Artificial Intelligence Models, Tools and Applications: 2nd Edition

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30 September 2026

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30 November 2026

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Topic Information

Dear Colleagues,

In recent years, the need for efficient Artificial Intelligence models, tools, and applications has become increasingly evident. Machine Learning and data science, along with the vast amounts of data they generate, provide a powerful new source of valuable insights. New and innovative approaches are needed to address the complex research challenges in this field. Within this context, Artificial Intelligence stands out as one of the most important research areas of our time. The research community, in particular, faces significant challenges in terms of data management and must resolve these by integrating emerging disciplines in information processing, as well as developing related tools and applications.

This Topic aims to bring together interdisciplinary approaches that focus on innovative applications and established Artificial Intelligence methodologies. Given that data is often heterogeneous and dynamic in nature, computer science researchers are encouraged to develop new or adapt existing AI models, tools, and applications to effectively address these challenges. The Topic welcomes submissions from anyone wishing to contribute relevant research work.

Dr. Phivos Mylonas
Dr. Katia Lida Kermanidis
Prof. Dr. Manolis Maragoudakis
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Computers computers	4.2	7.5	2012	17.5 Days	CHF 1800	Submit
Digital digital	-	4.8	2021	27.7 Days	CHF 1200	Submit
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400	Submit
Smart Cities smartcities	5.5	14.7	2018	25.2 Days	CHF 2000	Submit

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Published Papers (5 papers)

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All Applied Sciences Electronics Computers Digital Smart Cities

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18 pages, 1924 KB  

Open AccessArticle

A Hybrid Optimization Model for Transformer Fault Diagnosis Based on Gas Classification

by Junju Lai, Dongpeng Weng, Feng Xian, Yuandong Xie, Yujie Chen, Qian Zhou and Chao Yuan

Digital 2026, 6(1), 24; https://doi.org/10.3390/digital6010024 - 10 Mar 2026

Viewed by 188

Abstract

Dissolved gas analysis (DGA) provides valuable information for transformer condition monitoring, yet accurate multi-class fault identification remains challenging due to overlapping gas patterns and the sensitivity of classifier hyperparameters. This study proposes a hybrid optimization framework that combines Particle Swarm Optimization and Grey [...] Read more.

Dissolved gas analysis (DGA) provides valuable information for transformer condition monitoring, yet accurate multi-class fault identification remains challenging due to overlapping gas patterns and the sensitivity of classifier hyperparameters. This study proposes a hybrid optimization framework that combines Particle Swarm Optimization and Grey Wolf Optimization to tune the hyperparameters of a Support Vector Machine (SVM) for transformer fault diagnosis based on gas classification. The model is evaluated on a DGA dataset using a strict protocol that separates cross-validation–based tuning from held-out test assessment. Experimental results show that the proposed hybrid PSO-GWO-SVM achieves superior diagnostic performance and more stable convergence compared with representative single-optimizer baselines, demonstrating its potential for practical transformer fault identification. Full article

(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications: 2nd Edition)

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17 pages, 761 KB  

Open AccessArticle

Obstacle Avoidance in Mobile Robotics: A CNN-Based Approach Using CMYD Fusion of RGB and Depth Images

by Chaymae El Mechal, Mostefa Mesbah and Najiba El Amrani El Idrissi

Digital 2026, 6(1), 20; https://doi.org/10.3390/digital6010020 - 2 Mar 2026

Viewed by 190

Abstract

Over the last few years, deep neural networks have achieved outstanding results in computer vision, and have been widely integrated into mobile robot obstacle avoidance systems, where perception-driven classification supports navigation decisions. Most existing approaches rely on either color images (RGB) or depth [...] Read more.

Over the last few years, deep neural networks have achieved outstanding results in computer vision, and have been widely integrated into mobile robot obstacle avoidance systems, where perception-driven classification supports navigation decisions. Most existing approaches rely on either color images (RGB) or depth images (D) as the primary source of information, which limits their ability to jointly exploit appearance and geometric cues. This paper proposes a deep learning-based classification approach that simultaneously exploits RGB and depth information for mobile robot obstacle avoidance. The method adopts an early-stage fusion strategy in which RGB images are first converted into the CMYK color space, after which the K (black) channel is replaced by a normalized depth map to form a four-channel CMYD representation. This representation preserves chromatic information while embedding geometric structure in an intensity-consistent channel and is used as input to a convolutional neural network (CNN). The proposed method is evaluated using locally acquired data under different training options and hyperparameter settings. Experimental results show that, when using the baseline CNN architecture, the proposed fusion strategy achieves an overall classification accuracy of 93.3%, outperforming depth-only inputs (86.5%) and RGB-only images (92.9%). When the refined CNN architecture is employed, classification accuracy is further improved across all tested input representations, reaching approximately 93.9% for RGB images, 91.0% for depth-only inputs, 94.6% for the CMYK color space, and 96.2% for the proposed CMYD fusion. These results demonstrate that combining appearance and depth information through CMYD fusion is beneficial regardless of the network variant, while the refined CNN architecture further enhances the effectiveness of the fused representation for robust obstacle avoidance. Full article

(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications: 2nd Edition)

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22 pages, 4427 KB  

Open AccessArticle

Target Detection in Underground Mines Based on Low-Light Image Enhancement

by Haodong Guo, Kaibo Lu, Shanning Zhan, Jiangtao Li and Zhifei Wu

Digital 2026, 6(1), 13; https://doi.org/10.3390/digital6010013 - 25 Feb 2026

Viewed by 329

Abstract

Underground mines’ complex environments with dim lighting and high dust and humidity hamper feature extraction and reduce detection accuracy. To address this, we propose a low-light image enhancement-based target detection algorithm. Firstly, LIENet enhances low-light image quality and brightness via a dual-gamma curve [...] Read more.

Underground mines’ complex environments with dim lighting and high dust and humidity hamper feature extraction and reduce detection accuracy. To address this, we propose a low-light image enhancement-based target detection algorithm. Firstly, LIENet enhances low-light image quality and brightness via a dual-gamma curve and non-reference loss function-guided iterations. Secondly, the hierarchical feature extraction (HFE) method with a dual-branch structure captures long-term and local correlations, focusing on critical corner regions. Finally, HFE is combined with a feature pyramid structure for comprehensive feature representation through a top-down global adjustment. Our method, validated on a self-built dataset, outperforms other algorithms with an mAP@0.5 of 96.96% and mAP@0.5:0.95 of 71.1%, proving excellent low-light detection performance in mines. Full article

(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications: 2nd Edition)

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25 pages, 6684 KB  

Open AccessArticle

Physics-Guided Dynamic Sparse Attention Network for Gravitational Wave Detection Across Ground and Space-Based Observatories

by Tiancong Zhang and Wei Bian

Electronics 2026, 15(4), 838; https://doi.org/10.3390/electronics15040838 - 15 Feb 2026

Viewed by 293

Abstract

Ground-based and space-based gravitational wave (GW) detectors cover complementary frequency bands, laying the foundation for future multi-band collaborative observations. Detecting weak signals within non-stationary noise remains challenging. To address this, we propose a Physics-Guided Dynamic Sparse Attention (PGDSA) framework. The framework introduces a [...] Read more.

Ground-based and space-based gravitational wave (GW) detectors cover complementary frequency bands, laying the foundation for future multi-band collaborative observations. Detecting weak signals within non-stationary noise remains challenging. To address this, we propose a Physics-Guided Dynamic Sparse Attention (PGDSA) framework. The framework introduces a differentiable wavelet layer to explicitly embed sensitive frequency bands and time–frequency priors while utilizing intra-block Top-K sparse attention for efficient long-range temporal modeling. Training is performed on space-based simulation data with joint optimization for signal detection and waveform reconstruction. We then evaluate detection performance and zero-shot transfer capability on ground-based data. Experimental results show that PGDSA achieves an ROC-AUC of 0.886 on the Kaggle G2Net private leaderboard. On GWOSC O3 real data, the model yields high confidence scores for confirmed binary black hole events. On LISA simulation data, the framework achieves detection rates exceeding 99% for multiple signal types (SNR = 50, FAR = 1%) with waveform reconstruction Overlap comparable to baseline methods. These results demonstrate that PGDSA enables unified modeling across both space-based and ground-based scenarios. Full article

(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications: 2nd Edition)

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21 pages, 15857 KB  

Open AccessArticle

LogPPO: A Log-Based Anomaly Detector Aided with Proximal Policy Optimization Algorithms

by Zhihao Wang, Jiachen Dong and Chuanchuan Yang

Smart Cities 2026, 9(1), 5; https://doi.org/10.3390/smartcities9010005 - 26 Dec 2025

Cited by 1 | Viewed by 665

Abstract

Cloud-based platforms form the backbone of smart city ecosystems, powering essential services such as transportation, energy management, and public safety. However, their operational complexity generates vast volumes of system logs, making manual anomaly detection infeasible and raising reliability concerns. This study addresses the [...] Read more.

Cloud-based platforms form the backbone of smart city ecosystems, powering essential services such as transportation, energy management, and public safety. However, their operational complexity generates vast volumes of system logs, making manual anomaly detection infeasible and raising reliability concerns. This study addresses the challenge of data scarcity in log anomaly detection by leveraging Large Language Models (LLMs) to enhance domain-specific classification tasks. We empirically validate that domain-adapted classifiers preserve strong natural language understanding, and introduce a Proximal Policy Optimization (PPO)-based approach to align semantic patterns between LLM outputs and classifier preferences. Experiments were conducted using three Transformer-based baselines under few-shot conditions across four public datasets. Results indicate that integrating natural language analyses improves anomaly detection F1-Scores by 5–86% over the baselines, while iterative PPO refinement boosts classifier’s “confidence” in label prediction. This research pioneers a novel framework for few-shot log anomaly detection, establishing an innovative paradigm in resource-constrained diagnostic systems in smart city infrastructures. Full article

(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications: 2nd Edition)

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