Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Akhila, P.

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    Word Sense Disambiguation using Bidirectional LSTM
    (Institute of Electrical and Electronics Engineers Inc., 2019) Rakshith, J.; Savasere, S.; Ramachandran, A.; Akhila, P.; Koolagudi, S.G.
    Word Sense Disambiguation is considered one of the challenging problems in natural language processing(NLP). LSTM-based Word Sense Disambiguation techniques have been shown effective through experiments. Models have been proposed before that employed LSTM to achieve state-of-the-art results. This paper presents an implementation and analysis of a Bidirectional LSTM model using openly available datasets (Semcor, MASC, SensEval-2 and SensEval-3) and knowledge base (WordNet). Our experiments showed that a similar state of the art results could be obtained with much less data or without external resources like knowledge graphs and parts of speech tagging. © 2019 IEEE.
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    Hand classification based on fingerprint using Lightweight Convolutional Neural Network
    (Institute of Electrical and Electronics Engineers Inc., 2025) Akhila, P.; Koolagudi, S.G.
    Fingerprints are recognized as one of the most distinctive and reliable biometric identifiers that play a crucial role in forensic investigations by aiding in the swift identification of individuals. While traditional fingerprint analysis focuses on individual identification, determining the hand from which a particular fingerprint originates holds significant untapped potential. This paper proposes lightweight Convolutional Neural Networks to identify the hand from fingerprints. The model could achieve high accuracy on publicly available fingerprint datasets such as CASIA, SOCOFing, and NISTSD4. An in-depth analysis of the network prediction is conducted to determine the features that help the model identify the hand from the fingerprint. It is found that the position of core point, direction of ridge flow, inter-ridge distance at side ridges, and the slope of the ridges help the model identify the hand from fingerprints. © 2025 IEEE.