A5035126796- Glass properties and applications
- Phase-change materials and chalcogenides
- EEG and Brain-Computer Interfaces
- Chalcogenide Semiconductor Thin Films
- Luminescence Properties of Advanced Materials
- Neurological disorders and treatments
- Neuroscience and Neural Engineering
- Muon and positron interactions and applications
- Advanced Image and Video Retrieval Techniques
- Metaheuristic Optimization Algorithms Research
- Material Dynamics and Properties
- ECG Monitoring and Analysis
- Solid-state spectroscopy and crystallography
- Transition Metal Oxide Nanomaterials
- Pigment Synthesis and Properties
- Remote-Sensing Image Classification
- Non-Invasive Vital Sign Monitoring
- Wireless Body Area Networks
- Semiconductor Quantum Structures and Devices
- Advancements in Battery Materials
- Embedded Systems and FPGA Design
- Surface and Thin Film Phenomena
- Ferroelectric and Piezoelectric Materials
- Quantum and electron transport phenomena
- Fractal and DNA sequence analysis
National Institute of Technology Manipur
2015-2025
Punjabi University
2002
Views Icon Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Twitter Facebook Reddit LinkedIn Tools Reprints and Permissions Cite Search Site Citation Salam Devayani Devi, Loitongbam Surajkumar Singh, Khelchandra Thongam; Cardiovascular disease classification using continuous wavelet transform deep learning model. AIP Conf. Proc. 9 January 2025; 3159 (1): 020006. https://doi.org/10.1063/5.0247791 Download citation file: Ris (Zotero) Reference Manager EasyBib...
This study aims to identify the most accurate and reliable model for digit recognition in photographs. The models were tested using various metrics such as classification loss, accuracy, recall, mean average accuracy (mAP), F1 score. YOLO-NAS was found be effective, with a loss of 1.2, 0.85, recall 0.90, absolute performance 0.80. indicates that is valid competent identification tasks. However, YOLOv8 YOLOv5 showed significant deficiencies precision overall indicating need further...
Image segmentation has widespread applications in medical science, for example, classification of different tissues, identification tumors, estimation tumor size, surgery planning, and atlas matching. Clustering is a widely implemented unsupervised technique used image mainly because its simplicity fast computation. However, the quality efficiency clustering-based highly depended on initial value cluster centroid. In this paper, new hybrid approach based k-means clustering modified...