S
Suresh Dara
Researcher at Padmasri Dr. B. V. Raju Institute of Technology
Publications - 31
Citations - 401
Suresh Dara is an academic researcher from Padmasri Dr. B. V. Raju Institute of Technology. The author has contributed to research in topics: Computer science & Feature selection. The author has an hindex of 6, co-authored 19 publications receiving 206 citations. Previous affiliations of Suresh Dara include DIT University & Shri Mata Vaishno Devi University.
Papers
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Journal ArticleDOI
A Hamming distance based binary particle swarm optimization (HDBPSO) algorithm for high dimensional feature selection, classification and validation
Haider Banka,Suresh Dara +1 more
TL;DR: By choosing proper preprocessing method, fine tuned by HDBPSO with Hamming distance as a proximity measure, it is possible to find important feature subsets in gene expression data with better and competitive performances.
Proceedings ArticleDOI
Feature Extraction By Using Deep Learning: A Survey
Suresh Dara,Priyanka Tumma +1 more
TL;DR: The purpose of this paper presents an emerged survey of actual literatures on feature extraction methods since past five years, and described some of existing methodology of feature extraction.
Journal ArticleDOI
Machine Learning in Drug Discovery: A Review
TL;DR: A review of the literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials is provided in this article.
Journal ArticleDOI
Cancer microarray data feature selection using Multi-Objective Binary Particle Swarm Optimization algorithm
TL;DR: A Multi-Objective Binary Particle Swarm Optimization (MOBPSO) algorithm is proposed for analyzing cancer gene expression data using a fast heuristic based pre-processing technique to reduce some of the crude domain features from the initial feature set.
Journal ArticleDOI
Smart Heart Disease Prediction System with IoT and Fog Computing Sectors Enabled by Cascaded Deep Learning Model
TL;DR: The proposed model integrates Edge-Fog-Cloud computing for the accurate and fast delivery of outcomes and ensures its efficiency over the conventional models.