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Color Image Compression and Encryption Algorithm Based on 2D Compressed Sensing and Hyperchaotic System

computers, Materials & Continua 2024年第2期78卷 1977-1993页

作者： Zhiqing Dong Zhao Zhang Hongyan Zhou Xuebo Chen School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China School of Electronic and Information Engineering University of Science and Technology LiaoningAnshan114051China

With the advent of the information security era,it is necessary to guarantee the privacy,accuracy,and dependable transfer of *** study presents a new approach to the encryption and compression of color *** is predicated on 2D compressed sensing(CS)and the hyperchaotic ***,an optimized Arnold scrambling algorithm is applied to the initial color images to ensure strong ***,the processed images are con-currently encrypted and compressed using 2D *** them,chaotic sequences replace traditional random measurement matrices to increase the system’s ***,the processed images are re-encrypted using a combination of permutation and diffusion *** addition,the 2D projected gradient with an embedding decryption(2DPG-ED)algorithm is used to reconstruct *** with the traditional reconstruction algorithm,the 2DPG-ED algorithm can improve security and reduce computational ***,it has better *** experimental outcome and the performance analysis indicate that this algorithm can withstand malicious attacks and prove the method is effective.

关键词： Image encryption image compression hyperchaotic system compressed sensing

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A Novel Approach to Energy Optimization:Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

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computers, Materials & Continua 2024年第5期79卷 2945-2970页

作者： Muhammad Salman Qamar Ihsan ulHaq Amil Daraz Atif MAlamri Salman A.AlQahtani Muhammad Fahad Munir Department of Electrical and Computer Engineering International Islamic UniversityIslamabad44000Pakistan School of Information Science and Engineering NingboTech UniversityNingbo315100China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia

In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing ***,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable *** data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network *** mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring *** unique determination of this study is the shortest path to reach *** the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static *** this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the *** methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide *** addition,a method of using MS scheduling for efficient data collection is *** simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

关键词： Wireless Sensor Networks(WSNs) mobile sink(MS) rendezvous point(RP) machine learning Artificial Neural Networks(ANNs)

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Classifying Multi-Lingual Reviews Sentiment Analysis in Arabic and English Languages Using the Stochastic Gradient Descent Model

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computers, Materials & Continua 2025年第4期83卷 1275-1290页

作者： Yasser Alharbi Sarwar Shah Khan College of Computer Science and Engineering University of HailHail55436Saudi Arabia Department of Computer Science University of Engineering&TechnologyMardan23200Pakistan

Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the ***,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic *** study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie *** allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language *** adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better *** distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for *** proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and *** SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both *** indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English *** study helps deepen the understanding of sentiments across various linguistic *** many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.

关键词： Sentiment analysis stochastic gradient descent reviews English IMDb dataset Arabic dataset

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Formal Verification of Data Modifications in Cloud Block Storage Based on Separation Logic

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Chinese Journal of Electronics 2024年第1期33卷 112-127页

作者： Bowen ZHANG Zhao JIN Hanpin WANG Yongzhi CAO Key Laboratory of High Confidence Software Technologies (MOE) School of Computer Science Peking University School of Computer and Artificial Intelligence Zhengzhou University School of Computer Science and Cyber Engineering Guangzhou University

Cloud storage is now widely used, but its reliability has always been a major concern. Cloud block storage(CBS) is a famous type of cloud storage. It has the closest architecture to the underlying storage and can provide interfaces for other types. Data modifications in CBS have potential risks such as null reference or data *** verification of these operations can improve the reliability of CBS to some extent. Although separation logic is a mainstream approach to verifying program correctness, the complex architecture of CBS creates some challenges for verifications. This paper develops a proof system based on separation logic for verifying the CBS data modifications. The proof system can represent the CBS architecture, describe the properties of the CBS system state, and specify the behavior of CBS data modifications. Using the interactive verification approach from Coq, the proof system is implemented as a verification tool. With this tool, the paper builds machine-checked proofs for the functional correctness of CBS data modifications. This work can thus analyze the reliability of cloud storage from a formal perspective.

关键词： Formal verification Separation logic Cloud block storage Verification tool Coq

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QoS-Aware Application Assignment and Resource Utilisation Maximization Using AHP in Edge Computing

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IEEE Internet of Things Journal 2025年第11期12卷 17717-17728页

作者： Koganti, Yasasvitha Sridhar, Vidhyuth Yadav, Ram Narayan Pratap, Ajay Iitram Department of Electrical and Computer Science Engineering Ahmedabad380026 India Indiana University Department of Computer Science and Engineering Bloomington United States Banaras Hindu University Indian Institute of Technology Department of Computer Science and Engineering Varanasi India

Edge computing has emerged as a promising technology to satisfy the demand for data computational resources in Internet of Things (IoT) networks. With edge computing, processing of the massive data-intensive tasks can be done in the proximity of IoT users. Thus, the required constraints related to tasks such as latency requirements, and the Quality Of Service (QoS) can be guaranteed. However, the question that how to determine the task offloading strategy under various constraints of resources, distance, and cost is still an open issue. In this paper, we study the task offloading problem from a matching perspective and propose an Edge-User Assignment Algorithm (EUAA) that aims to maximize the resource utilization of edge servers and maximize the number of assigned IoT users. The main concern, in any matching algorithm, is how to generate the preference order of either side. To generate preference orders for edge servers, we use the concept of the Analytical Hierarchy Process (AHP). We have considered the following criteria: distance from users to the server, latency, available resources, and pricing. This generates the priority of the users for matching to edge servers. From IoT users' perspective, we use cost, and QoS parameters to improve their satisfaction. We have used state-of-the-art schemes to compare the performance of the proposed algorithm under different simulation scenarios. We compare the performance using the number of assigned users, servers' profit, number of satisfied users, edge server resources used, and execution time. The simulation results confirm that significant profit and satisfied user can be achieved by the proposed algorithm. © 2014 IEEE.

关键词： Computation offloading

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Human Stress Detection in and Through Sleep Patterns Using Machine Learning Algorithms

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Journal of The Institution of Engineers (India): Series B 2024年第6期105卷 1691-1713页

作者： Geetha, R. Gunanandhini, S. Srikanth, G. Umarani Sujatha, V. Department of Computer Science and Engineering S.A. Engineering College Chennai600077 India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Master of Computer Applications S.A. Engineering College Chennai600077 India

Stress has a remarkable impact on various cognitive functions, demanding timely and effective detection using strategies deployed across interdisciplinary domains. It influences decision-making, attention, learning, and problem-solving abilities. As a result, stress detection and modeling have become important areas of study in both psychology and computer science. This study links the fields of psychology and machine learning to deal with the urgent requirement of accurate stress detection methodologies and highlights sleep patterns as a key indicator for stress detection, discussing a novel approach to understand and determine stress levels. Psychologists use affective states to measure stress, which refers to a sense of feeling an underlying emotional state. However, most stress classification work has been limited to user-dependent models, which new users cannot use without additional training. This can be a significant time burden for new users trying to predict their affective states. Therefore, it is critical to address basic mental health issues in children and adults to prevent them from developing more complex problems on account of undergoing stress. The medical field processes vast amounts of medical data;the machine learning algorithms sift through patterns that might escape the human eye. The machine learning algorithms act as detectives, able to spot correlations and bring out a sense of complex information. The machine learning algorithms reveal fine correlations and patterns, aiding in more precise and prompt diagnoses particularly to focus fundamental mental health issues in individuals of all ages. This research work deploys an enhanced Multilayer Perceptron (MLP), exhibiting an extensive feature analysis for processing medical datasets, resulting in improved effectiveness in predicting stress levels. This helps us to diagnose issues more accurately and swiftly which improves the patient outcomes. The proposed and enhanced MLP model undergoes stri

关键词： Decision trees

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A probabilistic generative model for tracking multi-knowledge concept mastery probability

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Frontiers of computer science 2024年第3期18卷 155-170页

作者： Hengyu LIU Tiancheng ZHANG Fan LI Minghe YU Ge YU School of Computer Science and Engineering Northeastern UniversityShenyang 110169China Software College Northeastern UniversityShenyang 110169China

Knowledge tracing aims to track students’knowledge status over time to predict students’future performance *** a real environment,teachers expect knowledge tracing models to provide the interpretable result of knowledge *** chain-based knowledge tracing(MCKT)models,such as Bayesian Knowledge Tracing,can track knowledge concept mastery probability over ***,as the number of tracked knowledge concepts increases,the time complexity of MCKT predicting student performance increases exponentially(also called explaining away problem).When the number of tracked knowledge concepts is large,we cannot utilize MCKT to track knowledge concept mastery probability over *** addition,the existing MCKT models only consider the relationship between students’knowledge status and problems when modeling students’responses but ignore the relationship between knowledge concepts in the same *** address these challenges,we propose an inTerpretable pRobAbilistiC gEnerative moDel(TRACED),which can track students’numerous knowledge concepts mastery probabilities over *** solve explain away problem,we design long and short-term memory(LSTM)-based networks to approximate the posterior distribution,predict students’future performance,and propose a heuristic algorithm to train LSTMs and probabilistic graphical model *** better model students’exercise responses,we proposed a logarithmic linear model with three interactive strategies,which models students’exercise responses by considering the relationship among students’knowledge status,knowledge concept,and *** conduct experiments with four real-world datasets in three knowledge-driven *** experimental results show that TRACED outperforms existing knowledge tracing methods in predicting students’future performance and can learn the relationship among students,knowledge concepts,and problems from students’exercise *** also conduct several case *** case studies show that

关键词： probabilistic graphical model deep learning knowledge tracing learner modeling

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DDSS: Driver decision support system based on the driver behaviour prediction to avoid accidents in intelligent transport system

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 1-13页

作者： S, Balasubramani D, John Aravindhar Renjith, P.N. Ramesh, K. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

Accidents caused by drivers who exhibit unusual behavior are putting road safety at ever-greater risk. When one or more vehicle nodes behave in this way, it can put other nodes in danger and result in potentially catastrophic accidents. In order to anticipate and handle unusual driving behavior in Intelligent Transportation Systems (ITS), this research presents a unique Driver Decision Support System (DDSS). A reliable driving behavior prediction system is used by the suggested DDSS to categorize drivers as displaying normal or abnormal behavior. In order to prevent accidents in ITS scenarios, the system reliably detects anomalous driving patterns and advises nearby vehicles to change lanes or alter speed. The driver behavior prediction algorithm efficiently groups drivers into behavior categories using the K-Means clustering method. In order to evaluate the algorithm's efficacy, a comparative analysis is conducted by comparing its outcomes against those of Support Vector Machines (SVMs), Decision Trees, K-Nearest Neighbours (KNN), Logistic Regression, and Naïve Bayes. The integration of the Driver Decision Support System into the Intelligent Transportation System infrastructure serves to augment endeavours in accident prevention. Monitoring and analysis of driver behavior enable timely interventions, promoting safer driving practices and reducing accident risks. This research helps to create a more effective transportation system by reducing the number of accidents brought on by reckless driving. Because of its novel method to anticipating and controlling driver behavior, the proposed DDSS has promise for improving road safety and preventing accidents. The efficacy and the dependability of the driver behavior prediction algorithm are confirmed by the experimental assessment. © 2023

关键词： K-means clustering

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A Scalable Unsupervised and Back Propagation Free Learning With SACSOM: A Novel Approach to SOM-Based Architectures

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 955-967页

作者： Hirani, Gaurav R. Wang, Kevin I-Kai Abdulla, Waleed H. The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand

The field of computer vision is predominantly driven by supervised models, which, despite their efficacy, are computationally expensive and often intractable for many applications. Recently, research has expedited alternative avenues such as self-organizing maps (SOM)-based architectures, which offer significant advantages such as tractability, the absence of back-propagation, and feed-forward unsupervised learning. However, these SOM-based approaches frequently suffer from lower accuracy and limited generalization capabilities. To address these shortcomings, we propose a novel model called split and concur SOM (SACSOM). SACSOM overcomes the limitations of closely related SOM-based algorithms by utilizing multiple parallel branches, each equipped with its own SOM modules that process data independently with varying patch sizes. Furthermore, by creating groups of classes and using respective training samples to train independent subbranches in each branch, our approach accommodates datasets with a large number of classes. SACSOM employs a simple yet effective labeling technique requiring minimal labeled samples. The outputs from each branch, filtered by a threshold, contribute to the final prediction. Experimental validation on MNIST-digit, Fashion-MNIST, CIFAR-10, and CIFAR-100 demonstrates that SACSOM achieves competitive accuracy with significantly reduced computation time. Furthermore, it exhibits superior performance and generalization capabilities, even in high-noise scenarios. The weights of the single-layered SACSOM provide meaningful insights into the patch-based learning pattern, enhancing its tractability and making it ideal from the perspective of explainable AI. This study addresses the limitations of current clustering techniques, such as K-means and traditional SOMs, by proposing a lightweight, manageable, and fast architecture that does not require a GPU, making it suitable for low-powered devices. © 2024 IEEE.

关键词： Unsupervised learning

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An Improved Artificial Rabbits Optimization Algorithm with Chaotic Local Search and Opposition-Based Learning for engineering Problems and Its Applications in Breast Cancer Problem

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computer Modeling in engineering & sciences 2024年第11期141卷 1067-1110页

作者： Feyza AltunbeyÖzbay ErdalÖzbay Farhad Soleimanian Gharehchopogh Department of Software Engineering Firat UniversityElazig23119Turkey Department of Computer Engineering Firat UniversityElazig23119Turkey Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmia44867-57159Iran

Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in ***,for solving optimization problems,the ARO algorithm shows slow convergence speed and can fall into local *** overcome these drawbacks,this paper proposes chaotic opposition-based learning ARO(COARO),an improved version of the ARO algorithm that incorporates opposition-based learning(OBL)and chaotic local search(CLS)*** adding OBL to ARO,the convergence speed of the algorithm increases and it explores the search space *** maps in CLS provide rapid convergence by scanning the search space efficiently,since their ergodicity and non-repetitive *** proposed COARO algorithm has been tested using thirty-three distinct benchmark *** outcomes have been compared with the most recent optimization ***,the COARO algorithm’s problem-solving capabilities have been evaluated using six different engineering design problems and compared with various other *** study also introduces a binary variant of the continuous COARO algorithm,named *** performance of BCOARO was evaluated on the breast cancer *** effectiveness of BCOARO has been compared with different feature selection *** proposed BCOARO outperforms alternative algorithms,according to the findings obtained for real applications in terms of accuracy performance,and fitness *** experiments show that the COARO and BCOARO algorithms achieve promising results compared to other metaheuristic algorithms.

关键词： Artificial rabbit optimization binary optimization breast cancer chaotic local search engineering design problem opposition-based learning

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