Python
Smart E-Commmerce App using AR to Visualize Products in Realtime on Android
The goal of this project is to develop an Augmented Reality (AR) and 3D modeling system for online furniture shopping. The primary aim is to enhance the shopping experience by allowing customers to visualize furniture in their actual living spaces before making a purchase. This system will reduce uncertainty, improve engagement, and provide a more interactive and personalized shopping experience.
Smart Phone Based Remote Monitoring Tool for E-Learning
Student’s Attention Monitoring System in Learning Environments based on Artificial Intelligence
Transchain: Blockchain-Based Management of Allografts for Enhancing Data Provenance
Aim:
The aim of this project is to develop a secure, transparent, and tamper-proof blockchain-based system for managing allografts throughout their entire lifecycle—from donor registration to tissue processing, storage, distribution, and transplantation. The system seeks to ensure authenticity, complete data provenance, regulatory compliance, and real-time traceability by leveraging decentralized ledgers, smart contracts, and digital tagging technologies. This helps enhance patient safety, reduce fraud, and build trust among medical institutions and regulatory authorities.
Transfer Learning Strategies for Credit Card Fraud Detection
Understanding the Security Risks of Websites Using Cloud Storage for Direct User File Uploads
Aim:
To systematically analyze and identify security vulnerabilities in websites that allow users to upload files directly to cloud storage services, assess their real-world prevalence, and propose effective mitigation strategies to protect users, websites, and cloud infrastructure from potential abuse, data breaches, and operational disruptions.
WIPE: A Novel Web-Based Intelligent Packaging Evaluation via Machine Learning and Association Mining
Our study aims to introduce the Web-Based Intelligent Packaging Evaluation (WIPE) platform, which uses machine learning and association rule mining to assess packaging performance in e-commerce. By analyzing customer reviews, WIPE identifies packaging defects, their causes, and effects, offering a dynamic, real-world alternative to traditional laboratory methods.
By using a pre-trained BERT, it ensures precise predictions even with varying data quality. Additionally, the system captures the full context of customer feedback by generating dynamic word clouds, which visually represent common issues and sentiments, offering deeper insights into customer concerns.
