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سامانه دو سطحی تشخیص نفوذ برای شبکه اینترنت اشیا مبتنی بر یادگیری عمیق Two-level intrusion detection system for Internet of Things network based on deep learning 1 1 به موازات رشد استفاده از شبکه‌های اینترنت اشیا برای کاربردهای مختلف، تهدیدات و حملات مربوط به این نوع شبکه‌ها نیز افزایش پیدا کرده‌است. سامانه‌های تشخیص نفوذ به منظور تشخیص و شناسایی حملات در این‌گونه شبکه­‌ها طراحی و مورد استفاده قرار می‌گیرند و اقدام به شناسایی خرابکاری­‌ها و نفوذها و یا سوءاستفاده­‌هایی که از شبکه قرار است صورت بگیرد، کرده و این موضوع را به اطلاع مسئول مربوطه شبکه می‌­رسانند. در بیشتر سامانه­‌های تشخیص نفوذ، روش‌­ها و الگوریتم‌­های مختلفی از جمله الگوریتم‌­های مبتنی بر یادگیری ماشین و یادگیری عمیق استفاده می‌شود که هر کدام دارای مزایا و معایبی هستند، اما به طور معمول نسبت به روش‌­های ترکیبی نرخ صحت کمتری دارند. در سال‌های اخیر در تشخیص مبتنی بر ناهنجاری­ از ایده ترکیب طبقه­‌بندها استفاده شد‌ه‌است. ما در این پژوهش، برای افزایش سرعت الگوریتم در شناسایی و دستیابی به نرخ درستی و صحت بالاتر از ترکیب روش‌­های تحلیل مؤلفه اصلی یا PCA)) و شبکه­‌های عصبی پیچشی(CNN) برای طراحی سامانه تشخیص نفوذ پیشنهادی خود استفاده کرده‌ایم؛ از PCA به منظور کاهش ابعاد و حجم داده‌­های ورودی بهره بردیم تا به افزایش کارایی الگوریتم اصلی ما کمک کند و داده جدید تولیدشده با این الگوریتم در اختیار طبقه‌بند CNN قرار می­گیرد؛ همچنین ما از دو سطح از دسته‌بندی مبتنی بر شبکه عصبی عمیق پیچشی دودویی و چندکلاسه برای شناسایی حملات بهره بردیم. به این صورت که ابتدا حملات و داده­های نرمال به‌وسیله طبقه‌بند عمیق دودویی از هم جداسازی می‌­شوند؛ سپس به‌وسیله طبقه­‌بند عمیق چندکلاسه به شناسایی و تفکیک نوع حملات صورت‌گرفته پرداخته شده و دسته‌­بندی حملات صورت می‌­گیرد. بر اساس نتایج آزمایش‌­های انجام‌شده روی داده‌­های واقعی حملات، شاهد رشد نرخ صحت و درستی روش پیشنهادی نسبت به بسیاری از روش‌­های مطرح دیگر بوده‌­ایم. 2 Along with the growth in the use of Internet of Things networks for various applications, threats and attacks related to these types of networks have also increased. Intrusion detection systems are designed and used to detect and identify attacks in this type of networks, and to identify intrusions or abuses that are going to take place from the network, and to inform the relevant authorities about this issue. In most intrusion detection systems, various methods and algorithms are used, including deep neural networks (DNNs), support vector machines (SVM), or multilayer perceptron (MLP), and other traditional machine learning models. Each method has advantages and disadvantages, but it usually has a lower accuracy rate than combined methods. In recent years, the idea of combining classifications has been used for anomaly-based diagnosis. In this research, to reach better accuracy, we used the combination of principal component analysis (PCA) and convolutional neural network (CNN) algorithms to design our intrusion detection system. In the initial step of the proposed method, after preprocessing including conversions and normalizations, valuable features for classification are extracted. In this study, the NSL-KDD dataset, which has been mentioned in many scientific articles as a valid reference dataset in the field of intrusion detection, has been used. In fact, due to the high number of data dimensions and the high dispersion of feature values, we used a dimension reduction method. The dimensionality reduction method used in this research is principal component analysis (PCA). In the PCA method, the dimensions of the data are reduced in such a way that the reduced dimension data also includes the vital information of the dataset. We used PCA in order to reduce the size and volume of the input data to help increase the efficiency of our main algorithm and the new data generated with this algorithm is provided to the CNN classifier. A convolutional neural network is a special type of neural network with multiple layers that processes data that has a grid arrangement and then extracts important features from them. Here, accurate pattern learning and deep insight from the given data are our two main reasons for using CNN. In the proposed approach, we have two level classification including binary CNN and multi-class CNN, for detecting attacks and exact type of them, respectively. That is, firstly attacks and normal data are identified by binary classification and then by multi-class classification, the types of attacks are identified and separated. In fact, the type of attacks which includes one of DoS, U2R, R2L and Probe cases is determined using second convolutional neural network. Based on the obtained results, we have witnessed the growth of the accuracy rate of the proposed method compared to many other popular methods. In the evaluation of accuracy parameter values for different phases of training and testing, competitive results are observed for binary classification phase. Here we consider the number of 15 rounds. As it is clear from the graph related to training, the accuracy values in the final courses have reached 0.94. The accuracy of the test has also approached the value of 0.9 in the last round. Also, the results obtained in multi-class CNN are such that the accuracy value is 0.99 in the classification of the training data samples and 0.97 in the classification of the test data samples. Moreover, the cost graphs for training and testing courses of multi-class CNN are shown. The cost of training and testing in the final round is 0.06 and 0.09, respectively. 3 22 2023/07/21 1402/4/30 2024/08/18 1403/5/28 احمد تیموری Ahmad Teymouri کارشناس‌ارشد گروه مهندسی کامپیوتر، دانشگاه آزاد اسلامی، واحد تهران جنوب، تهران ایران trmahmad@gmail.com محمود دی پیر Mahmood Deypir دانشیار دانشکده مهندسی کامپیوتر، دانشگاه هوایی شهید ستاری، تهران ایران mdeypir@gmail.com Intrusion detection system Convolutional neural network (CNN) Binary classifier Multi-class classifier Principal component analysis (PCA). سامانه تشخیص نفوذ شبکه عصبی پیچشی طبقه‌بند دودویی طبقه‌بند چندکلاسه تحلیل مؤلفه‌های اصلی S. Prabavathy, K. Sundarakantham, and S. M. Shalinie. "Design of cognitive fog computing for intrusion detection in internet of things,", vol. 20, no. 3, pp. 291-298, 2018.##P. Kasinathan, C. Pastrone, M. A. Spirito, and M. Vinkovits. "Denial-of-Service detection in 6LoWPAN based Internet of Things,", pp. 600-607, 2013##G. Appice, A. Paolo Caforio, F. Andresini, & D. Malerba, "Improving cyber-threat detection by moving the boundary around the normal samples. In Machine Intelligence and Big Data Analytics for Cybersecurity Applications," pp. 105-127, 2021.‌##S. Hajj, R. El Sibai, J. Bou Abdo, J. Demerjian, A. Makhoul, & C. Guyeux, "Anomaly‐based intrusion detection systems: The requirements, methods, measurements, and datasets. Transactions on Emerging Telecommunications Technologies", 32(4), e4240, 2021.##M. Nobakht, V. Sivaraman, and R. Boreli, "A host-based intrusion detection and mitigation framework for smart home IoT using OpenFlow," 11th International conference on availability, reliability and security (ARES), pp. 147-156, 2016.##H. Bostani and M. Sheikhan, "Hybrid of anomaly-based and specification-based IDS for Internet of Things using unsupervised OPF based on MapReduce approach," Computer Communications, vol. 98, pp. 52-71, 2017.##M. Lopez-Martin, B. Carro, A. Sanchez-Esguevillas, and J. Lloret. "Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in iot," Sensors, vol. 17, no. 9, p. 1967, 2017.##S. Rathore and J. H. Park, "Semi-supervised learning based distributed attack detection framework for IoT," Applied Soft Computing, vol. 72, pp. 79-89, 2018.##Diro and N. Chilamkurti. "Distributed attack detection scheme using deep learning approach for Internet of Things," Future Generation Computer Systems, vol. 82, pp. 761-768, 2018.##V. Kumari and P. R. K. Varma. "A semi-supervised intrusion detection system using active learning SVM and fuzzy c-means clustering," in 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), pp. 481-485, 2017.##Z. K. Zhang, M. C. Y. Cho, C.-W. Wang, C.-W. Hsu, C.-K. Chen, and S. Shieh. "IoT security: ongoing challenges and research opportunities," in 2014 IEEE 7th international conference on service-oriented computing and applications, pp. 230-234, 2014.##M. Cheema, H. K. Qureshi, C. Chrysostomou, & M. Lestas, "Utilizing blockchain for distributed machine learning based intrusion detection in internet of things." In 2020 16th International Conference on Distributed Computing in Sensor Systems pp. 429-435, 2020.##T. Hagemann, & Katsarou, K. A systematic "review on anomaly detection for cloud computing environments. In 2020 3rd Artificial Intelligence and Cloud Computing Conference." pp. 83-96, 2020.##AbuGhazleh, A. Al-Rahayfeh, S. Atiewi, and A. Razaque. "Deep recurrent neural network for IoT intrusion detection system," Simulation Modelling Practice and Theory, vol. 101, pp. 10203, 2020.##Dhanabsl, S.P. Shantharajah, "A Study on NSL-KDD Dataset for Intrusion Detection System Based on Classification Algorithms." PP. 2319-5940, 2015.##Mackiewicz, and W. ratajczak, "Principal components analysis(PCA)" pp. 0098-3004, 1993.##S. Indolia, A. K. Goswami, S. P. Mishra, and P. Asopa, "Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach" pp. 10-1016, 2018.##S. Raza, L. Wallgren, and T. Voigt. "SVELTE: Real-time intrusion detection in the Internet of Things," Ad hoc networks, vol. 11, no. 8, pp. 2661-2674, 2013.##Jun and C. Chi. "Design of complex event-processing IDS in internet of things," in 2014 Sixth International Conference on Measuring Technology and Mechatronics Automation, pp. 226-229, 2014.##T. S. Naseri, and F. S. Gharehchopogh, "A Feature Selection Based on the Farmland Fertility Algorithm for Improved Intrusion Detection Systems". Journal of Network and Systems Management, 30(3), pp. 1-27, 2022.##S. K. Amalapuram, A.Tadwai, , R.Vinta, , S. S.Channappayya, and B. R. Tamma, "Continual Learning for Anomaly based Network Intrusion Detection". In 2022 14th International Conference on COMmunication Systems & NETworkS (COMSNETS), pp. 497-505, 2022.##D.Teixeira, , S. Malta, , and P.Pinto, "A Vote-Based Architecture to Generate Classified Datasets and Improve Performance of Intrusion Detection Systems Based on Supervised Learning". Future Internet, 14(3), 72,‌ 2022.##E. Gharavi, H. Veisi, "Using RST-based deep neural networks to improve text representation", Signal and Data Processing, 20 (1), pp. 181-197, 2023.##S. Abbasi, S. Nejatian, H. Parvin, K. Bagherifard, V. Rezaie, "The ensemble clustering with maximize diversity using evolutionary optimization algorithms", Signal and Data Processing, 19 (4), pp. 95-120, 2023.##S. Prabavathy, K. Sundarakantham, and S. M. Shalinie. "Design of cognitive fog computing for intrusion detection in internet of things,", vol. 20, no. 3, pp. 291-298, 2018.##P. Kasinathan, C. Pastrone, M. A. Spirito, and M. Vinkovits. "Denial-of-Service detection in 6LoWPAN based Internet of Things,", pp. 600-607, 2013##G. Appice, A. Paolo Caforio, F. Andresini, & D. Malerba, "Improving cyber-threat detection by moving the boundary around the normal samples. In Machine Intelligence and Big Data Analytics for Cybersecurity Applications," pp. 105-127, 2021.‌##S. Hajj, R. El Sibai, J. Bou Abdo, J. Demerjian, A. Makhoul, & C. Guyeux, "Anomaly‐based intrusion detection systems: The requirements, methods, measurements, and datasets. Transactions on Emerging Telecommunications Technologies", 32(4), e4240, 2021.##M. Nobakht, V. Sivaraman, and R. Boreli, "A host-based intrusion detection and mitigation framework for smart home IoT using OpenFlow," 11th International conference on availability, reliability and security (ARES), pp. 147-156, 2016.##H. Bostani and M. Sheikhan, "Hybrid of anomaly-based and specification-based IDS for Internet of Things using unsupervised OPF based on MapReduce approach," Computer Communications, vol. 98, pp. 52-71, 2017.##M. Lopez-Martin, B. Carro, A. Sanchez-Esguevillas, and J. Lloret. "Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in iot," Sensors, vol. 17, no. 9, p. 1967, 2017.##S. Rathore and J. H. Park, "Semi-supervised learning based distributed attack detection framework for IoT," Applied Soft Computing, vol. 72, pp. 79-89, 2018.##Diro and N. Chilamkurti. "Distributed attack detection scheme using deep learning approach for Internet of Things," Future Generation Computer Systems, vol. 82, pp. 761-768, 2018.##V. Kumari and P. R. K. Varma. "A semi-supervised intrusion detection system using active learning SVM and fuzzy c-means clustering," in 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), pp. 481-485, 2017.##Z. K. Zhang, M. C. Y. Cho, C.-W. Wang, C.-W. Hsu, C.-K. Chen, and S. Shieh. "IoT security: ongoing challenges and research opportunities," in 2014 IEEE 7th international conference on service-oriented computing and applications, pp. 230-234, 2014.##M. Cheema, H. K. Qureshi, C. Chrysostomou, & M. Lestas, "Utilizing blockchain for distributed machine learning based intrusion detection in internet of things." In 2020 16th International Conference on Distributed Computing in Sensor Systems pp. 429-435, 2020.##T. Hagemann, & Katsarou, K. A systematic "review on anomaly detection for cloud computing environments. In 2020 3rd Artificial Intelligence and Cloud Computing Conference." pp. 83-96, 2020.##AbuGhazleh, A. Al-Rahayfeh, S. Atiewi, and A. Razaque. "Deep recurrent neural network for IoT intrusion detection system," Simulation Modelling Practice and Theory, vol. 101, pp. 10203, 2020.##Dhanabsl, S.P. Shantharajah, "A Study on NSL-KDD Dataset for Intrusion Detection System Based on Classification Algorithms." PP. 2319-5940, 2015.##Mackiewicz, and W. ratajczak, "Principal components analysis(PCA)" pp. 0098-3004, 1993.##S. Indolia, A. K. Goswami, S. P. Mishra, and P. Asopa, "Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach" pp. 10-1016, 2018.##S. Raza, L. Wallgren, and T. Voigt. "SVELTE: Real-time intrusion detection in the Internet of Things," Ad hoc networks, vol. 11, no. 8, pp. 2661-2674, 2013.##Jun and C. Chi. "Design of complex event-processing IDS in internet of things," in 2014 Sixth International Conference on Measuring Technology and Mechatronics Automation, pp. 226-229, 2014.##T. S. Naseri, and F. S. Gharehchopogh, "A Feature Selection Based on the Farmland Fertility Algorithm for Improved Intrusion Detection Systems". Journal of Network and Systems Management, 30(3), pp. 1-27, 2022.##S. K. Amalapuram, A.Tadwai, , R.Vinta, , S. S.Channappayya, and B. R. Tamma, "Continual Learning for Anomaly based Network Intrusion Detection". In 2022 14th International Conference on COMmunication Systems & NETworkS (COMSNETS), pp. 497-505, 2022.##D.Teixeira, , S. Malta, , and P.Pinto, "A Vote-Based Architecture to Generate Classified Datasets and Improve Performance of Intrusion Detection Systems Based on Supervised Learning". Future Internet, 14(3), 72,‌ 2022.##E. Gharavi, H. Veisi, "Using RST-based deep neural networks to improve text representation", Signal and Data Processing, 20 (1), pp. 181-197, 2023.##S. Abbasi, S. Nejatian, H. Parvin, K. Bagherifard, V. Rezaie, "The ensemble clustering with maximize diversity using evolutionary optimization algorithms", Signal and Data Processing, 19 (4), pp. 95-120, 2023. ##

مطالعه مروری سیستماتیک بر مبانی، کاربرد‌ها و چالش‌های یادگیری مشارکتی A systematic review of the foundations, applications, and challenges of federated learning. 1 1 یادگیری مشارکتی یک طرح یادگیری ماشین در حال ‌رشد است که هدف آن حل مشکل جزیره‌ای‌شدن داده‌ها با حفظ حریم خصوصی آن‌هاست؛ در این ‌روش، چندین مشتری مانند دستگاه‌های تلفن ‌همراه، مؤسسات، سازمان‌ها با یک یا چند سرور مرکزی برای آموزش مدل‌های یادگیری ماشین به‌صورت غیرمتمرکز همکاری می‌کنند. برای نخستین‌ بار گوگل در سال 2016 یادگیری مشارکتی را برای پیش‌بینی ورودی متن کاربر در ده‌ها هزار دستگاه اندرویدی و حفظ حریم خصوصی داده‌ها در دستگاه‌ها  معرفی کرد که در واقع یک فناوری یادگیری ماشین توزیع‌شده رمزگذاری‌شده است که به شرکت‌کنندگان اجازه می‌دهد مدل آموزشی مشترکی را بسازند؛ در حالی‌که داده‌های اصلی را به‌صورت محلی حفظ می‌کنند. در سال‌های اخیر، مفهوم اصلی یادگیری مشارکتی به‌ طیف وسیع‌تری از روش‌های یادگیری ماشین غیرمتمرکز و حفظ حریم خصوصی گسترش یافته‌است. در این مقاله، مروری سیستماتیک بر مقالات مروری، مقالات منتخب و کتب منتشرشده در زمینه یادگیری مشارکتی ارائه می‌دهیم. در ابتدا یک نمای کلی از یادگیری مشارکتی ترسیم می‌کنیم که شامل معرفی و تشریح فرایند آن است؛ سپس به معرفی الگوریتم‌های موجود و سیر تکامل آن‌ها می‌پردازیم؛ همچنین، طبقه‌بندی و کاربرد انواع ساختارهای یادگیری مشارکتی در سه‌ گروه یادگیری مشارکتی افقی، یادگیری مشارکتی عمودی و یادگیری انتقال مشارکتی را مورد بررسی قرار می‌دهیم. در ادامه، با استناد به منابع منتخب این مقاله، به بررسی کاربردهای یادگیری مشارکتی در اینترنت اشیا، شهر هوشمند، امنیت، حفظ حریم خصوصی مدل و داده‌ها، انفورماتیک سلامت و مراقبت‌های بهداشتی می‌پردازیم؛ سپس روش‌های مختلف در هر یک از این زمینه‌ها مقایسه و مزایا، محدودیت‌ها و چالش‌های پیش‌روی هر کدام را مورد بحث قرار می‌دهیم. علاوه‌براین، به بررسی کاربردها و تفاوت‌های یادگیری ماشین مشارکتی، یادگیری عمیق مشارکتی و بلاک‌چین مشارکتی در اینترنت صنعتی اشیا می‌پردازیم وکاربردهای این فناوری‌ها در حوزه‌های ذخیره‌سازی، مدیریت‌داده‌ها و مدیریت منابع را نیز بررسی می‌کنیم. در پایان، به بررسی چالش‌ها، چشم‌اندازها و زمینه‌های پژوهشی آتی حوزه یادگیری مشارکتی می‌پردازیم.   2 Federated Learning (FL) is an innovative machine learning paradigm that tackles the challenge of data island while safeguarding data privacy. It enables decentralized model training by allowing multiple clients—such as mobile devices, institutions, or organizations—to collaboratively build models without transferring local data to a central server. This paradigm gained significant attention following Google’s 2016 initiative to predict user text input on Android devices while maintaining the privacy of locally stored data. A core feature of FL is its distributed and encrypted framework, enabling participants to contribute to a collective learning process without revealing their original data to a central entity or other participants. In recent years, FL has evolved to encompass a broader spectrum of decentralized machine learning techniques, while still maintaining privacy as a central tenet. This evolution has positioned FL as a critical technology in sectors where data privacy, security, and sovereignty are paramount. This paper presents a systematic review of the literature on federated learning, synthesizing insights from review articles, Books, key documents, and published research. The review is structured as follows: Overview of Federated Learning: This section introduces the foundational concepts of FL, detailing its origins, core principles, and operational processes. The decentralized structure and privacy-preserving techniques employed in FL are examined, along with real-world applications as examples. Algorithms and Evolution: This section explores the state-of-the-art algorithms driving FL and traces their development over time. Key innovations in aggregation techniques, optimization methods, and client-server communication protocols are highlighted, demonstrating how they have enhanced FL's scalability and efficiency. Classification and Applications of FL Architectures: Federated learning architectures are categorized into three main types: horizontal federated learning, vertical federated learning, and federated transfer learning. This section analyzes the application of these architectures across various domains, highlighting their distinctive features and associated challenges. Applications in IoT, Smart Cities, and Healthcare: Using selected case studies, this section evaluates the deployment of FL in the Internet of Things (IoT), smart cities, and healthcare. It assesses how FL enhances data privacy, security, and operational efficiency in these domains, focusing on practical implementations. Comparative Analysis: This section offers a comparative evaluation of the various methods and algorithms used in the aforementioned fields, identifying their relative strengths and weaknesses. Special attention is given to the challenges posed by large-scale FL deployments, including communication overhead, data heterogeneity, and model convergence. Federated Learning and Related Technologies: This section explores the integration of FL with related technologies, such as federated deep learning and federated blockchain, particularly within the context of the Industrial Internet of Things (IIoT). The potential of these technologies to improve storage, data management, and resource optimization is discussed in detail. Challenges and Future Directions: The final section addresses the ongoing challenges facing FL, including scalability, model accuracy, communication costs, and compliance with regulatory frameworks. Additionally, it proposes future research directions aimed at improving the practicality and widespread adoption of FL in industrial and commercial applications. This systematic review provides a comprehensive examination of federated learning’s current state, including its foundational concepts, applications, and challenges. It also outlines a forward-looking perspective on the advancements needed to establish FL as a key technology in privacy-centric, decentralized machine learning. 23 68 2023/07/212023/07/23 1402/5/1 2024/08/182024/08/21 1403/5/31 هاله فاتح Haleh Fateh دانشجوی دکتری دانشکده مهندسی کامپیوتر، دانشگاه صنعتی شاهرود، شاهرود، ایران ایران h.fateh@shahroodut.ac.ir محسن رضوانی Mohsen Rezvani دانشیار دانشکده مهندسی کامپیوتر، دانشگاه صنعتی شاهرود، شاهرود، ایران ایران mrezvani@shahroodut.ac.ir اسماعیل طحانیان Esmaeel Tahanian استادیار دانشکده مهندسی کامپیوتر، دانشگاه صنعتی شاهرود، شاهرود، ایران ایران e.tahanian@shahroodut.ac.ir Federated learning decentralized machine learning privacy-preserving Distributed artificial intelligence Internet of Things یادگیری مشارکتی یادگیری ماشین غیرمتمرکز حفظ حریم خصوصی هوش ‌مصنوعی توزیع‌شده اینترنت اشیا. Blanco-Justicia A, Domingo-Ferrer J et al. 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بهبود تشخیص صحنه در سنجش از راه‌دور با استفاده از یادگیری عمیق و انتخاب‌گر ویژگی Improving Scene Recognition in Remote Sensing Using Deep Learning and Feature Selector 1 1 تصاویر سنجش‌‌ازدور به‌عنوان منبع‌داده با ارزش می‌تواند در اندازه‌گیری و مشاهده ساختارهای دقیق در سطح زمین کمک کند. هدف این پژوهش ارائه راهکار تشخیص صحنه در سنجش از راه‌‌دور با به‌کارگیری روش‌های یادگیری عمیق است. برای رفع محدودیت‌های روش‌های پیشین، یک رویکرد ترکیبی استخراج ویژگی مطرح شده‌است که در آن دو نوع ویژگی عمیق محلی و سراسری و یک ‌نوع محلی دستی با یکدیگر ترکیب شده­اند. برای استخراج ویژگی‌ها یک شبکه‌ کانولوشن پیش‌آموزش‌دیده با بیست لایه تمام‌متصل پیشنهاد شده‌است؛ همچنین یک مرحله انتخاب ویژگی با استفاده از دو دسته الگوریتم‌های پالایه و بسته­بند در این ‌مدل تعبیه شده‌است؛ در نهایت با استفاده از الگوریتم‌های طبقه‌بندی مختلف تشخیص صحنه انجام می‌شود. ارزیابی راهکار پیشنهادی برای مجموعه‌داده­های UCM، AID ،RSSCN7 و NWPU-RESISC45 به‌ترتیب دقت 27/99% ، 91/97% ، 09/99% و 09/93% را کسب کرده‌است. 2 Remote sensing images as a valuable data source in Earth observation can help in measuring and observing detailed structures on the Earth's surface. Scene detection in remote sensing images has many applications in various fields such as urban planning, natural hazard detection, environmental monitoring, vegetation mapping, and geographic object detection. One of the key problems in the interpretation of remote sensing images is the scene classification of remote sensing images. Feature extraction is very important in scene detection and classification. Convolutional neural networks are one of the deep learning methods that have significantly increased the performance of tasks such as object recognition and scene classification, but their performance is highly dependent on the number of labeled images available, which are not available enough especially in the field of remote sensing. Recently, transfer learning, especially for the fine-tuning of pre-trained convolutional neural networks, has attracted more attention from researchers as a practical strategy for scene classification in remote sensing. However, the lack of use of local features and global deep model that is trained on the target data set is one of the limitations of current methods. Also, if these networks are not deep enough and the images do not pass through multiple filters, they cannot extract more semantic information, and the extracted features do not have high discrimination power, and as a result, scene recognition is not performed well. On the other hand, the features extracted through local features are very large, and not using feature selector methods reduces the accuracy of the model. In this research, to solve the mentioned limitations, a hybrid approach of feature extraction has been proposed in which three types of features including two types of deep local and global features and one type of manual local feature are combined with each other. To extract deep features, pre-trained convolutional networks have been used. The pre-trained networks used are: ResNet, InceptionNet, GoogleNet and EfficientNet_b0. In order to extract as much information as possible from the images, a convolutional network with 20 fully connected layers is proposed. Also, a combined feature selection stage consisting of two categories of filtering and packing algorithms is included in this model. Finally, scene detection is performed using several different classification algorithms. The different structure of pre-trained convolutional networks and their appropriate depth can be effective in improving the extraction of deep features. In addition, the combination of three categories of different features can provide a more comprehensive knowledge of images. The evaluation of the proposed solution on the UCM, AID, RSSCN7 and NWPU-RESISC45 datasets has obtained the accuracy of 99.27%, 97.91%, 99.09% and 93.09% respectively in identifying and classifying images. As a result, this solution has shown a better performance compared to the models that used the manual extraction of features, as well as the methods that use normal convolutional models. 69 84 2023/07/212023/07/232023/09/9 1402/6/18 2024/08/182024/08/212024/08/21 1403/5/31 سید علی جهانی Seyyed Ali Jahani کارشناس‌ارشد رشته مهندسی کامپیوتر، دانشگاه آزاد اسلامی، واحد اصفهان (خوراسگان)، اصفهان، ایران ایران alijahani1919@gmail.com کیوان محبی Keyvan Mohebbi استادیار گروه مهندسی کامپیوتر، دانشگاه آزاد اسلامی، واحد اصفهان (خوراسگان)، اصفهان، ایران ایران k.mohebbi@khuisf.ac.ir فرساد زمانی بروجنی Farsad Zamani Boroujeni استادیار گروه مهندسی کامپیوتر، واحد علوم‌وتحقیقات، دانشگاه آزاد اسلامی، تهران، ایران ایران Farsad.zamani@srbiau.ac.ir Remote Sensing Deep Learning Deep Feature Hybrid Learning Pre-Trained سنجش از راه دور یادگیری عمیق ویژگی عمیق یادگیری ترکیبی از پیش آموزش دیده Q. Hu et al., "Exploring the use of Google Earth imagery and object-based methods in land use/cover mapping," Remote Sensing, vol. 5, no. 11, pp. 6026-6042, 2013.##L. Gómez-Chova, D. Tuia, G. Moser, and G. Camps-Valls, "Multimodal classification of remote sensing images: A review and future directions," Proceedings of the IEEE, vol. 103, no. 9, pp. 1560-1584, 2015.##A. Tayyebi, B. C. Pijanowski, and A. H. Tayyebi, "An urban growth boundary model using neural networks, GIS and radial parameterization: An application to Tehran, Iran," Landscape and Urban Planning, vol. 100, no. 1-2, pp. 35-44, 2011.##Z. Y. Lv, W. Shi, X. Zhang, and J. A. Benediktsson, "Landslide inventory mapping from bitemporal high-resolution remote sensing images using change detection and multiscale segmentation," IEEE journal of selected topics in applied earth observations and remote sensing, vol. 11, no. 5, pp. 1520-1532, 2018.##T. R. Martha, N. Kerle, C. J. Van Westen, V. Jetten, and K. V. Kumar, "Segment optimization and data-driven thresholding for knowledge-based landslide detection by object-based image analysis," IEEE transactions on geoscience and remote sensing, vol. 49, no. 12, pp. 4928-4943, 2011.##F. Ghazouani, I. R. Farah, and B. Solaiman, "A multi-level semantic scene interpretation strategy for change interpretation in remote sensing imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 11, pp. 8775-8795, 2019.##Y. Li, Y. Zhang, X. Huang, and A. L. Yuille, "Deep networks under scene-level supervision for multi-class geospatial object detection from remote sensing images," ISPRS journal of photogrammetry and remote sensing, vol. 146, pp. 182-196, 2018.##Y. Gu, Y. Wang, and Y. Li, "A survey on deep learning-driven remote sensing image scene understanding: Scene classification, scene retrieval and scene-guided object detection," Applied Sciences, vol. 9, no. 10, p. 2110, 2019.##G. Cheng, X. Xie, J. Han, L. Guo, and G.-S. Xia, "Remote sensing image scene classification meets deep learning: Challenges, methods, benchmarks, and opportunities," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 3735-3756, 2020.##W. Wang, Y. Chen, and P. Ghamisi, "Transferring CNN With Adaptive Learning for Remote Sensing Scene Classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-18, 2022.##K. Xu, H. Huang, P. Deng, and Y. Li, "Deep feature aggregation framework driven by graph convolutional network for scene classification in remote sensing," IEEE Transactions on Neural Networks and Learning Systems, 2021.##S. Aggarwal, "Principles of remote sensing," Satellite remote sensing and GIS applications in agricultural meteorology, vol. 23, no. 2, pp. 23-28, 2004.##M. a. Ahmadi and R. Dianat, "Introducing a method for extracting features from facial images based on applying transformations to features obtained from convolutional neural networks," (in eng), Signal and Data Processing, Research vol. 17, no. 3, pp. 141-156, 2020.##A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, "A survey of the recent architectures of deep convolutional neural networks," Artificial intelligence review, vol. 53, no. 8, pp. 5455-5516, 2020.##M. A. Zare Chahooki and z. khalifeh zadeh, "A General Investigation on the Combination of Local and Global Feature Selection Methods for Request Identification on Telegram," (in eng), Signal and Data Processing, Applicable vol. 19, no. 2, pp. 175-196, 2022.##M. Imani and H. Ghassemian, "Supervised Feature Extraction of Face Images for Improvement of Recognition Accuracy," (in eng), Signal and Data Processing, Research vol. 16, no. 1, pp. 158-172, 2019.##J. Zhang, C. Lu, X. Li, H.-J. Kim, and J. Wang, "A full convolutional network based on DenseNet for remote sensing scene classification," Mathematical Biosciences and Engineering, vol. 16, no. 5, pp. 3345-3367, 2019.##H. Sun, S. Li, X. Zheng, and X. Lu, "Remote sensing scene classification by gated bidirectional network," IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 1, pp. 82-96, 2019.##J. Xie, N. He, L. Fang, and A. Plaza, "Scale-free convolutional neural network for remote sensing scene classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 9, pp. 6916-6928, 2019.##C. Shi, T. Wang, and L. Wang, "Branch feature fusion convolution network for remote sensing scene classification," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp 5194-5210, 2020.##S.-C. Hung, H.-C. Wu, and M.-H. Tseng, "Remote sensing scene classification and explanation using RSSCNet and LIME," Applied Sciences, vol. 10, no. 18, p. 6151, 2020.##X. Tang, Q. Ma, X. Zhang, F. Liu, J. Ma, and L. Jiao, "Attention consistent network for remote sensing scene classification," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 2030-2045, 2021.##R. M. Anwer, F. S. Khan, and J. Laaksonen, "Compact deep color features for remote sensing scene classification," Neural Processing Letters, vol. 53, no. 2, pp. 1523-1544, 2021.##T. Tian, L. Li, W. Chen, and H. Zhou, "SEMSDNet: A multiscale dense network with attention for remote sensing scene classification," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 5501-5514, 2021.##Q. Bi, H. Zhang, and K. Qin, "Multi-scale stacking attention pooling for remote sensing scene classification," Neurocomputing, vol. 436, pp. 147-161. 2021.##S. Mei, K. Yan, M. Ma, X. Chen, S. Zhang, and Q. Du, "Remote sensing scene classification using sparse representation-based framework with deep feature fusion," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 5867-5878, 2021.##J. Shen, T. Zhang, Y. Wang, R. Wang, Q. Wang, and M. Qi, "A Dual-Model Architecture with Grouping-Attention-Fusion for Remote Sensing Scene Classification," Remote Sensing, vol. 13, no. 3, p. 433, 2021.##J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "Imagenet: A large-scale hierarchical image database," in 2009 IEEE conference on computer vision and pattern recognition, 2009, pp. 248-255: Ieee.##B.-D. Liu, J. Meng, W.-Y. Xie, S. Shao, Y. Li, and Y. Wang, "Weighted spatial pyramid matching collaborative representation for remote-sensing-image scene classification," Remote Sensing, vol. 11, no. 5, p. 518, 2019.##B.-D. Liu, W.-Y. Xie, J. Meng, Y. Li, and Y. Wang, "Hybrid collaborative representation for remote-sensing image scene classification," Remote Sensing, vol. 10, no. 12, p. 1934, 2018.##Y. Liu, Y. Liu, and L. Ding, "Scene classification based on two-stage deep feature fusion," IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 2, pp. 183-186, 2017.##T. Gong, X. Zheng, and X. Lu, "Remote Sensing Scene Classification with Multi-task Learning," in Proceedings of the 7th China High Resolution Earth Observation Conference (CHREOC 2020), 2022, pp. 403-418: Springer.##R. Cao, L. Fang, T. Lu, and N. He, "Self-attention-based deep feature fusion for remote sensing scene classification," IEEE Geoscience and Remote Sensing Letters, vol. 18, no. 1, pp. 43-47, 2020.##Q. Bi, K. Qin, H. Zhang, Z. Li, and K. Xu, "RADC-Net: A residual attention based convolution network for aerial scene classification," Neurocomputing, vol. 377, pp. 345-359, 2020.##K. Xu, H. Huang, Y. Li, and G. Shi, "Multilayer feature fusion network for scene classification in remote sensing," IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 11, pp. 1894-1898, 2020.##W. Zhang, P. Tang, and L. Zhao, "Remote sensing image scene classification using CNN-CapsNet," Remote Sensing, vol. 11, no. 5, p. 494, 2019.##Q. Hu et al., "Exploring the use of Google Earth imagery and object-based methods in land use/cover mapping," Remote Sensing, vol. 5, no. 11, pp. 6026-6042, 2013.##L. Gómez-Chova, D. Tuia, G. Moser, and G. Camps-Valls, "Multimodal classification of remote sensing images: A review and future directions," Proceedings of the IEEE, vol. 103, no. 9, pp. 1560-1584, 2015.##A. Tayyebi, B. C. Pijanowski, and A. H. Tayyebi, "An urban growth boundary model using neural networks, GIS and radial parameterization: An application to Tehran, Iran," Landscape and Urban Planning, vol. 100, no. 1-2, pp. 35-44, 2011.##Z. Y. Lv, W. Shi, X. Zhang, and J. A. Benediktsson, "Landslide inventory mapping from bitemporal high-resolution remote sensing images using change detection and multiscale segmentation," IEEE journal of selected topics in applied earth observations and remote sensing, vol. 11, no. 5, pp. 1520-1532, 2018.##T. R. Martha, N. Kerle, C. J. Van Westen, V. Jetten, and K. V. Kumar, "Segment optimization and data-driven thresholding for knowledge-based landslide detection by object-based image analysis," IEEE transactions on geoscience and remote sensing, vol. 49, no. 12, pp. 4928-4943, 2011.##F. Ghazouani, I. R. Farah, and B. Solaiman, "A multi-level semantic scene interpretation strategy for change interpretation in remote sensing imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 11, pp. 8775-8795, 2019.##Y. Li, Y. Zhang, X. Huang, and A. L. Yuille, "Deep networks under scene-level supervision for multi-class geospatial object detection from remote sensing images," ISPRS journal of photogrammetry and remote sensing, vol. 146, pp. 182-196, 2018.##Y. Gu, Y. Wang, and Y. Li, "A survey on deep learning-driven remote sensing image scene understanding: Scene classification, scene retrieval and scene-guided object detection," Applied Sciences, vol. 9, no. 10, p. 2110, 2019.##G. Cheng, X. Xie, J. Han, L. Guo, and G.-S. Xia, "Remote sensing image scene classification meets deep learning: Challenges, methods, benchmarks, and opportunities," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 3735-3756, 2020.##W. Wang, Y. Chen, and P. Ghamisi, "Transferring CNN With Adaptive Learning for Remote Sensing Scene Classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-18, 2022.##K. Xu, H. Huang, P. Deng, and Y. Li, "Deep feature aggregation framework driven by graph convolutional network for scene classification in remote sensing," IEEE Transactions on Neural Networks and Learning Systems, 2021.##S. Aggarwal, "Principles of remote sensing," Satellite remote sensing and GIS applications in agricultural meteorology, vol. 23, no. 2, pp. 23-28, 2004.##M. a. Ahmadi and R. Dianat, "Introducing a method for extracting features from facial images based on applying transformations to features obtained from convolutional neural networks," (in eng), Signal and Data Processing, Research vol. 17, no. 3, pp. 141-156, 2020.##A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, "A survey of the recent architectures of deep convolutional neural networks," Artificial intelligence review, vol. 53, no. 8, pp. 5455-5516, 2020.##M. A. Zare Chahooki and z. khalifeh zadeh, "A General Investigation on the Combination of Local and Global Feature Selection Methods for Request Identification on Telegram," (in eng), Signal and Data Processing, Applicable vol. 19, no. 2, pp. 175-196, 2022.##M. Imani and H. Ghassemian, "Supervised Feature Extraction of Face Images for Improvement of Recognition Accuracy," (in eng), Signal and Data Processing, Research vol. 16, no. 1, pp. 158-172, 2019.##J. Zhang, C. Lu, X. Li, H.-J. Kim, and J. Wang, "A full convolutional network based on DenseNet for remote sensing scene classification," Mathematical Biosciences and Engineering, vol. 16, no. 5, pp. 3345-3367, 2019.##H. Sun, S. Li, X. Zheng, and X. Lu, "Remote sensing scene classification by gated bidirectional network," IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 1, pp. 82-96, 2019.##J. Xie, N. He, L. Fang, and A. Plaza, "Scale-free convolutional neural network for remote sensing scene classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 9, pp. 6916-6928, 2019.##C. Shi, T. Wang, and L. Wang, "Branch feature fusion convolution network for remote sensing scene classification," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp 5194-5210, 2020.##S.-C. Hung, H.-C. Wu, and M.-H. Tseng, "Remote sensing scene classification and explanation using RSSCNet and LIME," Applied Sciences, vol. 10, no. 18, p. 6151, 2020.##X. Tang, Q. Ma, X. Zhang, F. Liu, J. Ma, and L. Jiao, "Attention consistent network for remote sensing scene classification," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 2030-2045, 2021.##R. M. Anwer, F. S. Khan, and J. Laaksonen, "Compact deep color features for remote sensing scene classification," Neural Processing Letters, vol. 53, no. 2, pp. 1523-1544, 2021.##T. Tian, L. Li, W. Chen, and H. Zhou, "SEMSDNet: A multiscale dense network with attention for remote sensing scene classification," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 5501-5514, 2021.##Q. Bi, H. Zhang, and K. Qin, "Multi-scale stacking attention pooling for remote sensing scene classification," Neurocomputing, vol. 436, pp. 147-161. 2021.##S. Mei, K. Yan, M. Ma, X. Chen, S. Zhang, and Q. Du, "Remote sensing scene classification using sparse representation-based framework with deep feature fusion," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 5867-5878, 2021.##J. Shen, T. Zhang, Y. Wang, R. Wang, Q. Wang, and M. Qi, "A Dual-Model Architecture with Grouping-Attention-Fusion for Remote Sensing Scene Classification," Remote Sensing, vol. 13, no. 3, p. 433, 2021.##J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "Imagenet: A large-scale hierarchical image database," in 2009 IEEE conference on computer vision and pattern recognition, 2009, pp. 248-255: Ieee.##B.-D. Liu, J. Meng, W.-Y. Xie, S. Shao, Y. Li, and Y. Wang, "Weighted spatial pyramid matching collaborative representation for remote-sensing-image scene classification," Remote Sensing, vol. 11, no. 5, p. 518, 2019.##B.-D. Liu, W.-Y. Xie, J. Meng, Y. Li, and Y. Wang, "Hybrid collaborative representation for remote-sensing image scene classification," Remote Sensing, vol. 10, no. 12, p. 1934, 2018.##Y. Liu, Y. Liu, and L. Ding, "Scene classification based on two-stage deep feature fusion," IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 2, pp. 183-186, 2017.##T. Gong, X. Zheng, and X. Lu, "Remote Sensing Scene Classification with Multi-task Learning," in Proceedings of the 7th China High Resolution Earth Observation Conference (CHREOC 2020), 2022, pp. 403-418: Springer.##R. Cao, L. Fang, T. Lu, and N. He, "Self-attention-based deep feature fusion for remote sensing scene classification," IEEE Geoscience and Remote Sensing Letters, vol. 18, no. 1, pp. 43-47, 2020.##Q. Bi, K. Qin, H. Zhang, Z. Li, and K. Xu, "RADC-Net: A residual attention based convolution network for aerial scene classification," Neurocomputing, vol. 377, pp. 345-359, 2020.##K. Xu, H. Huang, Y. Li, and G. Shi, "Multilayer feature fusion network for scene classification in remote sensing," IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 11, pp. 1894-1898, 2020.##W. Zhang, P. Tang, and L. Zhao, "Remote sensing image scene classification using CNN-CapsNet," Remote Sensing, vol. 11, no. 5, p. 494, 2019. ##

بهبود الگوریتم برآورد داده‌های ازدست‌رفته علائم حیاتی در شبکه‌های حس‌گر بی‌سیم بدن مبتنی بر شبکه عصبی عمیق Improvement of missing vital signs data estimation algorithm in wireless body sensor networks based on deep neural networks 1 1 در شبکه‌های حس‌گر بی‌سیم به‌دلیل عوامل مختلفی از قبیل محدودبودن انرژی، قابلیت انتقال حس‌گرها، خرابی سخت‌افزار و مشکلات شبکه مانند برخورد بسته‌ها، پیوند غیرقابل اطمینان و آسیب‌های غیرمنتظره، مقدار حس‌شده به سرخوشه یا ایستگاه پایه نمی‌رسد؛ بنابراین ازبین‌رفتن داده‌ها در شبکه‌های حس‌گر بی‌سیم بسیار متداول است. از دست‌دادن داده‌های سنجیده شده، دقت WBAN را بسیار کاهش می‌دهد. از آنجا که WBAN با علائم حیاتی بدن انسان سر‌وکار دارد، قابلیت اطمینان شبکه از اهمیت زیادی برخوردار است. برای حل این مشکل، داده‌های گم‌شده باید برآورد شوند. به منظور پیش‌بینی مقادیر گم‌شده، یک مدل برآورد دادههازدست‌رفته بر اساس شبکه عصبی بازگشتی LSTM که وزن‌های شبکه به‌وسیله الگوریتم ازدحام ذرات (PSO) بهینه شده‌اند، در این مقاله ارائه شده است. نتایج نشان می‌دهد که sgdm-LSTM با ترکیب  PSOروش خوبی برای برآورد مقدار ازدست‌رفته است. در ضمن، نتایج تجربی نشان می‌دهد که میانگین خطای مربع ریشه مقدار برآوردشده کمتر از سایر روش‌هاست. این مقدار، با بهترین ابر پارامترهای شبکه LSTM و بهترین مقادیر برای الگوریتم PSO،  1.5898 است. 2 In a wireless sensor network (WSN), due to various factors such as limited power, sensor transferability, hardware failure and network problems such as packet collisions, unreliable connection and unexpected damage, the amount sensed to the header or base station is not arrives. Therefore, data loss is very common in wireless sensor networks. Loss of measured data greatly reduces WBAN accuracy. Because WBAN deals with the vital signs of the human body, network reliability is very important. To solve this problem, missing data must be estimated. Many methods are used to reconstruct lost sensor data based on temporal correlation, spatial correlation, interpolation method, or sparse theory. Due to the characteristics of vital signs data, they can be considered as a series of sequential information. So far, various methods have been developed to estimate missing data in time series data in different fields. These methods can be divided into two categories: statistical methods and machine learning-based methods. In order to predict missing values, a missing data estimation model based on LSTM recurrent neural network whose network weights are optimized by particle swarm algorithm (PSO) is presented in this paper. In this paper, we use the MIMIC-III Waveform database to test the algorithm and determine the algorithm parameters. However, due to the large volume of data and the difficulty of testing the algorithm on all data, we suffice to test 500 patients with this data, whose vital signs included heart rate, respiration, blood oxygen, and so on. After data preprocessing, network training, predicting lost values and calculating error values, it is observed that the proposed technique of sgdm-LSTM By combining the PSO algorithm is a suitable method for estimating lost values. In addition, experimental results show that the mean square root error of the estimated value is lower than other methods. This value is 1.5898 with the best LSTM network hyperparameters. 85 96 2023/07/212023/07/232023/09/92021/10/18 1400/7/26 2024/08/182024/08/212024/08/212024/12/4 1403/9/14 ابوالفضل ابراهیمی Abolufazl Ebrahimi کارشناس‌ارشد مهندسی برق و کامپیوتر، دانشگاه صنعتی قم، قم، ایران ایران ebrahimi.a@qut.ac.ir محبوبه شمسی Mahboubeh Shamsi دانشیار دانشکده مهندسی برق و کامپیوتر، دانشگاه صنعتی قم، قم، ایران ایران shamsi@qut.ac.ir مرتضی محجل Morteza Mohajjel استادیار دانشکده مهندسی برق و کامپیوتر، دانشگاه صنعتی قم، قم، ایران ایران mohajjel@qut.ac.ir WBAN Deep Learning Artificial Neural Network Missing Data Estimation برآورد داده ازدست‌رفته شبکه حس‌گر بی‌سیم بدن شبکه عصبی مصنوعی یادگیری عمیق L. Pan و J. Li، "K-Nearest Neighbor Based Missing Data Estimation Algorithm،" pp. 115-122، 2010.##Z. Gao، W. Cheng، X. Qiu و L. Meng، "A Missing Sensor Data Estimation Algorithm Based on،" 2015.##R. Kumar، D. Chaurasia، N. Chuahan و N. Chand، "Predicting Missing Values in Wireless Sensor Network using Spatial-Temporal Correlation،" International Journal of Computer Networks and Wireless Communications (IJCNWC)، 2017.##J. Pagán، "Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data،" 2015.##J. Q. Lin، H. C. Wu و S. C. Chan، "A New Regularized Recursive Dynamic Factor Analysis with Variable Forgetting Factor for Wireless Sensor Networks with Missing Data،" 2017 IEEE International Symposium on Circuits and Systems (ISCAS)، 2017.##Q. Zhen و T. Zhang، "A Missing Data Estimation Algorithm in Wireless Sensor Networks،" Boletín Técnico، 2017.##L. Zhao و F. Zheng، "Missing Data Reconstruction Using Adaptively Updated Dictionary in Wireless Sensor Networks،" تألیف Proceeding of science، 2017.##M. S. Saha و D. D. K. Anvekar، "Mitigation of Single Point Failure and Successful Data Recovery in Wireless Body Area Network،" International Journal of Network Infrastructure Security، 2017.##D. Sakurai، A. Santana و Y. Kawamura، "Estimation of Missing Data of Showcase Using Artificial Neural Networks،" تألیف IEEE 10th International Workshop on Computational Intelligence and Applications، 2017.##B. Kim، B. Lee و J. Cho، "ASRQ: Automatic Segment Repeat reQuest for IEEE 802.15.4-based WBAN،" IEEE SENSORS JOURNAL، 2016.##Y. Kawamura، K. Murakami، A. Santana، T. Iizaka و T. Matsui، "Differential Evolutionary Particle Swarm Optimization based ANN Training for Estimation of Missing Data of Refrigerated Showcase،" 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)، 2018(8).##Y. Tian، K. Zhang، J. Li، X. Lin و B. Yang، "LSTM-based Traffic Flow Prediction with Missing Data،" Neurocomputing، 2018.##S. Ghazal، M. Sauthier، D. Brossier، W. Bouachir، P. Jouvet و R. Noumeir، "Using machine learning models to predict oxygen saturation following ventilator support adjustment in critically ill children: a single center pilot study،" PLoS ONE، 2019.##H. Cheng، Z. Xie، L. Wu، Z. Yu و R. Li، "Data prediction model in wireless sensor networks based on bidirectional LSTM،" Wireless Communications and Networking، 2019.##S. Mujeeb، N. Javaid، M. Ilahi، Z. Wadud، F. Ishmanov و M. K. Afzal، "Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities،" sustainability، 2019.##R. Zhang، Z. Chen، S. Chen، J. Zheng، O. Büyüköztürk و H. Sun، "Deep long short-term memory networks for nonlinear structural seismic response prediction،" Computers and Structures، 2019.##F. Rundo، "Deep LSTM with Reinforcement Learning Layer for Financial Trend Prediction in FX High Frequency Trading Systems،" Applied Sciences، 2019.##K. Yan، X. Wang، Y. Du، N. Jin، H. Huang و H. Zhou، "Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy،" energies، 2018.##Y. Li، H. Wu و H. Liu، "Multi-step wind speed forecasting using EWT decomposition, LSTM principal computing, RELM subordinate computing and IEWT reconstruction،" Energy Conversion and Management، 2018.##X. Yang، S. Mao، H. Gao، Y. Duan و Q. Zou، "Novel Financial Capital Flow Forecast Framework Using Time Series Theory and Deep Learning: A Case Study Analysis of Yu'e Bao Transaction Data،" IEEE Access، 2019.##S. Zhao، Y. Zhang، S. Wang، B. Zhou و C. Cheng، "A recurrent neural network approach for remaining useful life prediction utilizing a novel trend features construction method،" Measurement، 2019.##T. Zhang، S. Song، S. Li، L. Ma، S. Pan و L. Han، "Research on Gas Concentration Prediction Models Based on LSTM Multidimensional Time Series،" Energies، 2019.##F. Lia، G. Renb و J. Lee، "Multi-step wind speed prediction based on turbulence intensity and hybrid deep neural networks،" Energy Conversion and Management، 2019.##K. W. Minmin Luo، "Heart rate prediction model based on neural network،" IOP Conference Series: Materials Science and Engineering، 2020##L. Zhou، C. Zhau، N. Liu، X. Yao، و Z. Cheng، "Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach ،" Engineering Applications of Artificial Intelligence، 2023##H. Farrell، T. Liang، S. Misra، "DEEP NEURAL NETWORKS FOR ESTIMATION AND INFERENCE ،" Econometrica ، 2021##Y. Pan، J. Mu، "Enhancing WBANs Network Performance Based on Deep Learning With Integrated Spatiotemporal Information" IEEE Wireless Communications ، 2024##K. W. Minmin Luo، " A Short-term Time Series Predictive Algorithm Based on Rolling Prediction and PSO-SVR،" 2024 IEEE 2nd International Conference on Control, Electronics and Computer Technology، 2024##مشیری، مریم، قادری زفرهایی، مصطفی و قانع گل محمدی، فرزان، " مقایسه دقت الگوریتم های یادگیری ماشین در تخمین داده های گمشده حاصل از آزمایش های ریزآرایه DNA"، مجله پژوهش های سلولی و مولکولی (مجله زیست شناسی ایران)، جلد 28، 1394.##ابراهیمی، ابوالفضل، شمسی، محبوبه و محجل، مرتضی، "برآورد داده های از دست رفته ی علائم حیاتی در شبکه های حس‌گر بی سیم بدن"،ششمین کنفرانس ملی پژوهش های کاربردی در مهندسی برق، مکانیک و مکاترونیک،تهران، 1399.##ابراهیمی، ابوالفضل، شمسی، محبوبه و محجل، مرتضی، "تنظیم بهینه پارامترهای شبکه عصبی عمیق در برآورد داده های از دست رفته ی علائم حیاتی در شبکه های حس‌گر بی سیم بدن"، مجله مدیریت مهندسی و رایانش نرم، شماره 16، صفحات 162-188، 1402.##عمرانپور، حسام و آزادیان، فهیمه، "ارائه یک رویکرد فازی برای بهینه‌سازی پیش‌بینی سری زمانی با مرتبه بالا"، فصلنامه پردازش علائم و داده ها، جلد 15 شماره 2، صفحات 3-16، 1397.##دانشپور، نگین و میرابوالقاسمی، سیده فاطمه، "پرکردن داده‌های گمشده در داده‌های سری زمانی چندمتغیره"، فصلنامه پردازش علائم و داده ها، جلد 19 شماره 2، صفحات 39-60، 1401.##عمرانپور، حسام و پورعلی، حدیثه، "ارائه مدل یادگیر ترکیب کرنل‌ها برای پیش‌بینی سری‌های زمانی براساس رگرسیون بردار پشتیبان و جستجوی فراابتکاری"، فصلنامه پردازش علائم و داده ها، جلد 19 شماره 1، صفحات 39-42، 1401.##L. Pan و J. Li، "K-Nearest Neighbor Based Missing Data Estimation Algorithm،" pp. 115-122، 2010.##Z. Gao، W. Cheng، X. Qiu و L. Meng، "A Missing Sensor Data Estimation Algorithm Based on،" 2015.##R. Kumar، D. Chaurasia، N. Chuahan و N. Chand، "Predicting Missing Values in Wireless Sensor Network using Spatial-Temporal Correlation،" International Journal of Computer Networks and Wireless Communications (IJCNWC)، 2017.##J. Pagán، "Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data،" 2015.##J. Q. Lin، H. C. Wu و S. C. Chan، "A New Regularized Recursive Dynamic Factor Analysis with Variable Forgetting Factor for Wireless Sensor Networks with Missing Data،" 2017 IEEE International Symposium on Circuits and Systems (ISCAS)، 2017.##Q. Zhen و T. Zhang، "A Missing Data Estimation Algorithm in Wireless Sensor Networks،" Boletín Técnico، 2017.##L. Zhao و F. Zheng، "Missing Data Reconstruction Using Adaptively Updated Dictionary in Wireless Sensor Networks،" تألیف Proceeding of science، 2017.##M. S. Saha و D. D. K. Anvekar، "Mitigation of Single Point Failure and Successful Data Recovery in Wireless Body Area Network،" International Journal of Network Infrastructure Security، 2017.##D. Sakurai، A. Santana و Y. Kawamura، "Estimation of Missing Data of Showcase Using Artificial Neural Networks،" تألیف IEEE 10th International Workshop on Computational Intelligence and Applications، 2017.##B. Kim، B. Lee و J. Cho، "ASRQ: Automatic Segment Repeat reQuest for IEEE 802.15.4-based WBAN،" IEEE SENSORS JOURNAL، 2016.##Y. Kawamura، K. Murakami، A. Santana، T. Iizaka و T. Matsui، "Differential Evolutionary Particle Swarm Optimization based ANN Training for Estimation of Missing Data of Refrigerated Showcase،" 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)، 2018(8).##Y. Tian، K. Zhang، J. Li، X. Lin و B. Yang، "LSTM-based Traffic Flow Prediction with Missing Data،" Neurocomputing، 2018.##S. Ghazal، M. Sauthier، D. Brossier، W. Bouachir، P. Jouvet و R. Noumeir، "Using machine learning models to predict oxygen saturation following ventilator support adjustment in critically ill children: a single center pilot study،" PLoS ONE، 2019.##H. Cheng، Z. Xie، L. Wu، Z. Yu و R. Li، "Data prediction model in wireless sensor networks based on bidirectional LSTM،" Wireless Communications and Networking، 2019.##S. Mujeeb، N. Javaid، M. Ilahi، Z. Wadud، F. Ishmanov و M. K. Afzal، "Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities،" sustainability، 2019.##R. Zhang، Z. Chen، S. Chen، J. Zheng، O. Büyüköztürk و H. Sun، "Deep long short-term memory networks for nonlinear structural seismic response prediction،" Computers and Structures، 2019.##F. Rundo، "Deep LSTM with Reinforcement Learning Layer for Financial Trend Prediction in FX High Frequency Trading Systems،" Applied Sciences، 2019.##K. Yan، X. Wang، Y. Du، N. Jin، H. Huang و H. Zhou، "Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy،" energies، 2018.##Y. Li، H. Wu و H. Liu، "Multi-step wind speed forecasting using EWT decomposition, LSTM principal computing, RELM subordinate computing and IEWT reconstruction،" Energy Conversion and Management، 2018.##X. Yang، S. Mao، H. Gao، Y. Duan و Q. Zou، "Novel Financial Capital Flow Forecast Framework Using Time Series Theory and Deep Learning: A Case Study Analysis of Yu'e Bao Transaction Data،" IEEE Access، 2019.##S. Zhao، Y. Zhang، S. Wang، B. Zhou و C. Cheng، "A recurrent neural network approach for remaining useful life prediction utilizing a novel trend features construction method،" Measurement، 2019.##T. Zhang، S. Song، S. Li، L. Ma، S. Pan و L. Han، "Research on Gas Concentration Prediction Models Based on LSTM Multidimensional Time Series،" Energies، 2019.##F. Lia، G. Renb و J. Lee، "Multi-step wind speed prediction based on turbulence intensity and hybrid deep neural networks،" Energy Conversion and Management، 2019.##K. W. Minmin Luo، "Heart rate prediction model based on neural network،" IOP Conference Series: Materials Science and Engineering، 2020##L. Zhou، C. Zhau، N. Liu، X. Yao، و Z. Cheng، "Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach ،" Engineering Applications of Artificial Intelligence، 2023##H. Farrell، T. Liang، S. Misra، "DEEP NEURAL NETWORKS FOR ESTIMATION AND INFERENCE ،" Econometrica ، 2021##Y. Pan، J. Mu، "Enhancing WBANs Network Performance Based on Deep Learning With Integrated Spatiotemporal Information" IEEE Wireless Communications ، 2024##K. W. Minmin Luo، " A Short-term Time Series Predictive Algorithm Based on Rolling Prediction and PSO-SVR،" 2024 IEEE 2nd International Conference on Control, Electronics and Computer Technology، 2024##مشیری، مریم، قادری زفرهایی، مصطفی و قانع گل محمدی، فرزان، " مقایسه دقت الگوریتم های یادگیری ماشین در تخمین داده های گمشده حاصل از آزمایش های ریزآرایه DNA"، مجله پژوهش های سلولی و مولکولی (مجله زیست شناسی ایران)، جلد 28، 1394.##ابراهیمی، ابوالفضل، شمسی، محبوبه و محجل، مرتضی، "برآورد داده های از دست رفته ی علائم حیاتی در شبکه های حس‌گر بی سیم بدن"،ششمین کنفرانس ملی پژوهش های کاربردی در مهندسی برق، مکانیک و مکاترونیک،تهران، 1399.##ابراهیمی، ابوالفضل، شمسی، محبوبه و محجل، مرتضی، "تنظیم بهینه پارامترهای شبکه عصبی عمیق در برآورد داده های از دست رفته ی علائم حیاتی در شبکه های حس‌گر بی سیم بدن"، مجله مدیریت مهندسی و رایانش نرم، شماره 16، صفحات 162-188، 1402.##عمرانپور، حسام و آزادیان، فهیمه، "ارائه یک رویکرد فازی برای بهینه‌سازی پیش‌بینی سری زمانی با مرتبه بالا"، فصلنامه پردازش علائم و داده ها، جلد 15 شماره 2، صفحات 3-16، 1397.##دانشپور، نگین و میرابوالقاسمی، سیده فاطمه، "پرکردن داده‌های گمشده در داده‌های سری زمانی چندمتغیره"، فصلنامه پردازش علائم و داده ها، جلد 19 شماره 2، صفحات 39-60، 1401.##عمرانپور، حسام و پورعلی، حدیثه، "ارائه مدل یادگیر ترکیب کرنل‌ها برای پیش‌بینی سری‌های زمانی براساس رگرسیون بردار پشتیبان و جستجوی فراابتکاری"، فصلنامه پردازش علائم و داده ها، جلد 19 شماره 1، صفحات 39-42، 1401. ##

شناسایی موجودیت‌های اسمی با استفاده از یادگیری عمیق و رویکرد تقویتی Identify the Named Entities Using Deep Learning and Reinforcement Approach 1 1 مطالعات اخیر نشان می‌دهد که شناسایی موجودیت‌های اسمی به یکی از موضوعات پرطرف‌دار در حوزه پردازش زبان طبیعی تبدیل شده‌است؛ این افزایش توجه بیشتر ناشی از رشد سریع روش‌های نوین در هوش مصنوعی، به‌ویژه شبکه‌های عصبی است. ظهور شبکه‌های عصبی عمیق به‌دلیل توانایی‌های برجسته آن‌ها در حل مسائل پیچیده پردازش زبان طبیعی، فرصت‌هایی نوین برای بهبود عملکرد سامانه‌های شناسایی موجودیت‌های اسمی فراهم کرده‌است؛ یکی از عوامل کلیدی در افزایش دقت این سامانه‌ها، بهره‌گیری از ویژگی‌های پیشرفته‌ای است که تا پیش از ظهور یادگیری عمیق قابل دسترسی نبودند، از جمله استفاده از مدل‌های زبانی که توانایی درک معنای متن‌های بزرگ را دارند. در این مقاله، یک روش پیشنهادی مبتنی بر مدل زبانی معنایی در زبان فارسی و شبکه عصبی عمیق طراحی شده‌است که با استفاده از منطق تکرار، مشکل وابستگی به داده‌های زیاد را کاهش می‌دهد. این روش روی مجموعه‌داده CoNLL 2003 انگلیسی و دو مجموعه‌داده فارسی آرمان و پیما ارزیابی شده‌است؛ نتایج ارزیابی به ترتیب مقادیر 95.3، 96.32 و 94.72 را برای معیار F نشان داده که حاکی از بهبود عملکرد نسبت به روش‌های پیشین است. 2 Named Entity Recognition (NER) has emerged as a critical and highly applicable task in the field of Natural Language Processing (NLP). Its significance stems from its essential role in numerous NLP applications, such as machine translation, question answering, text summarization, and information extraction. Recent studies highlight the substantial impact of advancements in Artificial Intelligence (AI), particularly Deep Neural Networks (DNNs), on improving the performance of NER systems. Deep Neural Networks, with their ability to learn complex patterns and extract rich features, have opened new horizons in addressing NLP challenges. These methods leverage advanced language models like BERT and GPT to enable deeper comprehension of linguistic structures and semantic relationships. One of their prominent capabilities is to capture long-term dependencies in complex sentences while reducing the reliance on manually engineered features. This research introduces a novel hybrid approach for Named Entity Recognition in both Persian and English languages, based on deep neural networks and semantic language models. To address the dependency on large datasets, the proposed method employs an iterative logic mechanism that facilitates effective learning with limited data. The proposed system was evaluated on three datasets: The CoNLL 2003 dataset for English, Two Persian datasets, Arman and Peyma. Experimental results demonstrate that the proposed method achieves F1-scores of 95.3, 96.32, and 94.72 on the CoNLL, Arman, and Peyma datasets, respectively. These scores reflect significant improvements over previous methods. The findings of this study suggest that combining advanced language models with deep neural networks can significantly enhance the accuracy and efficiency of NER systems. These achievements pave the way for developing effective NLP tools for low-resource languages, particularly Persian, and enable the application of this technology in both industrial and research contexts. 97 110 2023/07/212023/07/232023/09/92021/10/182021/05/15 1400/2/25 2024/08/182024/08/212024/08/212024/12/42024/02/25 1402/12/6 مهدی نقوی Mehdi Naghavi استادیار دانشکده و پژوهشکده رایانه، شبکه و ارتباطات، دانشگاه جامع امام حسین (ع)، تهران، ایران ایران mnaghavi@ihu.ac.ir محمدرضا حسنی‌آهنگر Mohmmad Reza Hassani Ahangar استادتمام دانشکده و پژوهشکده هوش مصنوعی و علوم شناختی، دانشگاه جامع امام حسین (ع)، تهران، ایران ایران mrhassani@ihu.ac.ir علی امیری جزه Ali Amiri Jezeh کارشناس ‌ارشد دانشکده و پژوهشکده رایانه، شبکه و ارتباطات، دانشگاه جامع امام حسین (ع)، تهران، ایران ایران aamirij@ihu.ac.ir Named Entity Named Entity Recognition Language Model Deep Learning موجودیت اسمی شناسایی موجودیت اسمی مدل زبانی یادگیری عمیق رویکرد تقویتی D. Nadeau and S. Sekine, "A survey of named entity recognition and classification," Lingvisticae Investigationes, vol. 30, no. 1, pp. 3-26, 2007.##J. Guo, G. Xu, X. Cheng, and H. Li, "Named entity recognition in query," in Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, 2009, pp. 267-274.##D. Petkova and W. B. Croft, "Proximity-based document representation for named entity retrieval," in Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, 2007, pp. 731-740.##B. Larsen, "A trainable summarizer with knowledge acquired from robust NLP techniques," Advances in automatic text summarization, vol. 71, 1999.##D. Mollá, M. Van Zaanen, and D. Smith, "Named entity recognition for question answering," 2006.##B. Babych and A. Hartley, "Improving machine translation quality with automatic named entity recognition," in Proceedings of the 7th International EAMT workshop on MT and other language technology tools, Improving MT through other language technology tools, Resource and tools for building MT at EACL 2003, 2003.##S. Scholar."Semantic Scholar." https://www.semanticscholar.org (accessed 2024).##I. Keraghel, S. Morbieu, and M. Nadif, "A survey on recent advances in named entity recognition," arXiv preprint, arXiv:2401.10825, 2024.##J. Yang, T. Zhang, C.-Y. Tsai, Y. Lu, and L. Yao, "Evolution and emerging trends of named entity recognition: Bibliometric analysis from 2000 to 2023," Heliyon, 2024.##Z. Hu, W. Hou, and X. Liu, "Deep learning for named entity recognition: A survey," Neural Computing and Applications, vol. 36, no. 16, pp. 8995-9022, 2024.##Y. Park, G. Son, and M. Rho, "Biomedical flat and nested named entity recognition: Methods, challenges, and advances," Applied Sciences, vol. 14, no. 20, 2024.##J. Liu, M. Sun, W. Zhang, G. Xie, Y. Jing, X. Li, and Z. Shi, "Dae-ner: Dual-channel attention enhancement for Chinese named entity recognition," Computer Speech & Language, vol. 85, p. 101581, 2024.##P. Deshmukh, N. Kulkarni, S. Kulkarni, K. Manghani, P. A. Khadkikar, and R. Joshi, "Named entity recognition for Indic languages: A comprehensive survey," in 2024 1st International Conference on Trends in Engineering Systems and Technologies (ICTEST), pp. 1-6, IEEE, 2024.##R. Grishman and B. M. Sundheim, "Message understanding conference-6: A brief history," in COLING 1996 Volume 1: The 16th International Conference on Computational Linguistics, 1996.##E. F. Sang and F. De Meulder, "Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition," arXiv preprint cs/0306050, 2003.##G. R. Doddington, A. Mitchell, M. A. Przybocki, L. A. Ramshaw, S. M. Strassel, and R. M. Weischedel, "The automatic content extraction (ace) program-tasks, data, and evaluation," in Lrec, 2004, vol. 2, no. 1: Lisbon, pp. 837-840.##G. Demartini, T. Iofciu, and A. P. De Vries, "Overview of the INEX 2009 entity ranking track," in International Workshop of the Initiative for the Evaluation of XML Retrieval, 2009: Springer, pp. 254-264.##K. Balog, P. Serdyukov, and A. P. de Vries, "Overview of the TREC 2011 Entity Track," in TREC, 2011, vol. 2011, p. 11.##M. Marrero, J. Urbano, S. Sánchez-Cuadrado, J. Morato, and J. M. Gómez-Berbís, "Named entity recognition: fallacies, challenges and opportunities," Computer Standards & Interfaces, vol. 35, no. 5, pp. 482-489, 2013.##M. L. Patawar and M. Potey, "Approaches to named entity recognition: a survey," International Journal of Innovative Research in Computer and Communication Engineering, vol. 3, no. 12, pp. 12201-12208, 2015.##H. Pu and W. Chen, "Review of multimodal named entity recognition studies," Data Analysis and Knowledge Discovery, vol. 8, no. 4, pp. 50-63, 2024.##Momtazi S, Torabi F. Named Entity Recognition in Persian Text using Deep Learning. JSDP 2020; 16 (4) :93-112##O. Khade, S. Jagdale, G. Takalikar, M. Inamdar, R. Joshi, and A. S. Ghotkar, "Enhancing code-mixing in named entity recognition: A comprehensive survey of deep learning models," in 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE), pp. 1-6, IEEE, 2024.##H. Pu and W. Chen, "Review of multimodal named entity recognition studies," Data Analysis and Knowledge Discovery, vol. 8, no. 4, pp. 50-63, 2024.##H. Wang, X. Xu, T. Wang, and B. Jing, "Research progress of multimodal named entity recognition," Journal of Zhengzhou University: Engineering Science, vol. 45, no. 2, 2024.##M. Zali and M. Firoozbakht, "Named Entities Recognition and Classification System for Persian Texts Based on Neural Network," Iranian Research Institute for Information Science and Technology, vol. 34, pp. 473-486, 2018.##Shahshahani M S, Mohseni M, Shakery A, Faili H. PAYMA: A Tagged Corpus of Persian Named Entities. JSDP 2019; 16 (1) :91-110##M. Mohseni and A. Tebbifakhr, "MorphoBERT: a Persian NER system with BERT and morphological analysis," in Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019-Short Papers, 2019, pp. 23-30.##E. Taher, S. A. Hoseini, and M. Shamsfard, "Beheshti-NER: Persian named entity recognition Using BERT," arXiv preprint arXiv:2003.08875, 2020.##M. H. Bokaei and M. Mahmoudi, "Improved deep persian named entity recognition," in 2018 9th International Symposium on Telecommunications (IST), 2018: IEEE, pp. 381-386.##J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "Bert: Pre-training of deep bidirectional transformers for language understanding," arXiv preprint arXiv:1810.04805, 2018.##K. Cho et al., "Learning phrase representations using RNN encoder-decoder for statistical machine translation," arXiv preprint arXiv:1406.1078, 2014.##I. Sutskever, O. Vinyals, and Q. V. Le, "Sequence to sequence learning with neural networks," arXiv preprint arXiv:1409.3215, 2014.##S. Ioffe and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift," in International conference on machine learning, 2015: PMLR, pp. 448-456.##N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: a simple way to prevent neural networks from overfitting," The journal of machine learning research, vol. 15, no. 1, pp. 1929-1958, 2014.##M. Schuster and K. K. Paliwal, "Bidirectional recurrent neural networks," IEEE transactions on Signal Processing, vol. 45, no. 11, pp. 2673-2681, 1997.##[G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely connected convolutional networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 4700-4708.##M. W. Browne, "Cross-validation methods," Journal of mathematical psychology, vol. 44, no. 1, pp. 108-132, 2000.##I. Yamada, A. Asai, H. Shindo, H. Takeda, and Y. Matsumoto, "LUKE: deep contextualized entity representations with entity-aware self-attention," arXiv preprint arXiv:2010.01057, 2020.##A. Baevski, S. Edunov, Y. Liu, L. Zettlemoyer, and M. Auli, "Cloze-driven pretraining of self-attention networks," arXiv preprint arXiv:1903.07785, 2019.##J. Yu, B. Bohnet, and M. Poesio, "Named entity recognition as dependency parsing," arXiv preprint arXiv:2005.07150, 2020.##Y. Jiang, C. Hu, T. Xiao, C. Zhang, and J. Zhu, "Improved differentiable architecture search for language modeling and named entity recognition," in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2pp. 3576-3581, 2019.##Z. Wang, J. Shang, L. Liu, L. Lu, J. Liu, and J. Han, "Crossweigh: Training named entity tagger from imperfect annotations," arXiv preprint arXiv:1909.01441, 2019.##J. Straková, M. Straka, and J. Hajič, "Neural architectures for nested NER through linearization," arXiv preprint arXiv:1908.06926, 2019.##Y. Luo, F. Xiao, and H. Zhao, "Hierarchical contextualized representation for named entity recognition," in Proceedings of the AAAI Conference on Artificial Intelligence, 2020, vol. 34, no. 05, pp. 8441-8448.##X. Li, J. Feng, Y. Meng, Q. Han, F. Wu, and J. Li, "A unified mrc framework for named entity recognition," arXiv preprint arXiv:1910.11476, 2019.##M. Farahani, M. Gharachorloo, M. Farahani, and M. Manthouri, "ParsBERT: Transformer-based Model for Persian Language Understanding," arXiv preprint arXiv:2005.12515, 2020.##L. Hafezi and M. Rezaeian, "Neural architecture for persian named entity recognition," in 2018 4th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), 2018: IEEE, pp. 61-64.##H. Poostchi, E. Z. Borzeshi, and M. Piccardi, "Bilstm-crf for persian named-entity recognition armanpersonercorpus: The first entity-annotated persian dataset," in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), 2018.##H. Poostchi, E. Z. Borzeshi, M. Abdous, and M. Piccardi, "PersoNER: Persian named-entity recognition," in COLING 2016-26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers, 2016.##D. Nadeau and S. Sekine, "A survey of named entity recognition and classification," Lingvisticae Investigationes, vol. 30, no. 1, pp. 3-26, 2007.##J. Guo, G. Xu, X. Cheng, and H. Li, "Named entity recognition in query," in Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, 2009, pp. 267-274.##D. Petkova and W. B. Croft, "Proximity-based document representation for named entity retrieval," in Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, 2007, pp. 731-740.##B. Larsen, "A trainable summarizer with knowledge acquired from robust NLP techniques," Advances in automatic text summarization, vol. 71, 1999.##D. Mollá, M. Van Zaanen, and D. Smith, "Named entity recognition for question answering," 2006.##B. Babych and A. Hartley, "Improving machine translation quality with automatic named entity recognition," in Proceedings of the 7th International EAMT workshop on MT and other language technology tools, Improving MT through other language technology tools, Resource and tools for building MT at EACL 2003, 2003.##S. Scholar."Semantic Scholar." https://www.semanticscholar.org (accessed 2024).##I. Keraghel, S. Morbieu, and M. Nadif, "A survey on recent advances in named entity recognition," arXiv preprint, arXiv:2401.10825, 2024.##J. Yang, T. Zhang, C.-Y. Tsai, Y. Lu, and L. Yao, "Evolution and emerging trends of named entity recognition: Bibliometric analysis from 2000 to 2023," Heliyon, 2024.##Z. Hu, W. Hou, and X. Liu, "Deep learning for named entity recognition: A survey," Neural Computing and Applications, vol. 36, no. 16, pp. 8995-9022, 2024.##Y. Park, G. Son, and M. Rho, "Biomedical flat and nested named entity recognition: Methods, challenges, and advances," Applied Sciences, vol. 14, no. 20, 2024.##J. Liu, M. Sun, W. Zhang, G. Xie, Y. Jing, X. Li, and Z. Shi, "Dae-ner: Dual-channel attention enhancement for Chinese named entity recognition," Computer Speech & Language, vol. 85, p. 101581, 2024.##P. Deshmukh, N. Kulkarni, S. Kulkarni, K. Manghani, P. A. Khadkikar, and R. Joshi, "Named entity recognition for Indic languages: A comprehensive survey," in 2024 1st International Conference on Trends in Engineering Systems and Technologies (ICTEST), pp. 1-6, IEEE, 2024.##R. Grishman and B. M. Sundheim, "Message understanding conference-6: A brief history," in COLING 1996 Volume 1: The 16th International Conference on Computational Linguistics, 1996.##E. F. Sang and F. De Meulder, "Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition," arXiv preprint cs/0306050, 2003.##G. R. Doddington, A. Mitchell, M. A. Przybocki, L. A. Ramshaw, S. M. Strassel, and R. M. Weischedel, "The automatic content extraction (ace) program-tasks, data, and evaluation," in Lrec, 2004, vol. 2, no. 1: Lisbon, pp. 837-840.##G. Demartini, T. Iofciu, and A. P. De Vries, "Overview of the INEX 2009 entity ranking track," in International Workshop of the Initiative for the Evaluation of XML Retrieval, 2009: Springer, pp. 254-264.##K. Balog, P. Serdyukov, and A. P. de Vries, "Overview of the TREC 2011 Entity Track," in TREC, 2011, vol. 2011, p. 11.##M. Marrero, J. Urbano, S. Sánchez-Cuadrado, J. Morato, and J. M. Gómez-Berbís, "Named entity recognition: fallacies, challenges and opportunities," Computer Standards & Interfaces, vol. 35, no. 5, pp. 482-489, 2013.##M. L. Patawar and M. Potey, "Approaches to named entity recognition: a survey," International Journal of Innovative Research in Computer and Communication Engineering, vol. 3, no. 12, pp. 12201-12208, 2015.##H. Pu and W. Chen, "Review of multimodal named entity recognition studies," Data Analysis and Knowledge Discovery, vol. 8, no. 4, pp. 50-63, 2024.##Momtazi S, Torabi F. Named Entity Recognition in Persian Text using Deep Learning. JSDP 2020; 16 (4) :93-112##O. Khade, S. Jagdale, G. Takalikar, M. Inamdar, R. Joshi, and A. S. Ghotkar, "Enhancing code-mixing in named entity recognition: A comprehensive survey of deep learning models," in 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE), pp. 1-6, IEEE, 2024.##H. Pu and W. Chen, "Review of multimodal named entity recognition studies," Data Analysis and Knowledge Discovery, vol. 8, no. 4, pp. 50-63, 2024.##H. Wang, X. Xu, T. Wang, and B. Jing, "Research progress of multimodal named entity recognition," Journal of Zhengzhou University: Engineering Science, vol. 45, no. 2, 2024.##M. Zali and M. Firoozbakht, "Named Entities Recognition and Classification System for Persian Texts Based on Neural Network," Iranian Research Institute for Information Science and Technology, vol. 34, pp. 473-486, 2018.##Shahshahani M S, Mohseni M, Shakery A, Faili H. PAYMA: A Tagged Corpus of Persian Named Entities. JSDP 2019; 16 (1) :91-110##M. Mohseni and A. Tebbifakhr, "MorphoBERT: a Persian NER system with BERT and morphological analysis," in Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019-Short Papers, 2019, pp. 23-30.##E. Taher, S. A. Hoseini, and M. Shamsfard, "Beheshti-NER: Persian named entity recognition Using BERT," arXiv preprint arXiv:2003.08875, 2020.##M. H. Bokaei and M. Mahmoudi, "Improved deep persian named entity recognition," in 2018 9th International Symposium on Telecommunications (IST), 2018: IEEE, pp. 381-386.##J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "Bert: Pre-training of deep bidirectional transformers for language understanding," arXiv preprint arXiv:1810.04805, 2018.##K. Cho et al., "Learning phrase representations using RNN encoder-decoder for statistical machine translation," arXiv preprint arXiv:1406.1078, 2014.##I. Sutskever, O. Vinyals, and Q. V. Le, "Sequence to sequence learning with neural networks," arXiv preprint arXiv:1409.3215, 2014.##S. Ioffe and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift," in International conference on machine learning, 2015: PMLR, pp. 448-456.##N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: a simple way to prevent neural networks from overfitting," The journal of machine learning research, vol. 15, no. 1, pp. 1929-1958, 2014.##M. Schuster and K. K. Paliwal, "Bidirectional recurrent neural networks," IEEE transactions on Signal Processing, vol. 45, no. 11, pp. 2673-2681, 1997.##[G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely connected convolutional networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 4700-4708.##M. W. Browne, "Cross-validation methods," Journal of mathematical psychology, vol. 44, no. 1, pp. 108-132, 2000.##I. Yamada, A. Asai, H. Shindo, H. Takeda, and Y. Matsumoto, "LUKE: deep contextualized entity representations with entity-aware self-attention," arXiv preprint arXiv:2010.01057, 2020.##A. Baevski, S. Edunov, Y. Liu, L. Zettlemoyer, and M. Auli, "Cloze-driven pretraining of self-attention networks," arXiv preprint arXiv:1903.07785, 2019.##J. Yu, B. Bohnet, and M. Poesio, "Named entity recognition as dependency parsing," arXiv preprint arXiv:2005.07150, 2020.##Y. Jiang, C. Hu, T. Xiao, C. Zhang, and J. Zhu, "Improved differentiable architecture search for language modeling and named entity recognition," in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2pp. 3576-3581, 2019.##Z. Wang, J. Shang, L. Liu, L. Lu, J. Liu, and J. Han, "Crossweigh: Training named entity tagger from imperfect annotations," arXiv preprint arXiv:1909.01441, 2019.##J. Straková, M. Straka, and J. Hajič, "Neural architectures for nested NER through linearization," arXiv preprint arXiv:1908.06926, 2019.##Y. Luo, F. Xiao, and H. Zhao, "Hierarchical contextualized representation for named entity recognition," in Proceedings of the AAAI Conference on Artificial Intelligence, 2020, vol. 34, no. 05, pp. 8441-8448.##X. Li, J. Feng, Y. Meng, Q. Han, F. Wu, and J. Li, "A unified mrc framework for named entity recognition," arXiv preprint arXiv:1910.11476, 2019.##M. Farahani, M. Gharachorloo, M. Farahani, and M. Manthouri, "ParsBERT: Transformer-based Model for Persian Language Understanding," arXiv preprint arXiv:2005.12515, 2020.##L. Hafezi and M. Rezaeian, "Neural architecture for persian named entity recognition," in 2018 4th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), 2018: IEEE, pp. 61-64.##H. Poostchi, E. Z. Borzeshi, and M. Piccardi, "Bilstm-crf for persian named-entity recognition armanpersonercorpus: The first entity-annotated persian dataset," in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), 2018.##H. Poostchi, E. Z. Borzeshi, M. Abdous, and M. Piccardi, "PersoNER: Persian named-entity recognition," in COLING 2016-26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers, 2016. ##

ارائه روش مبتنی بر الگوریتم ژنتیک برای مسئله ‌یافتن پایدارترین خوشه‌ها در خوشه‌بندی ترکیبی Presenting a Method based on Genetic Algorithm for finding the most Stable Clusters in Ensemble Clustering 1 1 خوشه‌بندی نقش حیاتی در روش‌های بازیابی اطلاعات برای سازمان‌دهی مجموعه‌های بزرگ، درونِ تعداد کمی خوشه معنادار دارد. یکی از مهم‌ترین انگیزه‌های استفاده از خوشه‌بندی، تعیین و آشکارکردن ساختار ذاتی و پنهان یک مجموعه‌داده است. کاربران انسانی به علت تفاوت در سلیقه و طرز تفکرات مختلف از کشف ساختار ذاتی و درونی مجموعه‌داده‌ای بزرگ متون ناتوان‌اند. الگوریتم‌های خوشه‌بندی ترکیبی چند الگوریتم خوشه‌بندی را با هم ترکیب می‌کنند تا در نهایت به یک سامانه کلی خوشه‌بندی برسند. روش‌های خوشه‌بندی ترکیبی برای یافتن راه‌های بهتری با استفاده از بیرون‌کشیدن اطلاعات از چندین افراز اولیه داده‌هاست. ازآنجاکه الگوریتم‌های خوشه‌بندی مختلف به نقاط مختلف داده نگاه می‌کنند، آن‌ها می‌توانند افراز‌های مختلفی را از این‌چنین داده‌هایی تولید کنند؛ با ترکیب افراز‌های به‌دست‌آمده از الگوریتم‌های مختلف، ایجاد یک افراز با کارایی بالا ممکن است، حتی اگر خوشه‌ها از هم بسیار متراکم باشند. در این مقاله، روشی جدید معرفی شده‌است که به‌جای استفاده از تمامی خوشه‌های اولیه تولیدشده، از پایدارترین آن‌ها که توسط شش روش مختلف تولید شده‌اند، استفاده می‌کند. برای انتخاب خوشه‌های پایدارتر از تابع توافقی مبتنی بر ماتریس هم‌بستگی استفاده می‌شود. انتخاب خوشه‌های پایدارتر بر اساس معیار پایداری خوشه مبتنی بر معیار فیشر انجام می‌گیرد و سپس خوشه‌های به‌دست‌آمده به‌وسیله الگوریتم ژنتیک مورد ارزیابی قرار می‌گیرد و طبق این الگوریتم پایدارترین خوشه‌ها انتخاب می‌شوند؛ درنهایت ماتریس هم‌بستگی به‌دست‌آمده از اجماع خوشه‌های بهینه، به‌عنوان یک ماتریس مشابهت در نظر گرفته می‌شود. یک الگوریتم خوشه‌بندی سلسله‌مراتبی به‌عنوان تابع جمع‌کننده نهایی در نظر گرفته می‌شود و ماتریس هم‌بستگی به‌دست‌آمده را به‌عنوان ورودی گرفته و خوشه‌بندی توافقی نهایی را برمی‌گرداند. نتایج تجربی روی چندین مجموعه‌داده نشان می‌دهد که روش پیشنهادی، خوشه‌های متنوع و با پایداری بالا تولید می‌کند. به طور مشخص، این روش در معیارهای NMI و ARI به ترتیب بهبودهای قابل توجهی به میزان ۱۲٪ و ۵٪ نسبت به بهترین روش‌های پیشین به دست آورده‌است. این نشان‌دهنده برتری روش خوشه‌بندی ترکیبی پیشنهادی مبتنی بر پایداری خوشه و الگوریتم‌های ژنتیک است. 2 Clustering is one of the fundamental tools in data analysis and data mining, enabling the extraction of hidden and meaningful structures from large datasets by grouping data based on intrinsic similarities. However, selecting optimal clusters in conventional clustering algorithms poses challenges, especially when clusters are dense or heterogeneous. In this study, a novel genetic algorithm-based method is proposed to identify the most stable clusters in ensemble clustering. By leveraging cluster stability criteria and a correlation matrix, the proposed approach improves the accuracy and stability of the final clustering results. The proposed method involves generating initial partitions of the data using six different clustering algorithms. Next, the Fisher criterion is applied to identify more stable clusters. These selected clusters are then evaluated and optimized using a genetic algorithm to construct an optimized correlation matrix. This matrix is subsequently fed into a hierarchical clustering algorithm, which produces the final consensus clustering. The proposed method was tested on standard datasets. Results demonstrated improvements of 12% and 5% in NMI and ARI metrics, respectively, compared to previous methods. The use of a genetic algorithm enabled the identification of clusters with higher stability and diversity, reducing the impact of noise and increasing the accuracy of the final clustering. Moreover, the method outperformed individual base clustering algorithms in providing more precise clustering results. Due to its ability to enhance the accuracy and stability of clustering, the proposed method holds potential for applications in domains such as big data analysis, machine learning, and information retrieval. The use of the Fisher criterion for selecting stable clusters and genetic algorithms for optimization are among the strengths of this research. This method not only preserves diversity among clusters but also significantly enhances clustering accuracy. Future studies could explore the combination of this approach with more advanced algorithms to assess its applicability to more complex datasets. 111 136 2023/07/212023/07/232023/09/92021/10/182021/05/152021/03/19 1399/12/29 2024/08/182024/08/212024/08/212024/12/42024/02/252024/02/25 1402/12/6 نوید صمیمی Navid Samimi گروه مهندسی کامپیوتر، واحد یاسوج، دانشگاه آزاد اسلامی، یاسوج، ایران ایران n_samimi@gmail.com صمد نجاتیان Samad Nejatian گروه مهندسی برق، واحد یاسوج، دانشگاه آزاد اسلامی، یاسوج، ایران ایران samad.nej.2007@gmail.com حمید پروین Hamid Parvin گروه مهندسی کامپیوتر، واحد نورآباد ممسنی، دانشگاه آزاد اسلامی، نورآباد ممسنی، ایران ایران parvin@iust.ac.ir کرم اله باقری فرد Karamolah Bagheri Fard گروه مهندسی کامپیوتر، واحد یاسوج، دانشگاه آزاد اسلامی، یاسوج، ایران ایران k.bagheri@iauyasooj.ac.ir وحیده رضایی Vahideh Rezaei Department of Mathematic, Yasuj Branch, Islamic Azad University, Yasuj, Iran ایران Ensemble clustering Cluster Stability Fisher Criterion Correlation matrix Genetic Algorithm خوشه‌بندی ترکیبی پایداری خوشه معیار فیشر ماتریس هم‌بستگی الگوریتم ژنتیک [1] W. J. Frawley, G. Piatetsky-Shapiro, and C. J. Matheus, "Knowledge discovery in databases: An overview", AI Magazine, vol. 13, no. 3, p. 57, 1992.##[2] D. J. Hand, H. Mannila, and P. Smyth, “Principles of Data Mining”, MIT Press, 2001.##[3] I. H. Witten and E. Frank, “Data Mining: Practical Machine Learning Tools and Techniques”, Morgan Kaufmann, 2005.##[4] A. K. Jain and R. C. Dubes, “Algorithms for Clustering”, Englewood Cliffs, NJ: Prentice Hall, 1988.##[5] H. Frigui and R. Krishnapuram, "A robust competitive clustering algorithm with applications in computer vision," IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 21, pp. 455–465, 1999.##[6] M. R. Anderberg, "Cluster Analysis for Applications”, Academic Press, Inc., New York, 1973.##[7] E. Diday and J. C. Simon, "Clustering analysis", Digital Pattern Recognition, K. S. Fu, Ed., Springer-Verlag, New York, pp. 47–94, 1976.##[8] R. S. Michalski and R. E. Stepp, "Automated construction of classification: Conceptual clustering versus numerical taxonomy", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 5, pp. 396–409, 1983.##[9] R. C. Dubes, "Cluster analysis and related issues", Handbook of Pattern Recognition & Computer Vision,World Scientific, 1993.##[10] S. Saha and S. Bandyopadhyay, "Application of multiobjective optimization for data clustering", Seminar (Machine Intelligence Unit, Indian Statistical Institute), Kolkata, India, 2008.##[11] N. Jardin and R. Sibson, “Mathematical Taxonomy”, Wiley, New York, 1971.##[12] N. Jardin and C. J. Van Rijsbergen, "The use of hierarchical clustering in information retrieval", Information Storage and Retrieval, vol. 7, pp. 217–240, 1971.##[13] M. Mojarad, S. Nejatian, H. Parvin, and M. Mohammadpoor, "A fuzzy clustering ensemble based on cluster clustering and iterative fusion of base clusters", Applied Intelligence, vol. 49, pp. 2567–2581, 2019.##[14] M. N. Ghaemi, "A survey: Clustering ensembles techniques", World Academy of Science, Engineering and Technology, pp. 636–646, 2009.##[15] H. Parvin, B. Miaei-Bidgoli, H. Alinejad-Rokny, and W. H. Punch, "Data weighing mechanisms for clustering ensembles", Computers & Electrical Engineering, vol. 39, no. 5, pp. 1433–1450, 2013.##[16] H. Parvin and B. Miaei-Bidgoli, "A clustering ensemble framework based on elite selection of weighted clusters," Advances in Data Analysis and Classification, vol. 7, no. 2, pp. 181–208, 2013.##[17] A. Topchy, B. Minaei-Bidgoli, and W. F. Punch, "Ensembles of partitions via data resampling," Proceedings of the International Conference on Information Technology, ITCC 04, Las Vegas, 2004.##[18] A. Topchy, A. K. Jain, and W. F. Punch, "Combining multiple weak clusterings", Proceedings of the 3rd IEEE International Conference on Data Mining, 331–338, 2003.##[19] A. Strehl and J. Ghosh, "Cluster ensembles - A knowledge reuse framework for combining multiple partitions", Journal of Machine Learning Research, vol. 3, pp. 583–617, 2002.##[20] R. Srikant and R. Agrawal, "Mining quantitative association rules in large relational tables”, Proceedings of the ACM SIGMOD Conference on Management of Data, Montreal, Canada, 1996.##[21] J. W. Chang and D. S. Jin, "A new cell-based clustering method for large-high dimensional data in data mining applications", Proceedings of the ACM Symposium on Applied Computing, 503–507, 2002.##[22] R. Miller and Y. Yang, "Association rules over interval data", Proceedings of the ACM SIGMOD International Conference on Management of Data, 452–461, 1997.##[23] R. Jiamthapthaksin, C. F. Eick, and S. Lee, "GAC-GEO: A generic agglomerative clustering framework for geo-referenced datasets," Knowledge and Information Systems, vol. 29, no. 3, pp. 597–628, 2011.##[24] S. Dudoit and J. Fridlyand, "Bagging to improve the accuracy of a clustering procedure", Bioinformatics, vol. 19, no. 9, pp. 1090–1099, 2003.##[25] M. H. F. Zarandi, M. R. Faraji, and M. Karbasian, "An exponential cluster validity index for fuzzy clustering with crisp and fuzzy data," Scientia Iranica, vol. 17, no. 2, pp. 95–110, 2010.##[26] J. C. Bezdek, "Cluster validity with fuzzy sets", Journal of Cybernetics, vol. 3, no. 3, pp. 58–73, 1973.##[27] F. Kovács, C. Legány, and A. Babos, "Cluster validity measurement techniques", Department of Automation and Applied Informatics, Budapest University of Technology and Economics, 2003.##[28] Y. Zhang, W. Wang, X. Zhang, and Y. Li, "A cluster validity index for fuzzy clustering", Information Sciences, vol. 178, no. 4, pp. 1205–1218, 2008.##[29] W. Wang and Y. Zhang, "fuzzy cluster validity indices", Fuzzy Sets and Systems, vol. 158, no. 19, pp. 2095–2117, 2007.##[30] M. J. A. Berry and G. Linoff, “Data Mining Techniques for Marketing”, Sales and Customer Support, 3rd ed., John Wiley & Sons, Inc., USA, 1996.##[31] M. Berry, S. Dumais, G. O’Brien, "Using linear algebra for intelligent information retrieval," SIAM Review, vol. 37, no. 4, pp. 573–595, 1995.##[32] M. U. Fayyad, G. Piatesky-Shapiro, and P. Smuth, “Advances in Knowledge Discovery and Data Mining”, AAAI Press, 1996.##[33] M. R. Razme Rezaee, B. P. F. Leleiveldt, and J. H. C. Reiber, "A new cluster validity index for the fuzzy c-mean", Pattern Recognition Letters, vol. 19, no. 3–4, pp. 237–246, 1998.##[34] A. Strehl and J. Ghosh, "Cluster ensembles - A knowledge reuse framework for combining multiple partitions", Journal of Machine Learning Research, vol. 3, no. 1, pp. 583–617, 2003.##[35] A. Fred and A. K. Jain, "Combining multiple clustering using evidence accumulation", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 6, pp. 835–850, 2005.##[36] N. Iam-On, T. Boongoen, S. Garrett, and C. Price, "A link-based approach to the cluster ensemble problem", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 11, pp. 2396–2409, 2011.##[37] A. Strehl and J. Ghosh, "Cluster ensembles: A knowledge reuse framework for combining multiple partitions", Journal of Machine Learning Research, vol. 3, pp. 583–617, 2002.##[38] Z. Zhou and W. Tang, "Clusterer ensemble", Knowledge-Based Systems, vol. 19, pp. 77–83, 2006.##[39] N. Nguyen and R. Caruana, "Consensus clusterings," Proceedings of the Seventh IEEE International Conference on Data Mining, Omaha, NE, USA, 28–31 October 2007, pp. 607–612.##[40] A. Y. Ng, M. I. Jordan, and Y. Weiss, "On spectral clustering: Analysis and an algorithm", Advances in Neural Information Processing Systems, MIT Press, Cambridge, MA, USA, vol. 14, 2002.##[41] M. Ester, H. Kriegel, J. Sander, and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise", KDD-96: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, AAAI Press, Menlo Park, CA, USA, 1996, pp. 226–231.##[42] A. Rodriguez and A. Laio, "Clustering by fast search and find of density peaks", Science, vol. 344, no. 6191, pp. 1492–1496, 2014.##[43] T. Alqurashi and W. Wang, "Clustering ensemble method," International Journal of Machine Learning and Cybernetics, vol. 10, no. 6, pp. 1227–1246, 2019.##[44] A. Tombros, R. Villa, and C. Rijsbergen, "The effectiveness of query-specific hierarchic clustering in information retrieval", Information Processing & Management, vol. 38, no. 4, pp. 559–582, 2002.##[45] M. R. Valizadeh and M. Zolghadri-Jahromi, "A proposed query-sensitive similarity measure for information retrieval", Iranian Journal of Science & Technology, Transaction B, Engineering, vol. 30, no. B2, 2006.##[46] A. Topchy, A. K. Jain, and W. Punch, "Clustering ensembles: Models of consensus and weak partitions", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 12, pp. 1689–1704, 2005.##[47] X. Z. Fern and W. Lin, "Cluster ensemble selection", Statistical Analysis and Data Mining: The ASA Data Science Journal, vol. 1, no. 3, pp. 128–141, 2008.##[48] A. L. Fred and A. K. Jain, "Combining multiple clustering using evidence accumulation", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 6, pp. 835–850, 2005.##[49] I. T. Christou, "Coordination of cluster ensembles via exact methods", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 2, pp. 279–293, 2011.##[50] V. Singh, L. Mukherjee, J. Peng, and J. Xu, "Ensemble clustering using semi-definite programming with applications", Machine Learning, vol. 79, no. 1–2, pp. 177–200, 2010.##[51] Sh. Zhou, R. Duan, Zh. Chen, W. Song, "Weighted ensemble clustering with multivariate randomness and random", Applied Soft Computing Journal, vol. 150, p. 111015, 2024.##[52] Z. Bian, J. Qu, J. Zhou, Zh. Jiang, Sh. Wang, "Weighted adaptively ensemble clustering method based on fuzzy Co-association matrix", Information Fusion, vol. 103, p. 102099, 2024.##[53] B. Shen, J. Jiang, F. Qian, D. Li, Y. Ye, Gh. Ahmadi, "Semi-supervised hierarchical ensemble clustering based on an innovative distance metric and constraint information", Engineering Applications of Artificial Intelligence, vol. 124, p. 106571, 2023.##[54] Y. Wu, R. Wu, J. Liu, X. Tang, "MetaWCE: Learning to Weight for Weighted Cluster Ensemble", Information Sciences, vol. 629, pp. 39–61, 2023.##[55] J. Xu, T. Li, D. Zhang, J. Wu, "Ensemble clustering via fusing global and local structure information", Expert Systems with Applications, vol. 237, p. 121557, 2024.##[56] Q. Gu, Y. Wang, P. Wang, X. Li, L. Chen, N. N. Xiong, D. Liu, "An improved weighted ensemble clustering based on two-tier uncertainty measurement", Expert Systems With Applications, vol. 238, p. 121672, 2024.##[57] J. Xua, T. Lia, "Ensemble clustering with low-rank optimal Laplacian matrix learning", Applied Soft Computing, 2023, doi: 10.1016/j.asoc.2023.111095.##[58] D. Aktaş, B. Lokman, T. İnkaya, G. Dejaegere, "Cluster ensemble selection and consensus clustering: A multi-objective optimization approach", European Journal of Operational Research, 2023, doi: 10.1016/j.ejor.2023.10.029.##[59] X. Niu, Ch. Zhang, X. Zhao, L. Hu, J. Zhang, "A multi-view ensemble clustering approach using joint affinity matrix", Expert Systems With Applications, vol. 216, p. 119484, 2023.##[60] Y. Jia, S. Tao, R. Wang, Y. Wang, "Ensemble Clustering via Co-Association Matrix Self-Enhancement", IEEE Transactions on Neural Networks and Learning Systems, 2023, ISSN: 2162-2388.##[61] رضایی، حمید و دانشپور، نگین، "ارائه روشی جدید برای خوشه بندی داده های مخلوط بر مبنای تعداد ویژگی مشابه"، مجله پردازش علائم و داده ها، جلد ۲۱ شماره ۱ صفحات ۵۲-۳۹، 1403.##[62] نجفی، فاطمه و پروین، حمید و میرزایی، کمال و نجاتیان، صمد و رضایی، سیده وحیده ، "یک روش خوشه‌بندی ترکیبی جدید مبتنی بر خوشه‌بند cmeans فازی با حفظ تنوع در اجماع"، مجله پردازش علائم و داده ها، جلد 17 شماره 4 صفحات 122-103، 1399.##W. J. Frawley, G. Piatetsky-Shapiro, and C. J. Matheus, "Knowledge discovery in databases: An overview", AI Magazine, vol. 13, no. 3, p. 57, 1992.##D. J. Hand, H. Mannila, and P. Smyth, "Principles of Data Mining", MIT Press, 2001.##I. H. Witten and E. Frank, "Data Mining: Practical Machine Learning Tools and Techniques", Morgan Kaufmann, 2005.##A. K. Jain and R. C. Dubes, "Algorithms for Clustering", Englewood Cliffs, NJ: Prentice Hall, 1988.##H. Frigui and R. Krishnapuram, "A robust competitive clustering algorithm with applications in computer vision," IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 21, pp. 455-465, 1999.##M. R. Anderberg, "Cluster Analysis for Applications", Academic Press, Inc., New York, 1973.##E. Diday and J. C. Simon, "Clustering analysis", Digital Pattern Recognition, K. S. Fu, Ed., Springer-Verlag, New York, pp. 47-94, 1976.##R. S. Michalski and R. E. Stepp, "Automated construction of classification: Conceptual clustering versus numerical taxonomy", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 5, pp. 396-409, 1983.##R. C. Dubes, "Cluster analysis and related issues", Handbook of Pattern Recognition & Computer Vision,World Scientific, 1993.##S. Saha and S. Bandyopadhyay, "Application of multiobjective optimization for data clustering", Seminar (Machine Intelligence Unit, Indian Statistical Institute), Kolkata, India, 2008.##N. Jardin and R. Sibson, "Mathematical Taxonomy", Wiley, New York, 1971.##N. Jardin and C. J. Van Rijsbergen, "The use of hierarchical clustering in information retrieval", Information Storage and Retrieval, vol. 7, pp. 217-240, 1971.##M. Mojarad, S. Nejatian, H. Parvin, and M. Mohammadpoor, "A fuzzy clustering ensemble based on cluster clustering and iterative fusion of base clusters", Applied Intelligence, vol. 49, pp. 2567-2581, 2019.##M. N. Ghaemi, "A survey: Clustering ensembles techniques", World Academy of Science, Engineering and Technology, pp. 636-646, 2009.##H. Parvin, B. Miaei-Bidgoli, H. Alinejad-Rokny, and W. H. Punch, "Data weighing mechanisms for clustering ensembles", Computers & Electrical Engineering, vol. 39, no. 5, pp. 1433-1450, 2013.##H. Parvin and B. Miaei-Bidgoli, "A clustering ensemble framework based on elite selection of weighted clusters," Advances in Data Analysis and Classification, vol. 7, no. 2, pp. 181-208, 2013.##A. Topchy, B. Minaei-Bidgoli, and W. F. Punch, "Ensembles of partitions via data resampling," Proceedings of the International Conference on Information Technology, ITCC 04, Las Vegas, 2004.##A. Topchy, A. K. Jain, and W. F. Punch, "Combining multiple weak clusterings", Proceedings of the 3rd IEEE International Conference on Data Mining, 331-338, 2003.##A. Strehl and J. Ghosh, "Cluster ensembles - A knowledge reuse framework for combining multiple partitions", Journal of Machine Learning Research, vol. 3, pp. 583-617, 2002.##R. Srikant and R. Agrawal, "Mining quantitative association rules in large relational tables", Proceedings of the ACM SIGMOD Conference on Management of Data, Montreal, Canada, 1996.##J. W. Chang and D. S. Jin, "A new cell-based clustering method for large-high dimensional data in data mining applications", Proceedings of the ACM Symposium on Applied Computing, 503-507, 2002.##R. Miller and Y. Yang, "Association rules over interval data", Proceedings of the ACM SIGMOD International Conference on Management of Data, 452-461, 1997.##R. Jiamthapthaksin, C. F. Eick, and S. Lee, "GAC-GEO: A generic agglomerative clustering framework for geo-referenced datasets," Knowledge and Information Systems, vol. 29, no. 3, pp. 597-628, 2011.##S. Dudoit and J. Fridlyand, "Bagging to improve the accuracy of a clustering procedure", Bioinformatics, vol. 19, no. 9, pp. 1090-1099, 2003.##M. H. F. Zarandi, M. R. Faraji, and M. Karbasian, "An exponential cluster validity index for fuzzy clustering with crisp and fuzzy data," Scientia Iranica, vol. 17, no. 2, pp. 95-110, 2010.##J. C. Bezdek, "Cluster validity with fuzzy sets", Journal of Cybernetics, vol. 3, no. 3, pp. 58-73, 1973.##F. Kovács, C. Legány, and A. Babos, "Cluster validity measurement techniques", Department of Automation and Applied Informatics, Budapest University of Technology and Economics, 2003.##Y. Zhang, W. Wang, X. Zhang, and Y. Li, "A cluster validity index for fuzzy clustering", Information Sciences, vol. 178, no. 4, pp. 1205-1218, 2008.##W. Wang and Y. Zhang, "fuzzy cluster validity indices", Fuzzy Sets and Systems, vol. 158, no. 19, pp. 2095-2117, 2007.##M. J. A. Berry and G. Linoff, "Data Mining Techniques for Marketing", Sales and Customer Support, 3rd ed., John Wiley & Sons, Inc., USA, 1996.##M. Berry, S. Dumais, G. O'Brien, "Using linear algebra for intelligent information retrieval," SIAM Review, vol. 37, no. 4, pp. 573-595, 1995.##M. U. Fayyad, G. Piatesky-Shapiro, and P. Smuth, "Advances in Knowledge Discovery and Data Mining", AAAI Press, 1996.##M. R. Razme Rezaee, B. P. F. Leleiveldt, and J. H. C. Reiber, "A new cluster validity index for the fuzzy c-mean", Pattern Recognition Letters, vol. 19, no. 3-4, pp. 237-246, 1998.##A. Strehl and J. Ghosh, "Cluster ensembles - A knowledge reuse framework for combining multiple partitions", Journal of Machine Learning Research, vol. 3, no. 1, pp. 583-617, 2003.##A. Fred and A. K. Jain, "Combining multiple clustering using evidence accumulation", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 6, pp. 835-850, 2005.##N. Iam-On, T. Boongoen, S. Garrett, and C. Price, "A link-based approach to the cluster ensemble problem", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 11, pp. 2396-2409, 2011.##A. Strehl and J. Ghosh, "Cluster ensembles: A knowledge reuse framework for combining multiple partitions", Journal of Machine Learning Research, vol. 3, pp. 583-617, 2002.##Z. Zhou and W. Tang, "Clusterer ensemble", Knowledge-Based Systems, vol. 19, pp. 77-83, 2006.##N. Nguyen and R. Caruana, "Consensus clusterings," Proceedings of the Seventh IEEE International Conference on Data Mining, Omaha, NE, USA, 28-31 October 2007, pp. 607-612.##A. Y. Ng, M. I. Jordan, and Y. Weiss, "On spectral clustering: Analysis and an algorithm", Advances in Neural Information Processing Systems, MIT Press, Cambridge, MA, USA, vol. 14, 2002.##M. Ester, H. Kriegel, J. Sander, and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise", KDD-96: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, AAAI Press, Menlo Park, CA, USA, 1996, pp. 226-231.##A. Rodriguez and A. Laio, "Clustering by fast search and find of density peaks", Science, vol. 344, no. 6191, pp. 1492-1496, 2014.##T. Alqurashi and W. Wang, "Clustering ensemble method," International Journal of Machine Learning and Cybernetics, vol. 10, no. 6, pp. 1227-1246, 2019.##A. Tombros, R. Villa, and C. Rijsbergen, "The effectiveness of query-specific hierarchic clustering in information retrieval", Information Processing & Management, vol. 38, no. 4, pp. 559-582, 2002.##M. R. Valizadeh and M. Zolghadri-Jahromi, "A proposed query-sensitive similarity measure for information retrieval", Iranian Journal of Science & Technology, Transaction B, Engineering, vol. 30, no. B2, 2006.##A. Topchy, A. K. Jain, and W. Punch, "Clustering ensembles: Models of consensus and weak partitions", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 12, pp. 1689-1704, 2005.##X. Z. Fern and W. Lin, "Cluster ensemble selection", Statistical Analysis and Data Mining: The ASA Data Science Journal, vol. 1, no. 3, pp. 128-141, 2008.##A. L. Fred and A. K. Jain, "Combining multiple clustering using evidence accumulation", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 6, pp. 835-850, 2005.##I. T. Christou, "Coordination of cluster ensembles via exact methods", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 2, pp. 279-293, 2011.##V. Singh, L. Mukherjee, J. Peng, and J. Xu, "Ensemble clustering using semi-definite programming with applications", Machine Learning, vol. 79, no. 1-2, pp. 177-200, 2010.##Sh. Zhou, R. Duan, Zh. Chen, W. Song, "Weighted ensemble clustering with multivariate randomness and random", Applied Soft Computing Journal, vol. 150, p. 111015, 2024.##Z. Bian, J. Qu, J. Zhou, Zh. Jiang, Sh. Wang, "Weighted adaptively ensemble clustering method based on fuzzy Co-association matrix", Information Fusion, vol. 103, p. 102099, 2024.##B. Shen, J. Jiang, F. Qian, D. Li, Y. Ye, Gh. Ahmadi, "Semi-supervised hierarchical ensemble clustering based on an innovative distance metric and constraint information", Engineering Applications of Artificial Intelligence, vol. 124, p. 106571, 2023.##Y. Wu, R. Wu, J. Liu, X. Tang, "MetaWCE: Learning to Weight for Weighted Cluster Ensemble", Information Sciences, vol. 629, pp. 39-61, 2023.##J. Xu, T. Li, D. Zhang, J. Wu, "Ensemble clustering via fusing global and local structure information", Expert Systems with Applications, vol. 237, p. 121557, 2024.##Q. Gu, Y. Wang, P. Wang, X. Li, L. Chen, N. N. Xiong, D. Liu, "An improved weighted ensemble clustering based on two-tier uncertainty measurement", Expert Systems With Applications, vol. 238, p. 121672, 2024.##J. Xua, T. Lia, "Ensemble clustering with low-rank optimal Laplacian matrix learning", Applied Soft Computing, 2023, doi: 10.1016/j.asoc.2023.111095.##D. Aktaş, B. Lokman, T. İnkaya, G. Dejaegere, "Cluster ensemble selection and consensus clustering: A multi-objective optimization approach", European Journal of Operational Research, 2023, doi: 10.1016/j.ejor.2023.10.029.##X. Niu, Ch. Zhang, X. Zhao, L. Hu, J. Zhang, "A multi-view ensemble clustering approach using joint affinity matrix", Expert Systems With Applications, vol. 216, p. 119484, 2023.##Y. Jia, S. Tao, R. Wang, Y. Wang, "Ensemble Clustering via Co-Association Matrix Self-Enhancement", IEEE Transactions on Neural Networks and Learning Systems, 2023, ISSN: 2162-2388.##رضایی، حمید و دانشپور، نگین، "ارائه روشی جدید برای خوشه بندی داده های مخلوط بر مبنای تعداد ویژگی مشابه"، مجله پردازش علائم و داده ها، جلد ۲۱ شماره ۱ صفحات ۵۲-۳۹، 1403.##نجفی، فاطمه و پروین، حمید و میرزایی، کمال و نجاتیان، صمد و رضایی، سیده وحیده ، "یک روش خوشه‌بندی ترکیبی جدید مبتنی بر خوشه‌بند cmeans فازی با حفظ تنوع در اجماع"، مجله پردازش علائم و داده ها، جلد 17 شماره 4 صفحات 122-103، 1399. ##

تشخیص بدافزارهای اندرویدی با رویکرد تخلیه بار در محاسبات ابری Detecting Android malware with offloading approach in cloud computing 1 1 اندروید در سال‌های اخیر به‌عنوان محبوب‌ترین سیستم‌عامل گوشی‌های هوشمند و دستگاه‌های تلفن‌ همراه ظاهر شده‌است؛ بااین‌حال، با توجه به خاصیت متن‌بازبودن این سیستم‌عامل، بدافزارهای زیادی در میان نرم‌افزارها در بازارهای اندروید پنهان شده‌اند که امنیت آن را با خطر جدی مواجه کرده‌است؛ لذا یافتن راه‌حل‌هایی برای تشخیص این بدافزارها از کارهای ضروری جهت جلوگیری از آلوده‌شدن تلفن‌ همراه است؛ به‌این‌منظور، در این پژوهش، برای تشخیص بدافزارها استفاده روش تخلیه محاسباتی در ساختار محاسبات ابر پیشنهادشده‌‌است که این روش باعث می‌شود تشخیص بدافزارها در زمان معقول، با دقت بالا و با استفاده از منابع کمتر انجام شود. در روش پیشنهادی ویژگی‌های برنامه‌های اندروید را در هنگام نصب و زمان اجرا سمت تلفن ‌همراه استخراج می‌کنیم و ویژگی‌های استخراج‌شده به ‌سمت سرور ابر برای تجزیه‌ و تحلیل ارسال می‌شود و با الگوریتم یادگیری ماشین بدافزارهای اندروید از برنامه‌های تمیز تشخیص داده می‌شوند. در این پژوهش، رویکرد پیشنهادی با استفاده از مجموعه‌داده Drebin آموزش و آزمایش شده‌است. نتایج به‌دست‌آمده، نشان می‌دهد که رویکرد پیشنهادی به‌دقت %44/96 برای شناسایی بدافزار دست‌یافت. 2 Today, the mobile phone is one of the smart devices that have become a necessity in everyday life and are used for various tasks such as shopping, banking, communicating with friends, family, etc. In recent years, the Android operating system has been able to gain more popularity than other mobile phone operating systems. The number of software related to this operating system is also expanding at a remarkable speed. Unfortunately, this issue is not hidden from the profit-seeking people, and the production of malware of this operating system has also grown in parallel with its development. Third-party Android app stores that have emerged in recent years have become a very strong source of malware distribution, as these stores have weak to non-existent measures to prevent malicious apps from being uploaded and distributed to users' devices. Therefore, one of the challenges that programmers are dealing with in this field is to find solutions to establish security in these types of devices, in such a way that it provides powerful security analysis capabilities while consuming few resources on the device itself. Software products such as Lookout, Norton, and Comodo Mobile Security mainly use signature-based methods to detect malware threats. However, malware attackers use techniques such as repackaging and obfuscation to circumvent signatures and defeat attempts to analyze their internal mechanisms. The ever-increasing sophistication of Android malware requires new defense techniques that can protect users against new threats while not using up all of a mobile device's processing and storage resources. Therefore, in the current research, a computational offloading method is presented in the cloud structure to identify Android malware. The solution proposed by this research first extracts the features of Android applications during installation and execution on the mobile phone, then sends these extracted features to the cloud servers. On the cloud server side, these features are analyzed and using machine learning algorithms, malware is distinguished from clean programs. The proposed approach is trained and tested using the Drebin dataset. The obtained results show that the proposed approach has achieved 96.44% accuracy for malware detection. 137 148 2023/07/212023/07/232023/09/92021/10/182021/05/152021/03/192022/08/28 1401/6/6 2024/08/182024/08/212024/08/212024/12/42024/02/252024/02/252024/08/18 1403/5/28 معصومه قاسمی Masoume Ghasemi دانش‌آموخته کارشناسی ارشد مهندسی کامپیوتر دانشکده فنی‌ومهندسی، دانشگاه شهرکرد، شهرکرد، ایران ایران masoume.ghasemi1995@gmail.com عباس حری abbas horri استادیار مهندسی کامپیوتر، دانشکده فنی‌ومهندسی، دانشگاه شهرکرد، شهرکرد، ایران ایران horri@sku.ac.ir محمداحسان بصیری Mohammad Ehsan basiri دانشیار مهندسی کامپیوتر، دانشکده فنی‌ومهندسی، دانشگاه شهرکرد، شهرکرد، ایران ایران basiri@sku.ac.ir Android Malware Machine Learning Cloud Computing بدافزار اندروید یادگیری ماشین محاسبات ابر [1] Number of Smartphone and Mobile Phone Users Worldwide in 2022/2023: Demographics, Statistics, Predictions. https:// financesonline.com/number-of-smartphone-users-worldwide/. 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WIW 2016, pp. 104–111, 2017.##[7] M. Deypir, A. Horri, "Instance based security risk value estimation for Android applications, " Journal of Information Security and Applications, 2018.##[8] K. Tam, S. J. Khan, A. Fattori, et al. "CopperDroid: Automatic Reconstruction of Android Malware Behaviors, " Systems Security Research Lab and Information Security Group Royal Holloway University of London, 2015.##[9] M. Qiao, A. H. Sung, Q. Liu, "Merging Permission and API Features for Android Malware Detection, " 5th IIAI International Congress on Advanced Applied Informatics, DOI 10.1109/ IIAI-AAI.2016.237, 2016.##[10] L. Wen, and H. Yu, "An Android Malware Detection System Based on Machine Learning, " AIP Conference Proceedings 1864, 020136-1–020136-7; doi: 10.1063/1.4992953, 2017.##[11] A. Pektas¸ and T. Acarman. "Deep learning for effective android malware detection using api call graph embeddings, " Soft Computing, 24(2):1027–1043, 2020.##[12] S.Y.Yerima, S. Sezer, and I. Muttik, "Android malware detection using parallel machine learning classifiers, " IET Information Security Vol. 9, Iss. 6, pp. 313–320, 2015.##[13] Z.Qin, Y. Xu, B. Liang, et al. "An Android malware static detection method, " Journal of Southeast University, 43(6):1162-1167, 2013.##[14] Y. Qiao, Y. Yang, J. He, et al, "CBM: Free, Automatic Malware Analysis Framework Using API Call Sequences, " Advances in Intelligent Systems and Computing 214, DOI: 10.1007/978-3-642-37832-4_21, Springer-Verlag Berlin Heidelberg 2014.##[15] G. He, B. Xu, L. Zhang, and H. Zhu, "On-Device Detection of Repackaged Android Malware via Traffic Clustering, " Security and Communication Networks, Volume 2020, Article ID 8630748, 19 pages, 2020.##[16] L. Xiao, Y. Li, X. Huangy, X. J. Du, "Cloud-based Malware Detection Game for Mobile Devices with Offloading, " IEEE Transactions on Mobile Computing, DOI 10.1109/TMC.2017.2687918, 2017.##[17] A. Zulkifli, I. R. A. Hamid, W. M. Shah, and Z. Abdullah, "Android malware detection based on network traffic using decision tree algorithm, " in Proceedings of the International Conference on Soft Computing and Data Mining, Springer, Senai, Malaysia, pp. 485–494, January 2018.##[18] V. Cardellini, V. De Nito Person´e, V. Di Valerio, et al, "A gametheoretic approach to computation offloading in mobile cloud computing, " Springer Mathematical Programming, pp. 1–29, 2013.##[19] Y. Wang, X. Lin, and M. Pedram, "A Bayesian game formulation of power dissipation and response time minimization imobile cloud computing system, " in Proc. IEEE Int’l Conf. Mobile Services, pp. 7 – 14, 2013.##[20] F. Saeik, M. Avgeris, D. Spatharakis, N. Santi, D. Dechouniotis, J. Violos, A. Leivadeas, N. Athanasopoulos, N. Mitton, S. Papavassiliou, "Task Offloading in Edge and Cloud Computing: A Survey on Mathematical, Artificial Intelligence and Control Theory Solution, " Journal Pre-proof, May 2021 Computer Networks 195(3):108177, 2021.##[21] P.Irolla1, A. Dey, "The duplication issue within the Drebin dataset, " Journal of Computer Virology and Hacking Techniques, 2018.##[22] D.Arp, M.Spreitzenbarth, M.Hubner, H.Gascon, K.Rieck, "DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket, " Conference Paper, February 2014.##[23] S. Garg, S. K. Peddoju, A. K. Sarje, "Network-based detection of Android malicious apps, " Institute of Technology Roorkee, Roorkee 247667, India, DOI 10.1007/s10207-016-0343-z, 2016.##[24] R. Jusoh, A. Firdaus, S. Anwar, et.al. "Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation), " PeerJ Comput. Sci. 7:e522, DOI 10.7717/peerj-cs.522, 2021.##[25] Y. Li, J. Liu, Q. Li, L. Xiao, "Mobile Cloud Offloading for Malware Detections with Learning, " The Third International Workshop on Security and Privacy in Big Data, NSFC (61271242,61001072), 2015##Number of Smartphone and Mobile Phone Users Worldwide in 2022/2023: Demographics, Statistics, Predictions. https:// financesonline.com/number-of-smartphone-users-worldwide/. Accessed: 2022-August-16.##Google Play: number of available apps by quarter 2022 - Statista. https://www.statista.com/statistics/289418/ number-of-available-apps-in-the-google-play-store-quarter. Accessed: 2022-August-16.##O. N. Elayan, A. M. Mustafa, "Android Malware Detection Using Deep Learning, " Procedia Computer Science., vol. 184, pp. 847-852.##M. K. Alzaylaee, S. Y. Yerima, and S. Sezer, "DL-Droid: Deep learning based android malware detection using real devices, " Computers & Security 89 (2020) 101663, vol. 89, February 2020.##Z. Yuan, Y. Lu, Z. Wang, and Y. Xue, "Droid-Sec: Deep learning in android malware detection, " ACM SIGCOMM Computer Communication Review, vol. 44, pp. 371-372, August 2014.##S. Hou, A. Saas, L. Chen, and Y. Ye, "Deep4MalDroid: A deep learning framework for android malware detection based on Linux kernel system call graphs, " Proc. - 2016 IEEE/WIC/ACM Int. Conf. Web Intell. Work. WIW 2016, 2016, pp. 104-111.##M. Deypir, A. Horri, "Instance based security risk value estimation for Android applications, " Journal of Information Security and Applications, vol. 40, pp. 20-30, June 2018.##K. Tam, S. J. Khan, A. Fattori, et al. "CopperDroid: Automatic Reconstruction of Android Malware Behaviors, " Systems Security Research Lab and Information Security Group Royal Holloway University of London, 2015, pp. 1-15.##M. Qiao, A. H. Sung, Q. 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حفظ حریم خصوصی در اینترنت اشیا برای انتقال داده‌ها در حوزه سلامت با استفاده از زنجیره بلوکی IoT privacy for the transmission of data in the field of health using blockchain 1 1 استفاده از زنجیره بلوکی در حفظ حریم خصوصی افراد موضوعی است که توانسته است نظرات پژوهش‌گران را به خود جلب کند و نیازمندی بسیار مهمی در سیستم­‌های اطلاعاتی و داده­ای به شمار می‌آید. از طرفی سیستم سلامت پزشکی با توجه به حساسیت‎های موجود در حفظ اطلاعات بیماران و افراد درگیر در سلامت پزشکی مانند پزشکان و پرستاران، دارای زمینه مساعدی برای به‌کارگیری سیستم قدرتمند زنجیره بلوکی برای حفظ حریم خصوصی است. در پژوهش حاضر بر حفظ محرمانگی داده‌های سلامت پزشکی در اینترنت اشیا مبتنی بر ابر با استفاده از زنجیره بلوکی و محاسبات لبه تأکید می‌شود به گونه‌­ای که این روش بتواند محرمانگی داده‌ها در این محیط‌ها و بسترهای پزشکی را به‎ویژه برای بیماران تحت مراقبت به شکل مطلوب فراهم آورد. ذخیره داده‌­های پزشکی با این فناوری در پاسخ به نیازمندی هم‌زمان کارایی سیستم و حفظ محرمانگی پیشنهاد شده‌است. به‌طور مشخص این روش از طریق بهره‌گیری از احراز هویت با روش نامتقارن در لبه ابر و تبادل کلید دیفی-هلمن برای موارد ناشناس‌بودن، استفاده از زنجیره بلوکی و پیرو آن درهم‌سازی SHA2 و رمزنگاری PKI سعی در ایجاد بستری با امنیت و به‎ویژه حریم خصوصی بالا جهت کاربردهای مراقبت پزشکی کرده‌است؛ این سیستم در ادامه شبیه­‌سازی و نشان داده شده‌است که می‌­تواند در سیستم سلامت پزشکی بر روی ترکیب ابر-اینترنت اشیا از منظر عملکردی تأثیر مناسبی داشته باشد. مطابق نتایج با اجرای روش، از نظر نقض SLA سیستم در شرایطی قرار می‌گیرد که حتی در صورت حمله نقض SLA وجود نداشته باشد و کارایی حفظ شود؛ همچنین درصد دستیابی به اطلاعات مفید توسط هکر نزدیک به صفر خواهد بود. با جلوگیری از ورود گره‎های مخرب گذردهی حدود 30 درصد افزایش نشان می­‌دهد. همچنین در مقایسه با برخی روش­‌های دیگر که در سال‌های اخیر توسط پژوهش‌گران ارائه شده‌­اند، خنثی‌سازی مهاجمان موجب بهبود حداقل 5 درصدی در کارایی سیستم می­‌شود. از دیگر محاسن این روش انعطاف‌­پذیری و مقیاس‌­پذیری بالا، مقاوم‍‎بودن، زمان اجرای مناسب و تأخیر کمابیش پایین است که به­‌واسطه استفاده از ابر لبه به‎وجود می‌­آید. 2 Data transmission and storage through blockchain is something that many studies have suggested for various security issues. Due to the sensitivities in securing information from patients and medical professionals, the healthcare system has a favorable context for deploying a powerful blockchain system for privacy. Blockchain application in the healthcare system allows physicians to store patient records with high security and make them available to other hospitals and clinics as needed. Besides increasing data transmission and storage security, this reduces data management risks and expenses. The present study emphasizes the confidentiality of healthcare data in the cloud-based Internet of Things (IoT) using blockchain and edge computing. Also this method uses SHA2 hashing and PKI encryption. Therefore, it can optimally provide data confidentiality in medical settings, especially for patients under care. The system proposed in this study includes five parts: users, IoT devices, edge devices, security server, and cloud computing. The proposed method uses the Diffie – Hellman key exchange in the authentication process for anomality case to achieve the goal of essential security compliance. This method is a cryptographic (encryption) protocol allowing two people or two organizations to create a shared password key without the need for any prior acquaintance and exchange it through an insecure connection path. This study used data sensed by IoT network sensors with medical data for loading on IoT and cloud simulated networks. These data were related to monitoring patients with cardiovascular disease and were sampled on 300 patients. The research dataset belonged to the Cleveland Clinic Foundation for Heart Disease Dataset. The study simulation software was NS-2.35, which used C++ and TCL programming languages. The research findings revealed that if there were no method for data encryption, much of the data would be exposed. This rate reaches 50% for 50 attackers. Encryption using the proposed method causes the percentage of data disclosed to be very slight, and it equals zero, even though attackers may guess the password or data. As network traffic grows, the throughput difference between blocking and non-blocking access methods increases. It suggests that by blocking the attacking nodes’ access, network traffic will not have a detrimental effect on attacking nodes by detecting and preventing their activity. However, if the access of these nodes is not blocked, the destructive impact is very high, and the network traffic will grow slightly. According to these results, in terms of SLA violation, the system is in a situation where even in case of an attack, there is no SLA violation and the efficiency is maintained. Also, the percentage of access to useful information by the hacker will be close to zero. By preventing the entry of malicious nodes, the throughput increases by about 30%. Some other advantages of this method are its high flexibility and comparability, robustness, and relatively low execution time and delay, which is caused by the use of cloud edge. It is estimated that the improvement rate of the proposed method is more than 5% compared to other related approaches. In the design presented in this study, the processes are highly simplified, and there will be a relatively low processing overhead. At the same time, the steps meet all the requirements for cloud and IoT data centers in healthcare applications. 149 178 2023/07/212023/07/232023/09/92021/10/182021/05/152021/03/192022/08/282022/05/31 1401/3/10 2024/08/182024/08/212024/08/212024/12/42024/02/252024/02/252024/08/182024/06/1 1403/3/12 عباس حسن پورعسکری Abbas Hassan Pour Askari دانشجوی دکترای گروه کامپیوتر، واحد علوم و تحقیقات- پردیس مرکز آموزش بین المللی قشم، دانشگاه آزاد اسلامی، قشم، ایران ایران Abas.hasanpoor@yahoo.com عمید خطیبی بردسیری Amid Khatibi Bardsiri استادیار گروه کامپیوتر، دانشگاه آزاد اسلامی واحد بردسیر، بردسیر، ایران ایران khatibi_amid@yahoo.com مختار محمدی قنات غستانی Mokhtar Mohammadi Ghanat Ghestani استادیار گروه کامپیوتر، دانشگاه آزاد اسلامی واحد بم، بم، ایران ایران mokhtarmohamadi@gmail.com cloud-based IoT health record system blockchain data confidentiality edge computing Cloud-IoT سیستم سوابق پزشکی زنجیره بلوکی محرمانگی داده‌ها محاسبات لبه. X. Wang, L. Bai, Q. Yang, L. Wang, and F. 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Baker, "Scaling blockchains to support electronic health record systems for hospitals," arXiv preprint arXiv:2001.05525, 2020.##A. Zhang and X. Lin, "Towards secure and privacy-preserving data sharing in e-health systems via consortium blockchain," Journal of Medical Systems, vol. 42, no. 8, p. 140, 2018.##A. Botta, W. De Donato, V. Persico, and A. Pescapé, "Integration of cloud computing and internet of things: A survey," Future Generation Computer Systems, vol. 56, pp. 684-700, 2016.##C. Perera, A. Zaslavsky, P. Christen, and D. Georgakopoulos, "Sensing as a service model for smart cities supported by internet of things," Transactions on Emerging Telecommunications Technologies, vol. 25, no. 1, pp. 81-93, 2014.##A. Shahnaz, U. Qamar, and A. Khalid, "Using blockchain for electronic health records," IEEE Access, vol. 7, pp. 147782-147795, 2019.##D. Gachet, M. de Buenaga, F. Aparicio, and V. 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Nagori, "Proof of authenticity-based electronic medical records storage on blockchain," in Smart Trends in Computing and Communications, Springer, Singapore, pp. 297-306, 2020.##A. Zhang and X. Lin, "Towards secure and privacy-preserving data sharing in e-health systems via consortium blockchain," Journal of Medical Systems, vol. 42, no. 8, p. 140, 2018.##S. Tanwar, K. Parekh, and R. Evans, "Blockchain-based electronic healthcare record system for healthcare 4.0 applications," Journal of Information Security and Applications, vol. 50, p. 102407, 2020.##G. Tripathi, M. A. Ahad, and S. Paiva, "S2HS-A blockchain based approach for smart healthcare system," Healthcare, Elsevier, 2019.##E. Gorelik, "Cloud computing models," Doctoral dissertation, Massachusetts Institute of Technology, 2013.##C. Choi, J.-H. Park, M. Na, and S. Jo, "Low-latency 5G architectures for mission-critical Internet of Things (IoT) services," Information and Communications Magazine, vol. 32, no. 9, pp. 17-23, 2015.##H. Wu, K. Yue, C.-H. Hsu, Y. Zhao, B. Zhang, and G. Zhang, "Deviation-based neighborhood model for context-aware QoS prediction of cloud and IoT services," Future Generation Computer Systems, vol. 76, pp. 550-560, 2017.##P. Bonte, F. Ongenae, F. De Backere, J. Schaballie, D. Arndt, S. Verstichel, E. Mannens, R. Van de Walle, and F. De Turck, "The MASSIF platform: A modular and semantic platform for the development of flexible IoT services," Knowledge and Information Systems, vol. 51, no. 1, pp. 89-126, 2017.##T. D. Cao, H. H. Hoang, H. X. Huynh, B. M. Nguyen, T. V. Pham, Q. Tran-Minh, ... and H. L. Truong, "IoT services for solving critical problems in Vietnam: A research landscape and directions," IEEE Internet Computing, vol. 20, no. 5, pp. 76-81, 2016.##G. Gang, Z. L., and J. Jun, "Internet of things security analysis," in Internet Technology and Applications (iTAP), 2011 International Conference on, pp. 1-4. IEEE, 2011.##A. Whitmore, A. Agarwal, and L. Da Xu, "The Internet of Things-A survey of topics and trends," Information Systems Frontiers, pp. 1-14, 2014.##J. Guo, R. Chen, and J. J. P. Tsai, "A mobile cloud hierarchical trust management protocol for IoT systems," in Mobile Cloud Computing, Services, and Engineering (MobileCloud), 2017 5th IEEE International Conference on, IEEE, 2017.##Y. Liu, J. E. Fieldsend, and G. Min, "A framework of fog computing: Architecture, challenges, and optimization," IEEE Access, vol. 5, pp. 25445-25454, 2017.##H. Kim and E. A. Lee, "Authentication and authorization for the Internet of Things," IT Professional, vol. 19, no. 5, pp. 27-33, 2017.##M. Mahmud, et al., "A brain-inspired trust management model to assure security in a cloud based IoT framework for neuroscience applications," arXiv preprint arXiv:1801.03984, 2018.##R. K. Behera, K. H. K. Reddy, and D. S. Roy, "Reliability modelling of service oriented Internet of Things," in 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO)(Trends and Future Directions), IEEE, 2015.##V. Kharchenko, et al., "Reliability and security issues for IoT-based smart business center: Architecture and Markov model," in 2016 Third International Conference on Mathematics and Computers in Sciences and in Industry (MCSI), IEEE, 2016.##V. Sharma, et al., "A consensus framework for reliability and mitigation of zero-day attacks in IoT," Security and Communication Networks, 2017.##C. Stergiou, et al., "Secure integration of IoT and cloud computing," Future Generation Computer Systems, vol. 78, pp. 964-975, 2018.##I. Makhdoom, I. Zhou, M. Abolhasan, J. Lipman, and W. Ni, "PrivySharing: A blockchain-based framework for privacy-preserving and secure data sharing in smart cities," Computers & Security, vol. 88, p. 101653, 2020.##H. Deng, Z. Qin, L. Sha, and H. Yin, "A flexible privacy-preserving data sharing scheme in cloud-assisted IoT," IEEE Internet of Things Journal, vol. 7, no. 12, pp. 11601-11611, 2020.##P. Tedeschi, K. E. Jeon, J. She, S. Wong, S. Bakiras, and R. Di Pietro, "Privacy-preserving and sustainable contact tracing using batteryless BLE beacons," arXiv preprint arXiv:2103.06221, 2021.##F. Firouzi, B. Farahani, M. Barzegari, and M. Daneshmand, "AI-driven data monetization: The other face of data in IoT-based smart and connected health," IEEE Internet of Things Journal, 2020.##R. Goyat, G. Kumar, R. Saha, M. Conti, M. K. Rai, R. Thomas, M. Alazab, and T. H.-K. Kim, "Blockchain-based data storage with privacy and authentication in Internet-of-Things," IEEE Internet of Things Journal, 2020.##A. Ali, et al., "HealthLock: Blockchain-based privacy preservation using homomorphic encryption in Internet of Things healthcare applications," Sensors, vol. 23, no. 15, p. 6762, 2023.##S. Das and S. Namasudra, "Lightweight and efficient privacy-preserving mutual authentication scheme to secure Internet of Things-based smart healthcare," Transactions on Emerging Telecommunications Technologies, 2023, e4716.##H. N. Alsuqaih, et al., "An efficient privacy-preserving control mechanism based on blockchain for E-health applications," Alexandria Engineering Journal, vol. 73, pp. 159-172, 2023.##M. I. Ahmed and G. Kannan, "Secure and lightweight privacy-preserving Internet of Things integration for remote patient monitoring," Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 9, pp. 6895-6908, 2022.##M. P. Mahmoudi-Nasr and K. H. Kimia, "A mutual authentication method for Internet of Things", Signal and Data Processing, vol. 19, no. 2, pp. 6, 2022. [Online]. Available: http://jsdp.rcisp.ac.ir/article-1-1134-fa.html##[S. Azizi, M. Ashouri-Talouki, and H. Mala, "An efficient and secure frequent multiparty summation protocol", Signal and Data Processing, vol. 15, no. 4, pp. 31-40, 2019. [Online]. Available: http://jsdp.rcisp.ac.ir/article-1-649-fa.html##A. Janosi, W. Steinbrunn, M. Pfisterer, and R. Detrano, "Heart Disease," UCI Machine Learning Repository [Online]. Available: https://archive.ics.uci.edu/dataset/45/heart+disease. [Accessed: Mar. 15, 2024].##X. Wang, L. Bai, Q. Yang, L. Wang, and F. Jiang, "A dual privacy-preservation scheme for cloud-based eHealth systems," Journal of Information Security and Applications, vol. 47, pp. 132-138, 2019.##M. A. Uddin, A. Stranieri, I. Gondal, and V. Balasubramanian, "Blockchain leveraged decentralized IoT eHealth framework," Internet of Things, vol. 9, p. 100159, 2020.##R. Duan, M. R. Boland, Z. Liu, Y. Liu, H. H. Chang, H. Xu, H. Chu, et al., "Learning from electronic health records across multiple sites: A communication-efficient and privacy-preserving distributed algorithm," Journal of the American Medical Informatics Association, vol. 27, no. 3, pp. 376-385, 2020.##A. Majeed, "Attribute-centric anonymization scheme for improving user privacy and utility of publishing e-health data," Journal of King Saud University-Computer and Information Sciences, vol. 31, no. 4, pp. 426-435, 2019.##A. Donawa, I. Orukari, and C. E. Baker, "Scaling blockchains to support electronic health record systems for hospitals," arXiv preprint arXiv:2001.05525, 2020.##A. Zhang and X. Lin, "Towards secure and privacy-preserving data sharing in e-health systems via consortium blockchain," Journal of Medical Systems, vol. 42, no. 8, p. 140, 2018.##A. Botta, W. De Donato, V. Persico, and A. Pescapé, "Integration of cloud computing and internet of things: A survey," Future Generation Computer Systems, vol. 56, pp. 684-700, 2016.##C. Perera, A. Zaslavsky, P. Christen, and D. Georgakopoulos, "Sensing as a service model for smart cities supported by internet of things," Transactions on Emerging Telecommunications Technologies, vol. 25, no. 1, pp. 81-93, 2014.##A. Shahnaz, U. Qamar, and A. Khalid, "Using blockchain for electronic health records," IEEE Access, vol. 7, pp. 147782-147795, 2019.##D. Gachet, M. de Buenaga, F. Aparicio, and V. Padrón, "Integrating internet of things and cloud computing for health services provisioning: The virtual cloud carer project," in 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, pp. 918-921. IEEE, 2012.##Y. Zhang, M. Qiu, C.-W. Tsai, M. M. Hassan, and A. Alamri, "Health-CPS: Healthcare cyber-physical system assisted by cloud and big data," IEEE Systems Journal, vol. 11, no. 1, pp. 88-95, 2015.##N. Alharbe, A. S. Atkins, and J. Champion, "Use of cloud computing with wireless sensor networks in an Internet of Things environment for a smart hospital network," in Proceedings of the Seventh International Conference on eHealth, Telemedicine, and Social Medicine, Lisbon, Portugal, pp. 22-27, 2015.##J. Zhou, T. Leppanen, E. Harjula, M. Ylianttila, T. Ojala, C. Yu, H. Jin, and L. T. Yang, "Cloudthings: A common architecture for integrating the internet of things with cloud computing," in Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 651-657. IEEE, 2013.##S. M. R. Islam, D. Kwak, M. H. Kabir, M. Hossain, and K.-S. Kwak, "The internet of things for health care: A comprehensive survey," IEEE Access, vol. 3, pp. 678-708, 2015.##S. Nakamoto, Bitcoin: A peer-to-peer electronic cash system. Manubot, 2019.##F. Hussain and U. Qamar, "Identification and correction of misspelled drugs' names in electronic medical records (EMR)," in International Conference on Enterprise Information Systems, vol. 3, pp. 333-338. SCITEPRESS, 2016.##S. I. Goldberg, M. Shubina, A. Niemierko, and A. Turchin, "A weighty problem: Identification, characteristics and risk factors for errors in EMR data," in AMIA Annual Symposium Proceedings, vol. 2010, p. 251. American Medical Informatics Association, 2010.##E. A. Mohammed, B. H. Far, and C. Naugler, "Applications of the MapReduce programming framework to clinical big data analysis: Current landscape and future trends," BioData Mining, vol. 7, no. 1, pp. 1-23, 2014.##Y. Liang and A. Kelemen, "Big Data science and its applications in health and medical research: Challenges and opportunities," J Biom Biostat, vol. 7, no. 307, 2016.##W. J. Gordon and C. Catalini, "Blockchain technology for healthcare: Facilitating the transition to patient-driven interoperability," Computational and Structural Biotechnology Journal, vol. 16, pp. 224-230, 2018.##L. Castaldo and V. Cinque, "Blockchain-based logging for the cross-border exchange of ehealth data in Europe," International ISCIS Security Workshop. Springer, Cham, 2018.##D. C. Nguyen, et al., "Blockchain for secure EHRs sharing of mobile cloud based e-Health systems," IEEE Access, vol. 7, pp. 66792-66806, 2019.##M. Qazi, D. Kulkarni, and M. Nagori, "Proof of authenticity-based electronic medical records storage on blockchain," in Smart Trends in Computing and Communications, Springer, Singapore, pp. 297-306, 2020.##A. Zhang and X. Lin, "Towards secure and privacy-preserving data sharing in e-health systems via consortium blockchain," Journal of Medical Systems, vol. 42, no. 8, p. 140, 2018.##S. Tanwar, K. Parekh, and R. Evans, "Blockchain-based electronic healthcare record system for healthcare 4.0 applications," Journal of Information Security and Applications, vol. 50, p. 102407, 2020.##G. Tripathi, M. A. Ahad, and S. Paiva, "S2HS-A blockchain based approach for smart healthcare system," Healthcare, Elsevier, 2019.##E. Gorelik, "Cloud computing models," Doctoral dissertation, Massachusetts Institute of Technology, 2013.##C. Choi, J.-H. Park, M. Na, and S. Jo, "Low-latency 5G architectures for mission-critical Internet of Things (IoT) services," Information and Communications Magazine, vol. 32, no. 9, pp. 17-23, 2015.##H. Wu, K. Yue, C.-H. Hsu, Y. Zhao, B. Zhang, and G. Zhang, "Deviation-based neighborhood model for context-aware QoS prediction of cloud and IoT services," Future Generation Computer Systems, vol. 76, pp. 550-560, 2017.##P. Bonte, F. Ongenae, F. De Backere, J. Schaballie, D. Arndt, S. Verstichel, E. Mannens, R. Van de Walle, and F. De Turck, "The MASSIF platform: A modular and semantic platform for the development of flexible IoT services," Knowledge and Information Systems, vol. 51, no. 1, pp. 89-126, 2017.##T. D. Cao, H. H. Hoang, H. X. Huynh, B. M. Nguyen, T. V. Pham, Q. Tran-Minh, ... and H. L. Truong, "IoT services for solving critical problems in Vietnam: A research landscape and directions," IEEE Internet Computing, vol. 20, no. 5, pp. 76-81, 2016.##G. Gang, Z. L., and J. Jun, "Internet of things security analysis," in Internet Technology and Applications (iTAP), 2011 International Conference on, pp. 1-4. IEEE, 2011.##A. Whitmore, A. Agarwal, and L. Da Xu, "The Internet of Things-A survey of topics and trends," Information Systems Frontiers, pp. 1-14, 2014.##J. Guo, R. Chen, and J. J. P. Tsai, "A mobile cloud hierarchical trust management protocol for IoT systems," in Mobile Cloud Computing, Services, and Engineering (MobileCloud), 2017 5th IEEE International Conference on, IEEE, 2017.##Y. Liu, J. E. Fieldsend, and G. Min, "A framework of fog computing: Architecture, challenges, and optimization," IEEE Access, vol. 5, pp. 25445-25454, 2017.##H. Kim and E. A. Lee, "Authentication and authorization for the Internet of Things," IT Professional, vol. 19, no. 5, pp. 27-33, 2017.##M. Mahmud, et al., "A brain-inspired trust management model to assure security in a cloud based IoT framework for neuroscience applications," arXiv preprint arXiv:1801.03984, 2018.##R. K. Behera, K. H. K. Reddy, and D. S. Roy, "Reliability modelling of service oriented Internet of Things," in 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO)(Trends and Future Directions), IEEE, 2015.##V. Kharchenko, et al., "Reliability and security issues for IoT-based smart business center: Architecture and Markov model," in 2016 Third International Conference on Mathematics and Computers in Sciences and in Industry (MCSI), IEEE, 2016.##V. Sharma, et al., "A consensus framework for reliability and mitigation of zero-day attacks in IoT," Security and Communication Networks, 2017.##C. Stergiou, et al., "Secure integration of IoT and cloud computing," Future Generation Computer Systems, vol. 78, pp. 964-975, 2018.##I. Makhdoom, I. Zhou, M. Abolhasan, J. Lipman, and W. Ni, "PrivySharing: A blockchain-based framework for privacy-preserving and secure data sharing in smart cities," Computers & Security, vol. 88, p. 101653, 2020.##H. Deng, Z. Qin, L. Sha, and H. Yin, "A flexible privacy-preserving data sharing scheme in cloud-assisted IoT," IEEE Internet of Things Journal, vol. 7, no. 12, pp. 11601-11611, 2020.##P. Tedeschi, K. E. Jeon, J. She, S. Wong, S. Bakiras, and R. Di Pietro, "Privacy-preserving and sustainable contact tracing using batteryless BLE beacons," arXiv preprint arXiv:2103.06221, 2021.##F. Firouzi, B. Farahani, M. Barzegari, and M. Daneshmand, "AI-driven data monetization: The other face of data in IoT-based smart and connected health," IEEE Internet of Things Journal, 2020.##R. Goyat, G. Kumar, R. Saha, M. Conti, M. K. Rai, R. Thomas, M. Alazab, and T. H.-K. Kim, "Blockchain-based data storage with privacy and authentication in Internet-of-Things," IEEE Internet of Things Journal, 2020.##A. Ali, et al., "HealthLock: Blockchain-based privacy preservation using homomorphic encryption in Internet of Things healthcare applications," Sensors, vol. 23, no. 15, p. 6762, 2023.##S. Das and S. Namasudra, "Lightweight and efficient privacy-preserving mutual authentication scheme to secure Internet of Things-based smart healthcare," Transactions on Emerging Telecommunications Technologies, 2023, e4716.##H. N. Alsuqaih, et al., "An efficient privacy-preserving control mechanism based on blockchain for E-health applications," Alexandria Engineering Journal, vol. 73, pp. 159-172, 2023.##M. I. Ahmed and G. Kannan, "Secure and lightweight privacy-preserving Internet of Things integration for remote patient monitoring," Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 9, pp. 6895-6908, 2022.##M. P. Mahmoudi-Nasr and K. H. Kimia, "A mutual authentication method for Internet of Things", Signal and Data Processing, vol. 19, no. 2, pp. 6, 2022. [Online]. Available: http://jsdp.rcisp.ac.ir/article-1-1134-fa.html##[S. Azizi, M. Ashouri-Talouki, and H. Mala, "An efficient and secure frequent multiparty summation protocol", Signal and Data Processing, vol. 15, no. 4, pp. 31-40, 2019. [Online]. Available: http://jsdp.rcisp.ac.ir/article-1-649-fa.html##A. Janosi, W. Steinbrunn, M. Pfisterer, and R. Detrano, "Heart Disease," UCI Machine Learning Repository [Online]. Available: https://archive.ics.uci.edu/dataset/45/heart+disease. [Accessed: Mar. 15, 2024]. ##