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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Senthilkumar, Sarath Rajan Jabak, Veer H. Vidyashankar, Gautham Divij, D. Saravanan, Srisakthi Vellore Institute of Technology School of Computer Science and Engineering Chennai Campus Chennai India

ISBN: (纸本)9798350370249

The following paper delves into the innovative integration of technology into the bus transportation system provided by education institutes. The paper explores the user-centric features offered by the system, aiming to enhance the overall commuting experience for passengers. The webapp serves as a comprehensive platform, providing users with detailed information about various bus routes, driver details for each route, and schedules for different time slots, including morning, afternoon shuttle, and evening buses. The standout feature of the system is its advanced geospatial functionality, allowing users to effortlessly locate the nearest bus stop based on the latitude and longitude coordinates they provide. © 2024 IEEE.

关键词： Google Maps API Haversine formula

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A Multichannel CNN-LSTM Approach for Fake News Detection: A Comparative Study

A Multichannel CNN-LSTM Approach for Fake News Detection: A ...

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

作者： Johnson, Deepika Roselind Srivats, R. Kalyanasundaram, V. Saimirra, R. Vellore Institute of Technology School of Computer Science and Engineering Chennai Campus Chennai India

ISBN: (纸本)9798331541170

The proliferation of fake news across various language sources poses a significant challenge in ensuring the dissemination of accurate and reliable information. Addressing this issue is crucial for maintaining the integrity of news content and public trust. We developed a novel approach combining Convolutional Neural Networks (CNN) for deep feature extraction and Long Short-Term Memory (LSTM) networks for subsequent detection of fake news. Our approach utilizes a Multichannel combined CNN-LSTM architecture. We collected an extensive dataset comprising 40,000 unique news articles from diverse sources to evaluate our model. Additionally, we integrated Gaussian and Random Forest models to benchmark performance. Our findings revealed that the LSTM component of the model achieved a remarkable accuracy of 97%, significantly outperforming the CNN's accuracy of 75%. The Gaussian model also recorded an accuracy of 75%, while the Random Forest model matched the LSTM's performance with an accuracy of 97%. The study demonstrates the effectiveness of our hybrid CNN-LSTM approach in detecting fake news, highlighting its potential as a robust tool for combating misinformation. Our results contribute to the growing body of research in fake news detection and underscore the value of leveraging deep learning techniques to enhance the accuracy and reliability of news content. © 2024 IEEE.

关键词： Convolutional neural networks

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An EDGE based end-2-end system architecture for retail stores

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International Journal of Information technology (Singapore) 2024年 1-29页

作者： Sundaresan, Vijay Anand Chandrapragasam, Tharini Rajaganesh, Thivish Naran Vimal, Pranhav Vadivelu, Manjula B.S.A Crescent Institute of Science and Technology Vandalur India School of Electrical and Communication Sciences B.S.A Crescent Institute of Science and Technology Vandalur India New York University NY United States Vellore Institute of Technology Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Retailers need real-time insights to know customer behavior and preferences to enhance operations efficiency and administrative cost reduction. There is a need to interconnect various legacy devices within the retail store that work on different technologies/standards like MATTER, Thread, WiFi, and Zigbee. This paper focuses on EDGE-based system design as a private retail network encompassing EDGE sensor nodes, EDGE gateway, and EDGE cloud. The proposed EDGE-based approach provides a detailed software architecture outlining the design and implementation methodology, enabling retailers to deliver a superior shopping experience. The performance evaluation clearly highlights the essential role of EDGE-based designs in retail shops for leveraging the full potential of battery-powered sensor nodes in diverse applications. © The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Connectivity EDGE cloud EDGE gateway EDGE sensor node Energy management Smart retail

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Mask Compliance in Public Services: A Comparative Study of Machine Learning and CNN Models for Mask Detection 2

Mask Compliance in Public Services: A Comparative Study of M...

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

作者： Abdullah, A. Sheik Altrin Lloyd Hudson, D. Naga Pranava Shashank, V. Aishwarya, S. Shri Varshan, P. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350328202

The Coronavirus Disease (COVID)-19 pandemic is fast changing our way of life and interfering with international travel and trade. The norm now is to wear a protective facemask. Many public service providers may soon demand users to correctly wear masks in order to access their services. This project offers a condensed method for accomplishing this goal by utilizing CNN models like VGG19, SVM, and Decision Trees as well as machine learning models. With an accuracy of 97%, the first methodology used Resnet50 as a feature extractor and the SVM + Decision tree (combined) - Stacking ensemble method as a classifier. Using the acquired dataset, the second methodology used the VGG19 model, which produced an impressive accuracy of 94.7%. The study will also look into how other elements, such lighting, camera angles, and facial orientations, affect how well the algorithms work. The results of this study can be applied to the development of more accurate and dependable mask detection systems, thereby reducing the transmission of infectious diseases in public areas. © 2024 IEEE.

关键词： Coronavirus

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Gaze-Driven Pneumonia Detection: Enhancing Diagnostic Accuracy with Eye-Tracking Insights

Gaze-Driven Pneumonia Detection: Enhancing Diagnostic Accura...

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2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Radhakrishnan, Shashvath Suganya, R. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350374711

Pneumonia, a prevalent respiratory infection, continues to pose a significant global health threat, underscoring the imperative for precise diagnostic approaches. This research aims to enhance pneumonia classification from chest X-ray images by incorporating gaze data and comparing its performance to a baseline model solely reliant on chest X-ray images. Leveraging gaze data along with chest X-ray images, our study proposes two distinct methodologies, both of which demonstrate improved performance. Our findings showcase compelling results, with the model integrating gaze data achieving a notable test accuracy of 81.32%. Additionally, the precision, recall, and F1 scores for this model stand at 0.84, 0.82, and 0.80 respectively, surpassing those of the baseline model utilizing only chest X-ray images. These results highlight the effectiveness of incorporating gaze data in pneumonia classification tasks. By showcasing the efficacy of integrating gaze data, this research will aid in the development of more robust and reliable decision support systems for medical imaging analysis. © 2024 IEEE.

关键词： Diagnosis

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DLRG-DravidianLangTech@EACL2024: Combating Hate Speech in Telugu Code-mixed Text on Social Media 4

DLRG-DravidianLangTech@EACL2024: Combating Hate Speech in Te...

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4th Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, DravidianLangTech 2024

作者： Rajalakshmi, Ratnavel Saptharishee, M. Hareesh, Teja S. Joshua, Gabriel R. Varsini, S.R. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798891760783

Detecting hate speech in code-mixed language is vital for a secure online space, curbing harmful content, promoting inclusive communication, and safeguarding users from discrimination. Despite the linguistic complexities of code-mixed languages, this study explores diverse pre-processing methods. It finds that the Transliteration method excels in handling linguistic variations. The research comprehensively investigates machine learning and deep learning approaches, namely Logistic Regression and Bi-directional Gated Recurrent Unit (Bi-GRU) models. These models achieved F1 scores of 0.68 and 0.70, respectively, contributing to ongoing efforts to combat hate speech in code-mixed languages and offering valuable insights for future research in this critical domain. © 2024 Association for Computational Linguistics.

关键词： Deep learning

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Deep Fake Detection using Transfer Learning: A Comparative study of Multiple Neural Networks

Deep Fake Detection using Transfer Learning: A Comparative s...

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

作者： Abhineswari M Kurupati Sai Charan Shrikarti BN Sujithra Kanmani R Shool of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

With the proliferation of sophisticated AI techniques, the creation and dissemination of deep fake images and videos have emerged as a pressing concern in today's digital landscape. Deep fake technology employs advanced machine learning algorithms to manipulate or synthesize realistic-looking media, often with malicious intent. This study evaluates the effectiveness of various pre-trained deep learning models using transfer learning for detecting deep fake images on the Face Forensics++ dataset. The models considered include MobileN etV2, ResN et50, Inception V3, EfficientN et, Xception, NASNetMobile, and a Custom CNN. Accuracies obtained from these models are compared to assess their performance in distinguishing between real and fake images. With the highest performance in MobileNetV2 with 89% followed by ResNet50 with 83 % and other models.

关键词： Deepfakes Accuracy Computational modeling Transfer learning Neural networks Pressing Signal processing

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Virtual Hue: A Web Application for Anemia Prognosis

Virtual Hue: A Web Application for Anemia Prognosis

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2024 International Conference on Smart Systems for Applications in Electrical sciences, ICSSES 2024

作者： Vivekananda, Gn Pradeep, L. Vellore Institute of Technology VIT School of Computer Science Engineering & Information Systems India

ISBN: (纸本)9798350364040

Anemia is a state of bad health condition where there is the presence of a low amount of red blood cells in the blood. We aim to build a simple Anemia prediction Web Application, that predicts whether a patient is anemic or not by which doctor can make a better decision in the diagnosis of the disease. The purpose of this study is to create a model that uses red blood cell indicators to predict anemia in adults. There are 1422 data records in the collection of data from Kaggle. After that, they undergo preprocessing, normalization, balancing, and the application of certain machine learning algorithms. It is followed by outcome analysis, validation, and verification. With an accuracy of 98.7%, the Ada Boost and Random Forest Classifier ensemble model exhibits the best performance. For the prediction of anemia, choosing the top-performing algorithm, stacking it with other algorithms, and using a voting classifier are all essential to increase accuracy regardless of any time constraints. © 2024 IEEE.

关键词： Decision trees

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Predictive Analytics Model for Heart Disease Prediction with Personalized Recommendation Systems using Machine Learning Techniques 15

Predictive Analytics Model for Heart Disease Prediction with...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Vishal, P. Joe, Praveen I.R. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350370249

In the era of rapid technological advancements, the persistent challenge of heart diseases, including sudden cardiac incidents, remains a pressing concern worldwide. Despite the potential of early warnings, these conditions continue to contribute significantly high mortality rates. This study involves machine learning (ML) techniques to forecast heart disease occurrences, enabling early awareness and intervention. Beyond mere prediction, this research innovates by integrating a system that provides personalized diet and exercise recommendations. Predictive analytics models such as Logistic Regression, Decision Trees, XGBoost and Random Forest are used for the prediction. K-means clustering for diet recommendation and ontology-based programming is used for exercise recommendation. This study identifies XGBoost as the best performer among other predictive analytics model in predicting heart disease. This study implements not only heart disease prediction but also gives personalized recommendations for the users which fosters proactive health management through personalized advice, marking a significant step in stopping high mortality rates due to heart disease. ©2024 IEEE.

关键词： Predictive analytics

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Brain Tumor Classification from Gene Expression Dataset Through Supervised Machine Learning Algorithms

Brain Tumor Classification from Gene Expression Dataset Thro...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Lokeswaran, S. Manikandan, P. Rajakumar, R. Marimuthu, M. Vellore Institute of Technology - Chennai Campus School of Computer Science and Engineering Chennai India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360660

Brain tumors are amongst the most prevalent diseases affecting the brain, can cause severe pain, and lead to various illnesses if not adequately treated. A notable factor contributing to the rising number of cancer cases worldwide is the lack of awareness and early intervention in tumor treatment. Early-stage diagnosis becomes crucial due to nonspecific symptoms, resulting in an improved prognosis. The main motive of this work is to categorize brain tumors using the gene expression dataset. The Gene expression dataset used in this study was a Gene Expression dataset which consists of 7130 features that include the information of 40 patients. Since the dataset contains many attributes, Principal Component Analysis (PCA) and Pearson correlation are used to choose features to minimize dimensionality for modeling. Machine learning approaches are utilized to categorize various types of brain tumor. The categorization of brain tumors involves the use of machine learning models such as Decision Tree Classifier (DT), Support Vector Machine Classifier (SVM), Logistic Regression (LR), K-Nearest Neighbor Classifier (KNN), and Random Forest Classifier (RF). The concert of the machine learning approaches was assessed via classification performance metrics including accuracy, recall, precision, confusion matrix, and f1-score. Out of 40 patients, 10 patients were identified to have Medulloblastoma, 10 were found to have Glioma, 10 were found to have RhabdoidTu, 6 were found to have Primitive Neuro-Ectodermal Tumor (PNET) and 4 were found to be NORMAL. The experimental investigation concluded that the Logistic Regression approach outpaced other approaches such as RF, SVM, DT, and KNN. © 2024 IEEE.

关键词： Self-supervised learning

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