Browsing by Author "Hegde, T."

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    Computational Modelling of Bioheat Transfer for Hyperthermia Using Finite Difference Method
    (Springer Science and Business Media Deutschland GmbH, 2023) Hegde, T.; Maniyeri, R.
    Bioheat transfer is a field which involves the study of thermal energy in living systems like tissues. Penne’s bioheat transfer equation is a popular model used in this field. The objective of this study is to develop a computational model to understand the effect of different heating methods like magnetic hyperthermia and laser treatment for living tissues with an embedded tumour. This is done by solving Penne’s bioheat transfer equation using finite difference method for a two-dimensional domain. Initially, Penne’s model in its one-dimensional form is used to observe heat transfer in a living tissue. After validating the results, the model is extended to a two-dimensional domain with an embedded tumour. The properties of the healthy tissue and the tumour cells are considered to be different. Using different heating methods, the temperature of the tumour is raised to 40–43 ℃ to damage the tumour cells, and the time taken for necrosis is found. The results obtained will be useful for tumour detection and also its treatment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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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)