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188 results on '"Bala, Pradip Kumar"'

30. An NLP-Based Cryptosystem to Control Spread of Fake News Through Social-Media

Author

Ray, Arghya, Bala, Pradip Kumar, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Das, Himansu, editor, Pattnaik, Prasant Kumar, editor, Rautaray, Siddharth Swarup, editor, and Li, Kuan-Ching, editor

Published
2020
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35. Towards Defining a Trustworthy Artificial Intelligence System Development Maturity Model

Author

Das, Sibanjan Debeeprasad, Bala, Pradip Kumar, and Mishra, Arindra Nath

Abstract

ABSTRACTThe trustworthiness of artificial intelligence (AI) has been challenged for quite some time. The AI results will be trustworthy and reliable if trust-related principles are built into the AI system along with effective AI governance and monitoring. Therefore, this study seeks to define a trustworthy AI system development maturity model to assist firms in understanding where they stand in terms of developing a trustworthy AI platform. Since trust is time-bound, it is difficult to regain once lost, and this maturity model helps to establish robust controls and governance mechanisms in AI systems from their inception to minimize risk and establish trust. Hence, this study starts by defining the trustworthy enhancing techniques (TET) to build a trustworthy AI system, then advances on to the Trustworthy AI system maturity model (TAS-MM). The study’s findings include a maturity model with four stages of trustworthy AI system growth and maturity progression.

Published
2024
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38. What drives MLOps adoption? An analysis using the TOE framework.

Author

Das, Sibanjan Debeeprasad and Bala, Pradip Kumar

Subjects
OCCUPATIONAL roles, TOES, RESEARCH personnel, CHI-squared test, LOGISTIC regression analysis
Abstract

MLOps is essential to streamline the machine learning (ML) development process, ensure ML models stay operational, and provide users with the desired value. MLOps enhances the auditability, dependability, repeatability, and quality of ML data, models, and systems. MLOps technologies tackle several operational difficulties in an ML process. This research used the TOE framework to identify drivers and challenges to adopting MLOps tool. Data were collected from 277 professionals from various industries and AI/ML-related job roles. The responses were analysed using a three-step approach – Data Profiling, Chi-square tests and Logistic regression (LR) model. The analysis uncovered that ML usage, performance drivers, and security drive MLOps adoption, whereas regulatory environment, organizational preparation, and ML infrastructure moderately influence it. The investigation shows that management/leadership needs to be aware of MLOps technologies' benefits. This study provides insights to AI/ML professionals, academics, researchers, and machine learning model users on MLOps adoption. [ABSTRACT FROM AUTHOR]

Published
2024
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39. Leveraging Stacking Framework for Fake Review Detection in the Hospitality Sector.

Author

Ashraf, Syed Abdullah, Javed, Aariz Faizan, Bellary, Sreevatsa, Bala, Pradip Kumar, and Panigrahi, Prabin Kumar

Subjects
HOSPITALITY, RESEARCH personnel, GOVERNMENT agencies
Abstract

Driven by motives of profit and competition, fake reviews are increasingly used to manipulate product ratings. This trend has caught the attention of academic researchers and international regulatory bodies. Current methods for spotting fake reviews suffer from scalability and interpretability issues. This study focuses on identifying suspected fake reviews in the hospitality sector using a review aggregator platform. By combining features and leveraging various classifiers through a stacking architecture, we improve training outcomes. User-centric traits emerge as crucial in spotting fake reviews. Incorporating SHAP (Shapley Additive Explanations) enhances model interpretability. Our model consistently outperforms existing methods across diverse dataset sizes, proving its adaptable, explainable, and scalable nature. These findings hold implications for review platforms, decision-makers, and users, promoting transparency and reliability in reviews and decisions. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Contributors

Author

Bakir, Sena, primary, Bala, Pradip Kumar, additional, Braconi, Daniela, additional, Bronze, Maria Rosário, additional, Capanoglu, Esra, additional, Catalkaya, Gizem, additional, Cicaloni, Vittoria, additional, Cole, Ian R., additional, Costa, Leonor, additional, Devi, Maguluri Sree, additional, Duong, Linh, additional, Bader, Farah, additional, Fischer, Arnout R.H., additional, Garcia-Garcia, Guillermo, additional, Guldiken, Burcu, additional, Gulsunoglu, Zehra, additional, Jagtap, Sandeep, additional, Kamilaris, Andreas, additional, Knutsen, Svein Halvor, additional, Kudari, Manjunath, additional, Li, Jie, additional, Martindale, Wayne, additional, Naika, Mahantesha B.N., additional, Nickerson, Michael, additional, Pathare, Pankaj, additional, Prenafeta-Boldú, Francesc X., additional, Rahimifard, Shahin, additional, Ray, Arghya, additional, Rowan, Neil J., additional, Sadhu, Dhanush Swaroop, additional, Sahlstrøm, Stefan, additional, Saldanha do Carmo, Cátia, additional, Santucci, Annalisa, additional, Serra, Ana Teresa, additional, Skouteris, George, additional, Spiga, Ottavia, additional, Sunagar, Suma, additional, Swainson, Mark, additional, Trollman, Hana, additional, and Van Loo, Ellen J., additional

Published
2021
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