Journal Description
Information
Information
is a scientific, peer-reviewed, open access journal of information science and technology, data, knowledge, and communication, and is published monthly online by MDPI. The International Society for Information Studies (IS4SI) is affiliated with Information and its members receive discounts on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), Ei Compendex, dblp, and other databases.
- Journal Rank: CiteScore - Q2 (Information Systems)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.1 (2022);
5-Year Impact Factor:
2.9 (2022)
Latest Articles
Production Scheduling Based on a Multi-Agent System and Digital Twin: A Bicycle Industry Case
Information 2024, 15(6), 337; https://doi.org/10.3390/info15060337 (registering DOI) - 6 Jun 2024
Abstract
The emerging digitalization in today’s industrial environments allows manufacturers to store online knowledge about production and use it to make better informed management decisions. This paper proposes a multi-agent framework enhanced with digital twin (DT) for production scheduling and optimization. Decentralized scheduling agents
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The emerging digitalization in today’s industrial environments allows manufacturers to store online knowledge about production and use it to make better informed management decisions. This paper proposes a multi-agent framework enhanced with digital twin (DT) for production scheduling and optimization. Decentralized scheduling agents interact to efficiently manage the work allocation in different segments of production. A DT is used to evaluate the performance of different scheduling decisions and to avoid potential risks and bottlenecks. Production managers can supervise the system’s decision-making processes and manually regulate them online. The multi-agent system (MAS) uses asset administration shells (AASs) for data modelling and communication, enabling interoperability and scalability. The framework was deployed and tested in an industrial pilot coming from the bicycle production industry, optimizing and controlling the short-term production schedule of the different departments. The evaluation resulted in a higher production rate, thus achieving higher production volume in a shorter time span. Managers were also able to coordinate schedules from different departments in a dynamic way and achieve early bottleneck detection.
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(This article belongs to the Special Issue Intelligent Agent and Multi-Agent System)
Open AccessArticle
Uncertainty-Driven Data Aggregation for Imitation Learning in Autonomous Vehicles
by
Changquan Wang and Yun Wang
Information 2024, 15(6), 336; https://doi.org/10.3390/info15060336 - 6 Jun 2024
Abstract
Imitation learning has shown promise for autonomous driving, but suffers from covariate shift, where the policy performs poorly in unseen environments. DAgger is a popular approach that addresses this by leveraging expert demonstrations. However, DAgger’s frequent visits to sub-optimal states can lead to
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Imitation learning has shown promise for autonomous driving, but suffers from covariate shift, where the policy performs poorly in unseen environments. DAgger is a popular approach that addresses this by leveraging expert demonstrations. However, DAgger’s frequent visits to sub-optimal states can lead to several challenges. This paper proposes a novel DAgger framework that integrates Bayesian uncertainty estimation via mean field variational inference (MFVI) to address this issue. MFVI provides better-calibrated uncertainty estimates compared to prior methods. During training, the framework identifies both uncertain and critical states, querying the expert only for these states. This targeted data collection reduces the burden on the expert and improves data efficiency. Evaluations on the CARLA simulator demonstrate that our approach outperforms existing methods, highlighting the effectiveness of Bayesian uncertainty estimation and targeted data aggregation for imitation learning in autonomous driving.
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(This article belongs to the Special Issue Emerging Research in Urban Computing and Intelligent Transport Systems)
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Open AccessArticle
Dynamic Workload Management System in the Public Sector
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Konstantinos C. Giotopoulos, Dimitrios Michalopoulos, Gerasimos Vonitsanos, Dimitris Papadopoulos, Ioanna Giannoukou and Spyros Sioutas
Information 2024, 15(6), 335; https://doi.org/10.3390/info15060335 - 6 Jun 2024
Abstract
Workload management is a cornerstone of contemporary human resource management with widespread applications in private and public sectors. The challenges in human resource management are particularly pronounced within the public sector: particularly in task allocation. The absence of a standardized workload distribution method
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Workload management is a cornerstone of contemporary human resource management with widespread applications in private and public sectors. The challenges in human resource management are particularly pronounced within the public sector: particularly in task allocation. The absence of a standardized workload distribution method presents a significant challenge and results in unnecessary costs in terms of man-hours and financial resources expended on surplus human resource utilization. In the current research, we analyze how to deal with the “race condition” above and propose a dynamic workload management model based on the response time required to implement each task. Our model is trained and tested using comprehensive employee data comprising 450 records for training, 100 records for testing, and 88 records for validation. Approximately 11% of the initial data are deemed either inaccurate or invalid. The deployment of the ANFIS algorithm provides a quantified capability for each employee to handle tasks in the public sector. The proposed idea is deployed in a virtualized platform where each employee is implemented as an independent node with specific capabilities. An upper limit of work acceptance is proposed based on a documented study and laws that suggest work time frames in each public body, ensuring that no employee reaches the saturation level of exhaustion. In addition, a variant of the “slow start” model is incorporated as a hybrid congestion control mechanism with exceptional outcomes, offering a gradual execution window for each node under test and providing a smooth and controlled start-up phase for new connections. The ultimate goal is to identify and outline the entire structure of the Greek public sector along with the capabilities of its employees, thereby determining the organization’s executive capacity.
Full article
(This article belongs to the Special Issue Information for Business and Management–Software Development for Data Processing and Management, 2nd Edition)
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Open AccessSystematic Review
Network Structure of Online Customer Reviews and Online Hotel Reviews: A Systematic Literature Review
by
Maria Helena Pestana, Manuel Gageiro, José António C. Santos and Margarida Custódio Santos
Information 2024, 15(6), 334; https://doi.org/10.3390/info15060334 - 6 Jun 2024
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This study conducts a bibliometric analysis of online customer and hotel review research, aiming to provide insights into where each field comes from, stands now and ought to go in the future. In particular, this study examines how the existing research on online
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This study conducts a bibliometric analysis of online customer and hotel review research, aiming to provide insights into where each field comes from, stands now and ought to go in the future. In particular, this study examines how the existing research on online customer reviews can benefit future hotel review research. Data collected from Web-of-Science and Scopus created an expanded network of 797 core articles and 19,374 citations to identify intellectual structures, developing trends, and future research gaps. This study offers a visual overview of journals, institutions, countries, research themes and authors to assess the overall directions hotels can take. It underscores the necessity for rigorous and relevant research amid the proliferation of online reviews and emphasises the imperative for academia to bridge the gap between theoretical insights and practical applications within the dynamic tourism industry. This study provides researchers and industry professionals with useful tools to understand and deal with the complexities of online reviews. It also highlights the important role these reviews play in shaping the future of tourism strategies.
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Open AccessArticle
Measuring Potential People’s Acceptance of Mobility as a Service: Evidence from Pilot Surveys
by
Corrado Rindone and Antonino Vitetta
Information 2024, 15(6), 333; https://doi.org/10.3390/info15060333 - 6 Jun 2024
Abstract
Sustainable mobility is one of the main challenges on a global level. In this context, the emerging Mobility as a Service (MaaS) plays an important role in the mobility of people. This paper investigates the main enabling factors for implementing the MaaS paradigm,
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Sustainable mobility is one of the main challenges on a global level. In this context, the emerging Mobility as a Service (MaaS) plays an important role in the mobility of people. This paper investigates the main enabling factors for implementing the MaaS paradigm, with a specific focus on the level of acceptance of this new technology. To achieve this objective, the proposed methodology for measuring the potential MaaS acceptance is based on a set of pilot surveys. The methodology integrates motivational surveys with Stated and Revealed Preference (SP, RP) and Technology Acceptance Models (TAM). The collected data are processed to obtain indicators that measure the potential level of MaaS acceptance. The main results of the two pilot experiments are illustrated by referring to urban and extra-urban mobility with or without physical barriers. The results obtained show that the level of MaaS acceptance grows with the increase in generalized transport costs perceived by the users.
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(This article belongs to the Special Issue Mobility as a Service: Opportunities and Challenges for the Sustainable Mobility)
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Open AccessReview
A Survey of Text-Matching Techniques
by
Peng Jiang and Xiaodong Cai
Information 2024, 15(6), 332; https://doi.org/10.3390/info15060332 - 5 Jun 2024
Abstract
Text matching, as a core technology of natural language processing, plays a key role in tasks such as question-and-answer systems and information retrieval. In recent years, the development of neural networks, attention mechanisms, and large-scale language models has significantly contributed to the advancement
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Text matching, as a core technology of natural language processing, plays a key role in tasks such as question-and-answer systems and information retrieval. In recent years, the development of neural networks, attention mechanisms, and large-scale language models has significantly contributed to the advancement of text-matching technology. However, the rapid development of the field also poses challenges in fully understanding the overall impact of these technological improvements. This paper aims to provide a concise, yet in-depth, overview of the field of text matching, sorting out the main ideas, problems, and solutions for text-matching methods based on statistical methods and neural networks, as well as delving into matching methods based on large-scale language models, and discussing the related configurations, API applications, datasets, and evaluation methods. In addition, this paper outlines the applications and classifications of text matching in specific domains and discusses the current open problems that are being faced and future research directions, to provide useful references for further developments in the field.
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(This article belongs to the Special Issue Applications of Information Extraction, Knowledge Graphs, and Large Language Models)
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Open AccessArticle
A Comparison of Mixed and Partial Membership Diagnostic Classification Models with Multidimensional Item Response Models
by
Alexander Robitzsch
Information 2024, 15(6), 331; https://doi.org/10.3390/info15060331 - 5 Jun 2024
Abstract
Diagnostic classification models (DCM) are latent structure models with discrete multivariate latent variables. Recently, extensions of DCMs to mixed membership have been proposed. In this article, ordinary DCMs, mixed and partial membership models, and multidimensional item response theory (IRT) models are compared through
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Diagnostic classification models (DCM) are latent structure models with discrete multivariate latent variables. Recently, extensions of DCMs to mixed membership have been proposed. In this article, ordinary DCMs, mixed and partial membership models, and multidimensional item response theory (IRT) models are compared through analytical derivations, three example datasets, and a simulation study. It is concluded that partial membership DCMs are similar, if not structurally equivalent, to sufficiently complex multidimensional IRT models.
Full article
(This article belongs to the Special Issue Second Edition of Predictive Analytics and Data Science)
Open AccessArticle
Model and Implementation of a Novel Heat-Powered Battery-Less IIoT Architecture for Predictive Industrial Maintenance
by
Raúl Aragonés, Joan Oliver, Roger Malet, Maria Oliver-Parera and Carles Ferrer
Information 2024, 15(6), 330; https://doi.org/10.3390/info15060330 - 5 Jun 2024
Abstract
The research and management of Industry 4.0 increasingly relies on accurate real-time quality data to apply efficient algorithms for predictive maintenance. Currently, Low-Power Wide-Area Networks (LPWANs) offer potential advantages in monitoring tasks for predictive maintenance. However, their applicability requires improvements in aspects such
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The research and management of Industry 4.0 increasingly relies on accurate real-time quality data to apply efficient algorithms for predictive maintenance. Currently, Low-Power Wide-Area Networks (LPWANs) offer potential advantages in monitoring tasks for predictive maintenance. However, their applicability requires improvements in aspects such as energy consumption, transmission range, data rate and constant quality of service. Commonly used battery-operated IIoT devices have several limitations in their adoption in large facilities or heat-intensive industries (iron and steel, cement, etc.). In these cases, the self-heating nodes together with the appropriate low-power processing platform and industrial sensors are aligned with the requirements and real-time criteria required for industrial monitoring. From an environmental point of view, the carbon footprint associated with human activity leads to a steady rise in global average temperature. Most of the gases emitted into the atmosphere are due to these heat-intensive industries. In fact, much of the energy consumed by industries is dissipated in the form of waste heat. With this scenario, it makes sense to build heat transformation collection systems as guarantors of battery-free self-powered IIoT devices. Thermal energy harvesters work on the physical basis of the Seebeck effect. In this way, this paper gathers the methodology that standardizes the modelling and simulation of waste heat recovery systems for IoT nodes, gathering energy from any hot surface, such as a pipe or chimney. The statistical analysis is carried out with the data obtained from two different IoT architectures showing a good correlation between model simulation and prototype behaviour. Additionally, the selected model will be coupled to a low-power processing platform with LoRaWAN connectivity to demonstrate its effectiveness and self-powering ability in a real industrial environment.
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(This article belongs to the Special Issue Internet of Things and Cloud-Fog-Edge Computing)
Open AccessArticle
Social CRM Strategies: A Key Driver of Strategic Information Exchange Capabilities and Relationship Quality
by
Ibrahim A. Elshaer, Alaa M. S. Azazz, Hala A. S. Elsaadany and Ahmed K. Elnagar
Information 2024, 15(6), 329; https://doi.org/10.3390/info15060329 - 5 Jun 2024
Abstract
This study aims to examine the influence of social customer relationship management (CRM) on relationship quality (RQ); the role of strategic information exchange capabilities (SIECs) as a mediator on the relationship between dimensions of social CRM and RQ was also investigated. A self-structured
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This study aims to examine the influence of social customer relationship management (CRM) on relationship quality (RQ); the role of strategic information exchange capabilities (SIECs) as a mediator on the relationship between dimensions of social CRM and RQ was also investigated. A self-structured questionnaire survey was conducted on the subordinates working at various family-style restaurants in Egypt. Following a simple random sampling procedure, 466 valid responses were used for data analysis. The findings reveal that three dimensions of social CRM, namely customer service quality (CSQ), integrated marketing channels (IMCs), and online communities (OCs), have statistically significant effects on RQ. Moreover, SIECs mediate the relationship between CS/IMCs/OCs and RQ. The other two dimensions, rewards (RDs) and value-added services (VSs), do not directly or indirectly affect RQ. This study opens new avenues in the existing literature by identifying the most relevant factors affecting RQ in the context of Egyptian restaurants. This study can enable policymakers and restaurant owners to formulate social CRM strategies and achieve customer satisfaction properly. This study explores the mediation mechanism of SIECs on the relationship between dimensions of social CRM and RQ.
Full article
(This article belongs to the Special Issue Knowledge Management, Digital Trust, and Corporate Social Responsibility in the Era of Social Media II)
Open AccessArticle
Evaluating Large Language Models for Structured Science Summarization in the Open Research Knowledge Graph
by
Vladyslav Nechakhin, Jennifer D’Souza and Steffen Eger
Information 2024, 15(6), 328; https://doi.org/10.3390/info15060328 - 5 Jun 2024
Abstract
Structured science summaries or research contributions using properties or dimensions beyond traditional keywords enhance science findability. Current methods, such as those used by the Open Research Knowledge Graph (ORKG), involve manually curating properties to describe research papers’ contributions in a structured manner, but
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Structured science summaries or research contributions using properties or dimensions beyond traditional keywords enhance science findability. Current methods, such as those used by the Open Research Knowledge Graph (ORKG), involve manually curating properties to describe research papers’ contributions in a structured manner, but this is labor-intensive and inconsistent among human domain-expert curators. We propose using Large Language Models (LLMs) to automatically suggest these properties. However, it is essential to assess the readiness of LLMs like GPT-3.5, Llama 2, and Mistral for this task before their application. Our study performs a comprehensive comparative analysis between the ORKG’s manually curated properties and those generated by the aforementioned state-of-the-art LLMs. We evaluate LLM performance from four unique perspectives: semantic alignment with and deviation from ORKG properties, fine-grained property mapping accuracy, SciNCL embedding-based cosine similarity, and expert surveys comparing manual annotations with LLM outputs. These evaluations occur within a multidisciplinary science setting. Overall, LLMs show potential as recommendation systems for structuring science, but further fine-tuning is recommended to improve their alignment with scientific tasks and mimicry of human expertise.
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(This article belongs to the Special Issue Information Extraction and Language Discourse Processing)
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Open AccessArticle
An Interactive Pedagogical Tool for Simulation of Controlled Rectifiers
by
Filipe Carvalho, Rui Chibante and Carlos Vaz de Carvalho
Information 2024, 15(6), 327; https://doi.org/10.3390/info15060327 - 4 Jun 2024
Abstract
Active learning approaches, incorporating student engagement through experimentation and problem solving, effectively foster higher-level thinking abilities and enhance academic performance. Interactive tools like simulators align with these methodologies, but commercially available simulators have limitations; particularly, their high cost and lack of customization features
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Active learning approaches, incorporating student engagement through experimentation and problem solving, effectively foster higher-level thinking abilities and enhance academic performance. Interactive tools like simulators align with these methodologies, but commercially available simulators have limitations; particularly, their high cost and lack of customization features pose significant challenges for many educational institutions. This article presents CORES, a web-based educational application designed to simulate controlled rectifier circuits. CORES eliminates the need for intricate circuit assembly and software installation by providing pre-built circuits so that users can concentrate on analyzing circuit behavior by manipulating the thyristor firing angle and load characteristics, while the application generates output voltage and current waveforms under steady-state conditions, minimizing computation time. CORES has proven to be a valuable pedagogical tool, surpassing commercial simulators in terms of accessibility, ease of use, and enriched learning experiences for power electronics students and educators.
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(This article belongs to the Special Issue Technology, Learning and Teaching of Electronics with Information Applications)
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Open AccessArticle
A Comparison of Bias Mitigation Techniques for Educational Classification Tasks Using Supervised Machine Learning
by
Tarid Wongvorachan, Okan Bulut, Joyce Xinle Liu and Elisabetta Mazzullo
Information 2024, 15(6), 326; https://doi.org/10.3390/info15060326 - 4 Jun 2024
Abstract
Machine learning (ML) has become integral in educational decision-making through technologies such as learning analytics and educational data mining. However, the adoption of machine learning-driven tools without scrutiny risks perpetuating biases. Despite ongoing efforts to tackle fairness issues, their application to educational datasets
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Machine learning (ML) has become integral in educational decision-making through technologies such as learning analytics and educational data mining. However, the adoption of machine learning-driven tools without scrutiny risks perpetuating biases. Despite ongoing efforts to tackle fairness issues, their application to educational datasets remains limited. To address the mentioned gap in the literature, this research evaluates the effectiveness of four bias mitigation techniques in an educational dataset aiming at predicting students’ dropout rate. The overarching research question is: “How effective are the techniques of reweighting, resampling, and Reject Option-based Classification (ROC) pivoting in mitigating the predictive bias associated with high school dropout rates in the HSLS:09 dataset?" The effectiveness of these techniques was assessed based on performance metrics including false positive rate (FPR), accuracy, and F1 score. The study focused on the biological sex of students as the protected attribute. The reweighting technique was found to be ineffective, showing results identical to the baseline condition. Both uniform and preferential resampling techniques significantly reduced predictive bias, especially in the FPR metric but at the cost of reduced accuracy and F1 scores. The ROC pivot technique marginally reduced predictive bias while maintaining the original performance of the classifier, emerging as the optimal method for the HSLS:09 dataset. This research extends the understanding of bias mitigation in educational contexts, demonstrating practical applications of various techniques and providing insights for educators and policymakers. By focusing on an educational dataset, it contributes novel insights beyond the commonly studied datasets, highlighting the importance of context-specific approaches in bias mitigation.
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(This article belongs to the Special Issue Real-World Applications of Machine Learning Techniques)
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Open AccessReview
Generative AI, Research Ethics, and Higher Education Research: Insights from a Scientometric Analysis
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Saba Mansoor Qadhi, Ahmed Alduais, Youmen Chaaban and Majeda Khraisheh
Information 2024, 15(6), 325; https://doi.org/10.3390/info15060325 - 2 Jun 2024
Abstract
In the digital age, the intersection of artificial intelligence (AI) and higher education (HE) poses novel ethical considerations, necessitating a comprehensive exploration of this multifaceted relationship. This study aims to quantify and characterize the current research trends and critically assess the discourse on
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In the digital age, the intersection of artificial intelligence (AI) and higher education (HE) poses novel ethical considerations, necessitating a comprehensive exploration of this multifaceted relationship. This study aims to quantify and characterize the current research trends and critically assess the discourse on ethical AI applications within HE. Employing a mixed-methods design, we integrated quantitative data from the Web of Science, Scopus, and the Lens databases with qualitative insights from selected studies to perform scientometric and content analyses, yielding a nuanced landscape of AI utilization in HE. Our results identified vital research areas through citation bursts, keyword co-occurrence, and thematic clusters. We provided a conceptual model for ethical AI integration in HE, encapsulating dichotomous perspectives on AI’s role in education. Three thematic clusters were identified: ethical frameworks and policy development, academic integrity and content creation, and student interaction with AI. The study concludes that, while AI offers substantial benefits for educational advancement, it also brings challenges that necessitate vigilant governance to uphold academic integrity and ethical standards. The implications extend to policymakers, educators, and AI developers, highlighting the need for ethical guidelines, AI literacy, and human-centered AI tools.
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(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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Open AccessArticle
Integrating Edge-Intelligence in AUV for Real-Time Fish Hotspot Identification and Fish Species Classification
by
U. Sowmmiya, J. Preetha Roselyn and Prabha Sundaravadivel
Information 2024, 15(6), 324; https://doi.org/10.3390/info15060324 - 31 May 2024
Abstract
Enhancing the livelihood environment for fishermen’s communities with the rapid technological growth is essential in the marine sector. Among the various issues in the fishing industry, fishing zone identification and fish catch detection play a significant role in the fishing community. In this
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Enhancing the livelihood environment for fishermen’s communities with the rapid technological growth is essential in the marine sector. Among the various issues in the fishing industry, fishing zone identification and fish catch detection play a significant role in the fishing community. In this work, the automated prediction of potential fishing zones and classification of fish species in an aquatic environment through machine learning algorithms is developed and implemented. A prototype of the boat structure is designed and developed with lightweight wooden material encompassing all necessary sensors and cameras. The functions of the unmanned boat (FishID-AUV) are based on the user’s control through a user-friendly mobile/web application (APP). The different features impacting the identification of hotspots are considered, and feature selection is performed using various classifier-based learning algorithms, namely, Naive Bayes, Nearest neighbors, Random Forest and Support Vector Machine (SVM). The performance of classifications are compared. From the real-time results, it is clear that the Naive Bayes classification model is found to provide better accuracy, which is employed in the application platform for predicting the potential fishing zone. After identifying the first catch, the species are classified using an AlexNet-based deep Convolutional Neural Network. Also, the user can fetch real-time information such as the status of fishing through live video streaming to determine the quality and quantity of fish along with information like pH, temperature and humidity. The proposed work is implemented in a real-time boat structure prototype and is validated with data from sensors and satellites.
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(This article belongs to the Special Issue Artificial Intelligence on the Edge)
Open AccessArticle
Architectural Framework to Enhance Image-Based Vehicle Positioning for Advanced Functionalities
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Iosif-Alin Beti, Paul-Corneliu Herghelegiu and Constantin-Florin Caruntu
Information 2024, 15(6), 323; https://doi.org/10.3390/info15060323 - 31 May 2024
Abstract
The growing number of vehicles on the roads has resulted in several challenges, including increased accident rates, fuel consumption, pollution, travel time, and driving stress. However, recent advancements in intelligent vehicle technologies, such as sensors and communication networks, have the potential to revolutionize
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The growing number of vehicles on the roads has resulted in several challenges, including increased accident rates, fuel consumption, pollution, travel time, and driving stress. However, recent advancements in intelligent vehicle technologies, such as sensors and communication networks, have the potential to revolutionize road traffic and address these challenges. In particular, the concept of platooning for autonomous vehicles, where they travel in groups at high speeds with minimal distances between them, has been proposed to enhance the efficiency of road traffic. To achieve this, it is essential to determine the precise position of vehicles relative to each other. Global positioning system (GPS) devices have an intended positioning error that might increase due to various conditions, e.g., the number of available satellites, nearby buildings, trees, driving into tunnels, etc., making it difficult to compute the exact relative position between two vehicles. To address this challenge, this paper proposes a new architectural framework to improve positioning accuracy using images captured by onboard cameras. It presents a novel algorithm and performance results for vehicle positioning based on GPS and video data. This approach is decentralized, meaning that each vehicle has its own camera and computing unit and communicates with nearby vehicles.
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(This article belongs to the Special Issue Modeling, Design, Analysis and Management of Embedded Control Systems for Automated Driving)
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Open AccessArticle
Prediction of Disk Failure Based on Classification Intensity Resampling
by
Sheng Wu and Jihong Guan
Information 2024, 15(6), 322; https://doi.org/10.3390/info15060322 - 31 May 2024
Abstract
With the rapid growth of the data scale in data centers, the high reliability of storage is facing various challenges. Specifically, hardware failures such as disk faults occur frequently, causing serious system availability issues. In this context, hardware fault prediction based on AI
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With the rapid growth of the data scale in data centers, the high reliability of storage is facing various challenges. Specifically, hardware failures such as disk faults occur frequently, causing serious system availability issues. In this context, hardware fault prediction based on AI and big data technologies has become a research hotspot, aiming to guide operation and maintenance personnel to implement preventive replacement through accurate prediction to reduce hardware failure rates. However, existing methods still have weaknesses in terms of accuracy due to the impacts of data quality issues such as the sample imbalance. This article proposes a disk fault prediction method based on classification intensity resampling, which fills the gap between the degree of data imbalance and the actual classification intensity of the task by introducing a base classifier to calculate the classification intensity, thus better preserving the data features of the original dataset. In addition, using ensemble learning methods such as random forests, combined with resampling, an integrated classifier for imbalanced data is developed to further improve the prediction accuracy. Experimental verification shows that compared with traditional methods, the F1-score of disk fault prediction is improved by 6%, and the model training time is also greatly reduced. The fault prediction method proposed in this paper has been applied to approximately 80 disk drives and nearly 40,000 disks in the production environment of a large bank’s data center to guide preventive replacements. Compared to traditional methods, the number of preventive replacements based on our method has decreased by approximately 21%, while the overall disk failure rate remains unchanged, thus demonstrating the effectiveness of our method.
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(This article belongs to the Special Issue Machine Learning Approaches for Imbalanced Domains: Emerging Trends and Applications)
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Open AccessArticle
Research on Facial Expression Recognition Algorithm Based on Lightweight Transformer
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Bin Jiang, Nanxing Li, Xiaomei Cui, Weihua Liu, Zeqi Yu and Yongheng Xie
Information 2024, 15(6), 321; https://doi.org/10.3390/info15060321 - 31 May 2024
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To avoid the overfitting problem of the network model and improve the facial expression recognition effect of partially occluded facial images, an improved facial expression recognition algorithm based on MobileViT has been proposed. Firstly, in order to obtain features that are useful and
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To avoid the overfitting problem of the network model and improve the facial expression recognition effect of partially occluded facial images, an improved facial expression recognition algorithm based on MobileViT has been proposed. Firstly, in order to obtain features that are useful and richer for experiments, deep convolution operations are added to the inverted residual blocks of this network, thus improving the facial expression recognition rate. Then, in the process of dimension reduction, the activation function can significantly improve the convergence speed of the model, and then quickly reduce the loss error in the training process, as well as to preserve the effective facial expression features as much as possible and reduce the overfitting problem. Experimental results on RaFD, FER2013, and FER2013Plus show that this method has significant advantages over mainstream networks and the network achieves the highest recognition rate.
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Open AccessEditorial
An Editorial for the Special Issue “Pervasive Computing in IoT”
by
Spyros Panagiotakis and Evangelos K. Markakis
Information 2024, 15(6), 320; https://doi.org/10.3390/info15060320 - 30 May 2024
Abstract
In the era of Internet of Things (IoT) we have entered, the “Monitoring–Decision–Execution” cycle of typical autonomic and automation systems is extended, so it includes distributed developments that might scale from a smart home or greenhouse to a smart city and from autonomous
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In the era of Internet of Things (IoT) we have entered, the “Monitoring–Decision–Execution” cycle of typical autonomic and automation systems is extended, so it includes distributed developments that might scale from a smart home or greenhouse to a smart city and from autonomous driving to emergency management [...]
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(This article belongs to the Special Issue Pervasive Computing in IoT)
Open AccessArticle
A Multimethod Approach for Healthcare Information Sharing Systems: Text Analysis and Empirical Data
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Amit Malhan, Robert Pavur, Lou E. Pelton and Ava Hajian
Information 2024, 15(6), 319; https://doi.org/10.3390/info15060319 - 29 May 2024
Abstract
This paper provides empirical evidence using two studies to explain the primary factors facilitating electronic health record (EHR) systems adoption through the lens of the resource advantage theory. We aim to address the following research questions: What are the main organizational antecedents of
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This paper provides empirical evidence using two studies to explain the primary factors facilitating electronic health record (EHR) systems adoption through the lens of the resource advantage theory. We aim to address the following research questions: What are the main organizational antecedents of EHR implementation? What is the role of monitoring in EHR system implementation? What are the current themes and people’s attitudes toward EHR systems? This paper includes two empirical studies. Study 1 presents a research model based on data collected from four different archival datasets. Drawing upon the resource advantage theory, this paper uses archival data from 200 Texas hospitals, thus mitigating potential response bias and enhancing the validity of the findings. Study 2 includes a text analysis of 5154 textual data, sentiment analysis, and topic modeling. Study 1’s findings reveal that joint ventures and ownership are the two main enablers of adopting EHR systems in 200 Texas hospitals. Moreover, the results offer a moderating role of monitoring in strengthening the relationship between joint-venture capability and the implementation of EHR systems. Study 2’s results indicate a positive attitude toward EHR systems. The U.S. was unique in the sample due to its slower adoption of EHR systems than other developed countries. Physician burnout also emerged as a significant concern in the context of EHR adoption. Topic modeling identified three themes: training, healthcare interoperability, and organizational barriers. In a multimethod design, this paper contributes to prior work by offering two new EHR antecedents: hospital ownership and joint-venture capability. Moreover, this paper suggests that the monitoring mechanism moderates the adoption of EHR systems in Texas hospitals. Moreover, this paper contributes to prior EHR works by performing text analysis of textual data to carry out sentiment analysis and topic modeling.
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(This article belongs to the Special Issue Information Systems in Healthcare)
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Open AccessArticle
GRAAL: Graph-Based Retrieval for Collecting Related Passages across Multiple Documents
by
Misael Mongiovì and Aldo Gangemi
Information 2024, 15(6), 318; https://doi.org/10.3390/info15060318 - 29 May 2024
Abstract
Finding passages related to a sentence over a large collection of text documents is a fundamental task for claim verification and open-domain question answering. For instance, a common approach for verifying a claim is to extract short snippets of relevant text from a
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Finding passages related to a sentence over a large collection of text documents is a fundamental task for claim verification and open-domain question answering. For instance, a common approach for verifying a claim is to extract short snippets of relevant text from a collection of reference documents and provide them as input to a natural language inference machine that determines whether the claim can be deduced or refuted. Available approaches struggle when several pieces of evidence from different documents need to be combined to make an inference, as individual documents often have a low relevance with the input and are therefore excluded. We propose GRAAL (GRAph-based retrievAL), a novel graph-based approach that outlines the relevant evidence as a subgraph of a large graph that summarizes the whole corpus. We assess the validity of this approach by building a large graph that represents co-occurring entity mentions on a corpus of Wikipedia pages and using this graph to identify candidate text relevant to a claim across multiple pages. Our experiments on a subset of FEVER, a popular benchmark, show that the proposed approach is effective in identifying short passages related to a claim from multiple documents.
Full article
(This article belongs to the Special Issue 2nd Edition of Information Retrieval and Social Media Mining)
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