Sinha Namrata Ieee Access Better _verified_ -

IEEE Access is a known open-access journal that often publishes longer, comprehensive articles compared to letters, and it is typical for authors to propose methods that provide "better" results than existing state-of-the-art.

Scholarship in the modern era is often defined by the synergy between innovative researchers and the platforms that disseminate their work. Dr. Namrata Sinha

18% better load balancing

Smart grid optimization is a crowded field. Sinha Namrata’s contribution? A reinforcement learning-based demand-response system that minimized peak load without sacrificing user comfort. Compared to a genetic algorithm baseline, the proposed strategy delivered and 33% faster convergence —numbers that reviewers praised as “significantly better than state-of-the-art.” sinha namrata ieee access better

Impact Factor:

As of 2024-2025, the journal maintains a strong standing in the scientific community: It holds a JCR Impact Factor of 3.6 .

Why IEEE Access was a suitable choice for her work:

Dr. Sinha's multidisciplinary approach extends to health tech, where she applies deep learning models to improve diagnostic accuracy: IEEE Access is a known open-access journal that

The "Better" Feature:

The model doesn't just highlight a dog’s ears in an image; it identifies the causal feature (e.g., ear shape AND texture) that, if removed, would change the prediction. During peer review, one reviewer noted, "This is the first time I’ve seen an IEEE Access paper that makes post-hoc explainability obsolete."

Another strand of research involves intrusion detection systems (IDS) for the Internet of Things. By combining ensemble learning with feature selection, Sinha Namrata achieved: Namrata Sinha 18% better load balancing Smart grid

The "Better" Metric:

Under the powerful Projected Gradient Descent (PGD) attack, baseline models saw accuracy drop from 92% to 34%. Namrata’s method dropped only to 81%—a 47-point improvement. Critically, this defense added only 7% overhead to inference time.