A
Akshansh Gupta
Researcher at Jawaharlal Nehru University
Publications - 28
Citations - 1401
Akshansh Gupta is an academic researcher from Jawaharlal Nehru University. The author has contributed to research in topics: Feature selection & Feature extraction. The author has an hindex of 10, co-authored 23 publications receiving 806 citations. Previous affiliations of Akshansh Gupta include Central Electronics Engineering Research Institute.
Papers
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Journal ArticleDOI
A review of clustering techniques and developments
Amit Saxena,Mukesh Prasad,Akshansh Gupta,Neha Bharill,Om Prakash Patel,Aruna Tiwari,Meng Joo Er,Weiping Ding,Chin-Teng Lin +8 more
TL;DR: The applications of clustering in some fields like image segmentation, object and character recognition and data mining are highlighted and the approaches used in these methods are discussed with their respective states of art and applicability.
Journal ArticleDOI
Edge of Things: The Big Picture on the Integration of Edge, IoT and the Cloud in a Distributed Computing Environment
Hesham El-Sayed,Sharmi Sankar,Mukesh Prasad,Deepak Puthal,Akshansh Gupta,Manoranjan Mohanty,Chin-Teng Lin +6 more
TL;DR: After analyzing the different network properties in the system, the results show that EC systems perform better than cloud computing systems, and this paper aims to validate the efficiency and resourcefulness of EC.
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Performance enhancement of mental task classification using EEG signal: a study of multivariate feature selection methods
TL;DR: Six popular multivariate filter methods have been investigated which are based on different criteria: distance measure, causal effect and mutual information to obtain a minimal set of relevant and non-redundant features for classification.
Proceedings ArticleDOI
Data-Driven Approach based on Feature Selection Technique for Early Diagnosis of Alzheimer’s Disease
Surendrabikram Thapa,Priyanka Singh,Deepak Kumar Jain,Neha Bharill,Akshansh Gupta,Mukesh Prasad +5 more
TL;DR: The need to combine neuropsychological scores like mini-mental state examination (MMSE) with MRI features to provide better decisional space for early diagnosis of AD is demonstrated.
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A modified intuitionistic fuzzy c-means algorithm incorporating hesitation degree
TL;DR: A modified intuitionistic fuzzy c-means algorithm incorporating hesitation degree is proposed and results show that mIFCM performs better in comparison to existing intuitionists fuzzy clustering algorithms.