Next Issue
Volume 16, February
Previous Issue
Volume 15, December
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.9
2.4
Journals
Information
Volume 16
Issue 1
share announcement

Information, Volume 16, Issue 1 (January 2025) – 70 articles

Cover Story (view full-size image): The paper presents a distributed UAV management system inspired by virtual circuit and datagram methods in packet-switching networks. By installing houses with wireless devices, UAVs navigate routes in a multi-hop network. UAVs are treated as packets, ground devices are treated as routers, and their connections are treated as links. To optimize connectivity, it is necessary to minimize relay nodes, connecting non-relay nodes to the nearest relay. This study proposes an innovative approach using Multipoint Relay (MPR) from the Optimized Link State Routing Protocol (OLSR), which maintained route construction success rates, lower relay node counts, and shorter route lengths. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
37 pages, 4559 KiB  
Article
Evaluating Mobile Telecom Apps: An Integrated Fuzzy MCDM Model Using Marketing Mix
by Hamzeh Mohammad Alabool
Information 2025, 16(1), 70; https://doi.org/10.3390/info16010070 - 20 Jan 2025
Viewed by 449
Abstract
App-based marketing has been widely used in the telecommunications industry to both serve and draw in new customers. Typically, telecom providers must invest an amount of company resources to develop and maintain the operations mechanism of information technology platforms (e.g., mobile apps); therefore, [...] Read more.
App-based marketing has been widely used in the telecommunications industry to both serve and draw in new customers. Typically, telecom providers must invest an amount of company resources to develop and maintain the operations mechanism of information technology platforms (e.g., mobile apps); therefore, it is important to take the issue of marketing effectiveness into account. For example, the mismatch between what telecom providers offer in their mobile apps and customers’ marketing requirements plays a significant role in determining unmet knowledge and presentation gaps that are related to the marketing domain. This research intends to propose an integrated Fuzzy MCDM model based on 4Ps (Product, Price, Place, Promotion) and 4Cs (Customer Needs, Cost, Convenience, Communication) models for evaluating mobile telecom applications (MTAs). Therefore, the 4Ps and 4Cs models are extended to develop a hierarchy model for evaluating MTAs. Next, fuzzy theory is applied to handle the subjectiveness of qualitative evaluation criteria while the Analytic Hierarchy Process (AHP) is applied to synthesize the weight and score of the evaluation criteria. The proposed model is applied to evaluate, rank, and analyze the MTA of three telecom providers in the Kingdom of Saudi Arabia (KSA) (e.g., STC, Zain, and Mobily). The conducted case study ensures the usability and applicability of the proposed model. The evaluation results offer several managerial actions for achieving ideal app-based marketing. Full article
(This article belongs to the Special Issue Advances in Telecommunication Networks and Wireless Technology)
►▼ Show Figures

Figure 1

16 pages, 2439 KiB  
Article
Improved RPCA Method via Fractional Function-Based Structure and Its Application
by Yong-Ke Pan and Shuang Peng
Information 2025, 16(1), 69; https://doi.org/10.3390/info16010069 - 20 Jan 2025
Viewed by 406
Abstract
With the advancement of oil logging techniques, vast amounts of data have been generated. However, this data often contains significant redundancy and noise. The logging data must be denoised before it is used for oil logging recognition. Hence, this paper proposed an improved [...] Read more.
With the advancement of oil logging techniques, vast amounts of data have been generated. However, this data often contains significant redundancy and noise. The logging data must be denoised before it is used for oil logging recognition. Hence, this paper proposed an improved robust principal component analysis algorithm (IRPCA) for logging data denoising, which addresses the problems of various noises in oil logging data acquisition and the limitations of conventional data processing methods. The IRPCA algorithm enhances both the efficiency of the model and the accuracy of low-rank matrix recovery. This improvement is achieved primarily by introducing the approximate zero norm based on the fractional function structure and by adding weighted kernel parametrization and penalty terms to enhance the model’s capability in handling complex matrices. The efficacy of the proposed IRPCA algorithm has been verified through simulation experiments, demonstrating its superiority over the widely used RPCA algorithm. We then present a denoising method tailored to the characteristics of logging data and based on the IRPCA algorithm. This method first involves the segregation of the original logging data to acquire background and foreground information. The background information is subsequently further separated to isolate the factual background and noise, resulting in the denoised logging data. The results indicate that the IRPCA algorithm is practical and effective when applied to the denoising of actual logging data. Full article
(This article belongs to the Section Artificial Intelligence)
►▼ Show Figures

Figure 1

11 pages, 678 KiB  
Article
An Integrated Approach of Video Game Therapy®: A Case Study
by Maura Crepaldi, Francesco Bocci, Marcello Sarini and Andrea Greco
Information 2025, 16(1), 68; https://doi.org/10.3390/info16010068 - 20 Jan 2025
Viewed by 684
Abstract
International literature in the field of rehabilitation and psychological support is increasingly characterized by the inclusion and use of video games and virtual media, even if the results are controversial. The theoretical basis on which the study presented is based is Video Game [...] Read more.
International literature in the field of rehabilitation and psychological support is increasingly characterized by the inclusion and use of video games and virtual media, even if the results are controversial. The theoretical basis on which the study presented is based is Video Game Therapy®. This approach uses commercial video games, which are generally free or available at a relatively low cost. These games possess many essential functions that make them practical as preventive tools or support for integration into traditional therapies. Video Game Therapy® allows the patient to reflect on emotional containment and cognitive self-regulation to establish a state of mental balance and well-being. It encourages insight and leads the player to reflect on some salient aspects of their character and lifestyle and their emotions and thoughts linked to specific life episodes relived in the game setting. Starting from these premises, the study shows promising results, presenting a single case of a boy with social isolation problems and relational difficulties, in which significant changes were highlighted in the perception, expression, and management of emotions, as well as in metacognition and self-efficacy. Full article
►▼ Show Figures

Graphical abstract

16 pages, 2883 KiB  
Article
Active Hard Sample Learning for Violation Action Recognition in Power Grid Operation
by Lingwen Meng, Di He, Guobang Ban, Guanghui Xi, Anjun Li and Xinshan Zhu
Information 2025, 16(1), 67; https://doi.org/10.3390/info16010067 - 20 Jan 2025
Viewed by 396
Abstract
Power grid operation occurs in complex, dynamic environments where the timely identification of operator violations is essential for safety. Traditional methods often rely on manual supervision and rule-based detection, leading to inefficiencies. Existing deep learning approaches, while powerful, require fully labeled data and [...] Read more.
Power grid operation occurs in complex, dynamic environments where the timely identification of operator violations is essential for safety. Traditional methods often rely on manual supervision and rule-based detection, leading to inefficiencies. Existing deep learning approaches, while powerful, require fully labeled data and long training times, thereby increasing costs. To address these challenges, we propose an active hard sample learning method specifically for the violation action recognition of operators in power grid operation. We design a hard instance sampling module with multi-strategy fusion based on active learning to improve training efficiency. This module identifies hard samples based on the consistency of models or samples, where we develop uncertainty evaluation and the instance discrimination strategy to assess the contributions of samples effectively. We utilize ResNet50 and ViT architectures with Faster-RCNN for detection and recognition, developed using PyTorch 2.0. The dataset comprises 2000 samples, and 30% and 60% labeled data are employed. Experimental results show significant improvements in model performance and training efficiency, demonstrating the method’s effectiveness in complex power grid environments. Our approach enhances safety monitoring and advances active learning and hard sample techniques in practical applications. Full article
►▼ Show Figures

Figure 1

27 pages, 1239 KiB  
Article
Cyber Insurance Adoption and Digitalisation in Small and Medium-Sized Enterprises
by Nazim Taskin, Aslı Özkeleş Yıldırım, Handan Derya Ercan, Martin Wynn and Bilgin Metin
Information 2025, 16(1), 66; https://doi.org/10.3390/info16010066 - 18 Jan 2025
Viewed by 819
Abstract
Digitalisation has significantly increased cybersecurity risks in organisations, notably for small to medium-sized enterprises (SMEs), in which IT departments often have relatively small teams and limited resources. Cyber insurance enables SMEs to navigate cybersecurity risks more economically, providing an essential risk transfer alternative [...] Read more.
Digitalisation has significantly increased cybersecurity risks in organisations, notably for small to medium-sized enterprises (SMEs), in which IT departments often have relatively small teams and limited resources. Cyber insurance enables SMEs to navigate cybersecurity risks more economically, providing an essential risk transfer alternative to costly reduction strategies. This article examines the antecedents, emergence, and application of cyber insurance as a solution to cybersecurity concerns against the backdrop of increasing digitalisation. The research adopts a quantitative deductive approach, with an analysis of relevant literature providing the basis for the development of 12 hypotheses, which are then tested via a survey of 168 SMEs in Turkey. Using the Technology–Organisation–Environment–Individual (TOE-I) model as a top-line conceptual framework, the article finds that cyber insurance policy adoption has facilitated a more rapid and secure digitalisation process and that the mitigation of financial risk associated with cyberattacks has allowed companies to invest more widely in information technologies and systems. The article clearly has its limitations, in that it is based on primary research in one European country, but the authors believe that it nevertheless provides some new insights into the potential benefits of cyber insurance, and the key issues SMEs must consider when considering adopting a cyber insurance policy. The findings will be of practical relevance to SMEs and other organisations reviewing their cybersecurity strategy and are also of relevance to the wider debate around the costs and benefits of digitalisation. Full article
(This article belongs to the Special Issue Feature Papers in Information in 2024–2025)
►▼ Show Figures

Figure 1

23 pages, 863 KiB  
Article
Fine-Grained Arabic Post (Tweet) Geolocation Prediction Using Deep Learning Techniques
by Marwa K. Elteir
Information 2025, 16(1), 65; https://doi.org/10.3390/info16010065 - 18 Jan 2025
Viewed by 445
Abstract
Leveraging Twitter data for crisis management necessitates the accurate, fine-grained geolocation of tweets, which unfortunately is often lacking, with only 1–3% of tweets being geolocated. This work addresses the understudied problem of fine-grained geolocation prediction for Arabic tweets, focusing on the Kingdom of [...] Read more.
Leveraging Twitter data for crisis management necessitates the accurate, fine-grained geolocation of tweets, which unfortunately is often lacking, with only 1–3% of tweets being geolocated. This work addresses the understudied problem of fine-grained geolocation prediction for Arabic tweets, focusing on the Kingdom of Saudi Arabia. The goal is to accurately assign tweets to one of thirteen provinces. Existing approaches for Arabic geolocation are limited in accuracy and often rely on basic machine learning techniques. Additionally, advancements in tweet geolocation for other languages often rely on distinct datasets, hindering direct comparisons and assessments of their relative performance on Arabic datasets. To bridge this gap, we investigate eight advanced deep learning techniques, including two Arabic pretrained language models (PLMs) on one constructed dataset. Through a comprehensive analysis, we assess the strengths and weaknesses of each technique for fine-grained Arabic tweet geolocation. Despite the success of PLMs in various tasks, our results demonstrate that a combination of Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM) layers yields the best performance, achieving a test accuracy of 93.85%. Full article
►▼ Show Figures

Figure 1

16 pages, 1760 KiB  
Article
Multimodal Assessment of Mental Workload During Automated Vehicle Remote Assistance: Modeling of Eye-Tracking-Related, Skin Conductance, and Cardiovascular Indicators
by Fabian Walocha, Andreas Schrank, Hoai Phuong Nguyen and Klas Ihme
Information 2025, 16(1), 64; https://doi.org/10.3390/info16010064 - 17 Jan 2025
Viewed by 347
Abstract
Remote assistance for highly automated vehicles (HAVs), i.e., third-party assistance from support staff outside the vehicle in times of the need for assistance, presents a solution to extend the capabilities of HAVs by integrating a third party for decision making in uncertain situations. [...] Read more.
Remote assistance for highly automated vehicles (HAVs), i.e., third-party assistance from support staff outside the vehicle in times of the need for assistance, presents a solution to extend the capabilities of HAVs by integrating a third party for decision making in uncertain situations. Similar to other control center positions, we expect the remote assistance tasks to exert high mental demands on the human operators. Therefore, we assessed impact of elevated mental workload during HAV remote assistance in a controlled environment in a user study (N = 37) with the goal of identifying cues to differentiate workload levels based on eye-tracking-related, skin conductance, and cardiovascular indicators. The results provide evidence that (A) elevated workload induced via a secondary task depreciates performance, and (B) we can identify workload levels person-independently as differences in tonic skin conductance (F(2,72) = 24.538, p < 0.001, partial η² = 0.405) and pupil dilation (F(2,72) = 13.872, p < 0.001, partial η² = 0.278), resulting in a classification accuracy of 58% in a three-class classification task. The results provide evidence that we are able to differentiate operator workload during remote assistance in a time-resolved way with the ultimate goal to provide adaptations to counteract task deficiencies. Full article
(This article belongs to the Section Information and Communications Technology)
►▼ Show Figures

Figure 1

23 pages, 13406 KiB  
Article
Object Detection Post Processing Accelerator Based on Co-Design of Hardware and Software
by Dengtian Yang, Lan Chen, Xiaoran Hao and Yiheng Zhang
Information 2025, 16(1), 63; https://doi.org/10.3390/info16010063 - 17 Jan 2025
Viewed by 571
Abstract
Deep learning significantly advances object detection. Post processes, a critical component of this process, select valid bounding boxes to represent the true targets during inference and assign boxes and labels to these objects during training to optimize the loss function. However, post processes [...] Read more.
Deep learning significantly advances object detection. Post processes, a critical component of this process, select valid bounding boxes to represent the true targets during inference and assign boxes and labels to these objects during training to optimize the loss function. However, post processes constitute a substantial portion of the total processing time for a single image. This inefficiency primarily arises from the extensive Intersection over Union (IoU) calculations required between numerous redundant bounding boxes in post processing algorithms. To reduce these redundant IoU calculations, we introduce a classification prioritization strategy during both training and inference post processes. Additionally, post processes involve sorting operations that contribute to their inefficiency. To minimize unnecessary comparisons in Top-K sorting, we have improved the bitonic sorter by developing a hybrid bitonic algorithm. These improvements have effectively accelerated the post processing. Given the similarities between the training and inference post processes, we unify four typical post processing algorithms and design a hardware accelerator based on this framework. Our accelerator achieves at least 7.55 times the speed in inference post processing compared to that of recent accelerators. When compared to the RTX 2080 Ti system, our proposed accelerator offers at least 21.93 times the speed for the training post process and 19.89 times for the inference post process, thereby significantly enhancing the efficiency of loss function minimization. Full article
►▼ Show Figures

Figure 1

17 pages, 1146 KiB  
Article
DFCNformer: A Transformer Framework for Non-Stationary Time-Series Forecasting Based on De-Stationary Fourier and Coefficient Network
by Yuxin Jin, Yuhan Mao and Genlang Chen
Information 2025, 16(1), 62; https://doi.org/10.3390/info16010062 - 17 Jan 2025
Viewed by 377
Abstract
Time-series data are widely applied in real-world scenarios, but the non-stationary nature of their statistical properties and joint distributions over time poses challenges for existing forecasting models. To tackle this challenge, this paper introduces a forecasting model called DFCNformer (De-stationary Fourier and Coefficient [...] Read more.
Time-series data are widely applied in real-world scenarios, but the non-stationary nature of their statistical properties and joint distributions over time poses challenges for existing forecasting models. To tackle this challenge, this paper introduces a forecasting model called DFCNformer (De-stationary Fourier and Coefficient Network Transformer), designed to mitigate accuracy degradation caused by non-stationarity in time-series data. The model initially employs a stabilization strategy to unify the statistical characteristics of the input time series, restoring their original features at the output to enhance predictability. Then, a time-series decomposition method splits the data into seasonal and trend components. For the seasonal component, a Transformer-based encoder–decoder architecture with De-stationary Fourier Attention (DSF Attention) captures temporal features, using differentiable attention weights to restore non-stationary information. For the trend component, a multilayer perceptron (MLP) is used for prediction, enhanced by a Dual Coefficient Network (Dual-CONET) that mitigates distributional shifts through learnable distribution coefficients. Ultimately, the forecasts of the seasonal and trend components are combined to generate the overall prediction. Experimental findings reveal that when the proposed model is tested on six public datasets, in comparison with five classic models it reduces the MSE by an average of 9.67%, with a maximum improvement of 40.23%. Full article
►▼ Show Figures

Graphical abstract

17 pages, 1748 KiB  
Article
Startup Survival Forecasting: A Multivariate AI Approach Based on Empirical Knowledge
by Francesc Font-Cot, Pablo Lara-Navarra, Claudia Sánchez-Arnau and Enrique A. Sánchez-Pérez
Information 2025, 16(1), 61; https://doi.org/10.3390/info16010061 - 16 Jan 2025
Viewed by 491
Abstract
Predicting the survival of startups is a complex challenge due to the multifaceted nature of entrepreneurial ecosystems and the dynamic interplay of internal and external factors. Despite advances in empirical research, existing models often lack integration with robust conceptual frameworks. This study addresses [...] Read more.
Predicting the survival of startups is a complex challenge due to the multifaceted nature of entrepreneurial ecosystems and the dynamic interplay of internal and external factors. Despite advances in empirical research, existing models often lack integration with robust conceptual frameworks. This study addresses these gaps by developing a multivariate AI-driven model for predicting startup survival, leveraging Lipschitz extensions, neural networks, and linear regression. Using a dataset of 20 startups, selected across diverse industries and evaluated on attributes such as team dynamics, market conditions, and financial metrics, the model demonstrated high accuracy and clustering capabilities. Key findings highlight the pivotal role of team dynamics and product differentiation in determining survival probabilities. By integrating conceptual insights with empirical data, the study bridges gaps in existing literature and offers a practical decision-making tool for entrepreneurs, investors, and policymakers. These findings underscore the importance of fostering collaborative, innovative ecosystems to enhance entrepreneurial success and societal well-being. Full article
(This article belongs to the Special Issue New Information Communication Technologies in the Digital Era)
►▼ Show Figures

Figure 1

21 pages, 12005 KiB  
Article
Shear Wave Velocity Prediction with Hyperparameter Optimization
by Gebrail Bekdaş, Yaren Aydın, Umit Işıkdağ, Sinan Melih Nigdeli, Dara Hajebi, Tae-Hyung Kim and Zong Woo Geem
Information 2025, 16(1), 60; https://doi.org/10.3390/info16010060 - 16 Jan 2025
Viewed by 443
Abstract
Shear wave velocity (Vs) is an important soil parameter to be known for earthquake-resistant structural design and an important parameter for determining the dynamic properties of soils such as modulus of elasticity and shear modulus. Different Vs measurement methods are [...] Read more.
Shear wave velocity (Vs) is an important soil parameter to be known for earthquake-resistant structural design and an important parameter for determining the dynamic properties of soils such as modulus of elasticity and shear modulus. Different Vs measurement methods are available. However, these methods, which are costly and labor intensive, have led to the search for new methods for determining the Vs. This study aims to predict shear wave velocity (Vs (m/s)) using depth (m), cone resistance (qc) (MPa), sleeve friction (fs) (kPa), pore water pressure (u2) (kPa), N, and unit weight (kN/m3). Since shear wave velocity varies with depth, regression studies were performed at depths up to 30 m in this study. The dataset used in this study is an open-source dataset, and the soil data are from the Taipei Basin. This dataset was extracted, and a 494-line dataset was created. In this study, using HyperNetExplorer 2024V1, Vs prediction based on depth (m), cone resistance (qc) (MPa), shell friction (fs), pore water pressure (u2) (kPa), N, and unit weight (kN/m3) values could be performed with satisfactory results (R2 = 0.78, MSE = 596.43). Satisfactory results were obtained in this study, in which Explainable Artificial Intelligence (XAI) models were also used. Full article
(This article belongs to the Special Issue Emerging Research on Neural Networks and Anomaly Detection)
►▼ Show Figures

Figure 1

14 pages, 2160 KiB  
Article
Quickly Finding the Semantically Optimal Presentation Order for a Set of Text Artifacts
by Daniel S. Soper
Information 2025, 16(1), 59; https://doi.org/10.3390/info16010059 - 16 Jan 2025
Viewed by 346
Abstract
This study considers how to quickly find the order in which to present a set of text artifacts on mobile apps or websites such that those artifacts are maximally semantically separated. Semantic separation is desirable because it ensures that users experience as much [...] Read more.
This study considers how to quickly find the order in which to present a set of text artifacts on mobile apps or websites such that those artifacts are maximally semantically separated. Semantic separation is desirable because it ensures that users experience as much novelty as possible from one item to the next, thereby improving user attention and engagement. Since an exhaustive search of all possible sequences of text items becomes increasingly infeasible as the length of the sequence grows, a new algorithm is proposed to quickly find the semantically optimal presentation order for a set of text artifacts. The performance of the proposed algorithm is evaluated using an extensive set of experiments involving three different types of text artifacts, seven different sequence lengths, and more than 600 experimental trials. The results demonstrate that the proposed algorithm can select statistically optimal sequences of text artifacts extremely quickly, regardless of the type of text artifacts being used as input or the length of the sequence. App and website developers who are seeking to hold users’ attention and improve user engagement may therefore find the proposed algorithm very attractive in comparison to an exhaustive search. Full article
(This article belongs to the Special Issue Text Mining: Challenges, Algorithms, Tools and Applications)
►▼ Show Figures

Graphical abstract

15 pages, 744 KiB  
Systematic Review
[18F]FDG PET-Based Radiomics and Machine Learning for the Assessment of Gliomas and Glioblastomas: A Systematic Review
by Francesco Dondi, Roberto Gatta, Maria Gazzilli, Pietro Bellini, Gian Luca Viganò, Cristina Ferrari, Antonio Rosario Pisani, Giuseppe Rubini and Francesco Bertagna
Information 2025, 16(1), 58; https://doi.org/10.3390/info16010058 - 16 Jan 2025
Viewed by 490
Abstract
Background: Some evidence of the value of 18F-fluorodesoxyglucose ([18F]FDG) positron emission tomography (PET) imaging for the assessment of gliomas and glioblastomas (GBMs) is emerging. The aim of this systematic review was to assess the role of [18F]FDG PET-based radiomics and machine learning (ML) [...] Read more.
Background: Some evidence of the value of 18F-fluorodesoxyglucose ([18F]FDG) positron emission tomography (PET) imaging for the assessment of gliomas and glioblastomas (GBMs) is emerging. The aim of this systematic review was to assess the role of [18F]FDG PET-based radiomics and machine learning (ML) in the evaluation of these neoplasms. Methods: A wide literature search of the PubMed/MEDLINE, Scopus, and Cochrane Library databases was made to find relevant published articles on the role of [18F]FDG PET-based radiomics and ML for the assessment of gliomas and GBMs. Results: Eight studies were included in the systematic review. Signatures, including radiomics analysis and ML, generally demonstrated a possible diagnostic value to assess different characteristics of gliomas and GBMs, such as the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter, the isocitrate dehydrogenase (IDH) genotype, alpha thalassemia/mental retardation X-linked (ATRX) mutation status, proliferative activity, differential diagnosis with solitary brain metastases or primary central nervous system lymphoma, and prognosis of these patients. Conclusion: Despite some intrinsic limitations of radiomics and ML affecting the studies included in the review, some initial insights on the promising role of these technologies for the assessment of gliomas and GBMs are emerging. Validation of these preliminary findings in multicentric studies is needed to translate radiomics and ML approaches in the clinical setting. Full article
(This article belongs to the Special Issue Computer Vision, Pattern Recognition and Machine Learning in Italy)
►▼ Show Figures

Figure 1

23 pages, 3787 KiB  
Article
Cloud-Based License Plate Recognition: A Comparative Approach Using You Only Look Once Versions 5, 7, 8, and 9 Object Detection
by Christine Bukola Asaju, Pius Adewale Owolawi, Chuling Tu and Etienne Van Wyk
Information 2025, 16(1), 57; https://doi.org/10.3390/info16010057 - 16 Jan 2025
Viewed by 534
Abstract
Cloud-based license plate recognition (LPR) systems have emerged as essential tools in modern traffic management and security applications. Determining the best approach remains paramount in the field of computer vision. This study presents a comparative analysis of various versions of the YOLO (You [...] Read more.
Cloud-based license plate recognition (LPR) systems have emerged as essential tools in modern traffic management and security applications. Determining the best approach remains paramount in the field of computer vision. This study presents a comparative analysis of various versions of the YOLO (You Only Look Once) object detection models, namely, YOLO 5, 7, 8, and 9, applied to LPR tasks in a cloud computing environment. Using live video, we performed experiments on YOLOv5, YOLOv7, YOLOv8, and YOLOv9 models to detect number plates in real time. According to the results, YOLOv8 is reported the most effective model for real-world deployment due to its strong cloud performance. It achieved an accuracy of 78% during cloud testing, while YOLOv5 showed consistent performance with 71%. YOLOv7 performed poorly in cloud testing (52%), indicating potential issues, while YOLOv9 reported 70% accuracy. This tight alignment of results shows consistent, although modest, performance across scenarios. The findings highlight the evolution of the YOLO architecture and its impact on enhancing LPR accuracy and processing efficiency. The results provide valuable insights into selecting the most appropriate YOLO model for cloud-based LPR systems, balancing the trade-offs between real-time performance and detection precision. This research contributes to advancing the field of intelligent transportation systems by offering a detailed comparison that can guide future implementations and optimizations of LPR systems in cloud environments. Full article
(This article belongs to the Section Information and Communications Technology)
►▼ Show Figures

Figure 1

28 pages, 404 KiB  
Review
Methodological and Technological Advancements in E-Learning
by Elias Dritsas and Maria Trigka
Information 2025, 16(1), 56; https://doi.org/10.3390/info16010056 - 15 Jan 2025
Viewed by 815
Abstract
The present survey examines the intersection of methodological advancements and technological innovations in e-learning, emphasizing their transformative impact on modern education. It systematically explores instructional design frameworks, adaptive learning systems, immersive technologies, and data-driven analytics, highlighting their role in fostering personalized, scalable, and [...] Read more.
The present survey examines the intersection of methodological advancements and technological innovations in e-learning, emphasizing their transformative impact on modern education. It systematically explores instructional design frameworks, adaptive learning systems, immersive technologies, and data-driven analytics, highlighting their role in fostering personalized, scalable, and inclusive learning environments. Through the integration of pedagogical theories with advanced tools like artificial intelligence (AI), augmented reality (AR), virtual reality (VR), and mixed reality (MR), this study demonstrates how e-learning systems enhance engagement, retention, and accessibility. The survey addresses critical challenges such as the digital divide, data privacy, and resistance to adoption, offering evidence-based strategies to mitigate these issues. It underscores the importance of bridging equity gaps while maintaining scalability and sustainability, particularly in underserved regions. By synthesizing state-of-the-art research and practical applications, this work provides actionable insights into the future of e-learning, advocating for a balanced approach to innovation that aligns technological capabilities with the diverse needs of global learners. The findings contribute to the broader discourse on sustainable, inclusive, and effective digital education ecosystems. Full article
(This article belongs to the Special Issue Trends in Artificial Intelligence-Supported E-Learning)
►▼ Show Figures

Figure 1

25 pages, 2703 KiB  
Article
Identifying the Impacts of Social Movement Mobilization on YouTube: Social Network Analysis
by Norhayatun Syamilah Osman, Jae-Hun Kim, Jae-Hong Park and Han-Woo Park
Information 2025, 16(1), 55; https://doi.org/10.3390/info16010055 - 15 Jan 2025
Viewed by 525
Abstract
This study explores the potential of social media in improving education, engagement, and mobilization for climate change initiatives. Using the theoretical framework of resource mobilization and methods such as social network analysis (SNA) and bipartite networks, it examines how effective deployment of resources [...] Read more.
This study explores the potential of social media in improving education, engagement, and mobilization for climate change initiatives. Using the theoretical framework of resource mobilization and methods such as social network analysis (SNA) and bipartite networks, it examines how effective deployment of resources such as information, social capital, and organizational capabilities can help in the progression of collective movements. Social media platforms, particularly YouTube, significantly influences network structures by facilitating resource mobilization and driving essential engagement. This study extracted data from NodeXL and found that YouTube is an effective medium in disseminating climate change information and delivering educational content to a multilingual audience. Additionally, video affordances such as storytelling, audio–visual effects, and concise narratives enhance viewer interest and engagement, increasing resource mobilization effectiveness. This research offers insights into optimizing social media use for effective resource mobilization and engagement in climate change initiatives. Full article
(This article belongs to the Special Issue 2nd Edition of Information Retrieval and Social Media Mining)
►▼ Show Figures

Figure 1

20 pages, 2538 KiB  
Review
Multi-Modal Fusion of Routine Care Electronic Health Records (EHR): A Scoping Review
by Zina Ben-Miled, Jacob A. Shebesh, Jing Su, Paul R. Dexter, Randall W. Grout and Malaz A. Boustani
Information 2025, 16(1), 54; https://doi.org/10.3390/info16010054 - 15 Jan 2025
Viewed by 616
Abstract
Background: Electronic health records (EHR) are now widely available in healthcare institutions to document the medical history of patients as they interact with healthcare services. In particular, routine care EHR data are collected for a large number of patients.These data span multiple [...] Read more.
Background: Electronic health records (EHR) are now widely available in healthcare institutions to document the medical history of patients as they interact with healthcare services. In particular, routine care EHR data are collected for a large number of patients.These data span multiple heterogeneous elements (i.e., demographics, diagnosis, medications, clinical notes, vital signs, and laboratory results) which contain semantic, concept, and temporal information. Recent advances in generative learning techniques were able to leverage the fusion of multiple routine care EHR data elements to enhance clinical decision support. Objective: A scoping review of the proposed techniques including fusion architectures, input data elements, and application areas is needed to synthesize variances and identify research gaps that can promote re-use of these techniques for new clinical outcomes. Design: A comprehensive literature search was conducted using Google Scholar to identify high impact fusion architectures over multi-modal routine care EHR data during the period 2018 to 2023. The guidelines from the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping review were followed. The findings were derived from the selected studies using a thematic and comparative analysis. Results: The scoping review revealed the lack of standard definition for EHR data elements as they are transformed into input modalities. These definitions ignore one or more key characteristics of the data including source, encoding scheme, and concept level. Moreover, in order to adapt to emergent generative learning techniques, the classification of fusion architectures should distinguish fusion from learning and take into consideration that learning can concurrently happen in all three layers of new fusion architectures (i.e., encoding, representation, and decision). These aspects constitute the first step towards a streamlined approach to the design of multi-modal fusion architectures for routine care EHR data. In addition, current pretrained encoding models are inconsistent in their handling of temporal and semantic information thereby hindering their re-use for different applications and clinical settings. Conclusions: Current routine care EHR fusion architectures mostly follow a design-by-example methodology. Guidelines are needed for the design of efficient multi-modal models for a broad range of healthcare applications. In addition to promoting re-use, these guidelines need to outline best practices for combining multiple modalities while leveraging transfer learning and co-learning as well as semantic and temporal encoding. Full article
(This article belongs to the Special Issue From Data to Diagnosis: Recent Advances of Machine Learning in Biomedical and Health Informatics)
►▼ Show Figures

Figure 1

26 pages, 8715 KiB  
Article
Interpretable Deep Learning for Pneumonia Detection Using Chest X-Ray Images
by Jovito Colin and Nico Surantha
Information 2025, 16(1), 53; https://doi.org/10.3390/info16010053 - 15 Jan 2025
Viewed by 466
Abstract
Pneumonia remains a global health issue, creating the need for accurate detection methods for effective treatment. Deep learning models like ResNet50 show promise in detecting pneumonia from chest X-rays; however, their black-box nature limits the transparency, which fails to meet that needed for [...] Read more.
Pneumonia remains a global health issue, creating the need for accurate detection methods for effective treatment. Deep learning models like ResNet50 show promise in detecting pneumonia from chest X-rays; however, their black-box nature limits the transparency, which fails to meet that needed for clinical trust. This study aims to improve model interpretability by comparing four interpretability techniques, which are Layer-wise Relevance Propagation (LRP), Adversarial Training, Class Activation Maps (CAMs), and the Spatial Attention Mechanism, and determining which fits best the model, enhancing its transparency with minimal impact on its performance. Each technique was evaluated for its impact on the accuracy, sensitivity, specificity, AUC-ROC, Mean Relevance Score (MRS), and a calculated trade-off score that balances interpretability and performance. The results indicate that LRP was the most effective in enhancing interpretability, achieving high scores across all metrics without sacrificing diagnostic accuracy. The model achieved 0.91 accuracy and 0.85 interpretability (MRS), demonstrating its potential for clinical integration. In contrast, Adversarial Training, CAMs, and the Spatial Attention Mechanism showed trade-offs between interpretability and performance, each highlighting unique image features but with some impact on specificity and accuracy. Full article
(This article belongs to the Special Issue Knowledge Management and Semantic Web Technologies for Explainable Artificial Intelligence)
►▼ Show Figures

Figure 1

18 pages, 1940 KiB  
Article
An Intelligent Fuzzy-Based Routing Protocol for Vehicular Opportunistic Networks
by Ermioni Qafzezi, Kevin Bylykbashi, Shunya Higashi, Phudit Ampririt, Keita Matsuo and Leonard Barolli
Information 2025, 16(1), 52; https://doi.org/10.3390/info16010052 - 15 Jan 2025
Viewed by 458
Abstract
Opportunistic networks are characterized by intermittent connectivity and dynamic topologies, which pose significant challenges for efficient message delivery, resource management, and routing decision-making. This paper introduces the Fuzzy Control Routing Protocol, a novel approach designed to address these challenges by leveraging fuzzy logic [...] Read more.
Opportunistic networks are characterized by intermittent connectivity and dynamic topologies, which pose significant challenges for efficient message delivery, resource management, and routing decision-making. This paper introduces the Fuzzy Control Routing Protocol, a novel approach designed to address these challenges by leveraging fuzzy logic to enhance routing decisions and improve overall network performance. The protocol considers buffer occupancy, angle to destination, and the number of unique connections of the target nodes to make context-aware routing decisions. It was implemented and evaluated using the FuzzyC framework for simulations and the opportunistic network environment simulator for realistic network scenarios. Simulation results demonstrate that the Fuzzy Control Routing Protocol achieves competitive delivery probability, efficient resource utilization, and low overhead compared to the Epidemic and MaxProp protocols. Notably, it consistently outperformed the Epidemic protocol across all metrics and exhibited comparable delivery probability to MaxProp while maintaining significantly lower overhead, particularly in low-density scenarios. The results demonstrate the protocol’s ability to adapt to varying network conditions, effectively balance forwarding and resource management, and maintain robust performance in dynamic vehicular environments. Full article
(This article belongs to the Special Issue Wireless Communication and Internet of Vehicles)
►▼ Show Figures

Figure 1

16 pages, 837 KiB  
Article
Barriers and Enablers of AI Adoption in Human Resource Management: A Critical Analysis of Organizational and Technological Factors
by Mitra Madanchian and Hamed Taherdoost
Information 2025, 16(1), 51; https://doi.org/10.3390/info16010051 - 15 Jan 2025
Viewed by 1371
Abstract
This paper examines the key factors recognized as transformative in the field of human resource management (HRM) and explores their influence on the global adoption of artificial intelligence (AI). While AI holds significant promise for enhancing HRM efficiency, employee engagement, and Decision Making, [...] Read more.
This paper examines the key factors recognized as transformative in the field of human resource management (HRM) and explores their influence on the global adoption of artificial intelligence (AI). While AI holds significant promise for enhancing HRM efficiency, employee engagement, and Decision Making, its implementation presents a range of organizational, technical, and ethical challenges that organizations worldwide must navigate. Change aversion, data security worries, and integration expenses are major roadblocks, but strong digital leadership, company culture, and advancements in NLP and machine learning are key enablers. This paper presents a complex analysis that questions the common perception of AI as only disruptive by delving into the relationship between power dynamics, corporate culture, and technology infrastructures. In this paper, we bring together research from several fields to help scholars and practitioners understand the nuances of AI adoption in HRM, with an emphasis on the importance of inclusive methods and ethical frameworks. Full article
(This article belongs to the Special Issue Advances in Human-Centered Artificial Intelligence)
►▼ Show Figures

Figure 1

21 pages, 1471 KiB  
Article
Integrating Motivation Theory into the AIED Curriculum for Technical Education: Examining the Impact on Learning Outcomes and the Moderating Role of Computer Self-Efficacy
by Shao-Hsun Chang, Kai-Chao Yao, Yao-Ting Chen, Cheng-Yang Chung, Wei-Lun Huang and Wei-Sho Ho
Information 2025, 16(1), 50; https://doi.org/10.3390/info16010050 - 14 Jan 2025
Viewed by 686
Abstract
The integration of artificial intelligence (AI) technologies into education has gained increasing attention, yet limited research examines how the curriculum design can enhance learning outcomes and influence learners’ intentions to continue AI learning. This study addresses this gap by integrating the theory of [...] Read more.
The integration of artificial intelligence (AI) technologies into education has gained increasing attention, yet limited research examines how the curriculum design can enhance learning outcomes and influence learners’ intentions to continue AI learning. This study addresses this gap by integrating the theory of planned behavior, technology acceptance model, theories of motivation, and computer self-efficacy to explore the factors affecting learners’ behavioral intentions in AI education. Using the AI course quality as the primary antecedent and “intention to continue taking courses” as the dependent variable, the study investigates the structural relationships and mediating variables between these factors. Data were collected through a stratified random sampling method from 19 universities in Taiwan, involving 200 students who had completed five core AI-related courses, including artificial intelligence, machine learning, internet of things, big data, and robotics. The analysis, conducted using PLS-SEM, revealed that AI course quality directly and indirectly influences learners’ behavioral intentions through mediating variables such as learning satisfaction, computer self-efficacy, technological literacy, and computer learning motivation. Moreover, AI course quality exerted a significant positive effect on computer motivation, which, in turn, influenced self-efficacy and learning outcomes. These findings provide valuable insights into the antecedents and processes shaping learners’ intentions to continue AI learning, offering practical and theoretical implications for AI education. Full article
(This article belongs to the Special Issue Artificial Intelligence and Games Science in Education)
►▼ Show Figures

Figure 1

18 pages, 1353 KiB  
Article
Enhancing Privacy While Preserving Context in Text Transformations by Large Language Models
by Tymon Lesław Żarski and Artur Janicki
Information 2025, 16(1), 49; https://doi.org/10.3390/info16010049 - 14 Jan 2025
Viewed by 640
Abstract
Data security is a critical concern for Internet users, primarily as more people rely on social networks and online tools daily. Despite the convenience, many users are unaware of the risks posed to their sensitive and personal data. This study addresses this issue [...] Read more.
Data security is a critical concern for Internet users, primarily as more people rely on social networks and online tools daily. Despite the convenience, many users are unaware of the risks posed to their sensitive and personal data. This study addresses this issue by presenting a comprehensive solution to prevent personal data leakage using online tools. We developed a conceptual solution that enhances user privacy by identifying and anonymizing named entity classes representing sensitive data while maintaining the original context by swapping source entities for functional data. Our approach utilizes natural language processing methods, combining machine learning tools such as MITIE and spaCy with rule-based text analysis. We employed regular expressions and large language models to anonymize text, preserving its context for further processing or enabling restoration to the original form after transformations. The results demonstrate the effectiveness of our custom-trained models, achieving an F1 score of 0.8292. Additionally, the proposed algorithms successfully preserved context in approximately 93.23% of test cases, indicating a promising solution for secure data handling in online environments. Full article
(This article belongs to the Special Issue Applications of Information Extraction, Knowledge Graphs, and Large Language Models)
►▼ Show Figures

Figure 1

26 pages, 1504 KiB  
Article
Encrypted Engagement: Mapping Messaging App Use in European News Consumption Patterns
by Răzvan Rughiniș, Dinu Țurcanu, Simona-Nicoleta Vulpe and Alexandru Radovici
Information 2025, 16(1), 48; https://doi.org/10.3390/info16010048 - 14 Jan 2025
Viewed by 525
Abstract
This study examines the emerging role of messaging apps and end-to-end encryption in news consumption patterns across the European Union. Using data from the Flash Eurobarometer 3153 “Media and News Survey 2023”, we employed K-Means cluster analysis to identify five distinct news consumer [...] Read more.
This study examines the emerging role of messaging apps and end-to-end encryption in news consumption patterns across the European Union. Using data from the Flash Eurobarometer 3153 “Media and News Survey 2023”, we employed K-Means cluster analysis to identify five distinct news consumer profiles. Our findings reveal that while messaging apps are used by 15% of EU residents for news consumption, their adoption varies significantly across demographic groups and regions. Notably, omnivorous news consumers show the highest usage (61%) and trust in these platforms, indicating a complementary role to traditional news sources. The study highlights a generational divide, with younger users and those still in education showing a stronger preference for messaging apps. Surprisingly, individuals without formal education also demonstrate high usage, challenging assumptions about the digital divide. This research offers updated, large-scale information on the evolving European news ecosystem, where private, encrypted channels are gaining importance alongside public platforms. Our findings have significant implications for media strategies, policymaking, and understanding the future of news dissemination in an increasingly digital and privacy-conscious Europe. Full article
(This article belongs to the Special Issue Cybersecurity, Cybercrimes, and Smart Emerging Technologies)
►▼ Show Figures

Figure 1

18 pages, 1638 KiB  
Article
Decoding Success: The Role of E-Learning Readiness in Linking Technological Skills and Employability in Hospitality Management Graduates
by Ibrahim A. Elshaer, Alaa M. S. Azazz, Abuelkassem A. A. Mohammad and Sameh Fayyad
Information 2025, 16(1), 47; https://doi.org/10.3390/info16010047 - 14 Jan 2025
Viewed by 484
Abstract
Technological advancement alongside global epidemics stimulated the widescale implementation of e-learning. However, it is reported that e-learning is in the experimental phase and still requires fundamental improvements, particularly in disciplines that go beyond theoretical knowledge. The current study examines the nexus between e-learning [...] Read more.
Technological advancement alongside global epidemics stimulated the widescale implementation of e-learning. However, it is reported that e-learning is in the experimental phase and still requires fundamental improvements, particularly in disciplines that go beyond theoretical knowledge. The current study examines the nexus between e-learning readiness, psychological motivation, technological skills, and employability skills among hospitality management undergraduates. It also explores the moderating effects of student engagement on the linkages among these variables. To that end, this study adopted a quantitative approach and used a self-administered questionnaire survey to collect primary data. The sample included a total of 428 participants who were recruited from undergraduates of hospitality management programs in Egyptian universities using the convenience sampling technique. Data analysis included performing PLS-SEM using Smart PLS 3.0 software. The results confirm the positive effects of psychological motivation and technological skills on both e-learning readiness and the employability skills of hospitality management undergraduates. The study also underscores the mediated role of e-learning readiness in the linkages between study predictors and outcomes. Additionally, the findings highlight the moderating effect of student engagement in supporting e-learning readiness and eventually employability skills. This study adds to the hospitality management body of knowledge and provides valuable insights for education institutions and policymakers to optimize e-learning experiences. Full article
(This article belongs to the Special Issue Trends in Artificial Intelligence-Supported E-Learning)
►▼ Show Figures

Figure 1

32 pages, 5367 KiB  
Article
TempoGRAPHer: Aggregation-Based Temporal Graph Exploration
by Evangelia Tsoukanara, Georgia Koloniari and Evaggelia Pitoura
Information 2025, 16(1), 46; https://doi.org/10.3390/info16010046 - 13 Jan 2025
Viewed by 400
Abstract
Graphs offer a generic abstraction for modeling entities and the interactions and relationships between them. Most real-world graphs, such as social and cooperation networks, evolve over time, and exploring their evolution may reveal important information. In this paper, we present TempoGRAPHer, a system [...] Read more.
Graphs offer a generic abstraction for modeling entities and the interactions and relationships between them. Most real-world graphs, such as social and cooperation networks, evolve over time, and exploring their evolution may reveal important information. In this paper, we present TempoGRAPHer, a system for analyzing and visualizing the evolution of temporal attributed graphs. TempoGRAPHer supports both temporal and attribute aggregation. It also allows graph exploration by identifying periods of significant growth, shrinkage, or stability. Temporal exploration is supported by two complementary strategies, namely skyline- and interaction-based exploration. Skyline-based exploration provides insights into the overall trends in the evolution, while interaction-based exploration offers a closer look at specific parts of the graph evolution history where significant changes occurred. We present experimental results demonstrating the efficiency of TempoGRAPHer. Additionally, we showcase the usefulness of our system in understanding graph evolution by presenting detailed scenarios, including exploring the evolution of a real contact network between primary school students and analyzing the collaborations in a co-authorship network between authors of the same gender over time. Full article
(This article belongs to the Special Issue Advances in Data and Network Sciences Applied to Computational Social Science)
►▼ Show Figures

Figure 1

26 pages, 949 KiB  
Article
Understanding Determinants of Management Simulation Games Adoption in Higher Educational Institutions Using an Integrated Technology Acceptance Model/Technology–Organisation–Environment Model: Educator Perspective
by Mirjana Pejić Bach, Maja Meško, Ana Marija Stjepić, Sarwar Khawaja and Fayyaz Hussain Quershi
Information 2025, 16(1), 45; https://doi.org/10.3390/info16010045 - 13 Jan 2025
Viewed by 548
Abstract
Background and Methods: A primary survey of a multi-national sample of higher institutional educators has been conducted to investigate the determinants of the adoption of management simulation games. The research model is developed based on the technology acceptance model (TAM) and technology–organisation–environment (TOE). [...] Read more.
Background and Methods: A primary survey of a multi-national sample of higher institutional educators has been conducted to investigate the determinants of the adoption of management simulation games. The research model is developed based on the technology acceptance model (TAM) and technology–organisation–environment (TOE). Structural equation modelling has been used to test the research model. The paper focuses on the use of management simulation games among educators in higher educational institutions (HEIs). Its purpose is to determine the factors influencing educators’ use of these games from both individual and institutional perspectives. The TAM captures the individual perspective, while the TOE framework addresses the institutional perspective. The structural equation model confirmed most of the TAM hypotheses. Results: However, the model does not support the hypotheses regarding the relationship between perceived ease of use and attitude toward usage or between perceived ease of use and perceived usefulness. The technological factors within the TOE framework did not significantly impact perceived usefulness, only perceived ease of use. Conclusion: The combined TAM-TOE model has demonstrated valid representativeness. Previous research on the usage of management simulation games has primarily focused on students, neglecting the broader perspective of educators in HEIs in business and economics within both the TAM and TOE frameworks. Full article
(This article belongs to the Special Issue ICT-Based Modelling and Simulation for Education)
►▼ Show Figures

Figure 1

16 pages, 615 KiB  
Article
Virtual Culinary Influence: Investigating the Impact of Food Vlogs on Viewer Attitudes and Restaurant Visit Intentions
by Tu-Anh Truong, Diana Piscarac, Seung-Mi Kang and Seung-Chul Yoo
Information 2025, 16(1), 44; https://doi.org/10.3390/info16010044 - 13 Jan 2025
Viewed by 751
Abstract
Emerging as a pivotal trend on social media, food review vlogs not only narrate culinary experiences but also boost local cuisine and economic growth. This study delves into how such vlogs and vlogger traits affect viewer attitudes and restaurant visit intentions, guided by [...] Read more.
Emerging as a pivotal trend on social media, food review vlogs not only narrate culinary experiences but also boost local cuisine and economic growth. This study delves into how such vlogs and vlogger traits affect viewer attitudes and restaurant visit intentions, guided by the stimulus–organism–response paradigm. Hypotheses were quantitatively assessed through an online survey with 347 participants from Amazon’s Mechanical Turk (MTurk), analyzed using SPSS 22 and AMOS 22 for structural equation modeling (SEM). The results indicated that informativeness, entertainment, and vividness influence viewers’ engagement with food review vlogs, while attractiveness and homophily are major predictors of parasocial relationships. Content engagement and parasocial relationships exerted positive influences on attitudes and visiting intentions toward the reviewed restaurants. The findings contribute to the empirical understanding of foodservice communication by identifying key characteristics of food review vlogs and vloggers that drive viewer engagement and behavioral intentions. Building on an established theoretical foundation, this study underscores the practical significance of the S–O–R model in digital marketing, offering actionable insights to empower content creators and marketers in driving audience engagement and shaping consumer behavior. Full article
(This article belongs to the Special Issue Recent Developments and Implications in Web Analysis)
►▼ Show Figures

Figure 1

18 pages, 7819 KiB  
Review
Low-Power Wake-Up Receivers for Resilient Cellular Internet of Things
by Siyu Wang, Trevor J. Odelberg, Peter W. Crary, Mason P. Obery and David D. Wentzloff
Information 2025, 16(1), 43; https://doi.org/10.3390/info16010043 - 13 Jan 2025
Viewed by 565
Abstract
Smart Cities leverage large networks of wirelessly connected nodes embedded with sensors and/or actuators. Cellular IoT, such as NB-IoT and 5G RedCap, is often preferred for these applications thanks to its long range, extensive coverage, and good quality of service. In these networks, [...] Read more.
Smart Cities leverage large networks of wirelessly connected nodes embedded with sensors and/or actuators. Cellular IoT, such as NB-IoT and 5G RedCap, is often preferred for these applications thanks to its long range, extensive coverage, and good quality of service. In these networks, wireless communication dominates power consumption, motivating research on energy-efficient yet resilient and robust wireless systems. Many IoT use cases require low latency but cannot afford high-power radios continuously operating to accomplish this. In these cases, wake-up receivers (WURs) are a promising solution: while the high-power main radio (MR) is turned off/idle, a lightweight WUR is continuously monitoring the RF channel; when it detects a wake-up sequence, the WUR will turn on the MR for subsequent communications. This article provides an overview of WUR hardware design considerations and challenges for 4G and 5G cellular IoT, summarizes the recent 3GPP activities to standardize NB-IoT and 5G wake-up signals, and presents a state-of-the-art WUR chip. Full article
(This article belongs to the Special Issue IoT-Based Systems for Resilient Smart Cities)
►▼ Show Figures

Graphical abstract

17 pages, 4632 KiB  
Article
Chinese Mathematical Knowledge Entity Recognition Based on Linguistically Motivated Bidirectional Encoder Representation from Transformers
by Wei Song, He Zheng, Shuaiqi Ma, Mingze Zhang, Wei Guo and Keqing Ning
Information 2025, 16(1), 42; https://doi.org/10.3390/info16010042 - 13 Jan 2025
Viewed by 417
Abstract
We assessed whether constructing a mathematical knowledge graph for a knowledge question-answering system or a course recommendation system, Named Entity Recognition (NER), is indispensable. The accuracy of its recognition directly affects the actual performance of these subsequent tasks. In order to improve the [...] Read more.
We assessed whether constructing a mathematical knowledge graph for a knowledge question-answering system or a course recommendation system, Named Entity Recognition (NER), is indispensable. The accuracy of its recognition directly affects the actual performance of these subsequent tasks. In order to improve the accuracy of mathematical knowledge entity recognition and provide effective support for subsequent functionalities, this paper adopts the latest pre-trained language model, LERT, combined with a Bidirectional Gated Recurrent Unit (BiGRU), Iterated Dilated Convolutional Neural Networks (IDCNNs), and Conditional Random Fields (CRFs), to construct the LERT-BiGRU-IDCNN-CRF model. First, LERT provides context-related word vectors, and then the BiGRU captures both long-distance and short-distance information, the IDCNN retrieves local information, and finally the CRF is decoded to output the corresponding labels. Experimental results show that the accuracy of this model when recognizing mathematical concepts and theorem entities is 97.22%, the recall score is 97.47%, and the F1 score is 97.34%. This model can accurately recognize the required entities, and, through comparison, this method outperforms the current state-of-the-art entity recognition models. Full article
►▼ Show Figures

Figure 1

28 pages, 1346 KiB  
Article
Cross-Cultural Perspectives on Fake News: A Comparative Study of Instagram Users in Greece and Portugal
by Evangelia Pothitou, Maria Perifanou and Anastasios A. Economides
Information 2025, 16(1), 41; https://doi.org/10.3390/info16010041 - 13 Jan 2025
Viewed by 695
Abstract
As our society increasingly relies on digital platforms for information, the spread of fake news has become a pressing concern. This study investigates the ability of Greek and Portuguese Instagram users to identify fake news, highlighting the influence of cultural differences. The responses [...] Read more.
As our society increasingly relies on digital platforms for information, the spread of fake news has become a pressing concern. This study investigates the ability of Greek and Portuguese Instagram users to identify fake news, highlighting the influence of cultural differences. The responses of 220 Instagram users were collected through questionnaires in Greece and Portugal. The data analysis investigates characteristics of Instagram posts, social endorsement, and platform usage duration. The results reveal distinct user behaviors: Greeks exhibit a unique inclination towards social connections, displaying an increased trust in friends’ content and investing more time on Instagram, reflecting the importance of personal connections in their media consumption. They also give less importance to a certain post’s characteristics, such as content opposing personal beliefs, emotional language, and poor grammar, spelling, or formatting when identifying fake news, compared to the Portuguese, suggesting a weaker emphasis on content quality in their evaluations. These findings show that cultural differences affect how people behave on Instagram. Hence, content creators, platforms, and policymakers need specific plans to make online spaces more informative. Strategies should focus on enhancing awareness of key indicators of fake news, such as linguistic quality and post structure, while addressing the role of personal and social networks in the spread of misinformation. Full article
(This article belongs to the Section Information Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-70
Previous Issue
Next Issue
Back to TopTop