Dr Venkata Phanikrishna B
Assistant Professor (Grade-II)
| Department of Computer Science and Information Technology
Indian Institute of Information Technology, Vadodara
Expertise:
Computer Science Artificial Intelligence
12
Publications
1
Projects
304
Scopus Citations
251
CrossRef
12 Years 5 Months Total Experience
Scopus
304
Citations
7
h-index
CrossRef
251
Citations
7
h-index
Google Scholar
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Professional Recognition
2nd Best Paper in IEEE conference
2020
SCEECS-2020 conference held at MNIT, Bhopal
Won the prize in ADCOM 2018 AI Innovation Jam
2018
IIIT Bangalore
B-Tech Best Project
2010
Impart-Technologies
Won Laptop in Quiz
2009
KIET's abhiyan Event
Research Award for journal publication and H-index
2023
VIT, Vellore
Awarded project as Principal Investigator under the Faculty Initiation Grant (FIG
2025
IIIT Vadodara
Community & Membership
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IEEE
Graduate Student MemberIndian Society for Technical Education (ISTE)
Life MemberInternational Association of Engineers (IAENG)
Life MemberPersonal Details
Doctor of Philosophy
2021
Rajiv Gandhi University, Itanagar
Master of Technology
2014
N/A
Bachelor of Technology
2010
N/A
Assistant Professor (Grade-II)
May 2024 – Present
Indian Institute of Information Technology, Vadodara
| Department of Computer Science and Information Technology
Assistant Professor (Grade-I)
Jun 2021 – May 2024
Vellore Institute of Technology, Vellore
Research Scholar
Jan 2016 – Feb 2021
National Institute of Technology, Rourkela
| Department of Computer Science and Engineering
Assistant Professor
Jun 2015 – Dec 2015
Aditya University
| Department of Computer Science and Engineering
Assistant Professor
Jan 2014 – Jun 2015
D.N.R. College of Engineering & Technology, Balusumudi, Bhimavaram- 534 202,(CC-9P)
Engineering and Technology
Scholarly Work
Machine Learning-Driven EEG Analysis for Early Detection and Prognostic Prediction in Parkinson’s Disease
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Scholarly Publications
Advancements in electrocardiography-based detection of obstructive sleep apnea: a deep learning approach
Automated EEG signal processing: A comprehensive investigation into preprocessing techniques and sub-band extraction for enhanced brain-computer interface applications
Deep Review of Machine Learning Techniques on Detection of Drowsiness Using EEG Signal
Analysis of EEG Signal for Drowsy Detection: A Machine Learning Approach
Development of single-channel electroencephalography signal analysis model for real-time drowsiness detection: SEEGDD
Automated classification system for drowsiness detection using convolutional neural network and electroencephalogram
Open AccessAutomatic classification methods for detecting drowsiness using wavelet packet transform extracted time-domain features from single-channel EEG signal
Statistical Channel Selection Method for Detecting Drowsiness through Single-Channel EEG-Based BCI System
Showing 1 to 8 of 12 publications