Green Deep Learning Techniques for Sensing and Experimental Multimodal Signal Processing
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 2
Special Issue Editors
Interests: machine/deep learning; neural networks; signal processing
Special Issues, Collections and Topics in MDPI journals
2. Artificial Intelligence Innovation and Incubation Institute (AI3), Fudan University, Shanghai, China
Interests: trustworthy AI; federated learning; edge computing; green AI, AI for science
Special Issue Information
Dear Colleagues,
Methodological and computational developments in Deep Learning, in addition to Generative AI and Large Language Models (LLMs), are strongly improving the enhancement and the information extraction in multimodal sensing, with application in varied fields (i.e., segmentation of scenes, classification and object detection, computer vision, assistive technologies, BCI, image and vision processing, and pathological EEG analysis).
However, in parallel with the novelties in Deep Learning, the international community is considering the excessive growth in energy consumption of big models training on the cloud, and the related consumption of water, which is a significant worry when taking SDGs into account.
In this Special issue, we would like to explore how the practice of Green AI, particularly on the edge, can substantially help to reduce the need for resources and compress the large quantity of data coming from distributed sensors for continuous monitoring, fault and anomaly detection, and related applications in, for example, healthcare.
We are looking forward to receiving good contributions for this Special Issue from researchers worldwide.
Prof. Dr. Francesco Carlo Morabito
Prof. Dr. Zenglin Xu
Dr. Muhammad Suffian
Guest Editors
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Keywords
- deep learning
- green AI
- energy-aware DL techniques
- edge computing
- hybrid learning methodologies
- uncertainty management in sensing
- data compression in healthcare
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