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Prediction of Common Diseases Lurking in Smokers and Alcoholics and its Impact on their Health

Prediction of Common Diseases Lurking in Smokers and Alcohol...

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

作者： Jeevan, B. Koushik Vishnu, D. Jagadish, R. Praveen Joe, R.I. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331541170

Smoking and alcohol intake have become very common in modern society, leading to many complications and health problems, including respiratory illnesses and cardiovascular diseases. Early intervention and targeted healthcare interventions are very much needed to mitigate these risks and improve patient outcomes. This research addresses the need by using patient data to predict potential future health risks. By analysing 19 health and lifestyle factors, including smoking, alcohol consumption, diabetes, hypertension, haemoglobin and such factors, the study developed a predictive model to identify diseases that patients may currently have or develop in the near future. After extensive hyperparameter tuning and analysis, the study utilized a deep learning model optimized with the ReduceLROnPlateau technique, Adam as the optimizer and binary cross-entropy as the loss function. With the best parameters, including a learning rate of 0.001, 200 epochs, and a batch size of 64, the model achieved the highest accuracy of 94.69%. Additionally, to assess and compare the efficiency of the deep learning model, the study explored Random Forest algorithm due to its robustness in handling complex datasets. Initially focused on smokers and alcoholics, the study expanded to include non-smokers and non-alcohol consumers, demonstrating the model's broad applicability. These findings highlight the model's strong predictive capability and its potential for widespread use in preventive healthcare. Further, the current study uses dashboard visualization that reveals common disorders that affect smokers and drinkers, providing the valuable insights on targeted treatments to improve patient care and outcomes in population. © 2024 IEEE.

关键词： Network security

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Enhanced Interpupillary Distance Identification for Children Using YOLOv5 and YOLOX Object Detection Models

Enhanced Interpupillary Distance Identification for Children...

引用

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

作者： Sandhya, P. Padmaja, V. Srivats, R. Kalyanasundaram, V. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331541170

Interpupillary distance (IPD), which is the distance between the centers of the pupils of the eyes, is a key measurement in many applications, including optics, virtual reality, and medicine. Measuring IPD in adults is generally easier than measuring it in children due to adults' more stable eye positions and greater cooperation during the measurement process. Additionally, adults have a longer attention span, which aids in maintaining focus. Historically, technological options for accurately measuring IPD in children have been limited compared to those available for *** this work, we present a novel approach to accurately measure the IPD of children aged 5 to 15 years using an object detection model. Data were personally collected from students in this age group and subsequently pre-processed and annotated using labelImg software. The YoloV5 and YoloX models were employed for object detection, with YoloV5 demonstrating superior performance on the dataset. This model was utilized to locate the centers of the pupils and calculate the IPD using the Euclidean distance method between the centers of the bounding boxes for the detected pupils. The model accurately detected IPD while accounting for even the slightest variations in different eye movements and gazes, providing precise results. Additionally, a chin rest and a brief flash were incorporated as a gaze correction methodology to further enhance measurement accuracy. The precise measurement of IPD enables the customization of spectacles for children, ensuring better fit and comfort. © 2024 IEEE.

关键词： Eye movements

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Disaster Scene Classification with Deep Learning: A Keras-Based Approach Utilizing Robotic Systems

Disaster Scene Classification with Deep Learning: A Keras-Ba...

引用

2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Arora, Gautam Bhavadharini, R.M. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350374711

This study presents an innovative technique that leverages convolutional neural networks (CNNs), an advanced computer vision methodology, to enhance the timely detection and classification of natural disasters. The objective is to develop a dependable model capable of categorizing various disaster- related photos and videos, such as cyclones, earthquakes, floods, and wildfires, by employing extensive datasets and sophisticated CNN architectures like Visual Geometry Group (VGG) including VGG16 and VGG19. A significant advancement introduced in this research is the seamless integration of robotics, enabling autonomous data collection and real-time decision-making at disaster sites. Through a technique encompassing model development and data preprocessing, the effectiveness of our approach in disaster scene classification is demonstrated. The findings exhibit remarkable performance: post 48 training epochs, the VGG19 model achieved an impressive 97% accuracy, while the VGG16 model achieved a commendable 93% accuracy. These outcomes underscore the potential of CNNs to revolutionize disaster management strategies and pave the way for swifter and more efficient responses to mitigate the impact of natural disasters on society. © 2024 IEEE.

关键词： Convolutional neural networks

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Automated Email and SMS Notification System for Unauthorized Entry using Yolov9 10

Automated Email and SMS Notification System for Unauthorized...

引用

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

作者： Dhanuka, Eeshaan Dhavakumar, P. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350353068

Security breaches present a serious risk in a variety of settings, thus strong detection and alerting systems are required to efficiently reduce risks. This study proposes a novel way for developing an Automated Email and SMS Notification System customized for unauthorized entrance detection using the YOLOv9 object identification model. By utilizing the capabilities of YOLOv9, the system quickly detects unwanted people or things in the monitored areas and sends out automatic emails and SMS notifications to selected stakeholders. Our technology improves security measures by combining cutting-edge machine learning techniques with communication technologies. This allows for the rapid response to possible attacks through real-time notifications. Test results show that the system is effective in identifying unwanted entries and reducing false positives, strengthening security protocols in many contexts. © 2024 IEEE.

关键词： Electronic mail

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An Unified Approach for Traffic Sign Classificationand Flow Prediction using Deep Learning Technique 15

An Unified Approach for Traffic Sign Classificationand Flow ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kavitha, D. Lekhana, Kommareddy Rajeshri, Vigrahala Rushitha, Chennareddygari School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331300579

Efficient and safe traffic management is an important concern in modern urban environments. To address this challenge, we propose an integrated deep learning based solution that unifies traffic sign classification and traffic flow prediction using the LeNet architecture. The seamless fusion of these two critical tasks enables a holistic approach to traffic management, benefiting autonomous vehicles, traffic control systems, and road safety. In this unified approach, LeNet, a seminal convolutional neural network (CNN), serves as the backbone for traffic sign classification. Lenet Network is a very famous kind of configuration of convolutional neural networks that can be used to classify *** this paper, we have used Lenet to classify traffic signs, mainly used for selfdriving cars. Leveraging LeNet's capability to recognize traffic signs with high accuracy, we train it on a comprehensive dataset comprising 43 different classes of traffic signs. This dataset encompasses a wide range of shapes, colors, and conditions, allowing the LeNetbased classifier not only to identify individual traffic signs but also to provide valuable context for downstream traffic flow prediction. Extensive experiments on diverse datasets validate the effectiveness of our unified approach. We demonstrate superior traffic sign classification accuracy using LeNet, surpassing previous state-of- art methods. Additionally, our traffic flow prediction capabilities exhibit impressive accuracy and robustness across various traffic scenarios. Traffic flow prediction helps people for effective route planning so that the people can chose their routes to save and fuel and it helps to reduce traffic congestion. This research represents a significant step toward enhancing traffic management systems efficiency and safety by leveraging deep learning techniques. Our unified approach, combining LeNet- based traffic sign classification with traffic flow prediction, holds great promise for smarter and more r

关键词： Convolutional neural networks

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Classification, Segmentation and Quantification of acute lymphoblastic leukemia cells in the bone marrow

Classification, Segmentation and Quantification of acute lym...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Jeffine, Theou Kiruthika, S. Joshan Athanesious, J. School of Computer Science and Engineering Vellore Institute of technology Chennai India

ISBN: (纸本)9798331510299

Cancer is a type of disease in which abnormal cells flourish and penetrate adjacent tissues, then spread to other parts of the body via the blood and lymph system. This abnormality results in DNA alterations in the bone marrow, which give rise to malignant cells in haematologic malignancies like leukaemia. This results in the abnormal production of white blood cells, where cancerous cells proliferate more quickly than healthy cells, compromising the ability of healthy cells to carry out their regular tasks, including fighting infections, distributing oxygen, and clotting blood. The primary objective was to create an efficient model that could classify the given microscopic image of bone marrow containing cancerous cells or not. If the image is classified as cancerous then the model will segment the cancer cells over the normal cells, in addition the model will quantify the presence of malignant cells to measure the spread in the blood. Segmentation and quantification along with the classification will provide better explainability to the model, thus to achieve this both the image processing techniques and the deep learning model are used to develop the Classification, Segmentation and Quantification (CSQ) of acute lymphoblastic leukemia cells in the bone marrow. The model is evaluated using the metrics like accuracy, precision, recall, specificity and F1score. Experimental results showed that the model performs well when compared to the existing methods. © 2024 IEEE.

关键词： Lung cancer

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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

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Navigating Spatial Insights: Comparative Exploration of Deep Learning Algorithms for Flood Detection using Remote Sensing Data 2

Navigating Spatial Insights: Comparative Exploration of Deep...

引用

2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Mittal, Bhavuk Vanzara, Jui Sajidha, S.A. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350360523

Detecting floods, which are highly destructive natural disasters, is crucial for minimizing their impact on both life and infrastructure [1]. Timely identification is essential, and satellites play a key role in assessing the extent of damage and pinpointing areas in urgent need of assistance following events such as floods, earthquakes, or wildfires. The "SEN12-FLOOD"dataset has emerged as a vital resource for flood detection, utilizing both synthetic aperture radar (SAR) and optical imaging. However, a significant challenge lies in the clarity of the images, making it difficult to accurately identify the extent of damage. In response, we propose a comprehensive methodology that incorporates data preprocessing, the comparison of multiple models, and efficient labeling techniques to enhance the accuracy of flood detection. The data preprocessing phase involves the removal of empty folders and irrelevant images, streamlining the dataset for optimal analysis. Additionally, we introduce band stacking, combining satellite imagery bands B2, B3, B4, and B8 to improve image clarity, providing a refined input for flood detection models. The methodology extends to model comparison, wherein multiple models are evaluated to identify the most effective one for accurate flood detection. © 2024 IEEE.

关键词： Satellite imagery

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Transformers in Cardiology: Automated ECG-Based Heart Disease Detection

Transformers in Cardiology: Automated ECG-Based Heart Diseas...

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2025 AI-Driven Smart Healthcare for Society 5.0, AdSoc5.0 2025

作者： Ganai, Aritra Bhowmick, Spandan Gupta, Aashi Parkavi, K. School of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India

ISBN: (纸本)9798331536336

This paper introduces a transformer-based approach for automated ECG analysis, overcoming the limitations of RNNs and CNNs in capturing long-range dependencies. Using self-attention, the model efficiently extracts local and global patterns from raw ECG signals, achieving superior accuracy, reduced training time, and better generalizability. Tested on public datasets, it demonstrates the potential of transformers to enhance heart disease diagnosis in clinical practice. © 2025 IEEE.

关键词： Electrocardiography

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PlantLDNet : Disease Detection in Leaf Images Using Deep Learning Models 5

PlantLDNet : Disease Detection in Leaf Images Using Deep Lea...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Nikhileswar, Gurram Sai Pampana, Surya Sai Jani Anbarasi, L. Bhavana, Chintalapudi Likhitha Nanditha, Rayanki Lakshmi Krishna, Kodela Chaithanya School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350385649

Automated disease diagnosis in the agricultural field has benefited from the recent advances in big data, deep learning, and computer vision across a wide range of applications. However, intelligent disease prediction is crucial for safely maintaining a good agricultural production. Moreover, unclear disease prognosis is caused by the degree of leaf disease inhomogeneity, the complexity of the background-which includes the low contrast with the surrounding area and the potential for shadows with a similar intensity. This research provides a custom Convolutional Neural Network (CNN) model called PlantLDNet that has hyperparameters configured optimum to distinguish between leaf images showing disease and those without it, hence avoiding the problems associated with vanishing gradient. A comparative study of the proposed custom CNN model PlantLDNet and earlier research works was conducted using the following metrics: precision, recall, accuracy, F1 score, loss accuracy, receiver operating characteristic (ROC) curves, and Area Under Curve (AUC) measure. The comparison findings show that the proposed PlantLDNet model has the highest classification prediction effectiveness (99.85%) with less computational complexity. It also has an elevated high F1-score of 93% when trained for Early Bright, 98% for Healthy, and 92% for Late Bright. © 2024 IEEE.

关键词： Convolutional neural networks

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