Machine Learning and Robots for Sustainability
A special issue of Sustainability (ISSN 2071-1050).
Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 2990
Special Issue Editors
Interests: robotics; computational mechanics; vehicle dynamics; multi-body dynamics; finite element modelling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In the era of Big Data and Digital Transformation the concept of sustainability goes beyond environmental concerns. This Special Issue is devoted to Machine Learning and Robots for Sustainability purposes. On the one hand, machine learning (ML) is the science of training devices or algorithms and statistical models that computer systems use to carry out tasks related to artificial intelligence (AI). On the other hand, robotics is the branch of science and technology that deals with the design, construction, operation, and application of robots and automated mechanical systems. Both hold promise as drivers of some of the most influential research in the twenty-first century since they generate innovation in a wide range of research fields, encompassing such diverse areas as healthcare, logistics and distribution, manufacturing industries, business technology, transportation, energy, environmental issues, etc. Both disciplines are inherently multidisciplinary by nature, involving mathematics, physics, and computing.
The main goals of this Special Issue are to (1) provide real-world applications in machine learning and robotics for sustainability purposes, (2) report on the latest progress in utilizing these groundbreaking technologies, and (3) share gained insights.
We invite authors to contribute original research articles addressing significant issues and contributing towards the development of new concepts, methodologies, applications, trends, and knowledge in science. Review articles describing the current state-of-the-art are also welcome.
Potential topics include but are not limited to the following:
- Machine learning in manufacturing and decision-making, optimization, control theory, machine vison (e.g., image classification, face recognition, image interpretation), computer-assisted applications, human–computer interaction, e-learning, ubiquitous computing, social networking, smartphones, medical imaging, medical technologies, intrusion detection, grasp detection, imitation learning, autonomous learning, and multiagent learning.
-Algorithms and statistical machine learning developments (e.g., Bayesian or probabilistic models)
-Applications of both disciplines to business and commerce, healthcare, autonomous driving, education, robotics, exploration, transportation, energy, drawing, music, safety, military applications, safety, autonomous vehicles, drones, and environmental care.
- Computational mechanics, vehicle dynamics, and multibody dynamics.
Dr. Carlos Llopis-Albert
Prof. Francisco Rubio
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. Sustainability 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.
Keywords
- machine learning
- robotics
- sustainability
