Prof. Madevamma S M
Assistant Professor
| Email-ID | madhushastri123@gmail.com |
Qualification
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Education |
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| Course / Degree | School / University | Grade / Score |
Year |
| S S L C | Government High School,Gagenahalli | 81.06% | 2009 |
| Second PUC | Government Girls Junior College , Krishnarajanagara | 59% | 2011 |
| BE in Computer Science | Government Engineering College,Kushalnagar | 73% | 2015 |
| Mtech | Maharaja Institute of Technology College, Mysore | 8.92 CGPA (Academic First Topper) | 2025 |
Professional experience
Php Web Developer
- 13-05-2016 – 30-12-2018
- Aegeaon Technology
- Developed and maintained dynamic web applications using PHP, MySQL, HTML, CSS, and Participated in debugging, testing, and deploying projects to production environments.
Lab Associate
- 05-08-2023 – 28-02-2024
- Navkis Engineering College,Hassan
- Assisted in conducting practical sessions and guided students in performing lab exercises. Maintained and managed lab equipment, software installations, and system troubleshooting. Supported faculty in preparing lab materials, documentation, and assessments.
- Ensured smooth operation of lab sessions by providing technical assistance and resolving student queries.
- Assistant Professor (Part Time)
- 06-08-2024 – 31-11-2025
- NDRK First Grade Degree College,Hassan
- Teaching undergraduate courses in Data Communication, Operating Systems, and related subjects.
- Conducting practical lab sessions in Python Programming, Shell Scripting, and HTML Web Preparing academic materials, assignments, and assessments for theory and lab courses.
- Guiding students in project development and providing academic
Skills
- Web Technologies (HTML,CSS, BOOTSTRAP, JAVASCRIPT)
- PHP
- Python C, C++
- Database Management System
Projects
Centralized Attendance System (BE Project)
Developed a centralized, web-based attendance management system to streamline and automate attendance tracking for students and faculty. The system allowed real-time recording, storage, and retrieval of attendance data in a secure and organized manner, minimizing manual effort and reducing errors.
Key Features:
Role-based access for Admin, Faculty, and Students.Attendance marking and monitoring with daily, monthly, and semester-wise reports.
Centralized database management for data consistency and backup.User-friendly dashboard for attendance analytics and notifications.
Volumetric Analysis of colon cancer using federated learning approach(Mtech Project)
This project aims to develop a secure and intelligent system for analyzing colon cancer using volumetric CT scan images.
Deep learning techniques were applied for tumor detection, segmentation, and classification from 3D medical data.
A Federated Learning approach was implemented to enable collaborative model training across multiple institutions without sharing sensitive patient data, thereby ensuring privacy and security.
The system was evaluated using metrics such as accuracy, sensitivity, and Dice coefficient, demonstrating effective diagnostic performance.
This project integrates medical image processing and privacy-preserving AI for healthcare applications.
Privacy-preserving analysis using Federated Learning without sharing raw medical data. Supports volumetric (3D) CT scan based colon cancer detection.
Secure multi-institutional collaborative model training High diagnostic accuracy with reduced data leakage risk