Artificial Intelligence Models, Tools and Applications: 2nd Edition

Abstract submission deadline

30 September 2026

Manuscript submission deadline

30 November 2026

Viewed by

1110

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
Electronics electronics	2.6	6.1	2012	16.4 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
Smart Cities smartcities	5.5	14.7	2018	25.2 Days	CHF 2000	Submit

Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

1. Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Related Topic

Published Papers (2 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Applied Sciences Electronics Computers Digital Smart Cities

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

25 pages, 6685 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 55

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)

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

Viewed by 541

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)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-2