Information

13 pages, 1381 KiB

Open AccessSystematic 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

Viewed by 2956

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)

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18 pages, 1657 KiB

Open AccessArticle

Identification of Emerging Technological Hotspots from a Multi-Source Information Perspective: Case Study on Blockchain Financial Technology

by Ruiyu Hu, Zemenghong Bao, Juncheng Jia and Kun Lv

Information 2024, 15(9), 581; https://doi.org/10.3390/info15090581 - 19 Sep 2024

Viewed by 904

Abstract

In recent years, propelled by societal transformations and technological advancements, emerging technologies founded upon diverse disciplines such as financial and information technology have rapidly evolved. Identifying the trends associated with these emerging technologies and extracting their salient topics is crucial in order to [...] Read more.

In recent years, propelled by societal transformations and technological advancements, emerging technologies founded upon diverse disciplines such as financial and information technology have rapidly evolved. Identifying the trends associated with these emerging technologies and extracting their salient topics is crucial in order to accurately grasp the developmental trajectory of these tools and for their efficient utilization. In this study, we chronologically categorize information derived from five types of multi-source data, including journal articles, patent inventions, and industry reports, into distinct periods. We employ the LDA (Latent Dirichlet Allocation) topic model to identify emerging technological themes within these periods and utilize a dual-index theme lifecycle analysis method to construct a hotspot theme distribution map, thereby facilitating the extraction of significant themes. Through empirical research on blockchain financial technology, we ultimately identify 22 thematic areas of blockchain finance and extracted eight prominent themes, including financial technology, cross-border payments, digital invoices, supply chain finance, and decentralization. By analyzing these themes alongside their respective popularity levels, we validate that the methods above can be used to effectively identify emerging technological hotspots and illuminate their developmental directions. Full article

(This article belongs to the Special Issue Advanced Methods for Multi-Source Information Management, Modeling, and Analysis)

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35 pages, 836 KiB

Open AccessArticle

Enhancing Task-Oriented Dialogue Systems through Synchronous Multi-Party Interaction and Multi-Group Virtual Simulation

by Ellie S. Paek, Talyn Fan, James D. Finch and Jinho D. Choi

Information 2024, 15(9), 580; https://doi.org/10.3390/info15090580 - 19 Sep 2024

Viewed by 1354

Abstract

This paper presents two innovative approaches: a synchronous multi-party dialogue system that engages in simultaneous interactions with multiple users, and multi-group simulations involving virtual user groups to evaluate the resilience of this system. Unlike most other chatbots that communicate with each user independently, [...] Read more.

This paper presents two innovative approaches: a synchronous multi-party dialogue system that engages in simultaneous interactions with multiple users, and multi-group simulations involving virtual user groups to evaluate the resilience of this system. Unlike most other chatbots that communicate with each user independently, our system facilitates information gathering from multiple users and executes 17 administrative tasks for group requests adeptly by leveraging a state machine-based framework for complete control over dialogue flow and a large language model (LLM) for robust context understanding. Assessing such a unique dialogue system poses challenges, as it requires many groups of users to interact with the system concurrently for an extended duration. To address this, we simulate various virtual groups using an LLM, each comprising 10–30 users who may belong to multiple groups, in order to evaluate the efficacy of our system; each user is assigned a persona and allowed to interact freely without scripts. As a result, our system shows average success rates of 87% for task completion and 89% for natural language understanding. Comparatively, our virtual simulation, which has an average success rate of 80%, is juxtaposed with a group of 15 human users, depicting similar task diversity and error trends. To our knowledge, it is the first work to show the LLM’s potential in both task execution and the simulation of a synchronous dialogue system to fully automate administrative tasks. Full article

(This article belongs to the Special Issue Feature Papers in Artificial Intelligence 2024)

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18 pages, 1217 KiB

Open AccessReview

User Experience and Usability of Voice User Interfaces: A Systematic Literature Review

by Akshay Madhav Deshmukh and Ricardo Chalmeta

Information 2024, 15(9), 579; https://doi.org/10.3390/info15090579 - 19 Sep 2024

Viewed by 4427

Abstract

As voice user interfaces (VUIs) rapidly transform the landscape of human–computer interaction, their potential to revolutionize user engagement is becoming increasingly evident. This paper aims to advance the field of human–computer interaction by conducting a bibliometric analysis of the user experience associated with [...] Read more.

As voice user interfaces (VUIs) rapidly transform the landscape of human–computer interaction, their potential to revolutionize user engagement is becoming increasingly evident. This paper aims to advance the field of human–computer interaction by conducting a bibliometric analysis of the user experience associated with VUIs. It proposes a classification framework comprising six research categories to systematically organize the existing literature, analyzes the primary research streams, and identifies future research directions within each category. This systematic literature review provides a comprehensive analysis of the development and effectiveness of VUIs in facilitating natural human–machine interaction. It offers critical insights into the user experience of VUIs, contributing to the refinement of VUI design to optimize overall user interaction and satisfaction. Full article

(This article belongs to the Special Issue Intelligent Information Technology)

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19 pages, 2296 KiB

Open AccessArticle

A Hybrid Approach to Ontology Construction for the Badini Kurdish Language

by Media Azzat, Karwan Jacksi and Ismael Ali

Information 2024, 15(9), 578; https://doi.org/10.3390/info15090578 - 19 Sep 2024

Viewed by 1194

Abstract

Semantic ontologies have been widely utilized as crucial tools within natural language processing, underpinning applications such as knowledge extraction, question answering, machine translation, text comprehension, information retrieval, and text summarization. While the Kurdish language, a low-resource language, has been the subject of some [...] Read more.

Semantic ontologies have been widely utilized as crucial tools within natural language processing, underpinning applications such as knowledge extraction, question answering, machine translation, text comprehension, information retrieval, and text summarization. While the Kurdish language, a low-resource language, has been the subject of some ontological research in other dialects, a semantic web ontology for the Badini dialect remains conspicuously absent. This paper addresses this gap by presenting a methodology for constructing and utilizing a semantic web ontology for the Badini dialect of the Kurdish language. A Badini annotated corpus (UOZBDN) was created and manually annotated with part-of-speech (POS) tags. Subsequently, an HMM-based POS tagger model was developed using the UOZBDN corpus and applied to annotate additional text for ontology extraction. Ontology extraction was performed by employing predefined rules to identify nouns and verbs from the model-annotated corpus and subsequently forming semantic predicates. Robust methodologies were adopted for ontology development, resulting in a high degree of precision. The POS tagging model attained an accuracy of 95.04% when applied to the UOZBDN corpus. Furthermore, a manual evaluation conducted by Badini Kurdish language experts yielded a 97.42% accuracy rate for the extracted ontology. Full article

(This article belongs to the Special Issue Knowledge Representation and Ontology-Based Data Management)

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23 pages, 1584 KiB

Open AccessArticle

Real-Time Identification and Nonlinear Control of a Permanent-Magnet Synchronous Motor Based on a Physics-Informed Neural Network and Exact Feedback Linearization

by Sergio Velarde-Gomez and Eduardo Giraldo

Information 2024, 15(9), 577; https://doi.org/10.3390/info15090577 - 19 Sep 2024

Viewed by 889

Abstract

This work proposes a novel method for the real-time identification and nonlinear control of a permanent-magnet synchronous motor (PMSM) based on a Physics-Informed Neural Network (PINN) and the exact feedback linearization approach. The proposed approach is presented in a direct-quadrature framework, where the [...] Read more.

This work proposes a novel method for the real-time identification and nonlinear control of a permanent-magnet synchronous motor (PMSM) based on a Physics-Informed Neural Network (PINN) and the exact feedback linearization approach. The proposed approach is presented in a direct-quadrature framework, where the quadrature current and the rotational speed are selected as outputs and the direct and quadrature voltages are selected as inputs. A nonlinear difference equation is selected to describe the physical dynamics of the PMSM, and a PINN is designed based on the aforementioned structure. A simplified training scheme is designed for the PINN based on a least-squares structure to facilitate online training in real time. A nonlinear controller based on exact feedback linearization is designed by considering the nonlinear model of the system identified based on the PINN. Therefore, the proposed approach involves identification and control in real time, where the PINN is trained online. In order to track the reference for the rotational speed, a nonlinear controller with integral action based on exact feedback linearization is designed based on a linear quadratic regulator. As a result, the proposed approach can be used to identify the system to be controlled in real time, and it is able to track any small change in the real model; in addition, it is robust to both external and internal disturbances, such as variations in torque load and resistance. The proposed approach is evaluated through simulation and using a real PMSM, and the results of reference tracking are evaluated under disturbances. The identification performance is evaluated by using a Taylor diagram under closed-loop and open-loop structures, where ARX and NARX structures are used for comparison. It is thereby verified that this novel proposed control approach involving a PINN-based model can adequately track the dynamics of a PMSM system, where the performance of the proposed nonlinear control is maintained even when using the identified model based on the PINN. Full article

(This article belongs to the Special Issue Feature Papers in Information in 2024–2025)

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16 pages, 1486 KiB

Open AccessArticle

Challenges and Trends in Student Evaluation of Teaching: Analysis of SET Data from the University of Peloponnese

by Ilias Papadogiannis, Costas Vassilakis, Manolis Wallace and Athanassios Katsis

Information 2024, 15(9), 576; https://doi.org/10.3390/info15090576 - 19 Sep 2024

Viewed by 1137

Abstract

This study examines the effectiveness of Student Evaluations of Teaching at the University of Peloponnese, which has systematically collected anonymous evaluations since 2015. The analysis focused on participation rates, average scores, and the correlation between student evaluations and their academic performance. Participation rates [...] Read more.

This study examines the effectiveness of Student Evaluations of Teaching at the University of Peloponnese, which has systematically collected anonymous evaluations since 2015. The analysis focused on participation rates, average scores, and the correlation between student evaluations and their academic performance. Participation rates were notably low, averaging 14.63%, with postgraduate students showing higher rates (27.33%) than undergraduates (10.77%). The average SET scores were moderately high, with postgraduates rating their courses slightly better (M = 4.137) than undergraduates (M = 3.899). A weak positive correlation was found between course grades and evaluations among undergraduates, whereas no significant correlation was observed for postgraduates. These findings highlight challenges in using SETs as reliable measures of teaching effectiveness and suggest the need for improved participation and more comprehensive evaluation methods. The results provide insights into enhancing assessment practices and contribute to the broader discourse on the validity of student evaluations in higher education. Full article

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17 pages, 4471 KiB

Open AccessArticle

Machine Learning Applications in Prediction Models for COVID-19: A Bibliometric Analysis

by Hai Lv, Yangyang Liu, Huimin Yin, Jingzhi Xi and Pingmin Wei

Information 2024, 15(9), 575; https://doi.org/10.3390/info15090575 - 18 Sep 2024

Viewed by 1581

Abstract

The COVID-19 pandemic has had a profound impact on global health, inspiring the widespread use of machine learning in combating the disease, particularly in prediction models. This study aimed to assess academic publications utilizing machine learning prediction models to combat COVID-19. We analyzed [...] Read more.

The COVID-19 pandemic has had a profound impact on global health, inspiring the widespread use of machine learning in combating the disease, particularly in prediction models. This study aimed to assess academic publications utilizing machine learning prediction models to combat COVID-19. We analyzed 2422 original articles published between 2020 and 2023 with bibliometric tools such as Histcite Pro 2.1, Bibliometrix, CiteSpace, and VOSviewer. The United States, China, and India emerged as the most prolific countries, with Stanford University producing the most publications and Huazhong University of Science and Technology receiving the most citations. The National Natural Science Foundation of China and the National Institutes of Health have made significant contributions to this field. Scientific Reports is the most frequent journal for publishing these articles. Current research focuses on deep learning, federated learning, image classification, air pollution, mental health, sentiment analysis, and drug repurposing. In conclusion, this study provides detailed insights into the key authors, countries, institutions, funding agencies, and journals in the field, as well as the most frequently used keywords. Full article

(This article belongs to the Special Issue Real-World Applications of Machine Learning Techniques)

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25 pages, 4208 KiB

Open AccessArticle

Adaptive and Scalable Database Management with Machine Learning Integration: A PostgreSQL Case Study

by Maryam Abbasi, Marco V. Bernardo, Paulo Váz, José Silva and Pedro Martins

Information 2024, 15(9), 574; https://doi.org/10.3390/info15090574 - 18 Sep 2024

Viewed by 2677

Abstract

The increasing complexity of managing modern database systems, particularly in terms of optimizing query performance for large datasets, presents significant challenges that traditional methods often fail to address. This paper proposes a comprehensive framework for integrating advanced machine learning (ML) models within the [...] Read more.

The increasing complexity of managing modern database systems, particularly in terms of optimizing query performance for large datasets, presents significant challenges that traditional methods often fail to address. This paper proposes a comprehensive framework for integrating advanced machine learning (ML) models within the architecture of a database management system (DBMS), with a specific focus on PostgreSQL. Our approach leverages a combination of supervised and unsupervised learning techniques to predict query execution times, optimize performance, and dynamically manage workloads. Unlike existing solutions that address specific optimization tasks in isolation, our framework provides a unified platform that supports real-time model inference and automatic database configuration adjustments based on workload patterns. A key contribution of our work is the integration of ML capabilities directly into the DBMS engine, enabling seamless interaction between the ML models and the query optimization process. This integration allows for the automatic retraining of models and dynamic workload management, resulting in substantial improvements in both query response times and overall system throughput. Our evaluations using the Transaction Processing Performance Council Decision Support (TPC-DS) benchmark dataset at scale factors of 100 GB, 1 TB, and 10 TB demonstrate a reduction of up to 42% in query execution times and a 74% improvement in throughput compared with traditional approaches. Additionally, we address challenges such as potential conflicts in tuning recommendations and the performance overhead associated with ML integration, providing insights for future research directions. This study is motivated by the need for autonomous tuning mechanisms to manage large-scale, heterogeneous workloads while answering key research questions, such as the following: (1) How can machine learning models be integrated into a DBMS to improve query optimization and workload management? (2) What performance improvements can be achieved through dynamic configuration tuning based on real-time workload patterns? Our results suggest that the proposed framework significantly reduces the need for manual database administration while effectively adapting to evolving workloads, offering a robust solution for modern large-scale data environments. Full article

(This article belongs to the Special Issue Machine Learning and Data Mining: Innovations in Big Data Analytics)

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21 pages, 1323 KiB

Open AccessArticle

Exploring Players’ Perspectives: A Comprehensive Topic Modeling Case Study on Elden Ring

by Fatemeh Dehghani and Loutfouz Zaman

Information 2024, 15(9), 573; https://doi.org/10.3390/info15090573 - 18 Sep 2024

Viewed by 1652

Abstract

Game reviews heavily influence public perception. User feedback is crucial for developers, offering valuable insights to enhance game quality. In this research, Metacritic game reviews for Elden Ring were analyzed for topic modeling using Latent Dirichlet Allocation (LDA), Bidirectional Encoder Representations from Transformers [...] Read more.

Game reviews heavily influence public perception. User feedback is crucial for developers, offering valuable insights to enhance game quality. In this research, Metacritic game reviews for Elden Ring were analyzed for topic modeling using Latent Dirichlet Allocation (LDA), Bidirectional Encoder Representations from Transformers (BERT), and a hybrid model combining both to identify effective methods for extracting underlying themes in player feedback. We analyzed and interpreted these models’ outputs to learn the game reviews. We aimed to identify the differences, similarities, and variations between the three to determine which provided more valuable and instructive information. Our findings indicate that each method successfully identified keywords with some similarities in identified words. The LDA model had the highest silhouette score, indicating the most distinct clustering. The LDA-BERT model had a 1% higher coherence score than LDA, indicating more meaningful topics. Full article

(This article belongs to the Special Issue Artificial Intelligence Methods for Human-Computer Interaction)

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26 pages, 436 KiB

Open AccessArticle

May the Source Be with You: On ChatGPT, Cybersecurity, and Secure Coding

by Tiago Espinha Gasiba, Andrei-Cristian Iosif, Ibrahim Kessba, Sathwik Amburi, Ulrike Lechner and Maria Pinto-Albuquerque

Information 2024, 15(9), 572; https://doi.org/10.3390/info15090572 - 18 Sep 2024

Viewed by 1430

Abstract

Software security is an important topic that is gaining more and more attention due to the rising number of publicly known cybersecurity incidents. Previous research has shown that one way to address software security is by means of a serious game, the CyberSecurity [...] Read more.

Software security is an important topic that is gaining more and more attention due to the rising number of publicly known cybersecurity incidents. Previous research has shown that one way to address software security is by means of a serious game, the CyberSecurity Challenges, which are designed to raise awareness of software developers of secure coding guidelines. This game, proven to be very successful in the industry, makes use of an artificial intelligence technique (laddering technique) to implement a chatbot for human–machine interaction. Recent advances in machine learning have led to a breakthrough, with the implementation and release of large language models, now freely available to the public. Such models are trained on a large amount of data and are capable of analyzing and interpreting not only natural language but also source code in different programming languages. With the advent of ChatGPT, and previous state-of-the-art research in secure software development, a natural question arises: to what extent can ChatGPT aid software developers in writing secure software? In this work, we draw on our experience in the industry, and also on extensive previous work to analyze and reflect on how to use ChatGPT to aid secure software development. Towards this, we conduct two experiments with large language models. Our engagements with ChatGPT and our experience in the field allow us to draw conclusions on the advantages, disadvantages, and limitations of the usage of this new technology. Full article

(This article belongs to the Special Issue Next-Generation Programming Education: Integrating Generative AI and Collaborative Tools for Cutting-Edge Learning Experiences)

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24 pages, 3952 KiB

Open AccessArticle

Confrontation of Capitalism and Socialism in Wikipedia Networks

by Leonardo Ermann and Dima L. Shepelyansky

Information 2024, 15(9), 571; https://doi.org/10.3390/info15090571 - 18 Sep 2024

Cited by 1 | Viewed by 691

Abstract

We introduce the Ising Network Opinion Formation (INOF) model and apply it to the analysis of networks of six Wikipedia language editions. In the model, Ising spins are placed at network nodes/articles and the steady-state opinion polarization of spins is determined from the [...] Read more.

We introduce the Ising Network Opinion Formation (INOF) model and apply it to the analysis of networks of six Wikipedia language editions. In the model, Ising spins are placed at network nodes/articles and the steady-state opinion polarization of spins is determined from the Monte Carlo iterations in which a given spin orientation is determined by in-going links from other spins. The main consideration was the opinion confrontation between capitalism, imperialism (blue opinion) and socialism, communism (red opinion). These nodes have fixed spin/opinion orientation while other nodes achieve their steady-state opinions in the process of Monte Carlo iterations. We found that the global network opinion favors socialism, communism for all six editions. The model also determined the opinion preferences for world countries and political leaders, showing good agreement with heuristic expectations. We also present results for opinion competition between Christianity and Islam, and USA Democratic and Republican parties. We argue that the INOF approach can find numerous applications for directed complex networks. Full article

(This article belongs to the Special Issue Network and Hypernetwork Science: Emerging Models, Paradigms, and Applications)

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19 pages, 7882 KiB

Open AccessArticle

A Modular XR Collaborative Platform for Occupational Safety and Health Training: A Case Study in Circular Logistics Facilities

by Ali Vatankhah Barenji, Jorge E. Garcia and Benoit Montreuil

Information 2024, 15(9), 570; https://doi.org/10.3390/info15090570 - 18 Sep 2024

Viewed by 1028

Abstract

Over the past few years, safety and health have become major concerns in the warehouse and logistics sectors. Each year, warehouse fatalities, injuries, and accidents cause unrecoverable losses and huge financial costs. In spite of all the advancements in methods, tools, equipment, and [...] Read more.

Over the past few years, safety and health have become major concerns in the warehouse and logistics sectors. Each year, warehouse fatalities, injuries, and accidents cause unrecoverable losses and huge financial costs. In spite of all the advancements in methods, tools, equipment, and regulations, the number of accidents, especially fatal ones, has not subsided significantly. As a result, safety professionals and researchers have explored new and innovative ways to combat this problem. In the circular logistics facility (CLF) industry, located inside warehouses and providing human muscle-oriented services to maintain pallets, both short-term safety incidents and long-term health concerns are present. Long-term health training is rarely discussed in the literature compared to short-term safety training. This is because health issues are more complex than safety issues, since biological outcomes may take time to develop, are affected by multiple resources, and cumulative injuries may occur. This paper contributes to warehouse health and safety by designing and developing a modular XR collaborative training and testing platform (MXC-P). The co-design process is applied to design each module in the MXC-P. Three main modules related to health and safety training for CLF were considered, namely personal protection equipment, pallet handling, and pallet repairing. On this platform, a virtual interactive world provides a solid hands-on training environment and generates syntactic data for evaluating long-term health risks. On the other hand, collaborative and modular environments provide a solution to geographically distributed systems, allowing employees to connect and train remotely. The effectiveness of the MXC-P is compared with traditional safety training in a pilot study. Based on the results, we can establish that the MXC-P is effective in teaching and testing hazard identification situations, especially those relating to short-term health. The results also indicate that trainees’ recall of knowledge would improve with the MXC-P. In addition to this, the MXC-P can also be used to test and evaluate a new system and generate syntactic data for evaluating long-term health. Full article

(This article belongs to the Special Issue Extended Reality and Cybersecurity)

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15 pages, 460 KiB

Open AccessArticle

Improving QoS Management Using Associative Memory and Event-Driven Transaction History

by Antonella Di Stefano, Massimo Gollo and Giovanni Morana

Information 2024, 15(9), 569; https://doi.org/10.3390/info15090569 - 18 Sep 2024

Viewed by 855

Abstract

Managing modern, web-based, distributed applications effectively is a complex task that requires coordinating several aspects, including understanding the relationships among their components, the way they interact, the available hardware, the quality of network connections, and the providers hosting them. A distributed application consists [...] Read more.

Managing modern, web-based, distributed applications effectively is a complex task that requires coordinating several aspects, including understanding the relationships among their components, the way they interact, the available hardware, the quality of network connections, and the providers hosting them. A distributed application consists of multiple independent and autonomous components. Managing the application involves overseeing each individual component with a focus on global optimization rather than local optimization. Furthermore, each component may be hosted by different resource providers, each offering its own monitoring and control interfaces. This diversity adds complexity to the management process. Lastly, the implementation, load profile, and internal status of an application or any of its components can evolve over time. This evolution makes it challenging for a Quality of Service (QoS) manager to adapt to the dynamics of the application’s performance. This aspect, in particular, can significantly affect the QoS manager’s ability to manage the application, as the controlling strategies often rely on the analysis of historical behavior. In this paper, the authors propose an extension to a previously introduced QoS manager through the addition of two new modules: (i) an associative memory module and (ii) an event forecast module. Specifically, the associative memory module, functioning as a cache, is designed to accelerate inference times. The event forecast module, which relies on a Weibull Time-to-Event Recurrent Neural Network (WTTE-RNN), aims to provide a more comprehensive view of the system’s current status and, more importantly, to mitigate the limitations posed by the finite number of decision classes in the classification algorithm. Full article

(This article belongs to the Special Issue Fundamental Problems of Information Studies)

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21 pages, 1072 KiB

Open AccessArticle

Community Detection Using Deep Learning: Combining Variational Graph Autoencoders with Leiden and K-Truss Techniques

by Jyotika Hariom Patil, Petros Potikas, William B. Andreopoulos and Katerina Potika

Information 2024, 15(9), 568; https://doi.org/10.3390/info15090568 - 16 Sep 2024

Viewed by 1148

Abstract

Deep learning struggles with unsupervised tasks like community detection in networks. This work proposes the Enhanced Community Detection with Structural Information VGAE (VGAE-ECF) method, a method that enhances variational graph autoencoders (VGAEs) for community detection in large networks. It incorporates community structure information [...] Read more.

Deep learning struggles with unsupervised tasks like community detection in networks. This work proposes the Enhanced Community Detection with Structural Information VGAE (VGAE-ECF) method, a method that enhances variational graph autoencoders (VGAEs) for community detection in large networks. It incorporates community structure information and edge weights alongside traditional network data. This combined input leads to improved latent representations for community identification via K-means clustering. We perform experiments and show that our method works better than previous approaches of community-aware VGAEs. Full article

(This article belongs to the Special Issue Optimization Algorithms and Their Applications)

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16 pages, 863 KiB

Open AccessArticle

The Enhancement of Statistical Literacy: A Cross-Institutional Study Using Data Analysis and Text Mining to Identify Statistical Issues in the Transition to University Education

by Antonio de la Hoz-Ruiz, Emma Howard and Raquel Hijón-Neira

Information 2024, 15(9), 567; https://doi.org/10.3390/info15090567 - 14 Sep 2024

Viewed by 782

Abstract

Statistics modules are included in most university degrees, independent of the degree area, and this means that many students face these modules underprepared and struggle because of a lack of statistics knowledge. The Maths Support Centre (MSC) in the University College Dublin (UCD) [...] Read more.

Statistics modules are included in most university degrees, independent of the degree area, and this means that many students face these modules underprepared and struggle because of a lack of statistics knowledge. The Maths Support Centre (MSC) in the University College Dublin (UCD) provides support for various mathematics-related subjects, with statistics students being the second-largest cohort of visitors. The overall goal of this paper is to identify the common statistical issues students face during the transition from secondary education to tertiary education. The main data set for this study is the data from UCD students who have accessed the UCD MSC since 2015/16 for statistics support; the categorization of statistical concepts has been made with the statistics module description for each statistics subject at the Universidad Rey Juan Carlos (URJC). First, we conducted a categorization of statistical concepts taught in university (based on URJC’s catergorization); after that, UCD MSC tutor comments were categorized and validated, and subsequently descriptive analyses and text mining were used on the UCD MSC comments to achieve a deeper understanding of the statistical issues. The statistical issues presented were categorized as descriptive statistics (22.8%), probability (44%), statistical inference (29.2%), and statistical software (4%). Students struggled with material that was introduced at university level rather than material seen at secondary level. Our findings on students’ main statistical issues contribute to the development of a suite of evidence-based educational applications and games to support undergraduate students internationally in first- and second-year statistical modules. Full article

(This article belongs to the Special Issue Advanced Computer and Digital Technologies)

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15 pages, 930 KiB

Open AccessArticle

The Effect of Augmented Reality on Learning Meiosis via Guided Inquiry and Pecha Kucha: A Quasi-Experimental Design

by António Faria and Guilhermina Lobato Miranda

Information 2024, 15(9), 566; https://doi.org/10.3390/info15090566 - 13 Sep 2024

Cited by 1 | Viewed by 1799

Abstract

This study investigates the effectiveness of using augmented reality (AR), combined with guided inquiry and the Pecha Kucha technique, on students’ academic outcomes when learning meiosis. The main objective was to analyse whether this combination presents significant differences in the academic performance of [...] Read more.

This study investigates the effectiveness of using augmented reality (AR), combined with guided inquiry and the Pecha Kucha technique, on students’ academic outcomes when learning meiosis. The main objective was to analyse whether this combination presents significant differences in the academic performance of students in the experimental group (EG) compared to the control group (CG), who did not use AR. The research employed a quasi-experimental design involving three 11th-grade classes from a secondary school in Lisbon. Knowledge tests were administered post-intervention and at follow-up to assess the impact. To ensure the normality of the distributions, a Shapiro–Wilk test was applied and, to guarantee the homogeneity of variances, a Levene test was utilised. Independent and paired sample t-tests were performed. The results indicated that the innovative approach, combining AR with guided inquiry and Pecha Kucha, enhanced student engagement and led to improved academic performance. The study highlights the importance of teacher support during guided inquiry, showing that proper guidance maximises learning outcomes. Findings suggest that integrating active methodologies and current technologies can enrich Biology teaching and improve understanding of complex concepts like meiosis. This research contributes to existing literature by demonstrating the potential of AR, guided inquiry, and the Pecha Kucha technique in enhancing educational outcomes. Full article

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24 pages, 4447 KiB

Open AccessArticle

LPG Semantic Ontologies: A Tool for Interoperable Schema Creation and Management

by Eleonora Bernasconi, Miguel Ceriani and Stefano Ferilli

Information 2024, 15(9), 565; https://doi.org/10.3390/info15090565 - 13 Sep 2024

Viewed by 698

Abstract

Ontologies are essential for the management and integration of heterogeneous datasets. This paper presents OntoBuilder, an advanced tool that leverages the structural capabilities of semantic labeled property graphs (SLPGs) in strict alignment with semantic web standards to create a sophisticated framework for data [...] Read more.

Ontologies are essential for the management and integration of heterogeneous datasets. This paper presents OntoBuilder, an advanced tool that leverages the structural capabilities of semantic labeled property graphs (SLPGs) in strict alignment with semantic web standards to create a sophisticated framework for data management. We detail OntoBuilder’s architecture, core functionalities, and application scenarios, demonstrating its proficiency and adaptability in addressing complex ontological challenges. Our empirical assessment highlights OntoBuilder’s strengths in enabling seamless visualization, automated ontology generation, and robust semantic integration, thereby significantly enhancing user workflows and data management capabilities. The performance of the linked data tools across multiple metrics further underscores the effectiveness of OntoBuilder. Full article

(This article belongs to the Special Issue Knowledge Graph Technology and its Applications II)

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23 pages, 1172 KiB

Open AccessArticle

Prevention of Cardiometabolic Syndrome in Children and Adolescents Using Machine Learning and Noninvasive Factors: The CASPIAN-V Study

by Hamid Reza Marateb, Mahsa Mansourian, Amirhossein Koochekian, Mehdi Shirzadi, Shadi Zamani, Marjan Mansourian, Miquel Angel Mañanas and Roya Kelishadi

Information 2024, 15(9), 564; https://doi.org/10.3390/info15090564 - 13 Sep 2024

Viewed by 856

Abstract

Cardiometabolic syndrome (CMS) is a growing concern in children and adolescents, marked by obesity, hypertension, insulin resistance, and dyslipidemia. This study aimed to predict CMS using machine learning based on data from the CASPIAN-V study, which involved 14,226 participants aged 7–18 years, with [...] Read more.

Cardiometabolic syndrome (CMS) is a growing concern in children and adolescents, marked by obesity, hypertension, insulin resistance, and dyslipidemia. This study aimed to predict CMS using machine learning based on data from the CASPIAN-V study, which involved 14,226 participants aged 7–18 years, with a CMS prevalence of 82.9%. We applied the XGBoost algorithm to analyze key noninvasive variables, including self-rated health, sunlight exposure, screen time, consanguinity, healthy and unhealthy dietary habits, discretionary salt and sugar consumption, birthweight, and birth order, father and mother education, oral hygiene behavior, and family history of dyslipidemia, obesity, hypertension, and diabetes using five-fold cross-validation. The model achieved high sensitivity (94.7% ± 4.8) and specificity (78.8% ± 13.7), with an area under the ROC curve (AUC) of 0.867 ± 0.087, indicating strong predictive performance and significantly outperformed triponderal mass index (TMI) (adjusted paired t-test; p < 0.05). The most critical selected modifiable factors were sunlight exposure, screen time, consanguinity, healthy and unhealthy diet, dietary fat type, and discretionary salt consumption. This study emphasizes the clinical importance of early identification of at-risk individuals to implement timely interventions. It offers a promising tool for CMS risk screening. These findings support using predictive analytics in clinical settings to address the rising CMS epidemic in children and adolescents. Full article

(This article belongs to the Section Artificial Intelligence)

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14 pages, 3163 KiB

Open AccessArticle

QYOLO: Contextual Query-Assisted Object Detection in High-Resolution Images

by Mingyang Gao, Wenrui Wang, Jia Mao, Jun Xiong, Zhenming Wang and Bo Wu

Information 2024, 15(9), 563; https://doi.org/10.3390/info15090563 - 12 Sep 2024

Viewed by 721

Abstract

High-resolution imagery captured by drones can detect critical components on high-voltage transmission towers, providing inspection personnel with essential maintenance insights and improving the efficiency of power line inspections. The high-resolution imagery is particularly effective in enhancing the detection of fine details such as [...] Read more.

High-resolution imagery captured by drones can detect critical components on high-voltage transmission towers, providing inspection personnel with essential maintenance insights and improving the efficiency of power line inspections. The high-resolution imagery is particularly effective in enhancing the detection of fine details such as screws. The QYOLO algorithm, an enhancement of YOLOv8, incorporates context queries into the feature pyramid, effectively capturing long-range dependencies and improving the network’s ability to detect objects. To address the increased network depth and computational load introduced by query extraction, Ghost Separable Convolution (GSConv) is employed, reducing the computational expense by half and further improving the detection performance for small objects such as screws. The experimental validation using the Transmission Line Accessories Dataset (TLAD) developed for this project demonstrates that the proposed improvements increase the average precision (AP) for small objects by 5.5% and the F1-score by 3.5%. The method also enhances detection performance for overall targets, confirming its efficacy in practical applications. 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)

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16 pages, 2706 KiB

Open AccessArticle

Classification of Moral Decision Making in Autonomous Driving: Efficacy of Boosting Procedures

by Amandeep Singh, Yovela Murzello, Sushil Pokhrel and Siby Samuel

Information 2024, 15(9), 562; https://doi.org/10.3390/info15090562 - 11 Sep 2024

Viewed by 934

Abstract

Autonomous vehicles (AVs) face critical decisions in pedestrian interactions, necessitating ethical considerations such as minimizing harm and prioritizing human life. This study investigates machine learning models to predict human decision making in simulated driving scenarios under varying pedestrian configurations and time constraints. Data [...] Read more.

Autonomous vehicles (AVs) face critical decisions in pedestrian interactions, necessitating ethical considerations such as minimizing harm and prioritizing human life. This study investigates machine learning models to predict human decision making in simulated driving scenarios under varying pedestrian configurations and time constraints. Data were collected from 204 participants across 12 unique simulated driving scenarios, categorized into young (24.7 ± 3.5 years, 38 males, 64 females) and older (71.0 ± 5.7 years, 59 males, 43 females) age groups. Participants’ binary decisions to maintain or change lanes were recorded. Traditional logistic regression models exhibited high precision but consistently low recall, struggling to identify true positive instances requiring intervention. In contrast, the AdaBoost algorithm demonstrated superior accuracy and discriminatory power. Confusion matrix analysis revealed AdaBoost’s ability to achieve high true positive rates (up to 96%) while effectively managing false positives and negatives, even under 1 s time constraints. Learning curve analysis confirmed robust learning without overfitting. AdaBoost consistently outperformed logistic regression, with AUC-ROC values ranging from 0.82 to 0.96. It exhibited strong generalization, with validation accuracy approaching 0.8, underscoring its potential for reliable real-world AV deployment. By consistently identifying critical instances while minimizing errors, AdaBoost can prioritize human safety and align with ethical frameworks essential for responsible AV adoption. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence with Applications)

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13 pages, 2995 KiB

Open AccessArticle

Transformer Fault Diagnosis Utilizing Feature Extraction and Ensemble Learning Model

by Gonglin Xu, Mei Zhang, Wanli Chen and Zhihui Wang

Information 2024, 15(9), 561; https://doi.org/10.3390/info15090561 - 11 Sep 2024

Viewed by 913

Abstract

This paper proposes a novel method for diagnosing faults in oil-immersed transformers, leveraging feature extraction and an ensemble learning algorithm to enhance diagnostic accuracy. Initially, Dissolved Gas Analysis (DGA) data from transformers undergo a cleaning process to ensure data quality and reliability. Subsequently, [...] Read more.

This paper proposes a novel method for diagnosing faults in oil-immersed transformers, leveraging feature extraction and an ensemble learning algorithm to enhance diagnostic accuracy. Initially, Dissolved Gas Analysis (DGA) data from transformers undergo a cleaning process to ensure data quality and reliability. Subsequently, an interactive ratio method is employed to augment features and project DGA data into a high-dimensional space. To refine the feature set, a combined Filter and Wrapper algorithm is utilized, effectively eliminating irrelevant and redundant features. The final step involves optimizing the Light Gradient Boosting Machine (LightGBM) model using IAOS algorithm for transformer fault classification; this model is an ensemble learning model. Experimental results demonstrate that the proposed feature extraction method enhances LightGBM model’s accuracy to 86.84%, representing a 6.58% improvement over the baseline model. Furthermore, optimization with IAOS algorithm increases the diagnostic accuracy of LightGBM model to 93.42%, an additional gain of 6.58%. Full article

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18 pages, 3952 KiB

Open AccessArticle

WGCAMNet: Wasserstein Generative Adversarial Network Augmented and Custom Attention Mechanism Based Deep Neural Network for Enhanced Brain Tumor Detection and Classification

by Fatema Binte Alam, Tahasin Ahmed Fahim, Md Asef, Md Azad Hossain and M. Ali Akber Dewan

Information 2024, 15(9), 560; https://doi.org/10.3390/info15090560 - 11 Sep 2024

Viewed by 1027

Abstract

Brain tumor detection and categorization of its subtypes are essential for early diagnosis and improving patient outcomes. This research presents a cutting-edge approach that employs advanced data augmentation and deep learning methodologies for brain tumor classification. For this work, a dataset of 6982 [...] Read more.

Brain tumor detection and categorization of its subtypes are essential for early diagnosis and improving patient outcomes. This research presents a cutting-edge approach that employs advanced data augmentation and deep learning methodologies for brain tumor classification. For this work, a dataset of 6982 MRI images from the IEEE Data Port was considered, in which a total of 5712 images of four classes (1321 glioma, 1339 meningioma, 1595 no tumor, and 1457 pituitary) were used in the training set and a total of 1270 images of the same four classes were used in the testing set. A Wasserstein Generative Adversarial Network was implemented to generate synthetic images to address class imbalance, resulting in a balanced and consistent dataset. A comparison was conducted between various data augmentation metholodogies demonstrating that Wasserstein Generative Adversarial Network-augmented results perform excellently over traditional augmentation (such as rotation, shift, zoom, etc.) and no augmentation. Additionally, a Gaussian filter and normalization were applied during preprocessing to reduce noise, highlighting its superior accuracy and edge preservation by comparing its performance to Median and Bilateral filters. The classifier model combines parallel feature extraction from modified InceptionV3 and VGG19 followed by custom attention mechanisms for effectively capturing the characteristics of each tumor type. The model was trained for 64 epochs using model checkpoints to save the best-performing model based on validation accuracy and learning rate adjustments. The model achieved a 99.61% accuracy rate on the testing set, with precision, recall, AUC, and loss of 0.9960, 0.9960, 0.0153, and 0.9999, respectively. The proposed architecture’s explainability has been enhanced by t-SNE plots, which show unique tumor clusters, and Grad-CAM representations, which highlight crucial areas in MRI scans. This research showcases an explainable and robust approach for correctly classifying four brain tumor types, combining WGAN-augmented data with advanced deep learning models in feature extraction. The framework effectively manages class imbalance and integrates a custom attention mechanism, outperforming other models, thereby improving diagnostic accuracy and reliability in clinical settings. Full article

(This article belongs to the Special Issue Applications of Deep Learning in Bioinformatics and Image Processing)

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11 pages, 6623 KiB

Open AccessArticle

Enhancing Flight Delay Predictions Using Network Centrality Measures

by Joseph Ajayi, Yao Xu, Lixin Li and Kai Wang

Information 2024, 15(9), 559; https://doi.org/10.3390/info15090559 - 10 Sep 2024

Cited by 1 | Viewed by 1093

Abstract

Accurately predicting flight delays remains a significant challenge in the aviation industry due to the complexity and interconnectivity of its operations. The traditional prediction methods often rely on meteorological conditions, such as temperature, humidity, and dew point, as well as flight-specific data like [...] Read more.

Accurately predicting flight delays remains a significant challenge in the aviation industry due to the complexity and interconnectivity of its operations. The traditional prediction methods often rely on meteorological conditions, such as temperature, humidity, and dew point, as well as flight-specific data like departure and arrival times. However, these predictors frequently fail to capture the nuanced dynamics that lead to delays. This paper introduces network centrality measures as novel predictors to enhance the binary classification of flight arrival delays. Additionally, it emphasizes the use of tree-based ensemble models, specifically random forest, gradient boosting, and CatBoost, which are recognized for their superior ability to model complex relationships compared to single classifiers. Empirical testing shows that incorporating centrality measures improves the models’ average performance, with random forest being the most effective, achieving an accuracy rate of 86.2%, surpassing the baseline by 1.7%. Full article

(This article belongs to the Special Issue Best IDEAS: International Database Engineered Applications Symposium)

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21 pages, 10483 KiB

Open AccessArticle

Evading Cyber-Attacks on Hadoop Ecosystem: A Novel Machine Learning-Based Security-Centric Approach towards Big Data Cloud

by Neeraj A. Sharma, Kunal Kumar, Tanzim Khorshed, A B M Shawkat Ali, Haris M. Khalid, S. M. Muyeen and Linju Jose

Information 2024, 15(9), 558; https://doi.org/10.3390/info15090558 - 10 Sep 2024

Viewed by 816

Abstract

The growing industry and its complex and large information sets require Big Data (BD) technology and its open-source frameworks (Apache Hadoop) to (1) collect, (2) analyze, and (3) process the information. This information usually ranges in size from gigabytes to petabytes of data. [...] Read more.

The growing industry and its complex and large information sets require Big Data (BD) technology and its open-source frameworks (Apache Hadoop) to (1) collect, (2) analyze, and (3) process the information. This information usually ranges in size from gigabytes to petabytes of data. However, processing this data involves web consoles and communication channels which are prone to intrusion from hackers. To resolve this issue, a novel machine learning (ML)-based security-centric approach has been proposed to evade cyber-attacks on the Hadoop ecosystem while considering the complexity of Big Data in Cloud (BDC). An Apache Hadoop-based management interface “Ambari” was implemented to address the variation and distinguish between attacks and activities. The analyzed experimental results show that the proposed scheme effectively (1) blocked the interface communication and retrieved the performance measured data from (2) the Ambari-based virtual machine (VM) and (3) BDC hypervisor. Moreover, the proposed architecture was able to provide a reduction in false alarms as well as cyber-attack detection. Full article

(This article belongs to the Special Issue Cybersecurity, Cybercrimes, and Smart Emerging Technologies)

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22 pages, 1665 KiB

Open AccessArticle

Design, Building and Deployment of Smart Applications for Anomaly Detection and Failure Prediction in Industrial Use Cases

by Ricardo Dintén and Marta Zorrilla

Information 2024, 15(9), 557; https://doi.org/10.3390/info15090557 - 10 Sep 2024

Viewed by 1719

Abstract

This paper presents a comparative analysis of deep learning techniques for anomaly detection and failure prediction. We explore various deep learning architectures on an IoT dataset, including recurrent neural networks (RNNs, LSTMs and GRUs), convolutional neural networks (CNNs) and transformers, to assess their [...] Read more.

This paper presents a comparative analysis of deep learning techniques for anomaly detection and failure prediction. We explore various deep learning architectures on an IoT dataset, including recurrent neural networks (RNNs, LSTMs and GRUs), convolutional neural networks (CNNs) and transformers, to assess their effectiveness in anomaly detection and failure prediction. It was found that the hybrid transformer-GRU configuration delivers the highest accuracy, albeit at the cost of requiring the longest computational time for training. Furthermore, we employ explainability techniques to elucidate the decision-making processes of these black box models and evaluate their behaviour. By analysing the inner workings of the models, we aim at providing insights into the factors influencing failure predictions. Through comprehensive experimentation and analysis on sensor data collected from a water pump, this study contributes to the understanding of deep learning methodologies for anomaly detection and failure prediction and underscores the importance of model interpretability in critical applications such as prognostics and health management. Additionally, we specify the architecture for deploying these models in a real environment using the RAI4.0 metamodel, meant for designing, configuring and automatically deploying distributed stream-based industrial applications. Our findings will offer valuable guidance for practitioners seeking to deploy deep learning techniques effectively in predictive maintenance systems, facilitating informed decision-making and enhancing reliability and efficiency in industrial operations. Full article

(This article belongs to the Special Issue Advances in Machine Learning and Intelligent Information Systems)

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17 pages, 1444 KiB

Open AccessReview

Governing with Intelligence: The Impact of Artificial Intelligence on Policy Development

by Muhammad Asfand Yar, Mahani Hamdan, Muhammad Anshari, Norma Latif Fitriyani and Muhammad Syafrudin

Information 2024, 15(9), 556; https://doi.org/10.3390/info15090556 - 10 Sep 2024

Viewed by 3897

Abstract

As the field of artificial intelligence (AI) continues to evolve, its potential applications in various domains, including public policy development, have garnered significant interest. This research aims to investigate the role of AI in shaping public policies through a qualitative examination of secondary [...] Read more.

As the field of artificial intelligence (AI) continues to evolve, its potential applications in various domains, including public policy development, have garnered significant interest. This research aims to investigate the role of AI in shaping public policies through a qualitative examination of secondary data and an extensive bibliographic review. By analyzing the existing literature, government reports, and relevant case studies, this study seeks to uncover the opportunities, challenges, and ethical considerations associated with leveraging AI in the formulation and implementation of public policies. This research will delve into the potential benefits of AI-driven policy analysis, such as enhanced decision-making processes, data-driven insights, and improved policy outcomes. Additionally, it will explore the risks and concerns surrounding AI’s influence on policy, including potential biases, privacy implications, and the need for transparency and accountability. The findings of this study will contribute to the ongoing discourse on the responsible and effective integration of AI in public policy development, fostering informed decision-making and promoting the ethical use of this transformative technology. Full article

(This article belongs to the Special Issue Feature Papers in Information in 2024–2025)

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15 pages, 3312 KiB

Open AccessArticle

Robust Mixed-Rate Region-of-Interest-Aware Video Compressive Sensing for Transmission Line Surveillance Video

by Lisha Gao, Zhoujun Ma, Shuo Han, Tiancheng Zhao, Qingcheng Liu and Zhangjie Fu

Information 2024, 15(9), 555; https://doi.org/10.3390/info15090555 - 10 Sep 2024

Viewed by 695

Abstract

Classic video compression methods usually suffer from long encode time and requires large memories, making it hard to deploy on edge devices; thus, video compressive sensing, which requires less resources during encoding, is receiving more attention. We propose a robust mixed-rate ROI-aware video [...] Read more.

Classic video compression methods usually suffer from long encode time and requires large memories, making it hard to deploy on edge devices; thus, video compressive sensing, which requires less resources during encoding, is receiving more attention. We propose a robust mixed-rate ROI-aware video compressive sensing algorithm for transmission line surveillance video compression. The proposed method compresses foreground targets and background frames separately and uses reversible neural network to reconstruct original frames. The result on transmission line surveillance video data shows that the proposed compressive sensing method can achieve 26.47, 34.71 PSNR and 0.6839, 0.9320 SSIM higher than existing methods on 1.5% and 15% measurement rates, and the proposed ROI extraction net can precisely retrieve regions under high noise levels. This research not only demonstrates the potential for a more efficient video compression technique in resource-constrained environments, but also lays a foundation for future advancements in video compressive sensing techniques and their applications in various real-time surveillance systems. Full article

(This article belongs to the Special Issue Advanced Methods for Multi-Source Information Management, Modeling, and Analysis)

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31 pages, 4888 KiB

Open AccessArticle

Efficient Cryptographic Solutions for Unbalanced Private Set Intersection in Mobile Communication

by Qian Feng, Shenglong Du, Wuzheng Tan and Jian Weng

Information 2024, 15(9), 554; https://doi.org/10.3390/info15090554 - 9 Sep 2024

Viewed by 835

Abstract

Private Set Intersection (PSI) is a cryptographic method in secure multi-party computation that allows entities to identify common elements in their datasets without revealing their private data. Traditional approaches assume similar-sized datasets and equal computational power, overlooking practical imbalances. In real-world applications, dataset [...] Read more.

Private Set Intersection (PSI) is a cryptographic method in secure multi-party computation that allows entities to identify common elements in their datasets without revealing their private data. Traditional approaches assume similar-sized datasets and equal computational power, overlooking practical imbalances. In real-world applications, dataset sizes and computational capacities often vary, particularly in the Internet of Things and mobile scenarios where device limitations restrict computational types. Traditional PSI protocols are inefficient here, as computational and communication complexities correlate with the size of larger datasets. Thus, adapting PSI protocols to these imbalances is crucial. This paper explores unbalanced PSI scenarios where one party (the receiver) has a relatively small dataset and limited computational power, while the other party (the sender) has a large amount of data and strong computational capabilities. It introduces three innovative solutions for unbalanced PSI: an unbalanced PSI protocol based on the Cuckoo filter, an unbalanced PSI protocol based on single-cloud assistance, and an unbalanced PSI protocol based on dual-cloud assistance, with each subsequent solution addressing the shortcomings of the previous one. Depending on performance and security needs, different protocols can be employed for applications such as private contact discovery. Full article

(This article belongs to the Special Issue Digital Privacy and Security, 2nd Edition)

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14 pages, 5209 KiB

Open AccessTechnical Note

Machine Learning Prediction of a Battery’s Thermal-Related Health Factor in a Battery Electric Vehicle Using Real-World Driving Data

by Natthida Sukkam, Tossapon Katongtung, Pana Suttakul, Yuttana Mona, Witsarut Achariyaviriya, Korrakot Yaibuathet Tippayawong and Nakorn Tippayawong

Information 2024, 15(9), 553; https://doi.org/10.3390/info15090553 - 9 Sep 2024

Viewed by 1022

Abstract

Electric vehicles (EVs) are alternatives to traditional combustion engine-powered vehicles. This work focuses on a thermal management system for battery EVs using liquid cooling and a machine learning (ML) model to predict their thermal-related health. Real-world data of EV operation, battery and cooling [...] Read more.

Electric vehicles (EVs) are alternatives to traditional combustion engine-powered vehicles. This work focuses on a thermal management system for battery EVs using liquid cooling and a machine learning (ML) model to predict their thermal-related health. Real-world data of EV operation, battery and cooling conditions were collected. Key influencing factors on the thermal-related health of batteries were identified. The ML model’s effectiveness was evaluated against experimental test data. The ML model proved effective in predicting and analyzing battery thermal health, suggesting its potential for use with the thermal management system. Full article

(This article belongs to the Special Issue Real-World Applications of Machine Learning Techniques)

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