Recent Advances in Machine Learning and Applications

Dear Colleagues, 

With the rapid advancement of digital technologies of cyber-physical systems, the high-dimensionality, noise contamination, incompleteness, inconsistency, and massive amounts of data from the ubiquity of the Internet of Things (IoT) have posed significant challenges for academic and industrial applications. Artificial intelligence models based on machine learning (ML) are used in data analytics and process optimization, which play significant roles in many research directions. Since 2012, various machine learning technologies have been quick to develop and have proven to be of substantial practical value in a diversity of application domains. Such technology has solved numerous complex industrial problems that have existed in the AI community for many years, such as predictive maintenance, process optimization, task scheduling, quality improvement, supply and demand forecasting, defect detection, vibration signal recognition, and many more. Machine learning is one of the liveliest areas of discussion and is central in current process technological developments. To review recent advances in machine learning, this Special Issue on "Recent Advances in Machine Learning and Applications" will focus on publishing high-quality original research studies that address challenges in the broad area of optimization and artificial intelligence in-process applications. Topics include but are not limited to the following:

• ML models and applications for predictive maintenance, quality control, and process optimization
• ML models and applications for smart manufacturing process monitoring and control
• ML models and application for intelligent manufacturing diagnostics, prognostics, and asset health management
• ML models and applications for scheduling and supply chain management
• ML models and applications for robotics and human–machine interaction
• ML algorithms and approaches to handling big data, data imbalance, uncertainty, data fusion, etc.

Prof. Dr. Chien-Chih Wang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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.