AI-driven alloy design focuses on leveraging machine learning and artificial intelligence to develop new materials with enhanced properties. This domain encourages participants to explore innovative approaches to material science, optimizing alloy compositions for specific applications such as aerospace, automotive, and construction. The goal is to create smarter, more efficient materials that meet the demands of modern engineering challenges.

Machine learning in quality inspection aims to enhance the accuracy and efficiency of quality control processes in manufacturing. Participants are encouraged to develop AI-based systems that can detect defects, predict failures, and ensure product consistency. By integrating machine learning algorithms, these solutions can significantly reduce human error, improve production speed, and maintain high-quality standards across various industries.

Gamification of driving involves integrating game-like elements into the driving experience to enhance safety, engagement, and efficiency. This domain encourages participants to develop innovative solutions that make driving more interactive and enjoyable while promoting safe driving habits. By leveraging technologies such as augmented reality, AI, and IoT, participants can create systems that reward good driving behavior, provide real-time feedback, and improve overall road safety.

Predictive maintenance using machine learning aims to predict equipment failures before they occur, reducing downtime and maintenance costs. Participants are encouraged to develop AI models that analyze sensor data, detect anomalies, and predict potential failures in industrial equipment. By implementing predictive maintenance solutions, industries can optimize their operations, extend the lifespan of their machinery, and ensure continuous production.

Driver behavior monitoring systems aim to enhance road safety by analyzing and improving driver habits. This domain encourages participants to develop AI-based solutions that monitor driver behavior in real-time, detect risky actions, and provide feedback to promote safer driving. By leveraging technologies such as computer vision, IoT, and machine learning, these systems can significantly reduce accidents and improve overall road safety.

Machine learning in 3D printing focuses on optimizing the 3D printing process by improving accuracy, reducing material waste, and enhancing print quality. Participants are encouraged to develop AI models that can predict and correct errors during the printing process, optimize designs for better performance, and reduce production costs. By integrating machine learning, 3D printing can become more efficient and accessible for various applications.

AI-based production scheduling optimization aims to streamline manufacturing processes by optimizing resource allocation, reducing downtime, and improving efficiency. Participants are encouraged to develop AI models that can analyze production data, predict bottlenecks, and optimize scheduling to meet production targets. By implementing AI-driven scheduling solutions, manufacturers can enhance productivity, reduce costs, and improve overall operational efficiency.

Intelligent IoT for society focuses on leveraging IoT technologies to create smart solutions that improve quality of life and address societal challenges. Participants are encouraged to develop IoT-based systems that enhance public services, improve infrastructure, and promote sustainability. By integrating AI and IoT, these solutions can provide real-time data, optimize resource usage, and create smarter, more connected communities.

Machine learning for biomedical image analysis and diagnosis aims to improve medical diagnostics by leveraging AI to analyze medical images. Participants are encouraged to develop AI models that can detect diseases, analyze medical images, and assist healthcare professionals in making accurate diagnoses. By integrating machine learning, these solutions can enhance diagnostic accuracy, reduce diagnostic time, and improve patient outcomes.

Solutions to real-time civil engineering tasks via machine learning aim to optimize construction processes, improve structural analysis, and enhance project management. Participants are encouraged to develop AI models that can predict structural failures, optimize construction schedules, and improve resource allocation. By integrating machine learning, civil engineering projects can become more efficient, cost-effective, and safer.