1403 21 2 60 104

به‌کارگیری روشهای دادهافزایی برای تحلیل احساسات کاربران درباره بازگشایی مدارس در دوران همه‌گیری کووید-19 Using Data Augmentation Techniques for Sentiment Analysis of Users’ Opinions on Reopening of Schools During the Covid-19 Epidemic 1 1 ازجمله روش‌­های موفق برای تحلیل احساسات، روش­‌های یادگیری باناظر است که با آموزش یک طبقه‌بند بر روی یک مجموعه‎داده آموزشی از نظرات دارای برچسب احساس، یک مدل پیش­‌بینی‌کننده می­‌سازند که قادر است، جملات جدید را طبقه­‌بندی کند. در زبان فارسی، نبود داده‌های آموزشیِ کافی و دقت کم ابزارهای پردازش زبان طبیعی، به‌کارگیری الگوریتم‌های باناظر و نیز استخراج ویژگی‎های باکیفیت را با چالش جدی روبه‎رو ساخته‌است. هدف مقاله حاضر به‌­کارگیری روش‌های یادگیری ماشین باناظر برای طبقه­‌بندی نظرات مطرح‎شده توسط کاربران فارسی زبان در رسانه‌های اجتماعی درباره بازگشایی مدارس در دوران همه‌گیری کووید-19 است. برای غلبه بر مشکل کمبود داده‌­های آموزشی یک روش ترکیبی برای داده‌افزایی پیشنهاد شده‌است که اندازه مجموعه آموزش را حدود 97درصد افزایش می­دهد. نتایج آزمایش‎های انجام‎گرفته نشان می‌دهد که با اعمال روش پیشنهادی برای داده‌افزایی و به‌کارگیری ویژگی‌های انتخابی در این مقاله، به‌ترتیب دقت 81 و 79درصد برای طبقه‌بندی نظرات با استفاده از الگوریتم‌های ماشین بردار پشتیبان و شبکه عصبی پیچشی حاصل می‌شود. 2 Sentiment analysis, also called opinion mining, is one of the sub-areas of natural language processing that aims to classify texts according to the sentiments, beliefs and attitudes expressed in them. In the most current research, texts are divided into two "positive" and "negative" categories. However, there are also other categories such as good/bad" and agree/disagree, every one of which has its applications. The purpose of this paper is to analyze the opinions expressed by users on social media about the reopening of schools during the Covid-19 outbreak using supervised machine learning techniques, and to classify them into two "agree" and "disagree" categories. Users' opinions, in this paper, are in Persian. The lack of sufficient datasets and also the low accuracy of natural language processing tools are the most important problems of text processing in Persian. Due to the mentioned limitations, the use of supervised machine learning algorithms and also the extraction of effective features for training machine learning classifiers in Persian are facing a serious challenge. In this paper, first, a small dataset of the users' opinions about the reopening of schools was collected and manually labeled. Then, a combined method was used for data augmentation of the dataset. In the proposed method, first, Persian sentences were translated into English. Then nouns, verbs and adjectives of the English sentences were replaced with their synonyms. Next, the English sentences were translated into Persian again. The new sentence with the class label of the initial sentence was added to the training set. Thus, the size of the training set increased by 97 percent. After that, the efficiency of employing the common pre-processing steps and using common feature sets in sentiment analysis of the English texts for Persian were evaluated and the best of them were selected. Considering the low accuracy of the Persian natural language processing tools, it was tried to select those features that were less dependent on the tools. Finally, machine learning classification was used to determine agree/disagree class of the user opinions of the test sets. The results of the experiments indicated that by applying the proposed method for data augmentation and using selected features in this paper, 81 and 79 percent precision was obtained for the polarity classification of opinions using SVM and CNN algorithms, respectively. 3 14 2023/06/9 1402/3/19 2024/02/25 1402/12/6 مرضیه میر Marziye Mir دانشجوی کارشناسی ارشد گروه فناوری اطلاعات، دانشکده مهندسی برق و کامپیوتر، دانشگاه سیستان و بلوچستان، زاهدان، ایران ایران marziyemir95@gmail.com سمیرا نوفرستی Samira Noferesti دانشیار گروه فناوری اطلاعات، دانشکده مهندسی برق و کامپیوتر، دانشگاه سیستان و بلوچستان، زاهدان، ایران ایران snoferesti@ece.usb.ac.ir Sentiment Analysis Opinion mining Supervised learning Deep learning Data augmentation Covid-19 تحلیل احساسات نظرکاوی یادگیری باناظر یادگیری عمیق داده‌افزایی کووید-19 Liu, B., "Sentiment analysis and opinion mining", Synthesis lectures on human language technologies, Vol. 5, No. 1, pp. 1-167, 2012.##Ahangari Ahangarkolaei, M., Sebti, A. and Yaghoubi, M., "Automatically generate sentiment lexicon for the Persian stock market", Signal and Data Processing, Vol. 20, No. 2, pp. 3-20, 2023.##Noferesti, S. and Shamsfard, M., "A semantic framework based on domain knowledge for opinion mining of drug reviews", Journal of applied research and technology, Vol. 20, No. 6, pp. 652-667, 2022.##Rajabi, Z., Valavi, M. and Hourali M., "Sentiment analysis methods in Persian text: A survey", Signal and Data Processing, Vol. 19, No. 2, pp. 107-132, 2019.##Catelli, R., Pelosi, S., Comito, C., Pizzuti, C. and Esposito, M., "Lexicon-based sentiment analysis to detect opinions and attitude towards COVID-19 vaccines on Twitter in Italy", Computers in Biology and Medicine, Vol. 158, pp. 106876, 2023.##Huang, M., Xie, H., Rao, Y., Liu, Y., Poon, L. K. and Wang, F. L., "Lexicon-based sentiment convolutional neural networks for online review analysis", IEEE Transactions on Affective Computing, 2020.##Baccianella, S., Esuli, A. and Sebastiani, F., "Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining", LREC. 2010.##Cambria, E., Liu, Q., Decherchi, S., Xing, F. and Kwok, K., "SenticNet 7: A commonsense-based neurosymbolic AI framework for explainable sentiment analysis", In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 3829-3839, 2022.##Dashtipour, K., Hussain, A., Zhou, Q., Gelbukh, A., Hawalah, A.Y. and Cambria, E., "PerSent: A freely available Persian sentiment lexicon", In International Conference on Brain Inspired Cognitive Systems, pp. 310-320, Springer, Cham, 2016.##Sabeti, B., Hosseini, P., Ghassem-Sani, G. and Mirroshandel, S.A., "LexiPers: An ontology based sentiment lexicon for Persian", arXiv preprint arXiv:1911.05263, 2019.##Revathy, G., Alghamdi, S.A., Alahmari, S.M., Yonbawi, S.R., Kumar, A. and Haq, M.A., "Sentiment analysis using machine learning: Progress in the machine intelligence for data science", Sustainable Energy Technologies and Assessments, Vo. 53, pp. 102557, 2022.##Wang, Y., Chen, Q., Shen, J., Hou, B., Ahmed, M. and Li, Z., "Aspect-level sentiment analysis based on gradual machine learning", Knowledge-Based Systems, Vol. 212, pp.106509, 2021.##Riaz, S., Fatima, M., Kamran, M. and Nisar, M.W., "Opinion mining on large scale data using sentiment analysis and k-means clustering", Cluster Computing, Vol. 22, No. 3, pp. 7149-7164, 2019.##Shams, M., Shakery, A. and Faili, H., "A non-parametric LDA-based induction method for sentiment analysis", In The 16th CSI international symposium on artificial intelligence and signal processing (AISP 2012). IEEE, 2012.##Najafzadeh, M., Rahati Quchan, S. and Ghaemi, R., "A Semi-supervised Framework Based on Self-constructed Adaptive Lexicon for Persian Sentiment Analysis", Signal and Data Processing, Vol. 15, No. 2, pp. 89-102, 2018.##Mendon, S., Dutta, P., Behl, A. and Lessmann, S., "A Hybrid approach of machine learning and lexicons to sentiment analysis: enhanced insights from twitter data of natural disasters", Information Systems Frontiers, pp.1-24, 2021.##Ahangari, M. and Sebti, A., "A Hybrid Approach to Sentiment Analysis of Iranian Stock Market User's Opinions", International Journal of Engineering, Vol. 36, No. 3, pp.573-584, 2023.##Imran, A.S., Daudpota, S.M., Kastrati, Z. and Batra, R., "Cross-cultural polarity and emotion detection using sentiment analysis and deep learning on COVID-19 related tweets", IEEE Access, Vol. 8, pp.181074-181090, 2020.##Manguri, K. H., Ramadhan, R. N. and Amin, P. R. M., "Twitter sentiment analysis on worldwide COVID-19 outbreaks", Kurdistan Journal of Applied Research, pp. 54-65, 2020.##Kaur, C. and Sharma, A., "Twitter Sentiment Analysis on Coronavirus using Textblob", EasyChair, 2020.##Ra, M., Ab, B. and Kc, S., "COVID-19 outbreak: Tweet based analysis and visualization towards the influence of coronavirus in the World", 2020.##Costola, M., Hinz, O., Nofer, M. and Pelizzon, L., "Machine learning sentiment analysis, Covid-19 news and stock market reactions", Research in International Business and Finance, pp. 101881, 2023.##Leelawat, N., Jariyapongpaiboon, S., Promjun, A., Boonyarak, S., Saengtabtim, K., Laosunthara, A., Yudha, A.K. and Tang, J., "Twitter data sentiment analysis of tourism in Thailand during the COVID-19 pandemic using machine learning", Heliyon, Vol. 8, No. 10, pp. e10894, 2022.##Miller, G. A., "WordNet: a lexical database for English", Communications of the ACM, Vol. 38, No. 11, pp. 39-41, 1995.##Liu, B., "Sentiment analysis and opinion mining", Synthesis lectures on human language technologies, Vol. 5, No. 1, pp. 1-167, 2012.##Ahangari Ahangarkolaei, M., Sebti, A. and Yaghoubi, M., "Automatically generate sentiment lexicon for the Persian stock market", Signal and Data Processing, Vol. 20, No. 2, pp. 3-20, 2023.##Noferesti, S. and Shamsfard, M., "A semantic framework based on domain knowledge for opinion mining of drug reviews", Journal of applied research and technology, Vol. 20, No. 6, pp. 652-667, 2022.##Rajabi, Z., Valavi, M. and Hourali M., "Sentiment analysis methods in Persian text: A survey", Signal and Data Processing, Vol. 19, No. 2, pp. 107-132, 2019.##Catelli, R., Pelosi, S., Comito, C., Pizzuti, C. and Esposito, M., "Lexicon-based sentiment analysis to detect opinions and attitude towards COVID-19 vaccines on Twitter in Italy", Computers in Biology and Medicine, Vol. 158, pp. 106876, 2023.##Huang, M., Xie, H., Rao, Y., Liu, Y., Poon, L. K. and Wang, F. L., "Lexicon-based sentiment convolutional neural networks for online review analysis", IEEE Transactions on Affective Computing, 2020.##Baccianella, S., Esuli, A. and Sebastiani, F., "Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining", LREC. 2010.##Cambria, E., Liu, Q., Decherchi, S., Xing, F. and Kwok, K., "SenticNet 7: A commonsense-based neurosymbolic AI framework for explainable sentiment analysis", In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 3829-3839, 2022.##Dashtipour, K., Hussain, A., Zhou, Q., Gelbukh, A., Hawalah, A.Y. and Cambria, E., "PerSent: A freely available Persian sentiment lexicon", In International Conference on Brain Inspired Cognitive Systems, pp. 310-320, Springer, Cham, 2016.##Sabeti, B., Hosseini, P., Ghassem-Sani, G. and Mirroshandel, S.A., "LexiPers: An ontology based sentiment lexicon for Persian", arXiv preprint arXiv:1911.05263, 2019.##Revathy, G., Alghamdi, S.A., Alahmari, S.M., Yonbawi, S.R., Kumar, A. and Haq, M.A., "Sentiment analysis using machine learning: Progress in the machine intelligence for data science", Sustainable Energy Technologies and Assessments, Vo. 53, pp. 102557, 2022.##Wang, Y., Chen, Q., Shen, J., Hou, B., Ahmed, M. and Li, Z., "Aspect-level sentiment analysis based on gradual machine learning", Knowledge-Based Systems, Vol. 212, pp.106509, 2021.##Riaz, S., Fatima, M., Kamran, M. and Nisar, M.W., "Opinion mining on large scale data using sentiment analysis and k-means clustering", Cluster Computing, Vol. 22, No. 3, pp. 7149-7164, 2019.##Shams, M., Shakery, A. and Faili, H., "A non-parametric LDA-based induction method for sentiment analysis", In The 16th CSI international symposium on artificial intelligence and signal processing (AISP 2012). IEEE, 2012.##Najafzadeh, M., Rahati Quchan, S. and Ghaemi, R., "A Semi-supervised Framework Based on Self-constructed Adaptive Lexicon for Persian Sentiment Analysis", Signal and Data Processing, Vol. 15, No. 2, pp. 89-102, 2018.##Mendon, S., Dutta, P., Behl, A. and Lessmann, S., "A Hybrid approach of machine learning and lexicons to sentiment analysis: enhanced insights from twitter data of natural disasters", Information Systems Frontiers, pp.1-24, 2021.##Ahangari, M. and Sebti, A., "A Hybrid Approach to Sentiment Analysis of Iranian Stock Market User's Opinions", International Journal of Engineering, Vol. 36, No. 3, pp.573-584, 2023.##Imran, A.S., Daudpota, S.M., Kastrati, Z. and Batra, R., "Cross-cultural polarity and emotion detection using sentiment analysis and deep learning on COVID-19 related tweets", IEEE Access, Vol. 8, pp.181074-181090, 2020.##Manguri, K. H., Ramadhan, R. N. and Amin, P. R. M., "Twitter sentiment analysis on worldwide COVID-19 outbreaks", Kurdistan Journal of Applied Research, pp. 54-65, 2020.##Kaur, C. and Sharma, A., "Twitter Sentiment Analysis on Coronavirus using Textblob", EasyChair, 2020.##Ra, M., Ab, B. and Kc, S., "COVID-19 outbreak: Tweet based analysis and visualization towards the influence of coronavirus in the World", 2020.##Costola, M., Hinz, O., Nofer, M. and Pelizzon, L., "Machine learning sentiment analysis, Covid-19 news and stock market reactions", Research in International Business and Finance, pp. 101881, 2023.##Leelawat, N., Jariyapongpaiboon, S., Promjun, A., Boonyarak, S., Saengtabtim, K., Laosunthara, A., Yudha, A.K. and Tang, J., "Twitter data sentiment analysis of tourism in Thailand during the COVID-19 pandemic using machine learning", Heliyon, Vol. 8, No. 10, pp. e10894, 2022.##Miller, G. A., "WordNet: a lexical database for English", Communications of the ACM, Vol. 38, No. 11, pp. 39-41, 1995.## ##

تشخیص شایعه در شبکه اجتماعی توییتر با استفاده از ویژگی‌های توییت و کاربر Rumor Detection on Twitter using tweet and user features 1 1 با شنیدن هر خبر در شبکه­‌های اجتماعی، واکنش­‌ها به آن متفاوت است و از زوایای مختلف موجب برانگیخته‌شدن حس کنجکاوی می‌شود. مهم­‌ترین بخش آن فهمیدن صحّت‌وسقم خبر است. شایعه، خبری نامعتبر است؛ یعنی هنوز تأیید نشده و ممکن است در صورت نداشتن اعتبار موجب خسارات جبران‌ناپذیری شود؛ ازاین‌رو، تشخیص آن بسیار مهم است. تشخیص شایعه و یا به‌عبارتی مشخص‌کردن اعتبار آن نقش اساسی در جلوگیری از خبر نادرست دارد. در این مقاله، با استفاده از ویژگی­‌های جدید دستی مبتنی بر توییت، کاربر و ترکیبی از این‌ دو و با استفاده از چهار دسته‌­بند یادگیری ماشین، شایعه موجود در شبکه­‌های اجتماعی تشخیص داده شد؛ همچنین با توجه به نامتعادل‌بودن مجموعه‌داده از روش بیش­‌نمونه‌­برداری استفاده و با توجه به تفاوت ویژگی­‌ها از نرمال‌سازی استفاده شده‌است. نتایج نشان‌ داد این روش با وجود سادگی نسبت به روش­‌های یادگیری ماشین و عمیق بهبود قابل ‌توجّهی داشته‌است و مقدار صحّت به 99/0 رسید. 2 When every news item is posted on social media, reactions to it are different and arouse curiosity from different viewpoints. The most important part is to understand the accuracy of the news. A rumor is invalid news, meaning it has not yet been confirmed and it may cause irreparable damage if it is not valid. Therefore, it is very important to detect it. Rumor detection, or in other words, determining its validity, plays an essential role in preventing fake news. Naturally, every phenomenon of normal and anomaly is transmitted to people through social networks. Every News Reactions to that news are different. Depending on the importance of the news, it may be widely covered or it may not have a specific reaction. But if the news spreads widely, it arouses curiosity from different angles. The news is false or true, or the news is valid or invalid. In this work, an attempt was made to identify rumors on social networks by using Hand-Crafted features based on tweets, users and a combination of the two, oversampling and normalization, and by using machine learning classification. Using 4 machine learning classifiers, including Support vector machine, Logistic regression, K-nearest neighbors and Random forest, the two rumors on social networks were detected. Two data sets, PHEME 2017 and PHEME 2018, have been used. The results on these two datasets show that in PHEME 2017, the random forest classifier shows an accuracy of 0.988 using tweet and combination features. Also, these features show a precision of 0.987, which is better than other classifiers used in this work. This classifier has a better recall than other classifiers along with logistic regression with a value of 0.986. Also, this classifier obtained better results with the two mentioned features, with 0.987. In the PHEME 2018 dataset, it obtained the RF classifier with an accuracy of 0.969 using tweet and combination features, and it has better performance in precision, recall and F1. In addition, the user feature in the classifier of k nearest neighbors brings better results than the other two features. 15 28 2023/06/92022/12/3 1401/9/12 2024/02/252024/08/22 1403/6/1 مسلم صامت عمرانی moslem samet omrani کارشناس ارشد هوش مصنوعی دانشکده مهندسی برق و کامپیوتر، دانشگاه تربیت مدرّس، تهران، ایران ایران moslem.ai1983@gmail.com محمد صنیعی آباده mohammad saniee abadeh دانشیار دانشکده مهندسی برق و کامپیوتر، دانشگاه تربیت مدرّس، تهران، ایران ایران saniee@modares.ac.ir نصراله مقدم چرکری nasrollah moghaddam charkari دانشیار دانشکده مهندسی برق و کامپیوتر، دانشگاه تربیت مدرّس، تهران، ایران ایران moghadam@modares.ac.ir rumor detection machine learning user Feature tweet Feature Hand-Crafted Feature تشخیص شایعه یادگیری ماشین ویژگی کاربر ویژگی توییت ویژگی دستی N. Diakopoulos, M. De Choudhury, and M. Naaman, "Finding and assessing social media information sources in the context of journalism," in Proceedings of the SIGCHI conference on human factors in computing systems, 2012, pp. 2451-2460.##A. Hermida, "Twittering the news: The emergence of ambient journalism," Journalism practice, vol. 4, no. 3, pp. 297-308, 2010.##S. Vieweg, "Microblogged contributions to the emergency arena: Discovery, interpretation and implications," Computer Supported Collaborative Work, pp. 515-516, 2010.##A. Zubiaga, A. Aker, K. Bontcheva, M. Liakata, and R. Procter, "Detection and resolution of rumours in social media: A survey," ACM Computing Surveys (CSUR), vol. 51, no. 2, pp. 1-36, 2018.##H. Slimi, I. Bounhas, and Y. Slimani, "TWITTER USERS CREDIBLITY EVALUATION BASED ON SOCIAL GRAPH IMPRESSION," in Proceedings of the 16th International Conference on Applied Computing (AC), Cagliari, Italy, 2019, pp. 11-18.##Y.-Z. Song, Y.-S. Chen, Y.-T. Chang, S.-Y. Weng, and H.-H. Shuai, "Adversary-Aware Rumor Detection," in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 2021, pp. 1371-1382.##N. Hassan, W. Gomaa, G. Khoriba, and M. Haggag, "Credibility detection in twitter using word n-gram analysis and supervised machine learning techniques," International Journal of Intelligent Engineering and Systems, vol. 13, no. 1, pp. 291-300, 2020.##M. R. I. Nawab, K. M. Shahiduzzaman, T. Eng, and M. N. Jamal, "Rumor Detection in Social Media with User Information Protection," European Journal of Electrical Engineering and Computer Science, vol. 4, no. 4, pp. 1-8, 2020.##M. Azer, M. Taha, H. H. Zayed, and M. Gadallah, "Credibility Detection on Twitter News Using Machine Learning Approach," International Journal of Intelligent Systems and Applications, vol. 13, no. 3, pp. 1-10, 2021.##R. Anggrainingsih, G. M. Hassan, and A. Datta, "BERT based classification system for detecting rumours on Twitter," arXiv preprint arXiv:2109.02975, 2021.##X. Yang, Y. Lyu, T. Tian, Y. Liu, Y. Liu, and X. Zhang, "Rumor detection on social media with graph structured adversarial learning," in Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, 2021, pp. 1417-1423.##J. Li, Y. Sujana, and H.-Y. Kao, "Exploiting microblog conversation structures to detect rumors," in Proceedings of the 28th International Conference on Computational Linguistics, 2020, pp. 5420-5429.##J. Ma, W. Gao, S. Joty, and K.-F. Wong, "An attention-based rumor detection model with tree-structured recursive neural networks," ACM Transactions on Intelligent Systems and Technology (TIST), vol. 11, no. 4, pp. 1-28, 2020.##M. Cheng, Y. Li, S. Nazarian, and P. Bogdan, "From rumor to genetic mutation detection with explanations: a GAN approach," Scientific Reports, vol. 11, no. 1, pp. 1-14, 2021.##S. Ni, J. Li, and H.-Y. Kao, "Rumor Detection on Social Media with Hierarchical Adversarial Training," arXiv preprint arXiv:2110.00425, 2021.##J. Li, S. Ni, and H.-Y. Kao, "Meet the truth: Leverage objective facts and subjective views for interpretable rumor detection," arXiv preprint arXiv:2107.10747, 2021.##K. Zhou, C. Shu, B. Li, and J. H. Lau, "Early rumour detection," in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 2019, pp. 1614-1623.##M. Z. Asghar, A. Habib, A. Habib, A. Khan, R. Ali, and A. Khattak, "Exploring deep neural networks for rumor detection," Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 4, pp. 4315-4333, 2021.##J. Ma, W. Gao, and K.-F. Wong, "Detect rumors on twitter by promoting information campaigns with generative adversarial learning," in The world wide web conference, 2019, pp. 3049-3055.##L. M. S. Khoo, H. L. Chieu, Z. Qian, and J. Jiang, "Interpretable rumor detection in microblogs by attending to user interactions," in Proceedings of the AAAI Conference on Artificial Intelligence, 2020, vol. 34, no. 05, pp. 8783-8790.##F. Xu, V. S. Sheng, and M. Wang, "Near real-time topic-driven rumor detection in source microblogs," Knowledge-Based Systems, vol. 207, p. 106391, 2020.##S. A. Alkhodair, S. H. Ding, B. C. Fung, and J. Liu, "Detecting breaking news rumors of emerging topics in social media," Information Processing & Management, vol. 57, no. 2, p. 102018, 2020.##Y. Sujana, J. Li, and H.-Y. Kao, "Rumor detection on Twitter using multiloss hierarchical BiLSTM with an attenuation factor," arXiv preprint arXiv:2011.00259, 2020.##جهانبخش نقده زلیخا، فیضی درخشی محمد رضا، شریفی آرش. «ارائه مدلی برای تشخیص شایعات فارسی مبتنی بر تحلیل ویژگی‌های محتوایی در متن شبکه‌های اجتماعی»، پردازش علائم و داده‌ها، ۱۴۰۰; ۱۸ (۱) :۵۰-۲۹##Z. Jahanbakhsh-Nagadeh, M.-R. Feizi-Derakhshi, and A. Sharifi, "A Model for Detecting of Persian Rumors based on the Analysis of Contextual Features in the Content of Social Networks," (in eng), Signal and Data Processing, Research vol. 18, no. 1, pp. 50-29, 2021, doi: 10.52547/jsdp.18.1.50.##N. Diakopoulos, M. De Choudhury, and M. Naaman, "Finding and assessing social media information sources in the context of journalism," in Proceedings of the SIGCHI conference on human factors in computing systems, 2012, pp. 2451-2460.##A. Hermida, "Twittering the news: The emergence of ambient journalism," Journalism practice, vol. 4, no. 3, pp. 297-308, 2010.##S. Vieweg, "Microblogged contributions to the emergency arena: Discovery, interpretation and implications," Computer Supported Collaborative Work, pp. 515-516, 2010.##A. Zubiaga, A. Aker, K. Bontcheva, M. Liakata, and R. Procter, "Detection and resolution of rumours in social media: A survey," ACM Computing Surveys (CSUR), vol. 51, no. 2, pp. 1-36, 2018.##H. Slimi, I. Bounhas, and Y. Slimani, "TWITTER USERS CREDIBLITY EVALUATION BASED ON SOCIAL GRAPH IMPRESSION," in Proceedings of the 16th International Conference on Applied Computing (AC), Cagliari, Italy, 2019, pp. 11-18.##Y.-Z. Song, Y.-S. Chen, Y.-T. Chang, S.-Y. Weng, and H.-H. Shuai, "Adversary-Aware Rumor Detection," in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 2021, pp. 1371-1382.##N. Hassan, W. Gomaa, G. Khoriba, and M. Haggag, "Credibility detection in twitter using word n-gram analysis and supervised machine learning techniques," International Journal of Intelligent Engineering and Systems, vol. 13, no. 1, pp. 291-300, 2020.##M. R. I. Nawab, K. M. Shahiduzzaman, T. Eng, and M. N. Jamal, "Rumor Detection in Social Media with User Information Protection," European Journal of Electrical Engineering and Computer Science, vol. 4, no. 4, pp. 1-8, 2020.##M. Azer, M. Taha, H. H. Zayed, and M. Gadallah, "Credibility Detection on Twitter News Using Machine Learning Approach," International Journal of Intelligent Systems and Applications, vol. 13, no. 3, pp. 1-10, 2021.##R. Anggrainingsih, G. M. Hassan, and A. Datta, "BERT based classification system for detecting rumours on Twitter," arXiv preprint arXiv:2109.02975, 2021.##X. Yang, Y. Lyu, T. Tian, Y. Liu, Y. Liu, and X. Zhang, "Rumor detection on social media with graph structured adversarial learning," in Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, 2021, pp. 1417-1423.##J. Li, Y. Sujana, and H.-Y. Kao, "Exploiting microblog conversation structures to detect rumors," in Proceedings of the 28th International Conference on Computational Linguistics, 2020, pp. 5420-5429.##J. Ma, W. Gao, S. Joty, and K.-F. Wong, "An attention-based rumor detection model with tree-structured recursive neural networks," ACM Transactions on Intelligent Systems and Technology (TIST), vol. 11, no. 4, pp. 1-28, 2020.##M. Cheng, Y. Li, S. Nazarian, and P. Bogdan, "From rumor to genetic mutation detection with explanations: a GAN approach," Scientific Reports, vol. 11, no. 1, pp. 1-14, 2021.##S. Ni, J. Li, and H.-Y. Kao, "Rumor Detection on Social Media with Hierarchical Adversarial Training," arXiv preprint arXiv:2110.00425, 2021.##J. Li, S. Ni, and H.-Y. Kao, "Meet the truth: Leverage objective facts and subjective views for interpretable rumor detection," arXiv preprint arXiv:2107.10747, 2021.##K. Zhou, C. Shu, B. Li, and J. H. Lau, "Early rumour detection," in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 2019, pp. 1614-1623.##M. Z. Asghar, A. Habib, A. Habib, A. Khan, R. Ali, and A. Khattak, "Exploring deep neural networks for rumor detection," Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 4, pp. 4315-4333, 2021.##J. Ma, W. Gao, and K.-F. Wong, "Detect rumors on twitter by promoting information campaigns with generative adversarial learning," in The world wide web conference, 2019, pp. 3049-3055.##L. M. S. Khoo, H. L. Chieu, Z. Qian, and J. Jiang, "Interpretable rumor detection in microblogs by attending to user interactions," in Proceedings of the AAAI Conference on Artificial Intelligence, 2020, vol. 34, no. 05, pp. 8783-8790.##F. Xu, V. S. Sheng, and M. Wang, "Near real-time topic-driven rumor detection in source microblogs," Knowledge-Based Systems, vol. 207, p. 106391, 2020.##S. A. Alkhodair, S. H. Ding, B. C. Fung, and J. Liu, "Detecting breaking news rumors of emerging topics in social media," Information Processing & Management, vol. 57, no. 2, p. 102018, 2020.##Y. Sujana, J. Li, and H.-Y. Kao, "Rumor detection on Twitter using multiloss hierarchical BiLSTM with an attenuation factor," arXiv preprint arXiv:2011.00259, 2020.##جهانبخش نقده زلیخا، فیضی درخشی محمد رضا، شریفی آرش. «ارائه مدلی برای تشخیص شایعات فارسی مبتنی بر تحلیل ویژگی‌های محتوایی در متن شبکه‌های اجتماعی»، پردازش علائم و داده‌ها، ۱۴۰۰; ۱۸ (۱) :۵۰-۲۹##Z. Jahanbakhsh-Nagadeh, M.-R. Feizi-Derakhshi, and A. Sharifi, "A Model for Detecting of Persian Rumors based on the Analysis of Contextual Features in the Content of Social Networks," (in eng), Signal and Data Processing, Research vol. 18, no. 1, pp. 50-29, 2021, doi: 10.52547/jsdp.18.1.50.## ##

پیاده‌سازی روشی برای مقابله با حمله تحلیل ‌توان بر روی الگوریتم مک‌الیس Implementation of a countermeasure method against DPA on McEliece Post Quantum Cryptosystem 1 1 یکی از مهم‌ترین مسائل امنیتی پیش‌رو، ظهور رایانه‌های کوانتومی و شکسته‌شدن الگوریتم‌های رمزنگاری کنونی است؛ ازاین‌رو توجه به الگوریتم‌های رمزنگاری پسا‌کوانتوم و بررسی روش‌های پیاده‌سازی و حملات ارائه‌شده روی آن‌ها موضوع مهمی است. یکی از چالش‌های پیاده‌سازی موجود در این دسته از الگوریتم‌ها طول کلید زیاد است که برای پیاده‌سازی روی سامانه‌های تعبیه‌شده[1] از اهمیت زیادی برخوردار است؛ ازطرفی مقابله با حملات کانال جانبی که ناشی از نشت اطلاعات از پیاده‎‌سازی سخت‌افزاری است، نیز یکی از چالش‌های موجود است. در این‌ مقاله، با تمرکز بر روی الگوریتم پساکوانتومی رمزنگاری مبتنی‌برکد QC-MDPC[2] مک‌الیس و با استفاده از روشی نوین در پیاده‌سازی، طول کلید نگه‌داری‌شده در سخت‌افزار برای تأمین امنیت هشتادبیتی از 1200 بایت به 180 بایت کاهش یافته‌است؛ همچنین با استفاده از روش پیاده‌سازی پوشانه‌گذاری آستانه[3]، با حمله تحلیل تفاضلی توان[4] مقابله و نشت‌های اطلاعاتی موجود در پیاده‌سازی‌های قبلی رفع شده‌است.   [1] embedded system [2] Quasi Cyclic Moderate Density Parity Check [3] Threshold Implementation (TI) Masking [4] Differential Power Analysis (DPA) 2 In recent years, embedded systems have continuously gained importance. This ubiquity is accompanied by an increased need for embedded security. Cryptography can address these security requirements. Many symmetric and asymmetric algorithms, such as AES, DES, RSA, ElGamal, and ECC, have been implemented on embedded devices. All frequently implemented public-key cryptosystems rely on the presumed hardness of either factoring the product of two large primes (FP) or computing discrete logarithms (DLP). These two problems are closely related. Therefore, solving these problems would have significant ramifications for classical public-key cryptography and, consequently, for all embedded devices that utilize these algorithms. Currently, both problems are believed to be computationally infeasible with a conventional computer. However, a quantum computer capable of performing computations on a few thousand qubits could solve both problems using Shor's algorithm[1]. Although a quantum computer of this scale has not been reported, it could become a reality within the next one to three decades. Consequently, the development and cryptanalysis of alternative post-quantum cryptosystems are crucial. Post-quantum cryptosystems refer to cryptosystems that are not susceptible to the critical security loss or complete compromise caused by quantum computers. One of the major security challenges is the development of quantum computers and the potential compromise of current cryptosystems in the future. Therefore, it is essential to consider post-quantum cryptosystem algorithms and the challenges of implementing and attacking them. Post-quantum cryptosystems encompass various types, including hash-based cryptography, multivariate-quadratic-equations cryptography, lattice-based cryptography, and code-based cryptography. In this study, our focus is on the QC-MDPC McEliece code-based algorithm. Post-quantum public keys must be designed to gain popularity in practice; they should be optimized for implementation and efficient in execution. McEliece encryption and decryption do not require computationally expensive processing, making it more suitable for implementation[2]. One of the implementation challenges for these algorithms is the large key length, which poses an important issue for implementation on embedded systems. Additionally, countering side-channel attacks caused by information leakage from hardware equipment is crucial. We have addressed this by reducing the key length from 1200 bytes to 180 bytes, providing 80-bit security, and introducing a new method for implementing the QC-MDPC McEliece cryptosystem. Differential power analysis attacks (DPA) exploit the relationship between power consumption and intermediate data to recover the key. In this study, we have used a masking technique for multiplication in the finite field in the syndrome computation part of the decryption algorithm. We have implemented the Threshold Implementation (TI) masking countermeasure for DPA to eliminate information leaks from the previous implementation. 29 42 2023/06/92022/12/32021/04/14 1400/1/25 2024/02/252024/08/222024/02/25 1402/12/6 زینب حاج حسینی Zeinab Haj-Hosseini کارشناس‌ارشد مهندسی فناوری اطلاعات، دانشگاه شاهد، تهران، ایران ایران zh_72_zh@yahoo.com محمدعلی دوستاری Mohammad-Ali Doostari استادیار گروه مهندسی کامپیوتر، دانشگاه شاهد، تهران، ایران ایران doostari@shahed.ac.ir حامد یوسفی Hamed Yusefi دانشجوی دکتری مهندسی الکترونیک دانشگاه شاهد و پژوهشگر پژوهشگاه توسعه فناوری‌‌های پیشرفته خواجه نصیرالدین‌طوسی، تهران، ایران ایران h.yusefi@rcdat.ir Post-Quantum Cryptosystem DPA McEliece QC-MDPC Codes رمزنگاری پسا‌کوانتومی تحلیل تفاضلی توان الگوریتم مک‌الیس کدهای QC-MDPC P. Shor, "Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer", SIAM Journal on Computing, vol. 26, no. 5, pp. 1484-1509, 1997.##S. Heyse, "Post-quantum cryptography: Implementing alternative public key schemes on embedded devices", PhD thesis, Ruhr-University Bochum, 2013.##R. McEliece, "A Public-Key Cryptosystem Based On Algebraic Coding Theory", Deep Space Network Progress Report, vol. 44, pp. 114-116, 1978.##T. Berson, "Failure of the McEliece public-key cryptosystem under message-resend and related-message attack", Advances in Cryptology - CRYPTO '97, pp. 213-220, 1997.##R. Misoczki, J. Tillich, N. Sendrier and P. Barreto, "MDPC-McEliece: New McEliece variants from Moderate Density Parity-Check codes", IEEE International Symposium on Information Theory, 2013.##S. Mangard, E. Oswald and T. Popp, Power analysis attacks: Revealing the secrets of smart cards, Springer US, 2008.##حامد یوسفی، محمود گردشی، محمد سبزی‌نژاد، «پیاده سازی حملۀ تحلیل توان ساده به الگوریتم AES روی میکروکنترلر PIC»، پردازش علائم و داده‌ها، دوره ۹، شماره ۱، 1391.##P. Kocher, J. Jaffe and B. Jun, "Differential Power Analysis", Advances in Cryptology - CRYPTO' 99, pp. 388-397, 1999.##E. Brier, C. Clavier and F. Olivier, "Correlation Power Analysis with a Leakage Model", Lecture Notes in Computer Science, pp. 16-29, 2004.##M. Masoumi and M. Ahmadian, "A practical differential power analysis attack against an FPGA implementation of AES cryptosystem", Ieeexplore.ieee.org, 2010. [Online]. Available: https://ieeexplore.ieee.org/document/6018719. [Accessed: 13- Jan- 2020].##P. Kocher, "Design and validation strategies for obtaining assurance in countermeasures to power analysis and related attacks", Proceedings of the NIST Physical Security Workshop, 2005.##Y. Ishai, A. Sahai and D. Wagner, "Private Circuits: Securing Hardware against Probing Attacks", Advances in Cryptology - CRYPTO 2003, pp. 463-481, 2003.##I. von Maurich and T. Güneysu, "Towards Side-Channel Resistant Implementations of QC-MDPC McEliece Encryption on Constrained Devices", Post-Quantum Cryptography, pp. 266-282, 2014.##C. Chen, T. Eisenbarth, I. von Maurich and R. Steinwandt, "Masking Large Keys in Hardware: A Masked Implementation of McEliece", Lecture Notes in Computer Science, pp. 293-309, 2016.##C. Chen, T. Eisenbarth, I. von Maurich and R. Steinwandt, "Horizontal and Vertical Side Channel Analysis of a McEliece Cryptosystem", IEEE Transactions on Information Forensics and Security, vol. 11, no. 6, pp. 1093-1105, 2016.##S. Belaïd, F. Benhamouda, A. Passelègue, E. Prouff, A. Thillard and D. Vergnaud, "Private Multiplication over Finite Fields", Advances in Cryptology - CRYPTO 2017, pp. 397-426,##P. Shor, "Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer", SIAM Journal on Computing, vol. 26, no. 5, pp. 1484-1509, 1997.##S. Heyse, "Post-quantum cryptography: Implementing alternative public key schemes on embedded devices", PhD thesis, Ruhr-University Bochum, 2013.##R. McEliece, "A Public-Key Cryptosystem Based On Algebraic Coding Theory", Deep Space Network Progress Report, vol. 44, pp. 114-116, 1978.##T. Berson, "Failure of the McEliece public-key cryptosystem under message-resend and related-message attack", Advances in Cryptology - CRYPTO '97, pp. 213-220, 1997.##R. Misoczki, J. Tillich, N. Sendrier and P. Barreto, "MDPC-McEliece: New McEliece variants from Moderate Density Parity-Check codes", IEEE International Symposium on Information Theory, 2013.##S. Mangard, E. Oswald and T. Popp, Power analysis attacks: Revealing the secrets of smart cards, Springer US, 2008.##حامد یوسفی، محمود گردشی، محمد سبزی‌نژاد، «پیاده سازی حملۀ تحلیل توان ساده به الگوریتم AES روی میکروکنترلر PIC»، پردازش علائم و داده‌ها، دوره ۹، شماره ۱، 1391.##P. Kocher, J. Jaffe and B. Jun, "Differential Power Analysis", Advances in Cryptology - CRYPTO' 99, pp. 388-397, 1999.##E. Brier, C. Clavier and F. Olivier, "Correlation Power Analysis with a Leakage Model", Lecture Notes in Computer Science, pp. 16-29, 2004.##M. Masoumi and M. Ahmadian, "A practical differential power analysis attack against an FPGA implementation of AES cryptosystem", Ieeexplore.ieee.org, 2010. [Online]. Available: https://ieeexplore.ieee.org/document/6018719. [Accessed: 13- Jan- 2020].##P. Kocher, "Design and validation strategies for obtaining assurance in countermeasures to power analysis and related attacks", Proceedings of the NIST Physical Security Workshop, 2005.##Y. Ishai, A. Sahai and D. Wagner, "Private Circuits: Securing Hardware against Probing Attacks", Advances in Cryptology - CRYPTO 2003, pp. 463-481, 2003.##I. von Maurich and T. Güneysu, "Towards Side-Channel Resistant Implementations of QC-MDPC McEliece Encryption on Constrained Devices", Post-Quantum Cryptography, pp. 266-282, 2014.##C. Chen, T. Eisenbarth, I. von Maurich and R. Steinwandt, "Masking Large Keys in Hardware: A Masked Implementation of McEliece", Lecture Notes in Computer Science, pp. 293-309, 2016.##C. Chen, T. Eisenbarth, I. von Maurich and R. Steinwandt, "Horizontal and Vertical Side Channel Analysis of a McEliece Cryptosystem", IEEE Transactions on Information Forensics and Security, vol. 11, no. 6, pp. 1093-1105, 2016.##S. Belaïd, F. Benhamouda, A. Passelègue, E. Prouff, A. Thillard and D. Vergnaud, "Private Multiplication over Finite Fields", Advances in Cryptology - CRYPTO 2017, pp. 397-426,## ##

طبقه‌بندی فیبریلاسیون دهلیزی با استفاده از الگوریتمPiCA-ESN و تبدیل Stockwell Atrial Fibrillation Classification Using PiCA-ESN Algorithm and Stockwell Transform 1 1 فیبریلاسیون دهلیزی (AF) شایع‌ترین نوع آریتمی قلبی است که با ایجاد بی‌نظمی در ضربان قلب همراه است. مطالعات بالینی نشان می‌دهند که بیماران مبتلا به AF اغلب علائم قابل مشاهده‌ای درطول فیبریلاسیون دهلیزی ندارند؛ ازاین‌رو، تشخیص این بیماری برای پزشک متخصص دشوار است. تشخیص خودکار AF با استفاده از سیگنال‌ الکتروکاردیوگرام (سیگنال ECG) به پزشک معالج در تشخیص کمک بسزایی می‌کند و خطر ابتلا به بیمارهای حاد قلبی و سکته مغزی را کاهش می‌دهد. در این مقاله، رویکردی جدید به‌منظور استخراج سیگنال‌ فیبریلاتور دهلیزی (موج f) از سیگنال‌ ECG با استفاده از الگوریتم PiCA-ESN و آنالیز زمان- فرکانس آن توسط تبدیل Stockwell برای طبقه‌بندی AF پایان‌یافته و پایان‌نیافته پیشنهاد شده‌است. ابتدا سیگنال‌ فیبریلاتور دهلیزی (موج f) از سیگنال ECG استخراج شده و پس از آن تصویر زمان-فرکانس موج f با استفاده از تبدیل Stockwell به‌دست می‌آید؛ در ادامه، دامنه تصویر زمان-فرکانس Stockwell توسط ویژگی‌های مختلف بازتوصیف و به‌ترکیب سه طبقه‌بند اعمال می‌شود. به‌علاوه در این مطالعه آزمایش جدیدی برای بررسی پایداری ویژگی‌ها درطول سیگنال پیشنهاد شده‌است. این مطالعه بر روی پایگاه ‌داده فیزیونت پیاده‌‌سازی شده‌ و نتایج به‌دست‌آمده نشان می‌دهند که روش پیشنهادی عملکرد بهتری در مقایسه با روش‌های برگزیده چالش فیزیونت با دستیابی به‌دقت 100درصد دارد. 2 Atrial fibrillation (AF) is a prevalent cardiac arrhythmia characterized by irregular heartbeats, often without noticeable symptoms in patients. Diagnosing AF is challenging for cardiologists, requiring advanced methods for accurate identification using electrocardiogram (ECG) signals. Automated AF diagnosis can significantly aid cardiologists in prompt identification, potentially reducing the risks associated with acute heart disease and stroke. Various non-invasive techniques based on ECG signal processing have been suggested to better understand the mechanisms by analyzing the atrial fibrillatory waves (f-waves). Different signal processing methods for f-wave extraction have been explored, which may be classified as follows: average beat subtraction and its advanced variants, QT-interval interpolation, principal and independent component analysis, nonlinear adaptive filtering using an echo state network, diffusion geometry, and extended Kalman filtering. This study aims to extract the f-wave from the ECG signal using the PiCA-ESN algorithm, which yields better results compared to other methods. Additionally, the f-wave's time-frequency behavior was analyzed using the Stockwell transform to differentiate between terminated and non-terminated AF states for the first time in this study. First, the PiCA-ESN algorithm facilitated the extraction of the f-wave from the ECG signal. Subsequently, the Stockwell transform was used to compute the time-frequency maps of the extracted f-wave. Various features were derived from the amplitude of the Stockwell transform and utilized in conjunction with three classifiers: MLP, SVM, and AdaBoost. The findings reveal that the proposed method outperforms selected methodologies from the Physionet Challenge 2004, achieving an impressive 100% accuracy in both tasks. Additionally, an experiment was conducted to assess the robustness of the proposed features across consecutive signal segments, validating their stability during signal analysis. 43 54 2023/06/92022/12/32021/04/142023/04/7 1402/1/18 2024/02/252024/08/222024/02/252024/08/22 1403/6/1 سارا میهن دوست sara mihandoost استادیار گروه مهندسی برق، دانشکده فناوری‌های صنعتی، دانشگاه صنعتی ارومیه، ارومیه، ایران ایران s.mihandoost@uut.ac.ir Electroencephalogram (ECG) Atrial fibrilation (AF) f-wave PiCA-ESN algorithm Stockwell transform (S transform) سیگنال الکتروکاردیوگرام(ECG) فیبریلاسیون دهلیزی(AF) موجf الگوریتمPiCA-ESN تبدیل Stockwell(تبدیل S) S. S. Chugh, J. L. Blackshear, W. K. Shen, S. C. Hammill, S. C, and B. J. Gersh, "Epidemiology and natural history of atrial fibrillation: clinical implications", Journal of the American College of Cardiology, vol. 37, no. 2, pp. 371-378, February 2001.##W. M. Feinberg, E. S. Cornell, S. D. Nightingale, L. A. Pearce, R. P. Tracy, R. G. Hart, and E. G. Bovill, "Relationship between prothrombin activation fragment F1. 2 and international normalized ratio in patients with atrial fibrillation", Stroke, vol. 28, no. 6, pp. 1101-1106, 1997.##S. Mihandoost, L. Sörnmo, M. Doyen, and J. Oster, "A comparative study of the performance of methods for f-wave extraction", Physiological Measurement, vol. 43, no. 10, pp. 105006, 2022.##F. Nilsson, M. Stridh, A. Bollmann, and L. Sornmo, "Predicting spontaneous termination of atrial fibrillation with time-frequency information", Computers in Cardiology, pp. 657-660, 2004.##S. S. Chugh, J. L. Blackshear, W. K. Shen, S. C. Hammill, S. C, and B. J. Gersh, "Epidemiology and natural history of atrial fibrillation: clinical implications", Journal of the American College of Cardiology, vol. 37, no. 2, pp. 371-378, 2001.##W. M. Feinberg, E. S. Cornell, S. D. Nightingale, L. A. Pearce, R. P. Tracy, R. G. Hart, and E. G. Bovill, "Relationship between prothrombin activation fragment F1. 2 and international normalized ratio in patients with atrial fibrillation", Stroke, vol. 28, no. 6, pp. 1101-1106, 1997.##P. Bonizzi, M. Stridh, L. Sörnmo L, and O. Meste, "Ventricular activity residual reduction in remainder ECGs based on short-term autoregressive model interpolation", Annual Computers in Cardiology Conference (CinC), vol. 36, pp. 813-816, 2009.##J. Slocum, A. Sahakian, and S. Swiryn, "Diagnosis of atrial fibrillation from surface electrocardiograms based on computer-detected atrial activity", Journal of electrocardiology, vol. 25, no. 1, pp. 1-8, 1992.##M. Stridh, and L. Sörnmo, "Spatiotemporal QRST cancellation techniques for analysis of atrial fibrillation", IEEE Transactions on Biomedical Engineering, vol. 48, no. 1, pp. 105-111, 2001.##J. Behar, J. Oster, and G. D. Clifford, "Combining and benchmarking methods of foetal ECG extraction without maternal or scalp electrode data", Physiological measurement, vol. 35, no. 8, pp.1569-1589, 2014.##H. Jaeger, "The "echo state" approach to analysing and training recurrent neural networks-with an erratum note", Bonn, Germany: German National Research Center for Information Technology, GMD Technical Report, vol. 148, no. 34, pp. 13, 2001.##A. Petrėnas, V. Marozas, L. Sörnmo, and A. Lukosevicius, "An echo state neural network for QRST cancellation during atrial fibrillation", IEEE Transactions on Biomedical Engineering, vol. 59, no. 10, pp, 2950-2957, 2012.##F. Castells, C. Mora, J. J. Rieta, D. Moratal-Pérez, and J. Millet, "Estimation of atrial fibrillatory wave from single-lead atrial fibrillation electrocardiograms using principal component analysis concepts", Medical and biological Engineering and Computing, vol. 43, no. 5, pp. 557-560, 2005.##R. Alcaraz, and J. J. Rieta, "Adaptive singular value cancellation of ventricular activity in single-lead atrial fibrillation electrocardiograms", Physiological measurement, vol. 29, no. 12, pp. 1351-1369, 2008.##R. Llinares, and J. Igual, "Application of constrained independent component analysis algorithms in electrocardiogram arrhythmias", Artif Intell Med, vol. 47, no. 2, pp, 121-133, 2009.##J. Malik, N. Reed, C. L. Wang, and H. T. Wu, "Single-lead f-wave extraction using diffusion geometry", Physiological measurement, vol. 38, no. 7, pp. 1310-1334, 2017.##[E. K. Roonizi, and R. Sassi, "An extended Bayesian framework for atrial and ventricular activity separation in atrial fibrillation", IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 6, pp. 1573-1580, 2016.##P. Langley, J. P. Bourke, and A. Murray, "Frequency analysis of atrial fibrillation", In Computers in Cardiology IEEE, pp. 65-68, 2000.##M. Stridh, and L. Sornmo, "Time-frequency characterization of atrial arrhythmias using principal decomposition", In 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol.2, pp. 1808-1811, 2001.##M. Stridh, L. Sornmo, C. J. Meurling, and S. B. Olsson, "Sequential characterization of atrial tachyarrhythmias based on ECG time-frequency analysis", IEEE Transactions on Biomedical Engineering, vol. 5, pp.100-114, 2004.##V. Zarzoso, D G. Latcu, A R Hidalgo-Muñoz, M. Meo, O. Meste, I. Popescu, and N. Saoudi, "Non-invasive prediction of catheter ablation outcome in persistent atrial fibrillation by fibrillatory wave amplitude computation in multiple electrocardiogram leads", Archives of cardiovascular diseases, vol. 109, no. 12, pp. 679-688, 2016.##R.K. Tripathy, M.R. Paternina, J.G. Arrieta, and P. Pattanaik, "Automated detection of atrial fibrillation ecg signals using two stage vmd and atrial fibrillation diagnosis index", Journal of Mechanics in Medicine and Biology, vol. 17, no. 7, pp. 1740044, 2017##R. J. Martis, U. R. Acharya, H. Prasad, C. K .Chua, and C. M. Lim, "Automated detectionof atrial fibrillation using bayesian paradigm", Knowledge-Based Systems, vol. 54, pp. 269-275, 2017.##A. Nguyen, S. Ansari, M. Hooshmand, K. Lin, H. Ghanbari, J. Gryak, and K. Najarian, "Comparative study on heart rate variability analysis for atrial fibrillation detection in short single-lead ecg recordings", In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 526-529, 2018.##S. Cerutti, L. Mainardi, A. Porta, and A. Bianchi, "Analysis of the dynamics of RR interval series for the detection of atrial fibrillation episodes", Computers in Cardiology IEEE, pp. 77-80, 1997.##T. Radhakrishnan, J. Karhade, S. K. Ghosh, P. R. Muduli, R. K. Tripathy, and U. R. Acharya, " AFCNNet: Automated detection of AF using chirplet transform and deep convolutional bidirectional long short term memory network with ECG signals", Computers in Biology and Medicine, vol. 137, pp.104783, 2021.##R. G. Stockwell, L. Mansinha, and R. Lowe, "Localization of the complex spectrum: The S-transform", IEEE Transaction on Signal Processing, vol. 44, pp. 998-100, 1996.##[GB. Moody, "Spontaneous Termination of Atrial Fibrillation" PhysioNet and Computers in Cardiology 2004, Apr. 11, 2004. [Online]. Available: https://physionet.org/content/aftdb/1.0.0/. [Accessed: Sept. 10, 2024].##G. Moody, "Spontaneous termination of atrial fibrillation: a challenge from physionet and computers in cardiology 2004", Computers in Cardiology, pp. 101-104, 2004.##R. Sameni, C. Jutten, and M. B. Shamsollahi, "Multichannel electrocardiogram decomposition using periodic component analysis", IEEE Transactions on Biomedical Engineering, vol. 55, no. 8, pp. 1935-1940, 2008.##J. Pan, and W. J. Tompkins, "A real-time QRS detection algorithm", IEEE Transactions on Biomedical Engineering, vol. 32, pp. 230-236, 1985.##A. Petrėnas, and M. Vaidotas, Atrial fibrillation from an engineering perspective, Edited by Leif Sörnmo, Berlin: Springer, 2018.##H. Hazeri, P. Zarjam, and G. Azemi, "Classification of normal/abnormal PCG recordings using a time-frequency approach", Analog Integrated Circuits and Signal Processing, vol. 109, no. 2 ,pp. 459-465, 2021.##M. Azimi Far, S.Nejatian, H. Parvin, K. Bagheri Fard, and V. Rezaei, "Predicting cardiac arrhythmia on ECG signal using an ensemble of optimal multicore support vector machines", Signal and Data Processing, vol. 19, no. 3, pp. 65-86, 2022.##عظیمی‌فر، مریم، نجاتیان، پروین، باقری فرد، کرم الله، رضایی وحیده، «پیش‌بینی آریتمی قلبی در سیگنال ECG با به‌کارگیری مجمعی از ماشین‌های بردار پشتیبان چندهسته‌ای بهینه»٬ پردازش علائم و داده‌ها، دوره ۱۹، شماره ۳، صفحات 65-86، ۱۴۰۱.##S. Petrutiu, A. V. Sahakian, and J. Ng, S. Swiryn, "Analysis of the surface electrocardiogram to predict termination of atrial fibrillation: the 2004 computers in cardiology/physionet challenge", In Computers in Cardiology, 2004, pp. 105-108.##D. Hayn, K. Edegger, D. Scherr, P. Lercher, B. Rotman, W. Klein, and G. Schreier, "Automated prediction of spontaneous termination of atrial fibrillation from electrocardiograms", In Computers in Cardiology 2004, 2004, pp. 117-120.##F. Cantini, F. Conforti, M. Varanini, F. Chiarugi, and G. Vrouchos, "Predicting the end of an atrial fibrillation episode: the physionet challenge", In Computers in Cardiology 2004, 2004, pp. 121-124.##M. Lemay, Z. Ihara, J. M. Vesin, and L. Kappenberger. "Computers in cardiology/physionet challenge 2004: AF classification based on clinical features", In Computers in Cardiology 2004, pp. 669-672, 2004.##B. Logan, and J. Healey, "Detection of spontaneous termination of atrial fibrillation", In Computers in Cardiology 2004, pp. 653-656, 2004.##Q. Xi, and S. Shkurovich, "Prediction of spontaneous termination of atrial fibrillation in surface ECG by frequency analysis", In Computers in Cardiology 2004, pp. 113-116, 2004.##L. Liu, F. Liu, X. Ren, Y. Li, B. Han, L. Zhang, S. Wei, "Predicting spontaneous termination of atrial fibrillation based on dual path network and feature selection", Biomedical Signal Processing and Control, 88, pp. 105606, 2024.##N. Ganapathy, D. Baumgärtel, and T. M. Deserno, "Automatic detection of atrial fibrillation in ECG using co-occurrence patterns of dynamic symbol assignment and machine learning". Sensors, vol. 21, no. 10, pp. 3542, 2021.##S. S. Chugh, J. L. Blackshear, W. K. Shen, S. C. Hammill, S. C, and B. J. Gersh, "Epidemiology and natural history of atrial fibrillation: clinical implications", Journal of the American College of Cardiology, vol. 37, no. 2, pp. 371-378, February 2001.##W. M. Feinberg, E. S. Cornell, S. D. Nightingale, L. A. Pearce, R. P. Tracy, R. G. Hart, and E. G. Bovill, "Relationship between prothrombin activation fragment F1. 2 and international normalized ratio in patients with atrial fibrillation", Stroke, vol. 28, no. 6, pp. 1101-1106, 1997.##S. Mihandoost, L. Sörnmo, M. Doyen, and J. Oster, "A comparative study of the performance of methods for f-wave extraction", Physiological Measurement, vol. 43, no. 10, pp. 105006, 2022.##F. Nilsson, M. Stridh, A. Bollmann, and L. Sornmo, "Predicting spontaneous termination of atrial fibrillation with time-frequency information", Computers in Cardiology, pp. 657-660, 2004.##S. S. Chugh, J. L. Blackshear, W. K. Shen, S. C. Hammill, S. C, and B. J. Gersh, "Epidemiology and natural history of atrial fibrillation: clinical implications", Journal of the American College of Cardiology, vol. 37, no. 2, pp. 371-378, 2001.##W. M. Feinberg, E. S. Cornell, S. D. Nightingale, L. A. Pearce, R. P. Tracy, R. G. Hart, and E. G. Bovill, "Relationship between prothrombin activation fragment F1. 2 and international normalized ratio in patients with atrial fibrillation", Stroke, vol. 28, no. 6, pp. 1101-1106, 1997.##P. Bonizzi, M. Stridh, L. Sörnmo L, and O. Meste, "Ventricular activity residual reduction in remainder ECGs based on short-term autoregressive model interpolation", Annual Computers in Cardiology Conference (CinC), vol. 36, pp. 813-816, 2009.##J. Slocum, A. Sahakian, and S. Swiryn, "Diagnosis of atrial fibrillation from surface electrocardiograms based on computer-detected atrial activity", Journal of electrocardiology, vol. 25, no. 1, pp. 1-8, 1992.##M. Stridh, and L. Sörnmo, "Spatiotemporal QRST cancellation techniques for analysis of atrial fibrillation", IEEE Transactions on Biomedical Engineering, vol. 48, no. 1, pp. 105-111, 2001.##J. Behar, J. Oster, and G. D. Clifford, "Combining and benchmarking methods of foetal ECG extraction without maternal or scalp electrode data", Physiological measurement, vol. 35, no. 8, pp.1569-1589, 2014.##H. Jaeger, "The "echo state" approach to analysing and training recurrent neural networks-with an erratum note", Bonn, Germany: German National Research Center for Information Technology, GMD Technical Report, vol. 148, no. 34, pp. 13, 2001.##A. Petrėnas, V. Marozas, L. Sörnmo, and A. Lukosevicius, "An echo state neural network for QRST cancellation during atrial fibrillation", IEEE Transactions on Biomedical Engineering, vol. 59, no. 10, pp, 2950-2957, 2012.##F. Castells, C. Mora, J. J. Rieta, D. Moratal-Pérez, and J. Millet, "Estimation of atrial fibrillatory wave from single-lead atrial fibrillation electrocardiograms using principal component analysis concepts", Medical and biological Engineering and Computing, vol. 43, no. 5, pp. 557-560, 2005.##R. Alcaraz, and J. J. Rieta, "Adaptive singular value cancellation of ventricular activity in single-lead atrial fibrillation electrocardiograms", Physiological measurement, vol. 29, no. 12, pp. 1351-1369, 2008.##R. Llinares, and J. Igual, "Application of constrained independent component analysis algorithms in electrocardiogram arrhythmias", Artif Intell Med, vol. 47, no. 2, pp, 121-133, 2009.##J. Malik, N. Reed, C. L. Wang, and H. T. Wu, "Single-lead f-wave extraction using diffusion geometry", Physiological measurement, vol. 38, no. 7, pp. 1310-1334, 2017.##[E. K. Roonizi, and R. Sassi, "An extended Bayesian framework for atrial and ventricular activity separation in atrial fibrillation", IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 6, pp. 1573-1580, 2016.##P. Langley, J. P. Bourke, and A. Murray, "Frequency analysis of atrial fibrillation", In Computers in Cardiology IEEE, pp. 65-68, 2000.##M. Stridh, and L. Sornmo, "Time-frequency characterization of atrial arrhythmias using principal decomposition", In 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol.2, pp. 1808-1811, 2001.##M. Stridh, L. Sornmo, C. J. Meurling, and S. B. Olsson, "Sequential characterization of atrial tachyarrhythmias based on ECG time-frequency analysis", IEEE Transactions on Biomedical Engineering, vol. 5, pp.100-114, 2004.##V. Zarzoso, D G. Latcu, A R Hidalgo-Muñoz, M. Meo, O. Meste, I. Popescu, and N. Saoudi, "Non-invasive prediction of catheter ablation outcome in persistent atrial fibrillation by fibrillatory wave amplitude computation in multiple electrocardiogram leads", Archives of cardiovascular diseases, vol. 109, no. 12, pp. 679-688, 2016.##R.K. Tripathy, M.R. Paternina, J.G. Arrieta, and P. Pattanaik, "Automated detection of atrial fibrillation ecg signals using two stage vmd and atrial fibrillation diagnosis index", Journal of Mechanics in Medicine and Biology, vol. 17, no. 7, pp. 1740044, 2017##R. J. Martis, U. R. Acharya, H. Prasad, C. K .Chua, and C. M. Lim, "Automated detectionof atrial fibrillation using bayesian paradigm", Knowledge-Based Systems, vol. 54, pp. 269-275, 2017.##A. Nguyen, S. Ansari, M. Hooshmand, K. Lin, H. Ghanbari, J. Gryak, and K. Najarian, "Comparative study on heart rate variability analysis for atrial fibrillation detection in short single-lead ecg recordings", In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 526-529, 2018.##S. Cerutti, L. Mainardi, A. Porta, and A. Bianchi, "Analysis of the dynamics of RR interval series for the detection of atrial fibrillation episodes", Computers in Cardiology IEEE, pp. 77-80, 1997.##T. Radhakrishnan, J. Karhade, S. K. Ghosh, P. R. Muduli, R. K. Tripathy, and U. R. Acharya, " AFCNNet: Automated detection of AF using chirplet transform and deep convolutional bidirectional long short term memory network with ECG signals", Computers in Biology and Medicine, vol. 137, pp.104783, 2021.##R. G. Stockwell, L. Mansinha, and R. Lowe, "Localization of the complex spectrum: The S-transform", IEEE Transaction on Signal Processing, vol. 44, pp. 998-100, 1996.##[GB. Moody, "Spontaneous Termination of Atrial Fibrillation" PhysioNet and Computers in Cardiology 2004, Apr. 11, 2004. [Online]. Available: https://physionet.org/content/aftdb/1.0.0/. [Accessed: Sept. 10, 2024].##G. Moody, "Spontaneous termination of atrial fibrillation: a challenge from physionet and computers in cardiology 2004", Computers in Cardiology, pp. 101-104, 2004.##R. Sameni, C. Jutten, and M. B. Shamsollahi, "Multichannel electrocardiogram decomposition using periodic component analysis", IEEE Transactions on Biomedical Engineering, vol. 55, no. 8, pp. 1935-1940, 2008.##J. Pan, and W. J. Tompkins, "A real-time QRS detection algorithm", IEEE Transactions on Biomedical Engineering, vol. 32, pp. 230-236, 1985.##A. Petrėnas, and M. Vaidotas, Atrial fibrillation from an engineering perspective, Edited by Leif Sörnmo, Berlin: Springer, 2018.##H. Hazeri, P. Zarjam, and G. Azemi, "Classification of normal/abnormal PCG recordings using a time-frequency approach", Analog Integrated Circuits and Signal Processing, vol. 109, no. 2 ,pp. 459-465, 2021.##M. Azimi Far, S.Nejatian, H. Parvin, K. Bagheri Fard, and V. Rezaei, "Predicting cardiac arrhythmia on ECG signal using an ensemble of optimal multicore support vector machines", Signal and Data Processing, vol. 19, no. 3, pp. 65-86, 2022.##عظیمی‌فر، مریم، نجاتیان، پروین، باقری فرد، کرم الله، رضایی وحیده، «پیش‌بینی آریتمی قلبی در سیگنال ECG با به‌کارگیری مجمعی از ماشین‌های بردار پشتیبان چندهسته‌ای بهینه»٬ پردازش علائم و داده‌ها، دوره ۱۹، شماره ۳، صفحات 65-86، ۱۴۰۱.##S. Petrutiu, A. V. Sahakian, and J. Ng, S. Swiryn, "Analysis of the surface electrocardiogram to predict termination of atrial fibrillation: the 2004 computers in cardiology/physionet challenge", In Computers in Cardiology, 2004, pp. 105-108.##D. Hayn, K. Edegger, D. Scherr, P. Lercher, B. Rotman, W. Klein, and G. Schreier, "Automated prediction of spontaneous termination of atrial fibrillation from electrocardiograms", In Computers in Cardiology 2004, 2004, pp. 117-120.##F. Cantini, F. Conforti, M. Varanini, F. Chiarugi, and G. Vrouchos, "Predicting the end of an atrial fibrillation episode: the physionet challenge", In Computers in Cardiology 2004, 2004, pp. 121-124.##M. Lemay, Z. Ihara, J. M. Vesin, and L. Kappenberger. "Computers in cardiology/physionet challenge 2004: AF classification based on clinical features", In Computers in Cardiology 2004, pp. 669-672, 2004.##B. Logan, and J. Healey, "Detection of spontaneous termination of atrial fibrillation", In Computers in Cardiology 2004, pp. 653-656, 2004.##Q. Xi, and S. Shkurovich, "Prediction of spontaneous termination of atrial fibrillation in surface ECG by frequency analysis", In Computers in Cardiology 2004, pp. 113-116, 2004.##L. Liu, F. Liu, X. Ren, Y. Li, B. Han, L. Zhang, S. Wei, "Predicting spontaneous termination of atrial fibrillation based on dual path network and feature selection", Biomedical Signal Processing and Control, 88, pp. 105606, 2024.##N. Ganapathy, D. Baumgärtel, and T. M. Deserno, "Automatic detection of atrial fibrillation in ECG using co-occurrence patterns of dynamic symbol assignment and machine learning". Sensors, vol. 21, no. 10, pp. 3542, 2021.## ##

نهان‌کاوی فایل‌های فشرده صوتی با استفاده از یادگیری ماشین Steganalysis of Compressed Audio Files Based on Machine Learning 1 1 علم پنهان‌سازیِ پیام حاوی اطلاعات در یک رسانه حامل را نهان‌نگاری و تلاش برای تشخیص وجود یا نبود پیام نهان‌شده در شیء پوششی را تحلیل نهان‌نگاری یا نهان‌کاوی می‌نامند. فرمت فشرده‌سازی MP3 در میان داده‌های صوتی به‌عنوان میزبانی مناسب و فراگیر برای نهان‌نگاری اطلاعات مورداستفاده قرار گرفته و شیوه‌های نهان‌نگاری مختلفی برای این منظور طراحی شده‌اند؛ در این پژوهش، هدف ارائه الگوریتمی برای نهان‌کاوی به‌طور خاص برای فایل‌های فشرده صوتی با قالب MP3 است که با نرم‌افزارMP3stego  نهان‌نگاری شده‌اند. برای تهیه دادگان نهان‌نگاری از فایل‌های متنی با متون تصادفی استفاده شده‌است. ابتدا با استفاده از اطلاعات جانبیِ مستخرج از فایل‌های MP3، ویژگی‌های لازم استخراج‌شده و دادگان صوتی که شامل دو دسته فایل‌های نهان‌نگاری‌شده و فایل‌های نهان‌نگاری‌نشده است، به دو بخش دادگان آموزش و دادگان آزمون تقسیم شده و در ادامه با استفاده از روش‌های یادگیری ماشین (ماشین بردار پشتیبان)، سامانه تشخیص فایل‌های آلوده و فایل‌های تمیز طراحی شده و درنهایت کارایی سامانه با استفاده از دادگان آزمون اندازه‌گیری می‌شود. در این مقاله، یک ویژگی جدید به‌نام قله‌داربودن طیف (SPK) از اطلاعات جانبی فایل MP3 استخراج می‌شود. سامانک پیشنهادی با استفاده از دادگان جداگانه آزمون که شامل فایل‌های تمیز و فایل‌های نهان‌نگاری‌شده با ظرفیت‌های نهان‌نگاری متنوع است، آزمایش شده و با دقت 100% و بدون خطا فایل‌های تمیز و آلوده را از هم متمایز می‌کند. نتایج حاصل حاکی از شناسایی دقیق موارد نهان‌نگاری‌شده درعین کاهش پیچیدگی محاسباتی و افزایش سرعت این نوع نهان‌کاوی نسبت به روش‌های ابداعی گذشته است. 2 The science of hiding a message containing information in a carrier medium is called steganography, and the attempt to detect the presence or absence of a hidden message in a cover medium is called steganalysis. The MP3 compression format has been used among audio data as a suitable and comprehensive host for information encryption, and various encryption methods have been designed for this purpose. In this research, the aim is to present an algorithm for audio ateganalysis, specifically for compressed audio files in MP3 format, in which some data has been embedded using MP3stego software. To prepare encrypted data, text files with random texts have been used. First, by using the side information extracted from MP3 files, the necessary features are extracted and the audio data, which includes two categories of stego files and clean files, is divided into two parts: training data and test data. And then, using machine learning techniques (support vector machine), the detection system of infected files and clean files is designed, and finally, the efficiency of the system is measured using the test data. In this paper, a new feature called spectral peakiness (SPK) is extracted from the side information of MP3 file. The proposed system was tested using separate test data, which includes clean files and stego files with various encryption capacities, and it distinguished clean and stego files with 100% accuracy and without error. The results indicate the perfect classification of stego and clean files while reducing the computational complexity and increasing the speed of steganalysis compared to other methods. Instead of using the audio signal information stored in the MP3 file, the proposed method uses the side information of the MP3 file, which is less dependent on the audio content of the file. In this method, the MDB side information in the compressed audio file is assumed as a sequence, and then, using a feature extraction method, a new feature in the frequency domain called spectral peakiness is calculated. This simple yet powerful feature is combined with features such as temporal average and spectral average of the MDB sequence and forms a low-dimensional (three-dimensional) feature vector. This feature vector will then be classified by a support vector machine (SVM) classifier as a suspicious file or a normal file. The feature extraction method, while being simple and having very few calculations, has 100% accuracy (recognition without any error) for MP3 files, even when the amount of the hidden information in the audio file is very low. 55 66 2023/06/92022/12/32021/04/142023/04/72021/10/10 1400/7/18 2024/02/252024/08/222024/02/252024/08/222022/02/20 1400/12/1 محسن سلیمانی mohsen soleimani دانشجوی دکترای، دانشکده مهندسی برق و کامپیوتر، دانشگاه ارومیه، ارومیه، ایران ایران mohsen1soleimani@gmail.com مهدی چهل امیرانی Mahdi Chehel Amirani استاد دانشکده مهندسی برق و کامپیوتر، دانشگاه ارومیه، ارومیه، ایران ایران m.amirani@urmia.ac.ir سید جهانشاه کبودیان Seied Jahanshah Kabodian استادیار دانشکده مهندسی برق و کامپیوتر، دانشگاه رازی، کرمانشاه، ایران ایران jkabudian@gmail.com Compressed Audio File Audio Steganography Audio Steganalysis MP3 MP3stego فایل فشرده صوتی نهان‌کاوی فایل‌های صوتی نهان‌نگاری در فایل‌های صوتی MP3Stego MP3 شیخ‌زادگان، جواد، آشنایی با نهان‌نگاری و پنهان‌سازی اطلاعات، تهران، انتشارات پژوهشکده پردازش هوشمند علائم، 1392.##H. Ghasemzadeh, and M. H. Kayvanrad, "Comprehensive review of audio steganalysis methods", IET Signal Processing, vol. 12, pp. 673-687, 2018.##دالوند، الهام، «نهان کاوی فایل‌های صوتی MP3 با روش‌های مبتنی بر اطلاعات جانبی»، پایان‌نامه کارشناسی ارشد، دانشگاه رازی، کرمانشاه، 1394.##R. Kuriakose, and P. Premalatha, "A novel Method for MP3 Steganalysis", Proceedings of Intelligent Computing, Communication and Devices, ICCD, New Delhi, India, 2015, pp. 605-611.##H. Song, T. Hu, Y. Huang, and M. Guo, "Detecting MP3Stego and Estimating the Hidden Size", in WSEAS International Conference on Computers, Athens, Greece, 2003, pp. 2881-2884.##X. Yu, R. Wang, D. Yan, and J. Zhu, "MP3 Audio Steganalysis Using Calibrated Side Information Feature", Journal of Computational Information Systems, vol. 8, pp. 4241-4248, 2012.##C. Jin, R. Wang, D. Yan Ma, P. and K. Yang, "A Novel Detection Scheme for MP3stego with Low Payload", in IEEE China Summit & International Conference on Signal and Information Processing, ChinaSIP, Xi'an, China, 2014, pp. 602-606.##دارابی، علیرضا، «نهان یابی فایل‌های صوتی MP3 با تاکید بر روش‌های پردازش سیگنال»، پایان نامه کارشناسی ارشد، دانشگاه رازی، کرمانشاه، 1395.##گروه واژه‌گزینی انجمن رمز ایران، واژه‌نامه و فرهنگ امنیت فضای تولید و تبادل اطلاعات، ویرایش دوم، تهران، مؤسسه انتشارات علمی دانشگاه صنعتی شریف، 1394.##مهدوی جعفری، سمیه، "نهان‌نگاری اصوات دیجیتال"، پایان نامه کارشناسی ارشد، دانشگاه شهید باهنر، کرمان، 1388.##سلیمیان ریزی، عاطفه، "روش‌های مخفی‌سازی اطلاعات در فایل‌های صوتی MP3"،. پایان نامه کارشناسی ارشد، دانشگاه صنعتی اصفهان، اصفهان، 1393.##R. Raissi, "The Theory behind MP3", Personal Report, Dec. 2002.##Music Audio Benchmark Data Set, Dortmund University, [Online], Available: https://www-ai.cs.tu-dortmund.de/audio.html [Accessed: Sept. 2, 2024]##Ogg MP3 converter, [Online], Available: http://www.ogg-mp3.net [Accessed: Sept. 2, 2024]##اشعری، فاطمه، ریاحی، نوشین، «نهان‌کاوی صوت مبتنی بر همبستگی بین فریم و کاهش بازگشتی ویژگی»، پردازش علائم و داده‌ها، دوره 15، شماره 3، صفحات 113-122، 1397.##شیخ‌زادگان، جواد، آشنایی با نهان‌نگاری و پنهان‌سازی اطلاعات، تهران، انتشارات پژوهشکده پردازش هوشمند علائم، 1392.##H. Ghasemzadeh, and M. H. Kayvanrad, "Comprehensive review of audio steganalysis methods", IET Signal Processing, vol. 12, pp. 673-687, 2018.##دالوند، الهام، «نهان کاوی فایل‌های صوتی MP3 با روش‌های مبتنی بر اطلاعات جانبی»، پایان‌نامه کارشناسی ارشد، دانشگاه رازی، کرمانشاه، 1394.##R. Kuriakose, and P. Premalatha, "A novel Method for MP3 Steganalysis", Proceedings of Intelligent Computing, Communication and Devices, ICCD, New Delhi, India, 2015, pp. 605-611.##H. Song, T. Hu, Y. Huang, and M. Guo, "Detecting MP3Stego and Estimating the Hidden Size", in WSEAS International Conference on Computers, Athens, Greece, 2003, pp. 2881-2884.##X. Yu, R. Wang, D. Yan, and J. Zhu, "MP3 Audio Steganalysis Using Calibrated Side Information Feature", Journal of Computational Information Systems, vol. 8, pp. 4241-4248, 2012.##C. Jin, R. Wang, D. Yan Ma, P. and K. Yang, "A Novel Detection Scheme for MP3stego with Low Payload", in IEEE China Summit & International Conference on Signal and Information Processing, ChinaSIP, Xi'an, China, 2014, pp. 602-606.##دارابی، علیرضا، «نهان یابی فایل‌های صوتی MP3 با تاکید بر روش‌های پردازش سیگنال»، پایان نامه کارشناسی ارشد، دانشگاه رازی، کرمانشاه، 1395.##گروه واژه‌گزینی انجمن رمز ایران، واژه‌نامه و فرهنگ امنیت فضای تولید و تبادل اطلاعات، ویرایش دوم، تهران، مؤسسه انتشارات علمی دانشگاه صنعتی شریف، 1394.##مهدوی جعفری، سمیه، "نهان‌نگاری اصوات دیجیتال"، پایان نامه کارشناسی ارشد، دانشگاه شهید باهنر، کرمان، 1388.##سلیمیان ریزی، عاطفه، "روش‌های مخفی‌سازی اطلاعات در فایل‌های صوتی MP3"،. پایان نامه کارشناسی ارشد، دانشگاه صنعتی اصفهان، اصفهان، 1393.##R. Raissi, "The Theory behind MP3", Personal Report, Dec. 2002.##Music Audio Benchmark Data Set, Dortmund University, [Online], Available: https://www-ai.cs.tu-dortmund.de/audio.html [Accessed: Sept. 2, 2024]##Ogg MP3 converter, [Online], Available: http://www.ogg-mp3.net [Accessed: Sept. 2, 2024]##اشعری، فاطمه، ریاحی، نوشین، «نهان‌کاوی صوت مبتنی بر همبستگی بین فریم و کاهش بازگشتی ویژگی»، پردازش علائم و داده‌ها، دوره 15، شماره 3، صفحات 113-122، 1397. ##

انتخاب بهترین مسیرهای نصب در توسعه دوربین‌های مداربسته شهری Choosing the Best Installation Paths In the Development of Urban CCTV Cameras 1 1 دوربین‎های مداربسته یکی از مهم‌ترین ابزارهایی است که در شهرها برای سامانه کنترل ترافیک استفاده می‌شود. شهروندان روزانه مسافرت‌های درون‌شهری زیادی انجام می‌دهند و عملکرد سامانه‌های نظارت شهری نیز پایش این مسیرهاست. در روش پیشنهادی، نقشه واقعی شهر به‌عنوان یک مدل انتخاب شده‌است. با جداسازی مسیر‌های اصلی از سایر مکان‌ها، گرافی از مسیرها به‌دست می‌آید؛ سپس با انتخاب تصادفی مجموعه‌ای از زوج‌رأس‌ها از گراف، به‌عنوان مبدأ و مقصد یک سفر داخل شهری و مسیریابی بین آن‌ها با الگوریتم دایکسترا، ترافیک مجازی ساخته می‌شود. برای تطابق ترافیک مجازی با ترافیک واقعی، احتمال انتخاب نقاط پر رفت‌و‌آمد بیشتر درنظرگرفته می‌شود. با ایجاد یک‌صدهزار مسیر برای مدل مورد مطالعه، می‌توان یال‎ها را با بالاترین تکرار به‌عنوان نتایج نهایی پیدا کرد و برای نصب دوربین پیشنهاد داد. ارزیابی نتایج نهایی با تکرار آزمایش‌های تصادفی و با استفاده از ضریب تشابه ژاکارد انجام گرفته است و میزان تشابه نتایج خروجی بررسی می‌شود. پایایی روش پیشنهادی با تحلیل ریاضی و با رسم نمودارها بیان می‌شود و تأثیر پارامترهای تأثیرگذار مانند تعداد مسافرت شهری، میزان احتمال انتخاب نقاط، تأثیر توپولوژی شهر و تعداد نتایج خروجی به‌شکل تحلیلی بیان‌ شده و میزان تشابه نتایج، 98درصد به‌دست آمد. مزیت روش پیشنهادی وابسته‌نبودن به ابزار خاص مانند دوربین‌های سنجش ترافیک و همچنین بدون وابستگی به مکان و توپولوژی خاص است. 2 Optimizing camera placement is a two-decade-old research problem. Many researches have solved the problem with different approaches. Some different methods such as genetic algorithm, reinforcement learning, and greedy algorithm have been developed to obtain the maximum surface coverage. Some researchers have considered specific applications in order to optimally cover a certain area such as a coastal area or a protected area under the coverage of CCTV cameras. Some researchers have also considered the camera's capabilities of vertical rotation or horizontal rotation or zooming in order to use these capabilities for optimization. With the development of drone manufacturing technology, this tool is also proposed for specific applications. But what is less discussed is the optimization of the placement of urban surveillance cameras in a real city map. Usually, due to the high cost, all city cameras are not installed at once, and cameras are added annually to develop the city traffic monitoring system. Therefore, it is necessary to prioritize the selection of the route and a very important factor in prioritization is traffic. Traffic is the most important factor in choosing the route for the placement of urban surveillance cameras because the streets with more traffic are exposed to more traffic accidents and should be the priority for video monitoring. Traffic data is usually big data, not available for all cities, and on the other hand, providing traffic data may violate citizens' privacy. Therefore, there are many methods for creating virtual traffic, which are classified into two categories: macro and micro. Macro methods model traffic as a physical phenomenon such as fluid or gas, but micro models, which are mostly used in artificial intelligence methods, consider traffic as a set of individual trips. In this work, we use the second method to create virtual traffic so that routes with more traffic are prioritized for installation. Citizens usually make a lot of intra-city trips, and the function of city monitoring systems is to monitor these routes. Therefore, the placement of surveillance cameras should also be in such a way that it considers the observation of these routes. In the proposed method, the real map of the city is selected as a model. Then, by separating the main paths and obtaining the skeleton of the path, a graph of the paths is obtained, the intersection point of the paths will be its vertex and the distance between the vertices will be the weight of the connecting edges. Now by randomly selecting two vertices from the graph as the origin and destination of an intra-city trip and routing between them with Dijkstra's algorithm, a trip is made. By repeating this process, virtual traffic is simulated. To create virtual traffic similar to real traffic, the probability of choosing high-traffic points is considered more than other points. Therefore, the probability of selecting vertices in the graph is different according to their location in the city. By creating one hundred thousand paths for the studied model, the edges with the highest repetition can be found as the final results and suggested for camera installation. The evaluation of the final results is done by repeating random experiments and using the Jaccard similarity coefficient, and the degree of similarity of the output results is checked. The reliability of the proposed method is expressed by mathematical analysis and by drawing graphs, and the impact of influential parameters such as the number of city trips, the probability of choosing points, the impact of city topology, and the number of output results are expressed analytically, and the similarity of the results is 98%. The advantage of the proposed method is not depending on special tools such as special cameras for traffic measurement, as well as not depending on a specific location and topology. 67 78 2023/06/92022/12/32021/04/142023/04/72021/10/102023/10/15 1402/7/23 2024/02/252024/08/222024/02/252024/08/222022/02/202024/11/4 1403/8/14 حسن صانعی آرانی hassan sanei arani دانشجوی دکترای مدیریت فناوری اطلاعات، دانشکده مدیریت و حسابداری، دانشگاه آزاد اسلامی واحد علوم و تحقیقات، تهران، ایران ایران hassan.sanei@srbiau.ac.ir مهدی اسماعیلی mahdi esmaili استادیار گروه کامپیوتر، دانشکده مهندسی برق و کامپیوتر، دانشگاه آزاد اسلامی واحد کاشان، کاشان، ایران ایران m.esmaeili@iaukashan.ac.ir محمد علی افشار کاظمی Mohmmad ali Afshar kazimi دانشیار گروه مدیریت صنعتی، دانشکده مدیریت و حسابداری، دانشگاه آزاد اسلامی واحد تهران مرکز، تهران، ایران ایران m.afsharkazemi@iauec.ac.ir placement of urban CCTV camera - Virtual traffic - Jaccard similarity coefficient - Dijkstra's algorithm - smart city جانمایی دوربین مداربسته شهری ترافیک مجازی ضریب تشابه ژاکارد الگوریتم دایکسترا شهر هوشمند Z. Su, Q. Zhang, Z. L¨u, C.-M. Li, W. Lin, and F. Ma, "Weightingbased variable neighborhood search for optimal camera placement," Proc. Conf. AAAI Artif. Intell., vol. 35, no. 14, pp. 12400-12408, 2021.##P. Bischoff, "Surveillance camera statistics: which cities have the most CCTV cameras?" Comparitech, 15-Aug-2019. [Online].Available: https://www.comparitech.com/vpn-privacy/the-worlds-mostsurveilled-cities/. [Accessed: 06-Jun-2023].##J. Liu, X. Wang, Y. Li, X. Kang, and L. Gao, "Method of evaluating and predicting traffic state of highway network based on deep learning," J. Adv. Transp., vol. 2021, pp. 1-9, 2021.##L. Fredianelli et al., "Traffic flow detection using camera images and machine learning methods in ITS for noise map and action plan optimization," Sensors (Basel), vol. 22, no. 5, p. 1929, 2022.##A. A. Altahir, V. S. Asirvadam, P. Sebastian, N. H. B. Hamid, and E. F. Ahmed, "Optimizing visual sensors placement with risk maps using dynamic programming," IEEE Sens. J., vol. 22, no. 1, pp. 393-404, 2022.##S. Kov'acs, B. Bolem'anyi, and J. Botzheim, "Placement of optical sensors in 3D terrain using a bacterial evolutionary algorithm," Sensors (Basel), vol. 22, no. 3, p. 1161, 2022.##A. M. Heyns, "Optimisation of surveillance camera site locations and viewing angles using a novel multi-attribute, multi-objective genetic algorithm: A day/night anti-poaching application," Comput. Environ. Urban Syst., vol. 88, no. 101638, p. 101638, 2021.##M. S. S. Suresh, A. Narayanan, and V. Menon, "Maximizing camera coverage in multicamera surveillance networks," IEEE Sens. J., vol. 20, no. 17, pp. 10170-10178, 2020.##A. Bhattacharya and M. Pal, "Vertex covering problems of fuzzy graphs and their application in CCTV installation," Neural Comput. Appl., vol. 33, no. 11, pp. 5483-5506, 2021.##Y. Chen, M. Tsukada, and H. Esaki, "Reinforcement learning based optimal camera placement for depth observation of indoor scenes," 2021.##W. J. Yun et al., "Cooperative multi-agent deep reinforcement learning for reliable surveillance via autonomous multi-UAV control," arXiv [eess.SY], 2022.##D. Susanj, D. Pincic, and K. Lenac, "Effective area coverage of 2D and 3D environments with directional and isotropic sensors," IEEE Access, vol. 8, pp. 185595-185608, 2020.##[X. Chen, Y. Zhu, H. Chen, Y. Ouyang, X. Luo, and X. Wu, "BIM-based optimization of camera placement for indoor construction monitoring considering the construction schedule," Autom. Constr., vol. 130, no. 103825, p. 103825, 2021.##N. Bisagno, A. Xamin, F. De Natale, N. Conci, and B. Rinner, "Dynamic camera reconfiguration with reinforcement learning and stochastic methods for crowd surveillance," Sensors (Basel), vol. 20, no. 17, p. 4691, 2020.##S. Jun, T.-W. Chang, H. Jeong, and S. Lee, "Camera placement in smart cities for maximizing weighted coverage with budget limit," IEEE Sens. J., vol. 17, no. 23, pp. 7694-7703, 2017.##C.-J. Liu, Z. Liu, Y.-J. Chai, and T.-T. Liu, "Review of virtual traffic simulation and its applications," J. Adv. Transp., vol. 2020, pp. 1-9, 2020.##Z. Xie, X. Liu, Y. Li, H. Zhang, and Q. Xiang, "Camera placement optimization for CCTV in rail transit using BIM," Measurement + control/Measurement and control, vol. 56, no. 9-10, pp. 1499-1509, Mar. 2023.##M. S. Eran and H. Hasranizam, "The Effectiveness of Crime Prevention Using GIS Technology and CCTV Application for Smart City," Earth and environmental sciences library, pp. 59-75, Jan. 2024.##G. Li, Y. Chen, Y. Wang, P. Nie, Z. Yu, and Z. He, "City-scale synthetic individual-level vehicle trip data," Sci. Data, vol. 10, no. 1, 2023.##V. T. N. Nha, S. Djahel, and J. Murphy, "A comparative study of vehicles' routing algorithms for route planning in smart cities," in 2012 First International Workshop on Vehicular Traffic Management for Smart Cities (VTM), 2012.##P. Udhan, A. Ganeshkar, P. Murugesan, A. R. Permani, S. Sanjeeva, and P. Deshpande, "Vehicle route planning using dynamically weighted Dijkstra's algorithm with traffic prediction," 2022.##Z. Su, Q. Zhang, Z. L¨u, C.-M. Li, W. Lin, and F. Ma, "Weightingbased variable neighborhood search for optimal camera placement," Proc. Conf. AAAI Artif. Intell., vol. 35, no. 14, pp. 12400-12408, 2021.##P. Bischoff, "Surveillance camera statistics: which cities have the most CCTV cameras?" Comparitech, 15-Aug-2019. [Online].Available: https://www.comparitech.com/vpn-privacy/the-worlds-mostsurveilled-cities/. [Accessed: 06-Jun-2023].##J. Liu, X. Wang, Y. Li, X. Kang, and L. Gao, "Method of evaluating and predicting traffic state of highway network based on deep learning," J. Adv. Transp., vol. 2021, pp. 1-9, 2021.##L. Fredianelli et al., "Traffic flow detection using camera images and machine learning methods in ITS for noise map and action plan optimization," Sensors (Basel), vol. 22, no. 5, p. 1929, 2022.##A. A. Altahir, V. S. Asirvadam, P. Sebastian, N. H. B. Hamid, and E. F. Ahmed, "Optimizing visual sensors placement with risk maps using dynamic programming," IEEE Sens. J., vol. 22, no. 1, pp. 393-404, 2022.##S. Kov'acs, B. Bolem'anyi, and J. Botzheim, "Placement of optical sensors in 3D terrain using a bacterial evolutionary algorithm," Sensors (Basel), vol. 22, no. 3, p. 1161, 2022.##A. M. Heyns, "Optimisation of surveillance camera site locations and viewing angles using a novel multi-attribute, multi-objective genetic algorithm: A day/night anti-poaching application," Comput. Environ. Urban Syst., vol. 88, no. 101638, p. 101638, 2021.##M. S. S. Suresh, A. Narayanan, and V. Menon, "Maximizing camera coverage in multicamera surveillance networks," IEEE Sens. J., vol. 20, no. 17, pp. 10170-10178, 2020.##A. Bhattacharya and M. Pal, "Vertex covering problems of fuzzy graphs and their application in CCTV installation," Neural Comput. Appl., vol. 33, no. 11, pp. 5483-5506, 2021.##Y. Chen, M. Tsukada, and H. Esaki, "Reinforcement learning based optimal camera placement for depth observation of indoor scenes," 2021.##W. J. Yun et al., "Cooperative multi-agent deep reinforcement learning for reliable surveillance via autonomous multi-UAV control," arXiv [eess.SY], 2022.##D. Susanj, D. Pincic, and K. Lenac, "Effective area coverage of 2D and 3D environments with directional and isotropic sensors," IEEE Access, vol. 8, pp. 185595-185608, 2020.##[X. Chen, Y. Zhu, H. Chen, Y. Ouyang, X. Luo, and X. Wu, "BIM-based optimization of camera placement for indoor construction monitoring considering the construction schedule," Autom. Constr., vol. 130, no. 103825, p. 103825, 2021.##N. Bisagno, A. Xamin, F. De Natale, N. Conci, and B. Rinner, "Dynamic camera reconfiguration with reinforcement learning and stochastic methods for crowd surveillance," Sensors (Basel), vol. 20, no. 17, p. 4691, 2020.##S. Jun, T.-W. Chang, H. Jeong, and S. Lee, "Camera placement in smart cities for maximizing weighted coverage with budget limit," IEEE Sens. J., vol. 17, no. 23, pp. 7694-7703, 2017.##C.-J. Liu, Z. Liu, Y.-J. Chai, and T.-T. Liu, "Review of virtual traffic simulation and its applications," J. Adv. Transp., vol. 2020, pp. 1-9, 2020.##Z. Xie, X. Liu, Y. Li, H. Zhang, and Q. Xiang, "Camera placement optimization for CCTV in rail transit using BIM," Measurement + control/Measurement and control, vol. 56, no. 9-10, pp. 1499-1509, Mar. 2023.##M. S. Eran and H. Hasranizam, "The Effectiveness of Crime Prevention Using GIS Technology and CCTV Application for Smart City," Earth and environmental sciences library, pp. 59-75, Jan. 2024.##G. Li, Y. Chen, Y. Wang, P. Nie, Z. Yu, and Z. He, "City-scale synthetic individual-level vehicle trip data," Sci. Data, vol. 10, no. 1, 2023.##V. T. N. Nha, S. Djahel, and J. Murphy, "A comparative study of vehicles' routing algorithms for route planning in smart cities," in 2012 First International Workshop on Vehicular Traffic Management for Smart Cities (VTM), 2012.##P. Udhan, A. Ganeshkar, P. Murugesan, A. R. Permani, S. Sanjeeva, and P. Deshpande, "Vehicle route planning using dynamically weighted Dijkstra's algorithm with traffic prediction," 2022. ##

رده‌بندی تصاویر سرطان پوست با استفاده از یادگیری عمیق جمعی دو سطحی Classification of skin cancer images using two-level ensemble deep learning 1 1 برخی از مشکلات پوستی خوش‌خیم و بی‌ضرر و برخی دیگر توده‌های بدخیمی هستند که با تشخیص به‌موقع‌ می‌توانند همچنان بی‌ضرر بمانند. در این پژوهش، یک روش یادگیری عمیق جمعی دوسطحی برای رده‌بندی تصاویر پزشکی سرطان پوست پیشنهاد می‏‌شود. در مدل پیشنهادی از یادگیری عمیق در دو سطح استفاده شده‌است و سپس در هر سطح از الگوریتم CatBoost برای ترکیب این مدل‌ها استفاده می‏‌شود. نتایج مدل پیشنهادی با شبکه‌‏ها‌ی عمیق تک‌سطحه و پژوهش‌های مشابه پیشین مقایسه ‌شده‌است. نتایج نشان‌می‌دهد که مدل پیشنهادی عملکرد بهتری در رده‌بندی تصاویر سرطان پوست دارد. عملکرد مدل پیشنهادی، چه در هر یک از کلاس‌ها و چه درکل، از تمامی مدل‌های یادگیری عمیق مستقل بهتر بوده‌است. همچنین نشان‌داده ‌شده‌ که استفاده از VGG-Ensemble در کنار روش پیشنهادی و ترکیب نتایج آن به‌کمک CatBoost و تشکیل یک مجمع دوسطحی، عملکرد آن را در هر کلاس نیز بهبود داده‌است. 2 Today, despite the tremendous advances in medical science and technology, access to a specialist doctor is still considered a major challenge. This challenge is of great importance for diseases such as cancer. Skin cancer is the 13th most common cancer in men and the 15th most common cancer in women. While some skin problems are benign and harmless, some of them can be malignant masses, which will remain harmless if they are diagnosed in time. When consulting a specialist doctor may be time-consuming and expensive, an intelligent system can be a fast alternative or, at least, an efficient preliminary treatment solution. For skin cancer, such intelligent system may utilize the images of suspicious skin masses labeled according to their benign or malignant state by specialist physicians. These labeled images are useful for training intelligent systems which should diagnose the potential problems in unseen new images. In this research, a novel deep learning-based approach is proposed for the problem of classifying skin cancer images into two categories of benign and malignant images. In the proposed model, powerful deep learning models for image classification including VGG, ResNet, and Inception are used in two levels. Specifically, we formed two ensembles; VGG ensemble which consists of VGG-16 and VGG-19 models and ResNet ensemble which consists of ResNet152, ResNet50, and Inception models. CatBoost algorithm is used in each level to combine the models on that ensemble. Finally, at the next level, two ensembles were combined using the CatBoost algorithm. The proposed ensemble model tries to improve the accuracy and consistency of the results by aggregating the deep models at its two levels. In order to show the utility of the proposed model, a subset of ISIC public dataset for skin cancer images is used for training and evaluation of models. The performance of the proposed ensemble model is compared with several deep neural networks and previous similar researches. Specifically, we compared the results achieved by the proposed model with those obtained by existing similar deep models and those used as building blocks of the proposed model. The results show that the proposed model performs better in classifying skin cancer images. The performance of the proposed model, both in each of the classes and in general, has been better than all independent deep learning models. It has also been shown that using VGG ensemble along with this proposed model by combining its results with the help of CatBoost and forming a two-level ensemble has improved its independent performance in each class. 79 90 2023/06/92022/12/32021/04/142023/04/72021/10/102023/10/152022/11/10 1401/8/19 2024/02/252024/08/222024/02/252024/08/222022/02/202024/11/42024/07/31 1403/5/10 پویان جابری Pouyan Jaberi کارشناسی ارشد مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه شهرکرد، شهرکرد، ایران ایران jaberi@stu.sku.ac.ir شهلا نعمتی Shahla Nemati استادیار گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه شهرکرد، شهرکرد، ایران ایران s.nemati@sku.ac.ir محمداحسان بصیری Mohammad Ehsan Basiri دانشیار گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه شهرکرد، شهرکرد، ایران ایران basiri@sku.ac.ir Deep Learning Ensemble Learning Skin Cancer Benign Malignant یادگیری عمیق یادگیری جمعی سرطان پوست خوش‌خیم بدخیم "https://data.worldbank.org/indicator/SH.MED.PHYS.ZS." Accessed: Nov. 04, 2022. [Online]. Available: https://data.worldbank.org/indicator/SH.MED.PHYS.ZS##B. Rahimi, H. Nadri, H. L. Afshar, and T. Timpka, "A systematic review of the technology acceptance model in health informatics," Appl Clin Inform, vol. 9, no. 03, pp. 604-634, 2018.##M. E. Basiri, S. Nemati, M. Abdar, S. Asadi, and U. R. Acharrya, "A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets," Knowl Based Syst, vol. 228, 2021, doi: 10.1016/j.knosys.2021.107242.##A. Garg and V. Mago, "Role of machine learning in medical research: A survey," Comput Sci Rev, vol. 40, p. 100370, 2021.##[A. Barragán-Montero et al., "Artificial intelligence and machine learning for medical imaging: A technology review," Physica Medica, vol. 83, pp. 242-256, 2021.##U. Kamath, J. Liu, and J. Whitaker, Deep learning for NLP and speech recognition, vol. 84. Cham, Switzerland: Springer, 2019.##M. Tsuneki, "Deep learning models in medical image analysis," Journal of Oral Biosciences, vol. 64, no. 3, pp.312-320, 2022.##S. Dong, P. Wang, and K. Abbas, "A survey on deep learning and its applications," Comput Sci Rev, vol. 40, p. 100379, 2021.##R. Sun et al., "Survey of Image Edge Detection," Frontiers in Signal Processing, vol. 2, p. 826967, 2022.##F. A. Spanhol, L. S. Oliveira, C. Petitjean, and L. Heutte, "Breast cancer histopathological image classification using convolutional neural networks," in 2016 international joint conference on neural networks (IJCNN), pp. 2560-2567, 2016.##[X. Dong, Z. Yu, W. Cao, Y. Shi, and Q. Ma, "A survey on ensemble learning," Front Comput Sci, vol. 14, no. 2, pp. 241-258, 2020.##M. A. Ganaie, M. Hu, and others, "Ensemble deep learning: A review," Engineering Applications of Artificial Intelligence, vol. 115, p.105151, 2021.##A. S. Kini et al., "Ensemble deep learning and internet of things-based automated COVID-19 diagnosis framework," Contrast Media Mol Imaging, vol. 2022, 2022.##A. K. Das, S. Ghosh, S. Thunder, R. Dutta, S. Agarwal, and A. Chakrabarti, "Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network," Pattern Analysis and Applications, vol. 24, no. 3, pp. 1111-1124, 2021.##[M. Dildar et al., "Skin cancer detection: a review using deep learning techniques," Int J Environ Res Public Health, vol. 18, no. 10, p. 5479, 2021.##M. A. Kassem, K. M. Hosny, and M. M. Fouad, "Skin lesions classification into eight classes for ISIC 2019 using deep convolutional neural network and transfer learning," IEEE Access, vol. 8, pp. 114822-114832, 2020.##S. Kalouche, A. Ng, and J. Duchi, "Vision-based classification of skin cancer using deep learning," 2015, conducted on Stanfords Machine Learning course (CS 229) taught, 2016.##A. Sagar and D. Jacob, "Convolutional neural networks for classifying melanoma images," bioRxiv, pp. 2020-2025, 2021.##"Skin Cancer: Malignant vs Benign, Kaggle," Skin Cancer: Malignant vs Benign, Kaggle,. Accessed: Jan. 01, 2022. [Online]. Available: https:// kaggle.com/datasets/fanconic/skin-cancer-malignant-vsbenign##K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.##K. He, X. Zhang, S. Ren, and J. Sun, "Identity mappings in deep residual networks," in European conference on computer vision, pp. 630-645, 2016.##C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the inception architecture for computer vision," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818-2826, 2016.##M. Al-Sarem, F. Saeed, W. Boulila, A. H. Emara, M. Al-Mohaimeed, and M. Errais, "Feature selection and classification using CatBoost method for improving the performance of predicting Parkinson's disease," in Advances on Smart and Soft Computing, Springer, pp. 189-199, 2021.##L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin, "CatBoost: unbiased boosting with categorical features," Adv Neural Inf Process Syst, vol. 31, 2018.##A. Géron, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. O'Reilly Media, Inc., 2022.##فروتن راد جواد، حورعلی مریم، کیوان راد محمدعلی، «دادگان پرسش‌وپاسخ زبان فارسی»، پردازش علائم و داده‌ها، ۲۰ (۴)، صفحات ۱۰۷-۱۲۰، 1402.##"https://data.worldbank.org/indicator/SH.MED.PHYS.ZS." Accessed: Nov. 04, 2022. [Online]. Available: https://data.worldbank.org/indicator/SH.MED.PHYS.ZS##B. Rahimi, H. Nadri, H. L. Afshar, and T. Timpka, "A systematic review of the technology acceptance model in health informatics," Appl Clin Inform, vol. 9, no. 03, pp. 604-634, 2018.##M. E. Basiri, S. Nemati, M. Abdar, S. Asadi, and U. R. Acharrya, "A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets," Knowl Based Syst, vol. 228, 2021, doi: 10.1016/j.knosys.2021.107242.##A. Garg and V. Mago, "Role of machine learning in medical research: A survey," Comput Sci Rev, vol. 40, p. 100370, 2021.##[A. Barragán-Montero et al., "Artificial intelligence and machine learning for medical imaging: A technology review," Physica Medica, vol. 83, pp. 242-256, 2021.##U. Kamath, J. Liu, and J. Whitaker, Deep learning for NLP and speech recognition, vol. 84. Cham, Switzerland: Springer, 2019.##M. Tsuneki, "Deep learning models in medical image analysis," Journal of Oral Biosciences, vol. 64, no. 3, pp.312-320, 2022.##S. Dong, P. Wang, and K. Abbas, "A survey on deep learning and its applications," Comput Sci Rev, vol. 40, p. 100379, 2021.##R. Sun et al., "Survey of Image Edge Detection," Frontiers in Signal Processing, vol. 2, p. 826967, 2022.##F. A. Spanhol, L. S. Oliveira, C. Petitjean, and L. Heutte, "Breast cancer histopathological image classification using convolutional neural networks," in 2016 international joint conference on neural networks (IJCNN), pp. 2560-2567, 2016.##[X. Dong, Z. Yu, W. Cao, Y. Shi, and Q. Ma, "A survey on ensemble learning," Front Comput Sci, vol. 14, no. 2, pp. 241-258, 2020.##M. A. Ganaie, M. Hu, and others, "Ensemble deep learning: A review," Engineering Applications of Artificial Intelligence, vol. 115, p.105151, 2021.##A. S. Kini et al., "Ensemble deep learning and internet of things-based automated COVID-19 diagnosis framework," Contrast Media Mol Imaging, vol. 2022, 2022.##A. K. Das, S. Ghosh, S. Thunder, R. Dutta, S. Agarwal, and A. Chakrabarti, "Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network," Pattern Analysis and Applications, vol. 24, no. 3, pp. 1111-1124, 2021.##[M. Dildar et al., "Skin cancer detection: a review using deep learning techniques," Int J Environ Res Public Health, vol. 18, no. 10, p. 5479, 2021.##M. A. Kassem, K. M. Hosny, and M. M. Fouad, "Skin lesions classification into eight classes for ISIC 2019 using deep convolutional neural network and transfer learning," IEEE Access, vol. 8, pp. 114822-114832, 2020.##S. Kalouche, A. Ng, and J. Duchi, "Vision-based classification of skin cancer using deep learning," 2015, conducted on Stanfords Machine Learning course (CS 229) taught, 2016.##A. Sagar and D. Jacob, "Convolutional neural networks for classifying melanoma images," bioRxiv, pp. 2020-2025, 2021.##"Skin Cancer: Malignant vs Benign, Kaggle," Skin Cancer: Malignant vs Benign, Kaggle,. Accessed: Jan. 01, 2022. [Online]. Available: https:// kaggle.com/datasets/fanconic/skin-cancer-malignant-vsbenign##K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.##K. He, X. Zhang, S. Ren, and J. Sun, "Identity mappings in deep residual networks," in European conference on computer vision, pp. 630-645, 2016.##C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the inception architecture for computer vision," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818-2826, 2016.##M. Al-Sarem, F. Saeed, W. Boulila, A. H. Emara, M. Al-Mohaimeed, and M. Errais, "Feature selection and classification using CatBoost method for improving the performance of predicting Parkinson's disease," in Advances on Smart and Soft Computing, Springer, pp. 189-199, 2021.##L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin, "CatBoost: unbiased boosting with categorical features," Adv Neural Inf Process Syst, vol. 31, 2018.##A. Géron, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. O'Reilly Media, Inc., 2022.##فروتن راد جواد، حورعلی مریم، کیوان راد محمدعلی، «دادگان پرسش‌وپاسخ زبان فارسی»، پردازش علائم و داده‌ها، ۲۰ (۴)، صفحات ۱۰۷-۱۲۰، 1402.## ##

ارائه روشی مؤثر برای بهبود تصاویر تاریک: بهبود بهینه نقشه روشنایی Providing an effective way to enhance low-light images: Enhanced Illumination Map Optimally 1 1 به‌طورمعمول جزئیات صحنه هدف در تصاویر ضبط‌شده در محیط‌های کم‌نور به‌خوبی قابل‌تشخیص نیستند. این مشکل می‌تواند عملکرد بسیاری از الگوریتم‌های دید رایانه را کاهش دهد؛ به‌همین‌دلیل در این ‌مقاله روشی جدید ارائه می‌شود تا با افزایش میزان روشنایی، جزئیات پنهان‌شده را دراین تصاویر نمایان سازد. در روش پیشنهادی ابتدا نقشه روشنایی اولیه تصویر محاسبه، سپس با استفاده از یک‌ مدل ریاضی جدید نقشه روشنایی اولیه بهبود داده می‌شود. مشتق‌پذیربودن تابع هدف مدل پیشنهادی وجه‌تمایز آن با سایر مدل‌های مشابه است. درکل روش‌های کلاسیک بهینه‌سازی مانند روش‌های نیوتن، گرادیان و ناحیه اعتماد نیازمند مشتق‌پذیری تابع هدف‌اند؛ بنابراین برای حل مدل پیشنهادی می‌توان از روش‌های متنوع‌تری در مقایسه باسایر مدل‌های مشابه استفاده کرد. خطی‌بودن قیدها و محدب‌بودن مدل پیشنهادی از دیگر ویژگی‌های مثبت این مدل است. نتایج بررسی‌ها نشان می‌دهد که روش پیشنهادی عملکرد خوبی در روشن‌کردن تصاویر تاریک و همچنین نمایان‌ساختن جزئیات صحنه هدف دارد و از این‌ منظر قابل ‌رقابت با بسیاری از روش‌های مطرح بهبود تصاویر تاریک است. 2 Low-light images often suffer from low brightness and contrast, which makes some scene details hard to see. This can affect the performance of many computer vision tasks, such as object recognition, tracking, scene understanding, and occlusion detection. Therefore, it is important and useful to enhance low-light images. One technique to enhance low-light images is based on the Retinex theory, which decomposes images into two components: reflection and illumination. Several mathematical models have been recently developed to estimate the illumination map using this theory. These methods first compute an initial illumination map and then refine it by solving a mathematical model. This paper introduces a novel method based on the Retinex theory to estimate the illumination map. The proposed method employs a new mathematical model with a differentiable objective function, unlike other similar models. This allows us to use more diverse methods to solve the proposed model, as classical optimization methods such as Newton, Gradient, and Trust-Region methods need the objective function to be differentiable. The proposed model also has linear constraints and is convex, which are desirable properties for optimization. We use the CPLEX solver to solve the proposed model, as it performs well and exploits the features of the model. Finally, we improve the illumination map obtained from the mathematical model using a simple linear transformation. This paper introduces a new method based on the Retinex theory for enhancing low-light images. The proposed method improves the illumination and the visibility of the scene details. We compare the performance of our method with six existing methods: AMSR, NPE, SRIE, DONG, MF, and LIME. We use four common metrics to evaluate the visual quality of the enhanced images: AMBE, LOE, SSIM, and NIQE. The results demonstrate that our method is competitive with many of the state-of-the-art methods for low-light image enhancement. 91 104 2023/06/92022/12/32021/04/142023/04/72021/10/102023/10/152022/11/102021/07/27 1400/5/5 2024/02/252024/08/222024/02/252024/08/222022/02/202024/11/42024/07/312024/08/22 1403/6/1 مهدی احمدنیا Mahdi Ahmadnia دانشجوی دکتری رشته تحقیق در عملیات گروه ریاضی کاربردی دانشکده ریاضی، دانشگاه فردوسی، مشهد، ایران ایران ahmadnia.mahdii@gmail.com مجتبی مغربی Mojtaba Maghrebi دانشیار گروه مهندسی عمران دانشکده مهندسی، دانشگاه فردوسی، مشهد، ایران ایران mojtabamaghrebi@um.ac.ir‬‏ رضا قنبری Reza Ghanbari دانشیار گروه ریاضی کاربردی دانشکده ریاضی، دانشگاه فردوسی، مشهد، ایران ایران ‫rghanbari@um.ac.ir Enhance illumination Enhance low-light images Illumination map Retinex theory Optimization model Image Processing افزایش روشنایی بهبود تصاویر تاریک نقشه روشنایی نظریه ریتاینکس مدل بهینه‌سازی پردازش تصویر D. Oneata, J. Revaud, J. Verbeek, and C. Schmid, "Spatio-temporal object detection proposals," 2014, doi: 10.1007/978-3-319-10578-9_48.##A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," 2012, doi: 10.1061/(ASCE)GT.1943-5606.0001284.##رضائی، معصومه، رضائیان، مهدی و ولی درهمی، «توصیف‌گر موضعی جدید با استفاده از نگاشت مرکاتور به‌منظور تشخیص اشیای سه‌بعدی»، مجله پردازش علائم و داده‌ها، شماره 1، صفحات 111-124، 1401##K. Zhang, L. Zhang, and M. H. Yang, "Fast Compressive Tracking," IEEE Trans. Pattern Anal. Mach. Intell., 2014, doi: 10.1109/TPAMI.2014.2315808.##Y. Liu, R. R. Martin, L. De Dominicis, and B. Li, "Using retinex for point selection in 3D shape registration," Pattern Recognit., 2014, doi: 10.1016/j.patcog.2013.12.015.##C. Jung, T. Sun, and L. Jiao, "Eye detection under varying illumination using the retinex theory," Neurocomputing, 2013, doi: 10.1016/j.neucom.2013.01.038.##C. Couprie, C. Farabet, L. Najman, and Y. LeCun, "Indoor semantic segmentation using depth information," 2013.##J. K. W. Wong, M. Maghrebi, A. Ahmadian Fard Fini, M. A. Alizadeh Golestani, M. Ahmadnia, and M. Er, "Development of a refined illumination and reflectance approach for optimal construction site interior image enhancement," Constr. Innov., 2024, doi: 10.1108/CI-02-2022-0044.##احمدی، سید محمد و دیانت، روح الله، «یک چارچوب توزیعی مبتنی‌بر خوشه‌بندی دومرحله‌ای برای شناسایی چهره درمقیاس بالا»، مجله پردازش علائم و داده‌ها، شماره 1، صفحات 53-70، 1403##S. Sarkar, V. Venugopalan, K. Reddy, M. Giering, J. Ryde, and N. Jaitly, "Occlusion Edge Detection in RGB-D Frames using Deep Convolutional Networks," 2014, [Online]. Available: http://arxiv.org/abs/1412.7007.##K. G. Lore, A. Akintayo, and S. Sarkar, "LLNet: A deep autoencoder approach to natural low-light image enhancement," Pattern Recognit., 2017, doi: 10.1016/j.patcog.2016.06.008.##W. Kim, R. Lee, M. Park, and S. H. Lee, "Low-Light Image Enhancement Based on Maximal Diffusion Values," IEEE Access, 2019, doi: 10.1109/ACCESS.2019.2940452.##X. Guo, Y. Li, and H. Ling, "LIME: Low-light image enhancement via illumination map estimation," IEEE Trans. Image Process., 2017, doi: 10.1109/TIP.2016.2639450.##S. Der Chen and A. R. Ramli, "Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation," IEEE Trans. Consum. Electron., 2003, doi: 10.1109/TCE.2003.1261233.##T. Arici, S. Dikbas, and A. Altunbasak, "A histogram modification framework and its application for image contrast enhancement," IEEE Trans. Image Process., 2009, doi: 10.1109/TIP.2009.2021548.##M. Veluchamy and B. Subramani, "Image contrast and color enhancement using adaptive gamma correction and histogram equalization," Optik (Stuttg)., 2019, doi: 10.1016/j.ijleo.2019.02.054.##[K. Srinivas and A. K. Bhandari, "Low light image enhancement with adaptive sigmoid transfer function," IET Image Process., vol. 14, no. 4, pp. 668-678, 2020, doi: 10.1049/iet-ipr.2019.0781.##M. A. Al Wadud, M. H. Kabir, M. A. A. Dewan, and O. Chae, "A dynamic histogram equalization for image contrast enhancement," IEEE Trans. Consum. Electron., 2007, doi: 10.1109/TCE.2007.381734.##Edwin H. Land, "The Retinex Theory of Color Vision," Sci. Am., 1977.##X. Fu, D. Zeng, Y. Huang, X. P. Zhang, and X. Ding, "A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation," 2016, doi: 10.1109/CVPR.2016.304.##D. Parihar, A. Singh, and K. Singh, "Illumination Estimation for Nature Preserving low-light image enhancement," pp. 1-11, 2020.##M. K. Ng and W. Wang, "A Total Variation Model for Retinex," SIAM J. Imaging Sci., 2011, doi: 10.1137/100806588.##S. Wang, J. Zheng, H. M. Hu, and B. Li, "Naturalness preserved enhancement algorithm for non-uniform illumination images," IEEE Trans. Image Process., 2013, doi: 10.1109/TIP.2013.2261309.##G. Fu, L. Duan, and C. Xiao, "A Hybrid L2 -LP Variational Model for Single Low-Light Image Enhancement with Bright Channel Prior," 2019, doi: 10.1109/ICIP.2019.8803197.##D. J. Jobson, Z. U. Rahman, and G. A. Woodell, "A multiscale retinex for bridging the gap between color images and the human observation of scenes," IEEE Trans. Image Process., 1997, doi: 10.1109/83.597272.##D. J. Jobson, Z. U. Rahman, and G. A. Woodell, "Properties and performance of a center/surround retinex," IEEE Trans. Image Process., 1997, doi: 10.1109/83.557356.##W. Wang, X. Wu, X. Yuan, and Z. Gao, "An Experiment-Based Review of Low-Light Image Enhancement Methods," IEEE Access. 2020, doi: 10.1109/ACCESS.2020.2992749.##Y. Chang, C. Jung, P. Ke, H. Song, and J. Hwang, "Automatic Contrast-Limited Adaptive Histogram Equalization with Dual Gamma Correction," IEEE Access, 2018, doi: 10.1109/ACCESS.2018.2797872.##R. Kimmel, M. Elad, D. Shaked, R. Keshet, and I. Sobel, "A variational framework for retinex," Int. J. Comput. Vis., 2003, doi: 10.1023/A:1022314423998.##J. Y. Kim, L. S. Kim, and S. H. Hwang, "An advanced contrast enhancement using partially overlapped sub-block histogram equalization," IEEE Trans. Circuits Syst. Video Technol., 2001, doi: 10.1109/76.915354.##Y. S. Chiu, F. C. Cheng, and S. C. Huang, "Efficient contrast enhancement using adaptive gamma correction and cumulative intensity distribution," 2011, doi: 10.1109/ICSMC.2011.6084119.##S. C. Huang, F. C. Cheng, and Y. S. Chiu, "Efficient contrast enhancement using adaptive gamma correction with weighting distribution," IEEE Trans. Image Process., 2013, doi: 10.1109/TIP.2012.2226047.##C. R. Nithyananda, A. C. Ramachandra, and Preethi, "Review on Histogram Equalization based Image Enhancement Techniques," 2016, doi: 10.1109/ICEEOT.2016.7755145.##X. Fu, D. Zeng, Y. Huang, Y. Liao, X. Ding, and J. Paisley, "A fusion-based enhancing method for weakly illuminated images," Signal Processing, 2016, doi: 10.1016/j.sigpro.2016.05.031.##Y. T. Kim, "Contrast enhancement using brightness preserving bi-histogram equalization," IEEE Trans. Consum. Electron., 1997, doi: 10.1109/30.580378.##Q. Wang and R. K. Ward, "Fast image/video contrast enhancement based on weighted thresholded histogram equalization," IEEE Trans. Consum. Electron., 2007, doi: 10.1109/TCE.2007.381756.##S. Der Chen and A. R. Ramli, "Minimum mean brightness error bi-histogram equalization in contrast enhancement," IEEE Trans. Consum. Electron., 2003, doi: 10.1109/TCE.2003.1261234.##N. Kong, "A Literature Review on Histogram Equalization and Its Variations for Digital Image Enhancement," Int. J. Innov. Manag. Technol., 2013, doi: 10.7763/ijimt.2013.v4.426.##T. K. Kim, J. K. Paik, and B. S. Kang, "Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering," IEEE Trans. Consum. Electron., 1998, doi: 10.1109/30.663733.##A. M. Reza, "Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement," J. VLSI Signal Process. Syst. Signal Image. Video Technol., 2004, doi: 10.1023/B:VLSI.0000028532.53893.82.##B. Liu, W. Jin, Y. Chen, C. Liu, and L. Li, "Contrast enhancement using non-overlapped sub-blocks and local histogram projection," IEEE Trans. Consum. Electron., 2011, doi: 10.1109/TCE.2011.5955195.##[R. R. Hussein, Y. I. Hamodi, and R. A. Sabri, "Retinex theory for color image enhancement: A systematic review," International Journal of Electrical and Computer Engineering. 2019, doi: 10.11591/ijece.v9i6.pp5560-5569.##H. Chang, M. K. Ng, W. Wang, and T. Zeng, "Retinex image enhancement via a learned dictionary," Opt. Eng., 2015, doi: 10.1117/1.oe.54.1.013107.##Y. O. Nam, D. Y. Choi, and B. C. Song, "Power-constrained contrast enhancement algorithm using multiscale retinex for OLED display," IEEE Trans. Image Process., 2014, doi: 10.1109/TIP.2014.2324288.##B. Gu, W. Li, M. Zhu, and M. Wang, "Local edge-preserving multiscale decomposition for high dynamic range image tone mapping," IEEE Trans. Image Process., 2013, doi: 10.1109/TIP.2012.2214047.##S. Pan, X. An, and H. He, "Adapting iterative retinex computation for high-dynamic-range tone mapping," J. Electron. Imaging, 2013, doi: 10.1117/1.jei.22.2.023006.##X. Lan, H. Shen, L. Zhang, and Q. Yuan, "A spatially adaptive retinex variational model for the uneven intensity correction of remote sensing images," Signal Processing, 2014, doi: 10.1016/j.sigpro.2014.01.017.##G. A. Rahman, Z. U., Jobson, D. J., & Woodell, "Retinex processing for automatic image enhancement," J. Electron. Imaging, 2002.##M. Li, J. Liu, W. Yang, X. Sun, and Z. Guo, "Structure-Revealing Low-Light Image Enhancement Via Robust Retinex Model," IEEE Trans. Image Process., 2018, doi: 10.1109/TIP.2018.2810539.##X. Ren, M. Li, W. H. Cheng, and J. Liu, "Joint Enhancement and Denoising Method via Sequential Decomposition," 2018, doi: 10.1109/ISCAS.2018.8351427.##Z. Huang, T. Zhang, Q. Li, and H. Fang, "Adaptive gamma correction based on cumulative histogram for enhancing near-infrared images," Infrared Phys. Technol., 2016, doi: 10.1016/j.infrared.2016.11.001.##K. León, D. Mery, F. Pedreschi, and J. León, "Color measurement in L*a*b* units from RGB digital images," Food Res. Int., 2006, doi: 10.1016/j.foodres.2006.03.006.##T. Kumar and K. Verma, "A Theory Based on Conversion of RGB image to Gray image," Int. J. Comput. Appl., 2010, doi: 10.5120/1140-1493.##S. Nocedal, Jorge and Wright, "Numerical optimization," Springer Sci. & Bus. Media, 2006.##R. Anand, D. Aggarwal, and V. Kumar, "A comparative analysis of optimization solvers," J. Stat. Manag. Syst., 2017, doi: 10.1080/09720510.2017.1395182.##Z. Wang, A. C. Bovik, and L. Lu, "Why is image quality assessment so difficult?," ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc., 2002, doi: 10.1109/ICASSP.2002.5745362.##Z. Ying, G. Li, and W. Gao, "A bio-inspired multi-exposure fusion framework for low-light image enhancement," arXiv. 2017.##Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity," IEEE Trans. Image Process., 2004, doi: 10.1109/TIP.2003.819861.##A. Mittal, R. Soundararajan, and A. C. Bovik, "Making a 'completely blind' image quality analyzer," IEEE Signal Process. Lett., 2013, doi: 10.1109/LSP.2012.2227726.##عابدی، زهرا و یزدیان دهکردی، مهدی، «توسعه روش‌های مبتنی بر رفع نوفه اسپکل تصویر جهت رفع نوفه ویدئو ویسار»، مجله پردازش علائم و داده‌ها، شماره 1، صفحات 137-152، 1401##C. H. Lee, J. L. Shih, C. C. Lien, and C. C. Han, "Adaptive multiscale retinex for image contrast enhancement," 2013, doi: 10.1109/ SITIS.2013.19.##X. Dong et al., "Fast efficient algorithm for enhancement of low lighting video," 2011, doi: 10.1109/ICME.2011.6012107.##C. Lee, C. S. Kim, and C. Lee, "Contrast enhancement based on layered difference representation of 2D histograms," IEEE Trans. Image Process., 2013, doi: 10.1109/TIP. 2013.2284059.##D. Oneata, J. Revaud, J. Verbeek, and C. Schmid, "Spatio-temporal object detection proposals," 2014, doi: 10.1007/978-3-319-10578-9_48.##A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," 2012, doi: 10.1061/(ASCE)GT.1943-5606.0001284.##رضائی، معصومه، رضائیان، مهدی و ولی درهمی، «توصیف‌گر موضعی جدید با استفاده از نگاشت مرکاتور به‌منظور تشخیص اشیای سه‌بعدی»، مجله پردازش علائم و داده‌ها، شماره 1، صفحات 111-124، 1401##K. Zhang, L. Zhang, and M. H. Yang, "Fast Compressive Tracking," IEEE Trans. Pattern Anal. Mach. Intell., 2014, doi: 10.1109/TPAMI.2014.2315808.##Y. Liu, R. R. Martin, L. De Dominicis, and B. Li, "Using retinex for point selection in 3D shape registration," Pattern Recognit., 2014, doi: 10.1016/j.patcog.2013.12.015.##C. Jung, T. Sun, and L. Jiao, "Eye detection under varying illumination using the retinex theory," Neurocomputing, 2013, doi: 10.1016/j.neucom.2013.01.038.##C. Couprie, C. Farabet, L. Najman, and Y. LeCun, "Indoor semantic segmentation using depth information," 2013.##J. K. W. Wong, M. Maghrebi, A. Ahmadian Fard Fini, M. A. Alizadeh Golestani, M. Ahmadnia, and M. Er, "Development of a refined illumination and reflectance approach for optimal construction site interior image enhancement," Constr. Innov., 2024, doi: 10.1108/CI-02-2022-0044.##احمدی، سید محمد و دیانت، روح الله، «یک چارچوب توزیعی مبتنی‌بر خوشه‌بندی دومرحله‌ای برای شناسایی چهره درمقیاس بالا»، مجله پردازش علائم و داده‌ها، شماره 1، صفحات 53-70، 1403##S. Sarkar, V. Venugopalan, K. Reddy, M. Giering, J. Ryde, and N. Jaitly, "Occlusion Edge Detection in RGB-D Frames using Deep Convolutional Networks," 2014, [Online]. Available: http://arxiv.org/abs/1412.7007.##K. G. Lore, A. Akintayo, and S. Sarkar, "LLNet: A deep autoencoder approach to natural low-light image enhancement," Pattern Recognit., 2017, doi: 10.1016/j.patcog.2016.06.008.##W. Kim, R. Lee, M. Park, and S. H. Lee, "Low-Light Image Enhancement Based on Maximal Diffusion Values," IEEE Access, 2019, doi: 10.1109/ACCESS.2019.2940452.##X. Guo, Y. Li, and H. Ling, "LIME: Low-light image enhancement via illumination map estimation," IEEE Trans. Image Process., 2017, doi: 10.1109/TIP.2016.2639450.##S. Der Chen and A. R. Ramli, "Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation," IEEE Trans. Consum. Electron., 2003, doi: 10.1109/TCE.2003.1261233.##T. Arici, S. Dikbas, and A. Altunbasak, "A histogram modification framework and its application for image contrast enhancement," IEEE Trans. Image Process., 2009, doi: 10.1109/TIP.2009.2021548.##M. Veluchamy and B. Subramani, "Image contrast and color enhancement using adaptive gamma correction and histogram equalization," Optik (Stuttg)., 2019, doi: 10.1016/j.ijleo.2019.02.054.##[K. Srinivas and A. K. Bhandari, "Low light image enhancement with adaptive sigmoid transfer function," IET Image Process., vol. 14, no. 4, pp. 668-678, 2020, doi: 10.1049/iet-ipr.2019.0781.##M. A. Al Wadud, M. H. Kabir, M. A. A. Dewan, and O. Chae, "A dynamic histogram equalization for image contrast enhancement," IEEE Trans. Consum. Electron., 2007, doi: 10.1109/TCE.2007.381734.##Edwin H. Land, "The Retinex Theory of Color Vision," Sci. Am., 1977.##X. Fu, D. Zeng, Y. Huang, X. P. Zhang, and X. Ding, "A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation," 2016, doi: 10.1109/CVPR.2016.304.##D. Parihar, A. Singh, and K. Singh, "Illumination Estimation for Nature Preserving low-light image enhancement," pp. 1-11, 2020.##M. K. Ng and W. Wang, "A Total Variation Model for Retinex," SIAM J. Imaging Sci., 2011, doi: 10.1137/100806588.##S. Wang, J. Zheng, H. M. Hu, and B. Li, "Naturalness preserved enhancement algorithm for non-uniform illumination images," IEEE Trans. Image Process., 2013, doi: 10.1109/TIP.2013.2261309.##G. Fu, L. Duan, and C. Xiao, "A Hybrid L2 -LP Variational Model for Single Low-Light Image Enhancement with Bright Channel Prior," 2019, doi: 10.1109/ICIP.2019.8803197.##D. J. Jobson, Z. U. Rahman, and G. A. Woodell, "A multiscale retinex for bridging the gap between color images and the human observation of scenes," IEEE Trans. Image Process., 1997, doi: 10.1109/83.597272.##D. J. Jobson, Z. U. Rahman, and G. A. Woodell, "Properties and performance of a center/surround retinex," IEEE Trans. Image Process., 1997, doi: 10.1109/83.557356.##W. Wang, X. Wu, X. Yuan, and Z. Gao, "An Experiment-Based Review of Low-Light Image Enhancement Methods," IEEE Access. 2020, doi: 10.1109/ACCESS.2020.2992749.##Y. Chang, C. Jung, P. Ke, H. Song, and J. Hwang, "Automatic Contrast-Limited Adaptive Histogram Equalization with Dual Gamma Correction," IEEE Access, 2018, doi: 10.1109/ACCESS.2018.2797872.##R. Kimmel, M. Elad, D. Shaked, R. Keshet, and I. Sobel, "A variational framework for retinex," Int. J. Comput. Vis., 2003, doi: 10.1023/A:1022314423998.##J. Y. Kim, L. S. Kim, and S. H. Hwang, "An advanced contrast enhancement using partially overlapped sub-block histogram equalization," IEEE Trans. Circuits Syst. Video Technol., 2001, doi: 10.1109/76.915354.##Y. S. Chiu, F. C. Cheng, and S. C. Huang, "Efficient contrast enhancement using adaptive gamma correction and cumulative intensity distribution," 2011, doi: 10.1109/ICSMC.2011.6084119.##S. C. Huang, F. C. Cheng, and Y. S. Chiu, "Efficient contrast enhancement using adaptive gamma correction with weighting distribution," IEEE Trans. Image Process., 2013, doi: 10.1109/TIP.2012.2226047.##C. R. Nithyananda, A. C. Ramachandra, and Preethi, "Review on Histogram Equalization based Image Enhancement Techniques," 2016, doi: 10.1109/ICEEOT.2016.7755145.##X. Fu, D. Zeng, Y. Huang, Y. Liao, X. Ding, and J. Paisley, "A fusion-based enhancing method for weakly illuminated images," Signal Processing, 2016, doi: 10.1016/j.sigpro.2016.05.031.##Y. T. Kim, "Contrast enhancement using brightness preserving bi-histogram equalization," IEEE Trans. Consum. Electron., 1997, doi: 10.1109/30.580378.##Q. Wang and R. K. Ward, "Fast image/video contrast enhancement based on weighted thresholded histogram equalization," IEEE Trans. Consum. Electron., 2007, doi: 10.1109/TCE.2007.381756.##S. Der Chen and A. R. Ramli, "Minimum mean brightness error bi-histogram equalization in contrast enhancement," IEEE Trans. Consum. Electron., 2003, doi: 10.1109/TCE.2003.1261234.##N. Kong, "A Literature Review on Histogram Equalization and Its Variations for Digital Image Enhancement," Int. J. Innov. Manag. Technol., 2013, doi: 10.7763/ijimt.2013.v4.426.##T. K. Kim, J. K. Paik, and B. S. Kang, "Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering," IEEE Trans. Consum. Electron., 1998, doi: 10.1109/30.663733.##A. M. Reza, "Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement," J. VLSI Signal Process. Syst. Signal Image. Video Technol., 2004, doi: 10.1023/B:VLSI.0000028532.53893.82.##B. Liu, W. Jin, Y. Chen, C. Liu, and L. Li, "Contrast enhancement using non-overlapped sub-blocks and local histogram projection," IEEE Trans. Consum. Electron., 2011, doi: 10.1109/TCE.2011.5955195.##[R. R. Hussein, Y. I. Hamodi, and R. A. Sabri, "Retinex theory for color image enhancement: A systematic review," International Journal of Electrical and Computer Engineering. 2019, doi: 10.11591/ijece.v9i6.pp5560-5569.##H. Chang, M. K. Ng, W. Wang, and T. Zeng, "Retinex image enhancement via a learned dictionary," Opt. Eng., 2015, doi: 10.1117/1.oe.54.1.013107.##Y. O. Nam, D. Y. Choi, and B. C. Song, "Power-constrained contrast enhancement algorithm using multiscale retinex for OLED display," IEEE Trans. Image Process., 2014, doi: 10.1109/TIP.2014.2324288.##B. Gu, W. Li, M. Zhu, and M. Wang, "Local edge-preserving multiscale decomposition for high dynamic range image tone mapping," IEEE Trans. Image Process., 2013, doi: 10.1109/TIP.2012.2214047.##S. Pan, X. An, and H. He, "Adapting iterative retinex computation for high-dynamic-range tone mapping," J. Electron. Imaging, 2013, doi: 10.1117/1.jei.22.2.023006.##X. Lan, H. Shen, L. Zhang, and Q. Yuan, "A spatially adaptive retinex variational model for the uneven intensity correction of remote sensing images," Signal Processing, 2014, doi: 10.1016/j.sigpro.2014.01.017.##G. A. Rahman, Z. U., Jobson, D. J., & Woodell, "Retinex processing for automatic image enhancement," J. Electron. Imaging, 2002.##M. Li, J. Liu, W. Yang, X. Sun, and Z. Guo, "Structure-Revealing Low-Light Image Enhancement Via Robust Retinex Model," IEEE Trans. Image Process., 2018, doi: 10.1109/TIP.2018.2810539.##X. Ren, M. Li, W. H. Cheng, and J. Liu, "Joint Enhancement and Denoising Method via Sequential Decomposition," 2018, doi: 10.1109/ISCAS.2018.8351427.##Z. Huang, T. Zhang, Q. Li, and H. Fang, "Adaptive gamma correction based on cumulative histogram for enhancing near-infrared images," Infrared Phys. Technol., 2016, doi: 10.1016/j.infrared.2016.11.001.##K. León, D. Mery, F. Pedreschi, and J. León, "Color measurement in L*a*b* units from RGB digital images," Food Res. Int., 2006, doi: 10.1016/j.foodres.2006.03.006.##T. Kumar and K. Verma, "A Theory Based on Conversion of RGB image to Gray image," Int. J. Comput. Appl., 2010, doi: 10.5120/1140-1493.##S. Nocedal, Jorge and Wright, "Numerical optimization," Springer Sci. & Bus. Media, 2006.##R. Anand, D. Aggarwal, and V. Kumar, "A comparative analysis of optimization solvers," J. Stat. Manag. Syst., 2017, doi: 10.1080/09720510.2017.1395182.##Z. Wang, A. C. Bovik, and L. Lu, "Why is image quality assessment so difficult?," ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc., 2002, doi: 10.1109/ICASSP.2002.5745362.##Z. Ying, G. Li, and W. Gao, "A bio-inspired multi-exposure fusion framework for low-light image enhancement," arXiv. 2017.##Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity," IEEE Trans. Image Process., 2004, doi: 10.1109/TIP.2003.819861.##A. Mittal, R. Soundararajan, and A. C. Bovik, "Making a 'completely blind' image quality analyzer," IEEE Signal Process. Lett., 2013, doi: 10.1109/LSP.2012.2227726.##عابدی، زهرا و یزدیان دهکردی، مهدی، «توسعه روش‌های مبتنی بر رفع نوفه اسپکل تصویر جهت رفع نوفه ویدئو ویسار»، مجله پردازش علائم و داده‌ها، شماره 1، صفحات 137-152، 1401##C. H. Lee, J. L. Shih, C. C. Lien, and C. C. Han, "Adaptive multiscale retinex for image contrast enhancement," 2013, doi: 10.1109/ SITIS.2013.19.##X. Dong et al., "Fast efficient algorithm for enhancement of low lighting video," 2011, doi: 10.1109/ICME.2011.6012107.##C. Lee, C. S. Kim, and C. Lee, "Contrast enhancement based on layered difference representation of 2D histograms," IEEE Trans. Image Process., 2013, doi: 10.1109/TIP. 2013.2284059.## ##