Machine Learning Algorithms in Natural Science
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".
Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 2052
Special Issue Editors
Interests: machine learning; artificial intelligence; e-learning; programming languages
Special Issues, Collections and Topics in MDPI journals
Interests: algorithms for bioinformatics and computational biology; logic decision algorithms; algorithms and data-structures for compressed computation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine learning algorithms are very powerful tools for analyzing data and learning patterns which they use to make predictions. They have been applied in many fields and areas of knowledge with great success. In the field of natural sciences there are innumerable problems resulting from large amounts of data being generated and it being necessary to carry out analysis of the information to solve different problems. Thus, machine learning could be applied to verify hypotheses about the existence of common characteristics between species; search for patterns in seismic phenomena and other natural phenomena; and search for patterns of behavior in animals, classification of animals, plants, living beings, or the environment. That is why the natural sciences constitute an ideal field of application for this type of algorithms and in particular for artificial intelligence techniques. In this Special Issue, we are open to any proposal that focuses on solving a problem in the field of natural sciences via the use of machine learning techniques. In particular, the following topics will be of interest to us:
- Application of ML techniques to natural phenomena (meteorology, seismology, volcanism, etc.).
- Application of ML techniques to classification problems in the field of natural sciences (animals, plants, viruses, diseases, etc.).
- Genetic analysis. Genomics.
- Problems of prediction and disease analysis.
- Epidemic prediction problems.
- Problems of prediction and analysis in the field of geology.
- Problems of prediction and analysis in the field of zoology.
Dr. Antonio Sarasa-Cabezuelo
Prof. Dr. Alberto Policriti
Guest Editors
Manuscript Submission Information
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Keywords
- application of ML techniques to natural phenomena (meteorology, seismology, volcanism, etc.)
- application of ML techniques to classification problems in the field of natural sciences (animals, plants, viruses, diseases, etc.)
- genetic analysis. genomics
- problems of prediction and disease analysis
- epidemic prediction problems
- problems of prediction and analysis in the field of geology
- problems of prediction and analysis in the field of zoology
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