Women Researcher Award
Amrithkala M Shetty
Nitte (Deemed to be University), India
Amrithkala M Shetty, affiliated with Nitte (Deemed to be University), is an Indian researcher whose scholarly work primarily focuses on computer science, artificial intelligence, natural language processing, recommender systems, and sentiment analysis. Her publication record demonstrates sustained contributions toward machine learning methodologies, transformer-based language models, and intelligent analytics for e-commerce applications. With publications indexed in Scopus and research appearing in peer-reviewed journals and conference proceedings, her academic profile reflects continuous engagement with contemporary computational research.[1]
Abstract
The academic contributions of Amrithkala M Shetty emphasize the application of artificial intelligence to text analytics, recommendation systems, and sentiment mining. Her research combines classical machine learning techniques with deep learning architectures, including convolutional neural networks and transformer models such as XLNet, to improve prediction accuracy for online review analysis. These studies contribute to practical decision-support systems while also advancing methodological understanding within computational intelligence and natural language processing.[2]
Keywords
Artificial Intelligence, Sentiment Analysis, Machine Learning, XLNet, Deep Learning, Transformer Models, Recommender Systems, Natural Language Processing, Computer Science.
Introduction
Research in intelligent text processing has become increasingly important because of the rapid growth of digital information and user-generated content. Amrithkala M Shetty’s work addresses this evolving landscape by developing computational methods that improve sentiment classification, recommendation accuracy, and automated interpretation of online reviews. Her publications demonstrate an interdisciplinary approach that integrates data mining, artificial intelligence, and predictive analytics for real-world applications.[3]
Research Profile
According to the provided research metrics, the author has produced 14 Scopus-indexed publications with 86 citations and an h-index of 4. Her scholarly interests include artificial intelligence, machine learning optimization, recommender systems, deep neural networks, and computational linguistics. These indicators reflect an emerging research profile with growing scholarly visibility.[1]
Research Contributions
- Comparative evaluation of transformer architectures for sentiment classification.
- Survey research on collaborative filtering recommender systems.
- Hyperparameter optimization using grid search techniques.
- Application of attention-based CNN models with pretrained embeddings.
- Machine learning approaches for e-commerce review analytics.
Publications
- Fine-tuning XLNet for Amazon Review Sentiment Analysis: A Comparative Evaluation of Transformer Models (ETRI Journal, 2026).
- A Collaborative Filtering Recommender Systems: Survey (Neurocomputing, 2025).
- Hyperparameter Optimization of Machine Learning Models Using Grid Search for Amazon Review Sentiment Analysis (2024).
- Sentiment Exploring on Feedback of E-commerce Data Using Machine Learning Algorithms (2024).
- Unleashing the Power of 2D CNN with Attention and Pre-trained Embeddings for Enhanced Online Review Analysis (2024).
Research Impact
The research portfolio illustrates practical engagement with modern artificial intelligence methods that support sentiment classification, recommender technologies, and predictive modeling. Publications in recognized journals and conference proceedings demonstrate consistent participation in advancing machine learning applications for digital commerce and intelligent decision-support systems. Citation metrics indicate growing recognition within the research community.[4]
Award Suitability
Based on the available scholarly record, Amrithkala M Shetty demonstrates sustained research activity in computer science and artificial intelligence. Her contributions to transformer-based sentiment analysis, recommender systems, optimization methods, and intelligent data analytics align with the objectives of the Women Researcher Award, which recognizes academic excellence, innovation, and meaningful contributions to scientific advancement within computing disciplines.[5]
Conclusion
The available evidence highlights a developing research career characterized by interdisciplinary work in artificial intelligence and machine learning. Through publications addressing sentiment analysis, recommender systems, and transformer architectures, Amrithkala M Shetty contributes to contemporary computational research while supporting practical applications in intelligent information processing. Her scholarly profile reflects continued academic engagement and potential for future impact.
External Links
References
- Elsevier. (n.d.). Scopus Author Details: Amrithkala M Shetty, Author ID 58767603900.
https://www.scopus.com/authid/detail.uri?authorId=58767603900
- ETRI Journal. Fine-tuning XLNet for Amazon Review Sentiment Analysis.
https://doi.org/10.4218/etrij.2024-0318
- Neurocomputing. A Collaborative Filtering Recommender Systems: Survey.
https://doi.org/10.1016/j.neucom.2024.128718
- Lecture Notes in Networks and Systems. Hyperparameter Optimization of Machine Learning Models Using Grid Search.
https://link.springer.com/chapter/10.1007/978-981-99-7814-4_36
- International Journal of Computers and Applications. Unleashing the Power of 2D CNN with Attention and Pre-trained Embeddings for Enhanced Online Review Analysis.
https://doi.org/10.1080/1206212X.2023.2283647