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AI Horizons: Present Status and Visions for the Next Era

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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 June 2025 | Viewed by 22545

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Special Issue Editors

Dr. Antonio Pagliaro

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INAF IASF Palermo, Via Ugo La Malfa 153, I-90146 Palermo, Italy
Interests: artificial intelligence; computer science; machine learning; deep learning; computer vision; high-energy astrophysics
Special Issues, Collections and Topics in MDPI journals

Dr. Pierluca Sangiorgi

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Guest Editor

Istituto Nazionale di Astrofisica INAF IASF Palermo, Via Ugo La Malfa 153, 90146 Palermo, Italy
Interests: software engineering; computer-aided system; semantic analysis; control software system; high-energy astrophysics
Special Issues, Collections and Topics in MDPI journals

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

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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.

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Keywords

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

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Published Papers (10 papers)

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Editorial

Jump to: Research, Review

4 pages, 195 KiB

Open AccessEditorial

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 5240

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, Review

23 pages, 1868 KiB

Open AccessArticle

Machine Learning-Enhanced Discrimination of Gamma-Ray and Hadron Events Using Temporal Features: An ASTRI Mini-Array Analysis

by Valentina La Parola, Giancarlo Cusumano, Saverio Lombardi, Antonio Alessio Compagnino, Antonino La Barbera, Antonio Tutone and Antonio Pagliaro

Appl. Sci. 2025, 15(7), 3879; https://doi.org/10.3390/app15073879 - 1 Apr 2025

Cited by 1 | Viewed by 337

Abstract

Imaging Atmospheric Cherenkov Telescopes (IACTs) have revolutionized our understanding of the universe at very high energies (VHEs), enabling groundbreaking discoveries of extreme astrophysical phenomena. These instruments capture the brief flashes of Cherenkov light produced when VHE particles interact with Earth’s atmosphere, providing unique insights into cosmic accelerators and high-energy radiation sources. A fundamental challenge in IACT observations lies in distinguishing the rare gamma-ray signals from an overwhelming background of cosmic-ray events. For every gamma-ray photon detected from even the brightest sources, thousands of cosmic-ray-induced atmospheric showers trigger the telescopes. This profound signal-to-background imbalance necessitates sophisticated discrimination techniques that can effectively isolate genuine gamma-ray events while maintaining high rejection efficiency for cosmic-ray backgrounds. The most common method involves the parametrization of the morphological feature of the shower images. However, we know that gamma-ray and hadron showers also differ in their time evolution. Here, we describe how the pixel time tags (i.e., the record of when each camera pixel is lit up by the incoming shower) can help in the discrimination between photonic and hadronic showers, with a focus on the ASTRI Mini-Array Cherenkov Event Reconstruction. Our methodology employs a Random Forest classifier with optimized hyperparameters, trained on a balanced dataset of gamma and hadron events. The model incorporates feature importance analysis to select the most discriminating temporal parameters from a comprehensive set of time-based features. This machine learning approach enables effective integration of both morphological and temporal information, resulting in improved classification performance, especially at lower energies. Full article

(This article belongs to the Special Issue AI Horizons: Present Status and Visions for the Next Era)

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30 pages, 5409 KiB

Open AccessArticle

YOLO-Type Neural Networks in the Process of Adapting Mathematical Graphs to the Needs of the Blind

by Mateusz Kawulok and Michał Maćkowski

Appl. Sci. 2024, 14(24), 11829; https://doi.org/10.3390/app142411829 - 18 Dec 2024

Viewed by 819

Abstract

This publication focuses on verifying the AI effectiveness in adapting traditional educational materials to digital form, with a focus on blind people. Despite the existence of solutions to assist visually impaired people, the adaptation of graphics is still problematic. To address these challenges, the use of machine learning, which is becoming increasingly prominent in modern solutions, can be effective. Of particular note are YOLO neural networks, known for their ability to analyze images accurately and in real time. The potential of these networks has not yet been fully validated in the context of mathematical graphics for the visually impaired. This research allowed for the determination of the effectiveness of selected versions of YOLO in recognizing relevant elements in mathematical graphs and the identification of the advantages and limitations of each version. It also helped to point out further potential developments in adapting graphs to accessible forms for blind people. The obtained results indicate that YOLOv5 and YOLOv8 have the most potential in this field. This research not only highlights the applicability of machine learning to accessibility challenges but also provides a foundation for the development of automated tools that can assist teachers in inclusive classroom environments. Full article

(This article belongs to the Special Issue AI Horizons: Present Status and Visions for the Next Era)

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14 pages, 3115 KiB

Open AccessArticle

Improving Web Readability Using Video Content: A Relevance-Based Approach

by Ehsan Elahi, Jorge Morato and Ana Iglesias

Appl. Sci. 2024, 14(23), 11055; https://doi.org/10.3390/app142311055 - 27 Nov 2024

Viewed by 972

Abstract

With the increasing integration of multimedia elements into webpages, videos have emerged as a popular medium for enhancing user engagement and knowledge retention. However, irrelevant or poorly placed videos can hinder readability and distract users from the core content of a webpage. This paper proposes a novel approach leveraging natural language processing (NLP) techniques to assess the relevance of video content on educational websites, thereby enhancing readability and user engagement. By using a cosine similarity-based relevance scoring method, we measured the alignment between video transcripts and webpage text, aiming to improve the user’s comprehension of complex topics presented on educational platforms. Our results demonstrated a strong correlation between automated relevance scores and user ratings, with an improvement of over 35% in relevance alignment. The methodology was evaluated across 50 educational websites representing diverse subjects, including science, mathematics, and language learning. We conducted a two-phase evaluation process: an automated scoring phase using cosine similarity, followed by a user study with 100 participants who rated the relevance of videos to webpage content. The findings support the significance of integrating NLP-driven video relevance assessments for enhanced readability on educational websites, highlighting the potential for broader applications in e-learning. Full article

(This article belongs to the Special Issue AI Horizons: Present Status and Visions for the Next Era)

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26 pages, 4266 KiB

Open AccessArticle

Fusing Machine Learning and AI to Create a Framework for Employee Well-Being in the Era of Industry 5.0

by Cosmina-Mihaela Rosca and Adrian Stancu

Appl. Sci. 2024, 14(23), 10835; https://doi.org/10.3390/app142310835 - 22 Nov 2024

Cited by 6 | Viewed by 1226

Abstract

Employees are the most valuable resources in any company, and their well-being directly influences work productivity. This research investigates integrating health parameters and sentiment analysis expressed in sent messages to enhance employee well-being within organizations in the context of Industry 5.0. Our primary aim is to develop a Well-Being Index (WBI) that quantifies employee health through various physiological and psychological parameters. A new methodology combining data collection from wearable devices from 1 January 2023 to 18 October 2024 and advanced text analytics was employed to achieve the WBI. This study uses the LbfgsMaximumEntropy ML classification algorithm to construct the Well-Being Model (WBM) and Azure Text Analytics for sentiment evaluation to assess negative messages among employees. The findings reveal a correlation between physiological metrics and self-reported well-being, highlighting the utility of the WBI in identifying areas of concern within employee behavior. We propose that the employee global indicator (EGI) is calculated based on the WBI and the dissatisfaction score component (DSC) to measure the overall state of mind of employees. The WBM exhibited a MacroAccuracy of 91.81% and a MicroAccuracy of 95.95% after 384 configurations were analyzed. Azure Text Analytics evaluated 2000 text messages, resulting in a Precision of 99.59% and an Accuracy of 99.7%. In this case, the Recall was 99.89% and F₁-score was 99.73%. In the Industry 5.0 environment, which focuses on the employee, a new protocol, the Employee KPI Algorithm (EKA), is integrated to prevent and identify employee stress. This study underscores the synergy between quantitative health metrics and qualitative sentiment analysis, offering organizations a framework to address employee needs proactively. Full article

(This article belongs to the Special Issue AI Horizons: Present Status and Visions for the Next Era)

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13 pages, 1217 KiB

Open AccessArticle

Predicting the Open Porosity of Industrial Mortar Applied on Different Substrates: A Machine Learning Approach

by Rafael Travincas, Maria Paula Mendes, Isabel Torres and Inês Flores-Colen

Appl. Sci. 2024, 14(23), 10780; https://doi.org/10.3390/app142310780 - 21 Nov 2024

Viewed by 812

Abstract

This study aims to evaluate the potential of machine learning algorithms (Random Forest and Support Vector Machine) in predicting the open porosity of a general-use industrial mortar applied to different substrates based on the characteristics of both the mortar and substrates. This study’s novelty lies in predicting the mortar’s porosity considering the substrate’s influence on which this mortar is applied. For this purpose, an experimental database comprising 1592 datapoints of industrial mortar applied to five different substrates (hollowed ceramic brick, solid ceramic brick, concrete block, concrete slab, and lightweight concrete block) was generated using an experimental program. The samples were characterized by bulk density, open porosity, capillary water absorption coefficient, drying index, and compressive strength. This database was then used to train and test the machine learning algorithms to predict the open porosity of the mortar. The results indicate that it is possible to predict the open porosity of mortar with good prediction accuracy, and that both Random Forest (RF) and Support Vector Machine (SVM) algorithms (RF = 0.880; SVM = 0.896) are suitable for this task. Regarding the main characteristics that influence the open porosity of the mortar, the bulk density and open porosity of the substrate are significant factors. Furthermore, this study employs a straightforward methodology with a machine learning no-code platform, enhancing the replicability of its findings for future research and practical implementations. Full article

(This article belongs to the Special Issue AI Horizons: Present Status and Visions for the Next Era)

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16 pages, 334 KiB

Open AccessArticle

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

Cited by 2 | Viewed by 2107

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 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

Open AccessArticle

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

Cited by 5 | Viewed by 3454

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 1622

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 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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Review

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17 pages, 2227 KiB

Open AccessReview

Unsupervised Learning in Precision Medicine: Unlocking Personalized Healthcare through AI

by Alfonso Trezza, Anna Visibelli, Bianca Roncaglia, Ottavia Spiga and Annalisa Santucci

Appl. Sci. 2024, 14(20), 9305; https://doi.org/10.3390/app14209305 - 12 Oct 2024

Cited by 6 | Viewed by 4054

Abstract

Integrating Artificial Intelligence (AI) into Precision Medicine (PM) is redefining healthcare, enabling personalized treatments tailored to individual patients based on their genetic code, environment, and lifestyle. AI’s ability to analyze vast and complex datasets, including genomics and medical records, facilitates the identification of hidden patterns and correlations, which are critical for developing personalized treatment plans. Unsupervised Learning (UL) is particularly valuable in PM as it can analyze unstructured and unlabeled data to uncover novel disease subtypes, biomarkers, and patient stratifications. By revealing patterns that are not explicitly labeled, unsupervised algorithms enable the discovery of new insights into disease mechanisms and patient variability, advancing our understanding of individual responses to treatment. However, the integration of AI into PM presents some challenges, including concerns about data privacy and the rigorous validation of AI models in clinical practice. Despite these challenges, AI holds immense potential to revolutionize PM, offering a more personalized, efficient, and effective approach to healthcare. Collaboration among AI developers and clinicians is essential to fully realize this potential and ensure ethical and reliable implementation in medical practice. This review will explore the latest emerging UL technologies in the biomedical field with a particular focus on PM applications and their impact on human health and well-being. Full article

(This article belongs to the Special Issue AI Horizons: Present Status and Visions for the Next Era)

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