Theoretical and Applied Problems in Human-Computer Intelligent Systems
The advancement in human–computer interaction technology has been a driving force in developing robotics, intelligent systems, and medical applications. The ability to understand human–computer interaction through visual and text analyses, as well as ensuring security, is critical for the success of intelligent systems. To achieve this, it is necessary to extract valuable insights from visual and text information and to implement robust security measures to protect against potential attacks. Despite these advancements, many challenges remain to be addressed in this field. Recently, deep learning, AI defense and attack, real-time learning, robot control, and other cutting-edge technologies have been explored and applied to intelligent systems. The increasing demand and complex real-world problems have encouraged the growth of academic research in human–computer interaction. This Topic brings together experts, engineers, and researchers worldwide to present their latest findings and advancements in human–computer interaction. The focus is on innovative theories related to intelligent systems and machine learning, emphasizing applications involving human–machine interaction through visual and text information.
Dr. Jiahui Yu
Prof. Dr. Charlie Yang
Prof. Dr. Zhenyu Wen
Dr. Dalin Zhou
Dr. Dongxu Gao
Dr. Changting Lin
- visual-based interaction
- data analysis
- robustness and security
- machine learning
- computer-aided medical analysis
- robotic control and design
- machine learning in signal transmission
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