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

共 30943 条记录，以下是431-440 订阅

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Detecting Depression in Social Media using Machine Learning Algorithms

Detecting Depression in Social Media using Machine Learning ...

引用

2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Goyal, Shivansh Sriram, Ajay Abirami, S. Nathezhtha, T. Vellore Institute of Technology School of Computer Science & Engineering Chennai India

ISBN: (纸本)9798331541170

Social media platforms such as Facebook, Twitter, and Instagram drastically change our world. After the Covid-19 outbreak, people become more inclined to discuss their sentiments and emotions on social media. Every year, over 300 million individuals worldwide suffer from depression, a prevalent mental condition that can lead to suicide. Early detection of depression on social media can help save lives. The proposed work aims to identify depressive qualities in tweets. The main challenge of this task is performing feature engineering and modeling to classify tweets as depressed or not. Additionally, estimating both depressive and non-depressive traits in a tweet is essential for fully understanding its content, which is crucial for determining how best to assist the user. We propose a probabilistic machine learning (ML) approach that provides not only a binary classification but also a probability estimate for the classification. This approach allows us to capture the uncertainty and varying degrees of depressive traits in tweets, leading to more nuanced insights. The proposed model is tested using datasets generated from mining depressive tweets and an equal number of random tweets, totaling 39,111 tweets. By improving data collection with precise keywords and enhancing feature engineering through proper tweet cleaning, the proposed model's accuracy increases by 3%. The probabilistic approach further enhances the model's ability to assess the likelihood of depressive characteristics. © 2024 IEEE.

关键词： Tweets

来源：评论

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Automated Safety Compliance Monitoring in Industrial Environments with Autonomous Rover

Automated Safety Compliance Monitoring in Industrial Environ...

引用

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

作者： Sanjay, K.S. Devi R, Kanniga Arjun, P. Geethan, Daniel Anandha Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350378092

In today's industrial sector, the efficient monitoring of safety compliance is essential. The number of industrial accidents has been on the rise and this study proposes an alternate approach to traditional monitoring systems, designed to enhance safety by integrating NVIDIA's Jetson Nano for data processing and computer vision supported by NVIDIA's DeepStream SDK pipeline for real-time video analytics. Equipped with IR and ultrasonic sensors, the rover autonomously navigates and detects obstacles while seamlessly sending over data from the edge device to the cloud through the integration of Azure IoT resources, it achieves efficient data communication, enabling real-time safety management and analysis. © 2024 IEEE.

关键词： Ultrasonic sensors

来源：评论

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Metal Surface Defect Detection Using Deep Learning Techniques

Metal Surface Defect Detection Using Deep Learning Technique...

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Abhinav, S. Elakiya, E. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331540685

Metal defect detection plays a crucial role in ensuring the quality and safety of industrial manufacturing processes. Traditional methods of defect detection often rely on manual inspection, which can be time-consuming and prone to human error. In recent years, the application of deep learning techniques, particularly Convolutional Neural Networks (CNNs), has shown promising results in automating defect detection tasks. This research explores the effectiveness of using CNN architectures, specifically InceptionNet, ResNet34, and VGG16, for automated metal defect detection. The study involves training and evaluating these models on a dataset of metal surface images containing various types of defects. The performance metrics such as accuracy, precision, recall, and F1-score are analysed to assess the robustness and efficacy of each model in defect classification. Additionally, the research investigates the impact of transfer learning by fine-tuning pre-trained CNN models on the defect detection task. The experimental results demonstrate the superior performance of deep CNNs compared to traditional methods, highlighting the potential of these techniques for real-world applications in industrial quality control and inspection. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

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An Integrated Framework for Real-Time Disaster Data Acquisition, Processing, and Resource Optimization in Crisis Management systems 4

An Integrated Framework for Real-Time Disaster Data Acquisit...

引用

4th International Conference on Ubiquitous Computing and Intelligent information systems, ICUIS 2024

作者： Saranya, G. Atchaya, M. Harshini, R. Choudhary, Kartik School of Computer Science Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331529635

The rapid onset and unpredictable nature of disasters necessitate a quick, organized, and efficient response. This paper proposes a comprehensive disaster management platform that integrates real-time disaster information, weather forecasting, and resource management to enhance disaster preparedness and response. The platform collects disaster data from geophysical monitoring stations, processes the data for consistency, and stores it in a MongoDB database. Geospatial visualization tools, such as Folium, are employed to present disaster events on interactive maps, helping decision-makers assess the situation and respond accordingly. Additionally, the platform includes features for weather forecasting and resource management, allowing users to monitor conditions and contribute essential resources in real time. This research demonstrates how leveraging modern technologies can improve disaster management systems, providing a more effective and coordinated response to natural and man-made disasters. © 2024 IEEE.

关键词： Weather forecasting

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

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

引用

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

来源：评论

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Adopting Ensemble Learning and Machine Learning Techniques for Predictive Modeling in Traffic Data Analysis 3

Adopting Ensemble Learning and Machine Learning Techniques f...

引用

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

作者： Santhosh Narayanan, B. Shyamala, L. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

In this study, the trends of traffic data are analyzed using predictive analysis techniques applied to the NYC taxi dataset. Various models were tested for forecasting travel time, fare, and speed. Notably, the Decision Tree Regression model demonstrated outstanding performance with an impressive R2 score of 0.9995 for travel time prediction. Additionally, an ensemble model consisting of Linear Regression and Decision Tree Regression achieved an accuracy of 0.93 for fare prediction. Furthermore, another ensemble model combining LSTM and DBN attained an R2 score of 0.90 for speed prediction. This study highlights the effectiveness of diverse predictive modeling techniques in accurately forecasting these features. © 2024 IEEE.

关键词： Long short-term memory

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Alzheimer's Disease Classification Using CUDA-Accelerated DenseNet201 and VGG16 5

Alzheimer's Disease Classification Using CUDA-Accelerated De...

引用

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

作者： Munugala, Shravya Marathe, Atharva Prashant Vijayakumar, Rahul Kumaran, K. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331518097

Automated classification of Alzheimer's disease through MRI analysis presents significant computational challenges due to complex deep learning architectures and largescale image processing requirements. This paper introduces a GPU-accelerated multi-branch neural network framework that combines modified DenseNet201 and VGG16 architectures for efficient Alzheimer's disease stage classification. The proposed model employs parallel feature extraction pathways, with each branch independently processing MRI inputs before feature fusion. CUDA optimization techniques are implemented across the processing pipeline, including data loading, augmentation, and model training phases, resulting in significantly reduced computational overhead. The framework classifies brain MRI scans into four categories: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented. Experimental results demonstrate that the proposed hybrid architecture achieves 98% classification accuracy while substantially reducing training time through GPU acceleration. The model's performance is further enhanced through adaptive learning rate scheduling and selective layer fine-tuning. This work establishes an efficient approach for deploying complex neural architectures in clinical neuroimaging applications where computational resources and processing time are critical constraints. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Classification of Healthy and Unhealthy Leaf Disease Using Modified MobileNetV2 5

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

引用

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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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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Enhancing Cyberbullying Detection Through Keyword Filtering: A Comparative Study of ML and DL Approaches

Enhancing Cyberbullying Detection Through Keyword Filtering:...

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Kaarthika, R. Hemamalini, R. Sujithra Kanmani, R. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540685

Cyberbullying represents the use of online platforms like social media, gaming platforms, and similar platforms to harass a person, hurt a person, or threaten a person. In this study, keyword filtering is introduced to enhance cyberbullying detection. Various machine learning models, including Logistic Regression, Random Forest, and Support Vector Machine, as well as a deep learning model Convolutional Neural Network, are implemented on original and keyword-labelled datasets. The performance of these models is compared using various evaluation metrics. Our findings demonstrate that the performance of models on the keyword-labeled dataset is significantly higher, with Logistic Regression achieving 98.24%, Random Forest at 99.77%, SVM at 99.67%, and CNN at 99.95% accuracy. This study highlights the effectiveness of keyword filtering features in enhancing the detection of cyberbullying, providing a robust approach to creating safer online environments. © 2024 IEEE.

关键词： Support vector regression

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