Advances in Machine Learning for Intelligent Engineering Systems and Applications

Dear Colleagues,

Latest advances in machine learning have contributed to great developments in many areas of interest to the engineering community. Data-driven or domain-oriented engineering applications can benefit significantly from the latest developments in machine learning theories and methods (including deep, reinforcement, transfer and extreme learning), but may also promote the development of learning algorithms, optimization approaches, fusion techniques for multimodal data, novel hardware and network architectures. The rapid development in these fields has also stimulated new research on sensors and sensor networks.

The purpose of this Special Issue is to provide a forum for engineers, data scientists, researchers and practitioners to present new academic research and industrial development on machine learning for engineering applications. The Special Issue aims at original research papers in the field, covering new theories, algorithms, systems, as well as new implementations and applications incorporating state-of-the-art machine learning techniques. Emphasis will be given on systems that incorporate new sensors and the configuration of them. Review articles and works on performance evaluation and benchmark datasets are also solicited.

Indicative domains of application of interest to the Special Issue include:

• Research on sensors for new critical engineering applications
• Sensors networks and drones to survey critical infrastructures
• Software and hardware architectures for new sensorial systems in managing critical infrastructures
• Electrical and mechanical engineering, production management and optimization, manufacturing, failure detection, energy management, smart grid
• Robotics and automation, computer vision and pattern recognition applications, critical infrastructure protection
• Civil engineering, construction management and optimization, structural health monitoring, earthquake engineering, urban planning
• Transportation, hydraulics, water power and environmental engineering
• Surveying and geospatial engineering, spatial planning, and remote sensing
• Materials science and engineering
• Biomedical engineering

Dr. Anastasios Doulamis
Dr. Nikolaos Doulamis
Dr. Athanasios Voulodimos
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Sensors 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 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.