Special Issue "Bioinspiration: The Path from Engineering to Nature"
A special issue of Computation (ISSN 2079-3197).
Deadline for manuscript submissions: 15 March 2022.
Interests: artificial neural networks; evolutionary computation; AI-assisted design and modelling
Special Issues, Collections and Topics in MDPI journals
Bioinspiration, understood as the use of biological processes for inspiration in engineering and computational designs, has become a widespread approach for both the engineer and the computational scientist to study, model, and resolve complex issues. Technological advances, such as the increasing affordability of high-performance computational resources, massive, fast, and accessible storage capacities, high-speed communication networks, along with a growing and vibrant international practitioner community, make the field an attractive opportunity to develop innovative solutions to the increasingly complex and uncertain issues humanity is facing today, which are impossible to face by means of classical or analytical paradigms.
Furthermore, it has also been recognized that bioinspired approaches stimulate synergy among scientific disciplines. Multi- and transdisciplinary work has become essential for the advancement of this area. Researchers from different knowledge fields can contribute toward unified goals, sharing their perspectives through discussion, interaction, and collaboration, which leads to bioinspired knowledge discovery and dissemination. Such processes enrich scientists’ respective areas of interest and open new possibilities for their studies.
It is with this horizon in mind that we have launched this Special Issue. Published works are expected to be the result of multi- and transdisciplinary efforts, which present innovative findings beyond each expert’s specific knowledge area. We look forward to contributions that not only propose new bioinspired engineering and computational methods and solutions but are exemplary of an effective and rewarding collaboration between colleagues from diverse areas that will continue to contribute to the growth of the field.
Prof. Dr. Juan Luis Crespo-Mariño
Prof. Andrés Segura-Castillo
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 papers will be 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. Computation 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 1400 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.
- signal and image detection, acquisition, analysis, and processing
- social network analysis and modeling
- pattern recognition for biological and related signals
- bioinformatics, biocomputing, and computational systems biology
- data mining and machine learning
- healthcare informatics
- biomedical devices
- machine learning in agriculture
- biodiversity informatics
- visual analytics for biological information
- high performance computing for health and life sciences
- models of biological learning
- brain–machine interfaces