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Computers
Special Issues
AI in Action: Innovations and Breakthroughs
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AI in Action: Innovations and Breakthroughs

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "AI-Driven Innovations".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 1197

Special Issue Editor

Dr. Xiaorui Shao

E-Mail Website
Guest Editor
College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
Interests: deep learning; artificial intelligence; time series feature mining; fault diagnosis; scheduling optimization

Special Issue Information

Dear Colleagues,

Artificial intelligence is rapidly transitioning from a theoretical discipline to a powerful force shaping every facet of our world. This Special Issue, titled “AI in Action: Innovations and Breakthroughs”, is dedicated to showcasing the tangible applications and transformative breakthroughs that are defining the current era of AI. We move beyond conceptual frameworks to present a curated collection of research papers and reviews that demonstrate how AI algorithms—from advanced deep learning to generative models—are solving complex, real-world challenges. This issue will explore cutting-edge advancements across diverse sectors including healthcare, climate science, finance, and autonomous systems, while also addressing the critical considerations of ethics, scalability, and societal impact. By bridging the gap between theory and practice, this Special Issue aims to serve as a vital resource for researchers, engineers, and industry leaders, offering a comprehensive snapshot of how AI is actively driving progress and reshaping the future.

We welcome the submission of high-quality research articles and reviews that demonstrate the practical application and transformative potential of artificial intelligence. Topics of interest include, but are not limited to, the following:

  • Real-world AI applications in science, industry, and society.
  • Breakthroughs in generative AI, deep learning, and other foundational models.
  • AI systems engineering, MLOps, and scalable deployment.
  • Explainable, ethical, and trustworthy AI in practice.
  • AI for scientific discovery and complex problem-solving.

Dr. Xiaorui Shao
Guest Editor

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. Computers is an international peer-reviewed open access monthly 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 1800 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
  • generative AI
  • healthcare AI
  • explainable AI
  • AI for science

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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23 pages, 2271 KB  
Article
Adaptive Particle Filter-Neural Network Fusion for Cooperative Localization of Multi-UAV Systems in GNSS-Denied Indoor Environments
by Zhongyi Wang, Hao Wang and Shuzhi Liu
Computers 2026, 15(3), 172; https://doi.org/10.3390/computers15030172 - 6 Mar 2026
Viewed by 141
Abstract
Accurate autonomous navigation of unmanned aerial vehicles (UAVs) in complex indoor environments where satellite signals are denied remains a critical challenge. Conventional state estimation methods, such as particle filters, often suffer from particle degeneracy and high computational costs, limiting their robustness and real-time [...] Read more.
Accurate autonomous navigation of unmanned aerial vehicles (UAVs) in complex indoor environments where satellite signals are denied remains a critical challenge. Conventional state estimation methods, such as particle filters, often suffer from particle degeneracy and high computational costs, limiting their robustness and real-time applicability. Here, we introduce an adaptive particle filter-neural network (PF-NN) fusion framework that achieves high-fidelity cooperative localization for multi-UAV systems. Our approach integrates a lightweight neural network that optimizes particle weight allocation by learning from motion consistency, thereby mitigating sample impoverishment. This is coupled with an adaptive resampling strategy that dynamically adjusts the particle population based on the effective sample size, balancing computational load with estimation accuracy. By fusing ultra-wideband (UWB) inter-vehicle ranging with visual landmark observations, the system leverages both global and local constraints to achieve robust state estimation. In simulations involving six UAVs in a complex indoor setting, our algorithm demonstrated superior performance, achieving an average root-mean-square error (RMSE) of 0.437 m. This work provides a robust and efficient solution for multi-UAV cooperative localization, paving the way for reliable autonomous operations in GNSS-denied scenarios such as search-and-rescue and industrial inspection. Full article
(This article belongs to the Special Issue AI in Action: Innovations and Breakthroughs)
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29 pages, 4856 KB  
Article
Evaluating LLMs for Source Code Generation and Summarization Using Machine Learning Classification and Ranking
by Hussain Mahfoodh, Mustafa Hammad, Bassam A. Y. Alqaralleh and Aymen I. Zreikat
Computers 2026, 15(2), 119; https://doi.org/10.3390/computers15020119 - 10 Feb 2026
Viewed by 656
Abstract
The recent use of large language models (LLMs) in code generation and code summarization tasks has been widely adopted by the software engineering community. New LLMs are emerging regularly with improved functionalities, efficiency, and expanding data that allow models to learn more effectively. [...] Read more.
The recent use of large language models (LLMs) in code generation and code summarization tasks has been widely adopted by the software engineering community. New LLMs are emerging regularly with improved functionalities, efficiency, and expanding data that allow models to learn more effectively. The lack of guidelines for selecting the right LLMs for coding tasks makes the selection a subjective choice by developers rather than a choice built on code complexity, code correctness, and linguistic similarity analysis. This research investigates the use of machine learning classification and ranking methods to select the best-suited open-source LLMs for code generation and code summarization tasks. This work conducts a comparison experiment on four open-source LLMs (Mistral, CodeLlama, Gemma 2, and Phi-3) and uses the MBPP coding question dataset to analyze code-generated outputs in terms of code complexity, maintainability, cyclomatic complexity, code structure, and LLM perplexity by collecting these as a set of features. An SVM classification problem is conducted on the highest correlated feature pairs, where the models are evaluated through performance metrics, including accuracy, area under the ROC curve (AUC), precision, recall, and F1 scores. The RankNet ranking methodology is used to evaluate code summarization model capabilities by measuring ROUGE and BERTScore accuracies between LLM code-generated summaries and the coding questions used from the dataset. The study results show a maximum accuracy of 49% for the code generation experiment, with the highest AUC score reaching 86% among the top four correlated feature pairs. The highest precision score reached is 90%, and the recall score reached up to 92%. Code summarization experiment results show Gemma 2 scored a 1.93 RankNet win probability score, and represented the highest ranking reached among other models. The phi3 model was the second-highest ranking with a 1.66 score. The research highlights the potential of machine learning to select LLMs based on coding metrics and paves the way for advancements in terms of accuracy, dataset diversity, and exploring other machine learning algorithms for other researchers. Full article
(This article belongs to the Special Issue AI in Action: Innovations and Breakthroughs)
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