Machine Learning in Electronic and Biomedical Engineering, 3rd Edition

Dear Colleagues,

In recent years, machine learning techniques have proven to be extremely useful in a wide variety of applications, and they are now rapidly garnering increasing interest, both in electronic and biomedical engineering.

The Special Issue seeks to collect contributions from researchers involved in developing and using machine learning techniques applied to the following:

• Embedded systems for artificial intelligence (AI) applications, in which the interest is focused on implementing these algorithms directly in the devices, thus reducing latency, communication costs, and privacy concerns;
• Edge computing, where the aim is to process AI algorithms locally on the device, i.e., where the data are generated, by focusing on compression techniques, dimensionality reduction, and parallel computation;
• Wearable sensors for collecting biological data;
• Human activity detection as well as the diagnosis and prognosis of patients are based on the investigation of data collected from sensors;
• Intelligent decision systems and automatic computer-aided diagnosis systems for the early detection and classification of diseases;
• Neuroimaging techniques, such as magnetic resonance, ultrasound imaging, and computed tomography, to aid in the diagnosis and prediction of diseases.

The aim of this Special Issue is to publish original research articles that cover recent advances in the theory and application of machine learning for electronic and biomedical engineering.

The topics of interest include, but are not limited to, the following:

• Machine learning applications for embedded systems;
• Machine learning for edge computation;
• Edge artificial intelligence and tiny machine learning;
• Deep learning model compression and acceleration;
• Image classification, detection, and semantic segmentation;
• Machine learning for autonomous guide;
• Machine learning for agriculture;
• Machine learning for industry;
• Deep neural networks for biomedical image processing;
• Machine learning methods for computer-aided diagnosis;
• Machine learning-based healthcare applications, such as sensor-based behavior analysis, human activity recognition, disease prediction, biomedical signal processing, and data monitoring.

Dr. Laura Falaschetti
Prof. Dr. Claudio Turchetti
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. Electronics 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 2400 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.

• machine learning
• neural networks
• edge computing
• edge artificial intelligence
• tiny machine learning
• sensors for IoT
• vision sensors
• autonomous guide
• medical image classification
• computer-aided diagnosis
• human activity recognition
• biosignals

• Machine Learning in Electronic and Biomedical Engineering, Volume II in Electronics (11 articles)