Browsing by Author "Guide - Dr. R. Vijayabhanu"

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    A Framework for Developing an Enhanced Convolutional Neural Network Based Ensemble Learning Model for Alzheimers Disease Classification Using MRI Brain Images
    (Avinashilingam, 2025-03) Chithra S; Guide - Dr. R. Vijayabhanu
    The integration of machine learning techniques in imaging domain is experiencing a deep transformation. It enables systems to analyze massive amount of data, distinguish patterns, and make forecasts with minimal human intervention. Machine Learning is applied to various domains in healthcare sector like disease diagnostics, treatment planning, drug detection and patient management. The machine learning models impact the complex and data exhaustive fields like oncology, cardiology, and neurology. In medical imaging machine learning models can examine MRIs, X-rays to identify irregularities like lumps, fractures, or organ deformities with high accuracy, regularly beating the capabilities of human clinicians. This present study focuses on the brain neuron images in classifying the Alzheimer’s Disease (AD) stages, which aids neurologists to understand complex changes in the brain. Through brain imaging analysis, the study strives to diagnose AD in its premature stages. AD is a deteriorating brain ailment caused by brain cells degeneration that impairs memory and intellectual damage that disturbs lots of old age individuals across the globe. Its a permanent brain ailment that steadily wear away thinking and memory skills which finally disturbs even the basic tasks. The memory and cognitive functions are affected in AD which is the reason for dementia in older population. These computational techniques use algorithms to analyze the brain images to classify patterns and features related to AD. To evaluate medical pictures and to discover neurological ailments like AD computational techniques like Machine Learning (ML) and Deep Learning (DL) techniques are applied in recent times. The objectives are to develop the classification potential of AD stages using ML and DL methods derived from ensemble classification framework. The contributions of this research work primarily focus on the preprocessing framework to eradicate the noises in the brain neuron MRIs by applying various de-noising filters to enhance the image deviations and to attain upgraded classification performance. The segmentation process is performed to for skull removal from the brain neuron image by applying thresholding methods to obtain a perfect image of the brain structure. To address the imbalance problems, a transfer learning approach is used for feature extraction. The first layer is transmitted, followed by the retrieval of features from Convolutional Neural Network using AlexNet model for feature retrieval. Lastly, classification is achieved from the extracted features using ML algorithms such as Decision Tree (DT), K-Nearest neighbor (KNN), Support Vector Machine (SVM), Neuro Evolution of Augmenting Topologies (NEAT), and BAGGING for AD stage classification. This study proposed two hybrid classification techniques like BAGGING_SVM and BAGGING_NEAT. The first hybrid classification technique combines BAGGING and SVM approaches to classify brain neuron images. The second hybrid classification technique combines the BAGGING and NEAT approaches to classify brain neuron images.
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    An Integrated Framework for COVID and Pneumonia Disease Prediction using Optimized Deep Learning Models
    (Avinashilingam, 2025-02) Kalaiselvi S R; Guide - Dr. R. Vijayabhanu
    MODIFIED EXTREME LEARNING MACHINE ALGORITHM WITH DETERMINISTIC WEIGHT MODIFICATION FOR INVESTMENT DECISIONS BASED ON SENTIMENT ANALYSIS Abstract: The trading of stocks contributes to the growth of the commodity economy by driving a significant quantity of capital into the stock market, which improves the organic configuration of corporate capital through capital concentration. Consequently, the stock market is seen as a measure of the financial activity of a nation or area. Specifically, since it can precisely depict the supply dynamics of the stock market, the trading price of the stock frequently acts as a measure of the price and quantity of the stock. Timely and precise stock price prediction and analysis are essential for both investor decision-making and the constancy of the national economy by increasing returns and decreasing risks. Consequently, researching stock projections can help depositors make wise decisions that will advance society and yield rewards for themselves. The intricacy of financial time series presents challenges that ML can handle with its strong data processing skills. Consequently, there are a lot of opportunities for ML and finance together, but there hasn't been enough research done in this field. Furthermore, the stock market is not entirely objective and does not always follow scientific principles due to humans' emotional, psychological, and behavioral traits. Recent studies have also demonstrated that investor sentiment may play a significant influence in stock market investing.The present study proposed a modified extreme learning machine (ELM) algorithm with deterministic weight adjustment to increase the precision and dependability of sentiment analysis-based investment decision-making. To capture investor mood, the approach incorporates financial sentiment research from news articles, social media, and market patterns. With deterministic weight initialization (DWM), the ELM algorithm achieves more consistent model performance than standard ELM techniques that use random weight initialization. The suggested model is a potent tool for sentiment-driven investing strategies since it shows improved prediction accuracy, quicker learning, and robustness in financial forecasting.