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Electronics
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AI-Based Pervasive Application Services
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AI-Based Pervasive Application Services

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

Deadline for manuscript submissions: closed (15 August 2025) | Viewed by 6755

Special Issue Editors

Dr. Sinan Chen

E-Mail Website
Guest Editor
Center of Mathematical and Data Sciences, Kobe University, Kobe 657-8501, Japan
Interests: smart home; cloud computing; computer vision; machine learning; software engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Jialong Li

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Waseda University, Tokyo 169-8050, Japan
Interests: self-adaptive system; software engineering; human-computer interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is well known that generative AI has revolutionized our expectations of computer-generated results in recent years, which has led to various types of simulated multimodal data, such as generative images, generative sounds, and even generative videos. However, while these technologies are rapidly advancing and evolving, significant barriers remain to their widespread application in everyday services for general households. Practical application services require explicit purposes and needs while considering computational resources, cost, user privacy, security, deployment, maintenance, and even ethical, moral, and cultural considerations.

It is a significant challenge to effectively match the continuous stream of advanced technologies with people's real-world needs to create widely adoptable application services. Additionally, for existing application services, there are significant challenges in effectively integrating emerging technologies to achieve upgrades and transformations. Evaluating and efficiently analyzing the next-generation application services' feasibility, appropriateness, utility, accuracy, and scalability are also critical issues.

As AI technology has developed, our cognition and daily lives need to be continually improved and transformed. As researchers in the relevant fields, we carry a responsibility and mission that require deep contemplation.

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

- Pervasive application services;

- AI with multimodal data;

- Generative AI for general households;

- Application services for general households;

- AI upgrades and transformations;

- Evaluating and analyzing with AI;

- Next-generation application services.

We look forward to receiving your contributions.

Dr. Sinan Chen
Dr. Jialong 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 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.

Keywords

  • practical technologies by XAI
  • high capability of humanity with AIGC
  • application services affordable for everyone
  • upgrades and transformations
  • personalized or easy-to-deploy
  • DX real-world evaluation and analysis
  • good usability and extendability

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Published Papers (3 papers)

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Research

18 pages, 5185 KB  
Article
SafeBladder: Development and Validation of a Non-Invasive Wearable Device for Neurogenic Bladder Volume Monitoring
by Diogo Sousa, Filipa Santos, Luana Rodrigues, Rui Prado, Susana Moreira and Dulce Oliveira
Electronics 2025, 14(17), 3525; https://doi.org/10.3390/electronics14173525 - 3 Sep 2025
Viewed by 547
Abstract
Neurogenic bladder is a debilitating condition caused by neurological dysfunction that impairs urinary control, often requiring timed intermittent catheterisation. Although effective, intermittent catheterisation is invasive, uncomfortable, and associated with infection risks, reducing patients’ quality of life. SafeBladder is a low-cost wearable device developed [...] Read more.
Neurogenic bladder is a debilitating condition caused by neurological dysfunction that impairs urinary control, often requiring timed intermittent catheterisation. Although effective, intermittent catheterisation is invasive, uncomfortable, and associated with infection risks, reducing patients’ quality of life. SafeBladder is a low-cost wearable device developed to enable real-time, non-invasive bladder volume monitoring using near-infrared spectroscopy (NIRS) and machine learning algorithms. The prototype employs LEDs and photodetectors to measure light attenuation through abdominal tissues. Bladder filling was simulated through experimental tests using stepwise water additions to containers and tissue-mimicking phantoms, including silicone and porcine tissue. Machine learning models, including Linear Regression, Support Vector Regression, and Random Forest, were trained to predict volume from sensor data. The results showed the device is sensitive to volume changes, though ambient light interference affected accuracy, suggesting optimal use under clothing or in low-light conditions. The Random Forest model outperformed others, with a Mean Absolute Error (MAE) of 25 ± 4 mL and R2 of 0.90 in phantom tests. These findings support SafeBladder as a promising, non-invasive solution for bladder monitoring, with clinical potential pending further calibration and validation in real-world settings. Full article
(This article belongs to the Special Issue AI-Based Pervasive Application Services)
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13 pages, 3705 KB  
Article
Multi-Agent Reinforcement Learning-Based Control Method for Pedestrian Guidance Using the Mojiko Fireworks Festival Dataset
by Masato Kiyama, Motoki Amagasaki and Toshiaki Okamoto
Electronics 2025, 14(6), 1062; https://doi.org/10.3390/electronics14061062 - 7 Mar 2025
Viewed by 959
Abstract
With increasing incidents due to congestion at events, effective pedestrian guidance has become a critical safety concern. Recent research has explored the application of reinforcement learning to crowd simulation, where agents learn optimal actions through trial and error to maximize rewards based on [...] Read more.
With increasing incidents due to congestion at events, effective pedestrian guidance has become a critical safety concern. Recent research has explored the application of reinforcement learning to crowd simulation, where agents learn optimal actions through trial and error to maximize rewards based on environmental states. This study investigates the use of reinforcement learning and simulation techniques to mitigate pedestrian congestion through improved guidance systems. We employ the Multi-Agent Deep Deterministic Policy Gradient (MA-DDPG), a multi-agent reinforcement learning approach, and propose an enhanced method for learning the Q-function for actors within the MA-DDPG framework. Using the Mojiko Fireworks Festival dataset as a case study, we evaluated the effectiveness of our proposed method by comparing congestion levels with existing approaches. The results demonstrate that our method successfully reduces congestion, with agents exhibiting superior cooperation in managing crowd flow. This improvement in agent coordination suggests the potential for practical applications in real-world crowd management scenarios. Full article
(This article belongs to the Special Issue AI-Based Pervasive Application Services)
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18 pages, 19074 KB  
Article
Deep Fashion Designer: Generative Adversarial Networks for Fashion Item Generation Based on Many-to-One Image Translation
by Jaewon Jung, Hyeji Kim and Jongyoul Park
Electronics 2025, 14(2), 220; https://doi.org/10.3390/electronics14020220 - 7 Jan 2025
Cited by 3 | Viewed by 4155
Abstract
Generative adversarial networks (GANs) have demonstrated remarkable performance in various fashion-related applications, including virtual try-ons, compatible clothing recommendations, fashion editing, and the generation of fashion items. Despite this progress, limited research has addressed the specific challenge of generating a compatible fashion item with [...] Read more.
Generative adversarial networks (GANs) have demonstrated remarkable performance in various fashion-related applications, including virtual try-ons, compatible clothing recommendations, fashion editing, and the generation of fashion items. Despite this progress, limited research has addressed the specific challenge of generating a compatible fashion item with an ensemble consisting of distinct categories, such as tops, bottoms, and shoes. In response to this gap, we propose a novel GANs framework, termed Deep Fashion Designer Generative Adversarial Networks (DFDGAN), designed to address this challenge. Our model accepts a series of source images representing different fashion categories as inputs and generates a compatible fashion item, potentially from a different category. The architecture of our model comprises several key components: an encoder, a mapping network, a generator, and a discriminator. Through rigorous experimentation, we benchmark our model against existing baselines, validating the effectiveness of each architectural choice. Furthermore, qualitative results indicate that our framework successfully generates fashion items compatible with the input items, thereby advancing the field of fashion item generation. Full article
(This article belongs to the Special Issue AI-Based Pervasive Application Services)
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