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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Prakash, V.S. Muthu, Annamalai Anandan, B. Velmurugan, V. Raja, T. Alex Stanley Purushothaman, V. Kristu Jayanti College Department of Computer Science Bengaluru India University of Technology and Applied Science Electrical Section Engineering Department Nizwa Oman Siddharth Institute of Engineering & Technology Department of Mechanical Engineering Andhrapradesh Puttur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Avadi Tamil Nadu Chennai India Bannari Amman Institute of Technology Department of Electrical and Electronics Engineering Sathyamangalam India Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350376685

The rapid global shift towards electric vehicles (EVs) marks a transformative era in transportation, driven by the imperative to reduce carbon emissions and reliance on fossil fuels. Central to the performance and longevity of EVs are Battery Management Systems (BMS), which optimize battery usage, enhance safety, and extend operational life. This comprehensive review explores current trends and future directions in BMS optimization, focusing on key technological advancements and their implications for electric mobility. Battery Management Systems (BMS) serve as critical components in electric vehicles, orchestrating the efficient operation of battery packs through real-time monitoring, state-of-charge (SoC) estimation, thermal management, and fault diagnosis. These systems not only ensure optimal performance but also safeguard against potential hazards, thereby enhancing vehicle reliability and user confidence. Current advancements in BMS technology emphasize enhanced accuracy in SoC estimation, leveraging sophisticated algorithms and sensor integration to provide precise information on battery charge levels. Coupled with innovative thermal management strategies, which include active cooling and heating systems, BMS can mitigate thermal stress and optimize battery performance across diverse environmental conditions. By synthesizing technological advancements, emerging trends, and potential challenges, it offers valuable insights for researchers, engineers, and policymakers aiming to advance the efficiency, reliability, and sustainability of electric mobility solutions worldwide. © 2024 IEEE.

关键词： State of charge

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ENCL: Empirical Evaluation of Consumer Behaviour Analysis Model Using Internet of Things (IoT) Based Enhanced Neural Classification Logic

ENCL: Empirical Evaluation of Consumer Behaviour Analysis Mo...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Merlin, S. Sudha Malathy, K. Ravi, S. Arun, M. Raut, S.P. Fawareh, H. Dmi College of Engineering Department of Electrical and Electronics Engineering Palanchur Chennai India S.A. Engineering College Department of Mca Chennai India Department of Vlsi Microelectronics Chennai India Kings College of Engineering Dept of Computer Science and Engineering Punalkulam Thanjavur India Yeshwantrao Chavan College of Engineering Department of Civil Engineering Nagpur India Zarqa University Faculty of Information Technology Software Engineering Dept Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The proliferation of consumer data can be attributed to 'the Internet of Things (IoT),' which makes it easier to access and more detailed than before. In this research our purpose is to evaluate the effectiveness of a consumer behavior analysis model using data from IoT. The quality of the model is evaluated on multiple parameters - accuracy, predictability and business sense for marketing. It uses the analytics of huge amounts for data from IoT devices to predict patterns, customer preferences and trends. The proposed method called as Enhanced Neural Classification Logic (ENCL) is cross-validated with the already existing method, which is termed as Consumer behavior Forecasting Strategy (CBFS), and helps users to share gather or process information in a fast and cost-effective way through the proposed method. The results are useful to grasp the promise of IoT in more effective consumer behavior analysis. The research portrays the strengths, weaknesses and possible areas of improvement in this model which will be beneficial for both academic and industry practitioners. As such, the objective of this study is to show how an IoT-based consumer behavior analysis can increase business growth and customer satisfaction. The experimental evaluations demonstrate significant improvements in predictive performance over traditional methods, providing actionable insights for targeted marketing and strategic decision-making. The results highlight ENCL's potential to transform consumer behavior analysis by delivering more precise and nuanced understandings of market dynamics. © 2024 IEEE.

关键词： CBFS Consumer Behavior Data Analysis ENCL Internet of Things IoT Marketing Strategy Model Evaluation

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A Node MCU Enabled Comprehensive Remote Healthcare Evaluation System for Elder People Care and Support

A Node MCU Enabled Comprehensive Remote Healthcare Evaluatio...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Sonia, R. Manikandan, S.K. Chinnammal, V. Venkatanaresh, M. Harirajkumar, J. B S Abdur Rahman Crescent Institute of Science and Technology Department of Computer Applications Chennai Vandalur India Velalar College of Engineering and Technology Department of Biomedical Engineering Erode638012 India Rajalakshmi Institute of Technology Department of ECE Chennai India Mohan Babu University School of Engineering Department of ECE Erstwhile Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India Sona College of Technology Department of Electronics and Instrumentation Engineering Salem India

ISBN: (纸本)9798350319200

The Internet of Things (IoT) has an opportunity to greatly improve our quality of life by providing immediate data that is acquired from an intelligent network of linked devices and then transferred via the internet. In this piece, we have discussed the Remote Medical Diagnosis System and the several life-threatening situations in which it possesses the potential to be a life-saving tool. Sometimes people get sick in areas where there are no hospitals or other medical facilities and they don't make it when they don't get the treatment they need because they can't obtain it. Sometimes they don't make it. This problem is made even more difficult in the rural areas of emerging countries. As part of the presentation, the temperature and heart rate of a person are both read and shown online. Through the use of a web browser, you will have immediate access to all of your medical records. An integrated TCP/IP protocol stack is required in order to send and receive sensor information, such as a patient's heart beat or electrocardiogram (ECG). Using several metrics and Electrocardiogram (ECG) readings, the NodeMCU ESP8266 WiFi module sends patient data to the remote monitoring site. This information may be accessed in real time. The patient's information and settings are stored on a distant Internet of Things server. In this instance, patient data is monitored using a smartphone that is linked to the cloud by a NodeMCU ESP8266. The NodeMCU ESP8266 comes equipped with its own built-in WiFi connectivity. Every user of the system, from doctors and nurses to patients themselves, has easy and rapid access to the system's database, where they may evaluate the findings. The findings of the analysis may be accessed by the patient or the doctor on an Internet of Things website or an Android application, respectively. © 2023 IEEE.

关键词： Android Application Elder People Care ESP8266 Health Support Internet of Things IoT NodeMCU Remote Healthcare Remote Server

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Transforming Healthcare: AI Models for Predictive Analysis in Medical Applications 4

Transforming Healthcare: AI Models for Predictive Analysis i...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Shalinirajan, R. Sargunavathi, S. Prithivirajan, V. Shobana, D. Kurada, Phaneendra Roghini, P. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Tamilnadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Mechatronics Engineering Tamil Nadu Chennai India Mlr Institute of Technology Department of Ece Dundigal Hyderabad India Dhanalakshmi College of Engineering Department of Eee Tamil Nadu Chennai India

ISBN: (纸本)9798350354218

With significant ramifications for predictive analysis in medical applications, the introduction of Artificial Intelligence (AI) into healthcare represents a revolutionary change. This study examines how AI models could transform healthcare, especially in the areas of illness management, diagnosis, and treatment. AI may analyze enormous datasets, find patterns, and provide incredibly accurate patient outcome forecasts by utilizing sophisticated algorithms and machine learning approaches. The vast amounts of sensitive medical data required for AI analysis necessitate robust safeguards to protect patient confidentiality and prevent data breaches. The interpretability and transparency of judgments made by AI are also questioned due to the black-box aspect of some AI models, particularly deep learning algorithms. Ensuring the dependability and security of AI systems requires the establishment of defined protocols for data management, model validation, and performance monitoring. The establishment of a regulatory framework that promotes innovation and protects the interests of public health requires cooperation between regulatory agencies, healthcare facilities, and AI developers. To sum up, artificial intelligence (AI) models have great potential for predictive analysis in medical applications. They could completely transform patient care and the delivery of healthcare. By harnessing AI's power, healthcare providers can achieve earlier diagnoses, more effective treatments, and better resource management. However, realizing this potential requires addressing ethical, privacy, and regulatory challenges to ensure AI is deployed responsibly and effectively, paving the way for a more predictive, preventive, and personalized approach to medicine. © 2024 IEEE.

关键词： AI models Gradient Boosting Machine learning Predictive analysis

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Transformer Encoder and Decoder Method for Forest Estimation and Change Detection 2

Transformer Encoder and Decoder Method for Forest Estimation...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Kotin, Kiran Kashinath Kumar, Sharan Alabdeli, Haider Kumar, Gotte Ranjith Ramachandra, A.C. Smt. Kamala and Sri Venkappa M. Agadi College of Engineering and Technology Department of Ece Karnataka Lakshmeshwar India H.K.E. Society's S.M.V. College of Engineering Department of Ece Karnataka Raichur India The Islamic University Najaf Iraq Sr University School of Computer Science and Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350317558

Forest Change Detection (FCD) is difficult component of natural assess monitoring and conservation strategy, enabled informing decision-making. Different techniques using strength of Artificial Intelligence (AI) has implemented to detect and classify modifications in forest cover by Remote Sensing (RS) data. In this research, proposed a Transformer Encoder and Decoder (TED) method for forest estimation and change detection. The Multi Feature Fusion (MFF) is used for effective combine features of various scales. Next, Multilevel decoding named as Multi Scale and Multi Decoder (MSMD) is used at fine the scales much excessively. At last, loss is function is used for minimizing loss of MSMD. The performance of proposed method is analysed with performance measures of Intersection over Union (IoU) and Accuracy. The proposed method attained IoU of 86.23% and 87.45% in forest and background Attained mIoU of 85.21% and 88.92% in forest and background respectively. The proposed method attained accuracy of 92.89% and 92.33% in forest and background. Attained mAcc of 90.54% and 91.48% in forest and background respectively. The proposed method performed effective than previous methods like Multiscale Channelwise Cross Attention Network (MCCANet), UNet and Generative Adversarial Networks (GAN). © 2024 IEEE.

关键词： Forest Change Detection Multi Feature Fusion Multi Scale and Multi Decoder Remote Sensing and Transformer Encoder and Decoder

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Design and Optimization of Feedback Controllers for Motion Control in the Manufacturing System for Digital Twin

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SN computer Science 2023年第6期4卷 1-12页

作者： Ramesh, P.S. Vanteru, Madhu Kumar Rajinikanth, E. Ramesh, J.V.N. Bhasker, B. Begum, A. Yasmine Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of ECE Balaji Institute of Technology and Science Narsampet Telangana Warangal India Department of ECE Sathyabama Institute of Science and Technology Jeppiaar Nagar Sholinganallur Tamil Nadu Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur District India Department of Information Technology MLR Institute of Technology Hyderabad India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Andhrapradesh Tirupati India

If the classic fatal development approach's conventional distribution procedure reveals structural flaws, digital twin (DT) can enable hardware-in-the-loop modeling of individual actual devices and cyber models that may be exploited to prevent the significant expense of production method transformation. A quad-play CMCO (configuration, motion, control, and optimization) concept structure is proposed for the development of the flow-type smart manufacturing system in the Industry 4.0 environment, which is dependent on the DT concept. The CMCO architecture system repetition mechanism is explained. The comprehensive embedding of the quad-play CMCO paradigm and the DT approach are two significant design approaches for permitting the customizable and development tools construction of flow category smart manufacturing systems. Depending on the CMCO concept, a version of the DT (digital twin) system-developed proposed methods are demonstrated. A literature review of the glass industrial sector is used to support the validity of the development cost that relies on DTs. The outcome demonstrates the viability and efficacy of the DT system-based design scheme. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Design approach Digital twin Motion control Optimization Smart manufacturing system

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Enhancing Speech Recognition for Hearing-Impaired Individuals Using Deep Learning Models 7

Enhancing Speech Recognition for Hearing-Impaired Individual...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Kalaiarasi, K. Medidhi, Winson Niresh Kumar, S. Thenappan, S. Sathiyaseelan, R. Mohana Sundaram, K. Saveetha Institute of Medical and Technical Sciences Department of ECE Saveetha School of Engineering Chennai India Vijayawada Andhra Pradesh Kanuru India St. Joseph's College of Engineering OMR Department of CSE Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Arunai Engineering College Department of Computer Science Engineering Tamil Nadu Thiruvannamalai India R.M.D. Engineering College Department of Computer Science and Engineering Thiruvallur Kavaraipettai India

ISBN: (纸本)9798350372816

This study examines the constraints of current methodologies, specifically in diverse acoustic environments, to surmount the obstacles associated with speech recognition for individuals with hearing impairments. Conventional processes frequently experience reduced accuracy and utility as a result of their incapability to dynamically adjust to evolving circumstances. This study presents an innovative approach that surpasses these constraints through the utilization of a transformer-based model. Through the implementation of sophisticated data augmentation techniques, the proposed model demonstrates exceptional performance across various acoustic environments, notwithstanding obstacles such as speaker adjustments and background noise. One notable characteristic of this methodology is its transformer architecture, which aptly captures subtle speech inflections by employing self-attention processes. Additionally, through independent learning, the model improves its ability to address the unique challenges associated with hearing-impaired speech. Integrating these components, the model achieves superior accuracy and adaptability compared to current technologies. © 2024 IEEE.

关键词： Self-supervised learning

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Reinforcement Learning for Dynamic Power Management in Embedded Systems 5

Reinforcement Learning for Dynamic Power Management in Embed...

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5th IEEE International Conference on Recent Trends in computer Science and Technology, ICRTCST 2024

作者： Panigrahi, Bhawani Sankar Pattanaik, Balachandra Pattanaik, Ojasvi Tilak Babu, S.B.G. Pavithra, G. Shaik, Bazani Vardhaman College of Engineering Department of IT Telangana Hyderabad India Saveetha Institute of Medical and Technical Sciences Adjunct Faculty Saveetha School of Engineering Tamilnadu Chennai India JNTU Vignan Institute of Management and Technology for Women Department of Computer Science and Engineering Hyderabad Ghatkesar India Aditya Engineering College Department of ECE Surampalem India Dayananda Sagar College of Engineering Department of Electronics & Communication Engineering Karnataka Bangalore India Ramachandra College Of Engineering Department of Mechanical Engineering Andhra Pradesh Eluru India

ISBN: (纸本)9798350351378

Itaddresses the challenges that are associated with dynamic power management in embedded systems by utilizing techniques that are derived from the discipline of reinforcement learning (RL). Given the increasing complexity of embedded systems and the need for solutions that are efficient for energy consumption, RL is a potential technology that can dynamically optimize power utilization. This is particularly pertinent in light of the requirement for solutions that are efficient for energy consumption. The dynamic workload fluctuations that are inherent in embedded systems are taken into consideration in this research, which studies the integration of RL algorithms to control power levels in real time in an adaptive manner. This research also takes into account the variable workloads that are endemic to embedded systems. To evaluate the efficacy of RL-based dynamic power management strategies in comparison to traditional methods, with a specific emphasis on the potential for enhanced energy efficiency and system responsiveness, the goal of this is to evaluate the effectiveness of these strategies. The findings not only contribute to the improvement of understanding of the use of reinforcement learning in the setting of embedded systems, but they also provide insights into the utility of RL in meeting the evolving power management requirements in contemporary computing environments. © 2024 IEEE.

关键词： Reinforcement learning

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Comparative Analysis on Sediment Classification using Convolutional Neural Network 3

Comparative Analysis on Sediment Classification using Convol...

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3rd International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Kumudham, R. Kumar, Sathish Megalan, Leo L Sivakumar, S. Venkateshkanna, T. Velmurugan, V. Chennai India Rajalakshmi Institute of Technology Department of Computer and Communication Engineering Chennai India Department of ECE School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai India Department of ECE Chennai126 India JAYA Engineering College Department of Electronics and Communication Engineering Chennai India R&D Institute of Science and Technology Vel Tech Rangarajan Dr. Sagunthala Department of ECE Chennai India

ISBN: (纸本)9781665462754

In this proposed work, we trained the neural network models for feature extraction and categorize the underwater seafloor sonar images into sediments, sands, mines, rocks, mud, gravels, etc using Convolutional Neural Network such as ResNet50, Inceptionv3 and VGG-19. Moreover, the performance of neural network is estimating by metrics measures such as validation accuracy, accuracy, loss, and accuracy loss upto 10 epochs. Hence assessment had performed among all three neural network models. The experimental output signifies that ResNet50 method achieves 94.4% appreciable accuracy with loss as 0.01 in distinguishing sonar imagery into sediments, sands, mud © 2022 IEEE.

关键词： Convolutional neural networks

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A Superficial Learning Strategy to Identify Email Spams Based on Hybrid Data Filtration and Classification Methodology

A Superficial Learning Strategy to Identify Email Spams Base...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Alekhya, A. Tagore Kumar, P. Bharaneedharan, M. Kavitha, S. Geetha, C. Thyagarajan, C. Qis College of Engineering and Technology Department of Electrical and Electronics Engineering Andhra Pradesh Ongole India Qis College of Engineering and Technology Department of Mechanical Engineering AndhraPradesh Ongole India S.A. Engineering College Department of Ece Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Cse Chennai India R.M.K. Engineering College Chennai Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Cse Chennai India

ISBN: (纸本)9798331543617

Spam email detection is a critical task in improving email security, especially with the increasing volume of spam emails that threaten users' privacy and productivity. In this study, we propose a hybrid approach combining Gated Recurrent Units (GRU) and Support Vector Machine (SVM) to identify spam emails effectively. The GRU component captures temporal dependencies in the email text, while SVM classifies the extracted features into spam or non-spam categories. The model was evaluated using various performance metrics, including accuracy, precision, recall, and F1-score, and compared with other models like standalone GRU, SVM, CNN, and Naive Bayes. The proposed hybrid GRU-SVM model achieved a superior accuracy of 96.8%, outperforming GRU (93.2%), SVM (91.5%), CNN (94.1%), and Naive Bayes (89.4%). Additionally, the model demonstrated a high true positive rate (98.2%) and a low false negative rate (1.8%) on a spam-heavy dataset, indicating its robustness in identifying spam emails. The results suggest that the hybrid model can significantly enhance spam email detection systems by improving classification accuracy and reducing false positives. © 2024 IEEE.

关键词： Support vector machines

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