The names Soumya and Nandana reflect deep cultural roots. In Sanskrit, Soumya denotes gentleness, moon-like calm, and grace, while Nandana translates to daughter, delight, or one who brings joy. Together with Krishnan , a standard patrilineal or familial surname in South India, these individuals exemplify the modern Indian woman balancing personal heritage with global career objectives.
Search results: Hmm, some hits for a book by someone named Soumya Nandana Krishnan titled "The Immortal Empire". That's from 2017, according to the first search result. There's also a blog post in Malayalam mentioning her in the context of an event in Thiruvananthapuram. Another article is about her book being published, and there's a YouTube video where she talks about her work. There's also a website mentioning her as an author and poet. soumya nandana krishnan
Here is an article profiling her career and artistic contributions. The names Soumya and Nandana reflect deep cultural roots
One leading figure is Dr. Soumya S. Krishnan, a biomedical scientist and faculty member at SUNY Downstate Health Sciences University. Her significant work covers pulmonary pharmacology, cancer biology, and lipid metabolism. Her doctoral research examined the anti-fibrotic mechanisms of the natural compound daidzein in pulmonary fibrosis, a serious lung disease, contributing valuable insights to the field of respiratory medicine. Search results: Hmm, some hits for a book
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The integration of Deep Learning (DL) into medical diagnostics has shown remarkable potential, yet the "black-box" nature of these models remains a significant barrier to clinical adoption. Physicians require not only accurate predictions but also a comprehensible rationale behind algorithmic decisions. This paper proposes a novel framework, , designed to classify cardiac arrhythmias from Electrocardiogram (ECG) signals while providing human-interpretable explanations. By combining Convolutional Neural Networks (CNNs) for feature extraction with attention-based mechanisms for localization, our model highlights specific regions of the ECG signal influencing the classification decision. We evaluate ECG-Net-X on the MIT-BIH Arrhythmia Database, achieving a classification accuracy of 98.4%. Furthermore, qualitative evaluation by cardiologists confirms that the attention maps align with known physiological biomarkers. This study bridges the gap between high-performance AI algorithms and the explainability required for trustworthy clinical application.