Sinha Namrata Ieee Access Link //top\\ May 2026

Title: Exploring the Research Contributions of Sinha Namrata in IEEE Access

While specific details about Namrata Sinha's publications in IEEE Access are not provided here, researchers like her often focus on cutting-edge areas such as artificial intelligence, machine learning, cybersecurity, and the Internet of Things (IoT). These areas are crucial in driving innovation and addressing complex challenges in our increasingly interconnected world.

IEEE Access stands out for its:

: Research involving the design and analysis of slanted polarized antennas using inverted resonators. Biomedical & Engineering Research

Namrata Sinha has contributed to technical literature in IEEE Access, a Q1-ranked, multidisciplinary journal focused on engineering and computing . Her research can be found on IEEE Xplore sinha namrata ieee access link

Review Process: It is known for its rapid peer review, with an average turnaround time of 4 to 6 weeks from submission to decision.

(At the end of this response I will suggest related search terms.) Title: Exploring the Research Contributions of Sinha Namrata

Namrata Sinha's research, published in IEEE Access and other journals, focuses on smart grids, renewable energy systems, and advanced control for sustainable energy integration. Her work is recognized for advancing electrical and electronics engineering through innovative modeling of hybrid systems. View her publications and profile at IEEE Xplore.

Abstract (150–200 words) This paper presents a robust deep learning framework for early detection and classification of faults in three-phase induction motors using vibration and stator-current signals. We design a data-preprocessing pipeline that includes resampling, denoising with wavelet thresholding, and time–frequency feature extraction via short-time Fourier transform (STFT) and continuous wavelet transform (CWT). A convolutional neural network (CNN) processes spectrogram/CWT images while a parallel 1D-CNN processes raw waveform data; features are fused and fed to fully connected layers for multi-class fault classification (bearing defects, rotor bar faults, eccentricity, healthy). We evaluate the model on an industrial testbed and the publicly available CWRU and Paderborn datasets, achieving average accuracy >98%, F1-score >0.97, and robust performance under variable loads and noise. Ablation studies quantify the contribution of each sensor modality and preprocessing step. The proposed method is computationally efficient for edge deployment and includes guidelines for transfer learning to adapt to new motor types. Her work is recognized for advancing electrical and