Dr. Swati Tyagi specializes in machine learning and artificial intelligence, contributing to data science, data mining, and finance with a focus on interpretable and ethical AI. Their work advances multimodal data fusion, sentiment prediction, and transparent decision systems while addressing real-world challenges in financial modeling, medical imaging, and cybersecurity. With strong expertise in algorithmic design, predictive analytics, and deep learning, they engage in diverse research collaborations involving bias mitigation, image segmentation, credit scoring, and automated analysis. Their interests include explainable machine learning, natural language processing, data fusion, financial analytics, and equitable AI frameworks that support trustworthy and domain-driven applications. Their contributions to scholarly publications, interdisciplinary projects, and AI innovations have earned recognition in areas such as gender-fair modeling, interpretable decision architectures, and high-utility computational solutions, reflecting a commitment to responsible and impactful AI research.
Featured Publications
Author(s). (2022). Analyzing machine learning models for credit scoring with explainable AI and optimizing investment decisions. arXiv. Cited by: 50.
Author(s). (2022). Comparative analysis of artificial intelligence and its powered technologies applications in the finance sector. Proceedings of the 5th International Conference on Contemporary Computing and Informatics. Cited by: 42.
Author(s). (2024). Analysis of multimodality fusion of medical image segmentation employing deep learning. Human Cancer Diagnosis and Detection Using Exascale Computing. Cited by: 26.
Author(s). (2023). Machine learning model-based financial market sentiment prediction and application. Proceedings of the 3rd International Conference on Advance Computing and Innovative Technologies. Cited by: 22.
Author(s). (2024). New perspectives, challenges, and advances in data fusion in neuroimaging. Human Cancer Diagnosis and Detection Using Exascale Computing. Cited by: 18.