Papers
2
Total Citations
3
H-Index
1
About
Ajeet Singh is a researcher at the forefront of applied artificial intelligence, with a focus on computer vision and biomedical signal processing. His work bridges the gap between advanced deep learning architectures and practical, real-world classification problems. Singh’s key contributions include developing a soft computing approach for pixel labeling in 2D images, where he fine-tuned a Region-based Convolutional Neural Network (R-CNN) to achieve more precise object segmentation. This work, published in 2023, has garnered early attention with 2 citations, signaling its growing relevance in the field of image analysis. In a parallel research stream, Singh has pioneered the use of Electromyogram (EMG) signals for hand gesture recognition. His 2024 study introduces a novel method that combines Short-Time Fourier Transform (STFT) with a Convolutional Neural Network (CNN) to extract and classify features from muscle activity data. By achieving high-accuracy classification of hand gestures, this work holds significant promise for advancing human-computer interaction and prosthetic control technologies. With a citation count already building for his recent publications, Ajeet Singh is establishing himself as an innovative voice in the integration of deep learning with both visual and physiological data.
Research Focus
Key Achievements
Top Papers
- 1
- 2