Attendance Monitoring Mobile Application
A newly developed Android mobile application uploaded to the Google Play Console as a team project. The application is designed to streamline attendance monitoring by using location-based verification to determine whether a student is within the designated attendance radius. It helps improve the accuracy and reliability of attendance records while reducing manual monitoring.
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Lightweight Context-Aware Authentication Model for IoT
Developed and evaluated a lightweight machine learning model for context-aware risk classification in IoT authentication. The notebook covers data preprocessing, feature selection, model training, performance evaluation, and risk-based authentication, with emphasis on lightweight and efficient deployment.
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This is my microservice of my Machine Learning model exported as TFLite about corn leaf disease classification and severity scoring. I used Python and Postman to create this microservice.
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This is a mobile application i published in Google Play Console. This app is intended for the non-academic club.
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