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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Wilfred Blessing, N.R. Gunasekaran, Hemalatha Hariharan, B. Al Jabri, Jaber Saleh Salim Kavitha, S.J. Sutherlin Subitha, G. College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman SRM Institute of Science and Technology Kattankulathur Department of Computational Intelligence Tamil Nadu India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Aruthenganvilai India

ISBN: (纸本)9798331505745

Improvements in patient care and early disease detection possibilities are two of the ways artificial intelligence is transforming the healthcare industry. Alzheimer's disease, a neurodegenerative disease that worsens with time, is among the most difficult diseases to exactly identify in early stages. Patients thus delay in treatment and experience a decline in their quality of life. Research is still under progress, thus knowledge about how AI-driven prediction models could be combined with readily available healthcare services remains lacking. This work presents a special artificial intelligence-powered system consisting of DenseNet-based deep learning for Alzheimer's disease prediction with an online consultation platform. The proposed approach analyzes brain MRI data and classifies Alzheimer's stages with higher accuracy using a DenseNet architecture, well-known for its exceptional performance in the field of feature extraction. Comprising three thousand magnetic resonance imaging (MRI) images, the dataset has been preprocessed for noise reduction and tuned for models performance. At 98.5%, the DenseNet model exceeded other methods including VGGNet and ResNet in accuracy. © 2025 IEEE.

关键词： Neurodegenerative diseases

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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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Adaptive Vehicle Control System for Improved Safety With Image Processing 5

Adaptive Vehicle Control System for Improved Safety With Ima...

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Vijayalakshmi, N. Priya, L. Ramaraj, S. Nanjundan, Menakadevi Palanisamy, Satheesh Kumar Olalekan Salau, Ayodeji Braide, Sepiribo Lucky Alliance University Alliance School of Advanced Compuitng Department of Computer Science and Engineering Bangalore India Department of Ece Kinathukadavu Tamilnadu Coimbatore641202 India Karpagam College of Engineering Department of Computer Science and Engineering India Karpagam Institute of Technology Department of Ece Coimbatore India Bms Institute of Technology and Management Department of Ece Karnataka Bengaluru India Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Chennai India Rivers State University Department of Electrical and Electronics Engineering Port Harcourt Nigeria

ISBN: (纸本)9798331542559

We propose the development of an Adaptive Vehicle Control (AVC) system using readily available components such as a Raspberry Pi microcontroller, a motor, a motor driver, and a Raspberry Pi camera. This system aims to enhance safety and convenience by automatically adjusting vehicle speed based on surrounding traffic conditions and recognizing relevant road signs like school zones, speed limits, and hospital zones. The process begins with real-time image capture using the Raspberry Pi camera module. These images are then analyzed using Python-based computer vision algorithms, primarily leveraging OpenCV. The image processing pipeline employs techniques such as object detection and template matching to identify specific road signs within the captured frames. Upon sign detection, the system interprets their meanings and determines the appropriate speed limits associated with them. Using the information from the detected signs and their corresponding speed limits, the system regulates the motor connected to the vehicle's throttle through GPIO pins on the Raspberry Pi. By adjusting the motor's speed, the AVC system ensures compliance with the specified speed limits across various road conditions. Safety measures are paramount, with fail-safe mechanisms implemented to prevent accidents in case of system errors or malfunctions. This AVC system offers a cost-effective and adaptable solution for integrating intelligent cruise control capabilities into vehicles. Leveraging the computational power of the Raspberry Pi and the versatility of image processing techniques, the system can adapt to diverse driving environments, thereby enhancing overall driving safety. This project contributes to the progression of smart transportation systems and lays the foundation for further advancements in autonomous driving technologies. © 2024 IEEE.

关键词： Adaptive cruise control

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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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Artificial Intelligence in Health Care Computing and Assessments in Oman: an Effectual Study 2

Artificial Intelligence in Health Care Computing and Assessm...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Wilfred Blessing, N.R. Gunasekaran, Hemalatha Hariharan, B. Jabri, Jaber Saleh Salim Al Kavitha, S.J. Sutherlin Subitha, G. College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Aruthenganvilai India

ISBN: (纸本)9798331505745

Health care plays a vital responsibility in a human life span. World countries targets to provide better care for their citizens. Digital transformations and innovations in Artificial Intelligence (AI) technologies helps in deep for the novel solutions to speed-up the decision making process in health management and patient consulting. AI branches including Machine Learning (ML), Deep Learning (DL), Neural Networks (NN), Natural Language Processing (NLP) and so forth can support innovations in heal care domain. This paper presents the comprehensive and noble review on recent existing tools and techniques and also recommends advanced techniques for health care industry that are to be used in Sultanate of Oman. Furthermore, overcoming AI challenges are resulted and presented through the study. This work mainly aimed to produce the multiple dimensions and challenges that helps to develop AI powered real-life applications in health care support system. © 2025 IEEE.

关键词： Nutrition

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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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An Improved Bearing Fault Investigation Scheme Using 1D CNN with PCA and SVM 3

An Improved Bearing Fault Investigation Scheme Using 1D CNN ...

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3rd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2025

作者： Kumar, Rajeev Anand, R.S. Akhtar, Muhammad Nameer Khanna, Rintu Punjab Engineering College (Deemed-to-be-University) Department of Electrical Engineering Chandigarh India Indian Institute of Technology Department of Electrical Engineering Roorkee India University of Petroleum and Energy Studies Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331521691

This study presents a novel approach for accurate motor bearing fault detection through a sequential process involving one-dimensional convolutional neural network (1D CNN) for feature extraction, Principal Component Analysis (PCA) for dimensionality reduction, and Support Vector Machine (SVM) for classification. The main objective of this approach is to improve the model performance for complex fault combinations by achieving high precision and recall scores. This study has been carried out using online available datasets of bearings faults provided by Bearing Data Center, Case Western Reserve University (CWRU), USA. In this study, bearing inner race, outer race and rolling ball defects are analysed under variable load operations using the proposed 1D-CNN scheme. This research highlights the potential of the proposed model to advance motor condition monitoring systems, offering a reliable and efficient method for fault detection across diverse operational scenarios. © 2025 IEEE.

关键词： Bearing Faults Convolutional Neural Network Principal Component Analysis Support Vector Machine

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