I. Jacob Raglend
Noorul Islam University, AAA College of Engineering and Technology
Papers
4
Total Citations
21
H-Index
3
About
I. Jacob Raglend is a researcher whose work spans robotics, artificial intelligence, and smart assistive technologies. His most recognized contributions lie in the domain of robotic kinematics, where he has applied neural network methodologies to solve complex problems in robot manipulator control. His investigations into both forward and inverse kinematics for five-joint robotic systems demonstrate a sustained commitment to advancing automation through intelligent computational approaches. Notably, his 2015 work employing Radial Basis Function Neural Networks to solve inverse kinematics problems has garnered the most attention in his field, accumulating 9 citations, reflecting its practical significance in industrial robotics. Complementing this, his earlier 2014 study on forward kinematics using artificial neural networks and a parallel exploration utilizing Elman networks illustrate his methodical approach to benchmarking multiple neural architectures for robotic applications. More recently, Raglend has expanded his research horizon into the Internet of Things and healthcare technology, contributing to the design and implementation of a machine learning-assisted smart wheelchair, highlighting his growing interest in human-centered, accessible technology solutions. His body of work reflects an evolving research trajectory bridging classical robotics with modern data-driven and IoT-enabled systems.
Research Focus
Key Achievements
Top Papers
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