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Your search keyword '"Jain, Tarun"' showing total 194 results
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194 results on '"Jain, Tarun"'

1. News Reporter: A Multi-lingual LLM Framework for Broadcast T.V News

Author

Jain, Tarun, Gao, Yufei, Vanga, Sridhar, and Singla, Karan

Subjects
Computer Science - Computation and Language
Abstract

Large Language Models (LLMs) have fast become an essential tools to many conversational chatbots due to their ability to provide coherent answers for varied queries. Datasets used to train these LLMs are often a mix of generic and synthetic samples, thus lacking the verification needed to provide correct and verifiable answers for T.V. News. We collect and share a large collection of QA pairs extracted from transcripts of news recordings from various news-channels across the United States. Resultant QA pairs are then used to fine-tune an off-the-shelf LLM model. Our model surpasses base models of similar size on several open LLM benchmarks. We further integrate and propose a RAG method to improve contextualization of our answers and also point it to a verifiable news recording., Comment: 5 pages, under review at ICASSP 2025

Published
2024

2. Twitter Trolling Detection Using Machine Learning

Author

Ghosh, Shubhra Bhunia, Kumar, Horesh, Joshi, Aditya, Kumar, Anshul, Jain, Tarun, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kumar, Rajesh, editor, Verma, Ajit Kumar, editor, Verma, Om Prakash, editor, and Wadehra, Tanu, editor

Published
2024
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8. Review on Customer Segmentation Methods Using Machine Learning

Author

Gupta, Rishi, Jain, Tarun, Sinha, Aditya, Tanwar, Vishwas, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Agrawal, Rajeev, editor, Mitra, Pabitra, editor, Pal, Arindam, editor, and Sharma Gaur, Madhu, editor

Published
2023
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9. Face Mask Waste Generation and Its Management During Covid-19

Author

Shrotriya, Anita, Tiwari, Pradeep Kumar, Jain, Tarun, Gupta, Rishi, Sinha, Aditya, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kumar, Raghvendra, editor, Pattnaik, Prasant Kumar, editor, and R. S. Tavares, João Manuel, editor

Published
2023
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10. Mental Health Analysis and Classification During Covid-19 Using Big Data Approach

Author

Badyal, Bhanvi, Digaari, Hrishabh, Jain, Tarun, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kumar, Raghvendra, editor, Pattnaik, Prasant Kumar, editor, and R. S. Tavares, João Manuel, editor

Published
2023
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19. Advancements in non-alcoholic fatty liver disease (NAFLD) detection: A machine learning approach.

Author

Chauhan, Som, Bhardwaj, Aditya, Jain, Tarun, and Pandey, Ankur

Subjects
NON-alcoholic fatty liver disease, LIVER biopsy, PUBLIC health, PREDICTION models, SENSITIVITY & specificity (Statistics)
Abstract

The growth of NAFLD has become an issue in public health because it can advance to more dangerous illnesses like cirrhosis and NASH. Detecting NAFLD on is vital which is why this project wants to explore the application of machine learning algorithms, for early diagnosis. Currently conventional diagnostic methods heavily rely on costly procedures such, as liver biopsies, which are not practical or sustainable considering the widespread prevalence of NAFLD. Currently traditional diagnostic methods heavily rely on costly procedures such, as liver biopsies, which are not practical or sustainable given the widespread occurrence of NAFLD. Harnessing the power of machine learning, with its potential to efficiently process huge volumes of data, predictive models can be methodically designed to reliably diagnose the existence of NAFLD based simply on clinical information. These advanced models have the ability to disclose detailed patterns and biomarkers closely connected with NAFLD by learning from varied patient cohorts. Consequently, this method delivers a non-invasive diagnostic solution with unparalleled levels of sensitivity and specificity. The study at hand exploited a big dataset comprising of a staggering 2024 observations and examinations obtained from 17549 meticulously conducted questionnaires. Out of the countless qualities accessible, a meticulous selection process led to the analysis of six specific attributes. These variables underwent different modifications, and the entire model development process occurred within the powerful framework of Google Collab. The results acquired were nothing short of remarkable; the suggested model displayed an unsurpassed accuracy rate of a breathtaking 100% in predicting the occurrence of NAFLD. The incorporation of machine learning approaches into the area of NAFLD detection marks a significant milestone in current medicine. This innovative development highlights the vital necessity of early detection and gives a realistic answer to the challenging diagnostic problems inherent in the management of NAFLD. The potential ramifications for public health and the healthcare sector as a whole cannot be emphasized, exposing machine learning's revolutionary role in increasing healthcare outcomes and greatly decreasing the social burden posed by NAFLD. In essence, this represents a great advance towards healthier individuals and a more robust society. [ABSTRACT FROM AUTHOR]

Published
2025
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37. 3D Printing-as-a-Service: An Economic Analysis of Pricing and Cocreation.

Author

Jain, Tarun, Hazra, Jishnu, and Gopal, Ram

Subjects
DESIGN failures, 3-D printers, PRODUCT design, THREE-dimensional printing, PRICES, CUSTOMER cocreation
Abstract

Three-dimensional (3D) printing technology has opened up possibilities for product design collaborations between device providers and customers. To enable an environment of cocreation, device providers are now renting 3D printers via the 3D-as-a-Service (3DaaS) model. Although prior research has examined pricing and quality issues in the traditional manufacturing setup, these studies have not analyzed such decisions in the 3D printing supply chain setting, where end users possess the ability to customize product designs. Therefore, several important questions remain unanswered from the perspective of the 3D printing device provider. For example, what is the appropriate pricing model for providing 3DaaS? How do factors such as the extent of design customization and the complexity influence the pricing strategy of the 3DaaS firm? Our analysis shows that if the customers' impact on the product quality is relatively high or low, the pay-per-build pricing model generates a higher profit than the fixed-fee pricing model. Interestingly, we also find that if customers frequently print highly intricate product designs, the firm might choose the pay-per-build pricing model, only if the likelihood of design failure for these complex structures is low. Otherwise, the firm might opt for the fixed-fee pricing model. [ABSTRACT FROM AUTHOR]

Published
2024
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46. Gamete and embryo donation guidance

Author

Gracia, Clarisa, Anderson, Jake, Flyckt, Rebecca, Hill, Micah, Jain, Tarun, Sakkas, Denny, Pier, Bruce, Steiner, Anne, Tanrikut, Cigdem, Yauger, Belinda, Hnsen, Karl, Kalra, Suleena, Ginsburg, Elizabeth, Robins, Jared, Shannon, Chevis, Goldstein, Jessica, Kawwass, Jennifer F., Shepherd, Gena, Shah, Nirali, Braverman, Andrea, Ahmady, Ali, Petok, William, Woodward, Julia, Kaplan, Danielle, and Moravek, Molly B.

Published
2024
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