Machine Learning on Various Data Sources in Smart Applications

Dear Colleagues,

Machine learning combines high-performance computing, leading to unusual solutions for multi-model data analysis problems. Machine learning-empowered systems can today achieve performance levels in various data analysis tasks comparable to, or even exceeding, those of humans. These advancements have the potential to open new high-impact applications in different environments. In this Special Issue, the authors will use theoretical, methodological, and experimental contributions to fully exploit machine learning solutions in smart applications. The topics will include, but are not limited to:

1. Lightweight machine learning models for visual and audio data analysis and applications.
2. Machine learning models for efficient multimodal data analysis and fusion.
3. Sensor data analysis based on machine learning.
4. Efficient deep learning methodologies for the Internet of Things.
5. Machine learning for applications in smart homes, smart lighting.
6. Machine learning for smart city applications.
7. Machine learning and deep learning for intelligent transportation systems.
8. Machine learning and deep learning for natural language processing and applications.
9. Machine learning and deep learning for medical sciences applications.
10. Machine learning for virtual reality applications.
11. Machine learning for Metaverse applications.

Prof. Dr. Rung-Ching Chen
Prof. Dr. Goutam Chakraborty
Dr. Christine Dewi
Guest Editors

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