Machine Learning in Electronic and Biomedical Engineering, 4th Edition

Dear Colleagues,

In recent years, machine learning techniques have proven to be extremely useful in a wide variety of applications and are now rapidly gaining increasing interest, both in electronics and biomedical engineering. The fast progress in artificial intelligence (AI) is currently opening new perspectives, not only for data analysis but also for the design, optimization, and deployment of intelligent systems across different domains.

This Special Issue seeks to collect contributions from researchers involved in developing and applying machine learning techniques in the following fields:

• Embedded systems for artificial intelligence (AI) applications, with a focus on implementing algorithms directly in devices to reduce latency, communication costs, and privacy concerns;
• Edge computing and TinyML, where the aim is to process AI algorithms locally on the device by focusing on compression techniques, dimensionality reduction, and parallel computation;
• Wearable sensors for collecting biological data and physiological data;
• Human activity recognition, diagnosis, and prognosis supported by advanced data-driven analysis of sensor and biomedical signals;
• Intelligent decision-making systems and computer-aided diagnosis (CAD) tools for early detection, classification, and prediction of diseases;
• Biomedical imaging and neuroimaging techniques (e.g., MRI, ultrasound imaging, CT) for disease diagnosis, progression monitoring, and treatment planning.

In addition to these consolidated areas, this Special Issue welcomes contributions in emerging AI paradigms that are expected to strongly impact both electronic and biomedical engineering:

• Generative AI for electronic design automation, circuit and system optimization, signal synthesis, and device modeling, as well as for synthetic biomedical data generation, data augmentation, drug discovery, and medical image reconstruction;
• Explainable AI and interpretable models to enhance reliability, accountability, and trust in safety-critical electronic systems such as autonomous devices, IoT infrastructures, and smart sensors, as well as in clinical decision-making and biomedical diagnostics;
• Multimodal AI for integrating heterogeneous data from sensor networks, electronic devices, and industrial monitoring systems, as well as from biosignals, medical imaging, clinical records, and genomics for holistic patient assessment;
• Federated and privacy-preserving learning for distributed learning across IoT devices, embedded systems, and smart electronics without sharing raw data, as well as for secure collaborative AI on sensitive biomedical information;
• AI-driven optimization and personalization of electronic systems, such as adaptive circuits, reconfigurable architectures, and intelligent hardware design, as well as AI-driven personalized and precision medicine where predictive models are tailored to individual patients;
• Digital twins of electronic circuits, devices, and industrial processes for design support, fault prediction, and lifecycle management, as well as digital twins of biological systems and healthcare processes for real-time monitoring, prediction, and optimization.

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 and edge systems;
• Edge artificial intelligence (EdgeAI), tiny machine learning (TinyML), and low-power AI;
• Machine learning for edge computation;
• Deep learning model compression, acceleration and hardware-aware optimization;
• Image classification, detection, semantic segmentation and object detection;
• Machine learning for autonomous guide;
• Machine learning for smart agriculture;
• Machine learning for smart industry;
• Deep learning and generative models for biomedical image and signal processing;
• AI methods for computer-aided diagnosis and prognosis;
• Machine-learning-based healthcare applications, such as sensor-based behavior analysis, human activity recognition, disease prediction, biomedical signal processing, and data monitoring;
• Multimodal and federated learning;
• Explainable AI and trustworthy machine learning;
• Digital twins.

Dr. Laura Falaschetti
Prof. 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
• generative AI
• explainable AI
• sensors for IoT
• wearable devices
• vision sensors
• autonomous guide
• smart agriculture
• smart industry
• medical image analysis
• computer-aided diagnosis
• human activity recognition
• biosignals
• digital twins
• multimodal learning

• Machine Learning in Electronic and Biomedical Engineering, 3rd Edition in Electronics (12 articles)