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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Mohandas, R. Elavarasi, M. Praveen, R.V.S. Chittapragada, Hemanand Nithiya, C. Prabagar, S. Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India Vels Institute of Science Technology & Advanced Studies Department of Bca - It Pallavaram India LTIMindtree Limited Digital Engineering and Assurance Hyderabad India Madanapalle Institute of Technology & Science Department of Computer Science and Engineering Madanapalle India R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Puduvoyal India Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350352931

The rapid growth of Internet of Things (IoT) devices has increased security weaknesses, rendering networks vulnerable to advanced cyber-attacks. Traditional detection systems frequently encounter difficulties in real-time anomaly detection and resource limitations, leaving IoT systems exposed. This study presents a hybrid model that combines Deep Belief Networks (DBN) for hierarchical feature extraction with Gradient Boosting (GB) to improve classification accuracy. Driven by the increasing incidence and complexity of cyber threats, the study seeks to rectify significant deficiencies in IoT security by providing a scalable, efficient, and high-performance detection system. Experimental findings confirm the model's efficacy, attaining 98.5% accuracy, an F1-score of 0.97, and a recall of 0.97, markedly surpassing current methodologies. The study addresses the issues of protecting IoT networks, suggests a comprehensive detection methodology, and offers a comparative analysis of performance indicators, laying the groundwork for refined, versatile approaches to counter new threats in IoT ecosystems. © 2024 IEEE.

关键词： Cyber attacks

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Multi-Criteria Decision-Making Model to Achieve Sustainable Developmental Goals in Industry 4.0 for Smart City Infrastructure

HighTech and Innovation Journal

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HighTech and Innovation Journal 2024年第4期5卷 1135-1153页

作者： Akila, D. Pal, Souvik Sarkar, Bikramjit Jayalaksshmi, S. Muthaiyah, Saravanan Anbananthen, Kalaiarasi Sonai Muthu Department of Computer Applications Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Management Information Systems Saveethas College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Computer Science and Engineering JIS College of Engineering Kalyani India Department of Information Technology Vels Institute of Science Technology and Advanced Studies India School of Business and Technology International Medical University Kuala Lumpur57000 Malaysia Faculty of Information Science and Technology Multimedia University Melaka75450 Malaysia

Due to a shortage of funding and other market challenges, Small and Medium-sized Enterprises (SMEs) face difficulties in adopting new technologies. Numerous technological obstacles negatively impact the long-term commercial achievement of SMEs. The deployment of Industry 4.0hopes to resolve these technological challenges. A sustainable city is a complex structure where economic, societal, and ecological components interact and compete. There is a scarcity of l methodologies for measuring interactions in this complex structure. Industry 4.0 aims to obtain higher performance effectiveness, profitability, and automation. The main goal is to develop a reliable method of evaluating small and medium-sized enterprises (SMEs) adopting Industry 4.0 technologies, particularly concerning smart city applications. This paper aims to determine the influence of Industry 4.0 in fostering economic efficiency and sustainability amongst these SMEs. The study introduces a multi-criteria decision-making (SC-MCDM) system designed to test an SME’s achievement of their targeted sustainable developmental goals. A technique for computing the interaction between various standards, i.e., (static interactions and dynamical pattern resemblance), as well as the weightage of variables of every indicator generated by the connection, is included within SC-MCDM. Furthermore, applying the suggested technique is validated by assessing the sustainable development goals of twelve Chinese cities within the Triple Bottom Line (TBL) paradigm. From a geographic-temporal viewpoint, spatial variations in city sustainability reveal regional sustainable inequalities. Indicator scores suggest that the most significant factors for most communities are the lack of research spending, falling financing in stationary assets, shortage of financial development, and inadequate shared transit. Furthermore, the growth of tertiary industries, improvement of energy performance, expansion of green areas, and reduction of poll

关键词： Sustainable city

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Secure Multi-Party Computation for Collaborative Data Analysis in Network Security 2

Secure Multi-Party Computation for Collaborative Data Analys...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, electrical and Electronics, ICIITCEE 2024

作者： Tamilselvi, P. Lathika, V. Jayachitra, S. Arunkumar, S. Balasubramani, M. Kalaichelvi, V. Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Department of Cse Salem India Psna College of Engineering and Technology Department of Ece Dindigul India Coimbatore Institute of Engineering and Technology Department of Ece Coimbatore India Vsb Engineering College Department of Computer Science and Engineering Karur India Sastra Deemed to Be University Srinivasa Ramanujan Centre Department of Cse Kumbakonam India

ISBN: (纸本)9798350306415

Within the scope of this research, we provide a Secure Multi-Party Computation (SMPC) architecture that has been adapted specifically for the purpose of collaborative data analysis in network protection. Significant improvements have been made to the framework in terms of performance metrics, security, and applicability in the real world. When measured against a baseline, comparative analysis showed a notable reduction of 25% in execution time, a decrease of 20% in communication overhead, and a drop of 16.67% in computational complexity. These enhancements highlight the framework's efficiency advantages, which are vital for maximising responsiveness and scalability in collaborative efforts to improve network security. As a result of the security evaluation, which demonstrates a shift from moderate to high privacy preservation, strong resilience to assaults, and low sensitivity to parameters, the suggested framework has been shown to be reliable. Real-world application scenarios further emphasise its adaptability, showing that its influence on threat intelligence sharing is moderate, while its impact on collaborative incident response is significant, and its impact on vulnerability analysis is low. These findings, when taken as a whole, establish the SMPC framework as a useful resource for organisations that want to improve the effectiveness of their joint efforts to secure networked environments. The research places a strong emphasis on the practical importance of the framework, which helps to build enhanced trust, collaboration, and resilience against developing cyber threats. Assessments of scalability, improvements to cryptographic protocols, and interaction with newly developing technology are some potential future topics for study. advanced threat analysis may also benefit. © 2024 IEEE.

关键词： Collaborative Data Analysis Cryptographic Protocols Network Security Privacy Preservation Secure Multi-Party Computation (SMPC)

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Detection analysis of malicious cyber attacks using machine learning algorithms

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Materials Today: Proceedings 2022年 68卷 26-34页

作者： Karthika, R.A. Maheswari, M. Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India Department of Computational Intelligence SRM Institute of Science and Technology KTR Chennai 603203 India

Cybersecurity is the practice of safeguarding information and the systems that store or process information. Cybersecurity violations are the foremost persecution instigated by cyber attackers through one or more systems on single or several networks or systems. This kind of cyber threats can ruthlessly trigger data loss or theft besides halting the network systems partially or fully. Among various cybersecurity attacks, the Denial of Service (DoS) attack is one of the most dominant hacking modus operandi over the internet. The hackers use the weapons at their disposal to disrupt traffic in and around the network surroundings or the target system by bombarding it with a lot of malicious requests. This paper introduced machine learning algorithms such as Gaussian Naïve Bayes and Nearest Centroid to identify the attacks via classifying the given dataset into normal and malicious traffic. Furthermore, metrics such as accuracy, f-score, FNR, precision, and prediction time etc., were calculated to identify the best performing model. © 2022

关键词： Denial-of-service attack

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Improved Contextual Understanding and Emotion Detection in Large-Scale Text Data with Hybrid Deep Learning Models 5

Improved Contextual Understanding and Emotion Detection in L...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Thilakavathy, P. Manikandan, G. Deepa, R. Jayalakshmi, V. Surendran, R. Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM institute of science and technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Ramapuram Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

With the growth of large-scale text datasets from reviews, social media, and other online sources, sentiment analysis is essential for understanding public opinion. Traditional models struggle to understand complicated linguistic sentiments and interdependencies. This research could improve sentiment classification in customer feedback analysis, market sentiment predictions, and social media monitoring. Addressed issues include ambiguous language, sentence context flipping, and the need for robust models that can manage massive datasets. Contextual Detection Text Analysis Using Deep Learning (CDTA-DL) improves contextual knowledge and emotion identification in this research. RNNs and CNNs are used in the CDTA-DL approach to extract spatial and sequential information from text. CDTA-DL helps large-scale sentiment analysis detect sarcasm, implicit emotions, and many expressions. In a comprehensive simulation investigation employing open-access sentiment analysis datasets, CDTA-DL surpassed standard deep learning models in accuracy, recall, and F1 scores. The recommended strategy may increase emotion recognition and contextual understanding, making it ideal for large-scale sentiment analysis in various domains. © 2024 IEEE.

关键词： Contrastive Learning

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Enhanced Camouflage Detection Using advanced Image Processing Techniques for Real-Time Object Recognition and Pattern Differentiation 5

Enhanced Camouflage Detection Using Advanced Image Processin...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Manikandan, G. Deepa, R. Jayalakshmi, V. Thilakavathy, P. Surendran, R. St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

The identification of objects that blend in with their surroundings has long been a concern in fields including defence, wildlife monitoring, and surveillance due to the difficulty of detecting such objects. This finding is significant because it could greatly enhance our capacity to detect camouflaged objects, transforming the field of real-time object detection. Existing image processing methods often fail miserably when adequately capturing this aspect. This dissertation's primary focus is identifying devices with complicated patterns, legacy interference, and inadequate contrast;nonetheless, in disguised scenarios, all of these features cause identification to be unsuccessful. This research often suggests a new approach called the Linear Edge Detecting Scheme for Camouflaged Object Detection (LEDS-COD) in these difficult cases. The LEDS-COD system can enhance item visibility in low-comparison settings by highlighting critical edges and descriptions. Pattern differentiation methodologies and modern facility-side recognition techniques are combined to accomplish this goal. The approach successfully differentiated items because it zeroed in on texture and comparative details that would otherwise go unnoticed by humans or advanced algorithms. LEDS-COD is useful in many areas, including the Navy, autonomous surveillance systems, and animal protection, where the real-time identification of hidden devices is essential. Results from a simulation study using various datasets, including concealed objects, show that LEDS-COD surpasses conventional detection methods in recognition accuracy, processing speed, and perseverance. The recommended technique exceeded the requirements for obtaining improved object detection estimates for challenging conditions like those with complex backdrops and changing illumination. According to this study, LEDS-COD can be devastating in improving real-time camouflage detection. © 2024 IEEE.

关键词： Accuracy Image processing Surveillance Image edge detection Wildlife Lighting Object detection Real-time systems Object recognition Protection

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Deep Learning for Sentiment Analysis with Adaptive Neural Networks for Emotion Classification 5

Deep Learning for Sentiment Analysis with Adaptive Neural Ne...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Jayalakshmi, V. Thilakavathy, P. Manikandan, G. Deepa, R. Surendran, R. SRM Institute of Science and Technology Department of Computer Science and Applications Tamilnadu Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM institute of Science and Technology Department of Computing Technologies Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

In understanding human emotions, sentiment analysis has been invaluable in social media monitoring, consumer comment analysis, and psychological evaluations. Customer service, human-computer interface, and public mood comprehension depend on appropriate emotion categorisation. Despite its potential, sentiment analysis struggles with sarcasm, multimodal data integration, and emotion processing. Implementing real-time applications requires improving deep learning model computational complexity. Multimodal Optimised Emotion Analysis using Deep Learning (MOEA-DL) uses CNNs for image processing, RNNs for text., and LSTM networks for sequential audio data. This improves emotion recognition by combining modality strengths. Marketing sentiment, mental health monitoring, social media sentiment, and consumer feedback use MOEA-DL. Multimodal solutions are better than text-based ones for communicating emotion. Customer contacts, film reviews, and social media platforms are used to evaluate the proposed strategy. According to simulations, MOEA-DL categorises emotions more accurately and robustly than previous sentiment analysis methods. Its adaptive neural network lets the system learn and perform well in many cultural and language environments. © 2024 IEEE.

关键词： Emotion Recognition

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Microgrid digital twins: concepts and their controlling through multi-agent systems

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International Journal of High Performance Systems Architecture 2023年第4期11卷 225-236页

作者： Negi, Gaurav Singh Mishra, Anupama Gupta, Mukul Kumar Saxena, Nitin Kumar Saini, DilipKumar Jang Bahadur Joshi, Kapil Electrical and Electronics Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India Department of Computer Science and Engineering Himalayan School of Science and Technology Swami Rama Himalayan University Uttarakhand Dehradun248140 India School of Engineering University of Petroleum and Energy Studies Dehradun248006 India Department of Electronics & Instrumentation Engineering MJP Rohilkhand University UP Bareilly243006 India Electrical and Electronics Engineering KIET Group of Institutions Ghaziabad201206 India Electrical and Computer Engineering University of Denver Denver80210 United States Department of Computer Science and Engineering School of Engineering and Technology Pimpri Chinchwad University Pune411044 India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India

Real-time grid analysis is made possible by the electric digital twin grid, which combines history and present data to convey system status and project future circumstances. Cooperative smart agents that can solve problems bigger than one agent's scope make up a multi-agent system (MAS). A micro-grid digital twin (MGDT) uses real-time data interchange and high-fidelity models to simulate a microgrid’s operation. Hybrid energy storage systems are managed by agents who maximise renewable resources and minimise expenses. Microgrids combine sensing, control, and intelligence technology in utility networks. Plant management, communication protocols, and power system operations are all made easier with MAS-based control. For dependable microgrid operation, dispersed energy resources are coordinated by MAS. In order to handle complexity and improve resilience and control of the energy system, this study investigates the application of MAS in microgrid control. Energy management and agent communication in microgrids are the main topics of MAS research. Integrating microgrid digital twins with MAS holds promise for advancing energy systems. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Multi agent systems

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Robust Domain Generalization for Multi-modal Object Recognition 5

Robust Domain Generalization for Multi-modal Object Recognit...

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5th International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2024

作者： Qiao, Yuxin Li, Keqin Lin, Junhong Wei, Rong Jiang, Chufeng Luo, Yang Yang, Haoyu Northern Arizona University Department of Computer Information Technology FlagstaffAZ United States Ama University Department of Computer Science Quezon Philippines Massachusetts Institute of Technology Electrical Engineering & Computer Science Department CambridgeMA United States Peking University Academy for Advanced Interdisciplinary Studies Beijing China The University of Texas at Austin Department of Computer Science AustinTX United States University of Southern California Department of Computer Science Los AngelesCA United States Georgia Institute of Technology College of Computing AtlantaGA United States

ISBN: (纸本)9798350366174

In multi-label classification, machine learning encounters the challenge of domain generalization when handling tasks with distributions differing from the training data. Existing approaches primarily focus on vision object recognition and neglect the integration of natural language. Recent advancements in vision-language pre-training leverages supervision from extensive visual-language pairs. This allows learning across diverse domains and enhances recognition in multi-modal scenarios, showcasing superior transfer learning capabilities in methods like CLIPood. However, CLIPood has several limitations: differences in the utilized loss, loss of generality in evaluating only a single backbone, and neglect of class-aware visual *** address these, we propose this paper that infers the actual loss based on the implementation, broadens evaluations to larger vision-language backbones, and introduces Mixup-CLIPood with a novel mix-up loss for enhanced class-aware visual fusion. © 2024 IEEE.

关键词： Visual languages

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Real-Time Implementation of Quadrotor UAV Control System Based on a Deep Reinforcement Learning Approach

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computers, Materials & Continua 2024年第12期81卷 4757-4786页

作者： Taha Yacine Trad Kheireddine Choutri Mohand Lagha Souham Meshoul Fouad Khenfri Raouf Fareh Hadil Shaiba Aeronautical Sciences Laboratory Aeronautical and Spatial Studies InstituteBlida 1 UniversityBlida0900Algeria Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Energy and Embedded Systems for Transportation Research Department ESTACA-LABMontigny-Le-Bretonneux78066France Department of Electrical Engineering University of SharjahSharjah27272United Arab Emirates Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The popularity of quadrotor Unmanned Aerial Vehicles(UAVs)stems from their simple propulsion systems and structural ***,their complex and nonlinear dynamic behavior presents a significant challenge for control,necessitating sophisticated algorithms to ensure stability and accuracy in *** strategies have been explored by researchers and control engineers,with learning-based methods like reinforcement learning,deep learning,and neural networks showing promise in enhancing the robustness and adaptability of quadrotor control *** paper investigates a Reinforcement Learning(RL)approach for both high and low-level quadrotor control systems,focusing on attitude stabilization and position tracking tasks.A novel reward function and actor-critic network structures are designed to stimulate high-order observable states,improving the agent’s understanding of the quadrotor’s dynamics and environmental *** address the challenge of RL hyper-parameter tuning,a new framework is introduced that combines Simulated Annealing(SA)with a reinforcement learning algorithm,specifically Simulated Annealing-Twin Delayed Deep Deterministic Policy Gradient(SA-TD3).This approach is evaluated for path-following and stabilization tasks through comparative assessments with two commonly used control methods:Backstepping and Sliding Mode Control(SMC).While the implementation of the well-trained agents exhibited unexpected behavior during real-world testing,a reduced neural network used for altitude control was successfully implemented on a Parrot Mambo mini *** results showcase the potential of the proposed SA-TD3 framework for real-world applications,demonstrating improved stability and precision across various test scenarios and highlighting its feasibility for practical deployment.

关键词： Deep reinforcement learning hyper-parameters optimization path following quadrotor twin delayed deep deterministic policy gradient and simulated annealing

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