Browsing by Author "Thomas, S.M."

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    A Multimodal Approach Integrating Convolutional and Recurrent Neural Networks for Alzheimer's Disease Temporal Progression Prediction
    (IEEE Computer Society, 2024) Durga Supriya, H.L.; Thomas, S.M.; Sowmya Kamath, S.
    Alzheimer's Disease (AD) poses a substantial healthcare challenge marked by cognitive decline and a lack of definitive treatments. As the global population ages, the prevalence of AD escalates, underscoring the need for more advanced diagnostic techniques. Current single-modality methods have limitations, emphasizing the critical need for early detection and precise diagnosis to facilitate timely interventions and the development of effective therapies. We propose a novel multimodal medical diagnostic framework for AD employing a hybrid deep learning model. This framework integrates a 3D Convolutional Neural Network (CNN) to extract detailed intra-slice features from MRI volumes and a Long Short-Term Memory (LSTM) network to capture intricate inter-sequence patterns indicative of AD progression. By leveraging longitudinal MRI data alongside spatial, temporal, biomarkers, and cognitive scores, our framework significantly enhances diagnostic accuracy, particularly in the early stages of the disease. We incorporate Grad-CAM to enhance the interpretability of MRI-based diagnoses by highlighting relevant brain regions. This multimodal approach achieves a promising accuracy of 92.65%, outperforming state-of-the-art works by a margin of 6%. © 2024 IEEE.
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    Impact of Vector Embeddings on the Performance of Tolerance Near Sets-based Sentiment Classifier for Text Classification
    (Elsevier B.V., 2023) Hegde, T.; Sanjay, K.S.; Thomas, S.M.; Kambhammettu, R.; Anand Kumar, M.; Ramanna, S.
    In recent years, Natural Language Processing (NLP) has gained significant attention, and sentiment analysis is an essential subfield of NLP that deals with identifying the sentiment or emotion conveyed in the text. Tolerance near sets (TNS) is a mathematical framework that has shown promising results in sentiment analysis tasks. However, the choice of word embeddings can significantly impact the performance of TNS-based classifiers. This paper investigates the impact of using different embeddings on the performance of tolerance near sets-based sentiment classifiers. This paper compares the use of different embeddings, including DistilBERT, MiniLM, and Word Embeddings, and their combinations, to understand their impact on TNS-based sentiment analysis. The TSC 2.0 model proposed in this paper achieves a weighted F1 score of 92.1% in one of the datasets, an improvement due to the sentence embeddings used. Experimental results have led to the observation that tie-breaking and variance-based classification may have led to a noticeable improvement in cases with more than three. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)