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 22.6 days after submission; acceptance to publication is undertaken in 3.3 days (median values for papers published in this journal in the first 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
Exploring the Path to Enhance Employee Creativity in Chinese MSMEs: The Influence of Individual and Team Learning Orientation, Transformational Leadership, and Creative Self-Efficacy
Information 2023, 14(8), 449; https://doi.org/10.3390/info14080449 - 08 Aug 2023
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This study examined the relationship between transformational leadership, learning orientation, creative self-efficacy, and employee creativity in manufacturing small and medium-sized enterprises (MSMEs) in China. A survey involving 742 employees was conducted, and hierarchical linear modeling (HLM) was employed to analyze the data. The
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This study examined the relationship between transformational leadership, learning orientation, creative self-efficacy, and employee creativity in manufacturing small and medium-sized enterprises (MSMEs) in China. A survey involving 742 employees was conducted, and hierarchical linear modeling (HLM) was employed to analyze the data. The result showed that transformational leadership has s significantly positive effect on employee creativity. Moreover, both individual and team-level learning orientations are positively related to employee creativity significantly. Creative self-efficacy (CSE) mediates the relationship between transformational leadership, team learning orientation, and individual learning orientation on employee creativity. These findings suggest that transformational leadership, learning orientation, and CSE enhance employee creativity in Chinese MSMEs. We discuss the implications of these findings and offer suggestions for future research.
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Association between Obesity and COVID-19: Insights from Social Media Content
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Information 2023, 14(8), 448; https://doi.org/10.3390/info14080448 - 08 Aug 2023
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The adoption of emerging technologies in healthcare systems plays a crucial part in anti-obesity initiatives. COVID-19 has intensified the Body Mass Index (BMI) discourses in AI (Artificial Intelligence)-powered social media. However, few studies have reported on the influence of digital content on obesity
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The adoption of emerging technologies in healthcare systems plays a crucial part in anti-obesity initiatives. COVID-19 has intensified the Body Mass Index (BMI) discourses in AI (Artificial Intelligence)-powered social media. However, few studies have reported on the influence of digital content on obesity prevention policies. Understanding the nature and forums of obese metaphors in social media is the first step in policy intervention. The purpose of this paper is to understand the mutual influence between obesity and COVID-19 and determine its policy implications. This paper analyzes the public responses to obesity using Twitter data collected during the COVID-19 pandemic. The emotional nature of tweets is analyzed using the NRC lexicon. The results show that COVID-19 significantly influences perceptions of obesity; this indicates that existing public health policies must be revisited. The study findings delineate prerequisites for obese disease control programs. This paper provides policy recommendations for improving social media interventions in health service delivery in order to prevent obesity.
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(This article belongs to the Special Issue Advances in Computing, Communication & Security)
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Prediction of Heatwave Using Advanced Soft Computing Technique
Information 2023, 14(8), 447; https://doi.org/10.3390/info14080447 - 07 Aug 2023
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At present, there is no suitable instrument available to simulate modeling the thermal performance of various areas of our states due to its complicated meteorological behavior. To accurately predict a heatwave, we studied the research gaps and current ongoing research on the prediction
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At present, there is no suitable instrument available to simulate modeling the thermal performance of various areas of our states due to its complicated meteorological behavior. To accurately predict a heatwave, we studied the research gaps and current ongoing research on the prediction of heatwaves. For the accurate prediction of a heatwave, we considered two soft computing concepts, (a) Rough Set Theory (RST) and (b) Support Vector Machine (SVM). All the ongoing research on the prediction of heatwaves is based on future predictions with an error margin. All the available techniques use a particular pattern of heatwave data, and these methods do not apply to vague data. This paper used an innovative RST and SVM technique, which can be applied to vague and imprecise datasets to produce the best outcomes. RST is helpful in finding the most significant attributes that will be alarming in the future. This analysis identifies the heat wave as the most prominent characteristic among various meteorological data. SVM is responsible for the future prediction of heat waves, which includes various parameters. By further classification of heatwaves, we found that a lack of greenery will increase the heatwave in the future. Although the survey was conducted based on a sampling distribution, we expect this result to represent the population as we collected our sample in a heterogeneous environment. These outcomes are validated using a statistical method.
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(This article belongs to the Special Issue Impact and Influence of Artificial Intelligence Technology and Computing)
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IPFS-Blockchain Smart Contracts Based Conceptual Framework to Reduce Certificate Frauds in the Academic Field
Information 2023, 14(8), 446; https://doi.org/10.3390/info14080446 - 07 Aug 2023
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In the digital age, ensuring the authenticity and security of academic certificates is a critical challenge faced by educational institutions, employers, and individuals alike. Traditional methods for verifying academic credentials are often cumbersome, time-consuming, and susceptible to fraud. However, the emergence of blockchain
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In the digital age, ensuring the authenticity and security of academic certificates is a critical challenge faced by educational institutions, employers, and individuals alike. Traditional methods for verifying academic credentials are often cumbersome, time-consuming, and susceptible to fraud. However, the emergence of blockchain technology offers a promising solution to address these issues. The proposed system utilizes a blockchain network, where each academic certificate is stored as a digital asset on the blockchain. These digital certificates are cryptographically secured, timestamped, and associated with unique identifiers, such as hashes or public keys, ensuring their integrity and immutability. Anyone with access to the blockchain network can verify a certificate’s authenticity, using the MetaMask extension and Ethereum network, eliminating the need for intermediaries and reducing the risk of fraudulent credentials. The main strength of the paper is that the data that are stored in the blockchain are unique identifiers of the encrypted data, which is encrypted by using an encryption technique that provides more security to the academic certificates. Furthermore, IPFS is also used to store large amounts of encrypted data.
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ECO4RUPA: 5G-IoT Inclusive and Intelligent Routing Ecosystem with Low-Cost Air Quality Monitoring
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Information 2023, 14(8), 445; https://doi.org/10.3390/info14080445 - 07 Aug 2023
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The increase and diversity of low-cost air quality (AQ) sensors, as well as their flexibility and low power consumption, offers us the opportunity to integrate them into broad AQ wireless sensor networks, with the aim of enabling real-time monitoring and higher spatial sampling
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The increase and diversity of low-cost air quality (AQ) sensors, as well as their flexibility and low power consumption, offers us the opportunity to integrate them into broad AQ wireless sensor networks, with the aim of enabling real-time monitoring and higher spatial sampling density of pollution in all parts of cities. Considering that the vast majority of the population lives in cities and the increase in respiratory/allergic problems in a large part of the population, it is of great interest to offer services and applications to improve their quality of life by avoiding pollution exposure in their movements in the open air. In the ECO4RUPA project, we focus on this kind of service, proposing an inclusive and intelligent routing ecosystem carried out using a network of low-cost AQ sensors with the support of 5G communications along with official AQ monitoring stations, using spatial interpolation techniques to enhance its spatial resolution. The goal of this service is to calculate healthy walking and/or cycling routes according to the particular citizen’s profile and needs. We provide and analyse the results of the proposed route planner under different scenarios (different timetables, congestion road traffic, and routes) and different user profiles, with a special interest in citizens with asthma and pregnant women, since both have special needs. In summary, our approach can lead to an approximately average reduction in pollution exposure of 17.82% while experiencing an approximately average increase in distance travelled of 9.8%.
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(This article belongs to the Special Issue IoT-Based Systems for Safe and Secure Smart Cities)
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Unlocking Sustainable Value through Digital Transformation: An Examination of ESG Performance
Information 2023, 14(8), 444; https://doi.org/10.3390/info14080444 - 07 Aug 2023
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Digital transformation has already begun to play a significant role in helping EU countries to achieve sustainable values by promoting environmental, social and governance (ESG) efficiency. It is rapidly changing the economic landscape, which leads to changes in all sectors and at all
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Digital transformation has already begun to play a significant role in helping EU countries to achieve sustainable values by promoting environmental, social and governance (ESG) efficiency. It is rapidly changing the economic landscape, which leads to changes in all sectors and at all levels. The European Union (EU) has set ambitious goals for sustainable development and climate change mitigation, such as the European Green Deal and the 2030 Agenda for Sustainable Development. The paper aims to test the spatial spillover effect of digitalization on ESG performance for EU countries for 2008–2020. The study applies the spatial Durbin model to check the research hypothesis. The empirical results revealed that the EU exhibits varying levels of ESG performance. Digital transformation has the potential to enhance ESG performance and has shown significant spatial spillover effects. The SDM estimates that a 1% increase in digital inclusion results in a minimal 0.001% increase in the ESG index. The statistically significant positive effects observed in key enablers, digital public services for businesses and citizens, highlight the contribution of digitalization to improving ESG performance. In addition, technological innovation serves as a critical conduit for transmitting digital transformation in the business and public sphere to ESG performance. Given these findings, policymakers are advised to strengthen digitalization efforts to narrow the digital divide, leveraging the digital economy as a potent instrument. Additionally, a dynamic and targeted strategy for digital economic development should be implemented to address ESG performance disparities effectively.
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(This article belongs to the Special Issue Digital Work—Information Technology and Commute Choice)
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Design and Selection of Inductor Current Feedback for the Sliding-Mode Controlled Hybrid Boost Converter
Information 2023, 14(8), 443; https://doi.org/10.3390/info14080443 - 07 Aug 2023
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The hybrid step-up converter is a fifth-order system with a dc gain greater than the traditional second-order step-up configuration. Considering their high order, several state variables are accessible for feedback purposes in the control of such systems. Therefore, choosing the best state variables
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The hybrid step-up converter is a fifth-order system with a dc gain greater than the traditional second-order step-up configuration. Considering their high order, several state variables are accessible for feedback purposes in the control of such systems. Therefore, choosing the best state variables is essential since they influence the system’s dynamic response and stability. This work proposes a methodical method to identify the appropriate state variables in implementing a sliding-mode (SM) controlled hybrid boost converter. A thorough comparison of two SM controllers based on various feedback currents is conducted. The frequency response technique is used to demonstrate how the SM method employing the current through the output inductor leads to an unstable response. The right-half s-plane poles and zeroes in the converter’s inner-loop transfer function, which precisely cancel one another, are what is causing the instability. On the other hand, a stable system may result from employing a SM controller with the current through the input inductor. Lastly, some experimental outcomes using the preferred SM control method are provided.
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(This article belongs to the Special Issue Advances in Electrical Engineering and Information Technologies)
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Analyzing Global Geopolitical Stability in Terms of World Trade Network Analysis
Information 2023, 14(8), 442; https://doi.org/10.3390/info14080442 - 04 Aug 2023
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The global economy operates as a complex and interconnected system, necessitating the application of sophisticated network methods for analysis. This study examines economic data from all countries across the globe, representing each country as a node and its exports as links, covering the
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The global economy operates as a complex and interconnected system, necessitating the application of sophisticated network methods for analysis. This study examines economic data from all countries across the globe, representing each country as a node and its exports as links, covering the period from 2008 to 2019. Through the computation of relevant indices, we can discern shifts in countries’ positions within the world trade network. By interpreting these changes through geopolitical perspectives, we can gain a deeper understanding of their root causes. The analysis reveals a notable trend of slow growth in the world trade network. Additionally, an intriguing observation emerges: countries naturally form stable groups, shedding light on the underlying structure of global trade relations. Furthermore, this research highlights the trade balance as a reflection of geopolitical strength, making it a valuable contribution to the study of the evolution of global geopolitical stability.
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(This article belongs to the Special Issue Complex Network Analysis in Security)
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Modeling Theory of Mind in Dyadic Games Using Adaptive Feedback Control
Information 2023, 14(8), 441; https://doi.org/10.3390/info14080441 - 04 Aug 2023
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A major challenge in cognitive science and AI has been to understand how intelligent autonomous agents might acquire and predict the behavioral and mental states of other agents in the course of complex social interactions. How does such an agent model the goals,
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A major challenge in cognitive science and AI has been to understand how intelligent autonomous agents might acquire and predict the behavioral and mental states of other agents in the course of complex social interactions. How does such an agent model the goals, beliefs, and actions of other agents it interacts with? What are the computational principles to model a Theory of Mind (ToM)? Deep learning approaches to address these questions fall short of a better understanding of the problem. In part, this is due to the black-box nature of deep networks, wherein computational mechanisms of ToM are not readily revealed. Here, we consider alternative hypotheses seeking to model how the brain might realize a ToM. In particular, we propose embodied and situated agent models based on distributed adaptive control theory to predict the actions of other agents in five different game-theoretic tasks (Harmony Game, Hawk-Dove, Stag Hunt, Prisoner’s Dilemma, and Battle of the Exes). Our multi-layer control models implement top-down predictions from adaptive to reactive layers of control and bottom-up error feedback from reactive to adaptive layers. We test cooperative and competitive strategies among seven different agent models (cooperative, greedy, tit-for-tat, reinforcement-based, rational, predictive, and internal agents). We show that, compared to pure reinforcement-based strategies, probabilistic learning agents modeled on rational, predictive, and internal phenotypes perform better in game-theoretic metrics across tasks. The outlined autonomous multi-agent models might capture systems-level processes underlying a ToM and suggest architectural principles of ToM from a control-theoretic perspective.
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(This article belongs to the Special Issue Intelligent Agent and Multi-Agent System)
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A Probabilistic Approach to Modeling Students’ Interactions in a Learning Management System for Facilitating Distance Learning
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Information 2023, 14(8), 440; https://doi.org/10.3390/info14080440 - 04 Aug 2023
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Learning mostly involves communication and interaction that leads to new information being processed, which eventually turns into knowledge. In the digital era, these actions pass through online technologies. Formal education uses LMSs that support these actions and, at the same time, produce massive
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Learning mostly involves communication and interaction that leads to new information being processed, which eventually turns into knowledge. In the digital era, these actions pass through online technologies. Formal education uses LMSs that support these actions and, at the same time, produce massive amounts of data. In a distance learning model, the assignments have an important role besides assessing the learning outcome; they also help students become actively engaged with the course and regulate their learning behavior. In this work, we leverage data retrieved from students’ online interactions to improve our understanding of the learning process. Focusing on log data, we investigate the students’ activity that occur close to and during assignment submission due dates. Additionally, their activity in relation to their academic achievements is examined and the response time in the forum communication is computed both for students and their tutors. The main findings include that students tend to procrastinate in the submission of their assignments mostly at the beginning of the course. Furthermore, the last-minute submissions are usually made late at night, which probably indicates poor management or lack of available time. Regarding forum interactions, our findings highlight that tutors tend to respond faster than students in the corresponding posts.
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(This article belongs to the Special Issue Artificial Intelligence and Games Science in Education)
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Machinability of Titanium Grade 5 Alloy for Wire Electrical Discharge Machining Using a Hybrid Learning Algorithm
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Information 2023, 14(8), 439; https://doi.org/10.3390/info14080439 - 03 Aug 2023
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Titanium alloys have found widespread use in aviation, automotive, and marine applications, which makes their implementation in mass production more challenging. Conventional methods of removing these alloy materials are unsuitable because of the high wear rate of cutting and slower rate of processing.
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Titanium alloys have found widespread use in aviation, automotive, and marine applications, which makes their implementation in mass production more challenging. Conventional methods of removing these alloy materials are unsuitable because of the high wear rate of cutting and slower rate of processing. The complexities of these materials have prompted the creation of cutting-edge machining methods. Wire Electrical Discharge Machining (WEDM) is a technique that has the potential to be useful for the removal of materials that are harder and electrically conductive. In order to create intricate designs, this method is frequently employed. The input factors, including pulse duration (on/off) and peak current, were taken into account during the experimental design process. The rate of material removal, surface roughness, dimensional deviation, and GD&T errors were opted for as performance indicators. The approach proposed by Taguchi was selected for the investigation of the process factors, and an Analysis of Variance was selected to find out the relative momentousness of each factor. From the analysis it is perceived that the applied current is the predominant factor that influences the chosen output characteristics. The aspiration of this article is to evolve a decision-making model based on a hybrid learning method which can be adopted to predict the selected output measures that affect the WEDM process. According to the findings, the value of the ANFIS-GRG, which was predicted to be 0.7777, was in fact closer to that value than any other value. The proposed model has the ability to help make a variety of different production processes more efficient. The analysis showed that the model’s functionality was enhanced, which helps producers make well-informed decisions.
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(This article belongs to the Special Issue Trends in Computational and Cognitive Engineering)
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An Evaluation of Feature Selection Robustness on Class Noisy Data
Information 2023, 14(8), 438; https://doi.org/10.3390/info14080438 - 03 Aug 2023
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With the increasing growth of data dimensionality, feature selection has become a crucial step in a variety of machine learning and data mining applications. In fact, it allows identifying the most important attributes of the task at hand, improving the efficiency, interpretability, and
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With the increasing growth of data dimensionality, feature selection has become a crucial step in a variety of machine learning and data mining applications. In fact, it allows identifying the most important attributes of the task at hand, improving the efficiency, interpretability, and final performance of the induced models. In recent literature, several studies have examined the strengths and weaknesses of the available feature selection methods from different points of view. Still, little work has been performed to investigate how sensitive they are to the presence of noisy instances in the input data. This is the specific field in which our work wants to make a contribution. Indeed, since noise is arguably inevitable in several application scenarios, it would be important to understand the extent to which the different selection heuristics can be affected by noise, in particular class noise (which is more harmful in supervised learning tasks). Such an evaluation may be especially important in the context of class-imbalanced problems, where any perturbation in the set of training records can strongly affect the final selection outcome. In this regard, we provide here a two-fold contribution by presenting (i) a general methodology to evaluate feature selection robustness on class noisy data and (ii) an experimental study that involves different selection methods, both univariate and multivariate. The experiments have been conducted on eight high-dimensional datasets chosen to be representative of different real-world domains, with interesting insights into the intrinsic degree of robustness of the considered selection approaches.
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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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IoTBChain: Adopting Blockchain Technology to Increase PLC Resilience in an IoT Environment
Information 2023, 14(8), 437; https://doi.org/10.3390/info14080437 - 02 Aug 2023
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The networks on a centralized cloud architecture that interconnect Internet of Things (IoT) gadgets are not limited by national or jurisdictional borders. To ensure the secure sharing of sensitive user data among IoT gadgets, it is imperative to maintain security, resilience and trustless
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The networks on a centralized cloud architecture that interconnect Internet of Things (IoT) gadgets are not limited by national or jurisdictional borders. To ensure the secure sharing of sensitive user data among IoT gadgets, it is imperative to maintain security, resilience and trustless authentication. As a result, blockchain technology has become a viable option to provide such noteworthy characteristics. Blockchain technology is foundational for resolving many IoT security and privacy issues. Blockchain’s safe decentralization can solve the IoT ecosystem’s security, authentication and maintenance constraints. However, blockchain, like any innovation, has drawbacks, mainly when used in crucial IoT systems such as programmable logic controller (PLC) networks. This paper addresses the most recent security and privacy issues relating to the IoT, including the perception, network and application layers of the IoT’s tiered architecture. The key focus is to review the existing IoT security and privacy concerns and how blockchain might be used to deal with these problems. This paper proposes a novel approach focusing on IoT capabilities and PLC device security. The new model will incorporate a proof-of-work-based blockchain into the (PLC) IoT ecosystem. This blockchain enables the transmission of binary data and the data logging of the (PLC) networks’ signals. This novel technique uses fewer resources than other sophisticated methods in that PLC devices communicate data while maintaining a high transmission, encryption and decoding speed. In addition to ensuring repeatability, our new model addresses the memory and tracing problems that different PLC manufacturers encounter.
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(This article belongs to the Special Issue Emerging Industrial Applications: Orchestration of Machine Learning, the IoT, and Blockchain)
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The Dark Threads That Weave the Web of Shame: A Network Science-Inspired Analysis of Body Shaming on Reddit
Information 2023, 14(8), 436; https://doi.org/10.3390/info14080436 - 02 Aug 2023
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Deep within online forums, we often stumble across body shaming. Words like “fat” and “ugly” are tossed around, hurting those they target. But can we peel back the layers of these online communities? In this study, social network analysis is used to shine
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Deep within online forums, we often stumble across body shaming. Words like “fat” and “ugly” are tossed around, hurting those they target. But can we peel back the layers of these online communities? In this study, social network analysis is used to shine a light on body shaming on Reddit, a well-known online platform. This paper presents a comprehensive social network analysis of body shaming on Reddit, one of the largest online platforms. The research delves into the intricacies of body shaming by identifying key actors, communities, and patterns of behavior and communication related to body shaming. The results show how behavior and communication differ across Reddit’s various subgroups, and how user activity and the length of comments can vary. Through the application of topic modeling, the main subjects discussed in each subgroup were identified. This enables an understanding of what drives discussions about body shaming. The findings provide valuable insights into the spread and normalization of harmful behaviors and attitudes related to body shaming, which can inform the development of targeted interventions aimed at reducing this harmful behavior and promoting more positive and inclusive attitudes towards body image and weight.
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(This article belongs to the Special Issue Network and Hypernetwork Science: Emerging Models, Paradigms, and Applications)
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Multiple Explainable Approaches to Predict the Risk of Stroke Using Artificial Intelligence
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Information 2023, 14(8), 435; https://doi.org/10.3390/info14080435 - 01 Aug 2023
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Stroke occurs when a brain’s blood artery ruptures or the brain’s blood supply is interrupted. Due to rupture or obstruction, the brain’s tissues cannot receive enough blood and oxygen. Stroke is a common cause of mortality among older people. Hence, loss of life
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Stroke occurs when a brain’s blood artery ruptures or the brain’s blood supply is interrupted. Due to rupture or obstruction, the brain’s tissues cannot receive enough blood and oxygen. Stroke is a common cause of mortality among older people. Hence, loss of life and severe brain damage can be avoided if stroke is recognized and diagnosed early. Healthcare professionals can discover solutions more quickly and accurately using artificial intelligence (AI) and machine learning (ML). As a result, we have shown how to predict stroke in patients using heterogeneous classifiers and explainable artificial intelligence (XAI). The multistack of ML models surpassed all other classifiers, with accuracy, recall, and precision of 96%, 96%, and 96%, respectively. Explainable artificial intelligence is a collection of frameworks and tools that aid in understanding and interpreting predictions provided by machine learning algorithms. Five diverse XAI methods, such as Shapley Additive Values (SHAP), ELI5, QLattice, Local Interpretable Model-agnostic Explanations (LIME) and Anchor, have been used to decipher the model predictions. This research aims to enable healthcare professionals to provide patients with more personalized and efficient care, while also providing a screening architecture with automated tools that can be used to revolutionize stroke prevention and treatment.
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(This article belongs to the Special Issue Health Data Information Retrieval)
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How Could Consumers’ Online Review Help Improve Product Design Strategy?
Information 2023, 14(8), 434; https://doi.org/10.3390/info14080434 - 01 Aug 2023
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This study aims to explore the utilization of user-generated content for product improvement and decision-making processes. In the era of big data, the channels through which enterprises obtain user feedback information are transitioning from traditional methods to online platforms. The original data for
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This study aims to explore the utilization of user-generated content for product improvement and decision-making processes. In the era of big data, the channels through which enterprises obtain user feedback information are transitioning from traditional methods to online platforms. The original data for this study were obtained from customer reviews of cordless hairdryers on JD.com. The specific process is as follows: First, we used the Python Requests package to crawl 20,157 initial comments. Subsequently, the initial data were cleaned, resulting in 1405 valid comments. Next, the cleaned and valid comments were segmented into Chinese words using the HanLP package. Finally, the Latent Dirichlet Allocation (LDA) method was applied for topic modeling. The visualization of the topic clustering was generated using pyLDAvis, and three optimal topics were identified. These topics were named “User Experience”, “Product Evaluation”, and “Product Features”, respectively. Through data analysis and expert consultation, this study developed product design improvement strategies based on online reviews and verified the validity of the developed cordless hairdryer design index system through a questionnaire survey, providing practical references and innovative theoretical foundations for future product design assessments.
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(This article belongs to the Special Issue Information for Business and Management–Software Development for Data Processing and Management)
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Detecting Abnormal Behaviors in Dementia Patients Using Lifelog Data: A Machine Learning Approach
Information 2023, 14(8), 433; https://doi.org/10.3390/info14080433 - 01 Aug 2023
Abstract
In this paper, a proof-of-concept method for detecting abnormal behavior in dementia patients based on a single case study is proposed. This method incorporates the collection of lifelog data using affordable sensors and the development of a machine-learning-based system. Such an approach has
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In this paper, a proof-of-concept method for detecting abnormal behavior in dementia patients based on a single case study is proposed. This method incorporates the collection of lifelog data using affordable sensors and the development of a machine-learning-based system. Such an approach has the potential to enable the prompt detection of abnormal behavior in dementia patients within nursing homes and to send alerts to caregivers, which could potentially reduce their workload and decrease the risk of accidents and injuries. In a proof-of-concept experiment conducted on a single dementia patient in a Korean nursing home, the proposed system, specifically the multilayer perceptron model, demonstrated exceptional performance, achieving an accuracy of 0.99, a precision of 1.00, a recall of 1.00, and an F1 score of 1.00. While being cost-effective and adaptable to various nursing homes, these results should be interpreted as preliminary, being based on a limited sample. Future research is aimed at validating and improving the performance of the abnormal behavior detection system by expanding the experiments to include lifelog data from multiple nursing homes and a larger cohort of dementia patients. The potential application of this system extends beyond healthcare and medical fields, reaching into smart home environments and various other facilities. This study underscores the potential of this system to enhance patient safety, alleviate family concerns, and reduce societal costs, thereby contributing to the improvement of the quality of life for dementia patients.
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(This article belongs to the Special Issue IoT-Based Systems for Safe and Secure Smart Cities)
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Topic Mining and Future Trend Exploration in Digital Economy Research
Information 2023, 14(8), 432; https://doi.org/10.3390/info14080432 - 01 Aug 2023
Abstract
This work proposes a new literature topic clustering analysis framework, based on which the topics of digital-economy-related studies are condensed. First, we calculated the word vector of keywords using the FastText model, and then the keywords were merged according to semantic similarity. A
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This work proposes a new literature topic clustering analysis framework, based on which the topics of digital-economy-related studies are condensed. First, we calculated the word vector of keywords using the FastText model, and then the keywords were merged according to semantic similarity. A hierarchical clustering method based on the Jaccard coefficient was employed to cluster the domain documents. Finally, the information gain method was applied to estimate the high-gain feature words for each category of topics. Based on the above framework, 23 categories of research topics were formed. We divided these topics into layers of digital technology, convergence innovation and digital governance, and we constructed a three-level digital economy research framework. Thereafter, the current hot spots and frontier trends were derived based on the number and growth rate of the literature. Our study revealed that the research on digital technology, which is the basic layer of the digital economy, has waned. The field related to the integration and innovation of digital technology and the real economy was the current research focus, among which the results with respect to “New Business Forms in the Digital Age”, “Circular Economy” and “Gig Economy” were abundant. The problems of the unbalanced development of the digital economy and digital monopoly have strengthened research on digital governance. Furthermore, research on “Regional Digital Economy”, “Chinese Digital Economy” and “Data Management” is in its initial stage and is a potential area of future research.
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(This article belongs to the Special Issue Digital Economy and Management)
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Exploring a Multi-Layered Cross-Genre Corpus of Document-Level Semantic Relations
Information 2023, 14(8), 431; https://doi.org/10.3390/info14080431 - 01 Aug 2023
Abstract
This paper introduces a multi-layered cross-genre corpus, annotated for coreference resolution, causal relations, and temporal relations, comprising a variety of genres, from news articles and children’s stories to Reddit posts. Our results reveal distinctive genre-specific characteristics at each layer of annotation, highlighting unique
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This paper introduces a multi-layered cross-genre corpus, annotated for coreference resolution, causal relations, and temporal relations, comprising a variety of genres, from news articles and children’s stories to Reddit posts. Our results reveal distinctive genre-specific characteristics at each layer of annotation, highlighting unique challenges for both annotators and machine learning models. Children’s stories feature linear temporal structures and clear causal relations. In contrast, news articles employ non-linear temporal sequences with minimal use of explicit causal or conditional language and few first-person pronouns. Lastly, Reddit posts are author-centered explanations of ongoing situations, with occasional meta-textual reference. Our annotation schemes are adapted from existing work to better suit a broader range of text types. We argue that our multi-layered cross-genre corpus not only reveals genre-specific semantic characteristics but also indicates a rich contextual interplay between the various layers of semantic information. Our MLCG corpus is shared under the open-source Apache 2.0 license.
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(This article belongs to the Special Issue Information Extraction and Language Discourse Processing)
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Open AccessArticle
Development of Technologies for the Detection of (Cyber)Bullying Actions: The BullyBuster Project
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Information 2023, 14(8), 430; https://doi.org/10.3390/info14080430 - 01 Aug 2023
Abstract
Bullying and cyberbullying are harmful social phenomena that involve the intentional, repeated use of power to intimidate or harm others. The ramifications of these actions are felt not just at the individual level but also pervasively throughout society, necessitating immediate attention and practical
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Bullying and cyberbullying are harmful social phenomena that involve the intentional, repeated use of power to intimidate or harm others. The ramifications of these actions are felt not just at the individual level but also pervasively throughout society, necessitating immediate attention and practical solutions. The BullyBuster project pioneers a multi-disciplinary approach, integrating artificial intelligence (AI) techniques with psychological models to comprehensively understand and combat these issues. In particular, employing AI in the project allows the automatic identification of potentially harmful content by analyzing linguistic patterns and behaviors in various data sources, including photos and videos. This timely detection enables alerts to relevant authorities or moderators, allowing for rapid interventions and potential harm mitigation. This paper, a culmination of previous research and advancements, details the potential for significantly enhancing cyberbullying detection and prevention by focusing on the system’s design and the novel application of AI classifiers within an integrated framework. Our primary aim is to evaluate the feasibility and applicability of such a framework in a real-world application context. The proposed approach is shown to tackle the pervasive issue of cyberbullying effectively.
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(This article belongs to the Special Issue Computer Vision, Pattern Recognition and Machine Learning in Italy)
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