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2nd International Conference on Cyber Physical Systems, Power Electronics and Electric Vehicles, ICPEEV 2024

作者： Matury, Sai Sathwik Sam Stewart, X. Ra, Nawin Kumba, Krishna School of Computer Science & Engineering Vellore Institute of Technology Chennai India School of Electrical Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350388664

The proposed work delves into the comprehensive design, development, and evaluation process of a 3D printed prosthetic arm, employing servo motors controlled by an Arduino Uno microcontroller to enable gesture mimicry through flex sensors integrated into a wearable glove. The primary objective is to provide an affordable and accessible solution for individuals with upper limb disabilities. The paper outlines the intricacies of the design process, system architecture, programming nuances, and rigorous testing procedures, showcasing promising results in terms of gesture accuracy, adaptability, and user-friendliness. The prosthetic arm demonstrates high precision in replicating various hand gestures. Future work will focus on enhancing the durability and responsiveness of the system to further improve its practical application. User feedback has been overwhelmingly positive, indicating significant potential for real-world adoption. © 2024 IEEE.

关键词： Servomotors

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Digital Regimen for Parkinson Disease: An Informative Approach Using Mobile Application 2

Digital Regimen for Parkinson Disease: An Informative Approa...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Seya Prakash, P. Gunasekaran, G. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Vellore Institute Technology Department of Smart Computing Vellore India

ISBN: (纸本)9798350328202

Parkinson's disease (PD) is an advanced neuro-degenerative condition where a large number of people around the world are profoundly affected. The scarcity of precise data regarding the typical circumstances faced by humans with PD severely restricts ongoing research and therapeutic endeavours. This proposed work aims to investigate various innovations to address this issue comprehensively. Specifically, this proposed work intends to develop a versatile application tailored to help the PD patients and incorporating smart medication reminders. Moreover, this proposed work will focus on creating exercise/workout plans & diet plans based on continuous data collection and patient feedback to enhance prescription management while minimizing side effects. Simultaneously, initiatives aimed at reducing the stress level and improve mental health. The development and implementation of this versatile applications hold significant promise in improving patient outcomes. By addressing the dearth of accurate information and fostering a systematic support ecosystem, it aspires to enhance the lives of individuals grappling with PD on a global scale. © 2024 IEEE.

关键词： Neurodegenerative diseases

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A novel and secured email classification and emotion detection using hybrid deep neural network

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 44-57页

作者： Krishnamoorthy, Parthiban Sathiyanarayanan, Mithileysh Proença, Hugo Pedro School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India MIT Square London United Kingdom University of Beira Interior Covilha Portugal

Compared to other social media data, email data differs from it in various topic-specific ways, including extensive replies, formal language, significant length disparities, high levels of anomalies, and indirect linkages. In this paper, the creation of a potent and computationally effective classifier to categorize spam and ham email documents is proposed. To assess and validate spam texts, this paper employs a variety of data mining-based classification approaches. On the benchmark Enron dataset, which is open to the public, tests were run. The final 7 Enron datasets were created by combining the six different types of Enron datasets that we had acquired. We preprocess the dataset at an early stage to exclude any useless phrases. This method falls under several categories, including Logistic Regression (LR), Convolutional Neural Networks (CNN), Random Forests (RF), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and suggested Deep Neural Networks (DNN). Using Bidirectional Long Short-Term Memory (BiLSTM), email documents may be screened for spam and labeled as such. In performance comparisons, DNN-BiLSTM outperforms other classifiers in terms of accuracy on all seven Enron datasets. In comparison to other machine learning classifiers, the findings demonstrate that DNN-BiLSTM and Convolutional Neural Networks can categorize spam with 96.39 % and 98.69 % accuracy, respectively. The report also covers the dangers of managing cloud data and the security problems that might occur. To safeguard data in the cloud while maintaining privacy, hybrid encryption is examined in this white paper. In the AES-Rabit hybrid encryption system, the symmetric session key exchange-based Rabit technique is combined with the benefits of the AES algorithm for faster data encryption. © 2024

关键词： Long short-term memory

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EVGPT - Fine-Tuned Large Language Model for Electric Vehicle Operational Assistance 4

EVGPT - Fine-Tuned Large Language Model for Electric Vehicle...

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4th IEEE International Conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2024

作者： Kanimozhi, S. Prithvi, G. Barath, M. Vaikash, K.G. Mathew, Abel Manoj Gukhan, V.B.S. School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Mechanical Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331528614

The optimization of electric vehicle (EV) utilization and efficiency is becoming increasingly essential in the ever-changing landscape of sustainable transportation. Consulting user manuals, online resources, and seeking expert advice for EV maintenance and operation is a significant obstacle to productivity. Electric Vehicle GPT (EVGPT) is a specialized Large Language Model (LLM) specifically designed to address queries related to EV maintenance, operation, and troubleshooting, filling the gaps that conventional LLMs may miss. This model serves as a comprehensive digital assistant for technicians, operators, and EV enthusiasts, providing timely and precise information while upholding response quality. This study offers an overview of the EVGPT model architecture and presents an operational evaluation based on its implementation in a specific EV model. ©2024 IEEE.

关键词： Electric vehicles

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A CNN Approach for Brain Tumor Detection Using Diverse Optimizers 3

A CNN Approach for Brain Tumor Detection Using Diverse Optim...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Revathi, Ar Venkataraman, Rishab Naveen Moola, Maitreya School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350375190

The ability to recognize brain tumors via medical imaging is crucial for brain tumor treatment. This proposed work focused on a technique for detecting brain tumors by employing Convolutional Neural Networks (CNNs) to achieve precise and prompt diagnosis of brain tumors. This frame utilizes a variety of optimizers such as Stochastic Gradient Descent, RMSprop, Adam (Adaptive Moment Estimation), Adadelta, Adagrad, Adamax, and Nadam. The model's performance is evaluated using accuracy, sensitivity, specificity, precision, and recall as the primary metrics. Leveraging a dataset of 6951 MRI images, results reveal varying performance across optimizers, with a peak accuracy of 98.8% emphasizing the need for careful selection based on specific requirements and dataset characteristics. Advancing brain tumor identification, aiming to enhance early intervention and patient outcomes through the integration of cutting-edge technology, particularly Convolutional Neural Networks (CNNs), which offer high precision in analyzing intricate patterns within medical imaging data. © 2024 IEEE.

关键词： Magnetic resonance imaging

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An Earthquake Alert system using Internet of Things 10

An Earthquake Alert system using Internet of Things

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Teja, N. Vishnu Akshay, S.P. Khaleed, K.Abu Kumar, C.Santan Manoj Kumar, G.P. Bhavadharini, R.M. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350353068

This project introduces an innovative earthquake detection system leveraging accelerometers for seismic sensing. By incorporating accelerometers from widely available devices like smartphones into a distributed sensor network, the system aims to provide a cost-effective and scalable solution for enhancing seismic monitoring capabilities. Real-time data processing, supported by advanced signal processing and machine learning algorithms, enables the system to differentiate between normal vibrations and seismic activities. Upon detecting anomalies, the system generates alerts to facilitate swift response and early warning mechanisms. Noteworthy features of this system include its affordability, easy deployment, and suitability for implementation in diverse environments, both urban and remote. The project contributes to advancing earthquake detection technologies, thereby bolstering preparedness and resilience against seismic events © 2024 IEEE.

关键词： Accelerometers

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Leveraging Data Analytics in Azure for Effective Churn Management 2

Leveraging Data Analytics in Azure for Effective Churn Manag...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Gokulnath, Shreejith Suthraye Revathi, M. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350328202

This research aims to develop optimized and cost-effective Azure architecture solutions by strategically selecting and utilizing Microsoft Azure services for specific use cases. The study leverages a simulated data-driven churn management case study within a social network context, emulating real-world data scenarios. The methodology employed involves a comprehensive understanding of the existing churn management script, enabling adaptability to diverse scenarios. This necessitates an in-depth exploration of Microsoft Azure's service offerings to ascertain their necessity and relevance. As the case study entails real-time streaming of event data, it is channeled into a staging area while concurrently undergoing batch processing for both account and subscription data. This synthesis culminates in a unified storage space tailored for analytics purposes. The implications of this research are twofold. First, the study yields a high-impact automated asset from a business standpoint. Second, it contributes expertise in the intricate process of migrating an entire asset from on-premises infrastructure to the cloud. Furthermore, using streamlined cost optimization achieved by judiciously selecting and configuring only essential resources, culminating in efficient outcomes. This paves the way for client-side stakeholders to make informed, data-driven decisions, leveraging the insights harnessed from the system. © 2024 IEEE.

关键词： Cost effectiveness

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Weed detection in precision agriculture: leveraging encoder-decoder models for semantic segmentation

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Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3547-3561页

作者： Thiagarajan, Shreya Vijayalakshmi, A. Grace, G. Hannah Shell India Markets Private Limited Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Division of Mathematics School of Advanced Sciences Vellore Institute of Technology Chennai India

Precision agriculture uses data gathered from various sources to improve agricultural yields and the effectiveness of crop management techniques like fertiliser application, irrigation management, and pesticide application. Reduced usage of agrochemicals is a key step towards more sustainable agriculture. Weed management robots which can perform tasks like selective sprinkling or mechanical weed elimination, contribute to this objective. A trustworthy crop/weed classification system that can accurately recognise and classify crops and weeds is required for these robots to function. In this paper, we explore various deep learning models for achieving reliable segmentation results in less training time. We classify every pixel of the images into different categories using semantic segmentation models. The models are based on an encoder-decoder architecture, where feature maps are extracted during encoding and spatial information is recovered during decoding. We examine the segmentation output on a beans dataset containing different weeds, which were collected under highly distinct environmental conditions, including cloudy, rainy, dawn, evening, full sun, and shadow. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Semantic Segmentation

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Data Driven Approach for Analysing the Relationship between Weather Patterns and Energy Consumption in Residential Buildings for Improved Energy Management 5

Data Driven Approach for Analysing the Relationship between ...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kowshik, S.B. Ramakrishnan, R. Amutha, S. Dhanush, R. Akshay, P. Preetam Kalyaan, P. School of Electronics Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331518097

Residential buildings are the most significant providers of global energy consumption. Hence, studying the weather patterns and energy use in residential buildings remains vital for effective energy management strategies. This work explores a data-driven approach to analyse the complex interplay between weather and energy consumption in residential settings. Traditional methods for understanding building energy use often rely on physical models or occupant surveys, which can be time-consuming and expensive. This research addresses the need for a more efficient and comprehensive approach to analyse the effect of weather patterns on energy consumption. Following deep learning sequence models were used to most accurately predict the energy consumption - LSTM (Long Short Term Memory), Bi-LSTM (Bidirectional LSTM) and Attention Based LSTM (ABLSTM) and find the ABLSTM model most accurate and suited for our requirement with RMAE value as 0.3086, R2 as 0.9990. This research provides individuals and communities with the knowledge to make informed decisions about their energy usage, potentially leading to reduced energy costs through optimized consumption and can contribute to improved energy security. © 2024 IEEE.

关键词： Building energy prediction Data driven Deep learning Energy shortage LSTM Sustainability Sustainable energy

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Super Long Range CNN For Video Enhancement in Handball Action Recognition 3

Super Long Range CNN For Video Enhancement in Handball Actio...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Pratik, V. Palani, Saravanan Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350372120

This paper explores the fusion of Longterm Recurrent Convolutional Neural Network (LRCNN) for action classification and Single Image Super-Resolution Convolutional Neural Network (SRCNN) for enhancing image quality in handball video sequences. Building upon previous work utilizing Mask R-CNN and an Optical Flow-Based Method for handball action detection, our study extends the framework by integrating LRCNN for precise action classification and SRCNN for improving spatial resolution and image quality as a hybrid model, Super Long Range Convolutional Neural Network (SLRCNN). LRCNN accurately classifies handball actions, while SRCNN enhances visual fidelity, leading to higher Peak Signal-to-Noise Ratio (PSNR) values. Through thorough experimentation and analysis, we assess the effectiveness of SLRCNN in capturing and classifying subtle handball actions, advancing sports analytics in computer vision with a holistic approach optimizing both action recognition accuracy and image quality metrics. © 2024 IEEE.

关键词： Convolutional neural networks

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