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پیش‌بینی بقای بیماران مبتلا به سرطان پستان با استفاده از بهینه سازی مدل DeepHit Predicting the Survival of Breast Cancer Patients via Deep Neural Networks 1 1 با توجه به اهمیت و شیوع سرطان پستان به‌عنوان دومین علت مرگ در بین بیماری‌های سرطانی در جهان، دسترسی به مدل­هایی که با دقت بالا بتوانند بقای این بیماران را در افراد مبتلا پیش‌بینی نمایند، موردتوجه است. هدف از این مطالعه، استفاده از شبکه عصبی عمیقِ بهینه‌سازی شده برای پیش‌بینی بقای بیماران مبتلا به سرطان پستان است. مطالعه حاضر یک مطالعه تحلیلی هست. داده‌های مورداستفاده از بانک داده‌ای METABRIC مربوط به طبقه‌بندی مولکولی از بیماران مبتلابه سرطان سینه مجمع بین‌المللی هست. تعداد کل بیماران موردبررسی 1981نفر هست. از این تعداد 888 نفر از بیماران تا لحظه مرگ تحت مراقبت و بقیه در حین مطالعه از ادامه مطالعه صرف‌نظر کرده‌اند. در این دیتاست به 21 ویژگی کلینیکی بیماران توجه شده است که در کل شامل 6 ویژگی کمی و 15 ویژگی کیفی هست. جهت پیش‌بینی بقا از مدل شبکه عصبی عمیق DeepHit بهینه‌سازی شده استفاده می­شود. مدل بهینه‌سازی شده توانسته است معیار 73/0 c_index= را، که معیاری برای سنجش قابلیت مدل‌های آنالیز بقا است کسب کند. مقایسه با مدل‌های قبلی بر اساس مجموعه داده‌های واقعی و مصنوعی نشان می‌دهد که DeepHit بهینه‌سازی شده به پیشرفت‌های عملکردی بزرگ و آماری قابل‌توجهی نسبت به روش‌های سطح بالا دست‌یافته است.   2 Predicting and estimating the time it takes for an event of interest to occur base on available information is special assistance in how to deal with the event and handle it or provide solution to prevent the occurrence of the event. In medicine, valuable information about evaluating the types of treatments and prognosis and providing solution to handle event can be gained by predicting the time that an event occurrence according to information recorded from patients. Many statistical solutions have been proposed for predicting the time that an event occurrence and the most professional method is Survival Analysis. The purpose of Survival Analysis is to predict the time that an event occurrence a model effective parameters in estimating the time, which can be control or eliminating problematic factors. Due to the importance and prevalence of breast cancer as the second leading cause of death among cancer patients in the world, access to models that can accurately predict the survival of breast cancer patients is very important. The present study is an analytical study. The data used in this study are taken from The Molecular Taxonomy Data of the International Federation of Breast Cancer (METABRIC) database, which is related to which is related to the molecular classification of breast cancer patients. The total number of patients studied was 1981. Of these, 888 patients were in care until the time of death and the rest did not continue the study during the study. In this database, 21 clinical features of patients have been considered, which includes a total of 6 quantitative features and 15 qualitative features. To predict survival, a deep neural network model called the optimized DeepHit is used. The optimized model has achieved the criterion of c_index = 0.73, which is a criterion for measuring the capability of survival analysis models. Comparisons with previous models based on real and synthetic datasets show that the optimized DeepHit has achieved great performance and statistically significant improvements over previous advanced methods. 3 12 2022/03/17 1400/12/26 2023/06/2 1402/3/12 سهیلا رضایی Soheila Rezaei گروه مهندسی برق ایران s.rezaie2261368@gmail.com حسین قیومی زاده Hossein Ghayoumi Zadeh گروه مهندسی برق ایران h.ghayoumizadeh@gmail.com محمدحسین قلی زاده Mohammad Hossein Gholizadeh گروه مهندسی برق ایران gholizadeh@vru.ac.ir علی فیاضی Ali Fayazi گروه مهندسی برق ایران a.fayazi@vru.ac.ir Deep learning Survival Analysis Breast cancer استخراج ویژگی یادگیری عمیق تجزیه و تحلیل بقا سرطان پستان [1]. El-Bendary, N. and N.A. Belal, A feature-fusion framework of clinical, genomics, and histopathological data for METABRIC breast cancer subtype classification. Applied Soft Computing, vol.91, pp.106238, 2020.##[2]. Singh, D. and A.K. Singh, Role of image thermography in early breast cancer detection-Past, present and future. Computer methods and programs in biomedicine, vol. 183, pp.105074, 2020.##[3]. Azamjah N, Soltan-Zadeh Y, Zayeri F. Global trend of breast cancer mortality rate: a 25-year study. Asian Pacific journal of cancer prevention: APJCP. 2019;20(7):2015.##[4]. Forouzanfar, M.H., et al., Breast and cervical cancer in 187 countries between 1980 and 2010: a systematic analysis. The lancet, vol. 378(9801), pp.1461-1484, 2011.##[5]. Delen, D., G. Walker, and A. Kadam, Predicting breast cancer survivability: a comparison of three data mining methods. Artificial intelligence in medicine, vol.34(2), pp.113-127, 2005.##[6]. Campone, M., et al., Taxanes in adjuvant breast cancer setting: which standard in Europe? Critical reviews in oncology/hematology, vol.55(3), pp.167-175, 2005.##[7]. Mohammadpour, A., et al., Breast Cancer, Genetic Factors and Methods of Diagnosis. Sarem Journal of Reproductive Medicine,vol. 4(4): 198-207, 2020.##[8]. Mukherjee, A., et al., Associations between genomic stratification of breast cancer and centrally reviewed tumour pathology in the METABRIC cohort. NPJ breast cancer, vol.4(1) ,pp. 1-9, 2018.##[9]. Rakha, E.A. and A.R. Green, Molecular classification of breast cancer: what the pathologist needs to know. Pathology, vol.49(2), pp.119, 2017.##[10]. Hao, J., et al., Interpretable deep neural network for cancer survival analysis by integrating genomic and clinical data. BMC medical genomics,vol. 12(10) ,pp. 1-13,2019.##[11]. Collett, D., Modelling survival data in medical research. 2015: CRC press.##[12]. Stevenson, M. and I. EpiCentre, An introduction to survival analysis. EpiCentre, IVABS, Massey University,2009.##[13]. Goel, M.K., P. Khanna, and J. Kishore, Understanding survival analysis: Kaplan-Meier estimate. International journal of Ayurveda research,vol.1(4), pp.274, 2010.##[14]. Therneau, T.M. and P.M. Grambsch, The cox model, in Modeling survival data: extending the Cox model,pp. 39-7,2000.##[15]. O'Brien, R.C., et al., Random Survival Forests Analysis of Intraoperative Complications as Predictors of Descemet Stripping Automated Endothelial Keratoplasty Graft Failure in the Cornea Preservation Time Study. JAMA ophthalmology,vol.139(2) ,pp.191-197,2021.##[16]. Gensheimer MF, Narasimhan B. A scalable discrete-time survival model for neural networks. PeerJ. 2019 Jan 25;7:e6257.##[17]. Faraggi, D. and R. Simon, A neural network model for survival data. Statistics in medicine,vol.14(1) ,pp.73-82, 1995.##[18]. Street, W.N. A Neural Network Model for Prognostic Prediction. in ICML. 1998. Citeseer.##[19]. Fotso, S., Deep neural networks for survival analysis based on a multi-task framework. arXiv preprint arXiv:1801.05512, 2018.##[20]. Yu, C.-N., et al., Learning patient-specific cancer survival distributions as a sequence of dependent regressors. Advances in neural information processing systems, vol.24, pp.1845-1853, 2011.##[21]. Katzman, J.L., et al., DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC medical research methodology, vol.18(1), pp.121, 2018.##[22]. Luck M, Sylvain T, Cardinal H, Lodi A, Bengio Y. Deep learning for patient-specific kidney graft survival analysis. arXiv preprint arXiv:1705.10245. 2017 May 29.##[23]. Lee, C., et al. Deephit: A deep learning approach to survival analysis with competing risks. in Thirty-second AAAI conference on artificial intelligence,2018.##[24]. Bilal, E., et al., Improving breast cancer survival analysis through competition-based multidimensional modeling. PLoS computational biology, vol.9(5), pp.10030-47, 2013.##[25]. Lu, J., et al., An efficient kernel discriminant analysis method. Pattern Recognition, vol. 38(10) ,pp.1788-179,2005.##[26]. Lee, M.-L.T. and G.A. Whitmore, Threshold regression for survival analysis: modeling event times by a stochastic process reaching a boundary. Statistical Science,21(4) ,pp.501-513, 2006.##[1]. El-Bendary, N. and N.A. Belal, A feature-fusion framework of clinical, genomics, and histopathological data for METABRIC breast cancer subtype classification. Applied Soft Computing, vol.91, pp.106238, 2020.##[2]. Singh, D. and A.K. Singh, Role of image thermography in early breast cancer detection-Past, present and future. Computer methods and programs in biomedicine, vol. 183, pp.105074, 2020.##[3]. Azamjah N, Soltan-Zadeh Y, Zayeri F. Global trend of breast cancer mortality rate: a 25-year study. Asian Pacific journal of cancer prevention: APJCP. 2019;20(7):2015.##[4]. Forouzanfar, M.H., et al., Breast and cervical cancer in 187 countries between 1980 and 2010: a systematic analysis. The lancet, vol. 378(9801), pp.1461-1484, 2011.##[5]. Delen, D., G. Walker, and A. Kadam, Predicting breast cancer survivability: a comparison of three data mining methods. Artificial intelligence in medicine, vol.34(2), pp.113-127, 2005.##[6]. Campone, M., et al., Taxanes in adjuvant breast cancer setting: which standard in Europe? Critical reviews in oncology/hematology, vol.55(3), pp.167-175, 2005.##[7]. Mohammadpour, A., et al., Breast Cancer, Genetic Factors and Methods of Diagnosis. Sarem Journal of Reproductive Medicine,vol. 4(4): 198-207, 2020.##[8]. Mukherjee, A., et al., Associations between genomic stratification of breast cancer and centrally reviewed tumour pathology in the METABRIC cohort. NPJ breast cancer, vol.4(1) ,pp. 1-9, 2018.##[9]. Rakha, E.A. and A.R. Green, Molecular classification of breast cancer: what the pathologist needs to know. Pathology, vol.49(2), pp.119, 2017.##[10]. Hao, J., et al., Interpretable deep neural network for cancer survival analysis by integrating genomic and clinical data. BMC medical genomics,vol. 12(10) ,pp. 1-13,2019.##[11]. Collett, D., Modelling survival data in medical research. 2015: CRC press.##[12]. Stevenson, M. and I. EpiCentre, An introduction to survival analysis. EpiCentre, IVABS, Massey University,2009.##[13]. Goel, M.K., P. Khanna, and J. Kishore, Understanding survival analysis: Kaplan-Meier estimate. International journal of Ayurveda research,vol.1(4), pp.274, 2010.##[14]. Therneau, T.M. and P.M. Grambsch, The cox model, in Modeling survival data: extending the Cox model,pp. 39-7,2000.##[15]. O'Brien, R.C., et al., Random Survival Forests Analysis of Intraoperative Complications as Predictors of Descemet Stripping Automated Endothelial Keratoplasty Graft Failure in the Cornea Preservation Time Study. JAMA ophthalmology,vol.139(2) ,pp.191-197,2021.##[16]. Gensheimer MF, Narasimhan B. A scalable discrete-time survival model for neural networks. PeerJ. 2019 Jan 25;7:e6257.##[17]. Faraggi, D. and R. Simon, A neural network model for survival data. Statistics in medicine,vol.14(1) ,pp.73-82, 1995.##[18]. Street, W.N. A Neural Network Model for Prognostic Prediction. in ICML. 1998. Citeseer.##[19]. Fotso, S., Deep neural networks for survival analysis based on a multi-task framework. arXiv preprint arXiv:1801.05512, 2018.##[20]. Yu, C.-N., et al., Learning patient-specific cancer survival distributions as a sequence of dependent regressors. Advances in neural information processing systems, vol.24, pp.1845-1853, 2011.##[21]. Katzman, J.L., et al., DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC medical research methodology, vol.18(1), pp.121, 2018.##[22]. Luck M, Sylvain T, Cardinal H, Lodi A, Bengio Y. Deep learning for patient-specific kidney graft survival analysis. arXiv preprint arXiv:1705.10245. 2017 May 29.##[23]. Lee, C., et al. Deephit: A deep learning approach to survival analysis with competing risks. in Thirty-second AAAI conference on artificial intelligence,2018.##[24]. Bilal, E., et al., Improving breast cancer survival analysis through competition-based multidimensional modeling. PLoS computational biology, vol.9(5), pp.10030-47, 2013.##[25]. Lu, J., et al., An efficient kernel discriminant analysis method. Pattern Recognition, vol. 38(10) ,pp.1788-179,2005.##[26]. Lee, M.-L.T. and G.A. Whitmore, Threshold regression for survival analysis: modeling event times by a stochastic process reaching a boundary. Statistical Science,21(4) ,pp.501-513, 2006.## ##

ارائه معماری سیستم کمک‌یار فرد نابینا برای عبور از تقاطع به کمک محاسبات ابری موبایل The Architecture of the Blind Assistant System for Passing Intersection by Mobile Cloud Computing 1 1 امروزه با پیشرفت فناوری‌ها در حوزه کمک به افراد نابینا، سیستم­های جهت ­یابی از اهمیت بسیاری برخوردار هستند. همچنین با پیشرفت تکنولوژی در حوزه محاسبات ابری و تلفن همراه می­ توان به این افراد کمک کرد. در این تحقیق یک معماری با استفاده از محاسبات ابری موبایل به منظور کمک به این افراد، در هنگام عبور از تقاطع پیشنهاد می­شود. معماری پیشنهادی شامل سه مؤلفه تلفن­ همراه، ابر و  نظارت است. اطلاعات مکان و زمان، جمع ­آوری و در پایگاه داده ابر ذخیره می­ شود تا در مواقع نیاز، آشنایان فرد نابینا بتوانند بر او نظارت داشته باشند. برای تشخیص وضعیت چراغ عابر پیاده عکس از تقاطع­ها با استفاده از دوربین گرفته می­ شود. با استفاده از ویژگی رنگ و عملیات ریخت­ شناسی، رنگ چراغ عابر پیاده تشخیص داده و به فرد نابینا گزارش داده می­ شود. نتایج حاصل از پیاده ­سازی روی تصاویر گرفته‌شده، کارایی خوب روش پیشنهادی در تشخیص وضعیت چراغ عابر پیاده را نشان می­ دهد که دارای میانگین دقت 100٪  برای روز و همچنین شب است. 2 Subject- Today, with the advancement of technologies to assist blind and visually impaired people, navigation systems are of great importance. As a result of emerging technologies in telecommunication and smartphones, these people can be helped. Identifying pedestrian and traffic lights is important to help pedestrians with visual impairments cross the intersection safely and securely. Background- researchers have studied the detection and identification of traffic lights in the assistive system or blind assist device. These researches can be divided into three main types: based on pattern matching, based on circular shape extraction, and based on color distribution. Methodology- In this research, an architecture based on mobile cloud computing is proposed, which can help blind pedestrians in crossing intersections. The architecture consists of three tiers: mobile phone, cloud, and supervision. The most important component is located on the mobile phone. It recognizes the color of pedestrian light by using image processing techniques. Spatial information (time and location) of the blind person is collected and held in a cloud storage database so that acquaintances can monitor him if needed. In order to detect the status of pedestrian lights, pictures of crossing streets with cameras will be captured. Using the features of color and morphology operations, the color of pedestrian lights is recognized and reported to the blind person. To this end, morphological operations are performed to eliminate small elements in the background and to restore the original size of the traffic light sign. Therefore, the operations of dilation, filling, and erosion are used. Result- We gathered a dataset including 280 photos of pedestrian lights (170 photos at day, 110 photos at night) in different illumination conditions (early day, noon, early night, night) and weather (sunny, cloudy, rainy). Matlab software and notebook system with Intel (R) Core (TM) i5 CPU and AMD Mobility Radeon HD 5100 graphics card were used to implement pedestrian traffic-light status detection. The scenario-based method is used to evaluate the system architecture and show that the proposed system can satisfy the investigated scenario. At last, the implementation results on taken images show excellent performance in detecting pedestrian lights with approximately 100% accuracy for the day and night. 13 26 2022/03/172022/02/25 1400/12/6 2023/06/22023/06/2 1402/3/12 نرجس حاجی زاده Narjes Hajizadeh دانشگاه آزاد بیرجند ایران Hajizadeh.narjes@gmail.com حامد وحدت نژاد Hamed Vahdat-Nejad دانشگاه بیرجند ایران vahdatnejad@birjand.ac.ir رمضان هاونگی Ramazan Havangi دانشگاه بیرجند ایران havangi@birjand.ac.ir Mobile cloud computing Pedestrian light Image processing Morphology operations محاسبات ابری موبایل چراغ عابر پیاده پردازش تصویر عملیات ریخت شناسی [1] S.Zafar, M.Asif, M.Bin Ahmad, T.M.Ghazal, T. Faiz, M. Ahmad, M.A.Khan, " Assistive Devices Analysis for Visually Impaired Persons: A Review on Taxonomy", IEEE Access 2022, 10, 13354-13366.##[2] M.D.Messaoudi ,B.J. Menelas ,H.Mcheick, "Review of Navigation Assistive Tools and Technologies for the Visually Impaired", Sensors,vol.22,no.20,2022.##[3] Q. Wang, Q. Zhang ,X.Liang ,Y.Wang1, C. Zhou, V.Mikulovich , "Traffic Lights Detection and Recognition Method Based on the Improved YOLOv4 Algorithm", Sensors, vol.22,no.1,2022.##[4] Y. Jie, Ch. Xiaomin, G. Pengfei, Xi. Zhonglong, "A New Traffic Light Detection and Recognition Algorithm for Electronic Travel Aid", International Conference on Intelligent Control and Information Processing (ICICIP), IEEE, 2013, pp 644-648.##[5] Q. Zhang, L. Cheng, R. Boutaba, 2010,"Cloud computing: state-of-the-art and research challenges", Journal of Internet Services and Applications Springer-Verlag, 2010, vol. 1, Issue 1, pp. 7-18.##[6] A.K.Pandey,S.Maneria, "Cloud computing methods based on IoT for better patient data planning: A research", 11th International Conference on System Modeling & Advancement in Research Trends (SMART),2022.##[7] H. Vahdat-Nejad, "context- Aware Middleware: A Review", in Context in Computing: A Cross-Disciplinary Approach for Modeling the Real World, Springer, 2014, pp. 83-96.##[8] M. Omachi, Sh. Omachi, "Detection of Traffic Light Using Structural Information", Signal Processing (ICSP), International Conference on, IEEE, 2010, pp. 809 - 812.##[9] M. Yusro, K. Hou, E. Pissaloux, H. Shi, "SEES: Concept and Design of a Smart Environment##Explorer Stick" International Conference on Human System Interaction (HSI), IEEE, 2013, pp. 70-77.##[10] P. Angin, B. Bhargava, S. Helal, "A Mobile-Cloud Collaborative Traffic Lights Detector for Blind Navigation" International Conference on Mobile Data Management (MDM), IEEE, 2010, pp. 396-401.##[11] Y. Freund, R. Schapire, "A Decision-Theoretic Generalization of On-line Learning and an Application to Boosting" Journal of Computer and System Sciences, Elsevier, 1997, vol. 55, Issue 1, pp. 119-139.##[12] Y. Chunhe, B. Ying, "A Traffic Light Detection Method", Springer-Verlag Berlin Heidelberg, 2012, vol. 163, pp. 745-751.##[13] AN. Lapyko, Li. Tung, B. Lin, "A Cloud-based Outdoor Assistive Navigation System for the Blind and Visually Impaired", Wireless and Mobile Networking Conference (WMNC), IEEE, 2014, pp. 1-8.##[14] Y. Jie, T. Niu,"A Novel Electronic Travel Aid for the Blind", International Conference on Photonics, 3D-Imaging, and Visualization, edited by Egui Zhu, Proc. of SPIE, SPIE, 2011, vol. 8205, 820503, pp. 1-4.##[15] W. Zong, Q. Chen, "Traffic Light Detection Based on Multi-feature Segmentation and Online Selecting Scheme", International Conference on Systems, Man, and Cybernetics (SMC), IEEE, 2014, pp. 204-209.##[16] M. Diaz-Cabrera, P. Cerri, "Robust real-time traffic light detection and distance estimation using a single camera", Expert Systems with Applications , Elsevier, 2015, vol. 42, Issue 8, pp. 3911-3923.##[17] X. Shi, N. Zhao, Y. Xia, "Detection and classification of traffic lights for automated setup of road surveillance systems", Journal Multimedia Tools and Applications, Springer, 2014, pp. 1-16.##[18] T. Adep, R. Nikaml, , S. Wanewel, & K. B. Naik, "Visual Assistant for Blind People using Raspberry Pi", International Journal of Scientific Research in Computer Science, Engineering and Information Technology ,vol.7,no.3,pp.671-675, 2021.##[19] K.Ding, K.Ma, S.Wang & E.P. Simoncelli , "Comparison of Full-Reference image quality models for optimization of image processing systems", International Journal of Computer Vision , vol.129, pp.1258-1281,2021.##[20] M. Mattsson, H. Crahn, F. Martensson, "Software Architecture Evaluation Methods for Performance, Maintainability, Testability, and Portability", Proceedings of second International Conference on the Quality of software Architectures (QoSA), 2006.##[21] L. Dobrica, E. Niemela, "A survey on software architecture analysis methods. Software Engineering", IEEE Transactions on Software Engineering, vol. 28, Issue 7, pp. 638-653,2002.##[1] S.Zafar, M.Asif, M.Bin Ahmad, T.M.Ghazal, T. Faiz, M. Ahmad, M.A.Khan, " Assistive Devices Analysis for Visually Impaired Persons: A Review on Taxonomy", IEEE Access 2022, 10, 13354-13366.##[2] M.D.Messaoudi ,B.J. Menelas ,H.Mcheick, "Review of Navigation Assistive Tools and Technologies for the Visually Impaired", Sensors,vol.22,no.20,2022.##[3] Q. Wang, Q. Zhang ,X.Liang ,Y.Wang1, C. Zhou, V.Mikulovich , "Traffic Lights Detection and Recognition Method Based on the Improved YOLOv4 Algorithm", Sensors, vol.22,no.1,2022.##[4] Y. Jie, Ch. Xiaomin, G. Pengfei, Xi. Zhonglong, "A New Traffic Light Detection and Recognition Algorithm for Electronic Travel Aid", International Conference on Intelligent Control and Information Processing (ICICIP), IEEE, 2013, pp 644-648.##[5] Q. Zhang, L. Cheng, R. Boutaba, 2010,"Cloud computing: state-of-the-art and research challenges", Journal of Internet Services and Applications Springer-Verlag, 2010, vol. 1, Issue 1, pp. 7-18.##[6] A.K.Pandey,S.Maneria, "Cloud computing methods based on IoT for better patient data planning: A research", 11th International Conference on System Modeling & Advancement in Research Trends (SMART),2022.##[7] H. Vahdat-Nejad, "context- Aware Middleware: A Review", in Context in Computing: A Cross-Disciplinary Approach for Modeling the Real World, Springer, 2014, pp. 83-96.##[8] M. Omachi, Sh. Omachi, "Detection of Traffic Light Using Structural Information", Signal Processing (ICSP), International Conference on, IEEE, 2010, pp. 809 - 812.##[9] M. Yusro, K. Hou, E. Pissaloux, H. Shi, "SEES: Concept and Design of a Smart Environment##Explorer Stick" International Conference on Human System Interaction (HSI), IEEE, 2013, pp. 70-77.##[10] P. Angin, B. Bhargava, S. Helal, "A Mobile-Cloud Collaborative Traffic Lights Detector for Blind Navigation" International Conference on Mobile Data Management (MDM), IEEE, 2010, pp. 396-401.##[11] Y. Freund, R. Schapire, "A Decision-Theoretic Generalization of On-line Learning and an Application to Boosting" Journal of Computer and System Sciences, Elsevier, 1997, vol. 55, Issue 1, pp. 119-139.##[12] Y. Chunhe, B. Ying, "A Traffic Light Detection Method", Springer-Verlag Berlin Heidelberg, 2012, vol. 163, pp. 745-751.##[13] AN. Lapyko, Li. Tung, B. Lin, "A Cloud-based Outdoor Assistive Navigation System for the Blind and Visually Impaired", Wireless and Mobile Networking Conference (WMNC), IEEE, 2014, pp. 1-8.##[14] Y. Jie, T. Niu,"A Novel Electronic Travel Aid for the Blind", International Conference on Photonics, 3D-Imaging, and Visualization, edited by Egui Zhu, Proc. of SPIE, SPIE, 2011, vol. 8205, 820503, pp. 1-4.##[15] W. Zong, Q. Chen, "Traffic Light Detection Based on Multi-feature Segmentation and Online Selecting Scheme", International Conference on Systems, Man, and Cybernetics (SMC), IEEE, 2014, pp. 204-209.##[16] M. Diaz-Cabrera, P. Cerri, "Robust real-time traffic light detection and distance estimation using a single camera", Expert Systems with Applications , Elsevier, 2015, vol. 42, Issue 8, pp. 3911-3923.##[17] X. Shi, N. Zhao, Y. Xia, "Detection and classification of traffic lights for automated setup of road surveillance systems", Journal Multimedia Tools and Applications, Springer, 2014, pp. 1-16.##[18] T. Adep, R. Nikaml, , S. Wanewel, & K. B. Naik, "Visual Assistant for Blind People using Raspberry Pi", International Journal of Scientific Research in Computer Science, Engineering and Information Technology ,vol.7,no.3,pp.671-675, 2021.##[19] K.Ding, K.Ma, S.Wang & E.P. Simoncelli , "Comparison of Full-Reference image quality models for optimization of image processing systems", International Journal of Computer Vision , vol.129, pp.1258-1281,2021.##[20] M. Mattsson, H. Crahn, F. Martensson, "Software Architecture Evaluation Methods for Performance, Maintainability, Testability, and Portability", Proceedings of second International Conference on the Quality of software Architectures (QoSA), 2006.##[21] L. Dobrica, E. Niemela, "A survey on software architecture analysis methods. Software Engineering", IEEE Transactions on Software Engineering, vol. 28, Issue 7, pp. 638-653,2002.## ##

CoviX-Net: سامانه مبتنی بر یادگیری عمیق برای تشخیص و تمایز عفونت کوید-19 و ذات‌الریه در تصاویر رادیوگرافی قفسه سینه CoviX-Net: A Deep Learning-based System for Diagnosis and Differentiation of Covid-19 Infection and Pneumonia in Chest Radiography Images 1 1 در این پژوهش، سامانه CoviX-Net مبتنی بر یادگیری عمیق برای تشخیص و تمایز بیماری کوید-19 و انواع ذات‌الریه از روی تصاویر رادیوگرافی سینه ارائه می‌شود. معماری مدل یادگیری CoviX-Net، بر اساس معماری اِکسپشن چند لایه و متناسب با کاربرد مورد هدف طراحی شده است. در این سامانه، از یادگیری انتقالی برای رفع مشکل کمبود داده آموزشی استفاده می‌شود. همچنین برای فراهم نمودن داده آموزشی کافی، یک پایگاه تصاویر جامع با بهره‌گیری مناسب از دو منبع مختلف از تصاویر قفسه سینه ایجاد شده است. برای جلوگیری از مشکل بیش‌برازش، فنون افزایش داده، تَنزلِ وزن و تنظیم ‌کننده‌های L2 استفاده شده است. نتایج ارزیابی‌ها نشان می‌دهد دقت CoviX-Net در حالت سه طبقه (کوید-19، ذات‌الریه و ریه طبیعی) %25/99، و در حالت چهار طبقه (کوید-19، ذات‌الریه باکتریایی، ذات‌الریه ویروسی و ریه طبیعی) %95 است که در مقایسه با دیگر روش‌های طبقه‌بندی مبتنی بر یادگیری عمیق با ساختار مشابه، بهبود دقت %5 و در مقایسه با روش مبتنی بر یادگیری عمیق انتقالی موازی، با ساختار پیچیده، بهبود دقت حدود نیم درصد را دارد. کلیه کدهای پیاده‌سازی CoviX-Net و مجموعه تصاویر گردآوری شده در دسترس عموم پژوهشگران قرار گرفته است. 2 Coronavirus (Covid-19) is a new infectious disease with a very high rate of infection and mortality. Therefore, its early detection has become one of the vital measures of human society. This virus is commonly tested using sputum or blood samples, and the result is usually announced within hours or even days after the test. However, even an hour delay in announcing the test results can lead to many more people being infected. Another way to diagnose the virus is to take a chest X-ray (chest radiography images), which is much faster and cheaper than other tests. However, the rate of human diagnostic error from these images is high, and more importantly, it is very difficult to distinguish Covid-19 infection from other infections such as pneumonia. In this paper, an intelligent system, termed CoviX-Net, based on deep machine learning techniques, is presented to diagnose and differentiate Covid-19 disease and various types of pneumonia (bacterial or viral) using chest radiographs. The CoviX-Net learning model is based on the Xception architecture, the accuracy of which is improved by the use of transfer learning and data augmentation techniques. To provide adequate training data, a comprehensive database is created by integrating two different sources of chest X-ray images. The evaluations performed on the test images show that the accuracy of CoviX-Net diagnosis in the three-class mode (Covid-19, pneumonia, and normal lung) is %99.25, and in the four-class mode (Covid-19, bacterial pneumonia, viral pneumonia, and normal lung) is %95. Compared to other similar deep learning-based classification methods, the accuracy is improved by 5%, and compared to the transfer learning-based parallel deep learning method, with a complex structure, the accuracy is improved by about half a percent. These promising results demonstrate the superiority of CoviX-Net against the competitors, and suggest that CoviX-Net can be a useful tool to assist clinicians and radiologists in diagnosing patients with Covid-19. All the implementation source codes and collected dataset are made publicly available. 27 46 2022/03/172022/02/252021/06/2 1400/3/12 2023/06/22023/06/22023/12/11 1402/9/20 وحید محمدیان Vahid Mohammadian Takaloo دانشگاه شهید مدنی آذربایجان ایران vahid.mohammadian@azaruniv.ac.ir مهدی هاشم زاده Mahdi Hashemzadeh دانشگاه شهید مدنی آذربایجان ایران meh_hashemzadeh@yahoo.com جلیل قویدل نیچران Jalil Ghavidel Neycharan دانشگاه شهید مدنی آذربایجان ایران j.ghavidel@azaruniv.ac.ir Machine vision Deep learning Covid-19 diagnosis Pneumonia Chest radiography images بینایی ماشین یادگیری عمیق تشخیص کوید-19 ذات‌الریه تصاویر رادیوگرافی قفسه سینه [1] C. 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تشخیص انجمن ها در شبکه های اجتماعی با استفاده از الگوریتم جستجوی هارمونی گسسته Identifying Community Structures in Social Networks using Discrete Harmony Search Algorithm 1 1 شبکه های اجتماعی امروزه نقش مهمی در زندگی روزمره افراد دارد. شناخت ساختار و گروه های تشکیل دهنده این شبکه ها می تواند اطلاعات مفیدی از وضعیت جامعه و افراد دهد. یکی از مباحثی که در این حوزه بحث می شود، ساخت گراف شبکه است که بر اساس اشیاء به عنوان گره های شبکه، و یال ها به عنوان تراکنش های بین این اشیاء شکل می گیرد. تشخیص ارتباطات بر اساس این گراف ها انجام می گیرد. یکی از روش هایی که برای ساخت و تشخیص این ارتباطات انجام می گیرد استفاده از الگوریتم های بهینه سازی است و مشخص شده است که الگوریتم جستجوی هارمونی یکی از الگوریتم های کارا در این حوزه است. با این وجود در مبحث تشخیص ساختارهای ارتباطی و جوامع، تا به حال کار پژوهشی با استفاده از الگوریتم جستجوی هارمونی انجام نگرفته است. در این مقاله، برای ساخت خوشه های شبکه بر اساس گراف شبکه و تشخیص ارتباطات مؤثر بر اساس معیارهای مختلف، روش جدیدی پیشنهاد می شود. این روش جدید در واقع نسخه گسسته ای از الگوریتم جستحوی هارمونی است که برای کشف جوامع و ساختارهای ارتباطی در شبکه های اجتماعی به کار می رود. آزمایش هایی بر روی چند شبکه مصنوعی و طبیعی انجام گرفته است و نتایج بدست آمده نشان می دهد که روش پیشنهادی دارای کارکرد مناسبی در مقایسه با دیگر روش های موجود دارد.   2 Nowadays, social networks play an important role in people's daily lives. A social network is a kind of social structure that consists of several nodes that can be individuals or organizations. Most importantly, these nodes are connected by one or more specific types of dependencies, such as friendships or work relationships. Understanding the structure and constituent groups of these networks can give us useful information about the state of society and individuals. In this article, a new solution to solve the problem of social structure detection is provided. Social structure means communities or associations in social networks. An important issue of this context is network graph construction based on objects as nodes and edges as transactions between these objects. Community detection is based on these graphs. The appropriate solution is to identify and create clusters of nodes that have strong connections with each other and at the same time have weaker connections between nodes of different clusters. Optimization algorithms can be used to construct and detect these connections. Harmonic search is one of the efficient optimization algorithms in this context. However, in the field of identifying communication structures and communities, so far, no research work has been done using the harmonic search algorithm. In this paper, a new method is proposed to construct network clusters based on network graphs. It can identify effective communications based on different criteria. In order to propose this method, first a new version of harmonic search algorithm is designed for discrete environments while the original version of which is for continuous environments. Then, according the problem, which is to discover appropriate structures in the social network graph, a new method is devised to solve it. This method tries to provide a suitable discrete version by relying on different operators to be applicable to solve the desired problem. In order to evaluate the proposed method, various experiments were carried out on several different networks. These networks have been used as benchmark in previous research work. For evaluation and comparison, two artificial networks and two real networks are considered. The evaluation results of the proposed method on these networks are presented based on different criteria and compared with four previous algorithms that are known in this field. Comparison results show that the proposed algorithm is relatively superior to other algorithms or at least produces similar results. The most important reason that can justify the relative performance superiority of the proposed algorithm or at least its competitive results is the better search capability of the problem search space. This leads to the discovery of more promising points and the production of better solutions.   47 60 2022/03/172022/02/252021/06/22022/02/3 1400/11/14 2023/06/22023/06/22023/12/112023/02/22 1401/12/3 محمود دی پیر Mahmood Deypir دانشگاه هوایی شهید ستاری ایران mdeypir@gmail.com احسان بیات Ehsan Bayat دانشگاه ازاد اسلامی واحد علوم و تحقیقات ایران ehsan.bayat70@gmail.com Social network Network graph Community detection Harmony search optimization algorithm. شبکه های اجتماعی گراف شبکه شناسایی انجمن الگوریتم بهینه سازی جستجوی هارمونی. [1] L. Wang, Y. Xu, Y. Mao, M. Fei, "A Discrete Harmony Search Algorithm," LSMS/ICSEE 2010, Part II, CCIS 98, © Springer-Verlag Berlin Heidelberg, pp. 37-43, 2010.##[2] M.Girvan, and M.E.J. "Newman, Community structure in social and biological networks," Proc. Natl. Acad. Sci. USA 99 (12): Doi: 10.1073/pnas.122653799. 2002, pp. 7821-7826.##[3] S. Fortunato, "Community detection in graphs," Physics Reports 486, pp. 75-174. 2010.##[4] K. Steinhaeuser, N.V. 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انتخاب کانال برای سیگنال های EEG با استفاده از اطلاعات متقابل جهت دسته بندی تصور حرکات مختلف Channel Selection in EEG Signals Using Mutual Information for Motor Imagery Classification 1 1 سیستم­های واسط مغز و رایانه مبتنی بر دسته­بندی تصور فعالیت‌های حرکتی با استفاده از سیگنال‌های چند کاناله EEG، نقش عمده‌ای در کنترل اندام‌های مصنوعی و ماشین­ها توسط افراد معلول دارند. مشکل اساسی این سیستم­ها بیش­برازش دسته­بند به­دلیل تعداد زیاد کانال­ها و نمونه­های آموزشی کم و نویزی می‌باشد. در این مقاله روشی برای انتخاب کانال­های بهینه، مبتنی بر بیشینه­سازی اطلاعات متقابل کانال‌های منتخب و تصور حرکت مدنظر ارائه شده است. توان زیرباندهایی با پهنای2 هرتز در باند 8 تا 30 هرتز و در فواصل 250 میلی­ثانیه­ای در بازه نیم تا دو و نیم ثانیه پس از شروع تصور حرکت به عنوان ویژگی برای هر کانال استخراج شده است. بعد ویژگی­های استخراج ­شده با ترکیب روش­های L1-PCA  و NWFE به 10 کاهش یافته است. صحت کل دسته‌بند SVM برای نمونه­های آزمایش دو شخص aa و al از داده­های مسابقات BCI III  87/94 و 51/96 درصد است در حالی‌که تعداد کانال‌ها از 118 به 7 کاهش یافته است. 2 Brain-computer interface systems based on classification of the motor imageries (MI) using multi-channel EEG signal play a major role in the control of artificial limbs and machines by people with disabilities. One of the main problems in classifying these signals to recognize different MI tasks is the large number of channels. The large number of channels causes a lot of cost and hassle during the measurement process, increasing computational load of the preprocessing, feature extraction, and classification, difficulty of interpretation of results, and over-fitting of the classifier due to the limited number and noisy training samples. Since not all measured channels for classifying a particular MI task have useful information, it would be beneficial to select the optimal channels for classifying desired MI tasks. Channel selection methods are categorized into wrapper, filtered, hybrid, and embedded categories. In this paper, a filtering method is used due to less computational cost and the independence of the classifier. The used criterion is very important in filtering methods. Criteria based on first- and second-order data moments are less efficient for non-Gaussian classes. The proposed method uses mutual information between candidate channels and class label as a comprehensive criterion and sequential forward selection search strategy. One of the problems in using this criterion is the accurate estimation of mutual information in the high dimensional spaces. The kpn entropy estimator is used to accurately estimate the mutual information in high dimensional space with limited number of training samples. The power of 2 Hz non overlapping sub-bands in the 8-30 Hz band and in 250 milliseconds non overlapping intervals in half to two and a half seconds after the onset of MI are extracted as features for each channel. The extracted features are reduced to 10 for each channel by combining the unsupervised L1-PCA and supervised NWFE dimensionality reduction methods. The reported results show the ability of the proposed method to select effective channels for classifying left and right hand and feet MI tasks. The overall accuracy of the SVM classifier on test samples for two subjects labeled aa and al from the BCI III competition dataset is 94.87% and 96.51%, respectively, while the number of channels is reduced from 118 to 7 channels. 61 72 2022/03/172022/02/252021/06/22022/02/32021/11/1 1400/8/10 2023/06/22023/06/22023/12/112023/02/222023/07/8 1402/4/17 مهدی کماندار Mehdi Kamandra دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته ایران mehdi_kamandar@yahoo.com عصمت راشدی Esmat Rashedi دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته ایران e.rashedi@kgut.ac.ir آیین صننعتی aeen sanatijavan دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته ایران sanatijavan.aeen@yahoo.com Brain Computer Interface EEG Signals Motor Imagery Classification Channel Selection Mutual Information kpn Entropy Estimation. سیستم‌های واسط مغز و رایانه سیگنال EEG دسته‌بندی تصور حرکات انتخاب کانال اطلاعات متقابل تخمین آنتروپی kpn. [1] Ortiz-Rosario A, Adeli H, Brain-computer interface technologies: from signal to action. Rev Neurosci, vol. 24, no. 5, pp. 537-552, 2013.##[2] A. Bashashati, M. Fatourechi, R. K. Ward, and G. E. Birch, A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals, J. Neural Eng., vol. 4, no. 2, pp. R32-R57, Jun.2007.##[3] Hudspeth, A.J., Jessell, T.M., Kandel, E.R., Schwartz, J.H. and Siegelbaum, S.A. eds., 2013. Principles of neural science, McGraw-Hill, Health Professions Division.##[4] Wolpaw JR, Birbaumer N, Heetderks WJ, McFarland DJ, Peckham PH, Schalk G, Donchin E, Quatrano LA, Robinson CJ,VaughanTM, Brain-computer interface technology: a review of the first international meeting, IEEE Trans Rehabilit Eng, vol. 8, no. 2, pp. 164-173, 2000.##[5] Alotaiby, T., El-Samie, F.E.A., Alshebeili, S.A. et al. A review of channel selection algorithms for EEG signal processing, EURASIP J. Adv. Signal Process. 2015.##[6] Yang, Yuan, Olexiy Kyrgyzov, Joe Wiart, and Isabelle Bloch. Subject-specific channel selection for classification of motor imagery electroencephalographic data, In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1277-1280. IEEE, 2013.##[7] Wei Qingguo, and Yanmei Wang. Binary multi-objective particle swarm optimization for channel selection in motor imagery based brain-computer interfaces, In 2011 4th International conference on biomedical engineering and informatics (BMEI), vol. 2, pp. 667-670. IEEE, 2011.##[8] A. Ghaemi, E. Rashedi, A. M. Pourrahimi, M. Kamandar, and F. Rahdari. Automatic channel selection in EEG signals for classification of left or right hand movement in Brain Computer Interfaces using improved binary gravitation search algorithm, Biomedical Signal Processing and Control 33, pp. 109-118, 2017.##[9] Shan, Haijun, Han Yuan, Shanan Zhu, and Bin He. EEG-based motor imagery classification accuracy improves with gradually increased channel number, In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1695-1698. IEEE, 2012.##[10] He, Lin, Youpan Hu, Yuanqing Li, and Daoli Li. Channel selection by Rayleigh coefficient maximization based genetic algorithm for classifying single-trial motor imagery EEG, Neurocomputing, pp. 423-433,2013##[11] Muhammad Zeeshan Baig, Nauman Aslam, Hubert P. H. Shum. Filtering techniques for channel selection in motor imagery EEG applications: a survey, Arti Intel Rev, vol. 53, pp.1207-1232, 2020.##[12] LemmS, BlankertzB, CurioG, MullerK-R. Spatio-spectral filters for improving the classification of single trial EEG, IEEE Trans Biomed Eng, vol. 52, pp. 1541-1548, 2005.##[13] AngKK, ChinZY, ZhangH, GuanC. Filter bank common spatial pattern (FBCSP) in brain-computer interface, In: IEEE world congress on computational intel-ligence. IEEE international joint conference on neural networks. IEEE, pp. 2390-7, 2008.##[14] ZhangY, ZhouG, JinJ, WangX, CichockiA. Optimizing spatial patterns with sparse filter bands for motor-imagery based brain-computer interface, J Neu-rosci Methods, pp. 85-91, 2015.##[15] ZhangY, NamCS, ZhouG, JinJ, WangX, CichockiA. Temporally constrained sparse group spatial patterns for motor imagery BCI, IEEE Trans Cybern, 48, pp. 3322-32, 2018.##[16] FarquharJ, HillJ, LalTN, SchölkopfB. Regularised CSP for sensor selection in BCI, In: Proceedings of the 3rd international brain-computer interface work-shop and training course; 2006.##[17] WuW, ChenZ, GaoX, LiY, BrownEN, GaoS. Probabilistic common spatial patterns for multichannel EEG analysis, IEEE Trans Pattern Anal Mach Intell, 37, pp. 639-53, 2015.##[18] Gómez-Herrero G. Automatic artifact removal (AAR) toolbox v1. 3 (Release 09.12. 2007) for MATLAB, Tampere University of Technology, 2007. [cited 2017 Aug 1]. Available from: http://germangh.github.io/ pubs/aardoc07.pdf.##[19] Bor-Chen Kuo and D. A. Landgrebe, Nonparametric weighted feature extraction for classification, in IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 5, pp. 1096-1105, May 2004, doi: 10.1109/TGRS.2004.825578.##[20] Li, Mingai, Jianyong Ma, and Songmin Jia. Optimal combination of channels selection based on common spatial pattern algorithm, In 2011 IEEE International Conference on Mechatronics and Automation, pp. 295-300. IEEE, 2011.##[21] J. Meng, G. Liu, G. Huang and X. Zhu, Automated selecting subset of channels based on CSP in motor imagery brain-computer interface system, 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO), Guilin, 2009, pp. 2290-2294, doi: 10.1109/ROBIO.2009.5420462.##[1] Ortiz-Rosario A, Adeli H, Brain-computer interface technologies: from signal to action. Rev Neurosci, vol. 24, no. 5, pp. 537-552, 2013.##[2] A. Bashashati, M. Fatourechi, R. K. Ward, and G. E. Birch, A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals, J. Neural Eng., vol. 4, no. 2, pp. R32-R57, Jun.2007.##[3] Hudspeth, A.J., Jessell, T.M., Kandel, E.R., Schwartz, J.H. and Siegelbaum, S.A. eds., 2013. Principles of neural science, McGraw-Hill, Health Professions Division.##[4] Wolpaw JR, Birbaumer N, Heetderks WJ, McFarland DJ, Peckham PH, Schalk G, Donchin E, Quatrano LA, Robinson CJ,VaughanTM, Brain-computer interface technology: a review of the first international meeting, IEEE Trans Rehabilit Eng, vol. 8, no. 2, pp. 164-173, 2000.##[5] Alotaiby, T., El-Samie, F.E.A., Alshebeili, S.A. et al. A review of channel selection algorithms for EEG signal processing, EURASIP J. Adv. Signal Process. 2015.##[6] Yang, Yuan, Olexiy Kyrgyzov, Joe Wiart, and Isabelle Bloch. Subject-specific channel selection for classification of motor imagery electroencephalographic data, In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1277-1280. IEEE, 2013.##[7] Wei Qingguo, and Yanmei Wang. Binary multi-objective particle swarm optimization for channel selection in motor imagery based brain-computer interfaces, In 2011 4th International conference on biomedical engineering and informatics (BMEI), vol. 2, pp. 667-670. IEEE, 2011.##[8] A. Ghaemi, E. Rashedi, A. M. Pourrahimi, M. Kamandar, and F. Rahdari. Automatic channel selection in EEG signals for classification of left or right hand movement in Brain Computer Interfaces using improved binary gravitation search algorithm, Biomedical Signal Processing and Control 33, pp. 109-118, 2017.##[9] Shan, Haijun, Han Yuan, Shanan Zhu, and Bin He. EEG-based motor imagery classification accuracy improves with gradually increased channel number, In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1695-1698. IEEE, 2012.##[10] He, Lin, Youpan Hu, Yuanqing Li, and Daoli Li. Channel selection by Rayleigh coefficient maximization based genetic algorithm for classifying single-trial motor imagery EEG, Neurocomputing, pp. 423-433,2013##[11] Muhammad Zeeshan Baig, Nauman Aslam, Hubert P. H. Shum. Filtering techniques for channel selection in motor imagery EEG applications: a survey, Arti Intel Rev, vol. 53, pp.1207-1232, 2020.##[12] LemmS, BlankertzB, CurioG, MullerK-R. Spatio-spectral filters for improving the classification of single trial EEG, IEEE Trans Biomed Eng, vol. 52, pp. 1541-1548, 2005.##[13] AngKK, ChinZY, ZhangH, GuanC. Filter bank common spatial pattern (FBCSP) in brain-computer interface, In: IEEE world congress on computational intel-ligence. IEEE international joint conference on neural networks. IEEE, pp. 2390-7, 2008.##[14] ZhangY, ZhouG, JinJ, WangX, CichockiA. Optimizing spatial patterns with sparse filter bands for motor-imagery based brain-computer interface, J Neu-rosci Methods, pp. 85-91, 2015.##[15] ZhangY, NamCS, ZhouG, JinJ, WangX, CichockiA. Temporally constrained sparse group spatial patterns for motor imagery BCI, IEEE Trans Cybern, 48, pp. 3322-32, 2018.##[16] FarquharJ, HillJ, LalTN, SchölkopfB. Regularised CSP for sensor selection in BCI, In: Proceedings of the 3rd international brain-computer interface work-shop and training course; 2006.##[17] WuW, ChenZ, GaoX, LiY, BrownEN, GaoS. Probabilistic common spatial patterns for multichannel EEG analysis, IEEE Trans Pattern Anal Mach Intell, 37, pp. 639-53, 2015.##[18] Gómez-Herrero G. Automatic artifact removal (AAR) toolbox v1. 3 (Release 09.12. 2007) for MATLAB, Tampere University of Technology, 2007. [cited 2017 Aug 1]. Available from: http://germangh.github.io/ pubs/aardoc07.pdf.##[19] Bor-Chen Kuo and D. A. Landgrebe, Nonparametric weighted feature extraction for classification, in IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 5, pp. 1096-1105, May 2004, doi: 10.1109/TGRS.2004.825578.##[20] Li, Mingai, Jianyong Ma, and Songmin Jia. Optimal combination of channels selection based on common spatial pattern algorithm, In 2011 IEEE International Conference on Mechatronics and Automation, pp. 295-300. IEEE, 2011.##[21] J. Meng, G. Liu, G. Huang and X. Zhu, Automated selecting subset of channels based on CSP in motor imagery brain-computer interface system, 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO), Guilin, 2009, pp. 2290-2294, doi: 10.1109/ROBIO.2009.5420462.## ##

پیش‌بینی ریزش کارمندان با استفاده از ‌الگوریتم‌های یادگیری گروهی مبتنی بر درخت ‌ Predicting employee turnover using tree-based ensemble ‎learning algorithms ‎ 1 1 یکی از مهم‌ترین نگرانی‌های مدیران ترک خدمت کارکنان کلیدی است؛ زیرا سازمان با ازدست‌دادن نیروهای ارزشمند خود، متحمل ازدست‌دادن دانش و تجربیاتی می‌شود که طی سال‌ها تلاش به‌دست‌آمده است؛ بنابراین پیش‌بینی ریزش کارکنان به مدیران منابع انسانی در استخدام نیروهای ماندگار و حفظ و نگهداری آنها کمک می‌کند. یکی از ابزارهای کارآمد دراین‌خصوص استفاده از روش‌های مختلف داده‌کاوی است. تعداد کم نمونه‌ها و نامتوازن بودن داده‌های ریزش کارکنان و تنظیم ابر پارامترها از جمله مشکلات استفاده از روش‌های داده‌کاوی برای پیش‌بینی ریزش کارکنان است. هدف  این تحقیق، ارائه روش‌های مناسب کاهش ویژگی و پیش‌پردازش داده‌ها به همراه ارائه راهکار برای تنظیم مناسب ابر پارامترها برای پیش‌بینی ریزش کارکنان با استفاده از تکنیک‌های مختلف داده‌کاوی و الگوریتم‌های یادگیری ماشین تجمعی است. باتوجه‌به نامتوازن بودن داده‌ها، از روش‌های کم نمونه‌گیری تصادفی و ترکیب آن با بیش نمونه‌گیری تصادفی برای متوازن‌سازی داده‌ها با نسبت‌های متفاوت استفاده شد. باتوجه‌به مثبت‌بودن همه داده‌ها از روش کاهش ابعاد تجزیۀ ماتریس نامنفی NMF استفاده گردید. با استفاده از روش‌های جستجوی ابر پارامترها، مقادیر بهینه ابر پارامترها برای الگوریتم‌های پیشنهادی، تعیین شد. برای ارزیابی روش پیشنهادی از مجموعه‌داده‌های استاندارد با اندازه‌های مختلف استفاده شده است. نتایج روش پیشنهادی با نتایج حاصل از سایر روش‌های مطرح در این حوزه مانند KNN, AdaBoost, DT و SVC مقایسه شده است. نتایج به‌دست‌آمده نشان می‌دهند که مدل پیشنهادی این تحقیق نسبت به تحقیقاتی که در گذشته روی همین داده‌ها صورت‌گرفته، دارای دقت پیش‌بینی بهتری است. طبق بررسی‌های انجام شده در این تحقیق که با استفاده از یک روش ترکیبی انتخاب ویژگی انجام شد، ویژگی‌های «سن»، «درآمد ماهیانه»، «نرخ روزانه»، «اضافه‌کاری» و «تعداد شرکت‌هایی که کارمند در آنها کارکرده»، بیش‌ترین تأثیر را بر ریزش کارکنان داشته‌اند. 2 Abstract Key employee's turnover is one of the most important concerns of Human Resource Managers (HRM); Because the organization by losing its valuable staff, suffers from the loss of skills and experience gained over the years, so predicting employee turnover helps HRMs to hire and retain permanent employees. One of the effective tools in this regard is the use of different data mining methods. Many researchers have done research in this field. This study reviewes recently published articles based on machine learning models, using Kaggle Human Resource (HR) databases [1-5] to compare them with this proposed models. In the article [9], the authors have selected 11 of the most important features by collecting common features from previous articles and filtering them using feature review and selection algorithms. After converting non-numerical variables to numerical and normalizing the data in the range [0,1], those attrition prediction approach is based on machine, deep and ensemble learning models and is experimented on a large-sized and a medium-sized simulated HR datasets and then a real small-sized dataset from a total of 450 responses. Those approach achieves higher Accuracy (0.96, 0.98 and 0.99 respectively) for the three datasets when compared previous solutions. In 2021, authors examined the relationship between features using Pearson correlation coefficient and selected 11 features with the highest correlation coefficient. Then used from six different machine learning algorithms including Random Forest (RF), Logistic Regression (LR), …, to predict employee turnover. The highest accuracy they obtained was 0.85 for RF [3]. In the article[1], the authors used two IBM datasets and a database containing HR information from a regional bank in the USA to predict employees turnover. After cleaning and preprocessing the data, the performance of 10 different machine learning algorithms such as Decision Tree (DT), RF, LR, Neural Network, …, was evaluated using ROC criteria on 10 small, medium, and large subsets of randomly selected, unassigned primary datasets. The average accuracy of algorithms is 0.83 in small datasets, 0.81 in medium datasets and 0.86 in large datasets. The authors of the paper [4] used three main experiments on IBM Watson simulated datasets to predict employees turnover. The first experiment involved training the original class-imbalanced dataset with the following machine learning models: support vector machine with several kernel functions, random forest and K-nearest neighbour (KNN). The second experiment focused on using adaptive synthetic (ADASYN) approach to overcome class imbalance, then retraining on the new dataset using the abovementioned machine learning models. As a result, training an ADASYN-balanced dataset with KNN (K = 3) achieved the highest performance, with 0.93 F1-score. this turnover prediction approach is based on tree-based ensemble learning models and is experimented on a large-sized standard simulated HR dataset (hr_data), including 15,000 samples with 10 features and a medium-sized (IBM) including 1470 samples with 34 features. The employees turnover rate in the IBM is 16.1% and in the hr_data is 23.8%, so datasets are unbalanced. To balance the data, the random-under-sampling technique and its combination of random-over-sampling with a ratio of 0.5965 for the IBM and 0.6558 for the hr_data has been used. In the preprocessing stage, Features with zero variance and samples containing the missing value were also removed. Then categorical (non-numeric) values ​​were converted to binary fields and then All features were scaled using data normalization in [0,1]. In order to reduce the feature dimensions in the IBM dataset, we used the "Non-negative Matrix Factorization" (NMF) technique (n_components=17, max_iter=500) and For initialization, non-negative singular value analysis method with zeros filled with X value has been used. After reviewing and cleaning the data, in the processing stage, six different classification algorithms, including KNN (k=1), RF (number of trees= 1500), DT, ExtraTreesClassifier (number of trees= 1000) and Support Vector Classifier were training on 70% of data. The optimal value of the hyperparameters for the algorithms, was set using RandomizedSearchCV and GridSearchCV techniques. In order to investigate the effect of balancing and Dimensionality Reduction on the performance of models, experiments were performed in 3 stages (befor balancing, after balancing befor Dimensionality Reduction, after balancing and Dimensionality Reduction) on 30% of the remaining data. The results shown in Table (2-4) indicate that this proposed model, which uses tree-based optimized ensemble learning algorithms with data balancing and NMF dimensionality reduction method, increases the f1score of turnover prediction. In the hr_data dataset, the best f1score for the RandomForest algorithm was 99.52% and for the IBM HR dataset, the best f1score for the ExtraTreesClassifier algorithm was 95.82%, which is higher than previous research. Table 5 compares the results of previous research with this research. Since, the prediction of employee attrition will not be enough without finding the characteristics that affect it, therefore, after building models and evaluating their performance, using a combined feature selection method by averaging the results of the single-variable feature selection method called "SelectKBest", and A wrapper feature selection method called "Recursive feature elimination" (RFE) with four learning algorithms RF, DT, ExtraTreesClassifier and AdaBoost, the most effective features were selected. SelectKBest combines the chi2 univariate statistical test with the selection of K features based on the statistical result between the features and the target variable. Also, in the RFE method, machine learning algorithms are used to remove the least important features after recursive training, so that finally the number of features reaches the set number (17 features in this article). The performance results of the models based on the selected features are shown in Table 6. The most effective characteristics are "age", "daily rate", "over  time", "NumCompaniesWorked" and, "monthly income" .   73 86 2022/03/172022/02/252021/06/22022/02/32021/11/12022/05/31 1401/3/10 2023/06/22023/06/22023/12/112023/02/222023/07/82023/07/18 1402/4/27 سیده محبوبه مزارعی Seyede Mahboobe Mazarei دانشگاه پیام نور ایران mahbanou60@gmail.com جعفر پورامینی Jafar pouramini دانشگاه پیام نور ایران J_pouramini@pnu.ac.ir data mining human resource management ensemble learning employee turnover داده‌کاوی مدیریت منابع انسانی ‎ ‎یادگیری گروهی ریزش کارکنان [1] Y. Zhao, M. K. Hryniewicki, F. Cheng, B. Fu, and X. Zhu, "Employee turnover prediction with machine learning: A reliable approach," in Proceedings of SAI intelligent systems conference, 2018: Springer, pp. 737-758.##[2] N. B. Yahia, J. Hlel, and R. Colomo-Palacios, "From big data to deep data to support people analytics for employee attrition prediction," IEEE Access, vol. 9, pp. 60447-60458, 2021.##[3] M. Pratt, M. Boudhane, and S. Cakula, "Employee Attrition Estimation Using Random Forest Algorithm," Baltic Journal of Modern Computing, vol. 9, no. 1, pp. 49-66, 2021.##[4] S. S. Alduayj and K. Rajpoot, "Predicting employee attrition using machine learning," in 2018 international conference on innovations in information technology (iit), 2018: IEEE, pp. 93-98.##[5] A. Huda and N. Ardi, "Predictive Analytic on Human Resource Department Data Based on Uncertain Numeric Features Classification," Int. J. Interact. Mob. Technol., vol. 15, no. 8, pp. 172-181, 2021.##[6] M. Al Akasheh, E. F. Malik, O. Hujran, and N. Zaki, "A Decade of Research on Data Mining Techniques for Predicting Employee Turnover: A Systematic Literature Review," Available at SSRN 4401862.##[7] P. Ajit, "Prediction of employee turnover in organizations using machine learning algorithms," algorithms, vol. 4, no. 5, p. C5, 2016.##[8] A. M. Esmaieeli Sikaroudi, R. Ghousi, and A. Sikaroudi, "A data mining approach to employee turnover prediction (case study: Arak automotive parts manufacturing", Journal of industrial and systems engineering, vol. 8, no. 4, pp. 106-121, 2015.##[9] S. H. Dolatabadi and F. Keynia, "Designing of customer and employee churn prediction model based on data mining method and neural predictor," in 2017 2nd International Conference on Computer and Communication Systems (ICCCS), 2017: IEEE, pp. 74-77.##[10] S. Khaziri Afravi, M. Sardari Zarchi, S. M. M. Fatemi Bushehri, "Factors affecting the tendency to leave the organization using algorithms based on multi-objective neural network and genetics", Human Resource Management in the Oil Industry, 1397.##[11] X. Cai et al., "DBGE: employee turnover prediction based on dynamic bipartite graph embedding," IEEE Access, vol. 8, pp. 10390-10402, 2020.##[12] P. K. Jain, M. Jain, and R. Pamula, "Explaining and predicting employees' attrition: a machine learning approach," SN Applied Sciences, vol. 2, pp. 1-11, 2020.##[13] M. Lazzari, J. M. Alvarez, and S. Ruggieri, "Predicting and explaining employee turnover intention," International Journal of Data Science and Analytics, vol. 14, no. 3, pp. 279-292, 2022.##[14] X. Gao, J. Wen, and C. Zhang, "An improved random forest algorithm for predicting employee turnover," Mathematical Problems in Engineering, vol. 2019, 2019.##[15] M. Teng, H. Zhu, C. Liu, and H. Xiong, "Exploiting network fusion for organizational turnover prediction," ACM Transactions on Management Information Systems (TMIS), vol. 12, no. 2, pp. 1-18, 2021.##[16] N. Jain, A. Tomar, and P. K. Jana, "A novel scheme for employee churn problem using multi-attribute decision making approach and machine learning," Journal of Intelligent Information Systems, vol. 56, pp. 279-302, 2021.##[1] Y. Zhao, M. K. Hryniewicki, F. Cheng, B. Fu, and X. Zhu, "Employee turnover prediction with machine learning: A reliable approach," in Proceedings of SAI intelligent systems conference, 2018: Springer, pp. 737-758.##[2] N. B. Yahia, J. Hlel, and R. Colomo-Palacios, "From big data to deep data to support people analytics for employee attrition prediction," IEEE Access, vol. 9, pp. 60447-60458, 2021.##[3] M. Pratt, M. Boudhane, and S. Cakula, "Employee Attrition Estimation Using Random Forest Algorithm," Baltic Journal of Modern Computing, vol. 9, no. 1, pp. 49-66, 2021.##[4] S. S. Alduayj and K. Rajpoot, "Predicting employee attrition using machine learning," in 2018 international conference on innovations in information technology (iit), 2018: IEEE, pp. 93-98.##[5] A. Huda and N. Ardi, "Predictive Analytic on Human Resource Department Data Based on Uncertain Numeric Features Classification," Int. J. Interact. Mob. Technol., vol. 15, no. 8, pp. 172-181, 2021.##[6] M. Al Akasheh, E. F. Malik, O. Hujran, and N. Zaki, "A Decade of Research on Data Mining Techniques for Predicting Employee Turnover: A Systematic Literature Review," Available at SSRN 4401862.##[7] P. Ajit, "Prediction of employee turnover in organizations using machine learning algorithms," algorithms, vol. 4, no. 5, p. C5, 2016.##]8[ ا. م. اسماعیلی سیکارودی، ر. قوسی، ع. اسماعیلی سیکارودی، «یک رویکرد داده‌کاوی برای پیش‌بینی ریزش کارکنان. مطالعه موردی: تولید قطعات خودروی اراک»، مجلۀ سیستم‌ها و صنعت، 2015.##[8] A. M. Esmaieeli Sikaroudi, R. Ghousi, and A. Sikaroudi, "A data mining approach to employee turnover prediction (case study: Arak automotive parts manufacturing", Journal of industrial and systems engineering, vol. 8, no. 4, pp. 106-121, 2015.##[9] س. حسن‌خانی دولت‌آبادی، ف. کی‌نیا، «طراحی مدل پیش‌بینی ریزش مشتری و کارمند بر اساس روش داده‌کاوی و پیش‌بینی‌کننده عصبی»، IEEE Xplore ، 2017.##[9] S. H. Dolatabadi and F. Keynia, "Designing of customer and employee churn prediction model based on data mining method and neural predictor," in 2017 2nd International Conference on Computer and Communication Systems (ICCCS), 2017: IEEE, pp. 74-77.##[10] س. خضیری عفراوی، م. سرداری زارچی، س. م. م. فاطمی بوشهری، «عوامل مؤثر بر تمایل به ترک سازمان با استفاده از الگوریتم‌های مبتنی‌بر شبکۀ عصبی و ژنتیک چندهدفه»، مدیریت منابع انسانی در صنعت نفت، 1397.##[10] S. Khaziri Afravi, M. Sardari Zarchi, S. M. M. Fatemi Bushehri, "Factors affecting the tendency to leave the organization using algorithms based on multi-objective neural network and genetics", Human Resource Management in the Oil Industry, 1397.##[11] X. Cai et al., "DBGE: employee turnover prediction based on dynamic bipartite graph embedding," IEEE Access, vol. 8, pp. 10390-10402, 2020.##[12] P. K. Jain, M. Jain, and R. Pamula, "Explaining and predicting employees' attrition: a machine learning approach," SN Applied Sciences, vol. 2, pp. 1-11, 2020.##[13] M. Lazzari, J. M. Alvarez, and S. Ruggieri, "Predicting and explaining employee turnover intention," International Journal of Data Science and Analytics, vol. 14, no. 3, pp. 279-292, 2022.##[14] X. Gao, J. Wen, and C. Zhang, "An improved random forest algorithm for predicting employee turnover," Mathematical Problems in Engineering, vol. 2019, 2019.##[15] M. Teng, H. Zhu, C. Liu, and H. Xiong, "Exploiting network fusion for organizational turnover prediction," ACM Transactions on Management Information Systems (TMIS), vol. 12, no. 2, pp. 1-18, 2021.##[16] N. Jain, A. Tomar, and P. K. Jana, "A novel scheme for employee churn problem using multi-attribute decision making approach and machine learning," Journal of Intelligent Information Systems, vol. 56, pp. 279-302, 2021.## ##

طراحی الگوریتم بهینه تشخیص سریع بیماری کووید19 برپایه اینترنت اشیا Designing an optimal diagnosis algorithm based on IoT for Covid-19 1 1 توسعه فناوری اطلاعات و استفاده از آن در سیستم بهداشتی، اقدامات بسیاری را برای محافظت و ارتقاء سلامت انسان انجام داده است، با این حال، جهان همچنان با تهدیدهای طولانی مدت و تکرار مجدد بیماریهای عفونی روبرو است. بیماری هدف در این پژوهش با توجه به اهمیت و فراگیری، کووید19 است. روش پژوهش از نظر پارادایم، تفسیری و از لحاظ استراتژی اکتشافی است. بر اساس نوع گردآوری داده‌ها از نوع پژوهش‌های کیفی بوده و با توجه به توسعه الگوریتم‌ها، روش تحقیق در این پژوهش مبتنی بر علم طراحی است. پژوهش در یک فرایند 7 مرحله‌ای انجام شد. ویژگی‌های اینترنت اشیا در پژوهش حاضر با نظر خبرگان استخراج شد و ویژگی‌های بدست آمده در آزمایش  الگوریتم «k نزدیک‌ترین همسایگی» بر روی داده‌ها برای تعیین بهترین مدل ایجاد شد. نتایج ارزیابی الگوریتم پیشنهادی برای پیش‌بینی بیماری کووید19، دقت بالاتر از 98 درصد را نشان دادند. همچنین حساسیت بالاتر از 98 درصد که برای تشخیص بیماری کووید19 اهمیت بالایی دارد و نشان دهنده کم بودن موارد منفی کاذب در نتایج آزمون است، در الگوریتم بدست آمد. 2 The development of information technology and its use in the health system has taken many measures to protect and promote human health, however, the world still faces long-term threats and recurrence of infectious diseases. Understanding the dynamics of infectious diseases is important in controlling the disease because the network and the mode of impact of infectious diseases are very complex. The management of infectious diseases can also be considered as a complex social system due to the fact that has many complexities (such as dimensions, parameters, interactions, behaviors and rules), for this reason, the approach of the present study is a multifaceted understanding of the spread of infectious diseases. To design the present model, an intelligent system with a combination of mathematical, machine learning and epidemiological dimensions is proposed. The disease studied in this study, due to its importance and prevalence, is Covid 19. In this study, with the approach of complex systems and using the Internet of Things and machine learning methods, an algorithm was presented that uses environmental and individual variables to predict the probability of disease in an individual. Therefore, this research can improve the prevention of infectious diseases by filling some of the gaps in 3 sections: 1- Re-emergence of infectious diseases and the potential of IoT and AI, 2- Speed of dissemination and importance of real-time tracking, and 3- Budget and cost. The evaluation of the algorithm in this study was determined by two criteria of sensitivity and specificity. The results of the proposed algorithm for predicting Covid 19 disease showed an accuracy of more than 98%. Sensitivity above 98% was also obtained. Which is very important for the diagnosis of Covid disease 19 and shows the low number of false negatives in the test results. Therefore, the proposed model, combined with the Internet of Things and machine learning, can cause early diagnosis and prevent the spread of the Covid-19 disease with high specificity and sensitivity. 87 102 2022/03/172022/02/252021/06/22022/02/32021/11/12022/05/312022/01/21 1400/11/1 2023/06/22023/06/22023/12/112023/02/222023/07/82023/07/182023/06/2 1402/3/12 سیده زهره حسینی Seyedeh Zohreh Hosseini دانشگاه آزاد اسلامی ایران zohreh.hoseini@gmail.com رضا رادفر Reza Radfar دانشگاه آزاد اسلامی ایران radfar@gmail.com امیراشکان نصیری‌پور Amirashkan Nasiripour دانشگاه آزاد اسلامی ایران drnp20@yahoo.com علی رجب‌زاده قطری Ali Rajabzadeh Ghatary دانشگاه تربیت مدرس ایران alirajabzadeh@gmail.com IoT Infectious Diseases Covid19 Machine Learning Artificial Intelligence اینترنت اشیاء بیماری‌های واگیر کووید19 یادگیری ماشین هوش مصنوعی ] 1[ N. A. o. 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مروری بر روش‌های شباهت‌سنجی متون کوتاه A survey on short text similarity measurement methods 1 1 مشابهت‌سنجی بین متون کوتاه یکی از نیازهای بنیادین در بسیاری از مسائل پردازش‌زبان‌طبیعی است؛ که باتوجه به اهمیت آن محققین کماکان به‌دنبال بهبود کیفیت الگوریتم‌های موجود هستند. در این مطالعه 150 مقاله بررسی شدند و دسته‌بندی جامعی برای روش‌های موجود ارائه شد. به‌طورکلی روش‌های ارائه‌شده را می‌توان در سه گروه دسته‌بندی کرد. گروه اول روش‌هایی که بر مشابهت لفظی تمرکز می‌کنند. در این روش‌ها متن به‌عنوان رشته‌ای از کاراکترها یا مجموعه‌ای از کلمات یا ترکیبی از این دو درنظر گرفته می‌شود. گروه دوم روش‌هایی هستند که به ارتباط معنایی کلمات نیز مبتنی‌بر پایگاه دانش یا تحلیل پیکره‌های متنی توجه دارند. در مطالعات اخیر از روش‌های یادگیری عمیق مبتنی‌بر ترنسفورمرها بهره‌برداری شده و نتایج حاکی‌از بهبود چشم‌گیر کیفیت  این روش‌هاست. گروه سوم به ترکیب روش‌های لفظی و معنایی و بعضا روش‌های تحلیل نحوی پرداخته‌اند. البته تحلیل‌گرهای نحوی باکیفیتی برای تمامی زبان‌ها نبوده و به‌کارگیری آن‌ها سرعت را نیز به‌مراتب کاهش می‌دهد. 2 Measuring similarity between two text snippets is one of the essential tasks in many NLP problems and it has been still one of the most challenging tasks in the field. Various methods have been proposed to measure text similarity. This survey reviews more than 150 of the related papers, introduces a comprehensive taxonomy with three main categories, and discusses the advantages and disadvantages of these methods. The first category is lexical methods that only focus on text pair’s surface similarity. These methods consider the text as a sequence of characters, tokens, or a mixture of these two. Some recent studies use deep learning techniques for detecting lexical similarity in alias detection task. The second category is semantic methods that take into consideration the meaning of the words based on some pre-prepared knowledge-bases like Wordnet or using Corpus-based methods. Some recent studies use modern deep learning techniques like transformers and Siamese networks to create document embedding that outperform other methods. The final category is hybrid methods that take advantage of all other methods even syntactic parsing in some cases. Note that high-quality syntactic parsers are not present for many languages and that using them has some side-effects on performance and speed. 103 126 2022/03/172022/02/252021/06/22022/02/32021/11/12022/05/312022/01/212022/04/20 1401/1/31 2023/06/22023/06/22023/12/112023/02/222023/07/82023/07/182023/06/22023/02/22 1401/12/3 احمد ربیعی زاده Ahmad Rabiei Zadeh آزمایشگاه هوش مصنوعی مرکز تحقیقات کامپیوتری علوم اسلامی نور ایران rabieizadeh@noornet.net حسین امیرخانی Hossein Amirkhani گروه مهندسی کامپیوتر دانشکده فنی دانشگاه قم، قم، ایران ایران amirkhani@qom.ac.ir short text similarity lexical similarity semantic similarity natural language processing sentence embedding transformer مشابهت معنایی متون مشابهت لفظی پردازش زبان طبیعی مشابه‌یابی بردار تعبیه جملات [1] W. Liu et al., "Semantic Matching from Different Perspectives," 2022, [Online]. Available: https://arxiv.org/abs/2202.06517.##[2] D. B. Bisandu, R. Prasad, and M. M. Liman, "Data clustering using efficient similarity measures," J. Stat. Manag. Syst., vol. 22, no. 5, pp. 901-922, 2019, doi: 10.1080/09720510.2019.1565443.##[3] E. Zafarani-Moattar, M. R. Kangavari, and A. M. Rahmani, "A Comparative Study on Transfer Learning and Distance Metrics in Semantic Clustering over the COVID-19 Tweets," pp. 1-22, 2021, [Online]. Available: http://arxiv.org/abs/2111.08658.##[4] H. A. Mohamed Hassan, G. Sansonetti, F. Gasparetti, A. Micarelli, and J. Beel, "BERT, ELMo, use and infersent sentence encoders: The Panacea for research-paper recommendation?," CEUR Workshop Proc., vol. 2431, no. September, pp. 6-10, 2019.##[5] S. Abujar, M. Hasan, and S. A. Hossain, "Sentence similarity estimation for text summarization using deep learning," in Advances in Intelligent Systems and Computing, 2019, vol. 828, no. January, pp. 155-164, doi: 10.1007/978-981-13-1610-4_16.##[6] A. A. Aliane and H. 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طراحی یک شبکه عصبی کانولوشنال مبتنی بر بانک فیلتر برای طبقه بندی تصاویر اعداد دست نویس Design of a filter bank-based convolutional neural network for handwritten digit images classification 1 1 در سال­های اخیر شبکه­های عصبی کانولوشنال به طور فزاینده­ای در کاربردهای مختلف بینایی ماشین و به ویژه در شناسایی و طبقه­بندی خودکار تصاویر مورد استفاده قرار گرفته­اند. این نوع از شبکه­های عصبی مصنوعی با شبیه­سازی عملکرد قشر بینایی مغز قدرتمندترین ساختار را در تجزیه و تحلیل داده­های بصری دارند. اما تنوع تصاویر دیجیتال و گوناگونی محتوی و ویژگی­های آن­ها ایجاب می­کند تا برای دستیابی به کارایی بالاتر در هر مسئله­ی طبقه­بندی، شبکه­های کانولوشنال به صورت اختصاصی طراحی و پارامترهای آن­ها به دقت تنظیم شوند. در این راستا، در پژوهش حاضر ضرایبی بهینه برای فیلترهای لایه­ی کانولوشن در شروع آموزش شبکه بکار رفته تا از این طریق دقت طبقه­بندی در شبکه افزایش و زمان آموزش کاهش یابد. این کار با طراحی و بکارگیری مجموعه­ای از فیلترهای تخصصی برای لایه­ی کانولوشن در قالب یک بانک فیلتر و جایگذاری آن­ها به جای فیلترهای تصادفی انجام پذیرفته و بر روی پایگاه داده­ی تصاویر اعداد دست­نویس MNIST ارزیابی شده است. آزمایشات ما بر روی شبکه­ی کانولوشنال تک لایه با سه نوع فیلترگذاری (فیلترهای عدد ثابت، عدد تصادفی و بانک فیلتر) میانگین دقت طبقه­بندی تصاویر اعداد دست­نویس MNIST را در 50 بار آموزش شبکه به ترتیب 94/74، 47/86 و 89/91 درصد و برای شبکه­ی کانولوشنال سه لایه به ترتیب 82/88، 16/96 و 14/99 درصد نشان دادند. این نتایج نشان می­دهند که فیلترهای بکار رفته در مدل پیشنهادی در مقایسه با فیلترهای تصادفی ویژگی­های موثرتری از تصاویر را استخراج نموده و با شروع آموزش شبکه از نقطه­­ی مناسبتر، بدون افزایش هزینه­ی محاسباتی دقت طبقه­بندی را افزایش داده­اند. بنابراین می­توان نتیجه گرفت که ضرایب اولیه­ی فیلترهای لایه­ی کانولوشن بر دقت طبقه­بندی شبکه­های کانولوشنال موثر است و با بکارگیری فیلترهای موثرتر در لایه­­ی کانولوشن می­توان این شبکه­ها را خاص مسئله ساخته و از این طریق کارآیی شبکه را افزایش داد.         2 Background: In recent years, convolutional neural networks (CNNs) have been increasingly used in various applications of machine vision. CNNs simulate the function of the brain's visual cortex and have a powerful structure for analyzing visual images. However, the diversity of digital images, their content, and their features necessitate that CNN networks are specially designed, and their parameters are carefully adjusted to achieve higher efficiency in any classification problem. In this regard, in many previous studies, researchers have attempted to increase the efficiency of the CNNs by setting their adjustable parameters more accurately. New method: New method: In this study, we presented a novel initializing method for the kernels of the first convolutional layer of the CNN networks. We designed a filter bank with specialized kernels and used them in the first convolution layer of the proposed models. These kernels, compared to the random kernels in traditional CNNs, extract more effective features from the input images without increasing the computational cost of the network, and improve the classification accuracy by covering all the important characteristics. Results: The dataset used in this paper was the MNIST database of handwritten digits. We examined the performance of CNN networks when three different types of kernels were used in their first convolution layer. The first group of kernels had constant coefficients; the second group had random coefficients, and finally, the kernels of the third group were specially designed to extract a wide range of image features. Our experiments on a single-layer CNN network with three types of kernels (constant numbers, random numbers, and filter-bank) showed the average classification accuracy of MNIST images in 50 times of network training to be 74.94%, 86.47%, and 91.89%, respectively, and for a three-layer CNN network, 88.82%, 96.16%, and 99.14%, respectively. Comparison with existing methods: Compared to the kernels with randomized coefficients, the use of specialized kernels in the first convolution layer of the CNN networks has several important advantages: 1) They can be designed to extract all important features of the input images, 2) They can be designed more effectively based on the problem in hand, 3) They cause the training to start from a more appropriate point, and in this way, the speed of training and the classification accuracy of the network increase. Conclusion: This study provides a novel method for initializing kernels in convolution layers of CNN networks to enhance their performance in image classification works. Our results show that compared to random kernels, the kernels used in the proposed models extract more effective features from the images at different frequencies and increase the classification accuracy by starting the training algorithm from a more appropriate point, without increasing the computational cost. Therefore, it can be concluded that the initial coefficients of the convolution layer kernels are effective on the classification accuracy of CNN networks, and by using more effective kernels in the convolution layers, these networks can be made specific to the problem and, in this way, increase the efficiency of the network.   127 140 2022/03/172022/02/252021/06/22022/02/32021/11/12022/05/312022/01/212022/04/202022/06/28 1401/4/7 2023/06/22023/06/22023/12/112023/02/222023/07/82023/07/182023/06/22023/02/222023/07/18 1402/4/27 علی دروگرمقدم ali derogarmoghadam دانشگاه صنعتی نوشیروانی بابل ایران ali_derogarmoghadam@stu.nit.ac.ir محمدرضا کرمی ملایی Mohammad Reza Karami Molaei دانشگاه صنعتی نوشیروانی بابل ایران mkarami@nit.ac.ir محمدرضا حسن زاده Mohammadreza Hassanzadeh دانشگاه صنعتی نوشیروانی بابل ایران m.hasanzadeh@nit.ac.ir convolutional neural network deep learning image classification handwritten digit شبکه های عصبی کانولوشنال یادگیری عمیق طبقه بندی تصاویر اعداد دست نویس [1] Javidi, B., "Image recognition and classification: algorithms, systems, and applications". 2002: CRC press.##[2] Lu D, Weng Q. 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مروری بر روش‌های نهان‌نگاری تصویر منطبق با محتوا A review of Content Adaptive Image Steganography methods 1 1 نهان‌نگاری هنر انتقال اطلاعات از طریق یک ارتباط محرمانه است. در نهان‌نگاری، اطلاعات حساس و مهم در یک محیط رسانه‌ای به نام پوشانه جاسازی می‌گردد؛ به‌طوری‌که رسانه حاوی پیام از نمونه رسانه اصلی قابل تمایز نبوده و وجود پیام مخفی حتی به‌صورت احتمالی قابل تشخیص نباشد. اعوجاج حاصل از جاسازی در نهان‌نگاری منطبق با محتوا (تطبیقی) به ساختار محلی تصویر وابسته است؛ ازاین‌رو، تغییرات در مناطق پیچیده کمتر قابل تشخیص بوده و درنتیجه از اولویت بالاتری برای جاسازی برخوردار خواهند بود. تاکنون رویکردهای مختلفی در زمینه نهان‌نگاری منطبق با محتوا ارائه شده است: مبتنی بر مدل، مبتنی بر هزینه و مبتنی بر یادگیری تقابلی. در رویکرد نهان‌نگاری مبتنی بر مدل سعی می‌شود مدل آماری پوشانه تا حد ممکن حفظ گردد؛ درحالی‌که هدف رویکرد مبتنی بر هزینه، کمینه‌سازی اعوجاج حاصل از مجموع هزینه‌های ویرایش پیکسل‌های حامل پیام است. در رویکرد یادگیری تقابلی، از رابطه رقابتی بین نهان‌نگار و نهان‌کاو برای حفظ مشخصات آماری تصویر و بهبود محرمانگی بهره گرفته می‌شود. در این مقاله مفاهیم و رویکردهای نهان‌نگاری معرفی می‌شود و سپس روش‌های پیشنهادی در نهان‌نگاری مورد بحث و بررسی قرار می‌گیرند. در بخش اول این مقاله به بیان مفهوم نهان‌نگاری و مرور تاریخچه آن پرداخته می‌شود. در بخش دوم، نهان‌نگاری دیجیتال تعریف می‌شود که شامل سه نوع نهان‌نگاری با ساخت، انتخاب یا ویرایش پوشانه می‌شود. در بخش سوم، نهان‌نگاری منطبق با محتوا مبتنی بر ویرایش پوشانه معرفی می‌شود که شامل دو مرحله یافتن مکان‌های مناسب برای جاسازی و سپس استفاده از الگوریتم مناسب برای جاسازی پیام است. راهکارهای مختلفی برای هر یک از این مراحل ارائه شده است که در این مقاله راهکارهای انتخاب یک بستر جاسازی بهینه مورد بررسی قرار می‌گیرند. این مسئله را می‌توان به‌صورت یک مسئله بهینه‌سازی فرموله نمود که هدف آن تخمین نقشه احتمال تغییرات به شرط به حداقل رساندن اعوجاج است. در این راستا، در بخش چهارم نهان‌نگاری بر اساس کمینه‌سازی اعوجاج تعریف می‌گردد و سپس مهم‌ترین روش‌های این رویکرد در بخش پنجم معرفی می‌شوند. در بخش ششم به معرفی رویکردهای نهان‌نگاری مبتنی بر یادگیری عمیق پرداخته می‌شود که از مزایای شبکه‌های مولد تقابلی یا نمونه‌های تقابلی برای بهبود محرمانگی بهره می‌گیرد و در بخش هفتم روش‌های ارائه شده در این حوزه معرفی می‌شوند. در بخش هشتم روش‌های معرفی شده از دیدگاه‌های مختلف مورد بررسی و مقایسه قرار می‌گیرند. نتایج مطالعه نشان می‌دهد که برخی راهکارها مانند هموارسازی تغییرات جاسازی، در نظر گرفتن تعاملات و همبستگی بین تغییرات، استفاده از اطلاعات جانبی و بهره‌گیری از یادگیری تقابلی می‌توانند نقش مؤثری در حفظ محرمانگی اطلاعات و بهبود امنیت ایفا کنند. در بخش نهم، پیشنهاد‌هایی ارائه می‌شود که می‌توانند برای تحقیقات آتی مورد توجه قرار گیرند و در پایان نتیجه‌گیری نهایی بیان می‌شود.   2 Steganography is the art of transferring information through secret communication. The essential aim of steganography is to minimize the distortion caused by embedding the secret message; so that the image containing the message (stego) cannot be distinguished from the original image (cover), and the existence of the hidden message cannot be detected. The distortion in content-adaptive steganography depends on the local structure of the image. The embedding changes into the areas with rich textures are less detectable than smooth areas, so the textured areas have a higher modification priority. In this regard, three main steganography approaches are proposed: model-based, cost-based, and adversarial. The model-based approach considers a statistical model for the cover image and tries to preserve this model during the embedding process. The cost-based one focuses on minimizing the distortion obtained from the sum of the heuristic costs of modified pixels. The adversarial approach uses the competition between steganography and steganalysis to improve the embedding performance. In the first section of this paper, the concept of steganography and its history is expressed. Digital steganography including three types of cover synthesis, selection, and modification is introduced in the second section. The focus of this paper is on steganography based on the cover modification. The goal is to estimate the best probability distribution of modifications, and embedding the message in the estimated places is left to existing coding algorithms. In the third section, the problem of estimating the probability distribution is formulated as an optimization problem with the aim of distortion minimization. The distortion-based methods compute the probability distribution of embedding changes using a pre-defined distortion function. In the additive distortion function, the embedding changes are assumed to be independent. Thus, the distortion function cannot capture interactions between changes caused by embedding, and it leads the performance to suboptimality. In this regard, the non-additive distortion functions are presented that consider the dependencies among the modification of adjacent pixels. The distortion-based methods include two model-based and cost-based approaches are introduced in the fourth section. Then, their most significant methods are reviewed in the fifth section. Considering the competitive nature of steganography and steganalysis, a new steganography approach is presented in the sixth section that takes advantage of adversarial learning to improve secrecy. Adversarial learning includes two strategies: Generative adversarial networks (GANs) and adversarial attacks. In the concept of steganography, the GAN-based strategy tries to train the steganographic network against a steganalysis network. This is an iterative and dynamic game between steganographic and steganalysis networks to reach the Nash equilibrium. Another strategy attempts to simulate an adversarial attack and generate stego images that deceive the steganalysis network. The adversarial-based steganography methods are reviewed in the seventh section. In the eighth section, different methods are compared from various points of view. The results of this study show that some techniques, such as smoothing the embedding changes, considering the interactions between the changes, using side-informed information, and exploring adversarial networks, can help to estimate the proper embedding probability map and improve performance and security. In the ninth section, suggestions are stated that can be considered for future research. Finally, the conclusion is expressed in the tenth section.   141 182 2022/03/172022/02/252021/06/22022/02/32021/11/12022/05/312022/01/212022/04/202022/06/282022/03/4 1400/12/13 2023/06/22023/06/22023/12/112023/02/222023/07/82023/07/182023/06/22023/02/222023/07/182023/07/18 1402/4/27 بهناز عبدالهی Behnaz Abdollahi دانشگاه فردوسی مشهد ایران b.abdollahi@mail.um.ac.ir احد هراتی Ahad Harati دانشگاه فردوسی مشهد ایران a.harati@um.ac.ir امیرحسین طاهری نیا AmirHossein Taherinia دانشگاه فردوسی مشهد ایران taherinia@um.ac.ir Content-adaptive steganography Steganalysis Distortion minimization Statistical model Adversarial learning نهان‌نگاری منطبق با محتوا نهان‌کاوی کمینه‌سازی اعوجاج مدل آماری یادگیری تقابلی [1] Fridrich, J. Steganography in digital media: principles, algorithms, and applications. Cambridge University Press, 2009.##[2] Kipper, G. 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روش سریع تعیین حالت پیشگویی درون قابی و اندازه واحد کدگذاری در HEVC Fast CU Size and Intra Mode Decision Algorithm in HEVC Standard 1 1 هدف این پژوهش، ارایه­ی روشی برای کاهش تعداد حالت­های پیشگویی درون قابی کدگذار HEVC است، چرا که بررسی همه حالت­ها در این فرآیند بسیار زمان­بر است. در این پژوهش روش جدیدی مبتنی بر عملگر گرادیان­گیر سوبل جهت تعیین سریع حالت­ پیشگویی درون­قابی و همچنین اندازه واحد کدگذاری در HEVC پیشنهاد شده است. زمان کدگذاری نسبت به نرم­افزار مرجعHEVC   (HM)با استفاده از روش پیشنهادی سریع، به­طور متوسط ۵۶٪ و با استفاده از روش پیشنهادی دقیق، به­طور متوسط ۲۹% کاهش یافته و در هر دو روش افت کیفیت ویدیوی نهایی قابل چشم­پوشی است. 2 With the expansion of the Internet and wireless technologies, video-related applications have been expanded much more than ever. But since network bandwidth is limited, better high-gain video compression methods remain essential. Recently, the new video compression standard called "High Efficiency Video Coding" (HEVC) has been introduced, which doubles the compression compared to the previous standard called "Advanced Video Coding", while preserving video quality at the same time. In HEVC standard, 35 intra prediction modes have been defined to improve the efficiency of encoding algorithm. The best intra prediction mode is chosen using a process called "Rate Distortion Optimization". The purpose of this study is to propose a method to reduce the number of intra prediction modes in RDO process, since it is a very time consuming process. A new method based on Sobel operator is proposed for fast determination of intra prediction mode as well as coding unit size in HEVC. Although Sobel operator is an essential part of our algorithm, other features were extracted from coding control part of HEVC algorithm to excel the speed of coding process, since the main purpose of fast coding algorithm should be to reduce complexity while producing a bitstream, which preserves the quality of decoded video. These features are parent CU mode, modes with lower cost functions and modes that are determined to be most probable via pervious researches. Parent CU mode is intra angle of coding unit that contains the current CU and is available at the time of calculating the best mode for current CU. Modes with lower cost functions are modes that have the least cost function value that are calculated by HM via a fast algorithm and modes that are determined to be most probable via pervious researches are DC and horizontal and vertical modes. Coding time compared to HEVC reference software (HM) using the proposed fast method is reduced by an average of 56% and by using the proposed precise method is reduced by an average of 29%. In proposed precise method, final video quality degradation is negligible. 183 196 2022/03/172022/02/252021/06/22022/02/32021/11/12022/05/312022/01/212022/04/202022/06/282022/03/42019/05/18 1398/2/28 2023/06/22023/06/22023/12/112023/02/222023/07/82023/07/182023/06/22023/02/222023/07/182023/07/182023/07/18 1402/4/27 سمانه پارسایی Samaneh Parsaee دانشگاه فردوسی مشهد ایران sa.parsaie@um.ac.ir حسین خانجانی Hossein Khanjani دانشگاه فردوسی مشهد ایران khanjani.hossein@mail.um.ac.ir مرتضی خادمی درح Morteza Khademi Doroh دانشگاه فردوسی مشهد ایران khademi@um.ac.ir عباس ابراهیمی مقدم Abbas Ebrahimi Moghadam دانشگاه فردوسی مشهد ایران a.ebrahimi@um.ac.ir هادی هادیزاده Hadi Hadizadeh دانشگاه صنعتی قوچان ایران h.hadizadeh@qiet.ac.ir Video Coding High Efficiency Video Coding (HEVC) Rate- distortion Optimization (RDO) Intra Prediction Sobel کدگذاری ویدیو کدگذاری پربازده ویدیو (HEVC) بهینه‌سازی نرخ – اعوجاج (RDO) پیشگویی درون‌قابی سوبل [1] M. Wien, High efficiency video coding: Coding tools and specification. Springer, 2014.##[2] M. Budagavi, High Efficiency Video Coding (hevc): Algorithms and Architectures. Springer, 2016.##[3] G. J. Sullivan, J. R. Ohm, "HEVC software guidelines," Joint Collaborative Team on Video Coding (JVT-VC) of ITU-T SG16 WP3 and ISO/IEC JTC1/SC29/WG11, document JCTVC-H1001, 8th Meeting: San Jose, CA, USA, Feb. 2012.##[4] R. C. Gonzalez and R. E. Woods, "Digital image processing," ed: Upper Saddle River, NJ: Prentice Hall, 2012.##[5] M. Jamali, S. Coulombe, and F. Caron, "Fast HEVC intra mode decision based on edge detection and SATD costs classification," in Data Compression Conference (DCC), 2015, 2015, pp. 43-52: IEEE.##[6] G. J. Sullivan, J. Ohm, W.-J. Han, and T. Wiegand, "Overview of the high efficiency video coding (HEVC) standard," IEEE Transactions on circuits and systems for video technology, vol. 22, no. 12, pp. 1649-1668, 2012.##[7] H. Brahmasury Jain and K. Rao, "Fast intra mode decision in high efficiency video coding," Polibits, no. 50, pp. 5-12, 2014.##[8] Q. Hu, Z. Shi, X. Zhang, and Z. Gao, "Fast HEVC intra mode decision based on logistic regression classification," in Broadband Multimedia Systems and Broadcasting (BMSB), 2016 IEEE International Symposium on, 2016, pp. 1-4: IEEE.##[9] W. Liao, D. Yang, and Z. Chen, "A fast mode decision algorithm for HEVC intra prediction," in Visual Communications and Image Processing (VCIP), 2016, 2016, pp. 1-4: IEEE.##[10] D. B. Patel, D. M. Chauhan, and K. C. Pathak, "Edges based intra-prediction in High Efficiency Video Coding (HEVC) standard," in Engineering and Technology (ICETECH), 2016 IEEE International Conference on, 2016, pp. 995-1000: IEEE.##[11] Y. Piao, J. Min, and J. Chen, "Encoder improvement of unified intra prediction, joint collaborative team on video coding (JCT-VC) of ITU-TSG16 WP3 and ISO," IEC JTC1/SC29/WG11, documentJCTVC-C2072010.##[12] R. Tian, Y. Zhang, R. Fan, and G. Wang, "Adaptive Fast Mode Decision for HEVC Intra Coding," in Digital Image Computing: Techniques and Applications (DICTA), 2016 International Conference on, 2016, pp. 1-6: IEEE.##[13] W. Xiang, C. Cai, Z. Wang, H. Zeng, and J. Chen, "Fast intra mode decision for HEVC," in Signal-Image Technology and Internet-Based Systems (SITIS), 2014 Tenth International Conference on, 2014, pp. 283-288: IEEE.##[14] M. Zhang, C. Zhao, and J. Xu, "An adaptive fast intra mode decision in HEVC," in Image Processing (ICIP), 2012 19th IEEE International Conference on, 2012, pp. 221-224: IEEE.##[15] L. Zhao, L. Zhang, S. Ma, and D. Zhao, "Fast mode decision algorithm for intraprediction in HEVC," in Visual Communications and Image Processing (VCIP), 2011 IEEE, 2011, pp. 1-4: IEEE.##[16] O. C. Cristina, U. R. Mihnea, and P. Ionut, "HEVC intra partitioning and mode decision using histograms of oriented gradients," in Electronicsand Telecommunications (ISETC), 2016 12th IEEE International Symposium on, 2016, pp. 277-280: IEEE.##[17] J. M. Ha, J. H. Bae, and M. H. Sunwoo, "Texture-based fast CU size decision algorithm for HEVC intra coding," in Circuits and Systems (APCCAS), 2016 IEEE Asia Pacific Conference on, 2016, pp. 702-705: IEEE.##[18] A. Heindel and A. Kaup, "Fast intra mode decision in HEVC using early distortion estimation," in Signal and Information Processing (ChinaSIP), 2015 IEEE China Summit and International Conference on, 2015, pp. 559-563: IEEE.##[19] J. Hou, D. Li, Z. Li, and X. Jiang, "Fast CU size decision based on texture complexity for HEVC intra coding," in Mechatronic Sciences, Electric Engineering and Computer (MEC), Proceedings 2013 International Conferenceon, 2013, pp. 1096-1099: IEEE.##[20] J. Kim, J. Yang, H. Lee, and B. Jeon, "Fast intra mode decision of HEVC based on hierarchical structure," in Information, Communications and Signal Processing (ICICS) 2011 8th International Conference on, 2011, pp. 1-4: IEEE.##[21] T. Mallikarachchi, A. Fernando, and H. K. Arachchi, "Efficient coding unit size selection based on texture analysis for hevc intra prediction," in Multimedia and Expo (ICME), 2014 IEEE International Conference on, 2014, pp. 1-6: IEEE.##[22] B. Min and R. C. Cheung, "A fast CU size decision algorithm for the HEVC intra encoder," IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 5, pp. 892-896, 2015.##[23] M. Radosavljević, G. Georgakarakos, S. Lafond, and D. Vukobratović, "Fast coding unit selection based on local texture characteristics for HEVC intra frame," in Signal and Information Processing (GlobalSIP), 2015 IEEE Global Conference on, 2015, pp. 1377-1381: IEEE.##[24] M. Ramezanpour and F. Zargari, "Early termination algorithm for CU size decision in HEVC intra coding," in Machine Vision and Image Processing (MVIP), 2015 9th Iranian Conference on, 2015, pp. 45-48: IEEE.##[25] K. Saurty, P. C. Catherine, and K. Soyjaudah, "Terminating CU splitting in HEVC intra prediction using the Hadamard Absolute Difference (HAD) cost," in SAI Intelligent Systems Conference (IntelliSys), 2015, 2015, pp. 836-841: IEEE.##[26] L. Shen, Z. Zhang, and Z. Liu, "Effective CU size decision for HEVC intracoding," IEEE Transactions on Image Processing, vol. 23, no. 10, pp. 4232-4241, 2014.##[27] S. Yan, L. Hong, W. He, and Q. Wang, "Group-based fast mode decision algorithm for intra prediction in HEVC," in Signal Image Technology and Internet Based Systems (SITIS), 2012 Eighth International Conference on, 2012, pp. 225-229: IEEE.##[28] H. Sun, D. Zhou, and S. Goto, "A low-complexity HEVC intra prediction algorithm based on level and mode filtering," in Multimedia and Expo (ICME), 2012 IEEE International Conference on, 2012, pp. 1085-1090: IEEE.##[29] J. Guo, G. Chen, J. Zheng, and D. Guo, "Gradient based fast CU splitting and mode decision algorithm for HEVC intra prediction," in Anti-counterfeiting, Security, and Identification (ASID), 2014 International Conference on, 2014, pp. 1-5: IEEE.##[30] L. Shen, Z. Zhang, P. An, "Fast CU Size Decision and Mode Decision Algorithm for HEVC Intra Coding". IEEE Transactions on Consumer Electronics, Vol 59, No. 1, pp. 207-213, Feb. 2013.##[31] T. Nishikori, T. Nakamura, T. Yoshitome, and K. Mishiba, "A fast CU decision using image variance in HEVC intra coding," in Industrial Electronics and Applications (ISIEA), 2013 IEEE Symposium on, 2013, pp. 52-56: IEEE.##[32] X. Shang, G. Wang, T. Fan, and Y. Li, "Fast CU size decision and PU mode decision algorithm in HEVC intra coding," in Image Processing (ICIP), 2015 IEEE International Conference on, 2015, pp. 1593-1597: IEEE.##[33] C.-F. Tseng and Y.-T. Lai, "Fast coding unit decision and mode selection for intra-frame coding in high-efficiency video coding," IET image processing, vol. 10, no. 3, pp. 215-221, 2016.##[34] Q. Zhang, J. Sun, Y. Duan, and Z. Guo, "A two-stage fast CU size decision method for HEVC intracoding," in Multimedia Signal Processing (MMSP), 2015 IEEE 17th International Workshop on, 2015, pp. 1-6: IEEE.##[35] X. Zhou, G. Shi, and W. Zhou, "Perceptual CU Size Decision and Fast Prediction Mode Decision Algorithm for HEVC Intra Coding," in Multimedia (ISM), 2016 IEEE International Symposium on, 2016, pp. 375-378: IEEE.##[36] G. Bjontegarrd, "Calculation of average PSNR differences between RD-curves," VCEG-M33, 2001.##[37] Jamali, M. and Coulombe, S., 2018. Fast HEVC intra mode decision based on RDO cost prediction. IEEE Transactions on Broadcasting, 65(1), pp.109-122.##[38] Gu, J., Tang, M., Wen, J. and Han, Y., 2017. Adaptive intra candidate selection with early depth decision for fast intra prediction in HEVC. IEEE Signal Processing Letters, 25(2), pp.159-163.##[1] M. Wien, High efficiency video coding: Coding tools and specification. Springer, 2014.##[2] M. Budagavi, High Efficiency Video Coding (hevc): Algorithms and Architectures. Springer, 2016.##[3] G. J. Sullivan, J. R. Ohm, "HEVC software guidelines," Joint Collaborative Team on Video Coding (JVT-VC) of ITU-T SG16 WP3 and ISO/IEC JTC1/SC29/WG11, document JCTVC-H1001, 8th Meeting: San Jose, CA, USA, Feb. 2012.##[4] R. C. Gonzalez and R. E. Woods, "Digital image processing," ed: Upper Saddle River, NJ: Prentice Hall, 2012.##[5] M. Jamali, S. Coulombe, and F. Caron, "Fast HEVC intra mode decision based on edge detection and SATD costs classification," in Data Compression Conference (DCC), 2015, 2015, pp. 43-52: IEEE.##[6] G. J. Sullivan, J. Ohm, W.-J. Han, and T. Wiegand, "Overview of the high efficiency video coding (HEVC) standard," IEEE Transactions on circuits and systems for video technology, vol. 22, no. 12, pp. 1649-1668, 2012.##[7] H. Brahmasury Jain and K. Rao, "Fast intra mode decision in high efficiency video coding," Polibits, no. 50, pp. 5-12, 2014.##[8] Q. Hu, Z. Shi, X. Zhang, and Z. Gao, "Fast HEVC intra mode decision based on logistic regression classification," in Broadband Multimedia Systems and Broadcasting (BMSB), 2016 IEEE International Symposium on, 2016, pp. 1-4: IEEE.##[9] W. Liao, D. Yang, and Z. Chen, "A fast mode decision algorithm for HEVC intra prediction," in Visual Communications and Image Processing (VCIP), 2016, 2016, pp. 1-4: IEEE.##[10] D. B. Patel, D. M. Chauhan, and K. C. Pathak, "Edges based intra-prediction in High Efficiency Video Coding (HEVC) standard," in Engineering and Technology (ICETECH), 2016 IEEE International Conference on, 2016, pp. 995-1000: IEEE.##[11] Y. Piao, J. Min, and J. Chen, "Encoder improvement of unified intra prediction, joint collaborative team on video coding (JCT-VC) of ITU-TSG16 WP3 and ISO," IEC JTC1/SC29/WG11, documentJCTVC-C2072010.##[12] R. Tian, Y. Zhang, R. Fan, and G. Wang, "Adaptive Fast Mode Decision for HEVC Intra Coding," in Digital Image Computing: Techniques and Applications (DICTA), 2016 International Conference on, 2016, pp. 1-6: IEEE.##[13] W. Xiang, C. Cai, Z. Wang, H. Zeng, and J. Chen, "Fast intra mode decision for HEVC," in Signal-Image Technology and Internet-Based Systems (SITIS), 2014 Tenth International Conference on, 2014, pp. 283-288: IEEE.##[14] M. Zhang, C. Zhao, and J. Xu, "An adaptive fast intra mode decision in HEVC," in Image Processing (ICIP), 2012 19th IEEE International Conference on, 2012, pp. 221-224: IEEE.##[15] L. Zhao, L. Zhang, S. Ma, and D. Zhao, "Fast mode decision algorithm for intraprediction in HEVC," in Visual Communications and Image Processing (VCIP), 2011 IEEE, 2011, pp. 1-4: IEEE.##[16] O. C. Cristina, U. R. Mihnea, and P. Ionut, "HEVC intra partitioning and mode decision using histograms of oriented gradients," in Electronicsand Telecommunications (ISETC), 2016 12th IEEE International Symposium on, 2016, pp. 277-280: IEEE.##[17] J. M. Ha, J. H. Bae, and M. H. Sunwoo, "Texture-based fast CU size decision algorithm for HEVC intra coding," in Circuits and Systems (APCCAS), 2016 IEEE Asia Pacific Conference on, 2016, pp. 702-705: IEEE.##[18] A. Heindel and A. Kaup, "Fast intra mode decision in HEVC using early distortion estimation," in Signal and Information Processing (ChinaSIP), 2015 IEEE China Summit and International Conference on, 2015, pp. 559-563: IEEE.##[19] J. Hou, D. Li, Z. Li, and X. Jiang, "Fast CU size decision based on texture complexity for HEVC intra coding," in Mechatronic Sciences, Electric Engineering and Computer (MEC), Proceedings 2013 International Conferenceon, 2013, pp. 1096-1099: IEEE.##[20] J. Kim, J. Yang, H. Lee, and B. Jeon, "Fast intra mode decision of HEVC based on hierarchical structure," in Information, Communications and Signal Processing (ICICS) 2011 8th International Conference on, 2011, pp. 1-4: IEEE.##[21] T. Mallikarachchi, A. Fernando, and H. K. Arachchi, "Efficient coding unit size selection based on texture analysis for hevc intra prediction," in Multimedia and Expo (ICME), 2014 IEEE International Conference on, 2014, pp. 1-6: IEEE.##[22] B. Min and R. C. Cheung, "A fast CU size decision algorithm for the HEVC intra encoder," IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 5, pp. 892-896, 2015.##[23] M. Radosavljević, G. Georgakarakos, S. Lafond, and D. Vukobratović, "Fast coding unit selection based on local texture characteristics for HEVC intra frame," in Signal and Information Processing (GlobalSIP), 2015 IEEE Global Conference on, 2015, pp. 1377-1381: IEEE.##[24] M. Ramezanpour and F. Zargari, "Early termination algorithm for CU size decision in HEVC intra coding," in Machine Vision and Image Processing (MVIP), 2015 9th Iranian Conference on, 2015, pp. 45-48: IEEE.##[25] K. Saurty, P. C. Catherine, and K. Soyjaudah, "Terminating CU splitting in HEVC intra prediction using the Hadamard Absolute Difference (HAD) cost," in SAI Intelligent Systems Conference (IntelliSys), 2015, 2015, pp. 836-841: IEEE.##[26] L. Shen, Z. Zhang, and Z. Liu, "Effective CU size decision for HEVC intracoding," IEEE Transactions on Image Processing, vol. 23, no. 10, pp. 4232-4241, 2014.##[27] S. Yan, L. Hong, W. He, and Q. Wang, "Group-based fast mode decision algorithm for intra prediction in HEVC," in Signal Image Technology and Internet Based Systems (SITIS), 2012 Eighth International Conference on, 2012, pp. 225-229: IEEE.##[28] H. Sun, D. Zhou, and S. Goto, "A low-complexity HEVC intra prediction algorithm based on level and mode filtering," in Multimedia and Expo (ICME), 2012 IEEE International Conference on, 2012, pp. 1085-1090: IEEE.##[29] J. Guo, G. Chen, J. Zheng, and D. Guo, "Gradient based fast CU splitting and mode decision algorithm for HEVC intra prediction," in Anti-counterfeiting, Security, and Identification (ASID), 2014 International Conference on, 2014, pp. 1-5: IEEE.##[30] L. Shen, Z. Zhang, P. An, "Fast CU Size Decision and Mode Decision Algorithm for HEVC Intra Coding". IEEE Transactions on Consumer Electronics, Vol 59, No. 1, pp. 207-213, Feb. 2013.##[31] T. Nishikori, T. Nakamura, T. Yoshitome, and K. Mishiba, "A fast CU decision using image variance in HEVC intra coding," in Industrial Electronics and Applications (ISIEA), 2013 IEEE Symposium on, 2013, pp. 52-56: IEEE.##[32] X. Shang, G. Wang, T. Fan, and Y. Li, "Fast CU size decision and PU mode decision algorithm in HEVC intra coding," in Image Processing (ICIP), 2015 IEEE International Conference on, 2015, pp. 1593-1597: IEEE.##[33] C.-F. Tseng and Y.-T. Lai, "Fast coding unit decision and mode selection for intra-frame coding in high-efficiency video coding," IET image processing, vol. 10, no. 3, pp. 215-221, 2016.##[34] Q. Zhang, J. Sun, Y. Duan, and Z. Guo, "A two-stage fast CU size decision method for HEVC intracoding," in Multimedia Signal Processing (MMSP), 2015 IEEE 17th International Workshop on, 2015, pp. 1-6: IEEE.##[35] X. Zhou, G. Shi, and W. Zhou, "Perceptual CU Size Decision and Fast Prediction Mode Decision Algorithm for HEVC Intra Coding," in Multimedia (ISM), 2016 IEEE International Symposium on, 2016, pp. 375-378: IEEE.##[36] G. Bjontegarrd, "Calculation of average PSNR differences between RD-curves," VCEG-M33, 2001.##[37] Jamali, M. and Coulombe, S., 2018. Fast HEVC intra mode decision based on RDO cost prediction. IEEE Transactions on Broadcasting, 65(1), pp.109-122.##[38] Gu, J., Tang, M., Wen, J. and Han, Y., 2017. Adaptive intra candidate selection with early depth decision for fast intra prediction in HEVC. IEEE Signal Processing Letters, 25(2), pp.159-163.## ##

یک سیستم پیشنهادگر محتوا-مشارکتی مبتنی بر خوشه‌بندی و هستان‌شناسی A Content-Collaborative Recommender System based on clustering and ontology 1 1 سامانه‌های پیشنهادگر سامانه‌هایی هستند که در گذر زمان یاد می‌گیرند که هر فرد یا مشتری احتمالاً چه کالا یا قلمی را می‌پسندد و آن را به او پیشنهاد می‌دهند. این سامانه‌ها اغلب بر اساس رفتارهای مشابه از دیگر افراد (احتمالاً مشابه) عمل می‌کنند. به‌طور کلی یافتن افراد مشابه، به علت زیاد بودن کاربران، فرایندی بسیار زمان‌بر و به علت کمبود اطلاعات، نادقیق است. به همین دلیل برخی از روش­ها، رو به افزایش سرعت آورده‌اند. از طرفی، برخی از روش­های دیگر، رو به افزودن اطلاعاتِ اضافه آورده تا در گذر این اطلاعات بتوانند دقت یافتن کاربران مشابه یا همسایه را افزایش دهند. برخی دیگر نیز، به روش­های ترکیبی رو آورده‌اند. اخیراً محققان با به‌کارگیری روش­های خوشه­بندی پایه که بر اساس یافتن شبیه‌ترین کاربران همسایه با کمک خوشه­بندی کاربران می‌باشد، و همچنین استفاده از روش‌های محتوا پایه و بعضاً اضافه نمودن هستان‌شناسی به روش‌های محتوا پایه توانسته‌اند با بهره‌گیری از مزایای این روش‌ها، برخی از چالش‌های فوق را تا حد قابل قبولی حل نمایند. در سامانه پیشنهادگر ترکیبی پیشنهادی، از یک سامانه دو مرحله‌ای استفاده کرده‌ایم که در مرحله اول، دو مدل پیش‌بینی‌های خود را انجام داده، سپس در مرحله دوم به‌وسیله یک مؤلفه ترکیب‌گر، نتایج دو بخش مرحله اول با یکدیگر ترکیب شده و نتایج به‌دست آمده را به‌عنوان نتایج نهایی سامانه به ما ارائه می‌دهد. در بخش اول، یک سامانه مبتنی بر پر‌کردن مقادیر گم شده، مقادیر خالی در ماتریس امتیازدهی را پر می‌کند. برای این مهم، از بین روش‌های پرکردن داده‌های گم شده، یک روش که با پرکردن مجموعه داده در شرایط بسیار تُنُک سازگار بود را طراحی کرده و سپس آن را به روش خودمان تعمیم داده‌ایم. در این راستا یک روش مبتنی بر خوشه‌بندی فاصله‌گری ارائه کرده‌ایم. در بخش دوم که خود یک سامانه پیشنهادگر ترکیبی هستان‌شناسی پایه می‌باشد، ابتدا به کمک یک خزنده وب، اطلاعات هر قلم را استخراج کرده، سپس در یک هستان‌شناسی پایه به کمک یک روش پیشنهادی، اقدام به بهبود ساختار هستان‌شناسی به‌وسیله حذف یال‌های همسان می‌نماییم. بدین ترتیب دقت اندازه‌گیری شباهت معنایی بین اقلام و کاربران در مراحل بعدی افزایش یافته و میزان اثربخشی پیشنهادات ارائه شده به‌طور با‌معنایی بهبود می‌یابد. شایان ذکر است این هستان‌شناسی یک هستان‌شناسی جامع نیست. درنهایت به کمک یک روش اندازه‌گیری شباهت ابتکاریِ هستان‌شناسی پایه، مشابهت قلم-قلم‌ها، کاربر-کاربرها، و کاربر-قلم‌ها را اندازه‌گیری می‌کنیم. به کمک این ماتریس مشابهت، کاربرها و قلم‌ها را خوشه‌بندی کرده و سپس برای هر کاربر، کاربرها و قلم‌های شبیه به آن را به‌عنوان یک ویژگی جدید در پروفایل کاربر ذخیره می‌نماییم. این کار به ما کمک می‌کند که در آینده، سرعت یافتن کاربرهای مشابه و قلم‌های مشابه را بالا ببریم. در حقیقت بر اساس این ویژگی، سرعت کل کار را افزایش داده‌ایم. از آنجایی که ما هدف خود را ساختن سامانه‌ای که یک موازنه بین دو معیار دقت و سرعت را برقرار کند قرار داده‌ایم، با استفاده از یک مجموعه داده واقعی، از این دو معیار جهت ارزیابی سامانه پیشنهادی استفاده می‌کنیم. نتایج مقایسه‌ی روش پیشنهادی ما با برخی روش‌های مشابه به‌روز ارائه شده در این حوزه (با استفاده از یک مجموعه داده یکسان) حاکی از آن است که روش ما از روش‌های سریع، کندتر است، اما از آنها دقیق‌تر می‌باشد. همچنین این نتایج بیانگر این موضوع است که روش پیشنهادی از روش‌های دقیق، سریع‌تر و کیفیت آن نیز قابل رقابت و یا حتی بهتر است.   2 Recommender systems are systems that, over time, learn what product(s) or item(s) each person or customer is (are) likely to like and recommend it (them) to him/her. These systems often operate based on similar behaviors from other (possibly similar) people. Finding similar people is generally a highly time-consuming process due to the large number of users and inaccurate due to the lack of information. For this reason, some methods have resorted to increasing speed. On the other hand, some other methods have added additional information so that they can increase the accuracy of finding similar or neighboring users. Some others have resorted to hybrid methods. Recently, by the use of basic clustering methods, which is based on finding the most similar neighbors with the help of users’ clustering, as well as by using basic content analysis methods and sometimes adding ontology to these methods, researchers have been able to take the advantage of these methods in order to solve some of the above challenges acceptably. In the proposed hybrid recommender system, we have used a two-stage system in which, in the first stage, two models of predictions are made, then in the second stage, by a combining component, the results of the first two parts are combined and the obtained results are given to us as the final results of the system. In the first part, a system based on imputation of missing values fills in the blanks in the scoring matrix. For this end, among the methods of the missing data imputation, we designed a method that was compatible with filling the data set in very sparse conditions, and then generalized it to our own method. In this regard, we have proposed a method based on the grey distance clustering. In the second part, which itself is a hybrid ontology-based recommender system, we first extract the information of each item with the help of a web crawler, then based on a basic article, we produce our own limited ontology, and after that we apply our proposed method. Then, with the help of a proposed method, we improve the ontology structure, thus increasing the accuracy of measuring semantic similarity between the items and users in later stages, and significantly improving the effectiveness of the created recommendations. It should be noted that this ontology is not comprehensive. Finally, we measure the similarity of item-items, user-users, and user-items using an innovative basic ontology similarity measurement method. By the use of this similarity matrix, we cluster users and items, and then store similar users and items as a new feature in the user/item profile for each user/item. This will help us speed up the process of looking for similar users and similar items in the future. In fact, based on this feature, we have increased the speed of the whole work. Since we have set our goal to build a system that makes a balance between the two criteria of accuracy and speed, we use these two criteria to evaluate the proposed system using a real data set. The results of comparing our proposed method with some up-to-date similar methods presented in this field (using the same data set) implies that our method is slower than fast methods, although it is more accurate than them. These results also suggest that the proposed method is faster than accurate methods and its quality is more competitive or even better than them. 197 224 2022/03/172022/02/252021/06/22022/02/32021/11/12022/05/312022/01/212022/04/202022/06/282022/03/42019/05/182023/04/23 1402/2/3 2023/06/22023/06/22023/12/112023/02/222023/07/82023/07/182023/06/22023/02/222023/07/182023/07/182023/07/182023/12/29 1402/10/8 پیام بحرانی Payam Bahrani 1گروه مهندسی کامپیوتر، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران ایران bahranipayam@gmail.com بهروز مینایی بیدگلی Behrouz Minaei Bidgoli دانشکده مهندسی کامپیوتر، دانشگاه علم و صنعت ایران، تهران، ایران ایران b_minaei@iust.ac.ir حمید پروین Hamid Parvin گروه مهندسی کامپیوتر، واحد نورآباد ممسنی، دانشگاه آزاد اسلامی، نورآباد ممسنی فارس، ایران ایران parvin@iust.ac.ir میترا میرزارضایی Mitra Mirzarzaei گروه مهندسی برق، دانشکده مهندسی سیستم‌های هوشمند و علوم داده، دانشگاه خلیج فارس، بوشهر، ایران ایران mirzarezaee@srbiau.ac.ir احمد کشاورز Ahmed Keshavarz گروه مهندسی برق، دانشکده مهندسی سیستم‌های هوشمند و علوم داده، دانشگاه خلیج فارس، بوشهر، ایران ایران a.keshavarz@pgu.ac.ir Recommender System Ontology Memory-based Filtering Model-based Filtering Clustering k-NN. سامانه پیشنهادگر هستان‌شناسی پالایش حافظه پایه پالایش مدل پایه خوشه‌بندی k-NN . 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(1400). ارائه یک سامانه پیشنهادگر حافظه پایه ترکیبی با استفاده از هستان شناسی و محتوا. پردازش علائم و داده ها، 18(4 (50 پیاپی) )، 89-124.## ##