited vocabulary to search for spoken documentation in real-world environments", 10th national conference of Iranian computer society, 2004.##[17] F. Mirzadeh, "Fuzzy based recognition of words in the Persian hand writing", M.S. Thesis, Sharif university of technology, 2007.##[18] "Research papers of Persian OCR", The OCR Research Council of the Persian writing and Language Teams, 2007.##[19] M. M. Homayoonpor and A. Salimi Badr, "Determining the boundary and type of syntactic expressions in Persian texts", Signal and data processing, Vol. 10, No. 2, P.P. 69-86, 2013.##[20] E. B. Tashk, A. Ahmadifard and H. Khosravi, "A two-step method for recognizing Persian handwritten words using the adaptive blocking of image gradients", Signal and data processing, Vol. 12, No. 3, P.P. 15-29, 2015.##[21] R. Deinat, M. Aliahmadi, M. Y. Akhlaghipour and B. Babaali, "Introducing a new information retrieval method applicable for speech recognized texts", Signal and data processing, Vol. 13, No. 4, P.P. 93-108, 2016.##[22] C. L. Liu, S. Jaeger, and M. Nakagawa, "Online recognition of Chinese characters: the state-of-the-art", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 2, pp. 198–213, 2004.##[23] D. Jurafsky and J. H. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Pearson Prentice Hall, 2009.##[24] H. N. Eseen and R. Kneser, "On Structuring Probabilistic Dependencies in Stochastic Language Modeling", Computer, Speech, and Language, vol. 8, pp. 1–38, 1994.##[25] H. Witten and T. C. Bell, "The zero-frequency problem: Estimating the probabilities of novel events in adaptive text compression", IEEE Transactions on Information Theory, vol. 37, no. 4, pp. 1085–1094, 1991.##[26] M. Bijankhan, "The role of the corpus in writing a grammar: An introduction to a software", Iranian Journal of Linguistics, vol. 19, no. 2, 2004.##[27] M. P. Harper, L. H. Jamieson, C. D. Mitchell, G. Ying, S. Potisuk, P. N. Srinivasan, R. Chen, C. B. Zoltowski, L. L. McPheters, and B. Pellom, "Integrating language models with speech recognition". AAAI-94 Workshop on the Integration of Natural Language and Speech Processing, Seattle, Washington, pp. 139-146, 1994.##[28] R. Plamondon and S. Srihari, "Online and off-line handwriting recognition: a comprehensive survey", Pattern Analysis and Machine, vol. 22, no. 1, pp. 63–84, 2000.##[29] S. Al-Emami and M. Usher, "On-line recognition of handwritten Arabic characters", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 704–710, 1990.##[30] S. Atkins, J. Clear, and N. Ostler, "Corpus design criteria", Literary and linguistic computing, vol. 7, no. 1, pp. 1–16, 1992.##[31] S. Connell and A. Jain, "Online handwriting recognition using multiple pattern class models", Michigan State University, 2000.##[32] S. Jaeger, C. L. Liu, and M. Nakagawa, "The state of the art in Japanese online handwriting recognition compared to techniques in western handwriting recognition", International Journal on Document Analysis and Recognition, vol. 6, no. 2, pp. 75–88, 2003.##[33] S. Katz, "Estimation of probabilities from sparse data for the language model component of a speech recognizer", IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 35, no. 3, pp. 400–401, 1987.## ##

طبقه‌بندی آریتمی‌های قلبی مبتنی بر ترکیب نتایج شبکه‌های عصبی با نظریه شواهد دمپستر- شفر Classification of Cardiac Arrhythmias based on combination of the results of Neural Networks using Dempster-Shefer Evidence Theory 1 1 آریتمی‌های قلبی یکی از شایع‌ترین  بیماری‌های قلبی است که ممکن است سبب مرگ بیمار شود. از‌این‌رو شناسایی آریتمی‌های قلبی بسیار مهم است. در این مقاله برای دسته‌بندی آریتمی‌های قلبی در سه طبقه PAC، PVC و Normal روشی مبنی بر ترکیب طبقه‌بندی‌کننده‌ها با استفاده از نظریه شواهد لحاظ شده است. بدین شکل که ابتدا پیک‌های R در ECG شناسایی شد؛ سپس ویژگی‌های  خطی ECG شامل RMSSD، SDNN و HR Mean و همچنین ویژگی غیر خطی آن با استفاده از SVD به‌دست آمد. ترکیب ویژگی‌های                       به‌دست‌آمده به شبکه‌های عصبی MLP، Cascade Feed Forward و RBF داده شد. اصل عدم قطعیت در مورد  پاسخ آن‌ها بررسی و در‌نهایت پاسخ این طبقه‌بندی‌کننده‌ها با استفاده از نظریه شواهد با یکدیگر ترکیب شدند. جهت پردازش ECG نیاز به حذف نوفه نبوده و روش پیشنهادی توانسته است در حضور نوفه، نوع آریتمی قلبی را در بهترین حالت با حساسیت 98 % تشخیص دهد.   2 Cardiac arrhythmias are one of the most common heart diseases that may cause the death of the patient. Therefore, it is extremely important to detect cardiac arrhythmias.  3 categories of arrhythmia, namely, PAC, PVC, and normal are considered in this paper based on classifier fusion using evidence theory. In this study, at first a sample is carrying out the ECG signal with 250 point.  Moreover, in each of the sampling, the maximum values will be obtained. Then, the average of the calculated values would be considered as adaptive thresholding and the total signals are multiplied by the inverse adaptive thresholding. After fixing the adaptive thresholding at number one, total resulting signal is becoming the power of 2. In this situation, the amounts smaller than one, are weakened and the larger than one amounts are reinforced. The smaller amount is removed and other amounts are held. Then, the maximum in each of the sampling is considered. In sampling areas that there is no peak, some maximum can be identified with zero value that these points should be removed from the set maximum. To find the maximum point where the maximum is close to the borders of sampling, two peaks may be placed in one field. This problem leads to removing one peak and non-recognition of the smaller peak. Some peaks near the border of sampling, for example the previous or next point on the border may be identified as the peak which eliminates the major peak and identifies the unrealistic peak. To solve this problem, the 80-point sampling is performed around each detected peak and the maximum value is obtained at the sampling areas. In this way, the correct peaks are identified and the wrong one will be deleted. In some parts, the peak signal is not quite sharp, and maybe two or more points that are adjacent to each other with the same value, will be considered as a peak. In other words, a closed peak is detected several times, which leads to detection of extra and incorrect peaks. In these circumstances, according to an amount that only belongs to one peak, just one of them should be considered and the other should be removed. After these steps, an obtained signal which includes peaks R, is compared with the original signal. To achieve the correct answer, it changes the number of sampling points and each time the result is compared with the previous values and with the original signal, too, until finally the major peaks will be identified. Then, HRV signal be will calculated. Linear properties contain root mean square of successive differences between normal intervals (RMSSD) and standard deviation of normal to normal intervals in a row (SDNN) and also heart rate (HR Mean) are calculated. Around each peak, 81 points window is inserted. These points for each peak is in one row. So resulting matrix (X) has 81 columns and its rows are the number of R peaks. SVD of matrix(X) is calculated. The obtained Matrix S will include the individual values. These singular signal values are non-linear features. If all used values are single, they can eclipse the linear features which will lead to the lack of features’ effect. Because of this reason, it is used only from the largest single value as a non-linear feature. The combination of linear and non-linear characteristics as input is applied to MLP, Cascade Feed Forward and RBF neural networks and every (single) answer is studied. The answers for each class have a level of probability that any classifier can independently be taken to the classification of cardiac arrhythmias. A class that has the greatest probability is allocated to the data. These probabilities show the uncertainty of the answers. Each of the classifiers is considered as a witness. All the possibilities for different classes of each witness uncertainties function are modeled and crime function is defined. In other words, belief structure is formed for evidence. At this stage, by combined Demster law, the mass functions will combine together. In this situation, the level of uncertainty is much reduced and the class with the highest crime will be selected as the answer. According to the survey results, the combination of linear and non-linear characteristics for training and testing the neural networks classifiers has increased the accuracy of the answer. In other words, the extraction of more features leads to better training the neural networks and increases the accuracy of the classifiers. It can be noted that the using classifiers uncertainty principle and combining them by using the evidence theory has increased the accuracy of the final classification. The results of this study show that the proposed method was able to classify cardiac arrhythmias in the presence of noise and provided an acceptable answer for the intended issue. In sum, the proposed method has been able to classify 3 categories of cardiac arrhythmia such as PVC, PAC and NORMAL with high accuracy. This is performed in the best situation with sensitivity greater than 0/98.   25 42 2015/09/262015/12/5 1394/9/14 2016/11/62016/10/29 1395/8/8 جمال قاسمی Jamal Ghasemi دانشگاه مازندران ایران j.ghasemi@umz.ac.ir سمیه کرد Somayeh Kord دانشگاه آزاد واحد نور ایران somayye.kord@yahoo.com محمد غلامی Mohamad Gholami دانشگاه مازندران ایران m.gholami@umz.ac.ir ECG signal Classifier Neural Networks Evidence theory سیگنال الکتروکاردیوگرام طبقه‌بندی‌کننده‌ها شبکه‌های عصبی نظریه شواهد [1] M. Satarpor, B. Mohammadzadeh Asl, "Recognition and estimation of T wave variation using ECG signal multitrack analysis ", JSDP, vol. 12(3), 69-80, 2015.##[2] M. Sharif Noughabi, H. Marvi, D. Darabian, " Farsi Accent Recognition based on speech signal using efficient features extraction and Combining of Classifiers, JSDP", vol. 13(2), 91-103, 2016.##[3] M. A. Khalilzadeh, R. Sarafan, M. Azarnoosh, "Lie detector system based on PhotoPlethysmoGraph(PPG) and Galvanic Skin Response(GSR) signals by means of neural network", JSDP, vol. 9(2), 49-60, 2013.##[4] J. Ghasemi, "Thesis segmentation of MRI brain fuzzy theory based on evidence", Mazandaran University, 2012.##[5]S. Kord, J. Ghasemi, "Classifieds cardiac arrhythmias with a combination of linear and nonlinear characteristics of ECG signal using probabilistic neural network",Second National Conference on Electrical Engineering Iran Islamic Azad University Gaz, 2014.##[6] M. Ankita, A. Meena, "Detection of Cardiac Arrhythmias Using Different Neural Networks: A Review", International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3, pp. 6992-6995, 2014.##[7] P. Auer, B. Harald and M. Wolfgang, "A learning rule for very simple universal approximators consisting of a single layer of perceptrons", Neural Networks, vol. 5, pp.786-795, 2008.##[8] E. Braunwald, "Heart Disease: A Textbook of Cardiovascular Medicine", Fifth Edition, Philadelphia, W.B. Saunders Co, pp. 108, 1997.##[9] A. P. Dempster, "Upper and lower probabilities induced by a ultivalued mapping", The Annals of Statistics, vol. 28, pp. 325-339, 1967.##[10] H. Demuth, M. Beale, M. Hagan, "Neural Network Toolbox Users Guide", the Math Works, Inc, Natrick, USA, 2009.##[11] A. Ebrahimzadeh, A. Khazaee, "Detection of premature ventricular contractions using MLP neural network: A comparative study", Elsevier, measurement, vol. 43, pp. 103-112, 2010.##[12] H. Gothwal, S. Kedawat, R. Kumar, "Cardiac arrhythmias detection in an ECG beat signal using fast fourier transform and artificial neural network", Journal of Biomedical Science & Engineering, vol. 4, pp. 289-296, 2011.##[13] J. Y. Halpern, R. Fagin, "Two views of belief: belief as generalized probability and belief as evidence", Artificial Intelligence, vol. 54, pp. 275-317, 1992.##[14] P. S. Hamilton, W. J. Tompkins, Quantitative Investigation of QRS Detection Rules Using the MIT/BIH Arrhytmia Database", IEEE Trans. On Biomed. Eng, vol. 33, pp. 1157-1167, 1986.##[15] J. C. Helton, "Uncertainty and sensitivity analysis in the presence of stochastic and subjective uncertainty", Journal of Statistical Computation and Simulation, vol. 57, pp. 3- 76, 1997.##[16] YZ. Hu, S. Palreddy, WJ. Tompkins, "A patient-adaptable ECG beat classifier using a mixture of experts approach", IEEE Trans Biomed Eng, Vol. 44, pp. 891-900, 1997.##[17] N. P. Hughes, L. T. Arassenko and S. J. Roberts, "Markov Models for Automated ECG Interval Analysis", oxford, 2004.##[18]L. Y. Jen, "Explaining critical clearing time with the rules extracted from a multilayer perceptron artificial neural network", Electr Power Energy Syst, vol. 33, pp. 873-878, 2010.##[19] L. Ju-Won, L. Gun-Ki, "Design of an Adaptive Filter with a Dynamic Structure for ECG Signal Processing", International Journal of Control, Automation, and Systems, Vol. 3, No. 1, pp. 137-142, 2005.##[20] M. Kania, M. Fereniec, R. Maniewski, "Wavelet Denoising for Multi-lead High Resolution ECG Signals", Measurement Science Review, Vol. 7, No. 2, pp. 30-33, 2007.##[21 ]S. Krimi, K. Ouni, N. Ellouze, "Using Hidden Markov Models for ECG Characterisation", InTech, ISBN: 978-953-307-208-1, 2011.##[22]V. S. Kumari, P. R. kumar, "Cardiac arrhythmia prediction using improved multilayer perceptron neural network", International Journal of Electronics, Communication & Instrumentation Engineering esearch and Development (IJECIERD), vol. 3, pp. 73-80, 2013.##[23] L. kuncheva, "Combining Pattern Classifiers: Methods and Algorithms", Hoboken, NJ, 2004.##[24] Y. Kutlu, K. Damla, "Feature Reduction Method Using Self Organizing Maps", International Conference on Electrical and Electronics Engineering, pp. 129-132, 2009.##[25] Sh. Lihuang, S. Yuning, Z. Shi and X. Zhongqiang, "A Precise Ambulatory ECG Arrhythmia Intelligent Analysis Algorithm Based On Support Vector Machine Classifiers", Proceedings of the 3rd International Conference on Biomedical Engineering and Informatics, 2010.##[26] R. G. Mark, G. B. Moody, "MIT/BIH Arrhythmia Database", 1991, Available from: http://www.ecg.mit.edu/dbinfo.html##[27] R. G. Mark, G. B. Moody, "The impact of the MIT/BIH Arrhythmia Database", IEEE Eng. Med. Biol, vol. 20, pp. 45-50, 1991.##[28] R. J. Martis, U. R. Achary, C. M. Lim, K.M. Mandana, A.K. Ray, C. Chakraborty, "Application of high order cumulant features for cardiac health diagnosis using ECG signals", International Journal of Neural Systems, vol. 23, pp. 1142- 1155, 2013a.##[29] R. J. Martis, U. R. Achary, K.M. Mandana, A.K. Ray, C. Chakraborty, "Application of principal component analysis to ECG signals for automated diagnosis of cardiac health", Expert Systems with Applications, Vol. 39, pp. 11792–11800, 2012.##[30] R. J. Martis, U. R. Achary, K.M. Mandana, A.K. Ray, C. Chakraborty, "Cardiac decision making using higher order spectra", Biomedical Signal Processing and Control, vol 8, 193-203, 2013b.##[31] S. S. Mehta,N. S. Lingayat, "Support Vector Machine for Cardiac Beat Detection in Single Lead Electrocardiogram", IAENG in IAENG International Journal of Applied Mathematics, vol. 36, pp. 20-26, 2011.##[32] G. Nazari Golpayegani, A. H. Jafari, "A novel approach in ECG beat recognition using adaptive neural fuzzy filter", J. Biomedical Science and Engineering, vol. 2, pp. 80-85, 2009.##[33] J. Pan, W. J. Tompkins, "A real-Time QRS Detection Algoritm", IEEE Trans. On Biomed. Eng, Vol. 3, pp. 230-236, 1985.##[34] Romero, L. Serrano, "ECG frequency domain features extraction: A new characteristics for arrhythmias classification", Engineering in Medicine and Biology Society, Proceedings of the 23rd Annual International Conference of the IEEE, vol. 2. pp. 2006-2008, 2001.##[35] M. B. Roman, Z. S. Ravilya, I. L. Ekaterina, "Comparison of linear and nonlinear calibration models based on near infrared (NIR) spectroscopy data for gasoline properties prediction", Chemometr Intell Lab, vol. 2, pp. 183-188, 2007.##[36] M. Roshan Joy, U. Rajendra Acharya, M. Lim Choo, "ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform", Biomedical Signal Processing and Control, BSPC-375, 2013.##[37] S. Safdar, S. Ahmad Khan, F. Arif, "Report Generation on ECGs Survey Data Analysis Using Threshold Based Inference Engine", International Journal of Information and Education Technology, Vol. 2, No. 3, pp 265-269, 2012.##[38] G. Shafer, "A mathematical theory of evidence", London, Princeton University Press, 1976.##[39] Z. S. Wang, J. D. Z. Chen, "Robust ECG R-R Wave Detection Using Evolutionary Programming Base Fuzzy Inference System (EPFIS) and Application to Accessing Brain Gut", Interaction Science Measurement and Technology, IEE Proccedings, vol. 6, 2000.##[40] M. Wozniak, B. Krawczyk, "Combined classifier based on feature space partitioning", International. Journal of Applied Mathematics and Computer Science, vol 22, pp. 855–866, 2012.##[41] Y. C. Yeh, C. W. Chiou and H. J. Lin, "Analyzing ECG for cardiac Arrhythmia using cluster analysis", Expert System with Application, vol. 39, pp. 1000- 1010, 2012.##[42] Y. C. Yeh, W. J. Wang and C. W. Chiou, "Heartbeat case determination using fuzzy logic method on ECG signals", International Journal of Fuzzy Systems, vol 11, 250-261, 2009.##[43] M. N. Zade, P. M. Palkar, P. N. Aerkewar, A. S. Pathan, "Detection of ECG Signal: A Survey", International Journal of Artificial Intelligence and Mechatronics, Vol. 1, Issue 5, pp. 126-130, 2013.##[44] L. A. Zadeh, "Fuzzy sets", IEEE Information Control, vol. IC-8, pp. 338–353, 1965.##[1] م. ستارپور، ب. محمدزاده اصل،" تشخیص و تخمین تغییرات موج T با استفاده از تحلیل چند لیدی سیگنال ECG "، پردازش علائم و داده ها، 12(3) ، 69-80، 1394.##[2] م. شریف نوقابی، ح. مروی، د. دارابیان،" تشخیص لهجه های گفتار زبان فارسی از روی سیگنال گفتار با استفاده از روش های استخراج ویژگی کارآمد و ترکیب طبقه بندها "، پردازش علائم و داده ها، 13(2) ، 91-103، 1395.##[3] م. ا. یونسی هروی، م. ع. خلیل زاده، ر. صرافان، م. آذرنوش،" تشخیص دروغ بر مبنای سیگنال های فوتوپلتیسموگراف و مقاومت الکتریکی پوست با استفاده از شبکه ی عصبی"، پردازش علائم و داده ها، 9(2) ، 49-60، 1391.##[4] ج. قاسمی ،"رساله دکترا قطعه بندی فازی ام آر آی مغز مبتنی بر نظریه شواهد"، دانشگاه مازندران، 1391.##[5] س. کرد، ج. قاسمی،" کلاسه بندی آریتمی‌های قلبی با ترکیب ویژگی‌های خطی و غیر خطی سیگنال الکتروکاردیوگرام با استفاده از شبکه عصبی احتمالی"، دومین همایش ملی مهندسی برق ایران دانشگاه آزاد اسلامی‌واحد بندرگز،1393.##[1] M. Satarpor, B. Mohammadzadeh Asl, "Recognition and estimation of T wave variation using ECG signal multitrack analysis ", JSDP, vol. 12(3), 69-80, 2015.##[2] M. Sharif Noughabi, H. Marvi, D. Darabian, " Farsi Accent Recognition based on speech signal using efficient features extraction and Combining of Classifiers, JSDP", vol. 13(2), 91-103, 2016.##[3] M. A. Khalilzadeh, R. Sarafan, M. Azarnoosh, "Lie detector system based on PhotoPlethysmoGraph(PPG) and Galvanic Skin Response(GSR) signals by means of neural network", JSDP, vol. 9(2), 49-60, 2013.##[4] J. Ghasemi, "Thesis segmentation of MRI brain fuzzy theory based on evidence", Mazandaran University, 2012.##[5]S. Kord, J. Ghasemi, "Classifieds cardiac arrhythmias with a combination of linear and nonlinear characteristics of ECG signal using probabilistic neural network",Second National Conference on Electrical Engineering Iran Islamic Azad University Gaz, 2014.##[6] M. Ankita, A. Meena, "Detection of Cardiac Arrhythmias Using Different Neural Networks: A Review", International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3, pp. 6992-6995, 2014.##[7] P. Auer, B. Harald and M. Wolfgang, "A learning rule for very simple universal approximators consisting of a single layer of perceptrons", Neural Networks, vol. 5, pp.786-795, 2008.##[8] E. Braunwald, "Heart Disease: A Textbook of Cardiovascular Medicine", Fifth Edition, Philadelphia, W.B. Saunders Co, pp. 108, 1997.##[9] A. P. Dempster, "Upper and lower probabilities induced by a ultivalued mapping", The Annals of Statistics, vol. 28, pp. 325-339, 1967.##[10] H. Demuth, M. Beale, M. Hagan, "Neural Network Toolbox Users Guide", the Math Works, Inc, Natrick, USA, 2009.##[11] A. Ebrahimzadeh, A. Khazaee, "Detection of premature ventricular contractions using MLP neural network: A comparative study", Elsevier, measurement, vol. 43, pp. 103-112, 2010.##[12] H. Gothwal, S. Kedawat, R. Kumar, "Cardiac arrhythmias detection in an ECG beat signal using fast fourier transform and artificial neural network", Journal of Biomedical Science & Engineering, vol. 4, pp. 289-296, 2011.##[13] J. Y. Halpern, R. Fagin, "Two views of belief: belief as generalized probability and belief as evidence", Artificial Intelligence, vol. 54, pp. 275-317, 1992.##[14] P. S. Hamilton, W. J. Tompkins, Quantitative Investigation of QRS Detection Rules Using the MIT/BIH Arrhytmia Database", IEEE Trans. On Biomed. Eng, vol. 33, pp. 1157-1167, 1986.##[15] J. C. Helton, "Uncertainty and sensitivity analysis in the presence of stochastic and subjective uncertainty", Journal of Statistical Computation and Simulation, vol. 57, pp. 3- 76, 1997.##[16] YZ. Hu, S. Palreddy, WJ. Tompkins, "A patient-adaptable ECG beat classifier using a mixture of experts approach", IEEE Trans Biomed Eng, Vol. 44, pp. 891-900, 1997.##[17] N. P. Hughes, L. T. Arassenko and S. J. Roberts, "Markov Models for Automated ECG Interval Analysis", oxford, 2004.##[18]L. Y. Jen, "Explaining critical clearing time with the rules extracted from a multilayer perceptron artificial neural network", Electr Power Energy Syst, vol. 33, pp. 873-878, 2010.##[19] L. Ju-Won, L. Gun-Ki, "Design of an Adaptive Filter with a Dynamic Structure for ECG Signal Processing", International Journal of Control, Automation, and Systems, Vol. 3, No. 1, pp. 137-142, 2005.##[20] M. Kania, M. Fereniec, R. Maniewski, "Wavelet Denoising for Multi-lead High Resolution ECG Signals", Measurement Science Review, Vol. 7, No. 2, pp. 30-33, 2007.##[21 ]S. Krimi, K. Ouni, N. Ellouze, "Using Hidden Markov Models for ECG Characterisation", InTech, ISBN: 978-953-307-208-1, 2011.##[22]V. S. Kumari, P. R. kumar, "Cardiac arrhythmia prediction using improved multilayer perceptron neural network", International Journal of Electronics, Communication & Instrumentation Engineering esearch and Development (IJECIERD), vol. 3, pp. 73-80, 2013.##[23] L. kuncheva, "Combining Pattern Classifiers: Methods and Algorithms", Hoboken, NJ, 2004.##[24] Y. Kutlu, K. Damla, "Feature Reduction Method Using Self Organizing Maps", International Conference on Electrical and Electronics Engineering, pp. 129-132, 2009.##[25] Sh. Lihuang, S. Yuning, Z. Shi and X. Zhongqiang, "A Precise Ambulatory ECG Arrhythmia Intelligent Analysis Algorithm Based On Support Vector Machine Classifiers", Proceedings of the 3rd International Conference on Biomedical Engineering and Informatics, 2010.##[26] R. G. Mark, G. B. Moody, "MIT/BIH Arrhythmia Database", 1991, Available from: http://www.ecg.mit.edu/dbinfo.html##[27] R. G. Mark, G. B. Moody, "The impact of the MIT/BIH Arrhythmia Database", IEEE Eng. Med. Biol, vol. 20, pp. 45-50, 1991.##[28] R. J. Martis, U. R. Achary, C. M. Lim, K.M. Mandana, A.K. Ray, C. Chakraborty, "Application of high order cumulant features for cardiac health diagnosis using ECG signals", International Journal of Neural Systems, vol. 23, pp. 1142- 1155, 2013a.##[29] R. J. Martis, U. R. Achary, K.M. Mandana, A.K. Ray, C. Chakraborty, "Application of principal component analysis to ECG signals for automated diagnosis of cardiac health", Expert Systems with Applications, Vol. 39, pp. 11792–11800, 2012.##[30] R. J. Martis, U. R. Achary, K.M. Mandana, A.K. Ray, C. Chakraborty, "Cardiac decision making using higher order spectra", Biomedical Signal Processing and Control, vol 8, 193-203, 2013b.##[31] S. S. Mehta,N. S. Lingayat, "Support Vector Machine for Cardiac Beat Detection in Single Lead Electrocardiogram", IAENG in IAENG International Journal of Applied Mathematics, vol. 36, pp. 20-26, 2011.##[32] G. Nazari Golpayegani, A. H. Jafari, "A novel approach in ECG beat recognition using adaptive neural fuzzy filter", J. Biomedical Science and Engineering, vol. 2, pp. 80-85, 2009.##[33] J. Pan, W. J. Tompkins, "A real-Time QRS Detection Algoritm", IEEE Trans. On Biomed. Eng, Vol. 3, pp. 230-236, 1985.##[34] Romero, L. Serrano, "ECG frequency domain features extraction: A new characteristics for arrhythmias classification", Engineering in Medicine and Biology Society, Proceedings of the 23rd Annual International Conference of the IEEE, vol. 2. pp. 2006-2008, 2001.##[35] M. B. Roman, Z. S. Ravilya, I. L. Ekaterina, "Comparison of linear and nonlinear calibration models based on near infrared (NIR) spectroscopy data for gasoline properties prediction", Chemometr Intell Lab, vol. 2, pp. 183-188, 2007.##[36] M. Roshan Joy, U. Rajendra Acharya, M. Lim Choo, "ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform", Biomedical Signal Processing and Control, BSPC-375, 2013.##[37] S. Safdar, S. Ahmad Khan, F. Arif, "Report Generation on ECGs Survey Data Analysis Using Threshold Based Inference Engine", International Journal of Information and Education Technology, Vol. 2, No. 3, pp 265-269, 2012.##[38] G. Shafer, "A mathematical theory of evidence", London, Princeton University Press, 1976.##[39] Z. S. Wang, J. D. Z. Chen, "Robust ECG R-R Wave Detection Using Evolutionary Programming Base Fuzzy Inference System (EPFIS) and Application to Accessing Brain Gut", Interaction Science Measurement and Technology, IEE Proccedings, vol. 6, 2000.##[40] M. Wozniak, B. Krawczyk, "Combined classifier based on feature space partitioning", International. Journal of Applied Mathematics and Computer Science, vol 22, pp. 855–866, 2012.##[41] Y. C. Yeh, C. W. Chiou and H. J. Lin, "Analyzing ECG for cardiac Arrhythmia using cluster analysis", Expert System with Application, vol. 39, pp. 1000- 1010, 2012.##[42] Y. C. Yeh, W. J. Wang and C. W. Chiou, "Heartbeat case determination using fuzzy logic method on ECG signals", International Journal of Fuzzy Systems, vol 11, 250-261, 2009.##[43] M. N. Zade, P. M. Palkar, P. N. Aerkewar, A. S. Pathan, "Detection of ECG Signal: A Survey", International Journal of Artificial Intelligence and Mechatronics, Vol. 1, Issue 5, pp. 126-130, 2013.##[44] L. A. Zadeh, "Fuzzy sets", IEEE Information Control, vol. IC-8, pp. 338–353, 1965.## ##

تحلیل ممیز غیرپارامتریک بهبودیافته برای دسته‌بندی تصاویر ابرطیفی با نمونه آموزشی محدود Modified Nonparametric Discriminant Analysis for Classification of Hyperspectral Images with Limited Training Samples 1 1 استخراج ویژگی نقش مهمی در بهبود دسته­بندی تصاویر ابرطیفی دارد. روش­های استخراج ویژگی غیرپارامتریک، نسبت به روش­های پارامتریک، برای داده­های با توزیع غیر نرمال‌ کارایی بهتری دارند و می­توانند ویژگی­های بیشتری را استخراج کنند. روش­های استخراج ویژگی غیرپارامتریک از ماتریس­های پراکندگی غیرپارامتریک برای محاسبه ماتریس انتقال استفاده می­کنند. تحلیل ممیز غیرپارامتریک[1]، یکی از روش­های غیرپارامتریک در استخراج ویژگی است که در آن برای تشکیل ماتریس­های پراکندگی غیرپارامتریک، از میانگین­های محلی هر نمونه و تابع وزن استفاده می­شود. میانگین محلی با استفاده از k نمونه همسایه به‌دست می­آید و تابع وزن، بر روی نمونه­های مرزی در تشکیل ماتریس­ پراکندگی بین­دسته­ای تأکید می­کند. در این مقاله، NDA بهبود‌یافته[2] به‌منظور اصلاح NDA معرفی شده است. در MNDA، تعداد نمونه­های همسایه در محاسبه میانگین محلی با توجه به موقعیت نمونه در فضای ویژگی به‌دست می­آید. روش پیشنهادی از توابع وزن جدید در تشکیل ماتریس­های پراکندگی استفاده می­کند. توابع وزن پیشنهادی تأکید روی نمونه­های مرزی در تشکیل ماتریس پراکندگی بین­دسته­ای و تأکید روی نمونه­های نزدیک به میانگین دسته، در تشکیل ماتریس پراکندگی درون دسته­ای دارند. علاوه براین، به‌منظور اجتناب از تکین‌شدن ماتریس پراکندگی درون‌دسته­ای، از تنظیم آن استفاده شده است. نتایج آزمایش­ها روی تصاویر ایندیانا و سالیناس نشان می­دهد که MNDA کاریی بهتری نسبت به روش­های استخراج ویژگی پارامتریک و غیرپارامتریک مورد مقایسه داشته است. بیشترین مقدار صحت متوسط دسته­بندی برای داده ایندیانا %34/80 است که با 18 نمونه آموزشی، دسته­بند ماشین بردار پشتیبان و 10 ویژگی استخراج شده از MNDA به‌دست آمده است. برای داده سالیناس، بیشترین مقدار صحت متوسط دسته­بندی، %31/94 است که با 18 نمونه آموزشی، دسته­بند ماشین بردار پشتیبان و 9 ویژگی استخراج‌شده از MNDA به‌دست آمده است. آزمایش­ها نشان می‌دهند که با استفاده از توابع وزن پیشنهادی و ماتریس پراکندگی درون‌دسته­ای تنظیم­شده، روش پیشنهادی نتایج بهتری را در دسته‌بندی تصاویر ابرطیفی با نمونه‌های آموزشی محدود به‌دست آورده است.   [1] Nonparametric Discriminant Analysis (NDA) [2] Modified NDA (MNDA) 2 Feature extraction performs an important role in improving hyperspectral image classification. Compared with parametric methods, nonparametric feature extraction methods have better performance when classes have no normal distribution. Besides, these methods can extract more features than what parametric feature extraction methods do. Nonparametric feature extraction methods use nonparametric scatter matrices to compute transformation matrix. Nonparametric Discriminant Analysis (NDA) is one of the nonparametric feature extraction methods in which, to form nonparametric scatter matrices, local means of samples and weight function are used. Local mean is calculated by k nearest neighbors of each sample and weight function emphasizes on boundary samples in between class scatter matrix formation. In this paper, modified NDA (MNDA) is proposed to improve NDA. In MNDA, the number of neighboring samples, when measuring local mean, are determined considering position of each sample in feature space. MNDA uses new weight functions in scatter matrix formation. Suggested weight functions emphasizes on boundary samples in between class scatter matrix formation and focus on samples close to class mean in within class scatter matrix formation. Moreover, within class scatter matrix is regularized to avoid singularity. Experimental results on Indian Pines and Salinas images show that MNDA has better performance compared to other parametric, nonparametric feature extraction methods. For Indian Pines data set, the maximum average classification accuracy is 80.34%, which is obtained by 18 training samples, support vector machine (SVM) classifier and 10 extracted features achieved by MNDA method. For Salinas data set, the maximum average classification accuracy is 94.31%, which is obtained by 18 training samples, SVM classifier and 9 extracted features achieved by MNDA method. Experiments show that using suggested weight functions and regularized within class scatter matrix, the proposed method obtained better results in hyperspectral image classification with limited training samples.   43 58 2015/09/262015/12/52015/03/14 1393/12/23 2016/11/62016/10/292016/10/17 1395/7/26 آزاده کیانی سرکله Azadeh Kianisarkaleh دانشگاه آزاد اسلامی، واحد علوم و تحقیقات ایران azade.kiyani@gmail.com محمد حسن قاسمیان Mohammad Hassan Ghassemian دانشگاه تربیت مدرس ایران ghassemi@modares.ac.ir Hyperspectral images Feature extraction Supervised classification Hughes Phenomenon Limited training samples تصاویر ابرطیفی استخراج ویژگی دسته‌بندی نظارت‌شده پدیده هیوز نمونه‌های آموزشی محدود [1] G. F. Hughes, "On the mean accuracy of statistical pattern recognizers," IEEE Transactions on Information Theory, vol. 14, pp. 55–63, 1968.##[2] M. Imani, and H. Ghassemian, "Binary coding based feature extraction in remote sensing high dimensional data," Information Sciences, vol. 342, pp. 191-208, 2016.##[3] S. A. Hosseini, and H. Ghassemian, "Rational function approximation for feature reduction in hyperspectral data," Remote Sensing Letters, vol. 7, pp. 101-110, 2015.##[4] S. A. Hosseini, and H. Ghassemian, "Hyper-spectral data feature extraction using rational function curve fitting," Signal and Data Processing, vol. 13, pp. 3-16, 2016.##[5] M. Imani, and H. Ghassemian, "Feature reduction of hyperspectral data for increasing of class separability and preserving of data structure," Signal and Data Processing, vol. 14, pp. 71-82, 2017.##[6] W. Liao, A. Pizurica, P. Scheunders, W. Philips, and Y. Pi, "Semisupervised local discriminant analysis for feature extraction in hyperspectral images," IEEE Transaction on Geoscience and Remote Sensing, vol. 51, pp. 184-198, 2013.##[7] Z. Feng, Sh. Yang, Sh. Wang, and L. Jiao, "Discriminative spectral–spatial margin-based semisupervised dimensionality reduction of hyperspectral data," IEEE Geoscience and Remote Sensing Letters, vol. 12, pp. 224-228, 2015.##[8] K. Fukunaga, Introduction to statistical pattern recognition. San Diego, CA, USA, Academic, 1990.##[9] X. He, and P. Niyogi, "Locality preserving projections," Advances in Neural Information Processing Systems, vol. 16, pp. 153–160, 2004.##[10] M. Kamandar, and H. Ghassemian, "Linear feature extraction for hyperspectral images based on information theoretic learning," IEEE Geoscience and Remote Sensing Letters, vol. 10, pp. 702-706, 2013.##[11] B. C. Kuo, and D. A. Landgrebe, "Nonparametric weighted feature extraction for classification" IEEE Transaction on Geoscience and Remote Sensing, vol. 42, pp. 1096–1105, 2004.##[12] J. Yang, P. Yu, and B. C. Kuo, "A nonparametric feature extraction and its application to nearest neighbor classification for hyperspectral image data," IEEE Transaction on Geoscience and Remote Sensing, vol. 48, pp. 1279–1293, 2010.##[13] M. Imani, and H. Ghassemian, "Feature reduction of hyperspectral images: discriminant analysis and the first principal component," journal of AI and Data Mining, vol. 3, pp. 1-9, 2015.##[14] M. Imani, and H. Ghassemian, "Feature extraction using attraction points for classification of hyperspectral images in a small sample size situation," IEEE Geoscience and Remote Sensing Letters, vol. 11, pp. 1325-1329, 2014.##https://doi.org/10.1109/LGRS.2013.2292892##[15] M. Imani, and H. Ghassemian, "Feature space discriminant analysis for hyperspectral data feature reduction," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 102, pp. 1–13, 2015.##[16] R. O. Duda, P. E. Hart, and D. G. Stock, Pattern classification, 2nd ed. New York, Wiley, 2001.##[17] C. Chang, and C. Lin, "LIBSVM : A library for support vector machines," ACM Transactions on Intelligent Systems and Technology, vol. 2, pp. 1–27, 2011.##[18] G. M. Foody, "Thematic map comparison: evaluating the statistical significance of differences in classification accuracy," Photogrammetric Engineering and Remote Sensing, vol. 70, pp. 627-633, 2004.##[1] G. F. Hughes, "On the mean accuracy of statistical pattern recognizers," IEEE Transactions on Information Theory, vol. 14, pp. 55–63, 1968.##[2] M. Imani, and H. Ghassemian, "Binary coding based feature extraction in remote sensing high dimensional data," Information Sciences, vol. 342, pp. 191-208, 2016.##[3] S. A. Hosseini, and H. Ghassemian, "Rational function approximation for feature reduction in hyperspectral data," Remote Sensing Letters, vol. 7, pp. 101-110, 2015.##[4] S. A. Hosseini, and H. Ghassemian, "Hyper-spectral data feature extraction using rational function curve fitting," Signal and Data Processing, vol. 13, pp. 3-16, 2016.##[5] M. Imani, and H. Ghassemian, "Feature reduction of hyperspectral data for increasing of class separability and preserving of data structure," Signal and Data Processing, vol. 14, pp. 71-82, 2017.##[6] W. Liao, A. Pizurica, P. Scheunders, W. Philips, and Y. Pi, "Semisupervised local discriminant analysis for feature extraction in hyperspectral images," IEEE Transaction on Geoscience and Remote Sensing, vol. 51, pp. 184-198, 2013.##[7] Z. Feng, Sh. Yang, Sh. Wang, and L. Jiao, "Discriminative spectral–spatial margin-based semisupervised dimensionality reduction of hyperspectral data," IEEE Geoscience and Remote Sensing Letters, vol. 12, pp. 224-228, 2015.##[8] K. Fukunaga, Introduction to statistical pattern recognition. San Diego, CA, USA, Academic, 1990.##[9] X. He, and P. Niyogi, "Locality preserving projections," Advances in Neural Information Processing Systems, vol. 16, pp. 153–160, 2004.##[10] M. Kamandar, and H. Ghassemian, "Linear feature extraction for hyperspectral images based on information theoretic learning," IEEE Geoscience and Remote Sensing Letters, vol. 10, pp. 702-706, 2013.##[11] B. C. Kuo, and D. A. Landgrebe, "Nonparametric weighted feature extraction for classification" IEEE Transaction on Geoscience and Remote Sensing, vol. 42, pp. 1096–1105, 2004.##[12] J. Yang, P. Yu, and B. C. Kuo, "A nonparametric feature extraction and its application to nearest neighbor classification for hyperspectral image data," IEEE Transaction on Geoscience and Remote Sensing, vol. 48, pp. 1279–1293, 2010.##[13] M. Imani, and H. Ghassemian, "Feature reduction of hyperspectral images: discriminant analysis and the first principal component," journal of AI and Data Mining, vol. 3, pp. 1-9, 2015.##[14] M. Imani, and H. Ghassemian, "Feature extraction using attraction points for classification of hyperspectral images in a small sample size situation," IEEE Geoscience and Remote Sensing Letters, vol. 11, pp. 1325-1329, 2014.##https://doi.org/10.1109/LGRS.2013.2292892##[15] M. Imani, and H. Ghassemian, "Feature space discriminant analysis for hyperspectral data feature reduction," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 102, pp. 1–13, 2015.##[16] R. O. Duda, P. E. Hart, and D. G. Stock, Pattern classification, 2nd ed. New York, Wiley, 2001.##[17] C. Chang, and C. Lin, "LIBSVM : A library for support vector machines," ACM Transactions on Intelligent Systems and Technology, vol. 2, pp. 1–27, 2011.##[18] G. M. Foody, "Thematic map comparison: evaluating the statistical significance of differences in classification accuracy," Photogrammetric Engineering and Remote Sensing, vol. 70, pp. 627-633, 2004.## ##

روشی جدید جهت استخراج موجودیت‌های اسمی در عربی کلاسیک A New Approach for Extracting Named Entity in Classical Arabic 1 1 تشخیص واحدهای اسمی به‌عنوان یکی از سامانه‌های پردازش زبان طبیعی عبارت از تشخیص اسامی خاص و طبقه‌بندی آن‌ها به یکی از گروه‌های شخص، مکان، سازمان و زمان است. این عملیات به دلیل تأثیر قابل توجه در بهبود کارایی دیگر حوزه‌های پردازش زبان طبیعی مانند ترجمه ماشین، بازیابی اطلاعات، خوشه‌بندی نتایج جستجو و پرسش و پاسخ، در سال‌های اخیر مورد توجه پژوهش‌گران در زبان عربی نیز قرار گرفته است. گرچه بیشتر پژوهش‌ها در این حوزه روی عربی استاندارد امروزی انجام ‌شده است، اما در این مطالعه عربی کلاسیک مورد توجه است. در همین راستا، روشی جدید جهت تشخیص واحدهای اسمی در زبان عربی ارائه می‌شود. در این پژوهش یک پیکره متنی عربی کلاسیک به نام نورکورپ، متشکل از ۱۳۰ هزار کلمه برچسب‌گذاری‌شده توسط متخصصان، معرفی می‌شود؛ همچنین از یک فرهنگ لغات شامل ۱۸۰۰۰ اسامی اشخاص که از کتب حدیثی استخراج شده است، به‌عنوان منابع خارجی استفاده می‌شود. مدل پیش‌بینی، بر اساس مجمع رده‌بندها و یک روش دو‌مرحله‌ای پیشنهاد شده است؛ به‌طوری‌که در مرحله نخست تشخیص واحدهای اسمی از طریق الگوریتم آدابوست M1 و در مرحله دوم طبقه‌بندی آن‌ها به گروه‌های از‌پیش‌تعیین‌شده توسط الگوریتم آدابوست M2 انجام می‌شود. به‌منظور غلبه بر چالش‌های زبان عربی عملیات نشانه‌گذاری، برچسب‌گذاری ادات سخن و قطعه‌کردن عبارت پایه به کار گرفته‌شده است. با استفاده از یک روش آماری، برخی از کلمات پر کاربرد در واحدهای اسمی به‌عنوان کلمات کلیدی استخراج شدند. نتیجه به‌دست‌آمده از مدل پیشنهادی در ارزیابی F-measure‌ معادل ۸۵/۸۶ درصد است که بیان‌گر عملکرد مطلوب مدل است. در آخر، روش پیشنهادی روی یک پیکره استاندارد امروزی به نام انرکورپ اعمال و نتایج با پیکره نورکورپ مقایسه شده‌اند.   2 In Natural Language Processing (NLP) studies, developing resources and tools makes a contribution to extension and effectiveness of researches in each language. In recent years, Arabic Named Entity Recognition (ANER) has been considered by NLP researchers due to a significant impact on improving other NLP tasks such as Machine translation, Information retrieval, question answering, query result clustering, etc. While most of these researches are based on Modern Standard Arabic (MSA), in this paper, we focus on Classical Arabic (CA) literature. We propose a corpus called NoorCorp with 130k labeled words for research purposes which is annotated by expert human resources manually. This corpus is based on a Historic-Islamic book of 1200 years ago including 1843 sentences and 127550 words. We also collected about 18k proper names from old Hadith books as a gazetteer which is called NoorGazet used as a future. In this paper, we propose a new approach to extract named entities (NEs) including person, location, organization and time. We use hybrid approach benefiting from advantages of Rule based approach and Machine learning approach. We divided the NoorCorp into two parts of training and test sets containing 80% and 20% of the data set respectively. Prediction model, based on Boosting method, was developed in two steps which Adaboost.M1 is employed to identify NEs and Adaboost.M2 is employed to classify NEs. There are many methods using multiple classifiers as voters and summing up their results, among which, ensemble methods are those which generate multiple hypotheses using the same base learner. We developed an ensemble consisting of 50 members (classifiers) based on decision stump to implement the weak learner. Since only 17% of the text data is composed of name entity labels, we had to deepen the tree while restricting pruning. We exploited tokenizing, part of speech (POS) tagging, and base phrase chunking (BPC) to overcome linguistic obstacles in Arabic including Meaning ambiguity, Optional diacritics, Complex morphology and Nonstandard written text. Moreover, using a statistical technique, the most frequently used words extracted as key words. Results show that performance of the method is better than decision tree as the base classifier. An overall F-measure value of 86.85 obtained which is better than base line about 20% and CART decision tree about 12%. Since CA corpus consists of simpler linguistic patterns compared to MSA, we applied the proposed approach on ANERCorp as Modern Standard Arabic corpus. Results show that the proposed model outcome on CA corpus is about 19% better than MSA. This result is due to the fact that there are plenty of NEs entered to MSA from other languages. These proper names do not have specific patterns and do not exist in the gazetteer. In addition, many NE’s are not distributed uniformly in ANERcorp which considerably reduces the results accuracy.      59 74 2015/09/262015/12/52015/03/142014/12/1 1393/9/10 2016/11/62016/10/292016/10/172017/03/24 1396/1/4 سید محمد باقر سجادی Seyed mohamad bagher Sajadi دانشگاه آزاد واحد تهران مرکز ایران mb.sajadi@qiau.ac.ir حسن رشیدی Hassan Rashidi دانشگاه علامه طباطبایی ایران Hrashi@atu.ac.ir بهروز مینایی بیدگلی Behrooz Minaei bidgoli دانشگاه علم و صنعت ایران B_minaei@iust.ac.ir Named entity recognition (NER) Ensemble learning Boosting method Classical Arabic Language تشخیص واحدهای اسمی مجمع رده‌بندها روش بوستینگ زبان عربی کلاسیک [1] D. Nadeau and S. Sekine, "A survey of named entity recognition and classification," Lingvisticae Investig., vol. 30, no. 1, pp. 3–26, 2007.##[2] M. Oudah and K. Shaalan, "A Pipeline Arabic Named Entity Recognition using a Hybrid Approach.," Coling, vol. 2, no. December 2012, pp. 2159–2176, 2012.##[3] S. Abuleil and M. Evens, "Extracting Names From Arabic Text for Question-Answering Systems.," Riao, pp. 638–647, 2004.##[4] R. Koulali and A. Meziane, "A contribution to arabic named entity recognition," in International Conference on ICT and Knowledge Engineering, 2012, pp. 46–52.##[5] K. Shaalan, "A Survey of Arabic Named Entity Recognition and Classification," Comput. Linguist., vol. 40, no. July 2013, pp. 469–510, 2014.##[6] N. Y. Habash, "Introduction to Arabic natural language processing," Synth. Lect. Hum. Lang. Technol., vol. 3, no. 1, pp. 1–187, 2010.##[7] H. Al-Jumaily, P. Martínez, J. L. Martínez-Fernández, and E. Van der Goot, "A real time Named Entity Recognition system for Arabic text mining," Lang. Resour. Eval., vol. 46, no. 4, pp. 543–563, 2012.##[8] M. Korayem, D. Crandall, and M. Abdul-Mageed, "Subjectivity and sentiment analysis of arabic: A survey," Adv. Mach. Learn. …, 2012.##[9] Y. Maynard, D., Tablan, V., Ursu, C., Cunningham, H. ve Wilks, "Named Entity Recognition from Diverse Text Types," in Recent Advances in Natural Language Processing, Springer, 2001, pp. 440–451.##[10] I. a Alkharashi, "Person Named Entity Generation and Recognition for Arabic Language," in the Proceedings of 2nd International Conference on Arabic Language Resources and Tools, Cairo, Egypt, 2009, pp. 205–208.##[11] B. Vazirnejad, F. Soltanzadeh, M. Mahdavi, and M. Moradi, "Sharif Text Editor: A Persian Editor and Spell Checker System.," JSDP, vol. 12, no. 4, pp. 43–52, 2016.##[12] I. A. Al-sughaiyer and I. A. Al-kharashi, "Arabic Morphological Analysis Techniques : A Comprehensive Survey," J. Am. Soc. Information Science and Technology, vol. 55, no. 3, pp. 189–213, 2004.##[13] K. Darwish, A. Abdelali, and H. Mubarak, "Using Stem-Templates to improve Arabic POS and Gender/Number Tagging," in International Conference on Language Resources and Evaluation (LREC-2014), 2014, pp. 2926–2931.##[14] I. Zitouni, J. Sorensen, X. Luo, and R. Florian, "The impact of morphological stemming on Arabic mention detection and coreference resolution," Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages, June 29, pp. 63–70, 2005.##[15] Y. Benajiba, P. Rosso, M. Bened, and J. Bened iRuiz, "ANERsys : An Arabic Named Entity Recognition System Based on Maximum Entropy," Names, pp. 143–153, 2007.##[16] Y. Benajiba and P. Rosso, "ANERsys 2.0: Conquering the NER Task for the Arabic Language by Combining the Maximum Entropy with POS-tag Information.," in 3rd Indian International Conference on Artificial Intelligence (IICAI-07), 2007, pp. 1814–1823.##[17] Y. Benajiba and P. Rosso, "Arabic named entity recognition using conditional random fields," Proc. Work. HLT NLP within …, 2008.##[18] Y. Benajiba, M. Diab, and P. Rosso, "Arabic named entity recognition using optimized feature sets," Proc. Conf. Empir. Methods Nat. Lang. Process. EMNLP 08, no. October, pp. 284–293, 2008.##[19] D. Valencia, "Arabic Named Entity Recognition," Audio, Speech, Lang. Process. IEEE Trans., vol. 17, no. May, pp. 151–152, 2010.##[20] S. Abdallah, K. Shaalan, and M. Shoaib, "Integrating rule-based system with classification for arabic named entity recognition," Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 7181 LNCS, no. PART 1, pp. 311–322, 2012.##[21] K. Shaalan and M. Oudah, "A hybrid approach to Arabic named entity recognition," J. Inf. Sci., vol. 40, no. 1, pp. 67–87, 2014.##[22] M. A. Meselhi, H. M. Abo Bakr, I. Ziedan, and K. Shaalan, "Hybrid Named Entity Recognition-Application to Arabic Language," in Computer Engineering & Systems (ICCES), 2014 9th International Conference on, 2014, pp. 80–85.##[23] M. A. Meselhi, H. M. A. Bakr, I. Ziedan, and K. Shaalan, "A Novel Hybrid Approach to Arabic Named Entity," in Machine Translation, Springer, 2014, pp. 93–103.##[24] F. Enríquez, F. L. Cruz, F. J. Ortega, C. G Vallejo, and J. A. Troyano, "A comparative study of classifier combination applied to NLP tasks," Inf. Fusion, vol. 14, no. 3, pp. 255–267, 2013.##[25] X. Carreras, L. Marquez, and L. Padró, "Named entity extraction using adaboost," 2002, pp. 1–4.##[26] X. Carreras, L. Màrquez, and L. Padró, "A simple named entity extractor using AdaBoost," … seventh Conf. Nat. …, 2003.##[27] G. Szarvas, R. Farkas, and A. Kocsor, "A Multilingual Named Entity Recognition System Using Boosting and C4.5 Decision Tree Learning Algorithms," Structure, pp. 267–278, 2006.##[28] M. Asgari Bidhendi and B. Minaei Bidgoli, "Extracting person names using name candidate injection in a conditional random field model for Arabic language," JSDP, vol. 11, no. 1, pp. 73–85, 2014.##[29] M. Rezaei Sharifabadi and P. Khosravizadeh, "Automatic Labeling of Semantic Roles in Persian Sentences using Dependency Trees," JSDP, vol. 13, no. 1, pp. 27–38, 2016.##[30] F. Al Shamsi and A. Guessoum, "A hidden Markov model-based POS tagger for Arabic," in Proceeding of the 8th International Conference on the Statistical Analysis of Textual Data, France, 2006, pp. 31–42.##[31] A. Salimibadr and M. M. Homayounpour, "Phrase chunking in Persian texts," JSDP, vol. 10, no. 2, pp. 69–86, 2014.##[32] M. Diab, "Second Generation AMIRA Tools for Arabic Processing : Fast and Robust Tokenization, POS tagging, and Base Phrase Chunking," Proc. Second Int. Conf. Arab. Lang. Resour. Tools, pp. 285–288, 2009.##[33] L. Kuncheva, "Combining Pattern Classifiers methods and algorithms. John Wiley&Sons," Inc. Publ. Hoboken, 2004.##[34] C. M. Bishop and others, Pattern recognition and machine learning, vol. 1. springer New York, 2006.##[35] R. Tabatabaei, M. R. Feizi-Derakhshi, and S. Masoumi, "Proposing an intelligent and semantic-based system for Evaluating Text Summarizers," JSDP, vol. 12, no. 2, pp. 3–11, 2015.##[11] ب. وزیرنژاد، ف. سلطانزاده، م. مهدوی و م. مرادی، "ویرایش‌گر متن شریف: سامانۀ ویرایش و خطایابی املایی زبان فارسی"، مجله پردازش علائم و داده‌ها، شماره۱۲، صفحات ۴۳-۵۲، ۱۳۹۴.##[28] م. عسگری بیدهندی و ب. مینایی بیدگلی، "تشخیص اسامی اشخاص با استفاده از تزریق کلمه‌های نامزد اسم در میدان‌های تصادفی شرطی برای زبان عربی"، مجله پردازش علائم و داده‌ها، شماره ۱۱، صفحات ۷۳-۸۵، ۱۳۹۳.##[29] م. رضائی شریف آبادی و پ. خسروی‌زاده، "برچسب‌زنی خودکار نقش‌های معنایی در جملات فارسی به کمک درخت‌های وابستگی"، مجله پردازش علائم و داده‌ها، شماره ۱۳، صفحات ۲۷-۳۸، ۱۳۹۵.##[31] آ. سلیمی بدر و م. همایون‌پور, "تعیین مرز و نوع عبارات نحوی در متون فارسی"، مجله پردازش علائم و داده‌ها، شماره ۱۰، صفحات ۶۹-۸۶، ۱۳۹۲.##[35] ر. طباطبائی، م. فیضی درخشی و س. معصومی، "ارائه یک سیستم هوشمند و معناگرا برای ارزیابی سیستم های خلاصه ساز متون"، مجله پردازش علائم و داده‌ها، شماره ۱۲، صفحات ۳-۱۱، ۱۳۹۴.##[1] D. Nadeau and S. Sekine, "A survey of named entity recognition and classification," Lingvisticae Investig., vol. 30, no. 1, pp. 3–26, 2007.##[2] M. Oudah and K. Shaalan, "A Pipeline Arabic Named Entity Recognition using a Hybrid Approach.," Coling, vol. 2, no. December 2012, pp. 2159–2176, 2012.##[3] S. Abuleil and M. Evens, "Extracting Names From Arabic Text for Question-Answering Systems.," Riao, pp. 638–647, 2004.##[4] R. Koulali and A. Meziane, "A contribution to arabic named entity recognition," in International Conference on ICT and Knowledge Engineering, 2012, pp. 46–52.##[5] K. Shaalan, "A Survey of Arabic Named Entity Recognition and Classification," Comput. Linguist., vol. 40, no. July 2013, pp. 469–510, 2014.##[6] N. Y. Habash, "Introduction to Arabic natural language processing," Synth. Lect. Hum. Lang. Technol., vol. 3, no. 1, pp. 1–187, 2010.##[7] H. Al-Jumaily, P. Martínez, J. L. Martínez-Fernández, and E. Van der Goot, "A real time Named Entity Recognition system for Arabic text mining," Lang. Resour. Eval., vol. 46, no. 4, pp. 543–563, 2012.##[8] M. Korayem, D. Crandall, and M. Abdul-Mageed, "Subjectivity and sentiment analysis of arabic: A survey," Adv. Mach. Learn. …, 2012.##[9] Y. Maynard, D., Tablan, V., Ursu, C., Cunningham, H. ve Wilks, "Named Entity Recognition from Diverse Text Types," in Recent Advances in Natural Language Processing, Springer, 2001, pp. 440–451.##[10] I. a Alkharashi, "Person Named Entity Generation and Recognition for Arabic Language," in the Proceedings of 2nd International Conference on Arabic Language Resources and Tools, Cairo, Egypt, 2009, pp. 205–208.##[11] B. Vazirnejad, F. Soltanzadeh, M. Mahdavi, and M. Moradi, "Sharif Text Editor: A Persian Editor and Spell Checker System.," JSDP, vol. 12, no. 4, pp. 43–52, 2016.##[12] I. A. Al-sughaiyer and I. A. Al-kharashi, "Arabic Morphological Analysis Techniques : A Comprehensive Survey," J. Am. Soc. Information Science and Technology, vol. 55, no. 3, pp. 189–213, 2004.##[13] K. Darwish, A. Abdelali, and H. Mubarak, "Using Stem-Templates to improve Arabic POS and Gender/Number Tagging," in International Conference on Language Resources and Evaluation (LREC-2014), 2014, pp. 2926–2931.##[14] I. Zitouni, J. Sorensen, X. Luo, and R. Florian, "The impact of morphological stemming on Arabic mention detection and coreference resolution," Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages, June 29, pp. 63–70, 2005.##[15] Y. Benajiba, P. Rosso, M. Bened, and J. Bened iRuiz, "ANERsys : An Arabic Named Entity Recognition System Based on Maximum Entropy," Names, pp. 143–153, 2007.##[16] Y. Benajiba and P. Rosso, "ANERsys 2.0: Conquering the NER Task for the Arabic Language by Combining the Maximum Entropy with POS-tag Information.," in 3rd Indian International Conference on Artificial Intelligence (IICAI-07), 2007, pp. 1814–1823.##[17] Y. Benajiba and P. Rosso, "Arabic named entity recognition using conditional random fields," Proc. Work. HLT NLP within …, 2008.##[18] Y. Benajiba, M. Diab, and P. Rosso, "Arabic named entity recognition using optimized feature sets," Proc. Conf. Empir. Methods Nat. Lang. Process. EMNLP 08, no. October, pp. 284–293, 2008.##[19] D. Valencia, "Arabic Named Entity Recognition," Audio, Speech, Lang. Process. IEEE Trans., vol. 17, no. May, pp. 151–152, 2010.##[20] S. Abdallah, K. Shaalan, and M. Shoaib, "Integrating rule-based system with classification for arabic named entity recognition," Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 7181 LNCS, no. PART 1, pp. 311–322, 2012.##[21] K. Shaalan and M. Oudah, "A hybrid approach to Arabic named entity recognition," J. Inf. Sci., vol. 40, no. 1, pp. 67–87, 2014.##[22] M. A. Meselhi, H. M. Abo Bakr, I. Ziedan, and K. Shaalan, "Hybrid Named Entity Recognition-Application to Arabic Language," in Computer Engineering & Systems (ICCES), 2014 9th International Conference on, 2014, pp. 80–85.##[23] M. A. Meselhi, H. M. A. Bakr, I. Ziedan, and K. Shaalan, "A Novel Hybrid Approach to Arabic Named Entity," in Machine Translation, Springer, 2014, pp. 93–103.##[24] F. Enríquez, F. L. Cruz, F. J. Ortega, C. G Vallejo, and J. A. Troyano, "A comparative study of classifier combination applied to NLP tasks," Inf. Fusion, vol. 14, no. 3, pp. 255–267, 2013.##[25] X. Carreras, L. Marquez, and L. Padró, "Named entity extraction using adaboost," 2002, pp. 1–4.##[26] X. Carreras, L. Màrquez, and L. Padró, "A simple named entity extractor using AdaBoost," … seventh Conf. Nat. …, 2003.##[27] G. Szarvas, R. Farkas, and A. Kocsor, "A Multilingual Named Entity Recognition System Using Boosting and C4.5 Decision Tree Learning Algorithms," Structure, pp. 267–278, 2006.##[28] M. Asgari Bidhendi and B. Minaei Bidgoli, "Extracting person names using name candidate injection in a conditional random field model for Arabic language," JSDP, vol. 11, no. 1, pp. 73–85, 2014.##[29] M. Rezaei Sharifabadi and P. Khosravizadeh, "Automatic Labeling of Semantic Roles in Persian Sentences using Dependency Trees," JSDP, vol. 13, no. 1, pp. 27–38, 2016.##[30] F. Al Shamsi and A. Guessoum, "A hidden Markov model-based POS tagger for Arabic," in Proceeding of the 8th International Conference on the Statistical Analysis of Textual Data, France, 2006, pp. 31–42.##[31] A. Salimibadr and M. M. Homayounpour, "Phrase chunking in Persian texts," JSDP, vol. 10, no. 2, pp. 69–86, 2014.##[32] M. Diab, "Second Generation AMIRA Tools for Arabic Processing : Fast and Robust Tokenization, POS tagging, and Base Phrase Chunking," Proc. Second Int. Conf. Arab. Lang. Resour. Tools, pp. 285–288, 2009.##[33] L. Kuncheva, "Combining Pattern Classifiers methods and algorithms. John Wiley&Sons," Inc. Publ. Hoboken, 2004.##[34] C. M. Bishop and others, Pattern recognition and machine learning, vol. 1. springer New York, 2006.##[35] R. Tabatabaei, M. R. Feizi-Derakhshi, and S. Masoumi, "Proposing an intelligent and semantic-based system for Evaluating Text Summarizers," JSDP, vol. 12, no. 2, pp. 3–11, 2015.## ##

الگوریتم ژنتیک با جهش آشوبی هوشمند و ترکیب چند‌نقطه‌ای مکاشفه‌ای برای حل مسئله رنگ‌آمیزی گراف Genetic Algorithm with Intelligence Chaotic Algorithm and Heuristic Multi-Point Crossover for Graph Coloring Problem 1 1 تخصیص مقدار رنگی را به هر یک از گره‌های گراف، به‌گونه‌ای که هیچ دو گره مجاوری دارای رنگ یکسانی نباشد و کمترین مقدار رنگی استفاده شود، مسئله رنگ‌آمیزی گراف گویند. این مسئله به‌عنوان یکی از مسائل NP-hard شناخته می‌شود که کاربردهای مختلفی در زمینه تخصیص پهنای باند، اختصاص حافظه به برنامه‌ها و همچنین، طراحی مدارهای مجتمع دارد. در مقاله حاضر، از الگوریتم ژنتیک و پدیده آَشوب برای حل این مسئله استفاده شده است. در روش پیشنهادی حاضر، عمل‌گر ترکیب چند‌نقطه‌ای مکاشفه‌ای به نام CMHn معرفی شده است. این عمل‌گر، با انتخاب چند نقطه برش در والدین و معتبر‌کردن یکی از زیر بخش‌های والدین (دومین زیربخش هر والد می‌تواند معتبر یا غیر معتبر باشد) آنها را با هم، با استفاده از روشی ابتکاری ترکیب می‌کند. برای اینکه بتوان از بهینه محلی فرار کرد و همچنین، برای یافتن فضای جستجوی جدید، از عمل‌گر جهش استفاده می‌شود. در این مقاله، عمل‌گر جهش آشوبی هوشمند معرفی شده است که با استفاده از فرمولی گره‌هایی را که برای جهش مناسب‌ترند، انتخاب و بر روی آنها جهش را اعمال می‌کند. همچنین، نیمی از جمعیت اولیه با استفاده از روش ابتکاری و نیمی از آن با روش تصادفی تولید شده است. به‌منظور ارزیابی الگوریتم پیشنهادی از نمونه گراف‌های DIMACS و Queen استفاده شده است. نتایج به‌دست‌آمده نشان می‌دهد که روش پیشنهادی در بیش‌تر گراف‌ها، به‌خصوص گراف‌های بسیار بزرگ (wap) و گراف‌های Queen جواب بهتری نسبت به تحقیقات مشابه ارائه می‌دهد. 2 Graph coloring is a way of coloring the vertices of a graph such that no two adjacent vertices have the same color. Graph coloring problem (GCP) is about finding the smallest number of colors needed to color a given graph. The smallest number of colors needed to color a graph G, is called its chromatic number. GCP is a well-known NP-hard problems and, therefore, heuristic algorithms are usually used to solve it. GCP has many applications such as: bandwidth allocation, register allocation, VLSI design, scheduling, Sudoku, map coloring and so on. We try genetic algorithm (GA) and chaos theory to solve GCP. We proposed a heuristic algorithm called CMHn to implement multi-point crossover operation in GA. To generate initial population, a fast greedy algorithm is used. In this algorithm, the degree of each node and the number colors in its neighbor is used to assign a color to each node. Mutation operation in GA is used to explore the search space and scape from the local optima. In this study, a chaotic mutation operation is presented to select some vertices and change their color.  The crossover and mutation parameters in the proposed algorithm is tuned based on some experiment. To evaluate the proposed algorithm, some experiment is conducted on DIMACS data set. Among DIMACS sample graphs, DSJ, Queen, Le450, Wap are well-known challenging samples for graph coloring. The proposed algorithm is executed 10 times on each sample and the best, worst and mean results are reported. Results show that the proposed algorithm can effectively solve GCP and have comparable outcome with the recent studies in this field. The proposed method outperforms other algorithms on very large graphs (Wap graphs).    75 96 2015/09/262015/12/52015/03/142014/12/12015/07/7 1394/4/16 2016/11/62016/10/292016/10/172017/03/242017/03/5 1395/12/15 سید علی ساداتی تیله بنی Seyyed Ali Sadati Tileboni دانشگاه صنعتی (نوشیروانی) بابل ایران seyyedali.sadati@nit.ac.ir حمید جزایری Hamid Jazayeriy دانشگاه صنعتی (نوشیروانی) بابل ایران jhamid@nit.ac.ir مجتبی ولی نتاج Mojtaba Valinataj دانشگاه صنعتی (نوشیروانی) بابل ایران m.valinataj@nit.ac.ir Graph coloring problem genetic algorithm heuristics multi-point crossover chaotic mutation مسئله رنگ‌آمیزی گراف الگوریتم ژنتیک روش ابتکاری ترکیب چند نقطه‌ای مکاشفه‌ای جهش آشوبی هوشمند [1] R. M. Karp, "Reducibility among combinat-orial problems," in Complexity of computer computations, ed: Springer, 1972, pp. 85-103.##[2] F. T. Leighton, "A graph coloring algorithm for large scheduling problems," Journal of research of the national bureau of standar-ds, vol. 84, pp. 489-506, 1979.##[3] N. R. Sabar, M. Ayob, R. Qu, and G. Kenda-ll, "A graph coloring constructive hyper-heuristic for examination timetabling problems," Applied Intelligence, vol. 37, pp. 1-11, 2012.##[4] J. Riihijarvi, M. Petrova, and P. Mahonen, "Frequency allocation for WLANs using graph colouring techniques," in Wireless On-demand Network Systems and Services, 2005. WONS 2005. Second Annual Confere-nce on, 2005, pp. 216-222.##[5] G. Chaitin, "Register allocation and spilling via graph coloring," Acm Sigplan Notices, vol. 39, pp. 66-74, 2004.##[6] D. Brélaz, "New methods to color the vertic-es of a graph," Communications of the ACM, vol. 22, pp. 251-256, 1979.##[7] J. C. Culberson and F. Luo, "Exploring the k-colorable landscape with iterated greedy," Cliques, coloring, and satisfiability: second DIMACS implementation challenge, vol. 26, pp. 245-284, 1996.##[8] C. Avanthay, A. Hertz, and N. Zufferey, "A variable neighborhood search for graph coloring," European Journal of Operational Research, vol. 151, pp. 379-388, 2003.##[9] D. C. Porumbel, J.-K. Hao, and P. Kuntz, "A search space "cartography" for guiding graph coloring heuristics," Computers & Operations Research, vol. 37, pp. 769-778, 2010.##[10] M. Chams, A. Hertz, and D. De Werra, "Some experiments with simulated anneal-ing for coloring graphs," European Journal of Operational Research, vol. 32, pp. 260-266, 1987.##[11] D. S. Johnson, C. R. Aragon, L. A. McGeoch, and C. Schevon, "Optimization by simulated annealing: an experimental evaluation; part II, graph coloring and number partitioning," Operations research, vol. 39, pp. 378-406, 1991.##[12] Y. Wang, C. Zhang, and Z. Liu, "A matrix approach to graph maximum stable set and coloring problems with application to multi-agent systems," Automatica, vol. 48, pp. 1227-1236, 2012.##[13] P. Galinier, A. Hertz, and N. Zufferey, "An adaptive memory algorithm for the k-color-ing problem," Discrete Applied Mathema-tics, vol. 156, pp. 267-279, 2008.##[14] J.-P. Hamiez and J.-K. Hao, "Scatter search for graph coloring," in Internation-al Conference on Artificial Evolution (Evolu-tion Artificielle), 2001, pp. 168-179.##[15] E. Malaguti, M. Monaci, and P. Toth, "A metaheuristic approach for the vertex color-ing problem," INFORMS Journal on Computing, vol. 20, pp. 302-316, 2008.##[16] E. Malaguti, M. Monaci, and P. Toth, "An exact approach for the vertex coloring probl-em," Discrete Optimization, vol. 8, pp. 174-190, 2011.##[17] I. Blöchliger and N. Zufferey, "A graph coloring heuristic using partial solutions and a reactive tabu scheme," Computers & Operations Research, vol. 35, pp. 960-975, 2008.##[18] T. Park and K. R. Ryu, "A dual-population genetic algorithm for adaptive diversity control," IEEE transactions on evolutionary computation, vol. 14, pp. 865-884,2010.##[19] Y. Tan, G. Tan, and X. Wu, "Hybrid real-coded genetic algorithm with chaotic local search for global optimization," JOURNAL OF INFORMATION ;COMPUTATIONAL SCIENCE, vol. 8, pp. 3171-3179, 2011.##[20] M. Last and S. Eyal, "A fuzzy-based lifetime extension of genetic algorithms," Fuzzy sets and systems, vol. 149, pp. 131-147, 2005.##[21] C. A. Glass and A. Prügel-Bennett, "Genetic algorithm for graph coloring: exploration of Galinier and Hao's algorithm," Journal of Combinatorial Optimization, vol. 7, pp. 229-236, 2003.##[22] D. C. Porumbel, J.-K. Hao, and P. Kuntz, "An evolutionary approach with diversity guarantee and well-informed grouping recombination for graph coloring," Comput-ers & Operations Research, vol. 37, pp. 1822-1832, 2010.##[23] f. hoseinkhani and b. nasersharif, "Two Fea-tuer Transformation Methods Based on Genetic Algorithm for Reducing Support Vector Machine Classification Error," Signal and Data Processing, vol. 12, pp. 23-39, 2015.##[24] S. Garcia, M. Saad, and O. Akhrif, "Nonlin-ear tuning of aircraft controllers using genetic global optimization: A new periodic mutation operator," Canadian Journal of Electrical and Computer Engineering, vol. 31, pp. 149-158, 2006.##[25] M. Rocha and J. Neves, "Preventing prema-ture convergence to local optima in genetic algorithms via random offspring generation," in International Conference on Industrial, Engineering and Other Applica-tions of Applied Intelligent Systems, 1999, pp. 127-136.##[26] Q. Lü, G. Shen, and R. Yu, "A chaotic approach to maintain the population diver-sity of genetic algorithm in network train-ing," Computational Biology and Che-mistry, vol. 27, pp. 363-371, 2003.##[27] G. Sadeghi Bajestani, A. Monzavi, and S. M. R. Hashemi Golpaygani, "Precisely chaotic models survey with Qualitative Bifurcation Diagram," Signal and Data Processing, vol. 13, pp. 17-34, 2016.##[28] E. N. Lorenz, "Deterministic nonperiodic flow," Journal of the atmospheric sciences, vol. 20, pp. 130-141, 1963.##https://doi.org/10.1175/1520-0469(1963)0202.0.CO;2##[29] J. Determan and J. A. Foster, "Using chaos in genetic algorithms," in Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on, 1999, pp. 2094-2101.##[30] M.-Y. Cheng and K.-Y. Huang, "Genetic algorithm-based chaos clustering approach for nonlinear optimization," Journal of Marine Science and Technology, vol. 18, pp. 435-441, 2010.##[31] C.-T. Cheng, W.-C. Wang, D.-M. Xu, and K. Chau, "Optimizing hydropower reservoir operation using hybrid genetic algorithm and chaos," Water Resources Management, vol. 22, pp. 895-909, 2008.##[32] E. Salari and K. Eshghi, "An ACO algorithm for graph coloring problem," in Computa-tional Intelligence Methods and Applicatio-ns, 2005 ICSC Congress on, 2005, p. 5 pp.##[33] I. Méndez-Díaz and P. Zabala, "A cutting plane algorithm for graph coloring," Discr-ete Applied Mathematics, vol. 156, pp. 159-179, 2008.##[34 P. San Segundo, "A new DSATUR-based algorithm for exact vertex coloring," Comp-uters & Operations Research, vol. 39, pp. 1724-1733, 2012.##[35] S. M. Douiri and S. Elbernoussi, "An Effec-tive Ant Colony Optimization Algor-ithm for the Minimum Sum Coloring Problem," in International Conference on Computational Collective Intelligence, 2013, pp. 346-355.##[36] Y. Jin, J.-K. Hao, and J.-P. Hamiez, "A memetic algorithm for the minimum sum coloring problem," Computers & Operations Research, vol. 43, pp. 318-327, 2014.##[37] S. Mahmoudi and S. Lotfi, "Modified cuck-oo optimization algorithm (MCOA) to solve graph coloring problem," Applied soft computing, vol. 33, pp. 48-64, 2015.##[38] S. M. Douiri and S. Elbernoussi, "Solving the graph coloring problem via hybrid gene-tic algorithms," Journal of King Saud University-Engineering Sciences, vol. 27, pp. 114-118, 2015.##[39] R. Marappan and G. Sethumadhavan, "Solu-tion to graph coloring problem using divide and conquer based genetic method," in Information Communication and Embedd-ed Systems (ICICES), 2016 Internat-ional Con-ference on, 2016, pp. 1-5.##[40] B. Ray, A. J. Pal, D. Bhattacharyya, and T. Kim, "An efficient ga with multipoint guided mutation for graph coloring problems," International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 3, pp. 51-58, 2010.##[41] S. Bakhtar, H. Jazayeriy, and M. Valinataj, "A multi-start path-relinking algorithm for the flexible job-shop scheduling problem," in Information and Knowledge Technology (IKT), 2015 7th Conference on, 2015, pp. 1-6.##[1] R. M. Karp, "Reducibility among combinat-orial problems," in Complexity of computer computations, ed: Springer, 1972, pp. 85-103.##[2] F. T. Leighton, "A graph coloring algorithm for large scheduling problems," Journal of research of the national bureau of standar-ds, vol. 84, pp. 489-506, 1979.##[3] N. R. Sabar, M. Ayob, R. Qu, and G. Kenda-ll, "A graph coloring constructive hyper-heuristic for examination timetabling problems," Applied Intelligence, vol. 37, pp. 1-11, 2012.##[4] J. Riihijarvi, M. Petrova, and P. Mahonen, "Frequency allocation for WLANs using graph colouring techniques," in Wireless On-demand Network Systems and Services, 2005. WONS 2005. Second Annual Confere-nce on, 2005, pp. 216-222.##[5] G. Chaitin, "Register allocation and spilling via graph coloring," Acm Sigplan Notices, vol. 39, pp. 66-74, 2004.##[6] D. Brélaz, "New methods to color the vertic-es of a graph," Communications of the ACM, vol. 22, pp. 251-256, 1979.##[7] J. C. Culberson and F. Luo, "Exploring the k-colorable landscape with iterated greedy," Cliques, coloring, and satisfiability: second DIMACS implementation challenge, vol. 26, pp. 245-284, 1996.##[8] C. Avanthay, A. Hertz, and N. Zufferey, "A variable neighborhood search for graph coloring," European Journal of Operational Research, vol. 151, pp. 379-388, 2003.##[9] D. C. Porumbel, J.-K. Hao, and P. Kuntz, "A search space "cartography" for guiding graph coloring heuristics," Computers & Operations Research, vol. 37, pp. 769-778, 2010.##[10] M. Chams, A. Hertz, and D. De Werra, "Some experiments with simulated anneal-ing for coloring graphs," European Journal of Operational Research, vol. 32, pp. 260-266, 1987.##[11] D. S. Johnson, C. R. Aragon, L. A. McGeoch, and C. Schevon, "Optimization by simulated annealing: an experimental evaluation; part II, graph coloring and number partitioning," Operations research, vol. 39, pp. 378-406, 1991.##[12] Y. Wang, C. Zhang, and Z. Liu, "A matrix approach to graph maximum stable set and coloring problems with application to multi-agent systems," Automatica, vol. 48, pp. 1227-1236, 2012.##[13] P. Galinier, A. Hertz, and N. Zufferey, "An adaptive memory algorithm for the k-color-ing problem," Discrete Applied Mathema-tics, vol. 156, pp. 267-279, 2008.##[14] J.-P. Hamiez and J.-K. Hao, "Scatter search for graph coloring," in Internation-al Conference on Artificial Evolution (Evolu-tion Artificielle), 2001, pp. 168-179.##[15] E. Malaguti, M. Monaci, and P. Toth, "A metaheuristic approach for the vertex color-ing problem," INFORMS Journal on Computing, vol. 20, pp. 302-316, 2008.##[16] E. Malaguti, M. Monaci, and P. Toth, "An exact approach for the vertex coloring probl-em," Discrete Optimization, vol. 8, pp. 174-190, 2011.##[17] I. Blöchliger and N. Zufferey, "A graph coloring heuristic using partial solutions and a reactive tabu scheme," Computers & Operations Research, vol. 35, pp. 960-975, 2008.##[18] T. Park and K. R. Ryu, "A dual-population genetic algorithm for adaptive diversity control," IEEE transactions on evolutionary computation, vol. 14, pp. 865-884,2010.##[19] Y. Tan, G. Tan, and X. Wu, "Hybrid real-coded genetic algorithm with chaotic local search for global optimization," JOURNAL OF INFORMATION ;COMPUTATIONAL SCIENCE, vol. 8, pp. 3171-3179, 2011.##[20] M. Last and S. Eyal, "A fuzzy-based lifetime extension of genetic algorithms," Fuzzy sets and systems, vol. 149, pp. 131-147, 2005.##[21] C. A. Glass and A. Prügel-Bennett, "Genetic algorithm for graph coloring: exploration of Galinier and Hao's algorithm," Journal of Combinatorial Optimization, vol. 7, pp. 229-236, 2003.##[22] D. C. Porumbel, J.-K. Hao, and P. Kuntz, "An evolutionary approach with diversity guarantee and well-informed grouping recombination for graph coloring," Comput-ers & Operations Research, vol. 37, pp. 1822-1832, 2010.##[23] f. hoseinkhani and b. nasersharif, "Two Fea-tuer Transformation Methods Based on Genetic Algorithm for Reducing Support Vector Machine Classification Error," Signal and Data Processing, vol. 12, pp. 23-39, 2015.##[24] S. Garcia, M. Saad, and O. Akhrif, "Nonlin-ear tuning of aircraft controllers using genetic global optimization: A new periodic mutation operator," Canadian Journal of Electrical and Computer Engineering, vol. 31, pp. 149-158, 2006.##[25] M. Rocha and J. Neves, "Preventing prema-ture convergence to local optima in genetic algorithms via random offspring generation," in International Conference on Industrial, Engineering and Other Applica-tions of Applied Intelligent Systems, 1999, pp. 127-136.##[26] Q. Lü, G. Shen, and R. Yu, "A chaotic approach to maintain the population diver-sity of genetic algorithm in network train-ing," Computational Biology and Che-mistry, vol. 27, pp. 363-371, 2003.##[27] G. Sadeghi Bajestani, A. Monzavi, and S. M. R. Hashemi Golpaygani, "Precisely chaotic models survey with Qualitative Bifurcation Diagram," Signal and Data Processing, vol. 13, pp. 17-34, 2016.##[28] E. N. Lorenz, "Deterministic nonperiodic flow," Journal of the atmospheric sciences, vol. 20, pp. 130-141, 1963.##https://doi.org/10.1175/1520-0469(1963)0202.0.CO;2##[29] J. Determan and J. A. Foster, "Using chaos in genetic algorithms," in Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on, 1999, pp. 2094-2101.##[30] M.-Y. Cheng and K.-Y. Huang, "Genetic algorithm-based chaos clustering approach for nonlinear optimization," Journal of Marine Science and Technology, vol. 18, pp. 435-441, 2010.##[31] C.-T. Cheng, W.-C. Wang, D.-M. Xu, and K. Chau, "Optimizing hydropower reservoir operation using hybrid genetic algorithm and chaos," Water Resources Management, vol. 22, pp. 895-909, 2008.##[32] E. Salari and K. Eshghi, "An ACO algorithm for graph coloring problem," in Computa-tional Intelligence Methods and Applicatio-ns, 2005 ICSC Congress on, 2005, p. 5 pp.##[33] I. Méndez-Díaz and P. Zabala, "A cutting plane algorithm for graph coloring," Discr-ete Applied Mathematics, vol. 156, pp. 159-179, 2008.##[34] P. San Segundo, "A new DSATUR-based algorithm for exact vertex coloring," Comp-uters & Operations Research, vol. 39, pp. 1724-1733, 2012.##[35] S. M. Douiri and S. Elbernoussi, "An Effec-tive Ant Colony Optimization Algor-ithm for the Minimum Sum Coloring Problem," in International Conference on Computational Collective Intelligence, 2013, pp. 346-355.##[36] Y. Jin, J.-K. Hao, and J.-P. Hamiez, "A memetic algorithm for the minimum sum coloring problem," Computers & Operations Research, vol. 43, pp. 318-327, 2014.##[37][ S. Mahmoudi and S. Lotfi, "Modified cuck-oo optimization algorithm (MCOA) to solve graph coloring problem," Applied soft computing, vol. 33, pp. 48-64, 2015.##[38] S. M. Douiri and S. Elbernoussi, "Solving the graph coloring problem via hybrid gene-tic algorithms," Journal of King Saud University-Engineering Sciences, vol. 27, pp. 114-118, 2015.##[39] R. Marappan and G. Sethumadhavan, "Solu-tion to graph coloring problem using divide and conquer based genetic method," in Information Communication and Embedd-ed Systems (ICICES), 2016 Internat-ional Con-ference on, 2016, pp. 1-5.##[40] B. Ray, A. J. Pal, D. Bhattacharyya, and T. Kim, "An efficient ga with multipoint guided mutation for graph coloring problems," International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 3, pp. 51-58, 2010.##[41] S. Bakhtar, H. Jazayeriy, and M. Valinataj, "A multi-start path-relinking algorithm for the flexible job-shop scheduling problem," in Information and Knowledge Technology (IKT), 2015 7th Conference on, 2015, pp. 1-6.## ##

فشرده‌سازی تصویر با کمک حذف و کدگذاری هوشمندانه اطلاعات تصویر و بازسازی آن با استفاده از الگوریتم های ترمیم تصویر Image Compression Based on Intelligent Information Removing and Inpainting Reconstruction Algorithms 1 1 روش‌های فشرده‌سازی با اتلاف، به‌دلیل ایجاد فشرده‌سازی بیشتر، کاربرد گسترده‌تری دارند. اگرچه روش‌های زیادی تا به حال برای فشرده‌سازی تصاویر پیشنهاد شده ، اما،  به استفاده از روش‌های هوشمندانه حذف اطلاعات، کمتر توجه شده است. ترمیم، مجموعه‌ای از روش‌هایی است که اصلاحاتی را بر روی تصاویر انجام می‌دهد؛ با این هدف که بیننده تفاوتی بین تصویر اصلاح‌شده و تصویر اصلی احساس نکند. در این مقاله، پس از بررسی و معرفی بعضی روش‌های ترمیم تصویر و روش‌های فشرده‌سازی تصویر با کمک ترمیم، روش جدیدی پیشنهاد می‌شود که علاوه‌بر‌این که باعث فشردگی قابل توجه تصویر در زمان ارسال می‌شود، نتیجه کیفی مناسبی نیز در گیرنده خواهد داشت. در روش پشنهادی، تصویر به نواحی ساختاری و بافتی تقسیم می‌شود و برای هر ناحیه بلوک‌های قابل حذفی که امکان بازسازی مناسبی در گیرنده با استفاده از روش‌های ترمیم دارند، شناسایی و حذف می‌شوند و اطلاعات کمکی لازم جهت ترمیم بهتر از آنها استخراج می‌شود. این بلوک‌ها به‌همراه بلوک‌های غیرقابل حذف تصویر پس از کد‌شدن، ارسال می‌شوند و در گیرنده پس از کدگشایی، بلوک‌های از‌دست‌رفته بازسازی و ترمیم می‌گردند تا در‌نهایت تصویر اولیه در گیرنده قابل استفاده باشد. ویژگی‌های روش پیشنهادی نخست متغیر‌بودن اندازه بلوک‌های حذفی است که باعث فشردگی بیشتر می‌شود و ثانیاً ارائه روش جدیدی جهت بازسازی بلوک‌های شامل لبه در گیرنده است که کیفیت بلوک‌های ترمیم شده این نواحی را افزایش می‌دهد.   2 Compression can be done by lossy or lossless methods. The lossy methods have been used more widely than the lossless compression. Although, many methods for image compression have been proposed yet, the methods using intelligent skipping proper to the visual models has not been considered in the literature. Image inpainting refers to the application of sophisticated algorithms to replace lost or corrupted parts of the data so that visual difference cannot be inferred from the reconstructed image. In this paper, first we review some of the image inpainting algorithms and some of the image compression techniques using the inpainting algorithms, we propose a new inpainting based image compression algorithm that can improve the compression rate considerably. We present image compression system based on the proposed parameter-assistant image inpainting method to more deeply exploit visual redundancy inherent in color images. We have shown that with carefully selected dropped regions and appropriately extracted parameters from them, dropped regions can be satisfactorily restored using the proposed PAI algorithm. Accordingly, our compression scheme has a higher coding performance compared with traditional methods in terms of the perceptual quality. To best represent the target region for inpainting, an effective region classifier is required. A generic solution is to study the distribution of each image region and find the best match among the candidates in the predefined model class. For simplicity, in our scheme, an entire image divided into three categories: gradated, structural, and non-featured, at non-overlapping block level of size S×S. The classification is performed based on edge content and color variance in each block. Simulation results show that our proposed method has reasonable visual quality in comparison with the other proposed image compression algorithms.     97 114 2015/09/262015/12/52015/03/142014/12/12015/07/72015/10/8 1394/7/16 2016/11/62016/10/292016/10/172017/03/242017/03/52017/05/20 1396/2/30 علی جمشیدی Ali Jamshidi دانشگاه شیراز ایران jamshidi@shirazu.ac.ir مهران یزدی Mehran Yazdi دانشگاه شیراز ایران yazdi@shirazu.ac.ir مریم السادات منافی Maryam Manafi دانشگاه شیراز ایران jamshidi801@gmail.com Image Inpainting Image Compression Intelligent Information Removing Coding ترمیم تصویر فشرده‌سازی تصاویر حذف هوشمندانه اطلاعات کد کردن [1] Shamsi gooshki A, Nezamabadi-pour H, Saryazdi S, Kabir E. "a relevance feedback approach based on similarity refinement in content based image retrieval". Journal of Signal and Data Processing, JDSP, vol. 11 (2) pp:43-55, 2015.##[2] Bertalmio, Marcelo, Guillermo Sapiro, Vincent Caselles, and Coloma Ballester. "Image inpainting." In Proceedings of the 27th annual conference on Computer graphics and interactive techniques, ACM Press/Addison-Wesley Publishing Co., 2000, pp. 417-424.##[3] Rane, Shantanu D., Guillermo Sapiro, and Marcelo Bertalmio. "Structure and texture filling-in of missing image blocks in wireless transmission and compression applications." IEEE Transactions on Image Processing, vol. 12, no. 3, pp: 296-303, March 2003.##[4] Criminisi, Antonio, Patrick Pérez, and Kentaro Toyama. "Region filling and object removal by exemplar-based image inpainting." IEEE Transactions on Image Processing, vol. 13, no. 9, pp: 1200-1212, September 2004.##[5] Xu, Zongben, and Jian Sun. "Image inpainting by patch propagation using patch sparsity." IEEE Transactions on Image Processing, vol. 19, no. 5, pp: 1153-1165, May 2010.##[6] Ružić, T., and A. Pižurica. "Context-aware patch-based image inpainting using Markov random field modeling." IEEE transactions on image processing, vol. 24, no. 1, pp: 444-456, January 2015.##[7] Weickert, Joachim. "From Optimized Inpainting with Linear PDEs Towards Competitive Image Compression Codecs." In Image and Video Technology: 7th Pacific-Rim Symposium, PSIVT 2015, Auckland, New Zealand, November 25-27, Revised Selected Papers, vol. 9431, Springer, 2016, pp: 63-68##[8] Peter, Pascal, and Joachim Weickert. "Compressing images with diffusion-and exemplar-based inpainting." In Scale Space and Variational Methods in Computer Vision, Springer International Publishing, pp. 154-165, April 2015.##[9] Liu, Dong, Xiaoyan Sun, Feng Wu, Shipeng Li, and Ya-Qin Zhang. "Image compression with edge-based inpainting." IEEE Transactions on Circuits and Systems for Video Technology, vol. 17, no. 10 pp: 1273-1287, January 2007.##[10] Xiong, Zhiwei, Xiaoyan Sun, and Feng Wu. "Block-based image compression with parameter-assistant inpainting." IEEE Transactions on Image Processing, vol. 19, no. 6, pp: 1651-1657, June 2010.##[11] Zhao, Chen, Jian Zhang, Siwei Ma, and Wen Gao. "Wavelet inpainting driven image compression via collaborative sparsity at low bit rates." In Image Processing (ICIP), 2013 20th IEEE International Conference on, IEEE, 2013, pp. 1685-1689.##[12] Bastani, Vahid, Mohammad Sadegh Helfroush, and Keyvan Kasiri. "Image compression based on spatial redundancy removal and image inpainting." Journal of Zhejiang University SCIENCE C, vol. 11, no. 2 pp: 92-100, January 2010.##[13] Guillemot, Christine, and Olivier Le Meur. "Image inpainting: Overview and recent advances." IEEE Signal Processing Magazine, vol. 31, no. 1 pp: 127-144, January 2014.##[14] Wang, Zhou, Hamid R. Sheikh, and Alan C. Bovik. "No-reference perceptual quality assessment of JPEG compressed images." In Image Processing 2002, International Conference on IEEE, vol. 1, 2002, pp. 472-477##[15] Bertalmio, Marcelo. "Strong-continuation, contrast-invariant inpainting with a third-order optimal PDE." IEEE Transactions on Image Processing, vol. 15, no. 7 pp: 1934-1938, July 2006.##[16] Chen, Peiying, and Yuandi Wang. "Fourth-order partial differential equations for image inpainting."ICALIP 2008. International Conference on Audio, Language and Image Processing, IEEE, 2008, pp. 1713-1717.##[17] Richard, Manuel M. Oliveira Brian Bowen, and McKenna Yu-Sung Chang. "Fast digital image inpainting." Appeared in the Proceedings of the International Conference on Visualization, Imaging and Image Processing (VIIP 2001), Marbella, Spain, pp. 106-107.##[18] Shen, Jianhong, and Tony F. Chan. "Mathematical models for local nontexture inpaintings." SIAM Journal on Applied Mathematics vol. 62, no. 3 pp: 1019-1043, 2002.##[19] Chan, Tony F., and Jianhong Shen. "Nontexture inpainting by curvature-driven diffusions." Journal of Visual Communication and Image Representation vol. 12, no. 4 pp: 436-449, December 2001.##[20] Bertalmio, Marcelo, Luminita Vese, Guillermo Sapiro, and Stanley Osher. "Simultaneous structure and texture image inpainting." IEEE Transactions on Image Processing, vol. 12, no. 8 pp: 882-889, August 2003.##[21] Rareş, Andrei, Marcel JT Reinders, and Jan Biemond. "Edge-based image restoration." IEEE Transactions on Image Processing, vol. 14, no. 10 pp: 1454-1468, October 2005.##[22] Ma, Wenjuan, Maolin Hu, and Pengyong Hu. "Image Inpainting under Single Image." In Congress on Image and Signal Processing, IEEE 2008. CISP'08. vol. 1, 2008, pp. 636-640.##[23] Gonzalez, Rafael C., and Richard E. Woods. Digital Image Processing. Prentice-Hall, New Jersey, 3rd Edition, 2007.##[24] Varghese, Sikha Mary, Alphonsa Johny, and Jubilant Job. "A survey on joint data-hiding and compression techniques based on SMVQ and image inpainting." In International Conference on Soft-Computing and Networks Security (ICSNS), IEEE, 2015, pp. 1-4.##https://doi.org/10.1109/ICSNS.2015.7292443##[25] Di, Wu, Ren Li, and Wu Shuang. "Inpainting intergrate with decomposition for image compression." In Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2015 IEEE, 2015, pp. 35-38.##[1] شمسی گوشکی، ا.، نظام آبادی پور، ح.، سریزدی، س.، کبیر، ا.، "روشی برای بازخورد ربط براساس بهبود تابع شباهت در بازیابی تصویر بر اساس محتوا" فصل نامه علمی پژوهشی «پردازش علائم و داده ها»، دوره 11، شماره 2، 1393-12، صفحات 43 تا 55##[1] Shamsi gooshki A, Nezamabadi-pour H, Saryazdi S, Kabir E. "a relevance feedback approach based on similarity refinement in content based image retrieval". Journal of Signal and Data Processing, JDSP, vol. 11 (2) pp:43-55, 2015.##[2] Bertalmio, Marcelo, Guillermo Sapiro, Vincent Caselles, and Coloma Ballester. "Image inpainting." In Proceedings of the 27th annual conference on Computer graphics and interactive techniques, ACM Press/Addison-Wesley Publishing Co., 2000, pp. 417-424.##[3] Rane, Shantanu D., Guillermo Sapiro, and Marcelo Bertalmio. "Structure and texture filling-in of missing image blocks in wireless transmission and compression applications." IEEE Transactions on Image Processing, vol. 12, no. 3, pp: 296-303, March 2003.##[4] Criminisi, Antonio, Patrick Pérez, and Kentaro Toyama. "Region filling and object removal by exemplar-based image inpainting." IEEE Transactions on Image Processing, vol. 13, no. 9, pp: 1200-1212, September 2004.##[5] Xu, Zongben, and Jian Sun. "Image inpainting by patch propagation using patch sparsity." IEEE Transactions on Image Processing, vol. 19, no. 5, pp: 1153-1165, May 2010.##[6] Ružić, T., and A. Pižurica. "Context-aware patch-based image inpainting using Markov random field modeling." IEEE transactions on image processing, vol. 24, no. 1, pp: 444-456, January 2015.##[7] Weickert, Joachim. "From Optimized Inpainting with Linear PDEs Towards Competitive Image Compression Codecs." In Image and Video Technology: 7th Pacific-Rim Symposium, PSIVT 2015, Auckland, New Zealand, November 25-27, Revised Selected Papers, vol. 9431, Springer, 2016, pp: 63-68##[8] Peter, Pascal, and Joachim Weickert. "Compressing images with diffusion-and exemplar-based inpainting." In Scale Space and Variational Methods in Computer Vision, Springer International Publishing, pp. 154-165, April 2015.##[9] Liu, Dong, Xiaoyan Sun, Feng Wu, Shipeng Li, and Ya-Qin Zhang. "Image compression with edge-based inpainting." IEEE Transactions on Circuits and Systems for Video Technology, vol. 17, no. 10 pp: 1273-1287, January 2007.##[10] Xiong, Zhiwei, Xiaoyan Sun, and Feng Wu. "Block-based image compression with parameter-assistant inpainting." IEEE Transactions on Image Processing, vol. 19, no. 6, pp: 1651-1657, June 2010.##[11] Zhao, Chen, Jian Zhang, Siwei Ma, and Wen Gao. "Wavelet inpainting driven image compression via collaborative sparsity at low bit rates." In Image Processing (ICIP), 2013 20th IEEE International Conference on, IEEE, 2013, pp. 1685-1689.##[12] Bastani, Vahid, Mohammad Sadegh Helfroush, and Keyvan Kasiri. "Image compression based on spatial redundancy removal and image inpainting." Journal of Zhejiang University SCIENCE C, vol. 11, no. 2 pp: 92-100, January 2010.##[13] Guillemot, Christine, and Olivier Le Meur. "Image inpainting: Overview and recent advances." IEEE Signal Processing Magazine, vol. 31, no. 1 pp: 127-144, January 2014.##[14] Wang, Zhou, Hamid R. Sheikh, and Alan C. Bovik. "No-reference perceptual quality assessment of JPEG compressed images." In Image Processing 2002, International Conference on IEEE, vol. 1, 2002, pp. 472-477##[15] Bertalmio, Marcelo. "Strong-continuation, contrast-invariant inpainting with a third-order optimal PDE." IEEE Transactions on Image Processing, vol. 15, no. 7 pp: 1934-1938, July 2006.##[16] Chen, Peiying, and Yuandi Wang. "Fourth-order partial differential equations for image inpainting."ICALIP 2008. International Conference on Audio, Language and Image Processing, IEEE, 2008, pp. 1713-1717.##[17] Richard, Manuel M. Oliveira Brian Bowen, and McKenna Yu-Sung Chang. "Fast digital image inpainting." Appeared in the Proceedings of the International Conference on Visualization, Imaging and Image Processing (VIIP 2001), Marbella, Spain, pp. 106-107.##[18] Shen, Jianhong, and Tony F. Chan. "Mathematical models for local nontexture inpaintings." SIAM Journal on Applied Mathematics vol. 62, no. 3 pp: 1019-1043, 2002.##[19] Chan, Tony F., and Jianhong Shen. "Nontexture inpainting by curvature-driven diffusions." Journal of Visual Communication and Image Representation vol. 12, no. 4 pp: 436-449, December 2001.##[20] Bertalmio, Marcelo, Luminita Vese, Guillermo Sapiro, and Stanley Osher. "Simultaneous structure and texture image inpainting." IEEE Transactions on Image Processing, vol. 12, no. 8 pp: 882-889, August 2003.##[21] Rareş, Andrei, Marcel JT Reinders, and Jan Biemond. "Edge-based image restoration." IEEE Transactions on Image Processing, vol. 14, no. 10 pp: 1454-1468, October 2005.##[22] Ma, Wenjuan, Maolin Hu, and Pengyong Hu. "Image Inpainting under Single Image." In Congress on Image and Signal Processing, IEEE 2008. CISP'08. vol. 1, 2008, pp. 636-640.##[23] Gonzalez, Rafael C., and Richard E. Woods. Digital Image Processing. Prentice-Hall, New Jersey, 3rd Edition, 2007.##[24] Varghese, Sikha Mary, Alphonsa Johny, and Jubilant Job. "A survey on joint data-hiding and compression techniques based on SMVQ and image inpainting." In International Conference on Soft-Computing and Networks Security (ICSNS), IEEE, 2015, pp. 1-4.##https://doi.org/10.1109/ICSNS.2015.7292443##[25] Di, Wu, Ren Li, and Wu Shuang. "Inpainting intergrate with decomposition for image compression." In Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2015 IEEE, 2015, pp. 35-38.## ##

یک روش جدید برای طبقه‎بندی نانوساختارها براساس آنالیز سری زمانی و منطق فازی A New Method for Classification of Nano-Structures based on Time Series Analysis and Fuzzy Logic 1 1 میزان پراکندگی نانوذرات در نانوساختارها، از مهم‌ترین شاخص‌هایی است که جهت تأیید کارآیی روش‌های پیشنهادی در زمینه سنتز نانومواد به‌کار می‎رود. تصاویر میکروسکوپی الکترونی روبشی نانوذرات دارای اطلاعات ساختاری، شیمیایی و مورفولوژیکی با وضوح بالا در مقیاس نانومتری نانومواد هستند. در این مقاله، یک الگوریتم جدید جهت طبقه‎بندی نانوساختارها با استفاده از این تصاویر ارائه شده ‎است؛ بدین منظور، ابتدا تصاویر میکروسکوپی الکترونی روبشی نانوذرات به سری زمانی تبدیل و مشخصات آنها از طریق روش‌های تحلیل سری زمانی مورد بررسی قرار گرفتند؛ سپس ویژگی‌های آماری این سری‌ها استخراج و به‌عنوان ورودی‎های یک سامانه استنتاج فازی برای طبقه‎بندی تصاویر میکروسکوپی نانوساختارها در سه گروه خوب، متوسط و بد در نظر گرفته ‎شدند. این الگوریتم برروی 65 تصویر میکروسکوپی نانوذرات با ابعاد یکسان (250×250 پیکسل) اعمال شده و دقتی بالاتر از 93 درصد را به دنبال داشته ‎است که بسیار مناسب است.   2 Dispersion of nanoparticles in nanostructures is one of the most important indicators designed to verify the effectiveness of proposed methods in the synthesis of nanomaterials. In the recent years, various methods have been suggested for the synthesis of nanostructures in which the Scanning Electron Microscopy (SEM) has been used to show the quality of the nanomaterial. The SEM images of nanoparticles contain structural, chemical and morphological information with high resolution in nanometer scale of nanomaterials. One of the challenges in the quality of dispersion’s nanostructures is detection of agglomeration degree. In some SEM images of nanoparticles, the particles have speeded uniformly and not aggregately. In some of the other SEM images, their particles are agglomerated. Also, there are a few SEM images of nanoparticles that their particles aren’t very aggregate or diffused. If the SEM images of nanoparticles with their particles speeded uniformly, are called good images, and the images with their aggregate particles are called bad images, and the images with their particle dispersion between good and bad images, are called average images, the nanomaterials could be classified in categories of good, average, and bad images. In this paper, a new algorithm has been provided to classify nanostructures using SEM images of nanoparticles. For this purpose, these images were transformed to time series at first (the time series extracted are unique for each SEM image of nanoparticles) and their specifications were investigated through time series analysis methods. Then, statistical specifications of these series were extracted. Six statistical specifications have been extracted for classification of nanostructures. These specifications are as follows: standard deviation, first and second kurtosis, interquartile range, the criterion of Pearson, and skewness. The extracted specifications were used as inputs of a fuzzy inference system for classifying microscopic images of nanostructures into three groups: good, average and bad. This algorithm has been tested on 65 nanoparticles microscopic images with identical size and resulted precision above 93 percent indicated validity of this algorithm.   115 130 2015/09/262015/12/52015/03/142014/12/12015/07/72015/10/82015/08/25 1394/6/3 2016/11/62016/10/292016/10/172017/03/242017/03/52017/05/202017/03/5 1395/12/15 نوشین بیگدلی دانشگاه بین المللی امام خمینی (ره) ایران nooshin_bigdeli@yahoo.com حامد جباری دانشگاه بین المللی امام خمینی (ره) ایران hamedjabbarie@gmail.com SEM image of nanoparticles Time series analyses Statistical features Fuzzy logic تصاویر میکروسکوپی الکترونی روبشی نانوذرات تحلیل سری زمانی ویژگی‎های آماری منطق فازی [1] J. Ramsden, Applied nanotechnology: the conversion of research results to products: William Andrew, 2013.##[2] G. M. Whitesides, "Nanoscience, nanotechnolo-gy, and chemistry," Small, vol. 1, no. 2, pp. 172-179, 2005.##[3] J. J. Ramsden, and J. Freeman, "The nanoscale," Collegium, vol. 3, 2008.##[4] K. Chatterjee, S. Sarkar, K. J. Rao, and S. Paria, "Core/shell nanoparticles in biomedical applica-tions," Advances in colloid and interface scienc-e, vol. 209, pp. 8-39, 2014.##[5] A. K. Hussein, "Applications of nanotechnology in renewable energies—A comprehensive overview and understanding," Renewable and Sustainable Energy Reviews, vol. 42, pp. 460-476, 2015.##[6] R. Misra, S. Acharya, and S. K. Sahoo, "Cancer nanotechnology: application of nanotechnology in cancer therapy," Drug Discovery Today, vol. 15, no. 19, pp. 842-850, 2010.##[7] R. Toy, L. Bauer, C. Hoimes, K. B. Ghaghada, and E. Karathanasis, "Targeted nanotechnology for cancer imaging," Advanced drug delivery reviews, vol. 76, pp. 79-97, 2014.##[8] H. Han, Z. Huang, and W. Lee, "Metal-assisted chemical etching of silicon and nanotechnology applications," Nano Today, vol. 9, no. 3, pp. 271-304, 2014.##[9] Y. Sun, and Y. Xia, "Shape-controlled synthesis of gold and silver nanoparticles," Science, vol. 298, no. 5601, pp. 2176-2179, 2002.##[10] H. Chen, S. Witharana, Y. Jin, C. Kim, and Y. Ding, "Predicting thermal conductivity of liquid suspensions of nanoparticles (nanofluids) based on rheology," Particuology, vol. 7, no. 2, pp. 151-157, 2009.##[11] V. Pokropivny, and V. Skorokhod, "Classifica-tion of nanostructures by dimension-ality and concept of surface forms engineering in nanomaterial science," Materials Science and Engineering: C, vol. 27, no. 5, pp. 990-993, 2007.##[12] V. Pokropivny, and V. Skorokhod, "New dimensionality classifications of nanostructur-es," Physica E: Low-dimensional Systems and nanostructures, vol. 40, no. 7, pp. 2521-2525, 2008.##[13] E. Kustov, and V. Nefedov, "Nanostructures: Compositions, structure, and classification," Russian Journal of Inorganic Chemistry, vol. 53, no. 14, pp. 2103-2170, 2008.##[14] A. A. Al-Mousa, A new systematic and quantitative approach to characterization of surface nanostructures using fuzzy logic: Santa Clara University, 2010.##[15] T.-c. Fu, "A review on time series data mining," Engineering Applications of Artificial Intellige-nce, vol. 24, no. 1, pp. 164-181, 2011.##[16] O. J. Pereira, L. de Almeida Pacheco, S. S. Barreto, and T. Cavalcante, "Pattern Recogni-tion using Multivariate Time Series for Fault Detection in a Thermoeletric Unit." p. 315.##[17] N. Ding, Y. Bésanger, and F. Wurtz, "Next-day MV/LV substation load forecaster using time series method," Electric Power Systems Resea-rch, vol. 119, pp. 345-354, 2015.##[18] J. A. Brady, "Considering Complexity: Image Matching With Time Series," University of California, Riverside, 2007.##[19] A. Elsayed, M. H. A. Hijazi, F. Coenen, M. Garcıa-Finana, V. Sluming, and Y. Zheng, "Image Categorisation Using Time Series Case Based Reasoning." pp. 423-436.##[20] M. D. Gupta, and S. Banerjee, "Similarity Based Retrieval in Case Based Reasoning for Analysis of Medical Images," World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, vol. 8, no. 3, pp. 539-545, 2015.##[21] T. Guyet, and H. Nicolas, "Long term analysis of time series of satellite images," Pattern Recognition Letters, vol. 70, pp. 17-23, 2016.##[22] L. A. Zadeh, "Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic," Fuzzy sets and systems, vol. 90, no. 2, pp. 111-127, 1997.##[1] J. Ramsden, Applied nanotechnology: the conversion of research results to products: William Andrew, 2013.##[2] G. M. Whitesides, "Nanoscience, nanotechnolo-gy, and chemistry," Small, vol. 1, no. 2, pp. 172-179, 2005.##[3] J. J. Ramsden, and J. Freeman, "The nanoscale," Collegium, vol. 3, 2008.##[4] K. Chatterjee, S. Sarkar, K. J. Rao, and S. Paria, "Core/shell nanoparticles in biomedical applica-tions," Advances in colloid and interface scienc-e, vol. 209, pp. 8-39, 2014.##[5] A. K. Hussein, "Applications of nanotechnology in renewable energies—A comprehensive overview and understanding," Renewable and Sustainable Energy Reviews, vol. 42, pp. 460-476, 2015.##[6] R. Misra, S. Acharya, and S. K. Sahoo, "Cancer nanotechnology: application of nanotechnology in cancer therapy," Drug Discovery Today, vol. 15, no. 19, pp. 842-850, 2010.##[7] R. Toy, L. Bauer, C. Hoimes, K. B. Ghaghada, and E. Karathanasis, "Targeted nanotechnology for cancer imaging," Advanced drug delivery reviews, vol. 76, pp. 79-97, 2014.##[8] H. Han, Z. Huang, and W. Lee, "Metal-assisted chemical etching of silicon and nanotechnology applications," Nano Today, vol. 9, no. 3, pp. 271-304, 2014.##[9] Y. Sun, and Y. Xia, "Shape-controlled synthesis of gold and silver nanoparticles," Science, vol. 298, no. 5601, pp. 2176-2179, 2002.##[10] H. Chen, S. Witharana, Y. Jin, C. Kim, and Y. Ding, "Predicting thermal conductivity of liquid suspensions of nanoparticles (nanofluids) based on rheology," Particuology, vol. 7, no. 2, pp. 151-157, 2009.##[11] V. Pokropivny, and V. Skorokhod, "Classifica-tion of nanostructures by dimension-ality and concept of surface forms engineering in nanomaterial science," Materials Science and Engineering: C, vol. 27, no. 5, pp. 990-993, 2007.##[12] V. Pokropivny, and V. Skorokhod, "New dimensionality classifications of nanostructur-es," Physica E: Low-dimensional Systems and nanostructures, vol. 40, no. 7, pp. 2521-2525, 2008.##[13] E. Kustov, and V. Nefedov, "Nanostructures: Compositions, structure, and classification," Russian Journal of Inorganic Chemistry, vol. 53, no. 14, pp. 2103-2170, 2008.##[14] A. A. Al-Mousa, A new systematic and quantitative approach to characterization of surface nanostructures using fuzzy logic: Santa Clara University, 2010.##[15] T.-c. Fu, "A review on time series data mining," Engineering Applications of Artificial Intellige-nce, vol. 24, no. 1, pp. 164-181, 2011.##[16] O. J. Pereira, L. de Almeida Pacheco, S. S. Barreto, and T. Cavalcante, "Pattern Recogni-tion using Multivariate Time Series for Fault Detection in a Thermoeletric Unit." p. 315.##[17] N. Ding, Y. Bésanger, and F. Wurtz, "Next-day MV/LV substation load forecaster using time series method," Electric Power Systems Resea-rch, vol. 119, pp. 345-354, 2015.##[18] J. A. Brady, "Considering Complexity: Image Matching With Time Series," University of California, Riverside, 2007.##[19] A. Elsayed, M. H. A. Hijazi, F. Coenen, M. Garcıa-Finana, V. Sluming, and Y. Zheng, "Image Categorisation Using Time Series Case Based Reasoning." pp. 423-436.##[20] M. D. Gupta, and S. Banerjee, "Similarity Based Retrieval in Case Based Reasoning for Analysis of Medical Images," World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, vol. 8, no. 3, pp. 539-545, 2015.##[21] T. Guyet, and H. Nicolas, "Long term analysis of time series of satellite images," Pattern Recognition Letters, vol. 70, pp. 17-23, 2016.##[22] L. A. Zadeh, "Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic," Fuzzy sets and systems, vol. 90, no. 2, pp. 111-127, 1997.## ##

سنجش عملکرد سامانه‌های رابط مغز و رایانه P300 Speller به‌ازای ماتریس نمایش ردیف و یا ستون (RCP) و نمایش حروف زبان فارسی Performance Analysis of a Persian text input brain–computer interface (BCI) P300 Speller system with row/column paradigm (RCP) 1 1 سامانه‌های رابط مغز و رایانه P300 Speller به‌عنوان عضوی از خانواده سامانه‌های رابط مغز و رایانه سعی دارد تا توانایی تایپ حروف و برقراری ارتباط با این شیوه را برای بیماران و معلولان فراهم آورد. یکی از موارد بسیار مهم در این سامانه‌ها، قابلیت شخصی‌سازی است. با توجه به آنکه بیش‌تر پژوهش‌های این حوزه بر اساس نمایش حروف انگلیسی انجام شده، در این پژوهش سعی شده است تا برای نخستین‌بار عملکرد یک سامانه ارتباط مغز و رایانه P300 Speller به‌ازای نمایش حروف زبان فارسی مورد سنجش قرار گیرد. در این پژوهش پس از ثبت داده از داوطلبان و سنجش عملکرد سامانه مورد بررسی، صحت تشخیص 21/88% و نرخ انتقال اطلاعات 74/6 بیت در دقیقه به‌ازای پانزده تکرار با  ترکیب روش کاهش بعد LDA و طبقه بند بیز به‌دست آمد. همچنین در این پژوهش اثر تغییر تعداد تکرار و کاهش زمان آزمایش نیز مورد بررسی قرار گرفت و نشان داده شد که به‌ازای کمینه تعداد تکرار، می‌توان به صحت تشخیص 06/80% و نرخ انتقال اطلاعات 43/42 بیت بر دقیقه دست یافت.   2 As a Brain computer interface system, BCI P300 Speller tries to help disabled people and patients to regain some of their lost ability with allowing communication via typing. The ability of personalization is one of the most important features in a BCI system, so the typing language as a personalization factor is an important feature in a BCI speller. Most prior researches on P300 Speller has focused on displaying English alphabet and there were only few studies made on other languages such as Chinese. In this research, we present a P300 Speller system, based on RCP, for Persian (Farsi) character input. RCP (Row or Column Paradigm) was introduced by Farwell and Donchin at 1988, and since then it has been considered as a benchmark in P300 BCI speller research. As a result, in this study also, Row or column paradigm was selected as the base stimulation pattern in P300 speller system. In order to evaluate the Persian row or column paradigm performance, we recorded EEG signals from volunteered subjects while the stimulation pattern was being displayed. It should be noted that the test was explained to each subject before testing, and for more experience and in order to reduce the error, each subject participated in an experiment test before attending the main test. These EEG signals were recorded from 8 channels based on ‘‘Fz’’, ‘‘Cz’’, ‘‘P3’’, ‘‘Pz’’, ‘‘P4’’, ‘‘O1’’, ‘‘Oz’’ and ‘‘O2’’ site in accordance to the International 10–20 system electrode placement system and by using Science Beam co.’s EEG recording device. The sample rate was 1 KHz which was down sampled to 250Hz. After recording, the EEG signals were filtered using a band passed filter And for classification, Linear discriminate analysis was used in combination with K-fold validation method for classifier training. As performance determination, we calculated accuracy and bit rate for the mentioned system based on recorded data from volunteers and reached the average accuracy of 88.21% and bit rate of 6.74 (bits/minute) (we use Linear LDA classifier for classification and the total trial number was set to 15). Furthermore, in this research performance was measured for different trial number and final results demonstrated that this system can achieve high average accuracy of 80.06% and average bit rate of 42.43 (bits/minute) by using only 2 repetitions.   131 140 2015/09/262015/12/52015/03/142014/12/12015/07/72015/10/82015/08/252015/10/27 1394/8/5 2016/11/62016/10/292016/10/172017/03/242017/03/52017/05/202017/03/52017/03/5 1395/12/15 محمد میکائیلی Mohammad Mikaeili دانشگاه شاهد ایران Mikaeili@shahed.ac.ir فروغ نجفی foroogh Najafi دانشگاه شاهد ایران foroogh.najafi.c@gmail.com Brian computer interface systems BCI P300 Speller P300 wave LDA classifier سامانه‌های رابط مغز و رایانه سامانه P300 Speller BCI روش کاهش بعد LDA مؤلفه P300 طبقه‌بند بیز [1] Z. Seyyedsalehi, AM. Nasrabadi, and V. Abootalebi. " Quadratic B-Spline Wavelet and Committee Machine for the P300 Detection in Brain Computer Interface.", Signal and Data Processing., vol. 2, no. 10, pp. 57-70, 2009.##[2] B. Allison, J. Pineda, "Effects of SOA and flash pattern manipulations on ERPs performance and preference: Implications for a BCI system", Int. J. Psychophysiol., vol. 59, no. 2, pp. 127-140, Feb. 2006.##[3] C.M. Bishop, Pattern Recognition and Machine Learning., Aug. 2006.##[4] M. Chang, T.M. Rutkowski, "Two-Step Input Spatial Auditory BCI for Japanese Kana Characters.", Advances in Cognitive Neurodynamics (V), pp. 383-389, 2016.##[5] D. J. Krusienski, E. W. Sellers, F. Cabestaing, S. Bayoudh, D. J. McFarland, T. M. Vaughan, J. R. Wolpaw, "A comparison of classification techniques for the P300 Speller", J. Neural Eng., vol. 3, pp. 299-305, Dec. 2006.##[6] J. Jin, E. W. Sellers, X. Wang, "Targeting an Efficient Target-to-Target Interval for P300 Speller Brain-Computer Interfaces", Medical & Biological Engineering & Computing, vol. 50, no. 3, pp. 289-296, Feb. 2012.##[7] F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, B. Arnaldi, "A Review of Classification Algorithms for EEG-Based Brain-Computer Interfaces", J. Neural Eng., vol. 4, pp. R1-R13, 2007.##[8] J. Jin, B. Allison, C. Brunner, B. Wang, X. Wang, J. Zhang, C. Neuper, G. Pfurtscheller, "P300 Chinese input system based on Bayesian LDA", Biomed. Tech., vol. 55, no. 1, pp. 5-18, 2010.##[9] D. J. McFarland, W. A. Sarnacki, G. Townsend, T. Vaughan, J. R. Wolpaw, "The P300-based brain–computer interface (BCI): Effects of stimulus rate", Clin. Neurophysiol., vol. 122, pp. 731-737, 2011.##[10] J. W. Minett, H.-Y. Zheng, M. C.-M. Fong, L. Zhou, G. Peng, and W. S.-Y. Wang, "A Chinese Text Input Brain-Computer Interface Based on the P300 Speller," International Journal of Human-Computer Interaction, vol. 28, pp. 472-483, 2012.##[11] R. Ortner, R. Prueckl, V. Putz, J. Scharinger, M. Bruckner, A. Schnuerer, and C. Guger, "Accuracy of a P300 Speller for Different Conditions: A Comparison," Proc. of the 5th Int. Brain-Computer Interface Conference, 2011, Graz, Austria, p. 196.##[12] A. E. Selim, M. A. Wahed and Y. M. Kadah., "MacHine learning methodologies in P300 speller Brain-Computer Interface systems, " in Radio Science Conference, pp. 1-9, 2009.##[13] E.W. Sellers, "A P300 Event-Related Potential Brain-Computer Interface (BCI): The Effects of Matrix Size and Inter Stimulus Interval on Performance", Biological Psychology, vol. 73, no. 3, pp. 242-252, 2006.##[14] A. Rakotomamonjy, V. Guigue, "BCI competition III: Dataset II-ensemble of SVMs for BCI P300 speller", IEEE Trans. Biomed. Eng., vol. 55, no. 3, pp. 1147-1154, Mar. 2008.##[15] D. E. Thompson, S. Blain-Moraes, J. E. Huggins, "Performance assessment in brain-computer interface-based augmentative and alternative communication", Biomed. Eng. Online, vol. 12, pp. 43, Jan. 2013.##[1] سیدصالحی سیده زهره، نصرآبادی علی مطیع، ابوطالبی وحید، "به‌کارگیری تحلیل زمان‌- فرکانس و ماشین‌ همیار در تشخیص خودکار مؤلّفه P300 جهت ارتباط مغز با رایانه"، پردازش علائم و داده‌ها، شماره 2 (پیاپی 10) ، صفحات 70-57، 1387.##[1] Z. Seyyedsalehi, AM. Nasrabadi, and V. Abootalebi. " Quadratic B-Spline Wavelet and Committee Machine for the P300 Detection in Brain Computer Interface.", Signal and Data Processing., vol. 2, no. 10, pp. 57-70, 2009.##[2] B. Allison, J. Pineda, "Effects of SOA and flash pattern manipulations on ERPs performance and preference: Implications for a BCI system", Int. J. Psychophysiol., vol. 59, no. 2, pp. 127-140, Feb. 2006.##[3] C.M. Bishop, Pattern Recognition and Machine Learning., Aug. 2006.##[4] M. Chang, T.M. Rutkowski, "Two-Step Input Spatial Auditory BCI for Japanese Kana Characters.", Advances in Cognitive Neurodynamics (V), pp. 383-389, 2016.##[5] D. J. Krusienski, E. W. Sellers, F. Cabestaing, S. Bayoudh, D. J. McFarland, T. M. Vaughan, J. R. Wolpaw, "A comparison of classification techniques for the P300 Speller", J. Neural Eng., vol. 3, pp. 299-305, Dec. 2006.##[6] J. Jin, E. W. Sellers, X. Wang, "Targeting an Efficient Target-to-Target Interval for P300 Speller Brain-Computer Interfaces", Medical & Biological Engineering & Computing, vol. 50, no. 3, pp. 289-296, Feb. 2012.##[7] F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, B. Arnaldi, "A Review of Classification Algorithms for EEG-Based Brain-Computer Interfaces", J. Neural Eng., vol. 4, pp. R1-R13, 2007.##[8] J. Jin, B. Allison, C. Brunner, B. Wang, X. Wang, J. Zhang, C. Neuper, G. Pfurtscheller, "P300 Chinese input system based on Bayesian LDA", Biomed. Tech., vol. 55, no. 1, pp. 5-18, 2010.##[9] D. J. McFarland, W. A. Sarnacki, G. Townsend, T. Vaughan, J. R. Wolpaw, "The P300-based brain–computer interface (BCI): Effects of stimulus rate", Clin. Neurophysiol., vol. 122, pp. 731-737, 2011.##[10] J. W. Minett, H.-Y. Zheng, M. C.-M. Fong, L. Zhou, G. Peng, and W. S.-Y. Wang, "A Chinese Text Input Brain-Computer Interface Based on the P300 Speller," International Journal of Human-Computer Interaction, vol. 28, pp. 472-483, 2012.##[11] R. Ortner, R. Prueckl, V. Putz, J. Scharinger, M. Bruckner, A. Schnuerer, and C. Guger, "Accuracy of a P300 Speller for Different Conditions: A Comparison," Proc. of the 5th Int. Brain-Computer Interface Conference, 2011, Graz, Austria, p. 196.##[12] A. E. Selim, M. A. Wahed and Y. M. Kadah., "MacHine learning methodologies in P300 speller Brain-Computer Interface systems, " in Radio Science Conference, pp. 1-9, 2009.##[13] E.W. Sellers, "A P300 Event-Related Potential Brain-Computer Interface (BCI): The Effects of Matrix Size and Inter Stimulus Interval on Performance", Biological Psychology, vol. 73, no. 3, pp. 242-252, 2006.##[14] A. Rakotomamonjy, V. Guigue, "BCI competition III: Dataset II-ensemble of SVMs for BCI P300 speller", IEEE Trans. Biomed. Eng., vol. 55, no. 3, pp. 1147-1154, Mar. 2008.##[15] D. E. Thompson, S. Blain-Moraes, J. E. Huggins, "Performance assessment in brain-computer interface-based augmentative and alternative communication", Biomed. Eng. Online, vol. 12, pp. 43, Jan. 2013.## ##

رفع اعوجاج هندسی متون به‌کمک اطلاعات هندسی خطوط متن Document Image Dewarping using geometrical information extracted from document lines 1 1 تصاویر سند تهیه‌شده توسط پویش‌گر یا دوربین دیجیتال، همواره با اعوجاج‌های فتومتریک و هندسی همراه هستند. وجود هر دو نوع اعوجاج، باعث کاهش دقت عملکرد نرم‌افزارهای شناسایی نویسه­ها (OCR) می­شوند. در این مقاله روشی نوین جهت رفع اعوجاج‌های هندسی از تصاویر متنی ارائه شده ­است. در روش پیشنهادی به‌منظور تصحیح اعوجاج هندسی، در ابتدا خطوط متن از تصویر استخراج و سپس هر خط متن به ستون­هایی به عرض مساوی شکسته می­شوند. برای هر قطعه استخراج‌شده از یک خط، راستای قطعه به‌نحوی تصحیح می‌شود که حروف موجود در آن قطعه در راستای افقی قرار گیرد. برای این منظور به‌ازای چرخش­های مختلف قطعۀ متن، افکنش افقی تصویر محاسبه می­شود و چرخشی از قطعه که بلندترین قله افکنش را ایجاد کند، راستای تصحیح‌شده آن قطعه در نظر گرفته می‌شود. بر این اساس یک نقطه مرجع که معرف راستای مبنا است، برای هر قطعه­خط هم‌راستا­شده با افق استخراج می‌شود. به‌کمک نقاط مرجع، هر قطعه از خط، انحنای آن خط متن به‌کمک برازش یک تابع درجۀ سه به‌دست می‌آید. درنهایت با استفاده از تخمین تبدیل پرسپکتیو، اعوجاج هندسی هر خط برطرف می‌شود. جهت افزایش پایداری روش پیشنهادی در تخمین انحنای خطوط متن با طول کم، از انحنای خطوط با طول بزرگ­تر مجاور آن خط استفاده شده ‌است. روش­ پیشنهادی بر روی پایگاه­های دادۀ فارسی و انگلیسی پیاده­سازی و با برخی روش‌های هم‌تراز آن مقایسه شده است. نتایج بیان‌گر قدرت و دقّت روش پیشنهادی در رفع اعوجاج هندسی است.   2 Document images produced by scanners or digital cameras usually have photometric and geometric distortions. If either of these effects distorts document, recognition of words from such a document image using OCR is subject to errors. In this paper we propose a novel approach to significantly remove geometric distortion from document images. In this method first we extract document lines from document using morphological operators. Then, extracted document lines are divided into a number of equal size column strips.  This allows to assume that each segment of line document is not curved. Each extracted document line segment is aligned horizontally. For this purpose, a segment line of document is rotated at different angels and for each rotation horizontal projection is obtained. The rotation angle with maximum peak at the corresponding projection signal is selected to align the line segment, horizontally. In order to estimate the geometrical distortion, for each document line a reference point is extracted from each line segment. These points indicate the position of a document line at starting column of line segments. Using reference points of a document line a polynomial function is fitted to each document line. At the end, geometric distortion for each part of the document is eliminated using a perspective transformation. This transformation is estimated based on the extracted polynomial function. To increase the stability of the proposed method for short text lines, the curve of adjacent text lines of longer length is used. A post processing stage is required after applying perspective transformation on document patches. Since this transformation is a continuous mapping but it is applied on digital images. To remove this distortion from the result, the consistency of each pixel value with the value of neighboring pixels are considered to correct the value of inconsistence pixels. The proposed method is implemented on Persian and English databases and has been compared with the existing methods. The results indicate the efficiency and accuracy of the proposed method in elimination of geometric distortions.   141 158 2015/09/262015/12/52015/03/142014/12/12015/07/72015/10/82015/08/252015/10/272015/08/22 1394/5/31 2016/11/62016/10/292016/10/172017/03/242017/03/52017/05/202017/03/52017/03/52017/03/5 1395/12/15 محمد امین طلوع بیدختی Mohammad Amin Tolou Beidokhti دانشگاه صنعتی شاهرود ایران M.a.Tolou.b@Gmail.com علیرضا احمدی فرد Alireza Ahmadyfard دانشگاه صنعتی شاهرود ایران ahmadyfard@shahroodut.ac.ir Geometric distortion document processing perspective Transformation Optical character recognition (OCR) اعوجاج هندسی پردازش دوبعدی اسناد تخمین تبدیل پرسپکتیو نویسه‌خوان نوری [1] E. bayesteh Tashak,A. Ahmadyfard and H. Khosravi "A two-step method for recognizing Persian handwritten words using adaptive divi-sion of gradient image" , JSDP, vol 12,PP 15-29 ,2015.##[2] H. Hasanpour and O. Rostami Ghadi " Image enhancment By Reducing the effect of failure factors on Intensity And reflection of the ima-ge"JSDP, vol 9, PP 12-23,2012.##[3] S.KhosraviRad, "Nonlinear distortion correct-ion in Persian documentary images" ,M.S. thesis, Shahrood ut, Shahrood ,Iran ,2012.##[4] H. Dehboyd, F. Razazi, Sh. Alirezei "Introduc-ing a new method for reducing image distortion in Persian text images captured by the camera" Sixth Conference of the Machine and Image Processing, Esfehan, Iran,2010.##[5] M.Shamgholi," Distortion correction and Image enhancement in Persian Books" M.S. thesis, Shahrood ut, Shahrood ,Iran ,2013.##[6] M.A. Tolou Beydokhti and A. Ahmadyfard##[7] A. Criminisi, I. Reid, and A. Zisserman, "A Plane Measuring Device," University of Oxfo-rd, 1993.##[8] B. Gatos, N. Pratikakis, and K. Ntirogiannis, "Segmentation Based Recovery of Arbitrarily Warped Document Images," in Ninth Internat-ional Conference on Document Analy-sis and Recognition (ICDAR), 2007.##[9] J. Kanai, T. A. Nartker, S. Rice, and G. Nagy, "Performance metrics for document understand-ing systems," in Proceedings of the Second International Conference on Document Analy-sis and Recognition, 1993, pp. 424-427.##[10] H. Khosravi and E. Kabir, "A blackboard approach towards integrated Farsi OCR syst-em," International Journal of Document Analy-sis and Recognition (IJDAR), vol. 12, pp. 21-32, 2009.##https://doi.org/10.1007/s10032-009-0087-7##[11] J. Liang, D. DeMenthon, and D. Doermann, "Geometric Rectification of Camera-captured Document Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 4, pp. 591-605, 2008.##[12] L. Likforman-Sulem and F. Claudie, "Extract-ing text lines in handwritten documents by perceptual grouping," in Advances in handwrit-ing and drawing: a multidisciplinary approach, paris, 1994, pp. 117-135.##[13] L. Likforman-Sulem, A. Hanimyan, and C. Faure, "A Hough based algorithm for extracting text lines in handwritten documents," in IEEE Proceedings of the Third International Confer-ence onDocument Analysis and Recogn-ition, 1995, pp. 774-777.##[14] A. Masalovitch and L. Mestetskiy, "Usage of continuous skeletal image representation for document images de-warping," in Proceedings of International Workshop on Camera-Based Document Analysis and Recognition, Curitiba, 2007, pp. 45-53.##[15] J. Mundy and A. Zisserman, Geometric invar-iance in computer vision. Cambridge, MA : MIT press, 1992, vol. 92.##[16] W. Niblack, "An introduction to digital image processing," Strandberg Publishing Company, 1985.##[17] A. H. Roger and C. R. Johnson, "Topics in matrix analysis," in Matrix analysis. Cambridge university press, 2012.##[18] J. Sauvola and M. Pietikainen, "Adaptive document image binarization," Pattern Recogni-tion, vol. 32, no. 2, pp. 225-236, 2000.##[19] F. Shafait and M. Breuel, "Document image dewarping contest," in 2nd Int. Workshop on Camera-Based Document Analysis and Recognition, Curitiba, Brazil, 2007, pp. 181-188.##[20] M.Shamgholi, M. H. Khosravi, and S. M. Riazi, "Document Image Dewarping Based on Text Line Detection and Surface Modeling," International Journal of Engineering-Transac-tions C: Aspects, vol. 27, no. 12, p. 1855, 2014.##[21] Z. Shi and V. Govindaraju, "Line separation for complex document images using fuzzy runleng-th," in IEEE Proceedings in First International Workshop on Document Image Analysis for Libraries, 2006, pp. 306-312.##[22] A. Ulges, C. H. Lampert, and T. Breuel, "Document capture using stereo vision," in ACM Proceedings of the 2004 ACM symposium on Document engineering, 2004, pp. 198-200.##[23] T. Wada, H. Ukida, and T. Matsuyama, "Shape from shading with interreflections under proximalLight Source-3D Shape Reconstruc-tion of Unfolded Book Surface From a Scanner Image," in IEEE Proceedings in Fifth Interna-tional Conference on Computer, 1995, pp. 66-71.##[24] K. Y.Wong, R. G. Casey, and F. M. Wahl, "Document analysis system," IBM journal of research and development, vol. 26, no. 6, pp. 647-656, Nov. 1982.##[25] OmniPage. [Online]. http://www.nuance.com##[1] ا. بایسته تاشک, ع. احمدی فرد و ح. خسروی, "یک روش دو مرحله ای برای بازشناسی کلمات دست نوشته فارسی به کمک بلوک بندی تطبیقی گرادیان تصویر" ، پردازش علائم و داده ها ، دوره 12،صفحات29-15 ، 1394.##[2] ح. حسن پور و ع. رستمی قادی "بهسازی تصویر با کاهش اثر عوامل خرابی بر مولفهی روشنایی و بازتابش تصویر," ، پردازش علائم و داده ها، دوره 9، صفحات23-13 ،1391.##[3] س. خسروی راد, "رفع اعوجاجات غیرخطی در تصاویر اسناد فارسی," پایان نامه ی ارشد، دانشگاه صنعتی شاهرود، شاهرود، ایران، 1391.##[4] ه. ده بوید, ف. رزازی, و ش. علیرضایی, "ارائه روشی نوین برای کاهش اعوجاج تصویربرداری در تصاویر متنی فارسی تصویربرداری شده توسط دوربین," ششمین کنفرانس ماشین بینایی و پردازش تصاوی، اصفهان، ایران، 1389.##[5]م.شامقلی, "رفع اعوجاج و بهبود کیفیت تصاویر اسکن شده از کتب فارسی," پایان نامه کارشناسی ارشد، دانشگاه صنعتی شاهرود، شاهرود ، ایران، 1392.##[6] م. ا. طلوع بیدختی و ع. احمدی فرد, "رفع اعوجاج فتومتریک از تصویر اسناد به کمک درون نگاری بهبودیافته," ششمین کنفرانس فناوری اطلاعات و دانش. دانشگاه صنعتی شاهرود، شاهرود، ایران، 1393.##[1] E. bayesteh Tashak,A. Ahmadyfard and H. Khosravi "A two-step method for recognizing Persian handwritten words using adaptive divi-sion of gradient image" , JSDP, vol 12,PP 15-29 ,2015.##[2] H. Hasanpour and O. Rostami Ghadi " Image enhancment By Reducing the effect of failure factors on Intensity And reflection of the ima-ge"JSDP, vol 9, PP 12-23,2012.##[3] S.KhosraviRad, "Nonlinear distortion correct-ion in Persian documentary images" ,M.S. thesis, Shahrood ut, Shahrood ,Iran ,2012.##[4] H. Dehboyd, F. Razazi, Sh. Alirezei "Introduc-ing a new method for reducing image distortion in Persian text images captured by the camera" Sixth Conference of the Machine and Image Processing, Esfehan, Iran,2010.##[5] M.Shamgholi," Distortion correction and Image enhancement in Persian Books" M.S. thesis, Shahrood ut, Shahrood ,Iran ,2013.##[6] M.A. Tolou Beydokhti and A. Ahmadyfard##[7] A. Criminisi, I. Reid, and A. Zisserman, "A Plane Measuring Device," University of Oxfo-rd, 1993.##[8] B. Gatos, N. Pratikakis, and K. Ntirogiannis, "Segmentation Based Recovery of Arbitrarily Warped Document Images," in Ninth Internat-ional Conference on Document Analy-sis and Recognition (ICDAR), 2007.##[9] J. Kanai, T. A. Nartker, S. Rice, and G. Nagy, "Performance metrics for document understand-ing systems," in Proceedings of the Second International Conference on Document Analy-sis and Recognition, 1993, pp. 424-427.##[10] H. Khosravi and E. Kabir, "A blackboard approach towards integrated Farsi OCR syst-em," International Journal of Document Analy-sis and Recognition (IJDAR), vol. 12, pp. 21-32, 2009.##https://doi.org/10.1007/s10032-009-0087-7##[11] J. Liang, D. DeMenthon, and D. Doermann, "Geometric Rectification of Camera-captured Document Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 4, pp. 591-605, 2008.##[12] L. Likforman-Sulem and F. Claudie, "Extract-ing text lines in handwritten documents by perceptual grouping," in Advances in handwrit-ing and drawing: a multidisciplinary approach, paris, 1994, pp. 117-135.##[13] L. Likforman-Sulem, A. Hanimyan, and C. Faure, "A Hough based algorithm for extracting text lines in handwritten documents," in IEEE Proceedings of the Third International Confer-ence onDocument Analysis and Recogn-ition, 1995, pp. 774-777.##[14] A. Masalovitch and L. Mestetskiy, "Usage of continuous skeletal image representation for document images de-warping," in Proceedings of International Workshop on Camera-Based Document Analysis and Recognition, Curitiba, 2007, pp. 45-53.##[15] J. Mundy and A. Zisserman, Geometric invar-iance in computer vision. Cambridge, MA : MIT press, 1992, vol. 92.##[16] W. Niblack, "An introduction to digital image processing," Strandberg Publishing Company, 1985.##[17] A. H. Roger and C. R. Johnson, "Topics in matrix analysis," in Matrix analysis. Cambridge university press, 2012.##[18] J. Sauvola and M. Pietikainen, "Adaptive document image binarization," Pattern Recogni-tion, vol. 32, no. 2, pp. 225-236, 2000.##[19] F. Shafait and M. Breuel, "Document image dewarping contest," in 2nd Int. Workshop on Camera-Based Document Analysis and Recognition, Curitiba, Brazil, 2007, pp. 181-188.##[20] M.Shamgholi, M. H. Khosravi, and S. M. Riazi, "Document Image Dewarping Based on Text Line Detection and Surface Modeling," International Journal of Engineering-Transac-tions C: Aspects, vol. 27, no. 12, p. 1855, 2014.##[21] Z. Shi and V. Govindaraju, "Line separation for complex document images using fuzzy runleng-th," in IEEE Proceedings in First International Workshop on Document Image Analysis for Libraries, 2006, pp. 306-312.##[22] A. Ulges, C. H. Lampert, and T. Breuel, "Document capture using stereo vision," in ACM Proceedings of the 2004 ACM symposium on Document engineering, 2004, pp. 198-200.##[23] T. Wada, H. Ukida, and T. Matsuyama, "Shape from shading with interreflections under proximalLight Source-3D Shape Reconstruc-tion of Unfolded Book Surface From a Scanner Image," in IEEE Proceedings in Fifth Interna-tional Conference on Computer, 1995, pp. 66-71.##[24] K. Y.Wong, R. G. Casey, and F. M. Wahl, "Document analysis system," IBM journal of research and development, vol. 26, no. 6, pp. 647-656, Nov. 1982.##[25] OmniPage. [Online]. http://www.nuance.com## ##

خوشه‌بندی خودکار داده‌ها با بهره‌گیری از الگوریتم رقابت استعماری بهبودیافته Automatic Clustering Using Improved Imperialist Competitive Algorithm 1 1 الگوریتم رقابت استعماری (ICA)، یکی از کاراترین الگوریتم‌های فرا‌ابتکاری برای پیدا‌کردن جواب بهینه سراسری در مسائل بهینه‌سازی است. در این مقاله از الگوریتم رقابت استعماری برای خوشه‌بندی خودکار مجموعه داده‌های بزرگ و واقعی بدون برچسب استفاده شده است. با بهره‌گیری از ساختار مناسب برای هر یک از کروموزم‌ها و استفاده از الگوریتم رقابت استعماری، در زمان اجرا تعداد بهینه خوشه‌ها هم‌زمان با خوشه‌بندی بهینه داده‌ها به‌دست می‌آید. همچنین برای افزایش دقت و افزایش سرعت هم‌گرایی، ساختار الگوریتم رقابت استعماری با تغییراتی همراه است. روش پیشنهادی (ACICA) نیاز به هیچ‌گونه دانش قبلی برای خوشه‌بندی داده‌ها ندارد. علاوه‌بر آن روش پیشنهادی  در مقایسه با سایر روش‌های خوشه‌بندی مبتنی بر الگوریتم‌های تکاملی، دقت بیشتری را دارد. از معیارهای ارزیابی خوشه‌بندی DB و CS به‌عنوان تابع هدف استفاده شده است. برای نشان‌دادن برتری روش پیشنهادی، میانگین مقدار بهینه تابع هدف و تعداد خوشه­های تعیین‌شده توسط روش پیشنهادی با سه الگوریتم خوشه­بندی خودکار مبتنی بر الگوریتم­های تکاملی مقایسه می‌شود.   2 Imperialist Competitive Algorithm (ICA) is considered as a prime meta-heuristic algorithm to find the general optimal solution in optimization problems. This paper presents a use of ICA for automatic clustering of huge unlabeled data sets. By using proper structure for each of the chromosomes and the ICA, at run time, the suggested method (ACICA) finds the optimum number of clusters while optimal clustering of the data simultaneously.To increase the accuracy and speed of convergence, the structure of ICA changes. As in different applications, there is a need for data clustering which the number of clusters is not known before it is necessary to have methods that can cluster data without knowing the correct prediction of the number of clusters. In the other words, the proposed algorithm requires no background knowledge to classify the data.  In addition, the proposed method is more accurate in comparison with other clustering methods based on evolutionary algorithms. In Imperialist Competitive Algorithm, firstly steps should be taken to increase search rates and explore possible solution while approaching to the global optimal response the steps should be reduced to ensure that the algorithm is not lost and it is not in the local optimal manner. For this purpose and improvement of imperialist competitive algorithm, mutation rate and revolution operator's operation rate are determined dynamically. DB and CS are cluster validity Indexes. In this paper, DB and CS cluster validity measurements are used as the objective function. To demonstrate the superiority of the proposed method, the average of fitness function and the number of clusters determined by the proposed method is compared with three automatic clustering algorithms based on evolutionary algorithms. The partitional clustering algorithms are based on three powerful well-known optimization algorithms, namely the genetic algorithm, the particle swarm optimization and differential evolutionary algorithm. 159 169 2015/09/262015/12/52015/03/142014/12/12015/07/72015/10/82015/08/252015/10/272015/08/222015/11/8 1394/8/17 2016/11/62016/10/292016/10/172017/03/242017/03/52017/05/202017/03/52017/03/52017/03/52017/03/5 1395/12/15 آرش چاقری Arash Chaghari دانشگاه تبریز ایران a.chaghari@tabrizu.ac.ir محمدرضا فیضی درخشی Mohammad-Reza Feizi-Derakhshi دانشگاه تبریز ایران mfezi@tabrizu.ac.ir Partitional Clustering Automatic Clustering Imperialist Competitive Algorithm (ICA) خوشه‌بندی تفکیکی خوشه‌بندی خودکار الگوریتم رقابت استعماری [1] I. Evangelou, "DG Hadjimitsis, AA Lazakidou, Clayton,"" in Data Mining and Knowledge Disco-very in Complex Image Data using Artific-ial Neural Networks", Workshop on Complex Reasoning an Geographical Data, Cyp-rus, 2001.##[2] T. Lillesand, R. W. Kiefer, and J. Chipman, Remote sensing and image interpretation. John Wiley & Sons, 2014.##[3] K. Fukunaga, Introduction to statistical pattern recognition. Academic press, 2013.##[4] H. Frigui and R. Krishnapuram, "A robust competitive clustering algorithm with applica-tions in computer vision," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 21, no. 5, pp. 450-465, 1999.##[5] Y. Leung, J.-S. Zhang, and Z.-B. Xu, "Clustering by scale-space filtering," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, no. 12, pp. 1396-1410, 2000.##[6] A. K. Jain, M. N. Murty, and P. J. Flynn, "Data clustering: a review," ACM computing surveys (CSUR), vol. 31, no. 3, pp. 264-323, 1999.##[7] J. Holland, "Adaption in natural and artificial systems," Ann Arbor MI: The University of Michigan Press, 1975.##[8] S. Z. Selim and K. Alsultan, "A simulated annealing algorithm for the clustering problem," Pattern recognition, vol. 24, no. 10, pp. 1003-1008, 1991.##[9] V. Di Gesú, R. Giancarlo, G. L. Bosco, A. Raimondi, and D. Scaturro, "GenClust: A genetic algorithm for clustering gene expression data," BMC bioinformatics, vol. 6, no. 1, p. 289, 2005.##[10] A. Beg, M. Z. Islam, and V. Estivill-Castro, "Genetic algorithm with healthy population and multiple streams sharing information for clustering," Knowledge-Based Systems, vol. 114, pp. 61-78, 2016.##[11] D. Bajer, G. Martinović, and J. Brest, "A population initialization method for evolution-ary algorithms based on clustering and Cauchy deviates," Expert Systems with Applications, vol. 60, pp. 294-310, 2016.##[12] J. de Andrade Silva, E. R. Hruschka, and J. Gama, "An evolutionary algorithm for cluster-ing data streams with a variable number of clusters," Expert Systems with Applications, vol. 67, pp. 228-238, 2017.##[13] S. Alam, G. Dobbie, and S. U. Rehman, "Analysis of particle swarm optimization based hierarchical data clustering approach-es," Swarm and Evolutionary Computation, vol. 25, pp. 36-51, 2015.##[14] Z. Halim, M. Waqas, and S. F. Hussain, "Clustering large probabilistic graphs using multi-population evolutionary algorithm," Information Sciences, vol. 317, pp. 78-95, 2015.##[15] M. G. Omran, A. Salman, and A. P. Engelbrecht, "Dynamic clustering using parti-cle swarm optimization with application in image segmentation," Pattern Analysis and Applications, vol. 8, no. 4, pp. 332-344, 2006.##[16] S. Bandyopadhyay and U. Maulik, "Genetic clustering for automatic evolution of clusters and application to image classification," Patt-ern Recognition, vol. 35, no. 6, pp. 1197-1208, 2002.##[17] S. Das, A. Abraham, and A. Konar, "Automatic clustering using an improved differential evolution algorithm," Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, vol. 38, no. 1, pp. 218-237, 2008.##[18] S. Saha and S. Bandyopadhyay, "A generaliz-ed automatic clustering algorithm in a multiobjective framework," Applied Soft Computing, vol. 13, no. 1, pp. 89-108, 2013.##[19] A. Konar, Computational intelligence: principles, techniques and applications. Sprin-ger Science & Business Media, 2006.##[20] P. Brucker, "On the complexity of clustering problems," in Optimization and operations research: Springer, 1978, pp. 45-54.##[21] D. L. Davies and D. W. Bouldin, "A cluster separation measure," Pattern Analysis and Machine Intelligence, IEEE Transactions on, no. 2, pp. 224-227, 1979.##[22] C.-H. Chou, M.-C. Su, and E. Lai, "A new cluster validity measure and its application to image compression," Pattern Analysis and Applications, vol. 7, no. 2, pp. 205-220, 2004.##[23] M. K. Pakhira, S. Bandyopadhyay, and U. Maulik, "Validity index for crisp and fuzzy clusters," Pattern recognition, vol. 37, no. 3, pp. 487-501, 2004.##[24] E. Atashpaz-Gargari and C. Lucas, "Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competi-tion," in Evolutionary computation, 2007. CEC 2007. IEEE Congress on, 2007, pp. 4661-4667: IEEE.##[1] I. Evangelou, "DG Hadjimitsis, AA Lazakidou, Clayton,"" in Data Mining and Knowledge Disco-very in Complex Image Data using Artific-ial Neural Networks", Workshop on Complex Reasoning an Geographical Data, Cyp-rus, 2001.##[2] T. Lillesand, R. W. Kiefer, and J. Chipman, Remote sensing and image interpretation. John Wiley & Sons, 2014.##[3] K. Fukunaga, Introduction to statistical pattern recognition. Academic press, 2013.##[4] H. Frigui and R. Krishnapuram, "A robust competitive clustering algorithm with applica-tions in computer vision," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 21, no. 5, pp. 450-465, 1999.##[5] Y. Leung, J.-S. Zhang, and Z.-B. Xu, "Clustering by scale-space filtering," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, no. 12, pp. 1396-1410, 2000.##[6] A. K. Jain, M. N. Murty, and P. J. Flynn, "Data clustering: a review," ACM computing surveys (CSUR), vol. 31, no. 3, pp. 264-323, 1999.##[7] J. Holland, "Adaption in natural and artificial systems," Ann Arbor MI: The University of Michigan Press, 1975.##[8] S. Z. Selim and K. Alsultan, "A simulated annealing algorithm for the clustering problem," Pattern recognition, vol. 24, no. 10, pp. 1003-1008, 1991.##[9] V. Di Gesú, R. Giancarlo, G. L. Bosco, A. Raimondi, and D. Scaturro, "GenClust: A genetic algorithm for clustering gene expression data," BMC bioinformatics, vol. 6, no. 1, p. 289, 2005.##[10] A. Beg, M. Z. Islam, and V. Estivill-Castro, "Genetic algorithm with healthy population and multiple streams sharing information for clustering," Knowledge-Based Systems, vol. 114, pp. 61-78, 2016.##[11] D. Bajer, G. Martinović, and J. Brest, "A population initialization method for evolution-ary algorithms based on clustering and Cauchy deviates," Expert Systems with Applications, vol. 60, pp. 294-310, 2016.##[12] J. de Andrade Silva, E. R. Hruschka, and J. Gama, "An evolutionary algorithm for cluster-ing data streams with a variable number of clusters," Expert Systems with Applications, vol. 67, pp. 228-238, 2017.##[13] S. Alam, G. Dobbie, and S. U. Rehman, "Analysis of particle swarm optimization based hierarchical data clustering approach-es," Swarm and Evolutionary Computation, vol. 25, pp. 36-51, 2015.##[14] Z. Halim, M. Waqas, and S. F. Hussain, "Clustering large probabilistic graphs using multi-population evolutionary algorithm," Information Sciences, vol. 317, pp. 78-95, 2015.##[15] M. G. Omran, A. Salman, and A. P. Engelbrecht, "Dynamic clustering using parti-cle swarm optimization with application in image segmentation," Pattern Analysis and Applications, vol. 8, no. 4, pp. 332-344, 2006.##[16] S. Bandyopadhyay and U. Maulik, "Genetic clustering for automatic evolution of clusters and application to image classification," Patt-ern Recognition, vol. 35, no. 6, pp. 1197-1208, 2002.##[17] S. Das, A. Abraham, and A. Konar, "Automatic clustering using an improved differential evolution algorithm," Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, vol. 38, no. 1, pp. 218-237, 2008.##[18] S. Saha and S. Bandyopadhyay, "A generaliz-ed automatic clustering algorithm in a multiobjective framework," Applied Soft Computing, vol. 13, no. 1, pp. 89-108, 2013.##[19] A. Konar, Computational intelligence: principles, techniques and applications. Sprin-ger Science & Business Media, 2006.##[20] P. Brucker, "On the complexity of clustering problems," in Optimization and operations research: Springer, 1978, pp. 45-54.##[21] D. L. Davies and D. W. Bouldin, "A cluster separation measure," Pattern Analysis and Machine Intelligence, IEEE Transactions on, no. 2, pp. 224-227, 1979.##[22] C.-H. Chou, M.-C. Su, and E. Lai, "A new cluster validity measure and its application to image compression," Pattern Analysis and Applications, vol. 7, no. 2, pp. 205-220, 2004.##[23] M. K. Pakhira, S. Bandyopadhyay, and U. Maulik, "Validity index for crisp and fuzzy clusters," Pattern recognition, vol. 37, no. 3, pp. 487-501, 2004.##[24] E. Atashpaz-Gargari and C. Lucas, "Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competi-tion," in Evolutionary computation, 2007. CEC 2007. IEEE Congress on, 2007, pp. 4661-4667: IEEE.## ##