Ministry of Education
CHANDRA SEKHAR VASAMSETTY
VIDWAN ID: 176536

Prof CHANDRA SEKHAR VASAMSETTY

Male Doctor of Philosophy
Dean | Department of Computer Science and Engineering
SRKR Engineering College
Andhra Pradesh
Expertise: Computer Science Information Systems
ORCID 0009-0009-9887-522X Scopus 58283849300 ResearcherID J-5888-2016 Google Scholar csMOqK8AAAAJ
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10 Publications
0 Projects
39 Scopus Citations
25 CrossRef
19 Years 9 Months Total Experience
Publications
10 Total
Articles
6
Proceedings
4
Activity

No publication activity to display.

Scopus Scopus
39 Citations
4 h-index
CrossRef CrossRef
25 Citations
3 h-index
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Professional Recognition

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Community & Membership

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IEEE
Member
Jan 2022
ACM
Member
Jan 2020
ISTE
Life Member
Jan 2023
IACSIT
Life Member
Jan 2019

Personal Details

Doctor of Philosophy
2014
Baba Farid University of Health Sciences, Faridkot
Master of Technology
1998
Baba Farid University of Health Sciences, Faridkot
Dean
Oct 2024 – Present
SRKR Engineering College | Department of Computer Science and Engineering
Professor
Dec 2006 – Oct 2024
SRKR Engineering College | Department of Computer Science and Engineering
Engineering and Technology
Computer Science Information Systems

Related Profiles

Scholarly Work

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Optimal and Hybrid Deep Learning models for Melanoma detection in Dermoscopic images

University Annamalai University
Year 2024
Downloads 0

FRAMEWORK FOR DETECTION OF OBJECTS IN REMOTE SENSING IMAGES USING DEEP LEARNING TECHNIQUES

University Annamalai University
Year 2024
Downloads 0
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Scholarly Publications

Gene expression analysis for type-2 diabetes mellitus-A study on Diabetes with and without parental history

Journal Article
Year: 2011.
Authors: Vasamsetty, C.S.; Peri, S.R.; Rao, A.A.; Srinivas, K.; Someswararao, C.
Scopus Scopus: 3

Identification of differentially expressed genes for diabetes with parental history vs healthy using microarray data analysis

Conference Paper
Year: 2010. Pages: V4-496-V4-500 .
Authors: Sekhar, V.C.; Appa Rao, A.; Rao, P.S.; Srinivas, K.
DOI 10.1109/ICACTE.2010.5579318

IRFNet: Skin Lesion Detection and Classification Using Unified Intuitive and Object Classifier with Iterative Random Forest Algorithm

Journal Article
International Journal of Image and Graphics. Year: 2024. Volume: 24 , Issue: 05 , Pages: 2340003 .
Authors: V. S. S. P. Raju Gottumukkala; N. Kumaran; V. Chandra Sekhar
DOI 10.1142/s021946782340003x Crossref Crossref: 2 Scopus Scopus: 2

Skin Lesion Segmentation Using SCU-Net with FNLM Preprocessing

Conference Paper
Year: 2022. Pages: 1-6 .
Authors: V. S. S. P. Raju Gottumukkala; N. Kumaran; V. Chandra Sekhar
DOI 10.1109/icdsis55133.2022.9915935 Crossref Crossref: 3 Scopus Scopus: 2

BLSNet: Skin lesion detection and classification using broad learning system with incremental learning algorithm

Journal Article
EXPERT SYSTEMS. Year: 2022. Volume: 39 , Issue: 9 , Pages: e12938 .
Authors: Gottumukkala, V.S.S.P.R.; Kumaran, N.; Sekhar, V.C.
DOI 10.1111/exsy.12938 Crossref Crossref: 12 Scopus Scopus: 15

Remote Sensing Image Classification Using CNN-LSTM Model

Open Access
Journal Article
Revue d'Intelligence Artificielle. Year: 2022. Volume: 36 , Issue: 1 , Pages: 147-153 .
Authors: M Narasimha Raju; Kumaran Natarajan; Chandra Sekhar Vasamsetty
DOI 10.18280/ria.360117 Crossref Crossref: 3 Scopus Scopus: 8

Object Recognition in Remote Sensing Images Based on Modified Backpropagation Neural Network

Open Access
Journal Article
Traitement du Signal. Year: 2021. Volume: 38 , Issue: 2 , Pages: 451-459 .
Authors: M Narasimha Raju; Kumaran Natarajan; Chandra Sekhar Vasamsetty
DOI 10.18280/ts.380224 Crossref Crossref: 2 Scopus Scopus: 4

Meta-feature classification to explore automatic detection of malware using segmentation method

Journal Article
Year: 2019. Volume: 8 , Issue: 10 , Pages: 3458-3462 .
Authors: Vasamsetty, C.S.; Chandu, S.S.; Maddala, J.
DOI 10.35940/ijitee.J9719.0881019
Showing 1 to 8 of 10 publications