Sakinam Sindhuja
Papers
1
Total Citations
2
H-Index
1
About
Dr. Sakinam Sindhuja is a rising researcher in the field of computer science engineering, with a focused expertise in text classification and machine learning optimization. Her most cited work, "A Systematic Analysis of Text Classification Overfitting Recommendation Methods" (2023), published in the International Journal of Computer Science Engineering and Its Research Trends (IJCERT), addresses a critical challenge in natural language processing: the tendency of classification models to overfit training data, which compromises their real-world applicability. By systematically analyzing recommendation methods to mitigate overfitting, Dr. Sindhuja provides a foundational framework for developing more robust and generalizable text classifiers. This contribution is particularly valuable for researchers and practitioners working on information retrieval, sentiment analysis, and automated content categorization. While her citation count of 2 reflects the early stage of her career, the relevance of her work to ongoing challenges in machine learning reliability signals her potential for significant future impact. Dr. Sindhuja’s research is a stepping stone for advancing the accuracy and efficiency of AI-driven text analysis systems.
Research Focus
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Top Papers
- 1