Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.2
4.3
Journals
Information
Editor’s Choice
share announcement

Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Order results
Result details
Results per page
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
19 pages, 2231 KB  
Article
Personality-Aware Course Recommender System Using Deep Learning for Technical and Vocational Education and Training
by Rana Hammad Hassan, Malik Tahir Hassan, Muhammad Shujah Islam Sameem and Muhammad Aasim Rafique
Information 2024, 15(12), 803; https://doi.org/10.3390/info15120803 - 12 Dec 2024
Cited by 22 | Viewed by 6203
Abstract
Personality represents enduring patterns, providing insights into an individual’s aptitude and behavior. Integrating these insights with learning tendencies shows promise in enhancing learning outcomes, optimizing returns on investment, and reducing dropout rates. This interdisciplinary study integrates techniques in advanced artificial intelligence (AI) with [...] Read more.
Personality represents enduring patterns, providing insights into an individual’s aptitude and behavior. Integrating these insights with learning tendencies shows promise in enhancing learning outcomes, optimizing returns on investment, and reducing dropout rates. This interdisciplinary study integrates techniques in advanced artificial intelligence (AI) with human psychology by analyzing data from the trades of Technical and Vocational Education and Training (TVET) education, by combining them with individual personality traits. This research aims to address dropout rates by providing personalized trade recommendations for TVET, with the goal of optimizing outcome-based personalized learning. The study leverages advanced AI techniques and data from a nationwide TVET program, including information on trades, trainees’ records, and the Big Five personality traits, to develop a Personality-Aware TVET Course Recommendation System (TVET-CRS). The proposed framework demonstrates an accuracy rate of 91%, and a Cohen’s Kappa score of 0.84, with an NMAE at 0.04 and an NDCG at 0.96. TVET-CRS can be effectively integrated into various aspects of the TVET cycle, including dropout prediction, career guidance, on-the-job training assessments, exam evaluations, and personalized course recommendations. Full article
(This article belongs to the Special Issue Accessibility and Inclusion in Education: Enabling Digital Technologies)
►▼ Show Figures

Figure 1

23 pages, 1262 KB  
Article
Leveraging Large Language Models in Tourism: A Comparative Study of the Latest GPT Omni Models and BERT NLP for Customer Review Classification and Sentiment Analysis
by Konstantinos I. Roumeliotis, Nikolaos D. Tselikas and Dimitrios K. Nasiopoulos
Information 2024, 15(12), 792; https://doi.org/10.3390/info15120792 - 10 Dec 2024
Cited by 38 | Viewed by 9280
Abstract
In today’s rapidly evolving digital landscape, customer reviews play a crucial role in shaping the reputation and success of hotels. Accurately analyzing and classifying the sentiment of these reviews offers valuable insights into customer satisfaction, enabling businesses to gain a competitive edge. This [...] Read more.
In today’s rapidly evolving digital landscape, customer reviews play a crucial role in shaping the reputation and success of hotels. Accurately analyzing and classifying the sentiment of these reviews offers valuable insights into customer satisfaction, enabling businesses to gain a competitive edge. This study undertakes a comparative analysis of traditional natural language processing (NLP) models, such as BERT and advanced large language models (LLMs), specifically GPT-4 omni and GPT-4o mini, both pre- and post-fine-tuning with few-shot learning. By leveraging an extensive dataset of hotel reviews, we evaluate the effectiveness of these models in predicting star ratings based on review content. The findings demonstrate that the GPT-4 omni family significantly outperforms the BERT model, achieving an accuracy of 67%, compared to BERT’s 60.6%. GPT-4o, in particular, excelled in accuracy and contextual understanding, showcasing the superiority of advanced LLMs over traditional NLP methods. This research underscores the potential of using sophisticated review evaluation systems in the hospitality industry and positions GPT-4o as a transformative tool for sentiment analysis. It marks a new era in automating and interpreting customer feedback with unprecedented precision. Full article
(This article belongs to the Special Issue Natural Language Processing (NLP) with Applications and Natural Language Understanding (NLU))
►▼ Show Figures

Graphical abstract

27 pages, 2255 KB  
Article
Harnessing AI in Anxiety Management: A Chatbot-Based Intervention for Personalized Mental Health Support
by Alexia Manole, Răzvan Cârciumaru, Rodica Brînzaș and Felicia Manole
Information 2024, 15(12), 768; https://doi.org/10.3390/info15120768 - 2 Dec 2024
Cited by 36 | Viewed by 23341
Abstract
Anxiety disorders represent one of the most widespread mental health challenges globally, yet access to traditional therapeutic interventions remains constrained, particularly in resource-limited settings. This study evaluated the effectiveness of an AI-powered chatbot, developed using ChatGPT, in managing anxiety symptoms through evidence-based cognitive-behavioral [...] Read more.
Anxiety disorders represent one of the most widespread mental health challenges globally, yet access to traditional therapeutic interventions remains constrained, particularly in resource-limited settings. This study evaluated the effectiveness of an AI-powered chatbot, developed using ChatGPT, in managing anxiety symptoms through evidence-based cognitive-behavioral therapy (CBT) techniques. Fifty participants with mild to moderate anxiety symptoms engaged with the chatbot over two observational phases, each lasting seven days. The chatbot delivered personalized interventions, including mindfulness exercises, cognitive restructuring, and breathing techniques, and was accessible 24/7 to provide real-time support during emotional distress. The findings revealed a significant reduction in anxiety symptoms in both phases, with an average improvement of 21.15% in Phase 1 and 20.42% in Phase 2. Enhanced engagement in Phase 2 suggested the potential for sustained usability and familiarity with the chatbot’s functions. While participants reported high satisfaction with the accessibility and personalization of the chatbot, its inability to replicate human empathy underscored the importance of integrating AI tools with human oversight for optimal outcomes. This study highlights the potential of AI-driven interventions as valuable complements to traditional therapy, providing scalable and accessible mental health support, particularly in regions with limited access to professional services. Full article
(This article belongs to the Special Issue Artificial Intelligence and Data Science for Health)
►▼ Show Figures

Figure 1

21 pages, 1440 KB  
Review
Digital Transformation in Energy Sector: Cybersecurity Challenges and Implications
by Saqib Saeed, Hina Gull, Muneera Mohammad Aldossary, Amal Furaih Altamimi, Mashael Saeed Alshahrani, Madeeha Saqib, Sardar Zafar Iqbal and Abdullah M. Almuhaideb
Information 2024, 15(12), 764; https://doi.org/10.3390/info15120764 - 2 Dec 2024
Cited by 36 | Viewed by 12852
Abstract
Digital transformation in energy sector organizations has huge benefits but also exposes them to cybersecurity challenges. In this paper, we carried out a systematic literature review on cybersecurity challenges and issues in the energy domain. Energy-associated assets are very critical for any nation [...] Read more.
Digital transformation in energy sector organizations has huge benefits but also exposes them to cybersecurity challenges. In this paper, we carried out a systematic literature review on cybersecurity challenges and issues in the energy domain. Energy-associated assets are very critical for any nation and cyber-attacks on these critical infrastructures can result in strategic, financial, and human losses. We investigated research papers published between 2019 and 2024 and categorized our work into three domains: oil and gas sector, the electricity sector, and the nuclear energy sector. Our study highlights that there is a need for more research in this important area to improve the security of critical infrastructures in the energy sector. We have outlined research directions for the scientific community to further strengthen the body of knowledge. This work is important for researchers to identify key areas to explore as well as for policymakers in energy sector organizations to improve their security operations by understanding the associated implications of cybersecurity. Full article
(This article belongs to the Special Issue Digital Privacy and Security, 2nd Edition)
►▼ Show Figures

Figure 1

45 pages, 1416 KB  
Article
A Comprehensive Review of Deep Learning: Architectures, Recent Advances, and Applications
by Ibomoiye Domor Mienye and Theo G. Swart
Information 2024, 15(12), 755; https://doi.org/10.3390/info15120755 - 27 Nov 2024
Cited by 255 | Viewed by 77033
Abstract
Deep learning (DL) has become a core component of modern artificial intelligence (AI), driving significant advancements across diverse fields by facilitating the analysis of complex systems, from protein folding in biology to molecular discovery in chemistry and particle interactions in physics. However, the [...] Read more.
Deep learning (DL) has become a core component of modern artificial intelligence (AI), driving significant advancements across diverse fields by facilitating the analysis of complex systems, from protein folding in biology to molecular discovery in chemistry and particle interactions in physics. However, the field of deep learning is constantly evolving, with recent innovations in both architectures and applications. Therefore, this paper provides a comprehensive review of recent DL advances, covering the evolution and applications of foundational models like convolutional neural networks (CNNs) and Recurrent Neural Networks (RNNs), as well as recent architectures such as transformers, generative adversarial networks (GANs), capsule networks, and graph neural networks (GNNs). Additionally, the paper discusses novel training techniques, including self-supervised learning, federated learning, and deep reinforcement learning, which further enhance the capabilities of deep learning models. By synthesizing recent developments and identifying current challenges, this paper provides insights into the state of the art and future directions of DL research, offering valuable guidance for both researchers and industry experts. Full article
(This article belongs to the Special Issue Feature Papers in Artificial Intelligence 2024)
►▼ Show Figures

Figure 1

42 pages, 2381 KB  
Review
AI for Decision Support: Balancing Accuracy, Transparency, and Trust Across Sectors
by Attila Kovari
Information 2024, 15(11), 725; https://doi.org/10.3390/info15110725 - 11 Nov 2024
Cited by 66 | Viewed by 31613
Abstract
This study seeks to understand the key success factors that underpin efficiency, transparency, and user trust in automated decision support systems (DSS) that leverage AI technologies across industries. The aim of this study is to facilitate more accurate decision-making with such AI-based DSS, [...] Read more.
This study seeks to understand the key success factors that underpin efficiency, transparency, and user trust in automated decision support systems (DSS) that leverage AI technologies across industries. The aim of this study is to facilitate more accurate decision-making with such AI-based DSS, as well as build trust through the need for visibility and explainability by increasing user acceptance. This study primarily examines the nature of AI-based DSS adoption and the challenges of maintaining system transparency and improving accuracy. The results provide practical guidance for professionals and decision-makers to develop AI-driven decision support systems that are not only effective but also trusted by users. The results are also important to gain insight into how artificial intelligence fits into and combines with decision-making, which can be derived from research when thinking about embedding systems in ethical standards. Full article
(This article belongs to the Special Issue Advancing Information Systems Through Artificial Intelligence: Innovative Approaches and Applications)
►▼ Show Figures

Graphical abstract

27 pages, 2374 KB  
Review
Cybersecurity at Sea: A Literature Review of Cyber-Attack Impacts and Defenses in Maritime Supply Chains
by Maria Valentina Clavijo Mesa, Carmen Elena Patino-Rodriguez and Fernando Jesus Guevara Carazas
Information 2024, 15(11), 710; https://doi.org/10.3390/info15110710 - 6 Nov 2024
Cited by 36 | Viewed by 17357
Abstract
The maritime industry is constantly evolving and posing new challenges, especially with increasing digitalization, which has raised concerns about cyber-attacks on maritime supply chain agents. Although scholars have proposed various methods and classification models to counter these cyber threats, a comprehensive cyber-attack taxonomy [...] Read more.
The maritime industry is constantly evolving and posing new challenges, especially with increasing digitalization, which has raised concerns about cyber-attacks on maritime supply chain agents. Although scholars have proposed various methods and classification models to counter these cyber threats, a comprehensive cyber-attack taxonomy for maritime supply chain actors based on a systematic literature review is still lacking. This review aims to provide a clear picture of common cyber-attacks and develop a taxonomy for their categorization. In addition, it outlines best practices derived from academic research in maritime cybersecurity using PRISMA principles for a systematic literature review, which identified 110 relevant journal papers. This study highlights that distributed denial of service (DDoS) attacks and malware are top concerns for all maritime supply chain stakeholders. In particular, shipping companies are urged to prioritize defenses against hijacking, spoofing, and jamming. The report identifies 18 practices to combat cyber-attacks, categorized into information security management solutions, information security policies, and cybersecurity awareness and training. Finally, this paper explores how emerging technologies can address cyber-attacks in the maritime supply chain network (MSCN). While Industry 4.0 technologies are highlighted as significant trends in the literature, this study aims to equip MSCN stakeholders with the knowledge to effectively leverage a broader range of emerging technologies. In doing so, it provides forward-looking solutions to prevent and mitigate cyber-attacks, emphasizing that Industry 4.0 is part of a larger landscape of technological innovation. Full article
►▼ Show Figures

Figure 1

25 pages, 1829 KB  
Review
Privacy-Preserving Techniques in Generative AI and Large Language Models: A Narrative Review
by Georgios Feretzakis, Konstantinos Papaspyridis, Aris Gkoulalas-Divanis and Vassilios S. Verykios
Information 2024, 15(11), 697; https://doi.org/10.3390/info15110697 - 4 Nov 2024
Cited by 103 | Viewed by 37703
Abstract
Generative AI, including large language models (LLMs), has transformed the paradigm of data generation and creative content, but this progress raises critical privacy concerns, especially when models are trained on sensitive data. This review provides a comprehensive overview of privacy-preserving techniques aimed at [...] Read more.
Generative AI, including large language models (LLMs), has transformed the paradigm of data generation and creative content, but this progress raises critical privacy concerns, especially when models are trained on sensitive data. This review provides a comprehensive overview of privacy-preserving techniques aimed at safeguarding data privacy in generative AI, such as differential privacy (DP), federated learning (FL), homomorphic encryption (HE), and secure multi-party computation (SMPC). These techniques mitigate risks like model inversion, data leakage, and membership inference attacks, which are particularly relevant to LLMs. Additionally, the review explores emerging solutions, including privacy-enhancing technologies and post-quantum cryptography, as future directions for enhancing privacy in generative AI systems. Recognizing that achieving absolute privacy is mathematically impossible, the review emphasizes the necessity of aligning technical safeguards with legal and regulatory frameworks to ensure compliance with data protection laws. By discussing the ethical and legal implications of privacy risks in generative AI, the review underscores the need for a balanced approach that considers performance, scalability, and privacy preservation. The findings highlight the need for ongoing research and innovation to develop privacy-preserving techniques that keep pace with the scaling of generative AI, especially in large language models, while adhering to regulatory and ethical standards. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: "Information Processes")
►▼ Show Figures

Graphical abstract

32 pages, 1091 KB  
Review
Geopolitical Ramifications of Cybersecurity Threats: State Responses and International Cooperations in the Digital Warfare Era
by Aisha Adeyeri and Hossein Abroshan
Information 2024, 15(11), 682; https://doi.org/10.3390/info15110682 - 1 Nov 2024
Cited by 22 | Viewed by 13412
Abstract
As the digital environment progresses, the complexities of cyber threats also advance, encompassing both hostile cyberattacks and sophisticated cyber espionage. In the face of these difficulties, cooperative endeavours between state and non-state actors have attracted considerable interest as crucial elements in improving global [...] Read more.
As the digital environment progresses, the complexities of cyber threats also advance, encompassing both hostile cyberattacks and sophisticated cyber espionage. In the face of these difficulties, cooperative endeavours between state and non-state actors have attracted considerable interest as crucial elements in improving global cyber resilience. This study examines cybersecurity governance’s evolving dynamics, specifically exploring non-state actors’ roles and their effects on global security. This highlights the increasing dangers presented by supply chain attacks, advanced persistent threats, ransomware, and vulnerabilities on the Internet of Things. Furthermore, it explores how non-state actors, such as terrorist organisations and armed groups, increasingly utilise cyberspace for strategic objectives. This issue can pose a challenge to conventional state-focused approaches to security management. Moreover, the research examines the crucial influence of informal governance processes on forming international cybersecurity regulations. The study emphasises the need for increased cooperation between governmental and non-governmental entities to create robust and flexible cybersecurity measures. This statement urges policymakers, security experts, and researchers to thoroughly examine the complex relationship between geopolitics, informal governance systems, and growing cyber threats to strengthen global digital resilience. Full article
(This article belongs to the Section Information Security and Privacy)
►▼ Show Figures

Figure 1

24 pages, 5888 KB  
Article
Fuzzy Logic Concepts, Developments and Implementation
by Reza Saatchi
Information 2024, 15(10), 656; https://doi.org/10.3390/info15100656 - 19 Oct 2024
Cited by 69 | Viewed by 23222
Abstract
Over the past few decades, the field of fuzzy logic has evolved significantly, leading to the development of diverse techniques and applications. Fuzzy logic has been successfully combined with other artificial intelligence techniques such as artificial neural networks, deep learning, robotics, and genetic [...] Read more.
Over the past few decades, the field of fuzzy logic has evolved significantly, leading to the development of diverse techniques and applications. Fuzzy logic has been successfully combined with other artificial intelligence techniques such as artificial neural networks, deep learning, robotics, and genetic algorithms, creating powerful tools for complex problem-solving applications. This article provides an informative description of some of the main concepts in the field of fuzzy logic. These include the types and roles of membership functions, fuzzy inference system (FIS), adaptive neuro-fuzzy inference system and fuzzy c-means clustering. The processes of fuzzification, defuzzification, implication, and determining fuzzy rules’ firing strengths are described. The article outlines some recent developments in the field of fuzzy logic, including its applications for decision support, industrial processes and control, data and telecommunication, and image and signal processing. Approaches to implementing fuzzy logic models are explained and, as an illustration, Matlab (version R2024b) is used to demonstrate implementation of a FIS. The prospects for future fuzzy logic developments are explored and example applications of hybrid fuzzy logic systems are provided. There remain extensive opportunities in further developing fuzzy logic-based techniques, including their further integration with various machine learning algorithms, and their adaptation into consumer products and industrial processes. Full article
(This article belongs to the Special Issue New Applications in Multiple Criteria Decision Analysis II)
►▼ Show Figures

Graphical abstract

29 pages, 8573 KB  
Review
Blockchain Consensus Mechanisms: A Bibliometric Analysis (2014–2024) Using VOSviewer and R Bibliometrix
by Joongho Ahn, Eojin Yi and Moonsoo Kim
Information 2024, 15(10), 644; https://doi.org/10.3390/info15100644 - 16 Oct 2024
Cited by 23 | Viewed by 12416
Abstract
Blockchain consensus mechanisms play a critical role in ensuring the security, decentralization, and integrity of distributed networks. As blockchain technology expands beyond cryptocurrencies into broader applications such as supply chain management and healthcare, the importance of efficient and scalable consensus algorithms has grown [...] Read more.
Blockchain consensus mechanisms play a critical role in ensuring the security, decentralization, and integrity of distributed networks. As blockchain technology expands beyond cryptocurrencies into broader applications such as supply chain management and healthcare, the importance of efficient and scalable consensus algorithms has grown significantly. This study provides a comprehensive bibliometric analysis of blockchain and consensus mechanism research from 2014 to 2024, using tools such as VOSviewer and R’s Bibliometrix package. The analysis traces the evolution from foundational mechanisms like Proof of ork (PoW) to more advanced models such as Proof of Stake (PoS) and Byzantine Fault Tolerance (BFT), with particular emphasis on Ethereum’s “The Merge” in 2022, which marked the historic shift from PoW to PoS. Key findings highlight emerging themes, including scalability, security, and the integration of blockchain with state-of-the-art technologies like artificial intelligence (AI), the Internet of Things (IoT), and energy trading. The study also identifies influential authors, institutions, and countries, emphasizing the collaborative and interdisciplinary nature of blockchain research. Through thematic analysis, this review uncovers the challenges and opportunities in decentralized systems, underscoring the need for continued innovation in consensus mechanisms to address efficiency, sustainability, scalability, and privacy concerns. These insights offer a valuable foundation for future research aimed at advancing blockchain technology across various industries. Full article
►▼ Show Figures

Graphical abstract

15 pages, 729 KB  
Article
Behavioral Intentions in Metaverse Tourism: An Extended Technology Acceptance Model with Flow Theory
by Qi Wu, Ming-Qi Li and Jun-Hui Wang
Information 2024, 15(10), 632; https://doi.org/10.3390/info15100632 - 13 Oct 2024
Cited by 24 | Viewed by 6438
Abstract
This study aims to develop a new theoretical framework from the perspective of the Technology Acceptance Model (TAM), incorporating flow theory, to explore the factors influencing behavioral intentions to participate in metaverse tourism. Using data from 518 respondents with metaverse experience and participation [...] Read more.
This study aims to develop a new theoretical framework from the perspective of the Technology Acceptance Model (TAM), incorporating flow theory, to explore the factors influencing behavioral intentions to participate in metaverse tourism. Using data from 518 respondents with metaverse experience and participation in metaverse tourism, the study employed R Studio and Structural Equation Modeling (SEM) to test the relationships between variables in the model. The results indicate that metaverse flow has a significant positive impact on users’ perceived usefulness and perceived ease of use, with flow demonstrating strong explanatory power as a precursor factor. Perceived usefulness and perceived ease of use are predictors of users’ attitudes to using metaverse technology. A positive attitude towards the metaverse can enhance users’ support for metaverse tourism and their behavioral intention to participate in it, while support also positively influences behavioral intention. Support for metaverse tourism acts as a clear mediator between attitudes and behavioral intention. The newly developed theoretical framework in this study provides a fresh perspective for metaverse tourism research and helps enrich empirical analysis in this field. By deeply analyzing tourists’ behavioral intentions, the study provides valuable insights for stakeholders to develop targeted marketing strategies and services, thus promoting the future development of metaverse tourism. Full article
(This article belongs to the Special Issue Extended Reality: A New Way of Interacting with the World, 2nd Edition)
►▼ Show Figures

Figure 1

15 pages, 3244 KB  
Article
Pneumonia Image Classification Using DenseNet Architecture
by Mihai Bundea and Gabriel Mihail Danciu
Information 2024, 15(10), 611; https://doi.org/10.3390/info15100611 - 6 Oct 2024
Cited by 19 | Viewed by 7516
Abstract
Pulmonary diseases, including pneumonia, represent a significant health challenge and are often diagnosed using X-rays. This study investigates the effectiveness of artificial intelligence (AI) in enhancing the diagnostic capabilities of X-ray imaging. Using Python and the PyTorch framework, we developed and trained several [...] Read more.
Pulmonary diseases, including pneumonia, represent a significant health challenge and are often diagnosed using X-rays. This study investigates the effectiveness of artificial intelligence (AI) in enhancing the diagnostic capabilities of X-ray imaging. Using Python and the PyTorch framework, we developed and trained several deep learning models based on DenseNet architectures (DenseNet121, DenseNet169, and DenseNet201) on a dataset comprising 5856 annotated X-ray images classified into two categories: Normal (Healthy) and Pneumonia. Each model was evaluated on its ability to classify images with metrics including binary accuracy, sensitivity, and specificity. The results demonstrated accuracy rates of 92% for Normal and 97% for Pneumonia. The models also showed significant improvements in diagnostic accuracy and reduced time for disease detection compared to traditional methods. This study underscores the potential of integrating convolutional neural networks (CNNs) with medical imaging to enhance diagnostic precision and support clinical decision-making in the management of pulmonary diseases. Further research is encouraged to refine these models and explore their application in other medical imaging domains. Full article
►▼ Show Figures

Figure 1

13 pages, 1381 KB  
Systematic Review
A Systematic Review of the Use and Effect of Virtual Reality, Augmented Reality and Mixed Reality in Physical Education
by Salvador Pérez-Muñoz, Raimundo Castaño Calle, Paula Teresa Morales Campo and Alberto Rodríguez-Cayetano
Information 2024, 15(9), 582; https://doi.org/10.3390/info15090582 - 21 Sep 2024
Cited by 21 | Viewed by 9354
Abstract
New technologies are tools that are present in daily life on a regular basis. In order to improve the didactic process, education must take into account these new technologies. In the field of physical education, the significance of these technologies is reflected in [...] Read more.
New technologies are tools that are present in daily life on a regular basis. In order to improve the didactic process, education must take into account these new technologies. In the field of physical education, the significance of these technologies is reflected in the existence of applications that can be carried out within the field, both for educational purposes and for physical fitness and health. This is due to the potential presented by virtual reality, augmented reality and mixed reality. The objective of this study was to examine the utilisation and impact of AR, VR and MR technologies in physical education at the compulsory stage. In order to achieve this objective, a design based on the PRISMA methodology for conducting systematic reviews was employed. The databases of WOS, Scopus, PubMed and Google Scholar were subjected to analysis. The results indicate that there has been a notable increase in research activity in this field in recent years. The analysis yielded four principal areas of focus, namely the utilisation of pedagogical methodologies, the enhancement of motor and health-related competencies, and moreover, the facilitation of optimal integration of students in physical education. The utilisation and consequences of novel technologies represent a suitable instrument for enhancing the educational experience of students enrolled in physical education programmes. Full article
(This article belongs to the Collection Augmented Reality Technologies, Systems and Applications)
►▼ Show Figures

Figure 1

34 pages, 786 KB  
Review
Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications
by Ibomoiye Domor Mienye, Theo G. Swart and George Obaido
Information 2024, 15(9), 517; https://doi.org/10.3390/info15090517 - 25 Aug 2024
Cited by 676 | Viewed by 78423
Abstract
Recurrent neural networks (RNNs) have significantly advanced the field of machine learning (ML) by enabling the effective processing of sequential data. This paper provides a comprehensive review of RNNs and their applications, highlighting advancements in architectures, such as long short-term memory (LSTM) networks, [...] Read more.
Recurrent neural networks (RNNs) have significantly advanced the field of machine learning (ML) by enabling the effective processing of sequential data. This paper provides a comprehensive review of RNNs and their applications, highlighting advancements in architectures, such as long short-term memory (LSTM) networks, gated recurrent units (GRUs), bidirectional LSTM (BiLSTM), echo state networks (ESNs), peephole LSTM, and stacked LSTM. The study examines the application of RNNs to different domains, including natural language processing (NLP), speech recognition, time series forecasting, autonomous vehicles, and anomaly detection. Additionally, the study discusses recent innovations, such as the integration of attention mechanisms and the development of hybrid models that combine RNNs with convolutional neural networks (CNNs) and transformer architectures. This review aims to provide ML researchers and practitioners with a comprehensive overview of the current state and future directions of RNN research. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Convolutional Neural Networks)
►▼ Show Figures

Figure 1

25 pages, 406 KB  
Systematic Review
Virtual and Augmented Reality in Science, Technology, Engineering, and Mathematics (STEM) Education: An Umbrella Review
by Yiqun Zhang, Miguel A. Feijoo-Garcia, Yiyin Gu, Voicu Popescu, Bedrich Benes and Alejandra J. Magana
Information 2024, 15(9), 515; https://doi.org/10.3390/info15090515 - 23 Aug 2024
Cited by 38 | Viewed by 11654
Abstract
The application of extended reality (XR) technology in education has been growing for the last two decades. XR offers immersive and interactive visualization experiences that can enhance learning by making it engaging. Recent technological advances have led to the availability of high-quality and [...] Read more.
The application of extended reality (XR) technology in education has been growing for the last two decades. XR offers immersive and interactive visualization experiences that can enhance learning by making it engaging. Recent technological advances have led to the availability of high-quality and affordable XR headsets. These advancements have spurred a wave of research focused on designing, implementing, and validating XR educational interventions. Limited literature focuses on the recent trends of XR within science, technology, engineering, and mathematics (STEM) education. Thus, this paper presents an umbrella review that explores the exploding field of XR and its transformative potential in STEM education. Using six online databases, the review zoomed in on 17 out of 1972 papers on XR for STEM education, published between 2020 and 2023, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. The results highlighted the types of XR technology applied (i.e., virtual reality and augmented reality), the specific STEM disciplines involved, the focus of each study reviewed, and the major findings from recent reviews. Overall, the educational benefits of using XR technology in STEM education are apparent: XR boosts student motivation, facilitates learning engagement, and improves skills, for example. However, using XR in education still has challenges that must be addressed, such as the physical discomfort of the learner wearing the XR headset and technical glitches. Besides revealing trends of using XR in STEM education, this umbrella review encourages reflection on current practices and suggests ways to apply XR to STEM education effectively. Full article
(This article belongs to the Special Issue Artificial Intelligence and Games Science in Education)
►▼ Show Figures

Figure 1

62 pages, 1897 KB  
Review
Construction of Knowledge Graphs: Current State and Challenges
by Marvin Hofer, Daniel Obraczka, Alieh Saeedi, Hanna Köpcke and Erhard Rahm
Information 2024, 15(8), 509; https://doi.org/10.3390/info15080509 - 22 Aug 2024
Cited by 79 | Viewed by 30896
Abstract
With Knowledge Graphs (KGs) at the center of numerous applications such as recommender systems and question-answering, the need for generalized pipelines to construct and continuously update such KGs is increasing. While the individual steps that are necessary to create KGs from unstructured sources [...] Read more.
With Knowledge Graphs (KGs) at the center of numerous applications such as recommender systems and question-answering, the need for generalized pipelines to construct and continuously update such KGs is increasing. While the individual steps that are necessary to create KGs from unstructured sources (e.g., text) and structured data sources (e.g., databases) are mostly well researched for their one-shot execution, their adoption for incremental KG updates and the interplay of the individual steps have hardly been investigated in a systematic manner so far. In this work, we first discuss the main graph models for KGs and introduce the major requirements for future KG construction pipelines. Next, we provide an overview of the necessary steps to build high-quality KGs, including cross-cutting topics such as metadata management, ontology development, and quality assurance. We then evaluate the state of the art of KG construction with respect to the introduced requirements for specific popular KGs, as well as some recent tools and strategies for KG construction. Finally, we identify areas in need of further research and improvement. Full article
(This article belongs to the Special Issue Knowledge Graph Technology and its Applications II)
►▼ Show Figures

Figure 1

19 pages, 2777 KB  
Article
Fabric Defect Detection in Real World Manufacturing Using Deep Learning
by Mariam Nasim, Rafia Mumtaz, Muneer Ahmad and Arshad Ali
Information 2024, 15(8), 476; https://doi.org/10.3390/info15080476 - 11 Aug 2024
Cited by 38 | Viewed by 16903
Abstract
Defect detection is very important for guaranteeing the quality and pricing of fabric. A considerable amount of fabric is discarded as waste because of defects, leading to substantial annual losses. While manual inspection has traditionally been the norm for detection, adopting an automatic [...] Read more.
Defect detection is very important for guaranteeing the quality and pricing of fabric. A considerable amount of fabric is discarded as waste because of defects, leading to substantial annual losses. While manual inspection has traditionally been the norm for detection, adopting an automatic defect detection scheme based on a deep learning model offers a timely and efficient solution for assessing fabric quality. In real-time manufacturing scenarios, datasets lack high-quality, precisely positioned images. Moreover, both plain and printed fabrics are being manufactured in industries simultaneously; therefore, a single model should be capable of detecting defects in all kinds of fabric. So training a robust deep learning model that detects defects in fabric datasets generated during production with high accuracy and lower computational costs is required. This study uses an indigenous dataset directly sourced from Chenab Textiles, providing authentic and diverse images representative of actual manufacturing conditions. The dataset is used to train a computationally faster but lighter state-of-the-art network, i.e., YOLOv8. For comparison, YOLOv5 and MobileNetV2-SSD FPN-Lite models are also trained on the same dataset. YOLOv8n achieved the highest performance, with a mAP of 84.8%, precision of 0.818, and recall of 0.839 across seven different defect classes. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence with Applications)
►▼ Show Figures

Figure 1

41 pages, 4974 KB  
Review
An Application-Driven Survey on Event-Based Neuromorphic Computer Vision
by Dario Cazzato and Flavio Bono
Information 2024, 15(8), 472; https://doi.org/10.3390/info15080472 - 9 Aug 2024
Cited by 18 | Viewed by 14988
Abstract
Traditional frame-based cameras, despite their effectiveness and usage in computer vision, exhibit limitations such as high latency, low dynamic range, high power consumption, and motion blur. For two decades, researchers have explored neuromorphic cameras, which operate differently from traditional frame-based types, mimicking biological [...] Read more.
Traditional frame-based cameras, despite their effectiveness and usage in computer vision, exhibit limitations such as high latency, low dynamic range, high power consumption, and motion blur. For two decades, researchers have explored neuromorphic cameras, which operate differently from traditional frame-based types, mimicking biological vision systems for enhanced data acquisition and spatio-temporal resolution. Each pixel asynchronously captures intensity changes in the scene above certain user-defined thresholds, and streams of events are captured. However, the distinct characteristics of these sensors mean that traditional computer vision methods are not directly applicable, necessitating the investigation of new approaches before being applied in real applications. This work aims to fill existing gaps in the literature by providing a survey and a discussion centered on the different application domains, differentiating between computer vision problems and whether solutions are better suited for or have been applied to a specific field. Moreover, an extensive discussion highlights the major achievements and challenges, in addition to the unique characteristics, of each application field. Full article
(This article belongs to the Special Issue Neuromorphic Engineering and Machine Learning)
►▼ Show Figures

Figure 1

26 pages, 2894 KB  
Review
The Implementation of “Smart” Technologies in the Agricultural Sector: A Review
by Fotis Assimakopoulos, Costas Vassilakis, Dionisis Margaris, Konstantinos Kotis and Dimitris Spiliotopoulos
Information 2024, 15(8), 466; https://doi.org/10.3390/info15080466 - 6 Aug 2024
Cited by 29 | Viewed by 12279
Abstract
The growing global population demands an increase in agricultural production and the promotion of sustainable practices. Smart agriculture, driven by advanced technologies, is crucial to achieving these goals. These technologies provide real-time information for crop monitoring, yield prediction, and essential farming functions. However, [...] Read more.
The growing global population demands an increase in agricultural production and the promotion of sustainable practices. Smart agriculture, driven by advanced technologies, is crucial to achieving these goals. These technologies provide real-time information for crop monitoring, yield prediction, and essential farming functions. However, adopting intelligent farming systems poses challenges, including learning new systems and dealing with installation costs. Robust support is crucial for integrating smart farming into practices. Understanding the current state of agriculture, technology trends, and the challenges in technology acceptance is essential for a smooth transition to Agriculture 4.0. This work reports on the pivotal synergy of IoT technology with other research trends, such as weather forecasting and robotics. It also presents the applications of smart agriculture worldwide, with an emphasis on government initiatives to support farmers and promote global adoption. The aim of this work is to provide a comprehensive review of smart technologies for precision agriculture and especially of their adoption level and results on the global scale; to this end, this review examines three important areas of smart agriculture, namely field, greenhouse, and livestock monitoring. Full article
(This article belongs to the Special Issue IoT-Based Systems for Resilient Smart Cities)
►▼ Show Figures

Figure 1

25 pages, 10486 KB  
Review
Digital Twins in Critical Infrastructure
by Georgios Lampropoulos, Xabier Larrucea and Ricardo Colomo-Palacios
Information 2024, 15(8), 454; https://doi.org/10.3390/info15080454 - 1 Aug 2024
Cited by 22 | Viewed by 6292
Abstract
This study aims to examine the use of digital twins in critical infrastructure through a literature review as well as a bibliometric and scientific mapping analysis. A total of 3414 documents from Scopus and Web of Science (WoS) are examined. According to the [...] Read more.
This study aims to examine the use of digital twins in critical infrastructure through a literature review as well as a bibliometric and scientific mapping analysis. A total of 3414 documents from Scopus and Web of Science (WoS) are examined. According to the findings, digital twins play an important role in critical infrastructure as they can improve the security, resilience, reliability, maintenance, continuity, and functioning of critical infrastructure in all sectors. Intelligent and autonomous decision-making, process optimization, advanced traceability, interactive visualization, and real-time monitoring, analysis, and prediction emerged as some of the benefits that digital twins can yield. Finally, the findings revealed the ability of digital twins to bridge the gap between physical and virtual environments, to be used in conjunction with other technologies, and to be integrated into various settings and domains. Full article
(This article belongs to the Section Review)
►▼ Show Figures

Figure 1

15 pages, 465 KB  
Article
Optimized Ensemble Learning Approach with Explainable AI for Improved Heart Disease Prediction
by Ibomoiye Domor Mienye and Nobert Jere
Information 2024, 15(7), 394; https://doi.org/10.3390/info15070394 - 8 Jul 2024
Cited by 93 | Viewed by 10351
Abstract
Recent advances in machine learning (ML) have shown great promise in detecting heart disease. However, to ensure the clinical adoption of ML models, they must not only be generalizable and robust but also transparent and explainable. Therefore, this research introduces an approach that [...] Read more.
Recent advances in machine learning (ML) have shown great promise in detecting heart disease. However, to ensure the clinical adoption of ML models, they must not only be generalizable and robust but also transparent and explainable. Therefore, this research introduces an approach that integrates the robustness of ensemble learning algorithms with the precision of Bayesian optimization for hyperparameter tuning and the interpretability offered by Shapley additive explanations (SHAP). The ensemble classifiers considered include adaptive boosting (AdaBoost), random forest, and extreme gradient boosting (XGBoost). The experimental results on the Cleveland and Framingham datasets demonstrate that the optimized XGBoost model achieved the highest performance, with specificity and sensitivity values of 0.971 and 0.989 on the Cleveland dataset and 0.921 and 0.975 on the Framingham dataset, respectively. Full article
(This article belongs to the Special Issue Artificial Intelligence and Data Science for Health)
►▼ Show Figures

Figure 1

15 pages, 1403 KB  
Article
BERTopic for Enhanced Idea Management and Topic Generation in Brainstorming Sessions
by Asma Cheddak, Tarek Ait Baha, Youssef Es-Saady, Mohamed El Hajji and Mohamed Baslam
Information 2024, 15(6), 365; https://doi.org/10.3390/info15060365 - 20 Jun 2024
Cited by 25 | Viewed by 12733
Abstract
Brainstorming is an important part of the design thinking process since it encourages creativity and innovation through bringing together diverse viewpoints. However, traditional brainstorming practices face challenges such as the management of large volumes of ideas. To address this issue, this paper introduces [...] Read more.
Brainstorming is an important part of the design thinking process since it encourages creativity and innovation through bringing together diverse viewpoints. However, traditional brainstorming practices face challenges such as the management of large volumes of ideas. To address this issue, this paper introduces a decision support system that employs the BERTopic model to automate the brainstorming process, which enhances the categorization of ideas and the generation of coherent topics from textual data. The dataset for our study was assembled from a brainstorming session on “scholar dropouts”, where ideas were captured on Post-it notes, digitized through an optical character recognition (OCR) model, and enhanced using data augmentation with a language model, GPT-3.5, to ensure robustness. To assess the performance of our system, we employed both quantitative and qualitative analyses. Quantitative evaluations were conducted independently across various parameters, while qualitative assessments focused on the relevance and alignment of keywords with human-classified topics during brainstorming sessions. Our findings demonstrate that BERTopic outperforms traditional LDA models in generating semantically coherent topics. These results demonstrate the usefulness of our system in managing the complex nature of Arabic language data and improving the efficiency of brainstorming sessions. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Economics and Business Management)
►▼ Show Figures

Graphical abstract

32 pages, 8136 KB  
Article
Social Media Influencers: Customer Attitudes and Impact on Purchase Behaviour
by Galina Ilieva, Tania Yankova, Margarita Ruseva, Yulia Dzhabarova, Stanislava Klisarova-Belcheva and Marin Bratkov
Information 2024, 15(6), 359; https://doi.org/10.3390/info15060359 - 18 Jun 2024
Cited by 40 | Viewed by 79320
Abstract
Social media marketing has become a crucial component of contemporary business strategies, significantly influencing brand visibility, customer engagement, and sales growth. The aim of this study is to investigate and determine the key factors guiding customer attitudes towards social media influencers, and, on [...] Read more.
Social media marketing has become a crucial component of contemporary business strategies, significantly influencing brand visibility, customer engagement, and sales growth. The aim of this study is to investigate and determine the key factors guiding customer attitudes towards social media influencers, and, on that basis, to explore their effects on purchase intentions regarding advertised products or services. A total of 376 filled-in questionnaires from an online survey were analysed. The main characteristics of digital influencers’ behaviour that affect consumer perceptions have been systematized and categorized through a combination of both traditional and advanced data analysis methods. Structural equation modelling (SEM), machine learning and multi-criteria decision-making (MCDM) methods were selected to uncover the hidden dependencies between variables from the perspective of social media users. The developed models elucidate the underlying relationships that shape the acceptance mechanism of influencers’ messages. The obtained results provide specific recommendations for stakeholders across the social media marketing value chain. Marketers can make informed decisions and optimize influencer marketing strategies to enhance user experience and increase conversion rates. Working collaboratively, marketers and influencers can create impactful and successful marketing campaigns that resonate with the target audience and drive meaningful results. Customers benefit from more tailored and engaging influencer content that aligns with their interests and preferences, fostering a stronger connection with brands and potentially affecting their purchase decisions. As the perception of customer satisfaction is an individual and evolving process, stakeholders should organize regular evaluations of influencer marketing data and explore the possibilities to ensure the continuous improvement of this e-marketing channel. Full article
►▼ Show Figures

Figure 1

33 pages, 2156 KB  
Article
Identification of Optimal Data Augmentation Techniques for Multimodal Time-Series Sensory Data: A Framework
by Nazish Ashfaq, Muhammad Hassan Khan and Muhammad Adeel Nisar
Information 2024, 15(6), 343; https://doi.org/10.3390/info15060343 - 11 Jun 2024
Cited by 17 | Viewed by 8068
Abstract
Recently, the research community has shown significant interest in the continuous temporal data obtained from motion sensors in wearable devices. These data are useful for classifying and analysing different human activities in many application areas such as healthcare, sports and surveillance. The literature [...] Read more.
Recently, the research community has shown significant interest in the continuous temporal data obtained from motion sensors in wearable devices. These data are useful for classifying and analysing different human activities in many application areas such as healthcare, sports and surveillance. The literature has presented a multitude of deep learning models that aim to derive a suitable feature representation from temporal sensory input. However, the presence of a substantial quantity of annotated training data is crucial to adequately train the deep networks. Nevertheless, the data originating from the wearable devices are vast but ineffective due to a lack of labels which hinders our ability to train the models with optimal efficiency. This phenomenon leads to the model experiencing overfitting. The contribution of the proposed research is twofold: firstly, it involves a systematic evaluation of fifteen different augmentation strategies to solve the inadequacy problem of labeled data which plays a critical role in the classification tasks. Secondly, it introduces an automatic feature-learning technique proposing a Multi-Branch Hybrid Conv-LSTM network to classify human activities of daily living using multimodal data of different wearable smart devices. The objective of this study is to introduce an ensemble deep model that effectively captures intricate patterns and interdependencies within temporal data. The term “ensemble model” pertains to fusion of distinct deep models, with the objective of leveraging their own strengths and capabilities to develop a solution that is more robust and efficient. A comprehensive assessment of ensemble models is conducted using data-augmentation techniques on two prominent benchmark datasets: CogAge and UniMiB-SHAR. The proposed network employs a range of data-augmentation methods to improve the accuracy of atomic and composite activities. This results in a 5% increase in accuracy for composite activities and a 30% increase for atomic activities. Full article
(This article belongs to the Special Issue Human Activity Recognition and Biomedical Signal Processing)
►▼ Show Figures

Figure 1

35 pages, 1616 KB  
Article
Decentralized Zone-Based PKI: A Lightweight Security Framework for IoT Ecosystems
by Mohammed El-Hajj and Pim Beune
Information 2024, 15(6), 304; https://doi.org/10.3390/info15060304 - 24 May 2024
Cited by 14 | Viewed by 3779
Abstract
The advent of Internet of Things (IoT) devices has revolutionized our daily routines, fostering interconnectedness and convenience. However, this interconnected network also presents significant security challenges concerning authentication and data integrity. Traditional security measures, such as Public Key Infrastructure (PKI), encounter limitations when [...] Read more.
The advent of Internet of Things (IoT) devices has revolutionized our daily routines, fostering interconnectedness and convenience. However, this interconnected network also presents significant security challenges concerning authentication and data integrity. Traditional security measures, such as Public Key Infrastructure (PKI), encounter limitations when applied to resource-constrained IoT devices. This paper proposes a novel decentralized PKI system tailored specifically for IoT environments to address these challenges. Our approach introduces a unique “zone” architecture overseen by zone masters, facilitating efficient certificate management within IoT clusters while reducing the risk of single points of failure. Furthermore, we prioritize the use of lightweight cryptographic techniques, including Elliptic Curve Cryptography (ECC), to optimize performance without compromising security. Through comprehensive evaluation and benchmarking, we demonstrate the effectiveness of our proposed solution in bolstering the security and efficiency of IoT ecosystems. This contribution underlines the critical need for innovative security solutions in IoT deployments and presents a scalable framework to meet the evolving demands of IoT environments. Full article
(This article belongs to the Special Issue Hardware Security and Trust)
►▼ Show Figures

Figure 1

43 pages, 428 KB  
Article
The Era of Artificial Intelligence Deception: Unraveling the Complexities of False Realities and Emerging Threats of Misinformation
by Steven M. Williamson and Victor Prybutok
Information 2024, 15(6), 299; https://doi.org/10.3390/info15060299 - 23 May 2024
Cited by 121 | Viewed by 42631
Abstract
This study delves into the dual nature of artificial intelligence (AI), illuminating its transformative potential that has the power to revolutionize various aspects of our lives. We delve into critical issues such as AI hallucinations, misinformation, and unpredictable behavior, particularly in large language [...] Read more.
This study delves into the dual nature of artificial intelligence (AI), illuminating its transformative potential that has the power to revolutionize various aspects of our lives. We delve into critical issues such as AI hallucinations, misinformation, and unpredictable behavior, particularly in large language models (LLMs) and AI-powered chatbots. These technologies, while capable of manipulating human decisions and exploiting cognitive vulnerabilities, also hold the key to unlocking unprecedented opportunities for innovation and progress. Our research underscores the need for robust, ethical AI development and deployment frameworks, advocating a balance between technological advancement and societal values. We emphasize the importance of collaboration among researchers, developers, policymakers, and end users to steer AI development toward maximizing benefits while minimizing potential harms. This study highlights the critical role of responsible AI practices, including regular training, engagement, and the sharing of experiences among AI users, to mitigate risks and develop the best practices. We call for updated legal and regulatory frameworks to keep pace with AI advancements and ensure their alignment with ethical principles and societal values. By fostering open dialog, sharing knowledge, and prioritizing ethical considerations, we can harness AI’s transformative potential to drive human advancement while managing its inherent risks and challenges. Full article
(This article belongs to the Section Information Applications)
35 pages, 6061 KB  
Article
Advanced Machine Learning Techniques for Predictive Modeling of Property Prices
by Kanchana Vishwanadee Mathotaarachchi, Raza Hasan and Salman Mahmood
Information 2024, 15(6), 295; https://doi.org/10.3390/info15060295 - 22 May 2024
Cited by 32 | Viewed by 13720
Abstract
Real estate price prediction is crucial for informed decision making in the dynamic real estate sector. In recent years, machine learning (ML) techniques have emerged as powerful tools for enhancing prediction accuracy and data-driven decision making. However, the existing literature lacks a cohesive [...] Read more.
Real estate price prediction is crucial for informed decision making in the dynamic real estate sector. In recent years, machine learning (ML) techniques have emerged as powerful tools for enhancing prediction accuracy and data-driven decision making. However, the existing literature lacks a cohesive synthesis of methodologies, findings, and research gaps in ML-based real estate price prediction. This study addresses this gap through a comprehensive literature review, examining various ML approaches, including neural networks, ensemble methods, and advanced regression techniques. We identify key research gaps, such as the limited exploration of hybrid ML-econometric models and the interpretability of ML predictions. To validate the robustness of regression models, we conduct generalization testing on an independent dataset. Results demonstrate the applicability of regression models in predicting real estate prices across diverse markets. Our findings underscore the importance of addressing research gaps to advance the field and enhance the practical applicability of ML techniques in real estate price prediction. This study contributes to a deeper understanding of ML’s role in real estate forecasting and provides insights for future research and practical implementation in the real estate industry. Full article
(This article belongs to the Special Issue Second Edition of Predictive Analytics and Data Science)
►▼ Show Figures

Figure 1

26 pages, 6156 KB  
Article
A Comparative Analysis of the Bayesian Regularization and Levenberg–Marquardt Training Algorithms in Neural Networks for Small Datasets: A Metrics Prediction of Neolithic Laminar Artefacts
by Maurizio Troiano, Eugenio Nobile, Fabio Mangini, Marco Mastrogiuseppe, Cecilia Conati Barbaro and Fabrizio Frezza
Information 2024, 15(5), 270; https://doi.org/10.3390/info15050270 - 10 May 2024
Cited by 24 | Viewed by 4026
Abstract
This study aims to present a comparative analysis of the Bayesian regularization backpropagation and Levenberg–Marquardt training algorithms in neural networks for the metrics prediction of damaged archaeological artifacts, of which the state of conservation is often fragmented due to different reasons, such as [...] Read more.
This study aims to present a comparative analysis of the Bayesian regularization backpropagation and Levenberg–Marquardt training algorithms in neural networks for the metrics prediction of damaged archaeological artifacts, of which the state of conservation is often fragmented due to different reasons, such as ritual, use wear, or post-depositional processes. The archaeological artifacts, specifically laminar blanks (so-called blades), come from different sites located in the Southern Levant that belong to the Pre-Pottery B Neolithic (PPNB) (10,100/9500–400 cal B.P.). This paper shows the entire procedure of the analysis, from its normalization of the dataset to its comparative analysis and overfitting problem resolution. Full article
(This article belongs to the Special Issue Techniques and Data Analysis in Cultural Heritage)
►▼ Show Figures

Figure 1

21 pages, 2447 KB  
Article
The Impact of Immersive Virtual Reality on Knowledge Acquisition and Adolescent Perceptions in Cultural Education
by Athanasios Christopoulos, Maria Styliou, Nikolaos Ntalas and Chrysostomos Stylios
Information 2024, 15(5), 261; https://doi.org/10.3390/info15050261 - 3 May 2024
Cited by 37 | Viewed by 9315
Abstract
Understanding local history is fundamental to fostering a comprehensive global viewpoint. As technological advances shape our pedagogical tools, Virtual Reality (VR) stands out for its potential educational impact. Though its promise in educational settings is widely acknowledged, especially in science, technology, engineering and [...] Read more.
Understanding local history is fundamental to fostering a comprehensive global viewpoint. As technological advances shape our pedagogical tools, Virtual Reality (VR) stands out for its potential educational impact. Though its promise in educational settings is widely acknowledged, especially in science, technology, engineering and mathematics (STEM) fields, there is a noticeable decrease in research exploring VR’s efficacy in arts. The present study examines the effects of VR-mediated interventions on cultural education. In greater detail, secondary school adolescents (N = 52) embarked on a journey into local history through an immersive 360° VR experience. As part of our research approach, we conducted pre- and post-intervention assessments to gauge participants’ grasp of the content and further distributed psychometric instruments to evaluate their reception of VR as an instructional approach. The analysis indicates that VR’s immersive elements enhance knowledge acquisition but the impact is modulated by the complexity of the subject matter. Additionally, the study reveals that a tailored, context-sensitive, instructional design is paramount for optimising learning outcomes and mitigating educational inequities. This work challenges the “one-size-fits-all” approach to educational VR, advocating for a more targeted instructional approach. Consequently, it emphasises the need for educators and VR developers to collaboratively tailor interventions that are both culturally and contextually relevant. Full article
(This article belongs to the Special Issue Innovation in Education, Training and Game Design with Immersive Technologies and Spatial Computing)
►▼ Show Figures

Figure 1

20 pages, 6891 KB  
Article
Enhanced Fault Detection in Bearings Using Machine Learning and Raw Accelerometer Data: A Case Study Using the Case Western Reserve University Dataset
by Krish Kumar Raj, Shahil Kumar, Rahul Ranjeev Kumar and Mauro Andriollo
Information 2024, 15(5), 259; https://doi.org/10.3390/info15050259 - 2 May 2024
Cited by 31 | Viewed by 12381
Abstract
This study introduces a novel approach for fault classification in bearing components utilizing raw accelerometer data. By employing various neural network models, including deep learning architectures, we bypass the traditional preprocessing and feature-extraction stages, streamlining the classification process. Utilizing the Case Western Reserve [...] Read more.
This study introduces a novel approach for fault classification in bearing components utilizing raw accelerometer data. By employing various neural network models, including deep learning architectures, we bypass the traditional preprocessing and feature-extraction stages, streamlining the classification process. Utilizing the Case Western Reserve University (CWRU) bearing dataset, our methodology demonstrates remarkable accuracy, particularly in deep learning networks such as the three variant convolutional neural networks (CNNs), achieving above 98% accuracy across various loading levels, establishing a new benchmark in fault-detection efficiency. Notably, data exploration through principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) provided valuable insights into feature relationships and patterns, aiding in effective fault detection. This research not only proves the efficacy of neural network classifiers in handling raw data but also opens avenues for more straightforward yet effective diagnostic methods in machinery health monitoring. These findings suggest significant potential for real-world applications, offering a faster yet reliable alternative to conventional fault-classification techniques. Full article
(This article belongs to the Section Information Applications)
►▼ Show Figures

Figure 1

21 pages, 4649 KB  
Article
Immersive Storytelling in Social Virtual Reality for Human-Centered Learning about Sensitive Historical Events
by Athina Papadopoulou, Stylianos Mystakidis and Avgoustos Tsinakos
Information 2024, 15(5), 244; https://doi.org/10.3390/info15050244 - 23 Apr 2024
Cited by 28 | Viewed by 10579
Abstract
History is a subject that students often find uninspiring in school education. This paper explores the application of social VR metaverse platforms in combination with interactive, nonlinear web platforms designed for immersive storytelling to support learning about a sensitive historical event, namely the [...] Read more.
History is a subject that students often find uninspiring in school education. This paper explores the application of social VR metaverse platforms in combination with interactive, nonlinear web platforms designed for immersive storytelling to support learning about a sensitive historical event, namely the Asia Minor Catastrophe. The goal was to design an alternative method of learning history and investigate if it would engage students and foster their independence. A mixed-methods research design was applied. Thirty-four (n = 34) adult participants engaged in the interactive book and VR space over the course of three weeks. After an online workshop, feedback was collected from participants through a custom questionnaire. The quantitative data from the questionnaire were analyzed statistically utilizing IBM SPSS, while the qualitative responses were coded thematically. This study reveals that these two tools can enhance historical education by increasing student engagement, interaction, and understanding. Participants appreciated the immersive and participatory nature of the material. This study concludes that these technologies have the potential to enhance history education by promoting active participation and engagement. Full article
(This article belongs to the Special Issue Innovation in Education, Training and Game Design with Immersive Technologies and Spatial Computing)
►▼ Show Figures

Figure 1

24 pages, 7051 KB  
Article
Sports Analytics: Data Mining to Uncover NBA Player Position, Age, and Injury Impact on Performance and Economics
by Vangelis Sarlis and Christos Tjortjis
Information 2024, 15(4), 242; https://doi.org/10.3390/info15040242 - 21 Apr 2024
Cited by 28 | Viewed by 21954
Abstract
In the intersecting fields of data mining (DM) and sports analytics, the impact of socioeconomic, demographic, and injury-related factors on sports performance and economics has been extensively explored. A novel methodology is proposed and evaluated in this study, aiming to identify essential attributes [...] Read more.
In the intersecting fields of data mining (DM) and sports analytics, the impact of socioeconomic, demographic, and injury-related factors on sports performance and economics has been extensively explored. A novel methodology is proposed and evaluated in this study, aiming to identify essential attributes and metrics that influence the salaries and performance of NBA players. Feature selection techniques are utilized for estimating the financial impacts of injuries, while clustering algorithms are applied to analyse the relationship between player age, position, and advanced performance metrics. Through the application of PCA-driven pattern recognition and exploratory-based categorization, a detailed examination of the effects on earnings and performance is conducted. Findings indicate that peak performance is typically achieved between the ages of 27 and 29, whereas the highest salaries are received between the ages of 29 and 34. Additionally, musculoskeletal injuries are identified as the source of half of the financial costs related to health problems in the NBA. The association between demographics and financial analytics, particularly focusing on the position and age of NBA players, is also investigated, offering new insights into the economic implications of player attributes and health. Full article
(This article belongs to the Special Issue New Information Communication Technologies in the Digital Era)
►▼ Show Figures

Figure 1

36 pages, 1803 KB  
Article
An Overview on the Advancements of Support Vector Machine Models in Healthcare Applications: A Review
by Rosita Guido, Stefania Ferrisi, Danilo Lofaro and Domenico Conforti
Information 2024, 15(4), 235; https://doi.org/10.3390/info15040235 - 19 Apr 2024
Cited by 313 | Viewed by 44134
Abstract
Support vector machines (SVMs) are well-known machine learning algorithms for classification and regression applications. In the healthcare domain, they have been used for a variety of tasks including diagnosis, prognosis, and prediction of disease outcomes. This review is an extensive survey on the [...] Read more.
Support vector machines (SVMs) are well-known machine learning algorithms for classification and regression applications. In the healthcare domain, they have been used for a variety of tasks including diagnosis, prognosis, and prediction of disease outcomes. This review is an extensive survey on the current state-of-the-art of SVMs developed and applied in the medical field over the years. Many variants of SVM-based approaches have been developed to enhance their generalisation capabilities. We illustrate the most interesting SVM-based models that have been developed and applied in healthcare to improve performance metrics on benchmark datasets, including hybrid classification methods that combine, for instance, optimization algorithms with SVMs. We even report interesting results found in medical applications related to real-world data. Several issues around SVMs, such as selection of hyperparameters and learning from data of questionable quality, are discussed as well. The several variants developed and introduced over the years could be useful in designing new methods to improve performance in critical fields such as healthcare, where accuracy, specificity, and other metrics are crucial. Finally, current research trends and future directions are underlined. Full article
(This article belongs to the Special Issue Computer Vision, Pattern Recognition and Machine Learning in Italy)
►▼ Show Figures

Figure 1

14 pages, 1146 KB  
Article
Literacy in Artificial Intelligence as a Challenge for Teaching in Higher Education: A Case Study at Portalegre Polytechnic University
by Eduardo Lérias, Cristina Guerra and Paulo Ferreira
Information 2024, 15(4), 205; https://doi.org/10.3390/info15040205 - 5 Apr 2024
Cited by 74 | Viewed by 12937
Abstract
The growing impact of artificial intelligence (AI) on Humanity is unavoidable, and therefore, “AI literacy” is extremely important. In the field of education—AI in education (AIED)—this technology is having a huge impact on the educational community and on the education system itself. The [...] Read more.
The growing impact of artificial intelligence (AI) on Humanity is unavoidable, and therefore, “AI literacy” is extremely important. In the field of education—AI in education (AIED)—this technology is having a huge impact on the educational community and on the education system itself. The present study seeks to assess the level of AI literacy and knowledge among teachers at Portalegre Polytechnic University (PPU), aiming to identify gaps, find the main opportunities for innovation and development, and seek the degree of relationship between the dimensions of an AI questionnaire, as well as identifying the predictive variables in this matter. As a measuring instrument, a validated questionnaire based on three dimensions (AI Literacy, AI Self-Efficacy, and AI Self-Management) was applied to a sample of 75 teachers in the various schools of PPU. This revealed an average level of AI literacy (3.28), highlighting that 62.4% of responses are at levels 3 and 4 (based on a Likert scale from 1 to 5). The results also demonstrate that the first dimension is highly significant for the total dimensions, i.e., for AI Literacy, and no factor characterizing the sample is a predictor, but finding a below-average result in the learning factor indicates a pressing need to focus on developing these skills. Full article
(This article belongs to the Special Issue Advancing Educational Innovation with Artificial Intelligence)
►▼ Show Figures

Figure 1

20 pages, 425 KB  
Article
Clustering-Based Joint Topic-Sentiment Modeling of Social Media Data: A Neural Networks Approach
by David Hanny and Bernd Resch
Information 2024, 15(4), 200; https://doi.org/10.3390/info15040200 - 4 Apr 2024
Cited by 19 | Viewed by 7439
Abstract
With the vast amount of social media posts available online, topic modeling and sentiment analysis have become central methods to better understand and analyze online behavior and opinion. However, semantic and sentiment analysis have rarely been combined for joint topic-sentiment modeling which yields [...] Read more.
With the vast amount of social media posts available online, topic modeling and sentiment analysis have become central methods to better understand and analyze online behavior and opinion. However, semantic and sentiment analysis have rarely been combined for joint topic-sentiment modeling which yields semantic topics associated with sentiments. Recent breakthroughs in natural language processing have also not been leveraged for joint topic-sentiment modeling so far. Inspired by these advancements, this paper presents a novel framework for joint topic-sentiment modeling of short texts based on pre-trained language models and a clustering approach. The method leverages techniques from dimensionality reduction and clustering for which multiple algorithms were considered. All configurations were experimentally compared against existing joint topic-sentiment models and an independent sequential baseline. Our framework produced clusters with semantic topic quality scores of up to 0.23 while the best score among the previous approaches was 0.12. The sentiment classification accuracy increased from 0.35 to 0.72 and the uniformity of sentiments within the clusters reached up to 0.9 in contrast to the baseline of 0.56. The presented approach can benefit various research areas such as disaster management where sentiments associated with topics can provide practical useful information. Full article
(This article belongs to the Special Issue 2nd Edition of Information Retrieval and Social Media Mining)
►▼ Show Figures

Figure 1

27 pages, 1316 KB  
Article
Toward Generating a New Cloud-Based Distributed Denial of Service (DDoS) Dataset and Cloud Intrusion Traffic Characterization
by MohammadMoein Shafi, Arash Habibi Lashkari, Vicente Rodriguez and Ron Nevo
Information 2024, 15(4), 195; https://doi.org/10.3390/info15040195 - 31 Mar 2024
Cited by 44 | Viewed by 8640
Abstract
The distributed denial of service attack poses a significant threat to network security. Despite the availability of various methods for detecting DDoS attacks, the challenge remains in creating real-time detectors with minimal computational overhead. Additionally, the effectiveness of new detection methods depends heavily [...] Read more.
The distributed denial of service attack poses a significant threat to network security. Despite the availability of various methods for detecting DDoS attacks, the challenge remains in creating real-time detectors with minimal computational overhead. Additionally, the effectiveness of new detection methods depends heavily on well-constructed datasets. This paper addresses the critical DDoS dataset creation and evaluation domain, focusing on the cloud network. After conducting an in-depth analysis of 16 publicly available datasets, this research identifies 15 shortcomings across various dimensions, emphasizing the need for a new approach to dataset creation. Building upon this understanding, this paper introduces a new public DDoS dataset named BCCC-cPacket-Cloud-DDoS-2024. This dataset is meticulously crafted, addressing challenges identified in previous datasets through a cloud infrastructure featuring over eight benign user activities and 17 DDoS attack scenarios. Also, a Benign User Profiler (BUP) tool has been designed and developed to generate benign user network traffic based on a normal user behavior profile. We manually label the dataset and extract over 300 features from the network and transport layers of the traffic flows using NTLFlowLyzer. The experimental phase involves identifying an optimal feature set using three distinct algorithms: ANOVA, information gain, and extra tree. Finally, this paper proposes a multi-layered DDoS detection model and evaluates its performance using the generated dataset to cover the main issues of the traditional approaches. Full article
(This article belongs to the Section Information Security and Privacy)
►▼ Show Figures

Figure 1

15 pages, 3562 KB  
Article
A Comparative Study of Machine Learning Classifiers for Enhancing Knee Osteoarthritis Diagnosis
by Aquib Raza, Thien-Luan Phan, Hung-Chung Li, Nguyen Van Hieu, Tran Trung Nghia and Congo Tak Shing Ching
Information 2024, 15(4), 183; https://doi.org/10.3390/info15040183 - 28 Mar 2024
Cited by 25 | Viewed by 5440
Abstract
Knee osteoarthritis (KOA) is a leading cause of disability, particularly affecting older adults due to the deterioration of articular cartilage within the knee joint. This condition is characterized by pain, stiffness, and impaired movement, posing a significant challenge in medical diagnostics and treatment [...] Read more.
Knee osteoarthritis (KOA) is a leading cause of disability, particularly affecting older adults due to the deterioration of articular cartilage within the knee joint. This condition is characterized by pain, stiffness, and impaired movement, posing a significant challenge in medical diagnostics and treatment planning, especially due to the current inability for early and accurate detection or monitoring of disease progression. This research introduces a multifaceted approach employing feature extraction and machine learning (ML) to improve the accuracy of diagnosing and classifying KOA stages from radiographic images. Utilizing a dataset of 3154 knee X-ray images, this study implemented feature extraction methods such as Histogram of Oriented Gradients (HOG) with Linear Discriminant Analysis (LDA) and Min–Max scaling to prepare the data for classification. The study evaluates six ML classifiers—K Nearest Neighbors classifier, Support Vector Machine (SVM), Gaussian Naive Bayes, Decision Tree, Random Forest, and XGBoost—optimized via GridSearchCV for hyperparameter tuning within a 10-fold Stratified K-Fold cross-validation framework. An ensemble model has also been made for the already high-accuracy models to explore the possibility of enhancing the accuracy and reducing the risk of overfitting. The XGBoost classifier and the ensemble model emerged as the most efficient for multiclass classification, with an accuracy of 98.90%, distinguishing between healthy and unhealthy knees. These results underscore the potential of integrating advanced ML methodologies for the nuanced and accurate diagnosis and classification of KOA, offering new avenues for clinical application and future research in medical imaging diagnostics. Full article
(This article belongs to the Section Information Applications)
►▼ Show Figures

Figure 1

20 pages, 1562 KB  
Article
Quantum-Mechanical Modelling of Asymmetric Opinion Polarisation in Social Networks
by Ivan S. Maksymov and Ganna Pogrebna
Information 2024, 15(3), 170; https://doi.org/10.3390/info15030170 - 20 Mar 2024
Cited by 15 | Viewed by 4771
Abstract
We propose a quantum-mechanical model that represents a human system of beliefs as the quantised energy levels of a physical system. This model represents a novel perspective on opinion dynamics, recreating a broad range of experimental and real-world data that exhibit an asymmetry [...] Read more.
We propose a quantum-mechanical model that represents a human system of beliefs as the quantised energy levels of a physical system. This model represents a novel perspective on opinion dynamics, recreating a broad range of experimental and real-world data that exhibit an asymmetry of opinion radicalisation. In particular, the model demonstrates the phenomena of pronounced conservatism versus mild liberalism when individuals are exposed to opposing views, mirroring recent findings on opinion polarisation via social media exposure. Advancing this model, we establish a robust framework that integrates elements from physics, psychology, behavioural science, decision-making theory, and philosophy. We also emphasise the inherent advantages of the quantum approach over traditional models, suggesting a number of new directions for future research work on quantum-mechanical models of human cognition and decision-making. Full article
(This article belongs to the Section Information and Communications Technology)
►▼ Show Figures

Figure 1

26 pages, 659 KB  
Article
A Comparison of Neural-Network-Based Intrusion Detection against Signature-Based Detection in IoT Networks
by Max Schrötter, Andreas Niemann and Bettina Schnor
Information 2024, 15(3), 164; https://doi.org/10.3390/info15030164 - 14 Mar 2024
Cited by 18 | Viewed by 5873
Abstract
Over the last few years, a plethora of papers presenting machine-learning-based approaches for intrusion detection have been published. However, the majority of those papers do not compare their results with a proper baseline of a signature-based intrusion detection system, thus violating good machine [...] Read more.
Over the last few years, a plethora of papers presenting machine-learning-based approaches for intrusion detection have been published. However, the majority of those papers do not compare their results with a proper baseline of a signature-based intrusion detection system, thus violating good machine learning practices. In order to evaluate the pros and cons of the machine-learning-based approach, we replicated a research study that uses a deep neural network model for intrusion detection. The results of our replicated research study expose several systematic problems with the used datasets and evaluation methods. In our experiments, a signature-based intrusion detection system with a minimal setup was able to outperform the tested model even under small traffic changes. Testing the replicated neural network on a new dataset recorded in the same environment with the same attacks using the same tools showed that the accuracy of the neural network dropped to 54%. Furthermore, the often-claimed advantage of being able to detect zero-day attacks could not be seen in our experiments. Full article
(This article belongs to the Special Issue Cyber Security and the Internet of Things (IoT): Theories, Approaches, Smart Applications, and New Trends)
►▼ Show Figures

Figure 1

19 pages, 2394 KB  
Article
Reducing the Power Consumption of Edge Devices Supporting Ambient Intelligence Applications
by Anastasios Fanariotis, Theofanis Orphanoudakis and Vassilis Fotopoulos
Information 2024, 15(3), 161; https://doi.org/10.3390/info15030161 - 12 Mar 2024
Cited by 17 | Viewed by 8288
Abstract
Having as a main objective the exploration of power efficiency of microcontrollers running machine learning models, this manuscript contrasts the performance of two types of state-of-the-art microcontrollers, namely ESP32 with an LX6 core and ESP32-S3 with an LX7 core, focusing on the impact [...] Read more.
Having as a main objective the exploration of power efficiency of microcontrollers running machine learning models, this manuscript contrasts the performance of two types of state-of-the-art microcontrollers, namely ESP32 with an LX6 core and ESP32-S3 with an LX7 core, focusing on the impact of process acceleration technologies like cache memory and vectoring. The research employs experimental methods, where identical machine learning models are run on both microcontrollers under varying conditions, with particular attention to cache optimization and vector instruction utilization. Results indicate a notable difference in power efficiency between the two microcontrollers, directly linked to their respective process acceleration capabilities. The study concludes that while both microcontrollers show efficacy in running machine learning models, ESP32-S3 with an LX7 core demonstrates superior power efficiency, attributable to its advanced vector instruction set and optimized cache memory usage. These findings provide valuable insights for the design of power-efficient embedded systems supporting machine learning for a variety of applications, including IoT and wearable devices, ambient intelligence, and edge computing and pave the way for future research in optimizing machine learning models for low-power, embedded environments. Full article
(This article belongs to the Special Issue Artificial Intelligence on the Edge)
►▼ Show Figures

Figure 1

30 pages, 4934 KB  
Article
A Survey of AI Techniques in IoT Applications with Use Case Investigations in the Smart Environmental Monitoring and Analytics in Real-Time IoT Platform
by Yohanes Yohanie Fridelin Panduman, Nobuo Funabiki, Evianita Dewi Fajrianti, Shihao Fang and Sritrusta Sukaridhoto
Information 2024, 15(3), 153; https://doi.org/10.3390/info15030153 - 9 Mar 2024
Cited by 59 | Viewed by 14019
Abstract
In this paper, we have developed the SEMAR (Smart Environmental Monitoring and Analytics in Real-Time) IoT application server platform for fast deployments of IoT application systems. It provides various integration capabilities for the collection, display, and analysis of sensor data on a single [...] Read more.
In this paper, we have developed the SEMAR (Smart Environmental Monitoring and Analytics in Real-Time) IoT application server platform for fast deployments of IoT application systems. It provides various integration capabilities for the collection, display, and analysis of sensor data on a single platform. Recently, Artificial Intelligence (AI) has become very popular and widely used in various applications including IoT. To support this growth, the integration of AI into SEMAR is essential to enhance its capabilities after identifying the current trends of applicable AI technologies in IoT applications. In this paper, we first provide a comprehensive review of IoT applications using AI techniques in the literature. They cover predictive analytics, image classification, object detection, text spotting, auditory perception, Natural Language Processing (NLP), and collaborative AI. Next, we identify the characteristics of each technique by considering the key parameters, such as software requirements, input/output (I/O) data types, processing methods, and computations. Third, we design the integration of AI techniques into SEMAR based on the findings. Finally, we discuss use cases of SEMAR for IoT applications with AI techniques. The implementation of the proposed design in SEMAR and its use to IoT applications will be in future works. Full article
(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)
►▼ Show Figures

Figure 1

12 pages, 2897 KB  
Article
FUSeg: The Foot Ulcer Segmentation Challenge
by Chuanbo Wang, Amirreza Mahbod, Isabella Ellinger, Adrian Galdran, Sandeep Gopalakrishnan, Jeffrey Niezgoda and Zeyun Yu
Information 2024, 15(3), 140; https://doi.org/10.3390/info15030140 - 1 Mar 2024
Cited by 35 | Viewed by 8175
Abstract
Wound care professionals provide proper diagnosis and treatment with heavy reliance on images and image documentation. Segmentation of wound boundaries in images is a key component of the care and diagnosis protocol since it is important to estimate the area of the wound [...] Read more.
Wound care professionals provide proper diagnosis and treatment with heavy reliance on images and image documentation. Segmentation of wound boundaries in images is a key component of the care and diagnosis protocol since it is important to estimate the area of the wound and provide quantitative measurement for the treatment. Unfortunately, this process is very time-consuming and requires a high level of expertise, hence the need for automatic wound measurement methods. Recently, automatic wound segmentation methods based on deep learning have shown promising performance; yet, they heavily rely on large training datasets. A few wound image datasets were published including the Diabetic Foot Ulcer Challenge dataset, the Medetec wound dataset, and WoundDB. Existing public wound image datasets suffer from small size and a lack of annotation. There is a need to build a fully annotated dataset to benchmark wound segmentation methods. To address these issues, we propose the Foot Ulcer Segmentation Challenge (FUSeg), organized in conjunction with the 2021 International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). It contains 1210 pixel-wise annotated foot ulcer images collected over 2 years from 889 patients. The submitted algorithms are reviewed in this paper and the dataset can be accessed through the Foot Ulcer Segmentation Challenge website. Full article
(This article belongs to the Special Issue Deep Learning in Medical Image Analysis: Foundations, Techniques, and Applications)
►▼ Show Figures

Figure 1

42 pages, 599 KB  
Review
Authorship Attribution Methods, Challenges, and Future Research Directions: A Comprehensive Survey
by Xie He, Arash Habibi Lashkari, Nikhill Vombatkere and Dilli Prasad Sharma
Information 2024, 15(3), 131; https://doi.org/10.3390/info15030131 - 28 Feb 2024
Cited by 23 | Viewed by 21280
Abstract
Over the past few decades, researchers have put their effort and paid significant attention to the authorship attribution field, as it plays an important role in software forensics analysis, plagiarism detection, security attack detection, and protection of trade secrets, patent claims, copyright infringement, [...] Read more.
Over the past few decades, researchers have put their effort and paid significant attention to the authorship attribution field, as it plays an important role in software forensics analysis, plagiarism detection, security attack detection, and protection of trade secrets, patent claims, copyright infringement, or cases of software theft. It helps new researchers understand the state-of-the-art works on authorship attribution methods, identify and examine the emerging methods for authorship attribution, and discuss their key concepts, associated challenges, and potential future work that could help newcomers in this field. This paper comprehensively surveys authorship attribution methods and their key classifications, used feature types, available datasets, model evaluation criteria and metrics, and challenges and limitations. In addition, we discuss the potential future research directions of the authorship attribution field based on the insights and lessons learned from this survey work. Full article
►▼ Show Figures

Figure 1

17 pages, 1151 KB  
Article
Optimization of Traditional Stock Market Strategies Using the LSTM Hybrid Approach
by Ive Botunac, Jurica Bosna and Maja Matetić
Information 2024, 15(3), 136; https://doi.org/10.3390/info15030136 - 28 Feb 2024
Cited by 31 | Viewed by 18269
Abstract
Investment decision-makers increasingly rely on modern digital technologies to enhance their strategies in today’s rapidly changing and complex market environment. This paper examines the impact of incorporating Long Short-term Memory (LSTM) models into traditional trading strategies. The core investigation revolves around whether strategies [...] Read more.
Investment decision-makers increasingly rely on modern digital technologies to enhance their strategies in today’s rapidly changing and complex market environment. This paper examines the impact of incorporating Long Short-term Memory (LSTM) models into traditional trading strategies. The core investigation revolves around whether strategies enhanced with LSTM technology perform better than traditional methods alone. Traditional trading strategies typically depend on analyzing current closing prices and various technical indicators to take trading action. However, by applying LSTM models, this study aims to forecast closing prices with greater accuracy, thereby improving trading performance. Our findings indicate that trading strategies that utilize LSTM models outperform traditional strategies. This improvement suggests a significant advantage in using LSTM models for market prediction and trading decision making. Acknowledging that no one-size-fits-all strategy works for every market condition or stock is crucial. As such, traders are encouraged to select and tailor their strategies based on thorough testing and analysis to best suit their needs and market conditions. This study contributes to a better understanding of how integrating LSTM models can enhance traditional trading strategies, offering a path toward more effective decision making in the unpredictable stock market. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) for Economics and Business Management)
►▼ Show Figures

Figure 1

61 pages, 7868 KB  
Article
Advances in Facial Expression Recognition: A Survey of Methods, Benchmarks, Models, and Datasets
by Thomas Kopalidis, Vassilios Solachidis, Nicholas Vretos and Petros Daras
Information 2024, 15(3), 135; https://doi.org/10.3390/info15030135 - 28 Feb 2024
Cited by 106 | Viewed by 49054
Abstract
Recent technological developments have enabled computers to identify and categorize facial expressions to determine a person’s emotional state in an image or a video. This process, called “Facial Expression Recognition (FER)”, has become one of the most popular research areas in computer vision. [...] Read more.
Recent technological developments have enabled computers to identify and categorize facial expressions to determine a person’s emotional state in an image or a video. This process, called “Facial Expression Recognition (FER)”, has become one of the most popular research areas in computer vision. In recent times, deep FER systems have primarily concentrated on addressing two significant challenges: the problem of overfitting due to limited training data availability, and the presence of expression-unrelated variations, including illumination, head pose, image resolution, and identity bias. In this paper, a comprehensive survey is provided on deep FER, encompassing algorithms and datasets that offer insights into these intrinsic problems. Initially, this paper presents a detailed timeline showcasing the evolution of methods and datasets in deep facial expression recognition (FER). This timeline illustrates the progression and development of the techniques and data resources used in FER. Then, a comprehensive review of FER methods is introduced, including the basic principles of FER (components such as preprocessing, feature extraction and classification, and methods, etc.) from the pro-deep learning era (traditional methods using handcrafted features, i.e., SVM and HOG, etc.) to the deep learning era. Moreover, a brief introduction is provided related to the benchmark datasets (there are two categories: controlled environments (lab) and uncontrolled environments (in the wild)) used to evaluate different FER methods and a comparison of different FER models. Existing deep neural networks and related training strategies designed for FER, based on static images and dynamic image sequences, are discussed. The remaining challenges and corresponding opportunities in FER and the future directions for designing robust deep FER systems are also pinpointed. Full article
(This article belongs to the Special Issue Deep Learning for Image, Video and Signal Processing)
►▼ Show Figures

Figure 1

23 pages, 1045 KB  
Review
Strategic Approaches to Cybersecurity Learning: A Study of Educational Models and Outcomes
by Madhav Mukherjee, Ngoc Thuy Le, Yang-Wai Chow and Willy Susilo
Information 2024, 15(2), 117; https://doi.org/10.3390/info15020117 - 18 Feb 2024
Cited by 48 | Viewed by 14270
Abstract
As the demand for cybersecurity experts in the industry grows, we face a widening shortage of skilled professionals. This pressing concern has spurred extensive research within academia and national bodies, who are striving to bridge this skills gap through refined educational frameworks, including [...] Read more.
As the demand for cybersecurity experts in the industry grows, we face a widening shortage of skilled professionals. This pressing concern has spurred extensive research within academia and national bodies, who are striving to bridge this skills gap through refined educational frameworks, including the integration of innovative information applications like remote laboratories and virtual classrooms. Despite these initiatives, current higher education models for cybersecurity, while effective in some areas, fail to provide a holistic solution to the root causes of the skills gap. Our study conducts a thorough examination of established cybersecurity educational frameworks, with the goal of identifying crucial learning outcomes that can mitigate the factors contributing to this skills gap. Furthermore, by analyzing six different educational models, for each one that can uniquely leverage technology like virtual classrooms and online platforms and is suited to various learning contexts, we categorize these contexts into four distinct categories. This categorization introduces a holistic dimension of context awareness enriched by digital learning tools into the process, enhancing the alignment with desired learning outcomes, a consideration sparsely addressed in the existing literature. This thorough analysis further strengthens the framework for guiding education providers in selecting models that most effectively align with their targeted learning outcomes and implies practical uses for technologically enhanced environments. This review presents a roadmap for educators and institutions, offering insights into relevant teaching models, including the opportunities for the utilization of remote laboratories and virtual classrooms, and their contextual applications, thereby aiding curriculum designers in making strategic decisions. Full article
(This article belongs to the Special Issue Technology, Learning and Teaching of Electronics with Information Applications)
►▼ Show Figures

Figure 1

24 pages, 4184 KB  
Article
Deep Reinforcement Learning for Autonomous Driving in Amazon Web Services DeepRacer
by Bohdan Petryshyn, Serhii Postupaiev, Soufiane Ben Bari and Armantas Ostreika
Information 2024, 15(2), 113; https://doi.org/10.3390/info15020113 - 15 Feb 2024
Cited by 13 | Viewed by 10042
Abstract
The development of autonomous driving models through reinforcement learning has gained significant traction. However, developing obstacle avoidance systems remains a challenge. Specifically, optimising path completion times while navigating obstacles is an underexplored research area. Amazon Web Services (AWS) DeepRacer emerges as a powerful [...] Read more.
The development of autonomous driving models through reinforcement learning has gained significant traction. However, developing obstacle avoidance systems remains a challenge. Specifically, optimising path completion times while navigating obstacles is an underexplored research area. Amazon Web Services (AWS) DeepRacer emerges as a powerful infrastructure for engineering and analysing autonomous models, providing a robust foundation for addressing these complexities. This research investigates the feasibility of training end-to-end self-driving models focused on obstacle avoidance using reinforcement learning on the AWS DeepRacer autonomous race car platform. A comprehensive literature review of autonomous driving methodologies and machine learning model architectures is conducted, with a particular focus on object avoidance, followed by hands-on experimentation and the analysis of training data. Furthermore, the impact of sensor choice, reward function, action spaces, and training time on the autonomous obstacle avoidance task are compared. The results of the best configuration experiment demonstrate a significant improvement in obstacle avoidance performance compared to the baseline configuration, with a 95.8% decrease in collision rate, while taking about 79% less time to complete the trial circuit. Full article
(This article belongs to the Special Issue Emerging Research in Urban Computing and Intelligent Transport Systems)
►▼ Show Figures

Figure 1

27 pages, 1281 KB  
Article
ForensicTransMonitor: A Comprehensive Blockchain Approach to Reinvent Digital Forensics and Evidence Management
by Saad Said Alqahtany and Toqeer Ali Syed
Information 2024, 15(2), 109; https://doi.org/10.3390/info15020109 - 13 Feb 2024
Cited by 23 | Viewed by 9685
Abstract
In the domain of computer forensics, ensuring the integrity of operations like preservation, acquisition, analysis, and documentation is critical. Discrepancies in these processes can compromise evidence and lead to potential miscarriages of justice. To address this, we developed a generic methodology integrating each [...] Read more.
In the domain of computer forensics, ensuring the integrity of operations like preservation, acquisition, analysis, and documentation is critical. Discrepancies in these processes can compromise evidence and lead to potential miscarriages of justice. To address this, we developed a generic methodology integrating each forensic transaction into an immutable blockchain entry, establishing transparency and authenticity from data preservation to final reporting. Our framework was designed to manage a wide range of forensic applications across different domains, including technology-focused areas such as the Internet of Things (IoT) and cloud computing, as well as sector-specific fields like healthcare. Centralizing our approach are smart contracts that seamlessly connect forensic applications to the blockchain via specialized APIs. Every action within the forensic process triggers a verifiable transaction on the blockchain, enabling a comprehensive and tamper-proof case presentation in court. Performance evaluations confirmed that our system operates with minimal overhead, ensuring that the integration bolsters the judicial process without hindering forensic investigations. Full article
►▼ Show Figures

Figure 1

18 pages, 925 KB  
Article
Chinese Cyberbullying Detection Using XLNet and Deep Bi-LSTM Hybrid Model
by Shifeng Chen, Jialin Wang and Ketai He
Information 2024, 15(2), 93; https://doi.org/10.3390/info15020093 - 6 Feb 2024
Cited by 23 | Viewed by 5574
Abstract
The popularization of the internet and the widespread use of smartphones have led to a rapid growth in the number of social media users. While information technology has brought convenience to people, it has also given rise to cyberbullying, which has a serious [...] Read more.
The popularization of the internet and the widespread use of smartphones have led to a rapid growth in the number of social media users. While information technology has brought convenience to people, it has also given rise to cyberbullying, which has a serious negative impact. The identity of online users is hidden, and due to the lack of supervision and the imperfections of relevant laws and policies, cyberbullying occurs from time to time, bringing serious mental harm and psychological trauma to the victims. The pre-trained language model BERT (Bidirectional Encoder Representations from Transformers) has achieved good results in the field of natural language processing, which can be used for cyberbullying detection. In this research, we construct a variety of traditional machine learning, deep learning and Chinese pre-trained language models as a baseline, and propose a hybrid model based on a variant of BERT: XLNet, and deep Bi-LSTM for Chinese cyberbullying detection. In addition, real cyber bullying remarks are collected to expand the Chinese offensive language dataset COLDATASET. The performance of the proposed model outperforms all baseline models on this dataset, improving 4.29% compared to SVM—the best performing method in traditional machine learning, 1.49% compared to GRU—the best performing method in deep learning, and 1.13% compared to BERT. Full article
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying article 1-50 on page 1 of 5.
Go to page     1 2 3 4 5 chevron_right last_page
Back to TopTop