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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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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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Enhancing Gesture-Controlled Virtual Mouse and Virtual Keyboard Using AI Techniques

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Journal of Mobile Multimedia 2024年第2期20卷 473-494页

作者： Kotti, Jayasri Padmaja, B. Deepa, D. Department of Information Technology GMR Institute of Technology Andhra Pradesh Rajam India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore India

Artificial Intelligence has become an essential part of modern technology. Although computer technology is advanced, it can still be improved to make it more user-friendly. One way to do this is to replace touchscreen desktops with a virtual mouse and keyboard. This can reduce the need for gadgets and enhance human-computer interaction. During the COVID-19 pandemic, reducing human intervention and dependency on devices has been critical in controlling the spread of the virus. This is where a battery-powered or Bluetooth mouse, powered by virtual reality technology, can be helpful. The virtual mouse is created using OpenCV and virtual reality technology, with the proposed system utilizing advanced tools such as MediaPipe and Python. The MediaPipe library is particularly useful in artificial intelligence projects, as it enhances the efficiency of the model. The system is an AI-based mouse and keyboard that can be controlled using hand gestures. The user interacts with the system through the camera output displayed on the screen, while the webcam serves as an input device. Python and OpenCV tools are used for implementation, making it applicable in pandemic situations and smart teaching systems. The proposed system works on Enhancing gesture Controlled Virtual Mouse and Virtual Keyboard through Virtual Assistant using AI Techniques. © 2024 River Publishers.

关键词： Hand gestures media pipe OpenCV virtual assistant virtual keyboard virtual mouse

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Liver disease Prediction using Ensemble Learning 3

Liver disease Prediction using Ensemble Learning

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

作者： Pilankar, Atharva Shyamala, L. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

The increasing prevalence of liver disease necessitates efficient identification methods to alleviate the diagnostic burden on healthcare providers. Machine learning offers a promising solution by evaluating vital parameters to predict liver disease, thereby reducing the risk of incorrect diagnoses and subsequent financial burdens on patients. Ensuring that predictive models generalize well across diverse populations and demographics is crucial for their wide applicability. Standardized evaluation metrics for liver disease prediction models are essential for unbiased comparisons and benchmarking. Demonstrating the superiority of new models over existing clinical methods is key to their adoption in clinical practice. This research explores the use of ensemble learning techniques to enhance the accuracy of liver disease predictions. By combining various supervised machine learning models, including Support Vector Machines (SVM), logistic regression, gradient boosting, decision trees, random forests, and artificial neural networks, the predictive outcomes have been improved. Each model undergoes extensive hyperparameter tuning to identify the optimal parameters for the given dataset. Additionally, feature selection techniques are employed to determine the most significant features contributing to the prediction. The study implements both stacking and bagging classification methodologies, achieving an accuracy rate of 79%. This hybrid approach demonstrates significant potential in improving the reliability and accuracy of liver disease predictions, offering a robust tool for early diagnosis and management of liver disease in diverse patient populations. © 2024 IEEE.

关键词： Support vector machines

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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...

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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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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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Diabetic Prediction with Ensemble Model and Feature Selection Using Information Gain Method 2

Diabetic Prediction with Ensemble Model and Feature Selectio...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Roy, Shreya Priyadarshini Beulah.s, Angelin Kasat, Avani Pankajalakshmi, P. Sivagami, M. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350327533

Diabetes is a chronic medical condition characterized by high levels of blood glucose levels, there is no specific cure for it, there are only ways to control it. Predicting diabetes in its early stages is very crucial as it affects the overall health of the patients when not predicted early. Through this work an Ensemble method of Logistic Regression, SVM, Naive Bayes and Random Forest Classifiers has been implemented. The result obtained has thoroughly been analyzed and documented. Proposed ensemble model obtained an accuracy of 85%. © 2024 IEEE.

关键词： Logistic regression

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A Cryptographically Secure Image Steganography Scheme Based On Arduino Microcontrollers 10

A Cryptographically Secure Image Steganography Scheme Based ...

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

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

ISBN: (纸本)9798350353068

In today's digitally pervasive environment, safeguarding sensitive information concealed within images has become increasingly critical. Conventional steganographic techniques may prove inadequate against sophisticated detection algorithms and statistical analysis. To address this challenge, integrating a pseudo-random number generator with hardware based entropy sources into the channel selection process offers a dynamic and adaptive solution to enhance information hiding security in images. As machine learning techniques for steganography become more sophisticated, they often introduce significant computational overhead. These ML-based algorithms require extensive computational resources for training, inference, and analysis, leading to increased complexity in terms of both time and computational power. By capitalizing on the inherent randomness introduced by the watchdog timer's RC oscillator in an arduino embedded microcontroller, we can amplify the unpredictability of channel selection during embedding. This paper proposes a novel hybrid model integrating Hardware Random Number Generators and Pseudo-Random Number Generators to augment entropy in the channel selection process within steganography. An examination of the performance of this hybrid model is presented. © 2024 IEEE.

关键词： Microcontrollers

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HBTrackr: AI-Based Heart Attack Prediction through ECG Monitoring on Wearable Devices 1

HBTrackr: AI-Based Heart Attack Prediction through ECG Monit...

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Micheal, A Ancy Gupta, Kriti Singh, Rishi Singh, Vikram Mohanty, Bipasha School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350365016

The topic proposes the existence of various mobile applications designed to predict and detect abnormal heartbeats, providing individuals with an accessible tool for monitoring their cardiac health. Advancement in AI supports heart attack prediction which can be deployed in various smart applications. In the paper, a novel AI model is implemented to read the Electrocardiogram (ECG) of the patient and understand the heart health of the person by wearing a smart watch. The proposed framework focuses on detection of sudden cardiac arrest and prediction of abnormal heartbeat using You Only Look Once(YOLOv8) in the wearable devices. The model is trained on ECG images dataset of cardiac patients using a matrix of features such as myocardial infarction, irregular heart rhythms, and healthy cardiac patterns. The model achieves high precision, recall, and F1 score, demonstrating its effectiveness in accurately classifying cardiac conditions. HBTrackr is an easy-to-use smartwatch app which is incorporated with an AI model that uses the pre-existing sensors in the watch. © 2024 IEEE.

关键词： Watches

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Spatial attention-based hybrid VGG-SVM and VGG-RF frameworks for improved cotton leaf disease detection

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Neural Computing and Applications 2025年第14期37卷 8309-8329页

作者： Pandiyaraju, V. Anusha, B. Senthil Kumar, A.M. Jaspin, K. Venkatraman, Shravan Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Marian Engineering College Trivandram Kerala Trivandrum India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai India Department of Information Science and Technology College of Engineering Guindy Anna University Tamil Nadu Chennai India

The agriculture industry's production and food quality have been impacted by plant leaf diseases in recent years. Hence, it is vital to have a system that can automatically identify and diagnose diseases at an initial stage for enhancing the quality of agricultural output and also for stopping the overall plant extinction. Most state-of-the-art methods do not provide appropriate accuracy of identification when the similarity among different diseases is higher and the input leaf images have complex background. In this work, a transfer learning-based automated crop disease recognition system is proposed to handle this problem. The cotton leaf disease detection framework being proposed here includes two key stages: (1) the feature extraction stage, where the convolutional neural network (CNN) model from the visual geometry group (VGG-16) is employed to obtain the deep features from cotton leaf images with complex background. (2) Cotton leaf disease detection stage: Here, the extracted optimal features are input to the machine learning classifiers, namely random forest (RF) and nonlinear support vector machine (SVM), for the initial classification of the diseases. For providing an optimal cotton crop disease detection performance, Kaggle’s four-class cotton disease dataset with complex background is chosen in this work. For the final decision making and analysis, two hybrid models named spatial attention-based hybrid VGG-RF and hybrid VGG-SVM have been proposed in this work for enhancing the decision accuracy. These hybrid models offered an average accuracy of approximately 98.29% and 99.31%, respectively, which is higher than the accuracy provided by the related work. This proved that the proposed hybrid model, namely spatial attention-based VGG-SVM and VGG-RF models, with spatial attention outperformed the state-of-the-art classifiers. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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