Deep Learning and Machine Learning Mathematical Models for Computer Assisted Diagnostic Systems, 2nd Edition
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: 31 January 2025 | Viewed by 1742
Special Issue Editors
Interests: structural vibration control; damage identification; large scale control; decentralized control; control of irrigation canals; automatic image analysis of blood cells
Special Issues, Collections and Topics in MDPI journals
Interests: structural health monitoring; condition monitoring; piezoelectric transducers; PZT; data science; wind turbines
Special Issues, Collections and Topics in MDPI journals
Interests: condition monitoring; data-based models; fault diagnosis; fault tolerant control; machine learning; structural health monitoring; sensors; wind turbines
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine and deep learning algorithms have recently seen broad use in computer-assisted diagnostic systems due to their dramatic advances in image analysis, computer vision, and time-series analysis. Deep and machine learning has demonstrated their huge potential to transform computer-aided diagnosis in a wide variety of areas that range from medical disease diagnostics and classification, through mechanical systems condition monitoring, to diagnosis for chemical industries, as well as structural health diagnosis of different structures as bridges, wind turbines, or buildings.
This Special Issue calls for innovative work that explores recent advances, prospects, and challenges in artificial intelligence applications to reduce the chances of either missing, misclassifying, or overdiagnosing suspicious targets on diagnostic, as well as to propel the path into computer-assisted prognostics. It is noteworthy that in this Special Issue the keyword ‘diagnostic’ has to be understood in a wide sense: medical, mechanical systems, civil engineering, chemical processes, and so on. The targeted audience includes both academic researchers and industrial practitioners. The purpose is to provide a platform to enhance interdisciplinary research and collaborations and to share the most innovative ideas in various related fields.
Prof. Dr. José Rodellar
Dr. Francesc Pozo
Dr. Yolanda Vidal
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. Mathematics 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.
Keywords
- deep learning
- machine learning
- artificial intelligence
- fault diagnosis
- damage diagnosis
- disease diagnosis
- medical decision making
- real-time diagnostics
