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Komal Kumar Napa on Muck Rack

Komal Kumar Napa

As seen in: Nature
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Articles

An optimized machine learning framework integrating feature selection and Bayesian–Tree-Structured Parzen estimator hyperparameter optimization for alzheimer’s disease prediction

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By Rajkumar Govindarajan, K. Thirunadanasikamani, Komal Kumar Napa, Haiter Lenin Allasi, Mary Vasanthi Soosaimariyan, C. Sheeja Herobin Rani
| Nature Verified
Abstract Alzheimer's disease (AD) is an increasing global health challenge due to its rising prevalence and the limited effectiveness of existing diagnostic approaches. Neuropathological changes often begin more than a decade before clinical symptoms emerge, yet most diagnostic methods detect the disease only after significant cognitive decline.
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Machine learning-based prediction and SHAP interpretation of mechanical properties in Kenaf/PALF/flax reinforced vinyl ester hybrid composites

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By S. Sathees Kumar, Komal Kumar Napa, V. Vignesh, S. Vincent Rajkumar, Pravat Ranjan Pati
| Nature Verified
Abstract Hybrid Natural Fiber Composites (HNFCs) made of kenaf, PALF, and flax fiber reinforced vinyl ester are the sustainable alternative for engineering applications, but the mechanical properties of these composites are dependent on complex and nonlinear interactions between the fibers, which make traditional experimental optimization not feasible.
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A federated attention-based stacked LSTM framework for interpretable malaria diagnosis under simulated non-IID federated conditions

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By Sangeetha Murugan, Komal Kumar Napa, Haiter Lenin Allasi, S. Sathya, Mary Vasanthi Soosaimariyan, K. Thirunadanasikamani, G. Geethamahalakshmi, Rajkumar Govindarajan
| Nature Verified
Abstract Malaria remains a major global health burden, particularly in low-resource regions where microscopic diagnosis relies heavily on expert interpretation and is prone to variability. Although deep learning models have demonstrated strong classification performance for automated malaria detection, most existing approaches operate as black-box systems and assume centralized, independent and identically distributed (IID) data.
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