Machine Learning Algorithms and Optimization in the Digital Transition
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".
Deadline for manuscript submissions: 30 June 2024 | Viewed by 1600
Special Issue Editors
2. Institute of Systems and Robotics, University of Coimbra, 3004-531 Coimbra, Portugal
Interests: machine learning; artificial intelligence; computer vision; data mining
Special Issues, Collections and Topics in MDPI journals
2. RCM2+ Research Centre in Asset Management and System Engineering, 3030-199 Coimbra, Portugal
3. CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, 62001-001 Covilhã, Portugal
Interests: production optimization through artificial supported condition maintenance policies
Special Issue Information
Dear Colleagues,
To optimize and manage modern industrial systems as well as facilities, many factors must be taken into account. Modern technologies in all sectors of activity depend on large amounts of sensors and data that facilitate multivariable analyses via algorithms to support decision making in the short, middle, and long terms. Classical and deep learning machine models have boosted the capacity to analyze large volumes of data and extract patterns that greatly contribute to informed decisions, making intelligent systems more prevalent and an important part of all organizations.
Industries and large institutions are always concerned about adjusting capacity and minimizing costs, in order to meet demand without delays or the excessive use of resources. This drives research focused on models that can provide consistent support in decision-making processes. Prediction techniques based on time series models and artificial intelligence are being used more frequently to meet these challenges and contribute to more informed decisions.
This Special Issue aims to cover the latest research, so that decisions made on the basis of the algorithms proposed are sound, adequate, and contribute to facilitate management as well as operational decisions. Original contributions on the above aspects, and related topics, are encouraged.
Dr. Mateus Mendes
Dr. Balduíno Mateus
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. Algorithms is an international peer-reviewed open access monthly 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 1600 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
- asset management
- clustering
- data analysis
- data mining
- decision-support systems
- deep learning
- fault detection
- knowledge-based systems
- machine learning
- object detection
- optimization
- predictive maintenance
- time series
