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Analysis of Personality Traits using Natural Language Processing and Deep Learning

Published in IEEE Second International Conference on Inventive Research in Computing Applications (ICIRCA), 2020

This paper presents a Deep Learning based approach to identifying a personality according to the MBTI personality types. Subsequently, we also discuss how this approach can be used as a personality test to reduce the time taken while accurately guaging personality traits.

Recommended citation: T. Pradhan, R. Bhansali, D. Chandnani and A. Pangaonkar, "Analysis of Personality Traits using Natural Language Processing and Deep Learning," 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2020, pp. 457-461, doi: 10.1109/ICIRCA48905.2020.9183090
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Prototype Design of a Multi-modal AI-Based Web Application for Hateful Content Detection in Social Media Posts

Published in Sense, Feel, Design. INTERACT 2021. Lecture Notes in Computer Science, vol 13198. Springer, 2021

This paper discusses the design of a multi-modal hate-speech detection system for a combination of image and textual data as found in memes using an ensemble of 2-D and 1-D Convolutional Neural Networks

Recommended citation: Pradhan, T., Bhutkar, G., Pangaonkar, A. (2022). Prototype Design of a Multi-modal AI-Based Web Application for Hateful Content Detection in Social Media Posts. In: Ardito, C., et al. Sense, Feel, Design. INTERACT 2021. Lecture Notes in Computer Science, vol 13198. Springer, Cham. https://doi.org/10.1007/978-3-030-98388-8_36
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Face Generation from Textual Features using Conditionally Trained Inputs to Generative Adversarial Networks

Published in Arxiv Pre-print, 2023

This paper discusses a novel approach to generating human faces given a textual description regarding the facial features by feeding in latent vector representations to Generative Adversarial Networks(GANs) without having to train the GANs.

Recommended citation: Shinde S., Pradhan T. et. al. arXiv:2301.09123
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Ensemble Graph Neural Networks for Structural Classification of HIV inhibiting Molecules

Published in Springer International Journal of Information Technology, 2024

This paper presents an ensemble graph neural network based approach to classify molecules as HIV inhibitors by using graph attention networks and graph convolutional networks.

Recommended citation: Pradhan, T., Ghorpade, A., Patil, S. et al. Ensemble graph neural networks for structural classification of HIV inhibiting molecules. Int. j. inf. tecnol. (2024).
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Talk 1 on Relevant Topic in Your Field

Published: March 01, 2012

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Tutorial 1 on Relevant Topic in Your Field

Published: March 01, 2013

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Talk 2 on Relevant Topic in Your Field

Published: February 01, 2014

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Conference Proceeding talk 3 on Relevant Topic in Your Field

Published: March 01, 2014

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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