ML and AI for Augmented Biosensing Applications
A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".
Deadline for manuscript submissions: 15 October 2024 | Viewed by 585
Special Issue Editor
Interests: biosensors and bioelectronics; microfluidics; nanotechnology and nanobiosensors; electrochemical impedance spectroscopy
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Artificial intelligence is already with us. It is a powerful tool that allows tasks that traditionally require a high level of human cognition to be performed more intelligently and efficiently. The development of biosensors, whether for biomedical, environmental, food, or agricultural applications, has escalated enormously in recent years. The systematic application of modern computer methods and machine learning (ML) on a large scale and massive amounts of data in the field and in the laboratory allows generating revolutionary advances in the knowledge and detection of diseases, quantification and evaluation of contaminants in foods and in the environment, as well as the study of specific markers for various applications, which are just being considered and are of great economic importance and social impact. The application of these new IT tools is undoubtedly enabling the development of new systems that are much more intelligent and efficient and allow many of the decisions to be offloaded to automatic and autonomous systems. However, there is still a long way to go in the area of augmented biosensor technology with this modern approach to computing. We propose this Special Issue on this 10th anniversary to showcase all the advances in the area, which will undoubtedly be of wide interest to our readers.
Topics include, but are not limited to, the following:
- Deep learning and machine learning for biosensing applications;
- Intelligent wearables and implantable biosensors;
- AI for environmental biosensor applications;
- AI biosensors for precision agriculture;
- AI-/ML-assisted development of novel bioreceptors and substrates for biosensing applications.
Dr. Rossana Madrid
Guest Editor
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