My Professional Profile
Academic background, technical skills, and project experience.
I’m Mohammed Saad Affan A, a Computer Science student at Vellore Institute of Technology (VIT), focused on building scalable full-stack applications and intelligent AI-driven systems.
Skills & Technologies
Featured Projects
Secure Vote System
Enterprise-grade online voting platform with role-based access, JWT authentication, one-vote enforcement, and secure MongoDB-backed election management. Features admin-controlled election lifecycle and real-time result tracking.
NeuroAdaptive UX
Client-side intelligent interface system that adapts in real-time to user interaction patterns. Tracks typing rhythm, mouse movement, and interaction intensity to dynamically adjust UI feedback without any backend or data storage.
Smart AI Attendance System
IoT-powered intelligent attendance system combining ESP32-CAM real-time face capture, deep learning face recognition, anti-spoofing detection, and automated attendance logging with LED/buzzer feedback.
Student Stress Prediction
Machine learning system predicting student stress levels from lifestyle and academic factors. Compares Logistic Regression, Random Forest, SVM+PCA, and XGBoost with optimized feature engineering achieving ~85% accuracy.
Bachelor’s of Computer Science
Vellore Institute of Technology (VIT)
Currently pursuing Bachelor’s of Computer Science with specialization in AI & ML, building strong foundations in scalable systems, programming, artificial intelligence, and modern software engineering.
Higher Secondary — Mathematics and Computer Science
Islamiah Boys Higher Secondary School
Successfully completed the English Access Microscholarship Program, strengthening communication and English proficiency. Performed among the top ranks in Computer Science at the school level during the academic year.
Matriculation
Islamiah Boys Higher Secondary School
Built strong foundations in mathematics, analytical thinking, and academic discipline.
Machine Learning Developer
AI/MLVellore Institute of Technology
Built a student stress prediction system using SVM, Random Forest, and XGBoost achieving ~85% accuracy through optimized feature engineering and hyperparameter tuning.
Member
CreativeVIT Film Society
Actively contributing to collaborative creative initiatives and technical event participation within VIT's film community.
Smart AI Attendance System
IoT + AIIoT-powered intelligent attendance system combining ESP32-CAM real-time face capture, deep learning face recognition, anti-spoofing detection, and automated attendance logging with LED/buzzer feedback.
SecureVote
Full-StackEnterprise-grade online voting platform with role-based access, JWT authentication, one-vote enforcement, and secure MongoDB-backed election management.
NeuroAdaptive UX
FrontendClient-side intelligent interface system that adapts in real-time to user interaction patterns without any backend or data storage.
Student Stress Prediction
MLMachine learning system predicting student stress levels from lifestyle and academic factors using optimized feature engineering achieving ~85% accuracy.
View my open-source contributions, projects, and repositories.
