M
M. Arif Wani
Researcher at University of Kashmir
Publications - 62
Citations - 469
M. Arif Wani is an academic researcher from University of Kashmir. The author has contributed to research in topics: Deep learning & Support vector machine. The author has an hindex of 9, co-authored 48 publications receiving 350 citations. Previous affiliations of M. Arif Wani include California State University & California State University, Bakersfield.
Papers
More filters
Journal ArticleDOI
Parallel Edge-Region-Based Segmentation Algorithm Targeted at Reconfigurable MultiRing Network
M. Arif Wani,Hamid R. Arabnia +1 more
TL;DR: The parallel edge-region-based segmentation algorithm targeted at reconfigurable MultiRing network is presented and various broadcasting mechanisms for utilizing the MultiRing for various stages of the algorithm are discussed.
Book
Advances in Deep Learning
TL;DR: This chapter discusses the application of deep learning in classification, application of Deep Learning in Segmentation, and application ofDeep learning in Fingerprint Recognition.
Journal ArticleDOI
A new cluster validity index using maximum cluster spread based compactness measure
M. Arif Wani,Romana Riyaz +1 more
TL;DR: A new compactness measure is introduced that depicts the typical behaviour of a cluster where more points are located around the centre and lesser points towards the outer edge of the cluster and a novel penalty function is proposed for determining the distinctness measure of clusters.
Journal ArticleDOI
A novel point density based validity index for clustering gene expression datasets
M. Arif Wani,Romana Riyaz +1 more
TL;DR: A new cluster validity index (ARPoints index) for the purpose of cluster validation is proposed and a new approach to determine the compactness measure and distinctness measure of clusters is presented.
Journal ArticleDOI
A fuzzy based local minima avoidance path planning in autonomous robots
Tawseef Ahmed Teli,M. Arif Wani +1 more
TL;DR: This paper provides a fuzzy based approach to path planning in addition to steering a robot out of local minima configuration with a comparative analysis of some non-fuzzy approaches.