Guest Editors
Dr. Man-Fai Leung, Anglia Ruskin University, Cambridge, U.K.
Dr. Wenming Cao, City University of Hong Kong, Hong Kong.
Dr. Keping Yu, Hosei University, Japan.
Summary
In the era of artificial intelligence (AI) and big data, the approaches for data analysis, information extraction, and underlying event analysis with state-of-the-art machine learning algorithms have grown radically. AI is a major research area with many real-world applications. For example, the introduction of different new techniques in machine learning and data mining provide efficient tools to assisted systems in healthcare such as medical diagnostics and patient monitoring. The techniques are widely used as a screening tool or as an aid to diagnosis so that fast and informed decisions could be made, especially those with big data sets from multiple sources. However, the black-box nature of AI hinders its applications in industry. This situation is even severely worse in complex data analytics. It is imperative to develop explainable AI models to provide safe, reliable, and efficient solutions integrated into applications. This Special Issue will accept original research and review articles on explainable AI techniques and their applications.
Potential topics include but are not limited to the following:
1. Explainable AI models
2. AI for daily living activities
3. Neural networks and fuzzy logic based systems
4. AI models for pattern recognition
5. Novel AI theory and algorithm
6. Intelligent wearable and assistive robotic devices
7. AI Based recommender system
8. Big data analytics
Keywords
Machine Learning; deep learning; optimization; neural networks; big data analytics; data mining; pattern recognition
Published Papers
Login
Register
Submit a Paper
Propose a Special lssue