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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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Voting Ensemble: Performance Improvement for Intrusion Detection System 3

Voting Ensemble: Performance Improvement for Intrusion Detec...

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

作者： Rajathi, C. Rukmani, P. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350372120

In the present digital world, security has become increasingly challenging. Many organizations rely on security mechanisms to safeguard their resources from intrusion. However, previous research indicates that despite the implementation of several security measures, organizations still face security breaches. Therefore, organizations must adopt an effective security mechanism. One such mechanism is the Intrusion Detection System (IDS), which examines network traffic to protect an organization from potential attacks. To handle the large volume of network traffic, a well-organized classification system is necessary. Machine learning classification, among the various approaches used in research, provides an effective and efficient solution for detecting intrusion and enhancing security. Despite significant efforts, IDS still faces challenges due to the nature of the concept drifting. To mitigate this issue, a hybrid ensemble approach is proposed. This approach combines the Majority Rule Ensemble (MRE) and Probability Consensus Ensemble (PCE). The ensemble is constructed using various classification algorithms such as Logistic Regression (LR), Naive Bayes (NBC), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). Each algorithm was individually built on the training dataset. The hybrid ensemble, which employs the MRE and PCE as aggregation methods, is then prepared by fitting it to the dataset. The proposed model's effectiveness is assessed using Network Security Laboratory - Knowledge Discovery and Databases (NSL-KDD) and a real-time testbed dataset. The results from the proposed hybrid ensemble show the improvement in the model's performance and the adaptability to the drifting nature of the intrusion dataset compared to a standalone model. © 2024 IEEE.

关键词： Network security

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Deep Neural Networks for Diagnosis of Parkinson's Disease Using Replicated Acoustic Features 1

Deep Neural Networks for Diagnosis of Parkinson's Disease Us...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Likhitha, Janga Sai Shahid, Afzal Hussain Reddy, Anamareddy Sravann School of Computer Science and Engineering Vellore Institute of Technology Amaravati India

ISBN: (纸本)9798350353648

Parkinson's disease is a neurodegenerative disease that affects a person's non-motor and motor functions causing tremors, stiffness, difficulty in moving, fatigue, anxiety, sleep disorders, etc. This condition happens because of the absence of dopamine-producing cells in the brain, and its symptoms worsen over time. This paper proposes a method for diagnosing Parkinson's disease using replicated acoustic features. This paper uses feature selection algorithms namely Fisher's, mRMR, Boruta, and ANOVA (Analysis of Variance) to find out the most discriminative features, and these features were fed into Random Forest (RF) and Deep neural networks (DNNs) to diagnose Parkinson's disease. 99.17% is the highest accuracy is achieved with the DNNs with Boruta and DNNs with ANOVA models. This is a significant improvement compared to previous studies. This result suggests that the replicated acoustic features can be used to diagnose Parkinson's disease. © 2024 IEEE.

关键词： Neurodegenerative diseases

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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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Prediction of drinking and irrigation chemical water quality indices using a multilinear regression approach in Cuddalore district, Tamil Nadu, India

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Water Supply 2025年第2期25卷 268-288页

作者： Sathiyamoorthy, Mahenthiran Gowrisankar, Ganesan Raman, Chandra Devi Kunasekaran, Krishnakumar Loganathan, Sankar Ramakrishnan, Devananth School of Civil Engineering Vellore Institute of Technology Tamil Nadu Vellore India Divecha Centre for Climate Change Indian Institute of Science Karnataka Bangalore India Sri Krishna College of Engineering and Technology Tamil Nadu Coimbatore India Vellore Institute of Technology Tamil Nadu Vellore India

Agriculture is predominantly practiced in the Cuddalore district, Tamil Nadu, India. The aim of this research work is to examine the groundwater suitability for drinking and irrigation activities and identify the factors influencing the groundwater quality. Physicochemical parameters such as pH, total dissolved solids, calcium, magnesium, sodium, potassium, chloride, sulphate, bicarbonate and nitrate are obtained for 62 groundwater samples (pre-monsoon and post-monsoon seasons). Based on the drinking water quality index (DWQI), most of the groundwater samples were good for drinking purposes in the region. The groundwater with elevated DWQI needs treatment before consumption. The parameters such as EC, Na%, SAR, MH and KR were integrated to produce a single irrigation water quality index (IWQI);it showed that most groundwater in the region is good for agricultural usage. Only a few samples were poor for agricultural usage. In these poor water quality areas, surface water and rainwater should be considered as alternative sources. There is a slight decrease in the concentration of groundwater ions during post-monsoon due to the dilution and dissolution processes. A multilinear regression model is also developed to predict the DWQI and the IWQI. The scatter diagram between various ions depicts that the geochemical process and anthropogenic sources influence the groundwater quality. © 2025 The Authors.

关键词： Potassium chloride

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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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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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