Computers最新文献

{"title":"A Blockchain-Based Electronic Health Record (EHR) System for Edge Computing Enhancing Security and Cost Efficiency","authors":"Valerio Mandarino, Giuseppe Pappalardo, Emiliano Tramontana","doi":"10.3390/computers13060132","DOIUrl":"https://doi.org/10.3390/computers13060132","url":null,"abstract":"Blockchain technology offers unique features, such as transparency, the immutability of data, and the capacity to establish trust without a central authority. Such characteristics can be leveraged to support the collaboration among several different software systems operating within the healthcare ecosystem, while ensuring data integrity and make electronic health records (EHRs) more easily accessible. To provide a solution based on blockchain technology, this paper has evaluated the main issues that arise when large amounts of data are expected, i.e., mainly cost and performance. A balanced approach that maximizes the benefits and mitigates the constraints of the blockchain has been designed. The proposed decentralized application (dApp) architecture employs a hybrid storage strategy that involves storing medical records locally, on users’ devices, while utilizing blockchain to manage an index of these data. The dApp clients facilitate interactions among participants, leveraging a smart contract to enable patients to set authorization policies, thereby ensuring that only designated healthcare providers and authorized entities have access to specific medical records. The blockchain data-immutability property is used to validate data stored externally. This solution significantly reduces the costs related to the utilization of the blockchain, while retaining its advantages, and improves performance, since the majority of data are available off-chain.","PeriodicalId":503381,"journal":{"name":"Computers","volume":"8 29","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141101050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Step-by-Step Methodology for Obtaining the Reliability of Building Microgrids Using Fault TreeAnalysis","authors":"Gustavo A. Patiño-Álvarez, Johan S. Arias-Pérez, Nicolás Muñoz-Galeano","doi":"10.3390/computers13060131","DOIUrl":"https://doi.org/10.3390/computers13060131","url":null,"abstract":"This paper introduces an improved methodology designed to address a practical deficit of existing methodologies by incorporating circuit-level analysis in the assessment of building microgrid reliability. The scientific problem at hand involves devising a systematic approach that integrates circuit modeling, Probability Density Function (PDF) selection, formulation of reliability functions, and Fault Tree Analysis (FTA) tailored specifically for the distinctive features of building microgrids. This method entails analyzing inter-component relationships to gain comprehensive insights into system behavior. By harnessing the circuit models and theoretical framework proposed herein, precise estimations of microgrid failure rates can be attained. To complement this approach, we propose a thorough investigation utilizing reliability curves and importance measures, providing valuable insights into individual device failure probabilities over time. Such time-based analysis plays a crucial role in proactively identifying potential failures and facilitating efficient maintenance planning for microgrid devices. We demonstrate the application of this methodology to the University of Antioquia (UdeA) Microgrid, a low-voltage system comprising critical components such as solar panels, microinverters, inverters/chargers, batteries, and charge controllers.","PeriodicalId":503381,"journal":{"name":"Computers","volume":"16 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141100047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning for Predicting Key Factors to Identify Misinformation in Football Transfer News","authors":"Ife Runsewe, Majid Latifi, Mominul Ahsan, J. Haider","doi":"10.3390/computers13060127","DOIUrl":"https://doi.org/10.3390/computers13060127","url":null,"abstract":"The spread of misinformation in football transfer news has become a growing concern. To address this challenge, this study introduces a novel approach by employing ensemble learning techniques to identify key factors for predicting such misinformation. The performance of three ensemble learning models, namely Random Forest, AdaBoost, and XGBoost, was analyzed on a dataset of transfer rumours. Natural language processing (NLP) techniques were employed to extract structured data from the text, and the veracity of each rumor was verified using factual transfer data. The study also investigated the relationships between specific features and rumor veracity. Key predictive features such as a player’s market value, age, and timing of the transfer window were identified. The Random Forest model outperformed the other two models, achieving a cross-validated accuracy of 95.54%. The top features identified by the model were a player’s market value, time to the start/end of the transfer window, and age. The study revealed weak negative relationships between a player’s age, time to the start/end of the transfer window, and rumor veracity, suggesting that for older players and times further from the transfer window, rumors are slightly less likely to be true. In contrast, a player’s market value did not have a statistically significant relationship with rumor veracity. This study contributes to the existing knowledge of misinformation detection and ensemble learning techniques. Despite some limitations, this study has significant implications for media agencies, football clubs, and fans. By discerning the credibility of transfer news, stakeholders can make informed decisions, reduce the spread of misinformation, and foster a more transparent transfer market.","PeriodicalId":503381,"journal":{"name":"Computers","volume":"13 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141104790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploiting Anytime Algorithms for Collaborative Service Execution in Edge Computing","authors":"L. Nogueira, Jorge Coelho, David Pereira","doi":"10.3390/computers13060130","DOIUrl":"https://doi.org/10.3390/computers13060130","url":null,"abstract":"The diversity and scarcity of resources across devices in heterogeneous computing environments can impact their ability to meet users’ quality-of-service (QoS) requirements, especially in open real-time environments where computational loads are unpredictable. Despite this uncertainty, timely responses to events remain essential to ensure desired performance levels. To address this challenge, this paper introduces collaborative service execution, enabling resource-constrained IoT devices to collaboratively execute services with more powerful neighbors at the edge, thus meeting non-functional requirements that might be unattainable through individual execution. Nodes dynamically form clusters, allocating resources to each service and establishing initial configurations that maximize QoS satisfaction while minimizing global QoS impact. However, the complexity of open real-time environments may hinder the computation of optimal local and global resource allocations within reasonable timeframes. Thus, we reformulate the QoS optimization problem as a heuristic-based anytime optimization problem, capable of interrupting and quickly adapting to environmental changes. Extensive simulations demonstrate that our anytime algorithms rapidly yield satisfactory initial service solutions and effectively optimize the solution quality over iterations, with negligible overhead compared to the benefits gained.","PeriodicalId":503381,"journal":{"name":"Computers","volume":"31 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141106329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Decision System on the Empirical Analysis of the Actual Usage of Interactive Entertainment: A Perspective of Sustainable Innovative Technology","authors":"Rex Revian A. Guste, A. K. Ong","doi":"10.3390/computers13060128","DOIUrl":"https://doi.org/10.3390/computers13060128","url":null,"abstract":"This study focused on the impact of Netflix’s interactive entertainment on Filipino consumers, seamlessly combining vantage points from consumer behavior and employing data analytics. This underlines the revolutionary aspect of interactive entertainment in the quickly expanding digital media ecosystem, particularly as Netflix pioneers fresh content distribution techniques. The main objective of this study was to find the factors impacting the real usage of Netflix’s interactive entertainment among Filipino viewers, filling a critical gap in the existing literature. The major goal of using advanced data analytics techniques in this study was to understand the subtle dynamics affecting customer behavior in this setting. Specifically, the random forest classifier with hard and soft classifiers was assessed. The random forest compared to LightGBM was also employed, alongside the different algorithms of the artificial neural network. Purposive sampling was used to obtain responses from 258 people who had experienced Netflix’s interactive entertainment, resulting in a comprehensive dataset. The findings emphasized the importance of hedonic motivation, underlining the requirement for highly engaging and rewarding interactive material. Customer service and device compatibility, for example, have a significant impact on user uptake. Furthermore, behavioral intention and habit emerged as key drivers, revealing interactive entertainment’s long-term influence on user engagement. Practically, the research recommends strategic platform suggestions that emphasize continuous innovation, user-friendly interfaces, and user-centric methods. This study was able to fill in the gap in the literature on interactive entertainment, which contributes to a better understanding of consumer consumption and lays the groundwork for future research in the dynamic field of digital media. Moreover, this study offers essential insights into the intricate interaction of consumer preferences, technology breakthroughs, and societal influences in the ever-expanding environment of digital entertainment. Lastly, the comparative approach to the use of machine learning algorithms provides insights for future works to adopt and employ among human factors and consumer behavior-related studies.","PeriodicalId":503381,"journal":{"name":"Computers","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141105766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Algorithms for the Analysis of Fast-Field-Cycling Nuclear Magnetic Resonance Dispersion Curves","authors":"Villiam Bortolotti, Pellegrino Conte, Germana Landi, Paolo Lo Meo, Anastasiia Nagmutdinova, Giovanni Vito Spinelli, Fabiana Zama","doi":"10.3390/computers13060129","DOIUrl":"https://doi.org/10.3390/computers13060129","url":null,"abstract":"Fast-Field-Cycling (FFC) Nuclear Magnetic Resonance (NMR) relaxometry is a powerful, non-destructive magnetic resonance technique that enables, among other things, the investigation of slow molecular dynamics at low magnetic field intensities. FFC-NMR relaxometry measurements provide insight into molecular motion across various timescales within a single experiment. This study focuses on a model-free approach, representing the NMRD profile R1 as a linear combination of Lorentzian functions, thereby addressing the challenges of fitting data within an ill-conditioned linear least-squares framework. Tackling this problem, we present a comprehensive review and experimental validation of three regularization approaches to implement the model-free approach to analyzing NMRD profiles. These include (1) MF-UPen, utilizing locally adapted L2 regularization; (2) MF-L1, based on L1 penalties; and (3) a hybrid approach combining locally adapted L2 and global L1 penalties. Each method’s regularization parameters are determined automatically according to the Balancing and Uniform Penalty principles. Our contributions include the implementation and experimental validation of the MF-UPen and MF-MUPen algorithms, and the development of a “dispersion analysis” technique to assess the existence range of the estimated parameters. The objective of this work is to delineate the variance in fit quality and correlation time distribution yielded by each algorithm, thus broadening the set of software tools for the analysis of sample structures in FFC-NMR studies. The findings underline the efficacy and applicability of these algorithms in the analysis of NMRD profiles from samples representing different potential scenarios.","PeriodicalId":503381,"journal":{"name":"Computers","volume":"48 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141102965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Improved Ensemble-Based Cardiovascular Disease Detection System with Chi-Square Feature Selection","authors":"A. E. Korial, I. I. Gorial, Amjad J. Humaidi","doi":"10.3390/computers13060126","DOIUrl":"https://doi.org/10.3390/computers13060126","url":null,"abstract":"Cardiovascular disease (CVD) is a leading cause of death globally; therefore, early detection of CVD is crucial. Many intelligent technologies, including deep learning and machine learning (ML), are being integrated into healthcare systems for disease prediction. This paper uses a voting ensemble ML with chi-square feature selection to detect CVD early. Our approach involved applying multiple ML classifiers, including naïve Bayes, random forest, logistic regression (LR), and k-nearest neighbor. These classifiers were evaluated through metrics including accuracy, specificity, sensitivity, F1-score, confusion matrix, and area under the curve (AUC). We created an ensemble model by combining predictions from the different ML classifiers through a voting mechanism, whose performance was then measured against individual classifiers. Furthermore, we applied chi-square feature selection method to the 303 records across 13 clinical features in the Cleveland cardiac disease dataset to identify the 5 most important features. This approach improved the overall accuracy of our ensemble model and reduced the computational load considerably by more than 50%. Demonstrating superior effectiveness, our voting ensemble model achieved a remarkable accuracy of 92.11%, representing an average improvement of 2.95% over the single highest classifier (LR). These results indicate the ensemble method as a viable and practical approach to improve the accuracy of CVD prediction.","PeriodicalId":503381,"journal":{"name":"Computers","volume":"6 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141112204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating Color-Blind User-Interface Accessibility via Simulated Interfaces","authors":"Amaan Jamil, Gyorgy Denes","doi":"10.3390/computers13020053","DOIUrl":"https://doi.org/10.3390/computers13020053","url":null,"abstract":"Over 300 million people who live with color vision deficiency (CVD) have a decreased ability to distinguish between colors, limiting their ability to interact with websites and software packages. User-interface designers have taken various approaches to tackle the issue, with most offering a high-contrast mode. The Web Content Accessibility Guidelines (WCAG) outline some best practices for maintaining accessibility that have been adopted and recommended by several governments; however, it is currently uncertain how this impacts perceived user functionality and if this could result in a reduced aesthetic look. In the absence of subjective data, we aim to investigate how a CVD observer might rate the functionality and aesthetics of existing UIs. However, the design of a comparative study of CVD vs. non-CVD populations is inherently hard; therefore, we build on the successful field of physiologically based CVD models and propose a novel simulation-based experimental protocol, where non-CVD observers rate the relative aesthetics and functionality of screenshots of 20 popular websites as seen in full color vs. with simulated CVD. Our results show that relative aesthetics and functionality correlate positively and that an operating-system-wide high-contrast mode can reduce both aesthetics and functionality. While our results are only valid in the context of simulated CVD screenshots, the approach has the benefit of being easily deployable, and can help to spot a number of common pitfalls in production. Finally, we propose a AAA–A classification of the interfaces we analyzed.","PeriodicalId":503381,"journal":{"name":"Computers","volume":"75 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139959837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable Software Defect Prediction from Project Effort and Static Code Metrics","authors":"Susmita Haldar, L. F. Capretz","doi":"10.3390/computers13020052","DOIUrl":"https://doi.org/10.3390/computers13020052","url":null,"abstract":"Software defect prediction models enable test managers to predict defect-prone modules and assist with delivering quality products. A test manager would be willing to identify the attributes that can influence defect prediction and should be able to trust the model outcomes. The objective of this research is to create software defect prediction models with a focus on interpretability. Additionally, it aims to investigate the impact of size, complexity, and other source code metrics on the prediction of software defects. This research also assesses the reliability of cross-project defect prediction. Well-known machine learning techniques, such as support vector machines, k-nearest neighbors, random forest classifiers, and artificial neural networks, were applied to publicly available PROMISE datasets. The interpretability of this approach was demonstrated by SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) techniques. The developed interpretable software defect prediction models showed reliability on independent and cross-project data. Finally, the results demonstrate that static code metrics can contribute to the defect prediction models, and the inclusion of explainability assists in establishing trust in the developed models.","PeriodicalId":503381,"journal":{"name":"Computers","volume":"33 29","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Achieving Better Energy Efficiency in Volume Analysis and Direct Volume Rendering Descriptor Computation","authors":"Jacob D. Hauenstein, Timothy S. Newman","doi":"10.3390/computers13020051","DOIUrl":"https://doi.org/10.3390/computers13020051","url":null,"abstract":"Approaches aimed at achieving improved energy efficiency for determination of descriptors—used in volumetric data analysis and one common mode of scientific visualisation—in one x86-class setting are described and evaluated. These approaches are evaluated against standard approaches for the computational setting. In all, six approaches for improved efficiency are considered. Four of them are computation-based. The other two are memory-based. The descriptors are classic gradient and curvature descriptors. In addition to their use in volume analyses, they are used in the classic ray-casting-based direct volume rendering (DVR), which is a particular application area of interest here. An ideal combination of the described approaches applied to gradient descriptor determination allowed them to to be computed with only 80% of the energy of a standard approach in the computational setting; energy efficiency was improved by a factor of 1.2. For curvature descriptor determination, the ideal combination of described approaches achieved a factor-of-two improvement in energy efficiency.","PeriodicalId":503381,"journal":{"name":"Computers","volume":"313 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139841412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}