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Electronics
Special Issues
AI-Driven Intelligent Systems in Energy, Healthcare, and Beyond
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AI-Driven Intelligent Systems in Energy, Healthcare, and Beyond

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 August 2026 | Viewed by 460

Special Issue Editors

Prof. Dr. Lin Meng

E-Mail Website
Guest Editor
Department of Electronic and Computer Engineering, Ritsumeikan University, Kusatsu 525-8577, Japan
Interests: processor architecture; high-performance computing; AI-based IoT; underwater drones; cultural heritage preservation and protection
Special Issues, Collections and Topics in MDPI journals
Dr. Xiangbo Kong

E-Mail Website
Guest Editor
Faculty of Information Engineering, Toyama Prefectural University, Imizu-shi 939-0398, Toyama, Japan
Interests: eartificial intelligence; image processing; embedded systems
Special Issues, Collections and Topics in MDPI journals
Dr. Hengyi Li

E-Mail Website
Guest Editor
School of Automation and Electrical Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China
Interests: artificial intelligence; high-performance computing; machine vision

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI), in conjunction with the Internet of Things (IoT) and robotics, is advancing intelligent, autonomous, and data-centric solutions across various sectors. Notably, energy and healthcare have emerged as two of the most impactful application domains. In the energy domain, the convergence of AI and IoT is modernizing infrastructure, optimizing consumption, and supporting sustainability goals. Real-time data from smart grids and renewable sources, when combined with AI-driven analytics, enable predictive maintenance, intelligent control, and demand-side optimization, contributing to more resilient and efficient energy systems. In healthcare, AI and robotics are reshaping diagnostics, treatment, and patient care through applications such as AI-driven imaging, wearable monitoring, robot-assisted surgeries, and autonomous rehabilitation. Advances in signal and image processing are improving diagnostic accuracy and enabling personalized healthcare delivery. These advancements are further empowered by progress in high-performance AI systems, including model compression, parallel computing, and deployment on edge and embedded platforms. Lifelong learning and self-adaptive algorithms enhance the responsiveness and robustness of AI systems in dynamic environments. This Special Issue aims to bring together cutting-edge research at the intersection of AI, high-performance computing, and robotics, with a focus on real-world intelligent applications in energy, healthcare, and beyond.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • AI-enabled energy optimization, including intelligent demand response, load balancing, and consumption forecasting;
  • IoT-based monitoring and predictive maintenance for energy systems and related domains;
  • Secure and resilient AI- and IoT-enabled cyber–physical energy systems;
  • High-performance AI architectures, including model compression, parallelization, and deployment on heterogeneous hardware;
  • Edge and embedded AI for low-latency, energy-efficient intelligence in constrained environments;
  • AI-powered robotics for domain-specific applications such as healthcare and industrial automation;
  • Signal and image processing for diagnostics, monitoring, and rehabilitation.

Prof. Dr. Lin Meng
Dr. Xiangbo Kong
Dr. Hengyi Li
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 250 words) can be sent to the Editorial Office for assessment.

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.

Keywords

  • artificial intelligence (AI)
  • internet of things (IoT)
  • high-performance computing
  • edge computing
  • embedded systems
  • robotic systems
  • image processing
  • energy system
  • healthcare

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Published Papers (2 papers)

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Research

14 pages, 449 KB  
Article
Graph Contrastive Learning via Noisy Training for Cold-Start Recommendation
by Tingting Fang, Guicheng Shen and Qiurui Sun
Electronics 2026, 15(4), 902; https://doi.org/10.3390/electronics15040902 - 23 Feb 2026
Abstract
This paper studies the problem of cold-start recommendation with graph contrastive learning. Graph contrastive learning has achieved state-of-the-art performance for the recommendation. However, it lacks robustness in cold-start scenarios due to noisy user–item interactions. Recent works have been proposed to improve the performance [...] Read more.
This paper studies the problem of cold-start recommendation with graph contrastive learning. Graph contrastive learning has achieved state-of-the-art performance for the recommendation. However, it lacks robustness in cold-start scenarios due to noisy user–item interactions. Recent works have been proposed to improve the performance of noisy user-item interactions; however, they can achieve effective performance only on existing user–item interactions, which are not cold-start interactions. The question of how to find an optimal graph contrastive learning method that is suitable for cold-start cases still remains to be explored. We propose a novel method, graph contrastive learning via noisy training (GCLNT), to alleviate the cold-start recommendation problem. Specifically, GCLNT identifies user–item interactions with different preferences, and assigns them to different preference environments. With such different preference environments, noisy training is used to enhance the model’s robustness. We evaluate GCLNT on three datasets, and the results demonstrate the effectiveness of GCLNT in handling the cold-start in recommender systems. Full article
(This article belongs to the Special Issue AI-Driven Intelligent Systems in Energy, Healthcare, and Beyond)
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17 pages, 3413 KB  
Article
DRAG: Dual-Channel Retrieval-Augmented Generation for Hybrid-Modal Document Understanding
by Zhe Xin, Shuyuan Xia and Xin Guo
Electronics 2026, 15(4), 843; https://doi.org/10.3390/electronics15040843 - 16 Feb 2026
Viewed by 164
Abstract
Large Language Models (LLMs) have acquired vast amounts of knowledge during pre-training. However, there are a lot of challenges when it is deployed in real-world applications, such as poor interpretability, hallucinations, and the inability to reference private data. To address these issues, Retrieval-Augmented [...] Read more.
Large Language Models (LLMs) have acquired vast amounts of knowledge during pre-training. However, there are a lot of challenges when it is deployed in real-world applications, such as poor interpretability, hallucinations, and the inability to reference private data. To address these issues, Retrieval-Augmented Generation (RAG) has been proposed. Traditional RAG relying on text-based retrievers often converts documents using Optical Character Recognition (OCR) before retrieval. While testing has revealed that it tends to overlook tables and images contained within the documents. RAG, relying on vision-based retrievers, often loses information on text-dense pages. To address these limitations, we propose DRAG: Dual-channel Retrieval-Augmented Generation for Hybrid-Modal Document Understanding, a novel retrieval paradigm. The DRAG method proposed in this paper primarily comprises two core improvements: first, a parallel dual-channel processing architecture is adopted to separately extract and preserve the visual structural information and deep semantic information of documents, thereby effectively enhancing information integrity; second, a novel dynamic weighted fusion mechanism is proposed to integrate the retrieval results from both channels, enabling precise screening of the most relevant information segments. Empirical results demonstrate that our method achieves Competitive performance across multiple general benchmarks. Furthermore, performance on biomedical datasets (e.g., BioM) specifically highlights its potential in specialized, vertical domains such as elderly care and rehabilitation, where documents are characterized by dense hybrid-modal information. Full article
(This article belongs to the Special Issue AI-Driven Intelligent Systems in Energy, Healthcare, and Beyond)
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