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Applied Sciences
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AI Horizons: Present Status and Visions for the Next Era
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AI Horizons: Present Status and Visions for the Next Era

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 November 2024 | Viewed by 5288

Special Issue Editors

Dr. Antonio Pagliaro

E-Mail Website
Guest Editor
INAF IASF Palermo, Via Ugo La Malfa 153, I-90146 Palermo, Italy
Interests: artificial intelligence; computer science; machine learning and deep learning; computer vision; high energy astrophysics
Dr. Pierluca Sangiorgi

E-Mail Website
Guest Editor
INAF IASF Palermo, Via Ugo La Malfa 153, I-90146 Palermo, Italy
Interests: software engineering; computer-aided system; semantic analysis; control software system; high-energy astrophysics

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore artificial intelligence's (AI’s) transformative impact across a multitude of scientific and practical domains, beyond the confines of experimental methodologies.

AI has emerged as a cornerstone in reshaping research landscapes, driving innovation, and fostering unprecedented advancements in how we gather, analyze, and interpret data. Its potential to revolutionize research practices and accelerate scientific discovery is immense, and its influence extends far beyond traditional experimental frameworks, permeating every aspect of scientific inquiry and application. In this Special Issue, we will investigate the current state of AI integration across varied fields, and discuss its future prospects.

The articles in this Special Issue will cover a wide range of topics, including but not limited to cutting-edge machine learning algorithms for predictive analytics, AI's role in enhancing data acquisition, processing, and interpretation, the automation and optimization of workflows through intelligent systems, strategic AI-driven decision-making, and the ethical implications and considerations of deploying AI solutions in diverse settings.

The contributions to this Special Issue will provide valuable insights into the benefits and limitations of utilizing AI, highlighting the ways in which AI technologies can augment human capabilities in various fields. Researchers, scientists, and practitioners from diverse domains are invited to submit their original research, reviews, and perspectives on the evolving landscape of AI applications.

Dr. Antonio Pagliaro
Dr. Pierluca Sangiorgi
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. Applied Sciences 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

  • artificial intelligence
  • experimental methodologies
  • machine learning
  • deep learning
  • data analysis
  • predictive modeling
  • image recognition
  • robotics and automation

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

Published Papers (4 papers)

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Editorial

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4 pages, 195 KiB  
Editorial
AI in Experiments: Present Status and Future Prospects
by Antonio Pagliaro and Pierluca Sangiorgi
Appl. Sci. 2023, 13(18), 10415; https://doi.org/10.3390/app131810415 - 18 Sep 2023
Cited by 1 | Viewed by 2600
Abstract
Artificial intelligence (AI) has become deeply intertwined with scientific inquiry and experimentation [...] Full article
(This article belongs to the Special Issue AI Horizons: Present Status and Visions for the Next Era)

Research

Jump to: Editorial

16 pages, 334 KiB  
Article
Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit
by Soukaina Rhazzafe, Fabio Caraffini, Simon Colreavy-Donnelly, Younes Dhassi, Stefan Kuhn and Nikola S. Nikolov
Appl. Sci. 2024, 14(13), 5809; https://doi.org/10.3390/app14135809 - 3 Jul 2024
Viewed by 546
Abstract
Electronic health records (EHRs) are a critical tool in healthcare and capture a wide array of patient information that can inform clinical decision-making. However, the sheer volume and complexity of EHR data present challenges for healthcare providers, particularly in fast-paced environments such as [...] Read more.
Electronic health records (EHRs) are a critical tool in healthcare and capture a wide array of patient information that can inform clinical decision-making. However, the sheer volume and complexity of EHR data present challenges for healthcare providers, particularly in fast-paced environments such as intensive care units (ICUs). To address this problem, the automatic summarization of the main problems of patients from daily progress notes can be extremely helpful. Furthermore, by accurately predicting ICU patients’ lengths of stay (LOSs), resource allocation and management can be optimized, allowing for a more efficient flow of patients within the healthcare system. This work proposes a hybrid method to summarize EHR notes and studies the potential of these summaries together with structured data for the prediction of LOSs of ICU patients. Our investigation demonstrates the effectiveness of combining extractive and abstractive summarization techniques with a concept-based method combined with a text-to-text transfer transformer (T5), which shows the most promising results. By integrating the generated summaries and diagnoses with other features, our study contributes to the accurate prediction of LOSs, with a support vector machine emerging as our best-performing classifier with an accuracy of 77.5%, surpassing existing systems and highlighting the potential for optimal allocation of resources within ICUs. Full article
(This article belongs to the Special Issue AI Horizons: Present Status and Visions for the Next Era)
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18 pages, 10602 KiB  
Article
A Novel Dataset for Fabric Defect Detection: Bridging Gaps in Anomaly Detection
by Rui Carrilho, Kailash A. Hambarde and Hugo Proença
Appl. Sci. 2024, 14(12), 5298; https://doi.org/10.3390/app14125298 - 19 Jun 2024
Viewed by 682
Abstract
Detecting anomalies in texture has become a significant concern across various industrial processes. One prevalent application of this is in inspecting patterned textures, especially in the domain of fabric defect detection, which is a commonly encountered scenario. This task entails dealing with a [...] Read more.
Detecting anomalies in texture has become a significant concern across various industrial processes. One prevalent application of this is in inspecting patterned textures, especially in the domain of fabric defect detection, which is a commonly encountered scenario. This task entails dealing with a wide array of colours and textile varieties, spanning a broad spectrum of fabrics. Due to the extensive diversity in colours, textures, and defect characteristics, fabric defect detection presents a complex and formidable challenge within the realm of patterned texture inspection. While recent trends have seen a rise in the utilization of deep learning methods for anomaly detection, there still exist notable gaps in this field. In this paper, we introduce a novel dataset comprising a diverse selection of fabrics and defects from a textile company based in Portugal. Our contributions encompass the provision of this unique dataset and the evaluation of state-of-the-art (SOTA) methods’ performance on our dataset. Full article
(This article belongs to the Special Issue AI Horizons: Present Status and Visions for the Next Era)
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19 pages, 473 KiB  
Article
Uncertainty in Automated Ontology Matching: Lessons from an Empirical Evaluation
by Inès Osman, Salvatore Flavio Pileggi and Sadok Ben Yahia
Appl. Sci. 2024, 14(11), 4679; https://doi.org/10.3390/app14114679 - 29 May 2024
Viewed by 706
Abstract
Data integration is considered a classic research field and a pressing need within the information science community. Ontologies play a critical role in such processes by providing well-consolidated support to link and semantically integrate datasets via interoperability. This paper approaches data integration from [...] Read more.
Data integration is considered a classic research field and a pressing need within the information science community. Ontologies play a critical role in such processes by providing well-consolidated support to link and semantically integrate datasets via interoperability. This paper approaches data integration from an application perspective by looking at ontology matching techniques. As the manual matching of different sources of information becomes unrealistic once the system scales up, the automation of the matching process becomes a compelling need. Therefore, we have conducted experiments on actual non-semantically enriched relational data with the support of existing tools (pre-LLM technology) for automatic ontology matching from the scientific community. Even considering a relatively simple case study—i.e., the spatio–temporal alignment of macro indicators—outcomes clearly show significant uncertainty resulting from errors and inaccuracies along the automated matching process. More concretely, this paper aims to test on real-world data a bottom-up knowledge-building approach, discuss the lessons learned from the experimental results of the case study, and draw conclusions about uncertainty and uncertainty management in an automated ontology matching process. While the most common evaluation metrics clearly demonstrate the unreliability of fully automated matching solutions, properly designed semi-supervised approaches seem to be mature for more generalized application. Full article
(This article belongs to the Special Issue AI Horizons: Present Status and Visions for the Next Era)
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