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Surveys in Information Systems and Applications
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Surveys in Information Systems and Applications

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Review".

Deadline for manuscript submissions: 28 February 2027 | Viewed by 50907

Editors

Dr. Marcin Paprzycki

E-Mail Website
Guest Editor
Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447 Warsaw, Poland
Interests: intelligent systems; software agents and agent systems; semantic technologies; high-performance computing; data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Robin Haunschild

E-Mail Website
Guest Editor
Max Planck Institute for Solid State Research, Heisenbergstr. 1, 70569 Stuttgart, Germany
Interests: scientometrics; bibliometrics; altmetrics; social network analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Giorgio Maria Di Nunzio

E-Mail Website
Guest Editor
Department of Information Engineering, University of Padua, 35131 Padova, Italy
Interests: medical information retrieval; interactive machine learning; digital humanities; computational terminology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vasco N. G. J. Soares

E-Mail Website
Guest Editor
1.​Polytechnic University of Castelo Branco, Av. Pedro Álvares Cabral, n° 12, 6000-084 Castelo Branco, Portugal
2.​Instituto de Telecomunicações, Rua Marquês d’Ávila e Bolama, 6201-001 Covilhã, Portugal
Interests: vehicular networks; Internet of Things; internet of everything; smart cities; delay/disruption-tolerant networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paulo Quaresma

E-Mail Website
Guest Editor
Algoritmi Research Center, Informatics Department, University of Évora, 7002–554 Évora, Portugal
Interests: artificial intelligence; natural language processing
Special Issues, Collections and Topics in MDPI journals
Dr. Luigi Laura

E-Mail Website
Guest Editor
Faculty of Engineering, Uninettuno University, 00186 Roma, Italy
Interests: graph algorithms; computational complexity; technology-enhanced learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Information is launching a new editorial initiative. In the diverse and complex field of computing, locating comprehensive and trustworthy overviews of the latest advancements and research trends within specific domains is challenging. To address this issue, we plan to introduce ‘survey papers’.

Each paper is welcome to explore any subfield that may interest the Information readership. The prepared papers are expected to provide a concise historical context of the progress made thus far, coupled with an overview of current advancements, all substantiated by a thorough bibliography. Contributions should outline the most pressing research questions of the moment and the most promising avenues for pursuing these inquiries. Additionally, recounting ‘unsuccessful paths’—as cautionary tales for upcoming researchers—holds its own merit.

Dr. Marcin Paprzycki
Dr. Robin Haunschild
Dr. Giorgio Maria Di Nunzio
Prof. Dr. Vasco N. G. J. Soares
Prof. Dr. Paulo Quaresma
Dr. Luigi Laura
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information 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

  • survey papers
  • information systems
  • information applications
  • information intelligence
  • information processes
  • information technology

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

Published Papers (6 papers)

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Review

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29 pages, 1030 KB  
Review
An Overview of Recent Interpretability and Explainability Approaches for Tree-Based Ensembles
by Alexandros Miteloudis and Ioannis Hatzilygeroudis
Information 2026, 17(5), 477; https://doi.org/10.3390/info17050477 - 13 May 2026
Viewed by 427
Abstract
Decision tree ensembles, such as Random Forests and Gradient Boosting Machines, achieve high predictive accuracy but often suffer from limited transparency due to their structural complexity. Due to this lack, interpretability challenges arise in domains where model understanding, accountability, and trust are essential. [...] Read more.
Decision tree ensembles, such as Random Forests and Gradient Boosting Machines, achieve high predictive accuracy but often suffer from limited transparency due to their structural complexity. Due to this lack, interpretability challenges arise in domains where model understanding, accountability, and trust are essential. So, many interpretability/explainability techniques have been proposed for tree-based ensembles. However, although there are enough surveys or overviews concerning interpretability/explainability in artificial intelligence or machine learning in general, there are very few surveys and overviews on interpretability/explainability for tree-based ensembles. This paper provides an overview of recent approaches to interpretability and explainability in decision tree ensembles. We present two categorizations: one based on the kind of technique/architecture used and the second based on the level of scope. The former is a unified taxonomy of acquired (or post hoc) and inherent methods further analyzed in two more levels. The latter concerns the distinction between local (or instance-related) and global (or model-related) methods. We additionally provide a survey of the interpretability/explainability methods/techniques used in various domain applications, like healthcare, finance, law, and privacy preservation. This overview clarifies the current landscape of interpretable/explainable ensemble learning, explicitly addressing emerging challenges. Ultimately, it aims to support researchers and practitioners in selecting and developing ensemble models that move beyond the traditional accuracy–interpretability trade-off, aligning predictive power with strict regulatory, operational, and domain-specific transparency requirements. Full article
(This article belongs to the Special Issue Surveys in Information Systems and Applications)
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18 pages, 741 KB  
Review
A Review of Tools and Technologies to Combat Deepfakes
by Dmitry Erokhin and Nadejda Komendantova
Information 2026, 17(4), 347; https://doi.org/10.3390/info17040347 - 3 Apr 2026
Cited by 1 | Viewed by 3542
Abstract
Deepfakes and adjacent synthetic-media capabilities have become a systemic challenge for information integrity, security, and digital trust. Countermeasures now span passive detection methods that infer manipulation from content traces, active provenance systems that cryptographically bind metadata to media, and watermarking approaches that embed [...] Read more.
Deepfakes and adjacent synthetic-media capabilities have become a systemic challenge for information integrity, security, and digital trust. Countermeasures now span passive detection methods that infer manipulation from content traces, active provenance systems that cryptographically bind metadata to media, and watermarking approaches that embed detectable signals into content or generative processes. This review presents a rigorous synthesis of tools and technologies to combat deepfakes across modalities (image, video, audio, and selected multimodal settings), drawing primarily from the peer-reviewed literature, standardized benchmarks, and official technical specifications and reports. The review analyzes detection methods, provenance and authentication technologies, with emphasis on cryptographic manifests and threat models, watermarking and content provenance, including diffusion-era watermarking and industrial deployments, adversarial robustness and attacker adaptation, datasets and benchmarks, evaluation metrics across tasks, and deployment and scalability constraints. A dedicated section addresses legal, ethical, and policy issues, focusing on emerging transparency obligations and platform governance. The review finds that no single countermeasure is sufficient in realistic adversarial settings. The strongest practical approach is a layered defense that combines provenance, watermarking, content-based detection, and human oversight. The study concludes with limitations of the current evidence base and prioritized research directions to improve generalization, interoperability, and trustworthy user experiences. Full article
(This article belongs to the Special Issue Surveys in Information Systems and Applications)
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32 pages, 414 KB  
Review
A Survey of Open-Source Autonomous Driving Systems and Their Impact on Research
by Nourdine Aliane
Information 2025, 16(4), 317; https://doi.org/10.3390/info16040317 - 17 Apr 2025
Cited by 5 | Viewed by 19015
Abstract
Open-source autonomous driving systems (ADS) have become a cornerstone of autonomous vehicle development. By providing access to cutting-edge technology, fostering global collaboration, and accelerating innovation, these platforms are transforming the automated vehicle landscape. This survey conducts a comprehensive analysis of leading open-source ADS [...] Read more.
Open-source autonomous driving systems (ADS) have become a cornerstone of autonomous vehicle development. By providing access to cutting-edge technology, fostering global collaboration, and accelerating innovation, these platforms are transforming the automated vehicle landscape. This survey conducts a comprehensive analysis of leading open-source ADS platforms, evaluating their functionalities, strengths, and limitations. Through an extensive literature review, the survey explores their adoption and utilization across key research domains. Additionally, it identifies emerging trends shaping the field. The main contributions of this survey include (1) a detailed overview of leading open-source platforms, highlighting their strengths and weaknesses; (2) an examination of their impact on research; and (3) a synthesis of current trends, particularly in interoperability with emerging technologies such as AI/ML solutions and edge computing. This study aims to provide researchers and practitioners with a holistic understanding of open-source ADS platforms, guiding them in selecting the right platforms for future innovation. Full article
(This article belongs to the Special Issue Surveys in Information Systems and Applications)
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63 pages, 22670 KB  
Review
Style Transfer Review: Traditional Machine Learning to Deep Learning
by Yao Xu, Min Xia, Kai Hu, Siyi Zhou and Liguo Weng
Information 2025, 16(2), 157; https://doi.org/10.3390/info16020157 - 19 Feb 2025
Cited by 15 | Viewed by 22204
Abstract
Style transfer is a technique that learns style features from different domains and applies these features to other images. It can not only play a role in the field of artistic creation but also has important significance in image processing, video processing, and [...] Read more.
Style transfer is a technique that learns style features from different domains and applies these features to other images. It can not only play a role in the field of artistic creation but also has important significance in image processing, video processing, and other fields. However, at present, style transfer still faces some challenges, such as the balance between style and content, the model generalization ability, and diversity. This article first introduces the origin and development process of style transfer and provides a brief overview of existing methods. Next, this article explores research work related to style transfer, introduces some metrics used to evaluate the effect of style transfer, and summarizes datasets. Subsequently, this article focuses on the application of the currently popular deep learning technology for style transfer and also mentions the application of style transfer in video. Finally, the article discusses possible future directions for this field. Full article
(This article belongs to the Special Issue Surveys in Information Systems and Applications)
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Other

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41 pages, 2342 KB  
Systematic Review
Artificial Intelligence in Complex Manufacturing Systems: A Systematic Review of Validation Rigor and Deployment Readiness in Predictive Maintenance
by Cesar Felipe Henao Villa, David Alberto Garcia Arango, Luis Fernando Garcés Giraldo, Rosana Alejandra Meleán Romero, Alejandro Valencia-Arias and José Alexander Velásquez Ochoa
Information 2026, 17(5), 456; https://doi.org/10.3390/info17050456 - 8 May 2026
Viewed by 1663
Abstract
This systematic review (PRISMA 2020) examines 89 studies—64 peer-reviewed articles and 25 arXiv preprints (2007–2026)—addressing the gap between AI research and operational predictive maintenance (PdM) deployment in complex manufacturing systems. Analyzing five thematic clusters in non-stationary and stochastic environments, we evaluated predictive performance [...] Read more.
This systematic review (PRISMA 2020) examines 89 studies—64 peer-reviewed articles and 25 arXiv preprints (2007–2026)—addressing the gap between AI research and operational predictive maintenance (PdM) deployment in complex manufacturing systems. Analyzing five thematic clusters in non-stationary and stochastic environments, we evaluated predictive performance and deployment readiness. Deep learning dominates remaining useful life (RUL) forecasting; however, 65.6% of studies employ weak or unclear validation protocols (Tier 0–1), lacking real-world robustness testing. Fault diagnosis increasingly integrates Edge-AI, yet Explainable AI (XAI) adoption remains scarce (15.6%), undermining industrial trustworthiness. No study reached operational field validation beyond temporal or cross-domain split, reflecting a systematic disconnection from deployed manufacturing systems. We introduce a novel Deployment Readiness Score (DRS) framework and identify critical barriers: data scarcity, environmental non-stationarity, computational constraints, and black-box model distrust. Recommendations include standardized temporal validation protocols, multi-site field studies, and architecture-integrated explainability. The 25 arXiv preprints (2024–2026) exhibit a mean DRS nearly three times that of the peer-reviewed corpus, signaling nascent convergence toward deployment-mature research. This review was not pre-registered. Full article
(This article belongs to the Special Issue Surveys in Information Systems and Applications)
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38 pages, 798 KB  
Systematic Review
Foundation Models in Software Engineering: A Taxonomy, Systematic Review, and In-Depth Analysis of Testing Support
by Shadi Banitaan, Mohammad Daoud, Hebah Alquran and Mohammad Akour
Information 2026, 17(1), 73; https://doi.org/10.3390/info17010073 - 12 Jan 2026
Cited by 1 | Viewed by 2332
Abstract
Foundation models are increasingly influencing software engineering research and practice, yet their adoption across the software development life cycle remains uneven and insufficiently characterized. This paper presents a systematic review of 224 recent studies investigating the application of foundation models to software engineering [...] Read more.
Foundation models are increasingly influencing software engineering research and practice, yet their adoption across the software development life cycle remains uneven and insufficiently characterized. This paper presents a systematic review of 224 recent studies investigating the application of foundation models to software engineering tasks. We introduce a two-dimensional taxonomy that systematically links software engineering life cycle phases with the foundation model capabilities employed, offering a unified view of current research practices. Our analysis reveals that existing work is heavily concentrated on implementation and testing activities, while earlier phases such as requirements engineering and architectural design, and process-oriented tasks, receive comparatively limited attention. Focusing on testing and quality assurance, we synthesize evidence across eight task categories, highlighting both demonstrated benefits and recurring challenges. This review is limited to peer-reviewed studies published between 2023 and 2025 and does not introduce new empirical models, focusing instead on synthesizing existing evidence. Overall, this review clarifies the current landscape of foundation model usage in software engineering and outlines actionable directions for future research and responsible adoption. Full article
(This article belongs to the Special Issue Surveys in Information Systems and Applications)
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