Journal Description
Information
Information
is a scientific, peer-reviewed, open access journal of information science and technology, data, knowledge, and communication, and is published monthly online by MDPI. The International Society for Information Studies (IS4SI) is affiliated with Information and its members receive discounts on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), Ei Compendex, dblp, and other databases.
- Journal Rank: CiteScore - Q2 (Information Systems)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.1 (2022);
5-Year Impact Factor:
2.9 (2022)
Latest Articles
A Multimethod Approach for Healthcare Information Sharing Systems: Text Analysis and Empirical Data
Information 2024, 15(6), 319; https://doi.org/10.3390/info15060319 (registering DOI) - 29 May 2024
Abstract
This paper provides empirical evidence using two studies to explain the primary factors facilitating electronic health record (EHR) systems adoption through the lens of the resource advantage theory. We aim to address the following research questions: What are the main organizational antecedents of
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This paper provides empirical evidence using two studies to explain the primary factors facilitating electronic health record (EHR) systems adoption through the lens of the resource advantage theory. We aim to address the following research questions: What are the main organizational antecedents of EHR implementation? What is the role of monitoring in EHR system implementation? What are the current themes and people’s attitudes toward EHR systems? This paper includes two empirical studies. Study 1 presents a research model based on data collected from four different archival datasets. Drawing upon the resource advantage theory, this paper uses archival data from 200 Texas hospitals, thus mitigating potential response bias and enhancing the validity of the findings. Study 2 includes a text analysis of 5154 textual data, sentiment analysis, and topic modeling. Study 1’s findings reveal that joint ventures and ownership are the two main enablers of adopting EHR systems in 200 Texas hospitals. Moreover, the results offer a moderating role of monitoring in strengthening the relationship between joint-venture capability and the implementation of EHR systems. Study 2’s results indicate a positive attitude toward EHR systems. The U.S. was unique in the sample due to its slower adoption of EHR systems than other developed countries. Physician burnout also emerged as a significant concern in the context of EHR adoption. Topic modeling identified three themes: training, healthcare interoperability, and organizational barriers. In a multimethod design, this paper contributes to prior work by offering two new EHR antecedents: hospital ownership and joint-venture capability. Moreover, this paper suggests that the monitoring mechanism moderates the adoption of EHR systems in Texas hospitals. Moreover, this paper contributes to prior EHR works by performing text analysis of textual data to carry out sentiment analysis and topic modeling.
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(This article belongs to the Special Issue Information Systems in Healthcare)
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Open AccessArticle
GRAAL: Graph-Based Retrieval for Collecting Related Passages across Multiple Documents
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Misael Mongiovì and Aldo Gangemi
Information 2024, 15(6), 318; https://doi.org/10.3390/info15060318 - 29 May 2024
Abstract
Finding passages related to a sentence over a large collection of text documents is a fundamental task for claim verification and open-domain question answering. For instance, a common approach for verifying a claim is to extract short snippets of relevant text from a
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Finding passages related to a sentence over a large collection of text documents is a fundamental task for claim verification and open-domain question answering. For instance, a common approach for verifying a claim is to extract short snippets of relevant text from a collection of reference documents and provide them as input to a natural language inference machine that determines whether the claim can be deduced or refuted. Available approaches struggle when several pieces of evidence from different documents need to be combined to make an inference, as individual documents often have a low relevance with the input and are therefore excluded. We propose GRAAL (GRAph-based retrievAL), a novel graph-based approach that outlines the relevant evidence as a subgraph of a large graph that summarizes the whole corpus. We assess the validity of this approach by building a large graph that represents co-occurring entity mentions on a corpus of Wikipedia pages and using this graph to identify candidate text relevant to a claim across multiple pages. Our experiments on a subset of FEVER, a popular benchmark, show that the proposed approach is effective in identifying short passages related to a claim from multiple documents.
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(This article belongs to the Special Issue 2nd Edition of Information Retrieval and Social Media Mining)
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Open AccessReview
Reliablity and Security for Fog Computing Systems
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Egor Shiriaev, Tatiana Ermakova, Ekaterina Bezuglova, Maria A. Lapina and Mikhail Babenko
Information 2024, 15(6), 317; https://doi.org/10.3390/info15060317 - 29 May 2024
Abstract
Fog computing (FC) is a distributed architecture in which computing resources and services are placed on edge devices closer to data sources. This enables more efficient data processing, shorter latency times, and better performance. Fog computing was shown to be a promising solution
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Fog computing (FC) is a distributed architecture in which computing resources and services are placed on edge devices closer to data sources. This enables more efficient data processing, shorter latency times, and better performance. Fog computing was shown to be a promising solution for addressing the new computing requirements. However, there are still many challenges to overcome to utilize this new computing paradigm, in particular, reliability and security. Following this need, a systematic literature review was conducted to create a list of requirements. As a result, the following four key requirements were formulated: (1) low latency and response times; (2) scalability and resource management; (3) fault tolerance and redundancy; and (4) privacy and security. Low delay and response can be achieved through edge caching, edge real-time analyses and decision making, and mobile edge computing. Scalability and resource management can be enabled by edge federation, virtualization and containerization, and edge resource discovery and orchestration. Fault tolerance and redundancy can be enabled by backup and recovery mechanisms, data replication strategies, and disaster recovery plans, with a residual number system (RNS) being a promising solution. Data security and data privacy are manifested in strong authentication and authorization mechanisms, access control and authorization management, with fully homomorphic encryption (FHE) and the secret sharing system (SSS) being of particular interest.
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(This article belongs to the Special Issue Digital Privacy and Security, 2nd Edition)
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Enhancing Arabic Dialect Detection on Social Media: A Hybrid Model with an Attention Mechanism
by
Wael M. S. Yafooz
Information 2024, 15(6), 316; https://doi.org/10.3390/info15060316 - 28 May 2024
Abstract
Recently, the widespread use of social media and easy access to the Internet have brought about a significant transformation in the type of textual data available on the Web. This change is particularly evident in Arabic language usage, as the growing number of
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Recently, the widespread use of social media and easy access to the Internet have brought about a significant transformation in the type of textual data available on the Web. This change is particularly evident in Arabic language usage, as the growing number of users from diverse domains has led to a considerable influx of Arabic text in various dialects, each characterized by differences in morphology, syntax, vocabulary, and pronunciation. Consequently, researchers in language recognition and natural language processing have become increasingly interested in identifying Arabic dialects. Numerous methods have been proposed to recognize this informal data, owing to its crucial implications for several applications, such as sentiment analysis, topic modeling, text summarization, and machine translation. However, Arabic dialect identification is a significant challenge due to the vast diversity of the Arabic language in its dialects. This study introduces a novel hybrid machine and deep learning model, incorporating an attention mechanism for detecting and classifying Arabic dialects. Several experiments were conducted using a novel dataset that collected information from user-generated comments from Twitter of Arabic dialects, namely, Egyptian, Gulf, Jordanian, and Yemeni, to evaluate the effectiveness of the proposed model. The dataset comprises 34,905 rows extracted from Twitter, representing an unbalanced data distribution. The data annotation was performed by native speakers proficient in each dialect. The results demonstrate that the proposed model outperforms the performance of long short-term memory, bidirectional long short-term memory, and logistic regression models in dialect classification using different word representations as follows: term frequency-inverse document frequency, Word2Vec, and global vector for word representation.
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(This article belongs to the Special Issue Recent Advances in Social Media Mining and Analysis)
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An Ecosystem for the Provision of Digital Accessibility for People with Special Needs
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Galina Bogdanova, Todor Todorov, Nikolay Noev, Negoslav Sabev, Neda Chehlarova, Mirena Todorova-Ekmekci and Aleksandar Krastev
Information 2024, 15(6), 315; https://doi.org/10.3390/info15060315 - 28 May 2024
Abstract
Digital technologies occupy an important place in today’s developing world. They are also strongly related to new trends in educational technologies. In this context, the digital accessibility of this new environment for people with various special needs is of particular concern. A novel
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Digital technologies occupy an important place in today’s developing world. They are also strongly related to new trends in educational technologies. In this context, the digital accessibility of this new environment for people with various special needs is of particular concern. A novel tool for assessment of the technological ecosystem, designed to provide digital accessibility to people with special needs, is described in the paper. The overall structure and the initial test of the system are discussed in the paper. The conceptual framework of the ecosystem and its ontological model are described. Special attention is paid to the accessibility of digital learning and e-learning for people with special needs from a robotic perspective.
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(This article belongs to the Special Issue Accessibility and Inclusion in Education: Enabling Digital Technologies)
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Determinants of Humanities and Social Sciences Students’ Intentions to Use Artificial Intelligence Applications for Academic Purposes
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Konstantinos Lavidas, Iro Voulgari, Stamatios Papadakis, Stavros Athanassopoulos, Antigoni Anastasiou, Andromachi Filippidi, Vassilis Komis and Nikos Karacapilidis
Information 2024, 15(6), 314; https://doi.org/10.3390/info15060314 - 28 May 2024
Abstract
Recent research emphasizes the importance of Artificial Intelligence applications as supporting tools for students in higher education. Simultaneously, an intensive exchange of views has started in the public debate in the international educational community. However, for a more proper use of these applications,
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Recent research emphasizes the importance of Artificial Intelligence applications as supporting tools for students in higher education. Simultaneously, an intensive exchange of views has started in the public debate in the international educational community. However, for a more proper use of these applications, it is necessary to investigate the factors that explain their intention and actual use in the future. With the Unified Theory of Acceptance and Use of Technology (UTAUT2) model, this work analyses the factors influencing students’ use and intention to use Artificial Intelligence technology. For this purpose, a sample of 197 Greek students at the School of Humanities and Social Sciences from the University of Patras participated in a survey. The findings highlight that expected performance, habit, and enjoyment of these Artificial Intelligence applications are key determinants influencing teachers’ intentions to use them. Moreover, behavioural intention, habit, and facilitating conditions explain the usage of these Artificial Intelligence applications. This study did not reveal any moderating effects. The limitations, practical implications, and proposed directions for future research based on these results are discussed.
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(This article belongs to the Special Issue Artificial Intelligence and Games Science in Education)
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Open AccessCorrection
Correction: AlJarrah et al. A Context-Aware Android Malware Detection Approach Using Machine Learning. Information 2022, 13, 563
by
Mohammed N. AlJarrah, Qussai M. Yaseen and Ahmad M. Mustafa
Information 2024, 15(6), 313; https://doi.org/10.3390/info15060313 - 28 May 2024
Abstract
In the published article [...]
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Open AccessArticle
Enhancing Personalized Recommendations: A Study on the Efficacy of Multi-Task Learning and Feature Integration
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Qinyong Wang, Enman Jin, Huizhong Zhang, Yumeng Chen, Yinggao Yue, Danilo B. Dorado, Zhongyi Hu and Minghai Xu
Information 2024, 15(6), 312; https://doi.org/10.3390/info15060312 - 27 May 2024
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Personalized recommender systems play a crucial role in assisting users in discovering items of interest from vast amounts of information across various domains. However, developing accurate personalized recommender systems remains challenging due to the need to balance model architectures, input feature combinations, and
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Personalized recommender systems play a crucial role in assisting users in discovering items of interest from vast amounts of information across various domains. However, developing accurate personalized recommender systems remains challenging due to the need to balance model architectures, input feature combinations, and fusion of heterogeneous data sources. This study investigates the impacts of these factors on recommendation performance using the MovieLens and Book Recommendation datasets. Six models, including single-task neural networks, multi-task learning, and baselines, were evaluated with various input feature combinations using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The multi-task learning approach achieved significantly lower RMSE and MAE by effectively leveraging heterogeneous data sources for personalized recommendations through a shared neural network architecture. Furthermore, incorporating user data and content data progressively enhanced performance compared to using only item identifiers. The findings highlight the importance of advanced model architectures and fusing heterogeneous data sources for high-quality recommendations, providing valuable insights for designing effective recommender systems across diverse domains.
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Harnessing Artificial Intelligence for Automated Diagnosis
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Christos B. Zachariadis and Helen C. Leligou
Information 2024, 15(6), 311; https://doi.org/10.3390/info15060311 - 27 May 2024
Abstract
The evolving role of artificial intelligence (AI) in healthcare can shift the route of automated, supervised and computer-aided diagnostic radiology. An extensive literature review was conducted to consider the potential of designing a fully automated, complete diagnostic platform capable of integrating the current
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The evolving role of artificial intelligence (AI) in healthcare can shift the route of automated, supervised and computer-aided diagnostic radiology. An extensive literature review was conducted to consider the potential of designing a fully automated, complete diagnostic platform capable of integrating the current medical imaging technologies. Adjuvant, targeted, non-systematic research was regarded as necessary, especially to the end-user medical expert, for the completeness, understanding and terminological clarity of this discussion article that focuses on giving a representative and inclusive idea of the evolutional strides that have taken place, not including an AI architecture technical evaluation. Recent developments in AI applications for assessing various organ systems, as well as enhancing oncology and histopathology, show significant impact on medical practice. Published research outcomes of AI picture segmentation and classification algorithms exhibit promising accuracy, sensitivity and specificity. Progress in this field has led to the introduction of the concept of explainable AI, which ensures transparency of deep learning architectures, enabling human involvement in clinical decision making, especially in critical healthcare scenarios. Structure and language standardization of medical reports, along with interdisciplinary collaboration between medical and technical experts, are crucial for research coordination. Patient personal data should always be handled with confidentiality and dignity, while ensuring legality in the attribution of responsibility, particularly in view of machines lacking empathy and self-awareness. The results of our literature research demonstrate the strong potential of utilizing AI architectures, mainly convolutional neural networks, in medical imaging diagnostics, even though a complete automated diagnostic platform, enabling full body scanning, has not yet been presented.
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(This article belongs to the Special Issue Real-World Applications of Machine Learning Techniques)
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In-Browser Implementation of a Gamification Rule Definition Language Interpreter
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Jakub Swacha and Wiktor Przetacznik
Information 2024, 15(6), 310; https://doi.org/10.3390/info15060310 - 27 May 2024
Abstract
One of the practical obstacles limiting the use of cloud-based gamification applications is the lack of an Internet connection of adequate quality. In this paper, we describe a practical solution to this problem by the implementation of client-side gamification rule processing so that
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One of the practical obstacles limiting the use of cloud-based gamification applications is the lack of an Internet connection of adequate quality. In this paper, we describe a practical solution to this problem by the implementation of client-side gamification rule processing so that most events generated by players can be processed without the need to involve server-side functions; therefore, only a handful of data have to be transmitted to the server for global state synchronization, and only when an Internet connection is available. For this purpose, we adopt a simple textual gamification rule definition format, implement the rule parser and event processor, and evaluate the solution in terms of performance in experimental conditions. The obtained results are optimistic, showing that the developed solution can easily handle rule sets and event streams of realistic sizes. The solution is planned to be integrated into the next version of the FGPE gamified programming education platform.
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(This article belongs to the Special Issue Cloud Gamification 2023)
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A Collaborative Allocation Algorithm of Communicating, Caching and Computing Resources in Local Power Wireless Communication Network
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Jiajia Tang, Sujie Shao, Shaoyong Guo, Ye Wang and Shuang Wu
Information 2024, 15(6), 309; https://doi.org/10.3390/info15060309 - 27 May 2024
Abstract
With the rapid development of new power systems, diverse new power services have imposed stricter requirements on network resources and performance. However, the traditional method of transmitting request data to the IoT management platform for unified processing suffers from large delays due to
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With the rapid development of new power systems, diverse new power services have imposed stricter requirements on network resources and performance. However, the traditional method of transmitting request data to the IoT management platform for unified processing suffers from large delays due to long transmission distances, making it difficult to meet the delay requirements of new power services. Therefore, to reduce the transmission delay, data transmission, storage and computation need to be performed locally. However, due to the limited resources of individual nodes in the local power wireless communication network, issues such as tight coupling between devices and resources and a lack of flexible allocation need to be addressed. The collaborative allocation of resources among multiple nodes in the local network is necessary to satisfy the multi-dimensional resource requirements of new power services. In response to the problems of limited node resources, inflexible resource allocation, and the high complexity of multi-dimensional resource allocation in local power wireless communication networks, this paper proposes a multi-objective joint optimization model for the collaborative allocation of communication, storage, and computing resources. This model utilizes the computational characteristics of communication resources to reduce the dimensionality of the objective function. Furthermore, a mouse swarm optimization algorithm based on multi-strategy improvements is proposed. The simulation results demonstrate that this method can effectively reduce the total system delay and improve the utilization of network resources.
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(This article belongs to the Special Issue Internet of Things and Cloud-Fog-Edge Computing)
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Privacy and Security Mechanisms for B2B Data Sharing: A Conceptual Framework
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Wanying Li, Woon Kwan Tse and Jiaqi Chen
Information 2024, 15(6), 308; https://doi.org/10.3390/info15060308 - 26 May 2024
Abstract
In the age of digitalization, business-to-business (B2B) data sharing is becoming increasingly important, enabling organizations to collaborate and make informed decisions as well as simplifying operations and hopefully creating a cost-effective virtual value chain. This is crucial to the success of modern businesses,
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In the age of digitalization, business-to-business (B2B) data sharing is becoming increasingly important, enabling organizations to collaborate and make informed decisions as well as simplifying operations and hopefully creating a cost-effective virtual value chain. This is crucial to the success of modern businesses, especially global business. However, this approach also comes with significant privacy and security challenges, thus requiring robust mechanisms to protect sensitive information. After analyzing the evolving status of B2B data sharing, the purpose of this study is to provide insights into the design of theoretical framework solutions for the field. This study adopts technologies including encryption, access control, data anonymization, and audit trails, with the common goal of striking a balance between facilitating data sharing and protecting data confidentiality as well as data integrity. In addition, emerging technologies such as homomorphic encryption, blockchain, and their applicability as well as advantages in the B2B data sharing environment are explored. The results of this study offer a new approach to managing complex data sharing between organizations, providing a strategic mix of traditional and innovative solutions to promote secure and efficient digital collaboration.
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(This article belongs to the Special Issue Digital Privacy and Security, 2nd Edition)
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Detecting the Use of ChatGPT in University Newspapers by Analyzing Stylistic Differences with Machine Learning
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Min-Gyu Kim and Heather Desaire
Information 2024, 15(6), 307; https://doi.org/10.3390/info15060307 - 25 May 2024
Abstract
Large language models (LLMs) have the ability to generate text by stringing together words from their extensive training data. The leading AI text generation tool built on LLMs, ChatGPT, has quickly grown a vast user base since its release, but the domains in
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Large language models (LLMs) have the ability to generate text by stringing together words from their extensive training data. The leading AI text generation tool built on LLMs, ChatGPT, has quickly grown a vast user base since its release, but the domains in which it is being heavily leveraged are not yet known to the public. To understand how generative AI is reshaping print media and the extent to which it is being implemented already, methods to distinguish human-generated text from that generated by AI are required. Since college students have been early adopters of ChatGPT, we sought to study the presence of generative AI in newspaper articles written by collegiate journalists. To achieve this objective, an accurate AI detection model is needed. Herein, we analyzed university newspaper articles from different universities to determine whether ChatGPT was used to write or edit the news articles. We developed a detection model using classical machine learning and used the model to detect AI usage in the news articles. The detection model showcased a 93% accuracy in the training data and had a similar performance in the test set, demonstrating effectiveness in AI detection above existing state-of-the-art detection tools. Finally, the model was applied to the task of searching for generative AI usage in 2023, and we found that ChatGPT was not used to revise articles to any appreciable measure to write university news articles at the schools we studied.
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(This article belongs to the Special Issue Applications of Information Extraction, Knowledge Graphs, and Large Language Models)
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Open AccessReview
Machine Learning for Smart Irrigation in Agriculture: How Far along Are We?
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Marco Del-Coco, Marco Leo and Pierluigi Carcagnì
Information 2024, 15(6), 306; https://doi.org/10.3390/info15060306 - 24 May 2024
Abstract
The management of water resources is becoming increasingly important in several contexts, including agriculture. Recently, innovative agricultural practices, advanced sensors, and Internet of Things (IoT) devices have made it possible to improve the efficiency of water use. However, it is the application of
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The management of water resources is becoming increasingly important in several contexts, including agriculture. Recently, innovative agricultural practices, advanced sensors, and Internet of Things (IoT) devices have made it possible to improve the efficiency of water use. However, it is the application of control strategies based on advanced machine learning techniques that enables the adoption of smart irrigation scheduling and the immediate economic, social, and environmental benefits. This challenging research area has attracted the attention of many researchers worldwide, who have proposed several technological and methodological solutions. Unfortunately, the results of these scientific efforts have not yet been categorized in a thematic survey, making it difficult to understand how far we are from optimal water management based on machine learning. This paper fills this gap by focusing on smart irrigation systems with an emphasis on machine learning. More specifically, the generic structure of a smart agriculture system is presented, and existing machine learning strategies and available datasets are discussed. Furthermore, several open issues are identified, especially in the processing of long-term data, also due to the lack of corresponding annotated datasets. Finally, some interesting future research directions to be pursued in order to build scalable, domain-independent approaches are proposed.
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(This article belongs to the Special Issue Feature Papers in Information in 2024–2025)
Open AccessArticle
Private Firm Valuation Using Multiples: Can Artificial Intelligence Algorithms Learn Better Peer Groups?
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Timotej Jagrič, Dušan Fister, Stefan Otto Grbenic and Aljaž Herman
Information 2024, 15(6), 305; https://doi.org/10.3390/info15060305 - 24 May 2024
Abstract
Forming optimal peer groups is a crucial step in multiplier valuation. Among others, the traditional regression methodology requires the definition of the optimal set of peer selection criteria and the optimal size of the peer group a priori. Since there exists no universally
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Forming optimal peer groups is a crucial step in multiplier valuation. Among others, the traditional regression methodology requires the definition of the optimal set of peer selection criteria and the optimal size of the peer group a priori. Since there exists no universally applicable set of closed and complementary rules on selection criteria due to the complexity and the diverse nature of firms, this research exclusively examines unlisted companies, rendering direct comparisons with existing studies impractical. To address this, we developed a bespoke benchmark model through rigorous regression analysis. Our aim was to juxtapose its outcomes with our unique approach, enriching the understanding of unlisted company transaction dynamics. To stretch the performance of the linear regression method to the maximum, various datasets on selection criteria (full as well as F- and NCA-optimized) were employed. Using a sample of over 20,000 private firm transactions, model performance was evaluated employing multiplier prediction error measures (emphasizing bias and accuracy) as well as prediction superiority directly. Emphasizing five enterprise and equity value multiples, the results allow for the overall conclusion that the self-organizing map algorithm outperforms the traditional linear regression model in both minimizing the valuation error as measured by the multiplier prediction error measures as well as in direct prediction superiority. Consequently, the machine learning methodology offers a promising way to improve peer selection in private firm multiplier valuation.
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(This article belongs to the Section Artificial Intelligence)
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Decentralized Zone-Based PKI: A Lightweight Security Framework for IoT Ecosystems
by
Mohammed El-Hajj and Pim Beune
Information 2024, 15(6), 304; https://doi.org/10.3390/info15060304 - 24 May 2024
Abstract
The advent of Internet of Things (IoT) devices has revolutionized our daily routines, fostering interconnectedness and convenience. However, this interconnected network also presents significant security challenges concerning authentication and data integrity. Traditional security measures, such as Public Key Infrastructure (PKI), encounter limitations when
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The advent of Internet of Things (IoT) devices has revolutionized our daily routines, fostering interconnectedness and convenience. However, this interconnected network also presents significant security challenges concerning authentication and data integrity. Traditional security measures, such as Public Key Infrastructure (PKI), encounter limitations when applied to resource-constrained IoT devices. This paper proposes a novel decentralized PKI system tailored specifically for IoT environments to address these challenges. Our approach introduces a unique “zone” architecture overseen by zone masters, facilitating efficient certificate management within IoT clusters while reducing the risk of single points of failure. Furthermore, we prioritize the use of lightweight cryptographic techniques, including Elliptic Curve Cryptography (ECC), to optimize performance without compromising security. Through comprehensive evaluation and benchmarking, we demonstrate the effectiveness of our proposed solution in bolstering the security and efficiency of IoT ecosystems. This contribution underlines the critical need for innovative security solutions in IoT deployments and presents a scalable framework to meet the evolving demands of IoT environments.
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(This article belongs to the Special Issue Hardware Security and Trust)
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A Framework Model of Mining Potential Public Opinion Events Pertaining to Suspected Research Integrity Issues with the Text Convolutional Neural Network Model and a Mixed Event Extractor
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Zongfeng Zou, Xiaochen Ji and Yingying Li
Information 2024, 15(6), 303; https://doi.org/10.3390/info15060303 - 24 May 2024
Abstract
With the development of the Internet, the oversight of research integrity issues has extended beyond the scientific community to encompass the whole of society. If these issues are not addressed promptly, they can significantly impact the research credibility of both institutions and scholars.
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With the development of the Internet, the oversight of research integrity issues has extended beyond the scientific community to encompass the whole of society. If these issues are not addressed promptly, they can significantly impact the research credibility of both institutions and scholars. This article proposes a text convolutional neural network based on SMOTE to identify short texts of potential public opinion events related to suspected scientific integrity issues from common short texts. The SMOTE comprehensive sampling technique is employed to handle imbalanced datasets. To mitigate the impact of short text length on text representation quality, the Doc2vec embedding model is utilized to represent short text, yielding a one-dimensional dense vector. Additionally, the dimensions of the input layer and convolution kernel of TextCNN are adjusted. Subsequently, a short text event extraction model based on TF-IDF and TextRank is proposed to extract crucial information, for instance, names and research-related institutions, from events and facilitate the identification of potential public opinion events related to suspected scientific integrity issues. Results of experiments have demonstrated that utilizing SMOTE to balance the dataset is able to improve the classification results of TextCNN classifiers. Compared to traditional classifiers, TextCNN exhibits greater robustness in addressing the problems of imbalanced datasets. However, challenges such as low information content, non-standard writing, and polysemy in short texts may impact the accuracy of event extraction. The framework can be further optimized to address these issues in the future.
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(This article belongs to the Special Issue Machine Learning Approaches for Imbalanced Domains: Emerging Trends and Applications)
Open AccessArticle
The Impact of Input Types on Smart Contract Vulnerability Detection Performance Based on Deep Learning: A Preliminary Study
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Izdehar M. Aldyaflah, Wenbing Zhao, Shunkun Yang and Xiong Luo
Information 2024, 15(6), 302; https://doi.org/10.3390/info15060302 - 24 May 2024
Abstract
Stemming vulnerabilities out of a smart contract prior to its deployment is essential to ensure the security of decentralized applications. As such, numerous tools and machine-learning-based methods have been proposed to help detect vulnerabilities in smart contracts. Furthermore, various ways of encoding the
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Stemming vulnerabilities out of a smart contract prior to its deployment is essential to ensure the security of decentralized applications. As such, numerous tools and machine-learning-based methods have been proposed to help detect vulnerabilities in smart contracts. Furthermore, various ways of encoding the smart contracts for analysis have also been proposed. However, the impact of these input methods has not been systematically studied, which is the primary goal of this paper. In this preliminary study, we experimented with four common types of input, including Word2Vec, FastText, Bag-of-Words (BoW), and Term Frequency–Inverse Document Frequency (TF-IDF). To focus on the comparison of these input types, we used the same deep-learning model, i.e., convolutional neural networks, in all experiments. Using a public dataset, we compared the vulnerability detection performance of the four input types both in the binary classification scenarios and the multiclass classification scenario. Our findings show that TF-IDF is the best overall input type among the four. TF-IDF has excellent detection performance in all scenarios: (1) it has the best F1 score and accuracy in binary classifications for all vulnerability types except for the delegate vulnerability where TF-IDF comes in a close second, and (2) it comes in a very close second behind BoW (within 0.8%) in the multiclass classification.
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(This article belongs to the Special Issue Machine Learning for the Blockchain)
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An Efficient EEG Signal Analysis for Emotion Recognition Using FPGA
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M. R. Ezilarasan and Man-Fai Leung
Information 2024, 15(6), 301; https://doi.org/10.3390/info15060301 - 24 May 2024
Abstract
Electroencephalography (EEG), electromyography (EMG), galvanic skin response (GSR), and electrocardiogram (ECG) are among the techniques developed for collecting psychophysiological data from humans. This study presents a feature extraction technique for identifying emotions in EEG-based data from the human brain. Independent component analysis (ICA)
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Electroencephalography (EEG), electromyography (EMG), galvanic skin response (GSR), and electrocardiogram (ECG) are among the techniques developed for collecting psychophysiological data from humans. This study presents a feature extraction technique for identifying emotions in EEG-based data from the human brain. Independent component analysis (ICA) was employed to eliminate artifacts from the raw brain signals before applying signal extraction to a convolutional neural network (CNN) for emotion identification. These features were then learned by the proposed CNN-LSTM (long short-term memory) algorithm, which includes a ResNet-152 classifier. The CNN-LSTM with ResNet-152 algorithm was used for the accurate detection and analysis of human emotional data. The SEED V dataset was employed for data collection in this study, and the implementation was carried out using an Altera DE2 FPGA development board, demonstrating improved performance in terms of FPGA speed and area optimization.
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(This article belongs to the Special Issue Human Activity Recognition and Biomedical Signal Processing)
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Open AccessArticle
The Personification of ChatGPT (GPT-4)—Understanding Its Personality and Adaptability
by
Leandro Stöckli, Luca Joho, Felix Lehner and Thomas Hanne
Information 2024, 15(6), 300; https://doi.org/10.3390/info15060300 - 24 May 2024
Abstract
Thanks to the publication of ChatGPT, Artificial Intelligence is now basically accessible and usable to all internet users. The technology behind it can be used in many chatbots, whereby the chatbots should be trained for the respective area of application. Depending on the
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Thanks to the publication of ChatGPT, Artificial Intelligence is now basically accessible and usable to all internet users. The technology behind it can be used in many chatbots, whereby the chatbots should be trained for the respective area of application. Depending on the application, the chatbot should react differently and thus, for example, also take on and embody personality traits to be able to help and answer people better and more personally. This raises the question of whether ChatGPT-4 is able to embody personality traits. Our study investigated whether ChatGPT-4’s personality can be analyzed using personality tests for humans. To test possible approaches to measuring the personality traits of ChatGPT-4, experiments were conducted with two of the most well-known personality tests: the Big Five and Myers–Briggs. The experiments also examine whether and how personality can be changed by user input and what influence this has on the results of the personality tests.
Full article
(This article belongs to the Collection Natural Language Processing and Applications: Challenges and Perspectives)
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