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1,070 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

4. 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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6. Measuring gender attitudes: Developing and testing Implicit Association Tests for adolescents in India

Author

Jain, Tarun, Dhar, Diva, Kapoor, Vrinda, Kapur, Vrinda, and Raj, Anita

Subjects
Gender Studies, Human Society, Clinical Research, Pediatric, Adolescent, Attitude, Female, Humans, Reproducibility of Results, Stereotyping, Students, Surveys and Questionnaires, General Science & Technology
Abstract

We develop and test gender attitude measures conducted with a school-based sample of adolescents aged 14-17 years in India. We test a measure with survey items and vignettes to capture gender-based value and stereotypes, an Implicit Association Test (IAT) capturing gender-based value, and an IAT capturing gender stereotype. All demonstrate good internal reliability, and both IATs are significantly associated with our survey measure suggesting criterion validity, though not confirming it due to the lack of a gold standard measure on gender attitudes. Finally, construct validity is indicated from the measures' positive significant associations with higher girls' mobility and education. The gender-related IAT tools developed are consistent and valid, and modestly correlated with gender-related behavior outcomes such as mobility and school enrolment.

Published
2022

8. Accelerated infection testing at scale: a proposal for inference with single test on multiple patients

Author

Jain, Tarun and Jain, Bijendra Nath

Subjects
Quantitative Biology - Other Quantitative Biology
Abstract

In pandemics or epidemics, public health authorities need to rapidly test a large number of individuals, both to determine the line of treatment as well as to know the spread of infection to plan containment, mitigation and future responses. However, the lack of adequate testing kits could be a bottleneck, especially in the case of unanticipated new diseases, such as COVID-19, where the testing technology, manufacturing capability, distribution, human skills and laboratories might be unavailable or in short supply. In addition, the cost of the standard PCR test is approximately USD 48, which is prohibitive for poorer patients and most governments. We address this bottleneck by proposing a test methodology that pools the sample from two (or more) patients in a single test. The key insight is that a single negative result from a pooled sample likely implies negative infection of all the individual patients. and It thereby rules out further tests for the patients. This protocol, therefore, requires significantly fewer tests. This may, however, result in somewhat increased false negatives. Our simulations show that combining samples from two patients with 7% underlying likelihood of infection implies that 36% fewer test kits are required, with 14% additional units of time for testing., Comment: 8 pages

Published
2020

12. 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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13. 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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14. 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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23. 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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24. Building Machine Learning Application Using Oracle Analytics Cloud

Author

Jain, Tarun, Agarwal, Mahek, Kumar, Ashish, Verma, Vivek Kumar, Yadav, Anju, 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, Nanda, Priyadarsi, editor, Verma, Vivek Kumar, editor, Srivastava, Sumit, editor, Gupta, Rohit Kumar, editor, and Mazumdar, Arka Prokash, editor

Published
2022
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25. An Empirical Analysis of Heart Disease Prediction Using Data Mining Techniques

Author

Kumar, Ashish, Sanjith, Sivapuram Sai, Cherukuru, Rajkishan, Verma, Vivek Kumar, Jain, Tarun, Yadav, Anju, 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, Nanda, Priyadarsi, editor, Verma, Vivek Kumar, editor, Srivastava, Sumit, editor, Gupta, Rohit Kumar, editor, and Mazumdar, Arka Prokash, editor

Published
2022
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42. Cost reduction collaboration between firm and social planner: An economic analysis.

Author

Arora, Stuti, Jain, Tarun, and Kiran, K

Subjects
COST control, COST effectiveness, JOB vacancies, SOCIAL services, MANUFACTURING processes
Abstract

Recently, firms like Toyota and social planners like the Government of India have invested in various initiatives leading to the firm's cost reduction. Further, such cost reduction collaborations also benefit the social planner because of higher industry output, and therefore, higher employment opportunities. In this paper, we investigate different scenarios in which the firm, or the social planner, or both exert efforts to reduce the firm's manufacturing cost. Interestingly, we find that under certain conditions, the firm may exert lower effort than the social planner. Our analysis reveals that when both players' cost reduction effectiveness is low, then collaboration results in higher payoffs for both the players. However, when the effectiveness of any player is high, then both players are better off if only one of them exerts effort. Our study further reveals that collaboration between the social planner and the firm always leads to a win–win situation. [ABSTRACT FROM AUTHOR]

Published
2025
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