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Message Verification Protocol Based on Bilinear Pairings and Elliptic Curves for Enhanced Security in Vehicular Ad Hoc Networks

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computers, Materials & Continua 2024年第10期81卷 1029-1057页

作者： Vincent Omollo Nyangaresi Arkan A.Ghaib Hend Muslim Jasim Zaid Ameen Abduljabbar Junchao Ma Mustafa A.Al Sibahee Abdulla J.Y.Aldarwish Ali Hasan Ali Husam A.Neamah Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennaiTamilnadu602105India Information Technology Department Management Technical CollegeSouthern Technical UniversityBasrah61004Iraq Department of Computer Science College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq College of Big Data and Internet Shenzhen Technology UniversityShenzhen518118China Huazhong University of Science and Technology Shenzhen InstituteShenzhen518000China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen518060China Computer Technology Engineering Department Iraq University CollegeBasrah61004Iraq Department of Mathematics College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq Institute of Mathematics University of DebrecenDebrecenH-4002Hungary Department of Electrical Engineering and Mechatronics Faculty of EngineeringUniversity of DebrecenDebrecen4028Hungary

Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation ***,the exchange of beacons and messages over public channels among vehicles and roadside units renders these networks vulnerable to numerous attacks and privacy *** address these challenges,several privacy and security preservation protocols based on blockchain and public key cryptography have been proposed ***,most of these schemes are limited by a long execution time and massive communication costs,which make them inefficient for on-board units(OBUs).Additionally,some of them are still susceptible to many *** such,this study presents a novel protocol based on the fusion of elliptic curve cryptography(ECC)and bilinear pairing(BP)*** formal security analysis is accomplished using the Burrows–Abadi–Needham(BAN)logic,demonstrating that our scheme is verifiably *** proposed scheme’s informal security assessment also shows that it provides salient security features,such as non-repudiation,anonymity,and ***,the scheme is shown to be resilient against attacks,such as packet replays,forgeries,message falsifications,and *** the performance perspective,this protocol yields a 37.88%reduction in communication overheads and a 44.44%improvement in the supported security ***,the proposed scheme can be deployed in VANETs to provide robust security at low overheads.

关键词： Attacks bilinear elliptic curve cryptography(ECC) privacy security vehicular ad hoc network(VANET)

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Advancements in Chatbot Technology: Enhancing User Experience through AI and NL

Advancements in Chatbot Technology: Enhancing User Experienc...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Arora, Yojna Singh, Ajeet Sharma, Abhay Aggarwal, Nitin Bansal, Nidhi Sharda University Department of Computer Science & Engineering Greater Noida India Moradabad Institute of Technology Department of Computer Science & Engineering Uttar Pradesh Moradabad India School of Computing and Intelligent System Manipal University Jaipur Department of IoT & IS Rajasthan Jaipur India Supervisor Oracle Development Database Admin Data Science PA United States Pune India

ISBN: (纸本)9798331517953

A chatbot is an AI-powered software or application designed to communicate with people. This technology can perform a variety of tasks, including providing instant responses and answers to users, delivering information, assisting with purchasing products, and enhancing customer service. In my paper, I present the fundamental principles and essential concepts of computing related to chatbots and its applications various sectors like Education, E commerce, Entertainment, Healthcare, Telecommunication, Customer Service etc. The paper also mentions detailed literature explain the contribution by various researchers in this fields. Architecture and step wise approach of creating a chatbot is presented lately. © 2024 IEEE.

关键词： Chatbots

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Dual Encoder Cross-Shape Transformer Network for Medical Image Segmentation in Internet of Medical Things for Consumer Health

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 538-549页

作者： Zhao, Yawu Wang, Shudong Zhang, Yulin Ren, Yande Zhang, Yuanyuan Pang, Shanchen the School of Computer Science and Technology China University of Petroleum Shandong Qingdao China the College of Mathematics and System Science Shandong University of Science and Technology Shandong Qingdao China the Department of Medical Imaging Center the Affiliated Hospital of Qingdao University Shandong Qingdao China the School of Information and Control Engineering Qingdao University of Technology Shandong Qingdao China

In emerging consumer healthcare, high-performance and robust medical image segmentation methods are essential for personalized diagnosis and treatment. Thus, early screening of aneurysms reduces the risk of aneurysm rupture and provides therapeutic and preventive measures. However, uncontrollable factors such as uncertainty in the size and location shape of tumors in medical images a significant challenge to medical image segmentation. These factors make extracting high-quality features from aneurysm images difficult, resulting in poor segmentation. Then, we designed a dual encoder cross-shape transform network (DECSTNet) to capture aneurysm feature information. The dual encoder structure can extract aneurysm feature information at different scales, the adaptive dynamic feature fusion module can fuse features at different scales between the encoders, and the cross-shape window transform layer can compute the width and height of the image in parallel for local self-attention, which enhances the interactive capability of the telematic information while realizing the complementarity of the local information. Through extensive experiments, we demonstrate the excellent segmentation performance of DECSTNet on the private and three public datasets. Noteworthy our method has significant segmentation performance and fewer parameters, making it well-suited to be deployed on IoMT diagnostic platforms for medical image segmentation to promote healthy patient consumption. © 2025 IEEE.

关键词： Semantic Segmentation

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Privacy Preservation for Cloud-Edge-Collaborative Energy Management system Using Post-Quantum Homomorphic Encryption

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IEEE Transactions on Smart Grid 2025年第4期16卷 3282-3294页

作者： Jiang, Cheng Li, Xue Du, Dajun Qian, Hong Wen, Mi Wu, Lei Findeisen, Rolf Shanghai University of Electric Power Shanghai Key Laboratory of Power Station Automation Technology College of Automation Engineering Shanghai200090 China Shanghai University Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronical Engineering and Automation Shanghai200072 China Shanghai University of Electric Power College of Computer Science and Technology Shanghai201306 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States TU Darmstadt Control and Cyber-Physical Systems Laboratory Darmstadt64227 Germany

Cloud-based energy management systems (EMS) in smart grids face privacy challenges, as existing methods based on traditional homomorphic encryption support limited operations and are vulnerable to quantum attacks. We propose a privacy preservation method for smart grid energy management system, leveraging edge-assisted computing and ring learning with errors (RLWE) homomorphic encryption. Our approach enables both addition and multiplication operations on encrypted smart grid data while defending against quantum threats. We demonstrate the methods security under the RLWE hardness assumption and validate its effectiveness through an experimental platform simulating smart grid environments. This work addresses computational limitations in current privacy-preserving techniques, enhancing data security for evolving smart grid technologies © 2010-2012 IEEE.

关键词： Power management (telecommunication)

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Biogas Fermentation Monitoring Research Based on Borderline-SMOTE Algorithm and Stacking Ensemble Learning 13

Biogas Fermentation Monitoring Research Based on Borderline-...

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13th International Conference of Information and Communication Technology, ICTech 2024

作者： Xiu, Pengfei Fu, Lijun Sun, Yukun Wang, Hongliang Li, Xuerao Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shandong University Big Data Technology and Cognitive Intelligence Laboratory University of Chinese Academy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (纸本)9798350376258

Anaerobic fermentation is the most important link to gas production, this paper solves the current problem with the help of the current complete machine learning technology, combining perception and intelligent processing, intelligent processing of information, and derive a decision-making basis for its control or management [1]. Aiming at the current problems of the anaerobic fermentation process in biogas engineering, the system design was carried out, and the anaerobic fermentation process of biogas and its main influencing factors were investigated, and the monitoring parameters of the system were selected to be temperature, PH value, liquid level and redox potential [2]. Previous studies are mainly based on a single prediction model, which leads to poor generalization ability of the model. In this study, unbalanced samples are handled by Borderline-SMOTE algorithm. Based on the Stacking integrated learning approach, a common single prediction model is used to construct a combined prediction model. The accuracy, precision, recall, F1 score and area under the ROC curve are used to evaluate the advantages and disadvantages of the combined prediction model over the single prediction model. ©2024 IEEE.

关键词： Decision making

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Effective and Imperceptible Adversarial Textual Attack Via Multi-objectivization

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ACM Transactions on Evolutionary Learning and Optimization 2024年第3期4卷 1-23页

作者： Liu, Shengcai Lu, Ning Hong, Wenjing Qian, Chao Tang, Ke Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen College of Engineering South Tower 1088 Xueyuan Avenue Guangdong Shenzhen518055 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong999077 Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen 10F Zhizhen Building Canghai Campus 3688 Nanhai Avenue Guangdong Shenzhen China National Key Laboratory for Novel Software Technology School of Artificial Intelligence Nanjing University Nanjing International College Area Xianlin Campus 163 Xianlin Avenue Jiangsu Nanjing China

The field of adversarial textual attack has significantly grown over the past few years, where the commonly considered objective is to craft adversarial examples (AEs) that can successfully fool the target model. However, the imperceptibility of attacks, which is also essential for practical attackers, is often left out by previous studies. In consequence, the crafted AEs tend to have obvious structural and semantic differences from the original human-written text, making them easily perceptible. In this work, we advocate leveraging multi-objectivization to address such an issue. Specifically, we reformulate the problem of crafting AEs as a multi-objective optimization problem, where the attack imperceptibility is considered as an auxiliary objective. Then, we propose a simple yet effective evolutionary algorithm, dubbed HydraText, to solve this problem. HydraText can be effectively applied to both score-based and decision-based attack settings. Exhaustive experiments involving 44,237 instances demonstrate that HydraText consistently achieves competitive attack success rates and better attack imperceptibility than the recently proposed attack approaches. A human evaluation study also shows that the AEs crafted by HydraText are more indistinguishable from human-written text. Finally, these AEs exhibit good transferability and can bring notable robustness improvement to the target model by adversarial training. © 2024 Copyright held by the owner/author(s).

关键词： Multiobjective optimization

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LSTM & MACD Approach: Prediction and Analysis of Stock Market

LSTM & MACD Approach: Prediction and Analysis of Stock Marke...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： L. Jerart Julus S.N. Syed Naweed D. Aadhavan S. Sudhakara Sankar R.S. Rampriya T. Kavitha Department of IT School of Computer Science Engineering and Information System Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information System Vellore Institute of Technology Vellore India Department of ECE Vel Tech University Chennai Tamilnadu India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Investing and researchers alike have long been fascinated by the volatile and dynamic nature of financial markets. For the prediction and analysis of stock market movements, we present a hybrid approach that combines Long Short-Term Memory (LSTM) and Moving Average Convergence Divergence (MACD). Leveraging the power of deep learning and technical analysis, this hybrid model offers a comprehensive solution for forecasting and understanding market trends. The LSTM component, a type of recurrent neural network, provides the model with the ability to capture and learn intricate patterns in historical stock price data, thus enabling accurate short and long-term predictions. The MACD indicator, a widely used tool in technical analysis, supplements the LSTM by identifying potential reversal points and providing valuable insights into market momentum. A hybrid approach will provide a stronger and more reliable forecasting framework by combining both LSTM and MACD. To validate the model's efficacy, we apply it to historical stock market data and conduct a thorough analysis of its predictive performance. Our findings suggest that the Hybrid LSTM-MACD approach outperforms traditional methods and offers a promising solution for investors, traders, and financial analysts seeking to navigate the complex world of stock market trading.

关键词： Analytical models Accuracy Predictive models Market research Data models Internet Stock markets Forecasting Long short term memory Convergence

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The Impact of Working From Home on Employee Collaboration, Happiness, and Career Growth: A Statistical Analysis

The Impact of Working From Home on Employee Collaboration, H...

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International Conference on Web Research (ICWR)

作者： Zahra Ghaderpour Majid Joudaki Mehdi Imani Department of Engineering Ayatollah Boroujerdi University Boroujerd Iran Department of Computer Engineering Faculty of Engineering Ayatollah Boroujerdi University Boroujerd Iran Department of Computer and System Sciences Stockholm University Stockholm Sweden

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

The COVID-19 pandemic significantly accelerated the adoption of working from home, raising concerns about its impact on employee collaboration, happiness (well-being), and career growth. This study investigates the correlations between working from home and three key factors: collaboration challenges, employees' happiness about household size, and promotion opportunities. Using a publicly available dataset containing 1,512 responses from workers in New South Wales, Australia, statistical analyses, including Pearson correlation and p-values, were applied to examine these relationships. The findings indicate a statistically significant positive correlation ( $\mathrm{r}=0.23, \mathrm{p}<0.05$ ) between working from home and collaboration challenges, suggesting that remote work may introduce obstacles to teamwork. Additionally, a significant negative correlation ( $\mathrm{r}=-0.06, \mathrm{p}<0.05$ ) was found between working from home and promotion chances, implying that employees working from home may experience slightly reduced career advancement opportunities. However, no significant correlation ( $\mathrm{r}=0.04, \mathrm{p}=0.161$ ) was observed between household size and employee happiness while working from home. These results suggest that remote work offers flexibility but may challenge collaboration and career growth. The study provides empirical evidence to guide organizations in refining working from home policies that balance employee happiness, collaboration efficiency, and career growth.

关键词： COVID-19 Correlation engineering profession Statistical analysis Pandemics Collaboration Organizations Remote working Teamwork Sustainable development

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Evaluation of an Intelligent Controller for Electric Vehicle Charging with Vienna Rectifier Topology

Evaluation of an Intelligent Controller for Electric Vehicle...

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International Conference on Power Electronic Drives

作者： Adithya Lingadahalli Nima Tashakor Hans-Joerg Wiehoff Hui Wang Stefan Goetz System Engineering Power Electronics Vitesco Technologies Regensburg Germany Electrical and Computer Engineering Department Duke University Durham USA Electrical and Computer Engineering Duke University Durham USA

ISBN: (数字)9798350372472

ISBN: (纸本)9798350372489

The Vienna rectifier is a widely used topology in a variety of industrial applications, including electric vehicle (EV) charging. Although conventional control approaches exist, the nonlinear and highly dynamic nature of the Vienna rectifier as well as many existing uncertainties within the system hinder the development of a performant controller based on classic control theory. This paper proposes a model-free controller based on reinforcement learning (RL) to optimize the performance of the DC-link voltage control loop with concurrent regulation of the power factor. We developed a state-space model of the Vienna rectifier in MATLAB/Simulink and a classical proportional-integral (PI) controller for quantitative comparison. The RL controller's ability to handle system nonlinearities and component uncertainties, such as inductor and capacitor tolerances, enables it to outperform classical control strategies. Furthermore, this paper benchmarks multiple RL-based algorithms including deep deterministic policy gradient (DDPG) as well as twin delayed DDPG (TD3) in the voltage control loop. The results demonstrate that TD3 can reduce 2.5 percent voltage ripple and has a shorter settling time of 0.75 s at the cost of higher computational requirements.

关键词： Reactive power PI control Uncertainty Rectifiers Reinforcement learning Electric vehicle charging Regulation Topology Voltage control Vehicle dynamics

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Detecting Rises in SBP from PPG for Identifying Autonomic Dysreflexia

Detecting Rises in SBP from PPG for Identifying Autonomic Dy...

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2023 IEEE Biomedical Circuits and systems Conference, BioCAS 2023

作者： Wang, Weinan Kilgore, Kevin L. Mohseni, Pedram Najafizadeh, Laleh Rutgers University Department of Electrical and Computer Engineering PiscatawayNJ United States Case Western Reserve University and the MetroHealth System Department of Physical Medicine & Rehabilitation ClevelandOH United States Case Western Reserve University Department of Electrical Computer and Systems Engineering ClevelandOH United States

ISBN: (纸本)9798350300260

Autonomic dysreflexia (AD) is a life-threatening and prevalent post-secondary health condition affecting individuals with spinal cord injury (SCI). Reliable and timely identification of AD requires continuous monitoring of changes in the blood pressure (BP), and alarming the patient of rapid BP elevations. However, conventional methods for identifying AD, including clinical observation and patient's self-report, lack timeliness in addressing the immediate threat of acute hypertension, leading to cardiovascular damages in SCI patients. To address this issue, we target the problem of detecting onsets of rapid systolic BP (SBP) elevations from photoplethysmogram (PPG) signals, and present a batched Residual Network-Long Short Term Memory (ResNet-LSTM) deep learning model as a solution. Results indicate that the model achieves an accuracy of 74.38% and F-1 score of 72.99% on 356 leave-out testing subjects. With only one sensor required for providing input signals, the proposed method holds promise for implementing low-cost continuous monitoring systems, for the timely detection of onsets of AD in SCI patients. © 2023 IEEE.

关键词： Autonomic Dysreflexia Blood Pressure Convolutional Neural Networks (CNN) Cuffless Blood Pressure Monitoring Photo-plethysmography (PPG) Spinal Cord Injury

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