Conference Papers

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

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

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    Sarcasm detection in tweets with BERT and GloVe embeddings
    (Association for Computational Linguistics (ACL), 2020) Khatri, A.; Pranav, P.
    Sarcasm is a form of communication in which the person states opposite of what he actually means. It is ambiguous in nature. In this paper, we propose using machine learning techniques with BERT and GloVe embeddings to detect sarcasm in tweets. The dataset is preprocessed before extracting the embeddings. The proposed model also uses the context in which the user is reacting to along with his actual response. © 2020 Association for Computational Linguistics.
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    Deepfake Image Detection using CNNs and Transfer Learning
    (Institute of Electrical and Electronics Engineers Inc., 2021) Kumar, N.; Pranav, P.; Nirney, V.; Geetha, V.
    Headways in deep learning has enabled the creation of fraudulent digital content with ease. This fraudulent digital content created is entirely indistinguishable from the original digital content. This close identicalness has what it takes to cause havoc. This fraudulent digital content, popularly known as deepfakes having the potential to change the truth and decay faith, can leave impressions on a large scale and even our daily lives. Deepfake is composed of two words, the first being deep: deep learning and the second being fake: fake digital content. Artificial intelligence forming the nucleus of any deepfake formulation technology empowers it to dodge most of the deepfake detection techniques through learning. This ability of deepfakes to learn and elude detection technologies is a matter of significant concern. In this research work, we focus on our efforts towards the detection of deepfake images. We follow two approaches for deepfake image detection, and the first is to build a custom CNN based deep learning network to detect deepfake images, and the second is to use the concept of transfer learning. © 2021 IEEE.
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    Comparative Performance Evaluation of Web-Based Book Recommender Systems
    (Institute of Electrical and Electronics Engineers Inc., 2022) Bhat, S.S.; Pranav, P.; Shashank, K.V.; Raghunandan, A.; Mohan, B.R.
    In today's world, recommendation algorithms are popularly utilised for personalization. To improve their business, e-commerce behemoths rely heavily on their recommendation algorithms. As a result, the quality of suggestions can have a big impact on how much money they make. As a result, effective evaluation of recommender systems is critical. Traditional evaluation measures are limited to error-based and accuracy-based metrics, and do not account for characteristics such as novelty, informedness, markedness, and so on. This research study aims to compare the effectiveness of two web-based book recommendation systems by using the measures like diversity, informedness, and markedness, which are less well-known but equally essential. © 2022 IEEE.