The Server Failure Forecast System aims to develop an intelligent system capable of detecting and alerting abnormal changes in system operations. This system allows for manual and automatic threshold adjustments for alerts, triggers scripts/commands based on specific conditions, and performs event/log correlation across applications to analyze and predict potential issues. It supports high availability, enables administrators to add new devices and services, and operates in various environments, including public cloud, hybrid cloud, private cloud, cloud-native, and on-premise. Additionally, the system analyzes transaction data, correlates information to identify root causes, supports real-time analysis, and integrates seamlessly with existing infrastructure and data warehouses without any disruptions.

The primary objective of this project is to develop a production scheduling system designed to optimize machinery management in the factory efficiently. The system will provide capabilities for processing and displaying data in the form of Gantt charts. Additionally, it will predict and recommend the best production plans. The system also enables comprehensive machinery data management, real-time status tracking of machine operations, and generating detailed and clear production plan reports.

This project develops a system for analyzing and detecting volatile organic compounds (VOCs) from patients' breath using AI to predict cancer risks accurately. The system displays results in numerical and graphical formats, supports usage on tablets and mobiles, and features comprehensive reporting with both online and offline data storage capabilities, ensuring quick and efficient access to patient data by medical professionals.

The MVDeep system is designed to optimize the selection and positioning of light sources and lenses for various applications. It features advanced capabilities to ensure precise and efficient operations, integrates seamlessly with cloud systems, and enables remote service updates.

This project develops an automated color sorting system using artificial intelligence (K-means Clustering) to enhance efficiency and accuracy in sorting products by color. The system aims to reduce labor costs and production time, increase production accuracy, minimize errors, and generate insights for analysis and quality improvement.