Advances in Machine Learning Applied to Intelligent Systems and Data Analytics, 2nd Edition

Dear Colleagues,

Advancements in machine learning (ML) are driving the development of autonomous and intelligent systems (AIS) in various domains, e.g., smart cities, transportation, healthcare, the economy, the environment, etc. Based on analytical models built from data samples, valuable insights can be mined and utilized to assist the decision-making and service delivery processes of AIS. To continuously and consistently elevate the levels of intelligence and automation in AIS, i.e., to be more independent of humans, advanced ML, e.g., deep learning, reinforcement learning, meta-learning, etc., are required to support both supervised and unsupervised analytical tasks via more accurate, robust, and self-interpretable models. Moreover, since the exploration of big data diversifies the data sources, which tend to be more isolated due to the engagement of laws and regulations about data protection and user privacy, the working paradigm of AIS and data analytics is shifting from being centralized to distributed, with multi-end resources being managed to learn and consume interknowledge in a collaborative and privacy-preserving manner. Driven by these emerging demands, novel solutions that are not limited to learning theories, algorithms, mechanisms, frameworks, systems, and services are required to impel the applications of advanced ML in AIS and data analytics.

This Special Issue will provide a forum for researchers to present their original contributions describing their experience and approaches toward a wide range of machine learning techniques applied to intelligent systems and data analytics. Submissions showcasing the latest developments in theoretical analysis, numerical experiments, practical applications, and data analytics are welcome.

Dr. Linlin You
Dr. Zhicong Chen
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• machine learning
• autonomous and intelligent systems
• ai-driven data analytics
• computing theory
• deep learning
• distributed learning
• meta-learning
• reinforcement learning
• supervised deep learning
• unsupervised deep learning
• novel learning applications