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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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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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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 ...

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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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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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Network Intrusion Detection System using Random Forest and Gradient Boosting Machines 4

Network Intrusion Detection System using Random Forest and G...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Bharathi, Indira Makhija, Rohan Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350349900

The recent technological advancements not only astonish us but also demonstrates that if they are misapplied, they can pose a serious threat. A Network intrusion Detection system (NIDS) is presented in this paper which leverages the abilities of Random Forest and Extreme gradient boosting Machine (Xgbm) classifiers and harnesses the ensemble of both classifiers which enhances the accuracy of the system and brings out the best in both the classifiers. The system is trained using the UNSWNB15 dataset. Additionally, the system also employs the statistical and Recursive Feature Elimination for feature selection which enhances the model's performance. © 2024 IEEE.

关键词： Network intrusion

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Optimizing Bone Fracture Diagnosis: A Hybrid Approach using EfficientNet-B3 and SVM for Accurate Classification 2

Optimizing Bone Fracture Diagnosis: A Hybrid Approach using ...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Jesher Joshua, M. Spoorthi, J.S. Keerthi, K. Divya Sankari, S. Pandiyaraji, V. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

The identification of osseous fractures has significant importance for defining precise medical diagnosis and con- structing treatment plans, especially in emergent and orthopedic contexts. This work proposes a new classification framework for different types of hand fractures through the integration of the EfficientNet-B3 deep learning model with an SVM classifying kernel. The feature extractor uses the pre-trained and then fine-tuned EfficientNet-B3 model on hand X-ray images to obtain both higher level and fine-grained features required to accurately detect the fractures. Subsequent features are then again passed to an SVM classifier which defines an ideal decision boundary mapping which increases the model's accuracy and interpretability. The dataset utilized in this study consists of 1,000 hand X-ray images obtained from different medical centers, which went through different preprocessing to enhance the image quality, as outlined below: Conversion of the images to grayscale benzylpenicilloyl hydrochloride - Gaussian blurring of the images, and edge detection on the images. In assessing the efficacy of the model, a variety of evaluative metrics, including accuracy, precision, recall, F1-score, and additional analytical tools, were employed, with the accuracy percentage reaching an impressive 93.5 percent in classification. A comparison of the proposed model using existing approaches is promising when it comes to the adaption of the data set into an automated system with a highly accurate classification of bone fracture, making it ideal for future clinical application. © 2024 IEEE.

关键词： Support vector machines

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Enhancing Sentiment Analysis of Customer Reviews Using Deep Learning, Data Augmentation and Explainable AI 4

Enhancing Sentiment Analysis of Customer Reviews Using Deep ...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Gokul Ram, K. Rajakumar, R. Ilavendhan, A. Padmanaban, R. Vijayaprabakaran, K. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

Sentiment analysis of customer reviews is crucial for understanding consumer feedback and improving business strategies in the e-commerce sector. This study proposes an enhanced deep learning framework for sentiment analysis, utilizing Data Augmentation to improve prediction accuracy. Additional layers are integrated into the sentiment analysis model to better differentiate emotions in customer feedback. Furthermore, Explainable AI (XAI) techniques, specifically LIME, enhance transparency and provide clear insights into the model's predictions, thereby increasing trust in the results. © 2024 IEEE.

关键词： Adversarial machine learning

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A Hybrid Deep Learning Algorithm for Improved ChatBot Accuracy and Relevance Through Advanced Retrieval-Augmented Generation

A Hybrid Deep Learning Algorithm for Improved ChatBot Accura...

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

作者： Varun, S. Seshasai Joseph, Linda School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331543617

This research presents the Hybrid Retrieval-Augmented Transformer Network (HRATN), a cutting-edge deep learning algorithm crafted to significantly elevate the performance of ChatBots through an innovative approach to Retrieval-Augmented Generation (RAG). The HRATN framework addresses several limitations inherent in conventional RAG models by seamlessly integrating a sophisticated retrieval mechanism with advanced generative transformer architecture, thereby enhancing both the accuracy of information retrieval and the coherence of the generated responses. The core of the HRATN model features a dense vector-based retrieval system that adeptly extracts relevant information from an expansive knowledge base, which is then fused with a transformer network equipped with advanced attention mechanisms to optimize response generation. A notable innovation in HRATN is its hybrid fusion layer, which effectively combines retrieved data with the outputs of the generative model to deliver responses that are not only more precise but also contextually more relevant. To assess the efficacy of HRATN, extensive experiments were carried out, comparing its performance against leading RAG models in standings of response correctness, relevance, and computational effectiveness. The results underscore that HRATN markedly enhances both the accuracy and relevance of responses while maintaining superior computational performance. This research underscores the potential of HRATN to make significant advancements in conversational AI, by equipping ChatBots with enhanced capabilities for providing more reliable and context-aware interactions, thus making a meaningful contribution to the broader fields of artificial intelligence and human-computer interaction. © 2024 IEEE.

关键词： Generative adversarial networks

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Heart Attack Prediction Using Ensemble Learning

Heart Attack Prediction Using Ensemble Learning

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

作者： Mali, Saurabh Veeramani, Karthika School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331540685

Heart attacks are a leading cause of death worldwide, making it a necessity to develop accurate prediction systems for timely intervention and prevention. This paper addresses the challenge of heart attack prediction through ensemble learning methodologies. Previous research has primarily focused on individual machine learning algorithms, often overlooking the potential of ensemble techniques to improve predictive accuracy. In this study, we employed a dataset comprising various clinical features and medical indicators. After preprocessing steps such as encoding categorical variables, scaling, and balancing data using SMOTE, we explored multiple machine learning models, including logistic regression, SVM, KNN, decision trees, random forests, gradient boosting, and artificial neural networks. Additionally, feature selection techniques and hyperparameter optimization were utilized to enhance model performance. Finally, we employed ensemble learning, specifically Stacking, to combine the strengths of individual models, resulting in a higher predictive accuracy of 93%. The results demonstrate the high efficiency of ensemble learning in heart attack prediction, showcasing a significant improvement over individual models. © 2024 IEEE.

关键词： Heart

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