Departments
Admin

Mr. Vysagh M

Assistant Professor

Areas of Specialization

Networking
Areas of Interest

Artificial Intelligence
Data Structures
Computer Networks

Educational Summary


Course University Year
M.Tech in Computer Science and Engineering University of Calicut 2015
B.Tech in Computer Science and Engineering University of Calicut 2012

Experience


From Date To Date Institution Name Designation
2016-06-06 2023-01-31 Nehru College of Engineering and Research Centre Assistant Professor

Papers Published


Vysagh M, S Sreeji, Shiji, T Ambika Devi Amma, “Security and Privacy Deep Learning Framework that Protect Healthcare Data Breaches”, IJRESM- International Journal of Research in Engineering, Science and Management, Volume-3, Issue-7, July2020.
Vysagh M, Reeja R Rajan, Ambikadevi Amma T “Securing Network against Pollution Attack using Tagging Scheme”, IJIRST - International Journal for Innovative Research in Science & Technology, Volume 1 , Issue 11, April 2015,pp.369-373.

Conferences / Workshop / FDPs Attended


ATAL FDP on "Exploring Explainable and Generative AI in Contemporary Machine Learning" at Coimbatore Institute of Technology, January 2024
“Cardiovascular Disease Prognosis using Deep Learning” in International Conference on Innovations in Computing Materials & Communication Technologies (ICICMCT’23) organized by IES College of Engineering on 15-05-2023
“Real Time Sign Language Gesture Recognition from Video Sequences” in International Conference on Emerging Trends in Signals Systems and Information (ICETSSI 2021) organized by Nehru College of Engineering and Research Centre on 28-05-2021.
ATAL online FDP on "Cyber Security" at Coimbatore Institute of Technology, February 2021
ATAL online FDP on "Quantum Computing" at K.S.Rangasamy College of Technology, January2021
ATAL online FDP on "Data Sciences" at SAINTGITS COLLEGE OF ENGINEERING, January2021
ATAL online FDP on "Augmented Reality (AR)/ Virtual Reality (VR)" at Dr. Mahalingam College of Engineering and Technology, December 2020
ATAL online FDP on "Cloud Technology" at Gandhi Institute of Technology and Management, November 2020

Projects Guided


Projects Name Details Year
FACIAL RECOGNITION ATTENDANCE SYSTEM This project offers an innovative solution that uses advanced facial recognition technology to improve student attendance management.Its flexibility is excellent and is ideal for deployment in dynamic and crowded learning environments.The results demonstrate the effectiveness of our method to improve the attendance process while maintaining a high level of accuracy and timeliness; thus providing schools with a reliable tool that is efficient and effective for managing student attendance. 2024
DECENTRALIZED IMMUTABLE CREDENTIAL ECOSYSTEM Traditional credential verification methods lack transparency and are susceptible to fraud and misrepresentation. There’s a need for a secure and tamper-proof system to store, verify and share educational qualifications and credentials. DICE addresses these issues by introducing a trustworthy and efficient solution for credential verification. DICE aims to establish a robust system using blockchain technology to ensure unchangeable credentials. 2024
CARDIOVASCULAR DISEASE PROGNOSIS USING DEEP LEARNING Cardiovascular diseases are considered as the most life-threatening syndromes with the highest mortality rate globally. The major factors of cardiovascular diseases are high blood pressure, family history, stress, age, gender, cholesterol, Body Mass Index (BMI), and unhealthy lifestyle. Based on these factors, researchers have proposed various approaches for early diagnosis. However, the accuracy of proposed techniques and approaches needs certain improvements due to the inherent criticality and life threatening risks of cardiovascular diseases. MaLCaDD (Machine Learning based Cardiovascular Disease Diagnosis) framework is proposed for the effective prediction of cardiovascular diseases with high precision. The validation of framework is performed through three benchmark datasets (i.e. Framingham, Heart Disease and Cleveland) and the accuracies of 99.1%, 98.0% and 95.5% are achieved respectively. 2023