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检索条件"机构=School of Computer Science Engineering and Information Systems Vellore Institute of Technology"

共 30959 条记录，以下是421-430 订阅

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Classification of Healthy and Unhealthy Leaf Disease Using Modified MobileNetV2 5

Classification of Healthy and Unhealthy Leaf Disease Using M...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Kumar, E. Sudheer Kumar, Aakash Vardhan, Harsha Prasanth, Rajaratnam Sai, Pindi Punith Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350389609

A study employing Convolutional Neural Network (CNN) architectures, specifically MobileNetV2 band ResNet-50, compares their performance in classifying tomato leaf diseases, aiming to address the substantial challenges faced by farmers due to these diseases. Previously, various kinds of diseases belonging to tomatoes were identified using the model, which leads to lesser accuracy and would require more computational energy. The dataset utilized for the research consists of 1900 healthy and 1800 unhealthy tomato plant images which includes all the diseases, with an additional 500 healthy and 1080 unhealthy plant images set aside for validation. MobileNetV2, renowned for its lightweight design, achieves an impressive accuracy of 95.3% after customizing the model with various layers makes it unique from the other existing models. While ResNet-50, known for its deeper architecture, attains a lower accuracy of 51%. The selection of MobileNetV2 as the preferred model is based on its superior accuracy and computational efficiency. The study includes analysis utilizing backpropagation to understand weight update trends within the chosen models. These results highlight the potential of deep learning approaches, specifically MobileNetV2 and ResNet-50, in automated disease diagnosis in agriculture, offering solutions to mitigate crop losses and enhance productivity in tomato cultivation. Overall, the study contributes significantly to advancing the field of automated disease diagnosis in agriculture by leveraging deep learning techniques, particularly MobileNetV2 and ResNet-50, for the classification of tomato leaf diseases. The insights gained from this research have profound implications for improving crop yield and sustainability in tomato cultivation practices, ultimately aiding in the advancement of agricultural technology and practices. © 2024 IEEE.

关键词： Fruits

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Adaptive Edge Computing for Autonomous Vehicles in Varying Environment 2

Adaptive Edge Computing for Autonomous Vehicles in Varying E...

引用

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

作者： Shreyas, K. Manjula, V. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350328202

This research offers an in-depth analysis of the integration of Autonomous Vehicle Platooning, Vehicle-to-Everything (V2X) communication, Edge Analytics, and Real-time Traffic Management. Central to the study is 'Platooning,' where autonomous vehicles optimize road space, fuel efficiency, and safety. V2X communication, a silent yet critical interaction between vehicles and their surroundings, lays the foundation for advanced safety and navigation in autonomous vehicles. When combined with V2X, platooning generates vast data, making Edge Analytics essential for filtering realtime intelligence for swift decisions. The study emphasizes the importance of Real-time Traffic Management in managing urban congestion, with Edge Analytics providing dynamic route planning. By focusing on algorithms and practical applications, the research aims to merge these technologies for efficient, safe, and responsive Autonomous vehicles (AV) operations, offering insights for urban planning and traffic management, and setting the stage for a sustainable transportation future. © 2024 IEEE.

关键词： Autonomous vehicles

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Prophet and NeuralProphet compared with Indian inflation data 11

Prophet and NeuralProphet compared with Indian inflation dat...

引用

11th IEEE International Conference on Signal Processing and Integrated Networks, SPIN 2024

作者： Subburaj, Brindha Agrawal, Aditya Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350308433

The word inflation is a commonly used expression to describe the overall increase in the costs of products and services in the market over time. In this research work, we will look at inflation as a whole, as well as for various population categories and goods within each population group. We will investigate demographic groups such as urban, rural, and combined, and we will compute the real Consumer Price Index (CPI) using the Reserve Bank of India's (RBI) weighted average technique. This research used statistics from 2011 to 2022. The RBI calculates the real CPI using a weighted average technique that takes into consideration the relative significance of various commodities within a population category. This technique is used to guarantee that the CPI accurately reflects the actual expense of living for the population group under consideration. It is essential to observe, that inflation varies significantly between various population groups and commodities. Because of higher living expenses and a higher demand for goods and services, urban areas generally have higher inflation rates. Rural regions, on the other hand have lower inflation rates because of reduced living expenses and lower demand for products and services. We can obtain a clearer grasp of how inflation impacts various segments of the population by analyzing data for these distinct population *** the basis of inflation statistics, this article compares two time-series forecasting models, Prophet and NeuralProphet. Both algorithms will be tested on the same data and their accuracy in forecasting will be contrasted. The root mean squared error (RMSE) and Mean absolute error (MAE) will be used as comparative statistics to assess accuracy. The performance of Prophet and NeuralProphet is evaluated in time-series forecasting and to look into the possible benefits of using parallelism on Prophet. This study's results will cast light on the relative strengths of these techniques. © 2024 IEEE.

关键词： Mean square error

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Vision Beyond the Haze: Advancements in Cataract Detection and Diagnosis 2

Vision Beyond the Haze: Advancements in Cataract Detection a...

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2nd International Conference on Self Sustainable Artificial Intelligence systems, ICSSAS 2024

作者： Kumar, E. Sudheer Reddy, N U. Praneeth Reddy, Depatla Hemika Kanish, Tulasi Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350368413

Eye diseases are among the most critical health issues impacting human life. One of the most frequently encountered eye problems is cataracts, due to which people suffer as they age. The cloudiness that forms on eye lenses is known as cataracts which can significantly damage vision. Diagnosing the disease in its acute stage rather than its onset should not blind the patient. This study presents an improved Cataract detection method using pre-trained convolutional neural networks (CNNs) on an Ocular Disease Intelligent Recognition (ODIR) dataset, Comparing the performance of ResNet50, ResNet101, and VGG19 architectures to find the most effective model for this project. The proposed approach uses deep learning to analyse fundus images, aiming to increase diagnostic accuracy, speed, and reliability. several preprocessing and data Augmentation techniques were utilized to prepare the data set, which provided robust training and improved model generalization. Samples were refined using pre-trained ImageNet weights and adjusted for binary classification of Cataract presence. Among the tested models, ResNet50 exhibited the best performance, achieving an overall accuracy of 99.08% It also showed good evaluation metrics, with a precision rate of 98.15% and 100% recall for cataract images, and 100% precision with 98.23% recall for normal images. The results highlight the potential of ResNet50 to provide accurate and effective cataract diagnosis, especially in areas where access to trained ophthalmologists is inaccessible. This study highlights the importance of using deep learning technologies to improve the early detection and treatment of cataracts, ultimately contributing to better patient outcomes and global eye health progress. © 2024 IEEE.

关键词： Convolutional neural networks

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An Intelligent System for Preventing Accidents Due to Driver Distractions 2

An Intelligent System for Preventing Accidents Due to Driver...

引用

2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Giridhar, S. Rhithik Raj, K. Sreeram, K.P. Menon, Nithish Ravikumar, Aswathy Sriraman, Harini Sriraman, Harini School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The paper presented introduces an innovative accident prevention system that employs deep learning models and sensors to identify and alert drivers who lose concentration while driving for a variety of reasons. The system targets drowsiness, cell phone use, and alcohol consumption, which are among the leading causes of automobile accidents. The proposed solution employs neural networks to identify specific patterns associated with driver inattention and generates an audible alert to refocus the driver's attention on the road. In addition, the system utilizes alcohol-detection sensors, as alcohol consumption is another significant cause of accidents. This system seeks to reduce the number of casualties by enhancing road safety considering the high number of daily collisions, which are primarily caused by speeding. The paper describes the design, architecture, and implementation of the system on a modest scale to provide a lifesaving, cost-effective solution. © 2024 Elsevier B.V.. All rights reserved.

关键词： Accidents

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Anxiety Monitoring and Digital Meditation Aids

Anxiety Monitoring and Digital Meditation Aids

引用

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Devi, Kanniga R. Muthuraman, Poorvesh Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350378092

Amidst a demanding world, accessible mental health support is crucial. This project proposes a portable hardware solution for anxiety reduction, integrating breathing exercises, like the physiological sigh, known for alleviating stress. The aim is to assist users in practicing these exercises during distress by leveraging wearable technology, particularly the Apple Watch. The research explores the development, design, and effectiveness of this solution, emphasizing the intersection of technology and mental health for a resilient society. This research paper explores the intricate development process, design considerations, and the overall effectiveness of this innovative solution. It emphasizes the potential of wearable technology in bridging the gap between technology and mental health, offering a personalized and adaptive approach to anxiety management. Through continuous user feedback and iterative improvements, the project aspires to contribute to a more resilient society, where individuals are empowered to manage their mental well-being through the seamless integration of technology and mindfulness practices. © 2024 IEEE.

关键词： Wearable computers

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Implementing OTP Verification and Zero Trust Security for Role-Based Access Control in Medical Records 3

Implementing OTP Verification and Zero Trust Security for Ro...

引用

3rd International Conference on Smart Technologies and systems for Next Generation Computing, ICSTSN 2024

作者： Harshini, B.V. Ulagarchana, U. Mounika, Perla Mary Shamala, L. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350391565

This paper proposes a comprehensive approach for enhancing security in medical record systems by incorporating One-Time Password (OTP) verification and Zero Trust Security principles within a Role-Based Access Control (RBAC) framework. With the increasing digitization of medical records and the growing threat of cyberattacks, ensuring confidentiality, integrity, and availability of patient information is paramount. OTP verification adds a layer of authentication, mitigating the risk of unauthorized access even in the event of compromised credentials. Integrating Zero Trust Security principles ensures that access permissions are continuously evaluated and verified based on various contextual factors, such as device health, user behavior, and network location, rather than relying solely on static credentials. Furthermore, RBAC facilitates granular access control, allowing administrators to assign roles and permissions based on users' responsibilities and organizational hierarchy. By combining OTP verification, Zero Trust Security, and RBAC, healthcare organizations can establish a robust security framework that minimizes the risk of data breaches and ensures compliance with regulatory requirements such as HIPAA. © 2024 IEEE.

关键词： Electronic health record

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DeepFrame: A Scientific Framework for Advancing Ocean Cleanup through Underwater Debris Detection and Geospatial Mapping

DeepFrame: A Scientific Framework for Advancing Ocean Cleanu...

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

作者： Jesher Joshua, M. Ragav, V. Pandiyaraju, V. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

The escalating concern of underwater debris demands innovative approaches for effective ocean cleanup. This study presents a comprehensive framework utilizing deep learning methods to detect, categorize, track, and geospatially map un derwater debris. By amalgamating cutting-edge object detection models, the system extracts real-time insights from underwater sensor data, facilitating the identification and categorization of debris based on various attributes. The incorporation of advanced tracking algorithms enables the monitoring of debris dynamics, contributing to predictive capabilities. Furthermore, geospatial mapping techniques are employed to project identified debris onto world maps, using latitude and longitude coordinates. This mapping feature aids in locating debris accumulation areas, thereby enabling strategic and efficient cleanup efforts. Through meticulous experimentation on authentic underwater debris datasets, our framework demonstrates its potential in precisely detecting, tracking, and mapping debris. The proposed solution leverages robust extended dataset of augmented images with its benchmark and novel Geo-Spatial mapping framework, which revolutionizes ocean conservation endeavors by providing valuable insights and ecologically-aware of underwater debris removal. The source code and the dataset have been made publicly available on GitHubl. © 2024 IEEE.

关键词： Mapping

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AgriAid: An Intelligent Farmer Companion Using Deep Learning Approach 2

AgriAid: An Intelligent Farmer Companion Using Deep Learning...

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

作者： Ilakiyaselvan, N. Dhandapani, Aarthi Khadar Nawas, K. Bhattacharya, Abhinov Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350306415

The percentage of farmers in India's enormous population is roughly 54.6%. However, despite having such a large population, they only contribute roughly 13.9% of the nation's total yearly GDP. This severe inequality may be reduced by improving farmers' access to information and expert advice. In order to increase crop output and quality, farmers typically depend on agricultural counselors and professionals to provide correct information for crop decision-making. Frequently, advisors or specialists in agriculture are not always available. A voice-based chatbot has several benefits, such as being real-time, constantly available, requiring little to no training to use, and providing instant access to information rather than requiring users to walk convoluted paths. In this study, we explain my design work for a farmer's helper, named Farmer Companion, to meet the needs of both rural and urban Indian farmers utilizing Supervised ML Algorithms and Deep Neural Networks, of which the best one is selected. A comparison of recent models that have been used in the literature is also included in the paper. © 2024 IEEE.

关键词： Artificial Neural Network Natural Language Processing Neural Networks Voice based Query System Voice-based Chatbot

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Unlocking Talent: Exploring the Potential of AI and ML Algorithms in Recruitment Process

Unlocking Talent: Exploring the Potential of AI and ML Algor...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Kundu, Sankhadip Palani, Saravanan Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350378092

A Recruitment process is complex and multifaceted. It requires a strategic approach to assess and select suitable candidates for the selected post while mitigating challenges along the way. This study aimed at predicting the likelihood of job candidates being hired, utilizing a diverse range of input features. Leveraging machine learning algorithms applied to a comprehensive dataset spanning over a decade and encompassing various populations, the research emphasizes the interpretability of the model's predictions. By integrating multiple machine learning algorithms, the study achieves remarkable prediction accuracy while providing valuable insights that HR professionals can interpret. Through meticulous analysis, distinct patterns and factors influencing hiring decisions are identified, offering practical implications for recruitment processes. The findings underscore the feasibility of predicting candidate hiring ability prior to employment, thereby facilitating the development of optimized recruitment strategies. This research significantly contributes to enhancing HR decision-making by furnishing a robust framework for evaluating job candidates' potential success. © 2024 IEEE.

关键词： Prediction models

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