GSRAP

A visualized system for project progress and project quality is developed using Angular for the front-end and Java Servlet for the back-end.

• Integration with the GitHub API, allowing users to register and log in using OAuth.
• Visualization of participants' project contributions, commit activity, and issue tracking, providing project managers with insights into the project progress.
• Integration with SonarQube to display real-time project quality information, including the issues detected by SonarQube scans. 

Intelligent Heart Rate 
Monitoring Alert Line Bot

Design a project that integrates smartwatches and LINE Bot to provide real-time notifications to caregivers about abnormal heart conditions. The project also includes a device administrator role to manage and monitor the performance of the devices.

• Deploy a Flask web application using GCP App Engine to provide device binding and settings pages. 
• Utilize BigQuery and Data Studio to visualize reports, displaying information such as heart rate, images, and threshold values.
• Use Firestore to trigger the LineBot API and send alerts, as well as provide links to replay recorded videos.
• Configure devices using AWS IoT Shadow and stream videos using KVS (Kinesis Video Streams).

Stroke Analysis (Visualization)

Using a publicly available stroke dataset from Kaggle, building a predictive model through machine learning, and using ChartJS to visualize the data. 

Users can input basic information, and the system will predict the probability of having a stroke.

Stroke Analysis (Machine Learning)

By utilizing SVM and AutoEncoder, a predictive model can be established. To address the issue of imbalanced data, techniques such as ADASYN and adjusting weights can be employed.