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1st International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2022

作者： Kazemi, Alireza Boostani, Reza Odeh, Mahmoud Al-Mousa, Mohammad Rasmi Shiraz University Shiraz Faculty of Science Mathematics Department Fars Iran Electrical and Computer Engineering School Shiraz University Shiraz CSEandIT Dept. Fars Iran Zarqa University Department of Management Information System Jordan Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan

ISBN: (纸本)9798350332742

Support Vector Machine (SVM) is originally a binary large-margin classifier emerged from the concept of structural risk minimization. Multiple solutions such as one-versus-one and one-versus-all have been proposed for creating multi-class SVM using elementary binary SVMs. Also multiple solutions have been proposed for SVM model selection, adjusting margin-parameter C and the Gaussian kernel variance. Here, an improved classifier named SVM-SVM is proposed for multi-class problems which increases accuracy and decreases dependency to margin-parameter selection. SVM-SVM adopts two K-class one-vs-one SVMs in a cascaded two-layer structure. In the first layer, input features are fed to one-vs-one SVM with non-linear kernels. We introduce this layer as a large-margin non-linear feature transform that maps input feature space to a discriminative K(K-1)/2 dimensional space. To assess our hierarchical classifier, some datasets from the UCI repository are evaluated. Standard one-vs-one SVM and one-vs-one fuzzy SVM are used as reference classifiers in experiments. Results show significant improvements of our proposed method in terms of test accuracy and robustness to the model (margin and kernel) parameters in comparison with the reference classifiers. Our observations suggest that a multi-layer (deep) SVM structures can gain the same benefits as is seen in the deep neural nets (DNNs). © 2022 IEEE.

关键词： Support vector machines

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An adaptive system for detecting malicious queries in web attacks

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Science China(Information Sciences) 2018年第3期61卷 175-190页

作者： Ying DONG Yuqing ZHANG Hua MA Qianru WU Qixu LIU Kai WANG Wenjie WANG National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of Sciences School of Mathematics and Statistics Xidian University Security Department Alibaba Group Zhanlu Laboratory Tencent Incorporation

Web request query strings(queries), which pass parameters to a referenced resource, are always manipulated by attackers to retrieve sensitive data and even take full control of victim web servers and web applications. However, existing malicious query detection approaches in the literature cannot cope with changing web attacks. In this paper, we introduce a novel adaptive system(AMOD) that can adaptively detect web-based code injection attacks, which are the majority of web attacks, by analyzing queries. We also present a new adaptive learning strategy, called SVM HYBRID, leveraged by our system to minimize manual work. In the evaluation, an up-to-date detection model is trained on a ten-day query dataset collected from an academic institute's web server logs. The evaluation shows our approach overwhelms existing approaches in two respects. Firstly, AMOD outperforms existing web attack detection methods with an F-value of 99.50%and FP rate of 0.001%. Secondly, the total number of malicious queries obtained by SVM HYBRID is3.07 times that by the popular support vector machine adaptive learning(SVM AL) method. The malicious queries obtained can be used to update the web application firewall(WAF) signature library.

关键词： web attacks adaptive learning intrusion detection anomaly detection SVM

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Bending of a circular flexible plate taking into account the initial curvature 49

Bending of a circular flexible plate taking into account the...

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49th International Conference on Vibroengineering

作者： Mamatova, G.U. Sugirbekova, A.K. Bekaulov, N.M. Department of Chemical Physics and Materials Science Al-Farabi Kazakh National University Almaty050040 Kazakhstan Department of Chair of Mathematics Physics and Informatics Teaching Methods Abai Kazakh National Pedagogical University Almaty050010 Kazakhstan Department of Computer Engineering and Information Security International IT University Almaty050000 Kazakhstan

In this article, an analytical solution of the problem of bending a round plate with an initial curvature is obtained. A round plate with an initial curvature as a structural element is widely used, the calculation of which leads to many questions related to the design of round Foundation plates, turbine disks, flexible shaft connections and other. The nonlinear theory of round flexible plates provides the key to explaining the process of loss of stability, which often leads to complete destruction of the structure. Therefore, there is a high need for analytical methods for solving problems of calculating the stress-strain state of round flexible plates, taking into account the initial curvature. These problems are mathematically reduced to differential equations with variable coefficients, the exact solution of which, as a rule, does not exist. Therefore, the construction of analytical solutions to these problems is very relevant. Copyright © 2020 G. U. Mamatova, et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

关键词： Differential equations

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Set Stabilization of Large-Scale Stochastic Boolean Networks:A Distributed Control Strategy

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 806-808页

作者： Lin Lin Jinde Cao Jianquan Lu Leszek Rutkowski the Department of Mechanical Engineering the University of Hong KongHong Kong China the School of Mathematics Southeast UniversityNanjing 210096China IEEE the School of Mathematics and the Frontiers Science Center for Mobile Information Communication and Security Southeast UniversityNanjing 210096China the School of Mathematics the Southeast UniversityNanjing 210096 the School of Electronic Information and Electrical Engineering Chengdu UniversityChengdu 610106China the Systems Research Institute of the Polish Academy of Sciences Warsaw 01-447 the Institute of Computer Science AGH University of Science and TechnologyKraków 30-059 the Information Technology Institute University of Social SciencesŁódź90-113Poland

Dear Editor,This letter deals with the set stabilization of stochastic Boolean control networks(SBCNs)by the pinning control strategy,which is to realize the full control for systems by imposing control inputs on a fr... 详细信息

关键词： Boolean stabilization letter

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Pioneering Advanced Deep Multi-Task Learning Model for Precise COVID-19 Diagnosis 2

Pioneering Advanced Deep Multi-Task Learning Model for Preci...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Kordnoori, Shirin Alqammaz, Abdullah Sabeti, Maliheh Boostani, Reza Mostafaei, Hamidreza Agha Banihashemi, Saeed Seyed Islamic Azad University North Tehran Branch Department of Computer Engineering Tehran Iran Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Shiraz University Faculty of Electrical and Computer Engineering Cse and It Departmnet Shiraz Iran Islamic Azad University Shiraz University North Tehran Branch Department of Statistics Tehran Iran Islamic Azad University North Tehran Branch Department of Mathematics Tehran Iran

ISBN: (纸本)9798350373363

To address the urgent need for intelligent systems capable of simultaneously identifying COVID-19 cases and delineating lung infection regions in CT scan images, we introduce a multi-task architecture. Initially, our model, an enhanced U-Net with a classification branch, aims to balance segmentation and classification tasks using binary preprocessing algorithms. The CBAM attention module further improves task coherence. Rigorous evaluation demonstrates our model's superiority over previous studies, achieving an impressive 95% classification accuracy and a Dice index of 88.37 ± 0.01 for segmentation, supported by robust metrics. We also extend our multi-task framework to complex U-Net++ and ResUNet architectures with positive outcomes. Future exploration includes integrating ensemble learning within this framework. Our work signifies significant progress in enhancing diagnostic capabilities in complex medical image analysis, elevating standards for COVID-19 detection and lung infection analysis through multi-task learning and shared feature extraction. © 2023 IEEE.

关键词： COVID-19

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Chimp Optimization Algorithm Based Feature Selection with Machine Learning for Medical Data Classification

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computer Systems Science & engineering 2023年第12期47卷 2791-2814页

作者： Firas Abedi Hayder M.A.Ghanimi Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Ali Hashim Abbas Zahraa H.Kareem Hussein Muhi Hariz Ahmed Alkhayyat Department of Mathematics College of EducationAl-Zahraa University for WomenKarbalaIraq Biomedical Engineering Department College of EngineeringUniversity ofWarith Al-AnbiyaaKarbalaIraq Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt College of Information Technology Imam Jaafar Al-Sadiq UniversityAl-Muthanna66002Iraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Computer Engineering Department Mazaya University CollegeDhi QarIraq College of Technical Engineering the Islamic UniversityNajafIraq

Datamining plays a crucial role in extractingmeaningful knowledge fromlarge-scale data repositories,such as data warehouses and *** rule mining,a fundamental process in data mining,involves discovering correlations,patterns,and causal structures within *** the healthcare domain,association rules offer valuable opportunities for building knowledge bases,enabling intelligent diagnoses,and extracting invaluable information *** paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System(MLARMC-HDMS).The MLARMC-HDMS technique integrates classification and association rule mining(ARM)***,the chimp optimization algorithm-based feature selection(COAFS)technique is employed within MLARMC-HDMS to select relevant *** by the foraging behavior of chimpanzees,the COA algorithm mimics their search strategy for ***,the classification process utilizes stochastic gradient descent with a multilayer perceptron(SGD-MLP)model,while the Apriori algorithm determines attribute *** propose a COA-based feature selection approach for medical data classification using machine learning *** approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature *** evaluate the performance of our approach on various medical datasets employing diverse machine learning *** results demonstrate that our proposed approach surpasses alternative feature selection methods,achieving higher accuracy and precision rates in medical data classification *** study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features,thereby enhancing the diagnosis and treatment of various *** provide further validation,we conduct detailed experiments on a benchmark

关键词： Association rule mining data classification healthcare data machine learning parameter tuning data mining feature selection MLARMC-HDMS COA stochastic gradient descent Apriori algorithm

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On an attack on RSA with small CRT-exponents

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Science China(Information Sciences) 2010年第8期53卷 1511-1518页

作者： HAN LiDong 1,2 , WANG XiaoYun 3 & XU GuangWu 3,4 1 Key Laboratory of Cryptologic Technology and Information security, Ministry of Education, Shandong University, Jinan 250100, China 2 School of mathematics, Shandong University, Jinan 250100, China 3 Institute for Advanced Study, Tsinghua University, Beijing 100084, China 4 department of Electrical engineering and computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA 1. Key Laboratory of Cryptologic Technology and Information Security Ministry of Education Shandong University Jinan 250100 China2. School of Mathematics Shandong University Jinan 250100 China3. Institute for Advanced Study Tsinghua University Beijing 100084 China4. Department of Electrical Engineering and Computer Science University of Wisconsin-Milwaukee Milwaukee WI 53201 USA

This paper concerns the RSA system with private CRT-exponents. Since Chinese remainder representation provides efficiency in computation, such system is of some practical significance. In this paper, an existing attack to small private CRT-exponents is analyzed. It is indicated that this attack makes nice use of lattice in RSA analysis, but some argument does not hold in general. Several counterexamples are constructed. Refinements and more precise statements of the attack are given.

关键词： RSA CRT cryptanalysis continued fraction lattice reduction

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Detection of Liver Disease Using Machine Learning Approach 5

Detection of Liver Disease Using Machine Learning Approach

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Kshirsagar, Pravin Ramdas Reddy, Dhoma Harshavardhan Dhingra, Mallika Dhabliya, Dharmesh Gupta, Ankur G H Raisoni College of Engineering Department of Artificial Intelligence Maharashtra Nagpur India Trainee Security Analyst JunoClinic Davman Technology Services Private Limited Maharashtra Mumbai India Manipal University Jaipur Department of Mathematics and Statistics Rajasthan Jaipur India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Vaish College of Engineering Department of Computer Science and Engineering Haryana Rohtak India

ISBN: (纸本)9798350398267

For more effective therapy, it's critical to get an early diagnosis of liver illness. Due to the disease's modest symptoms, it is a very difficult challenge for medical experts to forecast the disease in its early stages. Frequently, the symptoms show up only when it's too late. This study uses machine learning techniques to enhance the detection of liver illness in an effort to solve this problem. The major goal of this study is to distinguish between liver patients and healthy people using classification algorithms. The prevalence of liver illness has been rising globally in the twenty-first century. According to the most recent survey data, the death rate from liver disease has increased by almost 2 million per year globally. 3.5% of deaths globally are caused by liver disease generally. As one of the most fatal diseases, chronic liver disease can be readily cured with early diagnosis and therapy. The lifespan of a patient with Chronic Liver Disease (CLD) would increase due to the rapid development of artificial intelligence (AI), including various machine learning algorithms like SVM, K-mean clustering, KNN, Random forest, Logistic regression, etc. The foundation of this research is the use of machine learning algorithms to predict liver disease. Pre-processing, feature extraction, and classification are just a few of the stages that go into predicting liver disease. In this study, a hybrid classification system is suggested for predicting liver illness. © 2022 IEEE.

关键词： Logistic regression

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A Scalable Platform to Collect, Store, Visualize and Analyze Big Data in Real- Time 3

A Scalable Platform to Collect, Store, Visualize and Analyze...

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Kshirsagar, Pravin Ramdas Reddy, Dhoma Harshavardhan Dhingra, Mallika Dhabliya, Dharmesh Gupta, Ankur S B Jain College of Engineering and Technology Department of Artificial Intelligence Maharashtra Nagpur India Trainee Security Analyst JunoClinic Davman Technology Services Private Limited Maharashtra Mumbai India Manipal University Jaipur Department of Mathematics and Statistics Rajasthan Jaipur India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Vaish College of Engineering Department of Computer Science and Engineering Haryana Rohtak India

ISBN: (纸本)9798350336238

Examination and evaluation of sentiment The most popular area for dissecting and extracting knowledge from communication data from many sources, such as Facebook, Instagram, Twitter, Amazon, and so on, is known as mining. It plays a fundamental role in enabling the organizations to work productively on advancing the business system and gaining the most comprehensive understanding of the buyer's feedback on their product. It entails calculating a person's behavior in terms of his buying preferences and then placing yourself in his shoes about the commercial aspect of a relationship. You may think of this component as a situation, person, blog post, or tangible experience. Appraisals, checks, and Reflections created by customers may be divided into smaller, more notable pieces for usage by large businesses. The analysis of comparable purchasing behavior may be used to understand the needs of the customer and foresee future opportunities to assist them. E-business Associations may track the uses and passions associated with their Particulars via this internal assessment and adopt better marketing mechanism to give a tailored shopping experience for their customers, so improving their hierarchical advantage. By using the Programming interface key, Twitter data for this article was collected from Twitter. Similarly, we really want to finish setting up the systems. Additionally, the system supported NLP approaches, thus we wish to implement the calculation as a crucial backslide. The exploratory problems exhibit perfection. © 2023 IEEE.

关键词： Big data

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Efficient Privacy-Preserving ML for IoT: Cluster-Based Split Federated Learning Scheme for Non-IID Data 7

Efficient Privacy-Preserving ML for IoT: Cluster-Based Split...

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7th Cyber security in Networking Conference, CSNet 2023

作者： Arafeh, Mohamad Wazzeh, Mohamad Ould-Slimane, Hakima Talhi, Chamseddine Mourad, Azzam Otrok, Hadi Department of Software and It Engineering Montreal Canada Department of Mathematics and Computer Science Canada New York University Division of Science Abu Dhabi United Arab Emirates Cyber Security Systems and Applied Ai Research Center Lebanese American University Department of Csm Beirut Lebanon Khalifa University Department of Eecs Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350342871

This paper proposes a scheme addressing the challenges of integrating privacy-preserving distributed machine learning in the Internet of Things (IoT) context while improving the efficiency of the learning process and accelerating the convergence of the model. Specifically, it addresses the presence of non-independent and identically distributed (Non-IID) data and device heterogeneity, which occurs during parallel training and are common characteristics of IoT environments. In this context, SplitFed is a privacy-preserving technique that combines Federated Learning (FL) and Split Learning (SL) to enable fast and privacy-preserving model training on IoT devices. Through parallelism, FL allows fast and parallel model training. Furthermore, SL enables weak devices to train models through model splitting. However, SplitFed suffers from the increasing convergence time due to the FL parallelism introducing Non-IID and stragglers issues. In this paper, we aim to address the aforementioned problems by proposing a clustering parallelization scheme. As such, similar clients are combined based on their weight divergence and considered a single FL unit addressing the Non-IID problem. Moreover, to improve time efficiency and overcome the stragglers' presence, we offer a second clustering stage that separates clients by efficiency and uses FedAsync for model aggregation. © 2023 IEEE.

关键词： Internet of things

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