Research Article
Application of Graph Theory, Critical Path & Machine Learning to Find Inversions of a Kinematic Chain
@INPROCEEDINGS{10.4108/eai.28-4-2025.2358086, author={R Giri Prasad and G S N Murthy and Ch S V V S N Murthy and A Ramesh and M Sreenivasa Reddy and V.V. Kamesh}, title={Application of Graph Theory, Critical Path \& Machine Learning to Find Inversions of a Kinematic Chain}, proceedings={Proceedings of the 4th International Conference on Information Technology, Civil Innovation, Science, and Management, ICITSM 2025, 28-29 April 2025, Tiruchengode, Tamil Nadu, India, Part II}, publisher={EAI}, proceedings_a={ICITSM PART II}, year={2025}, month={10}, keywords={graph conversion longest path k-nn( r )}, doi={10.4108/eai.28-4-2025.2358086} }- R Giri Prasad
G S N Murthy
Ch S V V S N Murthy
A Ramesh
M Sreenivasa Reddy
V.V. Kamesh
Year: 2025
Application of Graph Theory, Critical Path & Machine Learning to Find Inversions of a Kinematic Chain
ICITSM PART II
EAI
DOI: 10.4108/eai.28-4-2025.2358086
Abstract
In the analysis of mechanisms, Graph theory is being applied since many years. Representing a k-chain as a planar graph and analyzing by various numerical methods is possible by representing link as node and joint as path in the graph network. In the analysis of Networks i.e., computer networks, project networks and transportation networks, the largest time duration is taken as one of the key element based on which Critical path and Critical activities are found. K-nearest neighbours is one of the Machine learning technique generally used for the classification of the elements. In the present paper, a new method is pro-posed in which all the above techniques are combined to find the inversions of a k-chain. Results for 8-link 1-dof k-chains are analyzed and presented.
