Projects

A collection of things I've built โ€” from mobile apps to web tools, simulators, and more.

Additional Projects

๐Ÿฆ

Zoo Ticket Booking System

OOP-based booking simulation โ€” Java

๐Ÿ“ฐ

Fake News Detection Model

ML classifier using Scikit-learn, Pandas & TF-IDF โ€” Python

๐Ÿ“

Note App

Simple Android note-taking app with intuitive interface โ€” Kotlin

๐ŸŽฌ

Cinema Booking System

Windows Forms + SQL Server โ€” C#

Research Papers

๐Ÿง  Cognitive Psychology ยท UI/UX Ethics
2026

The Manufactured Reality and the Emerging Mind

AI-Generated Visual Content ยท Cognitive Development ยท Ethical UI/UX

This research investigates the unprecedented cognitive and social challenges posed by AI-generated visual content โ€” particularly deepfakes โ€” on Generations Z and Alpha. The study introduces the Awarenessโ€“Action Gap: the empirically verified phenomenon where knowing content is fabricated is insufficient to neutralise its influence on judgement and belief. Using a Four-Dimensional Comparative Matrix spanning Digital Awareness, Behavioural Engagement, Cognitive Dependency, and Critical Resilience, the paper proposes six Ethical UI/UX Design Models that embed transparency and informed consent architecturally within digital interfaces โ€” moving beyond awareness campaigns toward structural, design-level protection.

Awarenessโ€“Action Gap Ethical UI/UX Gen Z & Alpha Deepfakes Critical Resilience
๐Ÿ“Š Comparative Matrix Analysis ๐Ÿ“š 4 Peer-Reviewed Sources
๐Ÿ“„ View Paper (PDF)
๐Ÿ›ก๏ธ AI Ethics ยท Fake News Detection
2025

AI for Fake News Detection in Social Media

Dual-Role AI ยท Algorithmic Bias ยท Trust Architecture in UI/UX

This research examines the growing challenge of misinformation on social media and the dual role Artificial Intelligence plays โ€” simultaneously as a tool for generating and for detecting fake news. Key findings reveal that fake news spreads 70% faster than real news, and that AI systems alone achieve only 60% accuracy, rising to 90% when combined with human oversight. The paper critically analyzes algorithmic bias โ€” where false positive rates reach 61.3% for non-native English speakers versus 15% for native speakers โ€” and argues that the real challenge is not purely technical but fundamentally a UI/UX and ethical design problem. Effective solutions demand transparent, explainable systems built on a Human-in-the-Loop approach and a Trust Architecture that protects users within the digital information ecosystem.

NLP & Transformers BERT / GPT Algorithmic Bias Human-in-the-loop Trust Architecture
๐ŸŽฏ 99% Accuracy Tools Analyzed ๐Ÿ“– Case Study: COVID-19
๐Ÿ“„ View Paper (PDF)