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Vibrating Particles System Algorithm for Solving Classification Problems

computer systems science & Engineering 2022年第12期43卷 1189-1206页

作者： Mohammad Wedyan Omar Elshaweesh Enas Ramadan Ryan Alturki Department of Autonomous Systems Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan Department of Software Engineering Information Technology CollegeAL-Hussein bin Talal UniversityMa’an71111Jordan Department of Computer Science Prince Abdullah Bin Ghazi Faculty of Communication and Information TechnologyAl-Balqa Applied UniversityAl-Salt19117Jordan Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21961Saudi Arabia

Big data is a term that refers to a set of data that,due to its largeness or complexity,cannot be stored or processed with one of the usual tools or applications for data management,and it has become a prominent word in recent years for the massive development of *** immediately thereafter,the term“big data mining”emerged,i.e.,mining from big data even as an emerging and interconnected field of *** is an important stage in data mining since it helps people make better decisions in a variety of situations,including scientific endeavors,biomedical research,and industrial *** probabilistic neural network(PNN)is a commonly used and successful method for handling classification and pattern recognition *** this study,the authors proposed to combine the probabilistic neural network(PPN),which is one of the data mining techniques,with the vibrating particles system(VPS),which is one of the metaheuristic algorithms named“VPS-PNN”,to solve classi-fication problems more *** data set is eleven common benchmark medical datasets from the machine-learning library,the suggested method was *** suggested VPS-PNN mechanism outperforms the PNN,biogeography-based optimization,enhanced-water cycle algorithm(E-WCA)and the firefly algorithm(FA)in terms of convergence speed and classification accuracy.

关键词： Vibrating particles system(VPS) probabilistic neural network(PNN) classification problem data mining

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A hybrid machine learning approach for analysis of stegomalware

International Journal of Industrial Engineering and Operatio...

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International Journal of Industrial Engineering and Operations Management 2023年第2期5卷 104-117页

作者： Kadebu, Prudence Shoniwa, Robert T.R. Zvarevashe, Kudakwashe Mukwazvure, Addlight Mapanga, Innocent Thusabantu, Nyasha Fadzai Gotora, Tatenda Trust Department of Computer Engineering University of Zimbabwe Harare Zimbabwe Zimbabwe Information and Communication Technology Harare Zimbabwe Victoria Institute of Technology Adelaide Australia A Division of Zimbabwe Institution of Engineers Zimbabwe Information and Communication Technology Harare Zimbabwe Department of Academics Harare Institute of Technology Belvedere Zimbabwe Department of Computer Science Midlands State University Gweru Zimbabwe Department of Information Systems Women's University in Africa Harare Zimbabwe

Purpose: Given how smart today’s malware authors have become through employing highly sophisticated techniques, it is only logical that methods be developed to combat the most potent threats, particularly where the malware is stealthy and makes indicators of compromise (IOC) difficult to detect. After the analysis is completed, the output can be employed to detect and then counteract the attack. The goal of this work is to propose a machine learning approach to improve malware detection by combining the strengths of both supervised and unsupervised machine learning techniques. This study is essential as malware has certainly become ubiquitous as cyber-criminals use it to attack systems in cyberspace. Malware analysis is required to reveal hidden IOC, to comprehend the attacker’s goal and the severity of the damage and to find vulnerabilities within the system. Design/methodology/approach: This research proposes a hybrid approach for dynamic and static malware analysis that combines unsupervised and supervised machine learning algorithms and goes on to show how Malware exploiting steganography can be exposed. Findings: The tactics used by malware developers to circumvent detection are becoming more advanced with steganography becoming a popular technique applied in obfuscation to evade mechanisms for detection. Malware analysis continues to call for continuous improvement of existing techniques. State-of-the-art approaches applying machine learning have become increasingly popular with highly promising results. Originality/value: Cyber security researchers globally are grappling with devising innovative strategies to identify and defend against the threat of extremely sophisticated malware attacks on key infrastructure containing sensitive data. The process of detecting the presence of malware requires expertise in malware analysis. Applying intelligent methods to this process can aid practitioners in identifying malware’s behaviour and features. This is especially e

关键词： Machine learning Malware analysis Malware detection Steganography Stegomalware

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Edge-Driven Industrial Computing Power Networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

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Reduced-Complexity Approach for Opacity Enforcement Based on Extended Insertion Functions with Limited Insertion Events 20

Reduced-Complexity Approach for Opacity Enforcement Based on...

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20th IEEE International Conference on Networking, Sensing and Control, ICNSC 2023

作者： Li, Xiaoyan Hadjicostis, Christoforos N. Li, Zhiwu North University of China School of Information and Communication Engineering Taiyuan China University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus Macau University of Science and Technology Institute of Systems Engineering China

ISBN: (纸本)9798350369502

Opacity is a security and privacy property that guarantees predefined secret behavior of a cyber-physical system will not be inferred with certainty by a malicious entity. If a system is opacity-violating, extended insertion functions that insert fake symbols can be implemented to enforce opacity. By constricting the ways in which the fake symbols are inserted, this paper addresses opacity enforcement under the framework of finite state automata using extended insertion functions under event insertion constraints and limited insertion events constraints, i.e., only a limited number of symbols in a specified set can be inserted before and after an actual system output. The notion of (kb,ka)-EIC-enforceability is proposed to describe how to enforce opacity via extended insertion functions in the presence of both event insertion constraints and limited insertion events constraints. A necessary and sufficient condition to check (kb,ka)-EIC-enforceability is derived by constructing an appropriate verifier. © 2023 IEEE.

关键词： Opacity

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Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Disease Prediction

Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Dis...

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2023 International Conference on Energy, Materials and communication Engineering, ICEMCE 2023

作者： Karthikeyan, D. Nivasini, R. Raja, Kiruba Thangam Madhivadhani, D. Sivakumar, B. Krishna Priya, A.T.R. Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur Chennai India Iitm Research Park Guvi Geek Networks Tamilnadu Chennai India Vit University School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Vellore India Kings Engineering College Department of Electronics and Communication Engineering Sriperumbudur Chennai India Rohini College of Engineering and Technology Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798350393378

Magnetic resonance imaging (MRI) has been used to study the structural makeup of the brain and analyse several neurological disorders and diseased areas. For the adoption of preventative measures, early recognition of Alzheimer's disease (AD) patients is essential. Here, a thorough inspection of the tissue arrangements obtained by MRI images of Outcome and Assessment Information (OASIS) dataset enables an exact characterization of certain brain diseases. There have been a number of division techniques for diagnosing AD that range in complexity. Compassion has been tested by deep learning techniques used to segment the structure of the brain and classify AD because they have the potential to uncover important information from vast amounts of data. In this paper the deep learning technique of Hybrid Dragonfly based GWO convolutional Neural Network (CNN) is achieved promising result for the diagnosis of AD. At image preprocessing wiener filter is used for removing the additive noises and the Gray Level Co-Occurrence Matrix (GLCM) extraction is implemented for texture analyzing. As a result, hybrid deep learning methods of CNN has the accuracy as 90% and the result of the image prediction are presented in this paper. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Deep learning and encryption algorithms based model for enhancing biometric security for artificial intelligence era

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Journal of Ambient Intelligence and Humanized Computing 2024年 1-14页

作者： Byeon, Haewon Shabaz, Mohammad Surbakti, Herison Keshta, Ismail Soni, Mukesh Bhatnagar, Vaibhav Department of Digital Anti-Aging Healthcare Inje University Gimhae50834 Korea Republic of Model Institute of Engineering and Technology J&K Jammu India Information and Communication Technology Rangsit University International College Pathumthani Lak Hok Thailand Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of CSE University Centre for Research & ampDevelopment Chandigarh University Punjab Mohali140413 India Department of Computer Applications Manipal University Jaipur Jaipur India

The significance of facial recognition in the era of artificial intelligence lies in its utilization of facial features as a type of biometric characteristic possessing uniqueness and irreversibility. However, exposing these features to attacks, tampering, or unauthorized disclosure poses considerable threats to user privacy and security. A privacy and security solution based on deep learning and encryption algorithms is proposed to tackle this issue. This solution employs the FaceNet deep learning algorithm to extract facial features efficiently. The combination of biometric feature blurriness and cryptographic system precision is achieved, utilizing the CKKS fully homomorphic encryption algorithm for operations in the ciphertext domain of facial recognition. The SM4 algorithm is used to enhance the resilience of facial feature ciphertext against malicious attacks. By leveraging the properties of symmetric ciphers, a balance is achieved between security and computational efficiency. The management of the symmetric key used in the SM4 algorithm is conducted through the employment of the SM9 asymmetric encryption algorithm. Experimental results and analysis demonstrate that the proposed solution enhances the security of data transmission, storage, and comparison without compromising the accuracy and efficiency of facial recognition. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Differential privacy

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Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Hanzel, Krzysztof Grzechca, Damian Ziebinski, Adam Chruszczyk, Lukas Janus, Artur Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Telecommunications and Teleinformatics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Electronics Electrical Engineering and Microelectronics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice Poland Silesian University of Technology Student of Electrical EngineeringFaculty of Electrical Engineering Gliwice Poland

ISBN: (纸本)9798350324457

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested;however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV - so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime. © 2023 IEEE.

关键词： AGV load estimation machine learning Prediction of energy consumption random forest

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Database Deadlock Diagnosis for Large-Scale ORM-Based Web Applications

Database Deadlock Diagnosis for Large-Scale ORM-Based Web Ap...

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International Conference on Data Engineering

作者： Zhiyuan Dong Zhaoguo Wang Chuanwei Yi Xian Xu Jinyuan Zhang Jinyang Li Haibo Chen Institute of Parallel and Distributed Systems Shanghai Jiao Tong University Department of Computer Science New York University

Today, most database-backed web applications depend on the database to handle deadlocks. At runtime, the database monitors the progress of transaction execution to detect deadlocks and abort affected transactions. However, this common detect-and-recover strategy is costly to performance as aborted transactions waste CPU *** avoid deadlock-induced performance degradation, developers aim to reorganize the application code to remove deadlocks. Unfortunately, doing so is difficult for web applications. Not only do their implementations include hundreds of thousands of LoCs, but they also use third-party object-relational mapping (ORM) frameworks which hide database access details. Consequently, it is hard for developers to accurately diagnose *** propose WeSEER, a deadlock diagnosis tool for web applications. To overcome the opacity of ORMs, WeSEER performs concolic execution on unit tests to extract a web application’s transactions as a sequence of template statements with symbolic inputs as well as path conditions that enable the sequence. WeSEER then analyzes the extracted transactions based on fine-grained lock modeling to identify potential deadlocks and report the code locations that cause them. We implement WeSEER for Java-based (OpenJDK) web applications, and use it to analyze two popular open-source e-commerce applications, Broadleaf and Shopizer. WeSEER has successfully identified 18 potential deadlocks in Broadleaf and Shopizer. Eliminating these identified deadlocks can result in up to 39.5× and 4.5× throughput improvement for Broadleaf and Shopizer, respectively.

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Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization

Efficiently Assemble Normalization Layers and Regularization...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Khiem Le Long Ho Cuong Do Danh Le-Phuoc Kok-Seng Wong Department of Computer Science and Engineering University of Notre Dame IN USA College of Engineering and Computer Science Vin University Hanoi Vietnam Open Distributed Systems Technical University Berlin Berlin Germany

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Domain shift is a formidable issue in Machine Learning that causes a model to suffer from performance degradation when tested on unseen domains. Federated Domain Gener-alization (FedDG) attempts to train a global model using collaborative clients in a privacy-preserving manner that can generalize well to unseen clients possibly with domain shift. However, most existing FedDG methods either cause additional privacy risks of data leakage or induce signifi-cant costs in client communication and computation, which are major concerns in the Federated Learning paradigm. To circumvent these challenges, here we introduce a novel architectural method for FedDG, namely gPerXAN 1 1 https://***/lhkhiem28/gPerXAN, which relies on a normalization scheme working with a guiding regularizer: In particular, we carefully design Personalized eXplicitly Assembled Normalization to enforce client mod-els selectively filtering domain-specific features that are bi-ased towards local data while retaining discrimination of those features. Then, we incorporate a simple yet effec-tive regularizer to guide these models in directly capturing domain-invariant representations that the global model's classifier can leverage. Extensive experimental results on two benchmark datasets, i.e., PACS and Office-Home, and a real-world medical dataset, Camelyon17, indicate that our proposed method outperforms other existing methods in ad-dressing this particular problem.

关键词： Degradation Data privacy computer vision Costs Filtering Federated learning Computational modeling

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A Novel Peak-to-Average Power Ratio Reduction for 5G Advanced Waveforms

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computers, Materials & Continua 2022年第10期73卷 1637-1648页

作者： Rajneesh Pareek Karthikeyan Rajagopal Himanshu Sharma Nidhi Gour Arun Kumar Sami Althahabi Haya Mesfer Alshahrani Mohamed Mousa Manar Ahmed Hamza Department of Computer Science&Engineering JECRC UniversityJaipur303905India Centre for Nonlinear Systems Chennai Institute of TechnologyChennaiIndia Department of Electronics and Communication JECRC UniversityJaipur303905India Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Electrical Engineering Department Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Multi and single carrier waveforms are utilized in cellular systems for high-speed data *** The Fifth Generation(5G)system,several waveform techniques based on multi carrier waveforms are ***,the Peak to Average Power Ratio(PAPR)is seen as one of the significant concerns in advanced waveforms as it degrades the efficiency of the *** proposed article documents the study,progress,and implementation of PAPR reduction algorithms for the 5G radio *** compare the PAPR algorithm performance for advanced and conventional *** simulation results reveal that the advanced Partial Transmission Sequence(PTS)and Selective Mapping(SLM)methods enhanced the throughput and gain of the 5G ***,we have also analyzed the performance of Orthogonal Time Frequency Space Modulation(OTFSM)based on a single carrier system and found that PAPR is significantly low and is best suited to fading *** is seen that the conventional algorithms lower the PAPR but increase the *** proposed PTS and SLM have shown good performance with low computational complexity.

关键词： PAPR 5G OFDM NOMA OTFSM PTS SLM

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