Subjects -> COMMUNICATIONS (Total: 518 journals)
    - COMMUNICATIONS (446 journals)
    - DIGITAL AND WIRELESS COMMUNICATION (31 journals)
    - HUMAN COMMUNICATION (19 journals)
    - MEETINGS AND CONGRESSES (7 journals)
    - RADIO, TELEVISION AND CABLE (15 journals)

COMMUNICATIONS (446 journals)                  1 2 3 | Last

Showing 1 - 200 of 480 Journals sorted by number of followers
Evidence Based Library and Information Practice     Open Access   (Followers: 592)
Information Technologies & International Development     Open Access   (Followers: 86)
Information, Communication & Society     Hybrid Journal   (Followers: 79)
Journal of Communication     Hybrid Journal   (Followers: 64)
Convergence The International Journal of Research into New Media Technologies     Hybrid Journal   (Followers: 50)
Augmentative and Alternative Communication     Hybrid Journal   (Followers: 48)
e-learning and education (eleed)     Open Access   (Followers: 40)
Communication Theory     Hybrid Journal   (Followers: 36)
Journal of Computer-Mediated Communication     Open Access   (Followers: 35)
New Media and Mass Communication     Open Access   (Followers: 33)
Communication     Open Access   (Followers: 31)
Journal of the Association for Information Systems     Open Access   (Followers: 31)
Communication, Culture & Critique     Hybrid Journal   (Followers: 30)
Electronic Journal of Knowledge Management     Open Access   (Followers: 30)
Journalism & Mass Communication Quarterly     Hybrid Journal   (Followers: 29)
Health Information Management Journal     Hybrid Journal   (Followers: 29)
Advances in Journalism and Communication     Open Access   (Followers: 29)
Journal of Medical Internet Research     Open Access   (Followers: 28)
Advances in Image and Video Processing     Open Access   (Followers: 28)
New Review of Film and Television Studies     Hybrid Journal   (Followers: 27)
Discourse, Context & Media     Open Access   (Followers: 26)
Proceedings of the American Society for Information Science and Technology     Hybrid Journal   (Followers: 26)
Communication Papers : Media Literacy & Gender Studies     Open Access   (Followers: 25)
Journal of Media and Communication Studies     Open Access   (Followers: 25)
International Journal of Advanced Media and Communication     Hybrid Journal   (Followers: 23)
Journal of Information, Communication and Ethics in Society     Hybrid Journal   (Followers: 23)
Information & Communications Technology Law     Hybrid Journal   (Followers: 22)
Framework : The Journal of Cinema and Media     Full-text available via subscription   (Followers: 22)
Quarterly Review of Film and Video     Hybrid Journal   (Followers: 21)
Journalism & Mass Communication Educator     Hybrid Journal   (Followers: 21)
Screen     Hybrid Journal   (Followers: 20)
Journal of International and Intercultural Communication     Hybrid Journal   (Followers: 20)
Global Media and Communication     Hybrid Journal   (Followers: 19)
Language and Speech     Hybrid Journal   (Followers: 19)
Journal of Media Psychology     Hybrid Journal   (Followers: 19)
Journal of Science Communication     Open Access   (Followers: 19)
Journalism & Communication Monographs     Hybrid Journal   (Followers: 19)
Human Communication Research     Hybrid Journal   (Followers: 18)
IEEE Transactions on Smart Grid     Hybrid Journal   (Followers: 18)
Speech, Language and Hearing     Hybrid Journal   (Followers: 17)
Communication Booknotes Quarterly     Hybrid Journal   (Followers: 16)
Journal of Magnetic Resonance Imaging     Hybrid Journal   (Followers: 16)
International Journal of Information Technology, Communications and Convergence     Hybrid Journal   (Followers: 16)
Journal for the History of Rhetoric     Hybrid Journal   (Followers: 16)
Journal of Media Ethics : Exploring Questions of Media Morality     Hybrid Journal   (Followers: 15)
Public Relations Review     Hybrid Journal   (Followers: 15)
Quarterly Journal of Speech     Hybrid Journal   (Followers: 15)
Communications of the Association for Information Systems     Open Access   (Followers: 15)
International Journal of Computer Science and Telecommunications     Open Access   (Followers: 15)
Media and Communication     Open Access   (Followers: 15)
Journal of Broadcasting & Electronic Media     Hybrid Journal   (Followers: 14)
Journal of the American College of Radiology     Hybrid Journal   (Followers: 14)
Global Media Journal     Open Access   (Followers: 14)
Communications in Mobile Computing     Open Access   (Followers: 14)
Celebrity Studies     Hybrid Journal   (Followers: 14)
International Journal of Information and Communication Technology Education     Full-text available via subscription   (Followers: 13)
Journal of Technical Writing and Communication     Full-text available via subscription   (Followers: 12)
Chinese Journal of Communication     Hybrid Journal   (Followers: 12)
MedieKultur. Journal of media and communication research     Open Access   (Followers: 12)
Pragmatics and Society     Hybrid Journal   (Followers: 12)
Qualitative Studies     Open Access   (Followers: 12)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 12)
IEICE - Transactions on Fundamentals of Electronics, Communications and Computer Sciences     Full-text available via subscription   (Followers: 11)
IET Communications     Open Access   (Followers: 11)
Qualitative Research Reports in Communication     Hybrid Journal   (Followers: 11)
International Journal of Business Communication     Hybrid Journal   (Followers: 10)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 10)
Informal Logic     Open Access   (Followers: 10)
Communication & Language at Work     Open Access   (Followers: 10)
C&SC - Communication & Social Change     Open Access   (Followers: 10)
Openings : Studies in Book Art     Open Access   (Followers: 10)
Creative Artist : A Journal of Theatre and Media Studies     Open Access   (Followers: 10)
Journal of Radiotherapy in Practice     Hybrid Journal   (Followers: 9)
Magnetic Resonance Imaging     Hybrid Journal   (Followers: 9)
Interaction Studies     Hybrid Journal   (Followers: 9)
Journal of Language and Politics     Hybrid Journal   (Followers: 9)
Fibreculture Journal     Open Access   (Followers: 9)
Journal of Islamic Manuscripts     Hybrid Journal   (Followers: 9)
Comedy Studies     Hybrid Journal   (Followers: 9)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 9)
Communication & Sport     Hybrid Journal   (Followers: 9)
tripleC : Communication, Capitalism & Critique. Open Access Journal for a Global Sustainable Information Society     Open Access   (Followers: 9)
International Journal of Ad Hoc and Ubiquitous Computing     Hybrid Journal   (Followers: 8)
Seminars in Interventional Radiology     Hybrid Journal   (Followers: 8)
Information Design Journal     Hybrid Journal   (Followers: 8)
Myth & Symbol     Hybrid Journal   (Followers: 8)
Black Camera     Full-text available via subscription   (Followers: 8)
Cross-cultural Communication     Open Access   (Followers: 8)
Investigative Radiology     Hybrid Journal   (Followers: 7)
Pediatric Radiology     Hybrid Journal   (Followers: 7)
Technical Communication     Full-text available via subscription   (Followers: 7)
African Journal of Information and Communication     Open Access   (Followers: 7)
Annals of Telecommunications     Hybrid Journal   (Followers: 7)
Intelligent Information Management     Open Access   (Followers: 7)
African Journal of Information Systems     Open Access   (Followers: 7)
China Communications     Full-text available via subscription   (Followers: 7)
Journal of Radio & Audio Media     Hybrid Journal   (Followers: 6)
Sign Language Studies     Full-text available via subscription   (Followers: 6)
Review of Cognitive Linguistics     Hybrid Journal   (Followers: 6)
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Journal of Professional Communication     Open Access   (Followers: 6)
Informatics     Open Access   (Followers: 6)
Porn Studies     Hybrid Journal   (Followers: 6)
Journal of Advertising Education     Hybrid Journal   (Followers: 5)
The Communication Review     Hybrid Journal   (Followers: 5)
Journal of Asian Pacific Communication     Hybrid Journal   (Followers: 5)
Journal of Graph Theory     Hybrid Journal   (Followers: 5)
Middle East Journal of Culture and Communication     Hybrid Journal   (Followers: 5)
CIC. Cuadernos de Informacion y Comunicacion     Open Access   (Followers: 5)
Women's Studies in Communication     Hybrid Journal   (Followers: 5)
Global Advances in Business Communication     Open Access   (Followers: 5)
Transactions on Emerging Telecommunications Technologies     Hybrid Journal   (Followers: 4)
Journal of Radiology Nursing     Hybrid Journal   (Followers: 4)
Neuroimaging Clinics of North America     Full-text available via subscription   (Followers: 4)
Telecommunication Systems     Hybrid Journal   (Followers: 4)
Terminology     Hybrid Journal   (Followers: 4)
Tijdschrift voor Communicatiewetenschappen     Full-text available via subscription   (Followers: 4)
Gesture     Hybrid Journal   (Followers: 4)
Communicatio : South African Journal for Communication Theory and Research     Hybrid Journal   (Followers: 4)
Media International Australia     Hybrid Journal   (Followers: 4)
International Review of Pragmatics     Hybrid Journal   (Followers: 4)
International Journal of Cooperative Information Systems     Hybrid Journal   (Followers: 4)
International Journal of Information Communication Technologies and Human Development     Full-text available via subscription   (Followers: 4)
Medical Writing     Hybrid Journal   (Followers: 4)
Journal of Media Innovations     Open Access   (Followers: 4)
Journal of Development and Communication Studies     Open Access   (Followers: 4)
International Journal of Autonomous and Adaptive Communications Systems     Hybrid Journal   (Followers: 3)
Magnetic Resonance Materials in Physics, Biology and Medicine     Hybrid Journal   (Followers: 3)
Solid State Nuclear Magnetic Resonance     Hybrid Journal   (Followers: 3)
Journal of Location Based Services     Hybrid Journal   (Followers: 3)
Etudes de communication     Open Access   (Followers: 3)
Science China Information Sciences     Hybrid Journal   (Followers: 3)
Language, Interaction and Acquisition     Hybrid Journal   (Followers: 3)
Sign Language & Linguistics     Hybrid Journal   (Followers: 3)
Kaleidoscope : A Graduate Journal of Qualitative Communication Research     Open Access   (Followers: 3)
Pacific Asia Journal of the Association for Information Systems     Open Access   (Followers: 3)
Journal of Community Informatics     Open Access   (Followers: 3)
International Journal of Intelligence Science     Open Access   (Followers: 3)
International Journal of Interdisciplinary Telecommunications and Networking     Full-text available via subscription   (Followers: 3)
Journal of International Communication     Hybrid Journal   (Followers: 3)
MediaTropes     Open Access   (Followers: 3)
Nonprofit Communications Report     Hybrid Journal   (Followers: 3)
Journal of Media Literacy Education     Open Access   (Followers: 3)
Seton Hall Journal of Sports and Entertainment Law     Open Access   (Followers: 3)
Social Networking     Open Access   (Followers: 3)
Imaging Decisions MRI     Hybrid Journal   (Followers: 2)
Journal of Cardiovascular Computed Tomography     Hybrid Journal   (Followers: 2)
Language Problems & Language Planning     Hybrid Journal   (Followers: 2)
Research Journal of Information Technology     Open Access   (Followers: 2)
Área Abierta     Open Access   (Followers: 2)
Comunicación y Medios     Open Access   (Followers: 2)
Middle East Media Educator     Open Access   (Followers: 2)
Revue française des sciences de l’information et de la communication     Open Access   (Followers: 2)
Journal of Argumentation in Context     Hybrid Journal   (Followers: 2)
Metaphor and the Social World     Hybrid Journal   (Followers: 2)
Journal of Organizational Knowledge Communication     Open Access   (Followers: 2)
GSTF Journal on Media & Communications     Open Access   (Followers: 2)
Chasqui. Revista Latinoamericana de Comunicación     Open Access   (Followers: 2)
Bioelectromagnetics     Hybrid Journal   (Followers: 1)
Radioelectronics and Communications Systems     Hybrid Journal   (Followers: 1)
McMaster Journal of Communication     Open Access   (Followers: 1)
Palabra Clave     Open Access   (Followers: 1)
International Journal of Knowledge and Systems Science     Full-text available via subscription   (Followers: 1)
Documentación de las Ciencias de la Información     Open Access   (Followers: 1)
International Journal of Trust Management in Computing and Communications     Hybrid Journal   (Followers: 1)
South African Journal of Communication Disorders     Open Access   (Followers: 1)
FLEKS : Scandinavian Journal of Intercultural Theory and Practice     Open Access   (Followers: 1)
Public Journal of Semiotics     Open Access   (Followers: 1)
Journal of Language and Sexuality     Hybrid Journal   (Followers: 1)
Language and Dialogue     Hybrid Journal   (Followers: 1)
Anuario electrónico de estudios en Comunicación Social "Disertaciones"     Open Access   (Followers: 1)
3C TIC     Open Access   (Followers: 1)
Cuadernos.info     Open Access  
Comunicación     Open Access  
De Signos y Sentidos     Open Access  
Distúrbios da Comunicação     Open Access  
Wacana : Jurnal Sosial dan Humaniora     Open Access  
Question     Open Access  
Llengua, societat i comunicació     Open Access  
Informatio. Revista del Instituto de Información de la Facultad de Información y Comunicación     Open Access  
Communiquer : Revue de communication sociale et publique     Open Access  
Cuadernos de H Ideas     Open Access  
Punto Cero     Open Access  
Netcom     Open Access  
Observatorio (OBS*)     Open Access  
Vivat Academia     Open Access  
Commons. Revista de Comunicación y Ciudadanía Digital     Open Access  
Journal of Information and Organizational Sciences     Open Access  
Questions de communication     Open Access  
Quaderni     Open Access  
Communication et organisation     Open Access  
International Journal of Telework and Telecommuting Technologies     Full-text available via subscription  
Virtualidad, Educación y Ciencia     Open Access  
Revista Contracampo     Open Access  
Mediaciones Sociales     Open Access  
Historia y Comunicación Social     Open Access  
Pixel-Bit. Revista de Medios y Educacion     Open Access  
Cuadernos de Informacion     Open Access  
Journal of Modern Periodical Studies     Full-text available via subscription  
Tic & société     Open Access  

        1 2 3 | Last

Similar Journals
Journal Cover
Informatics
Number of Followers: 6  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2227-9709
Published by MDPI Homepage  [258 journals]
  • Informatics, Vol. 11, Pages 42: The Mappability of Clinical Real-World
           Data of Patients with Melanoma to Oncological Fast Healthcare
           Interoperability Resources (FHIR) Profiles: A Single-Center
           Interoperability Study

    • Authors: Jessica Swoboda, Moritz Albert, Catharina Lena Beckmann, Georg Christian Lodde, Elisabeth Livingstone, Felix Nensa, Dirk Schadendorf, Britta Böckmann
      First page: 42
      Abstract: (1) Background: Tumor-specific standardized data are essential for AI-based progress in research, e.g., for predicting adverse events in patients with melanoma. Although there are oncological Fast Healthcare Interoperability Resources (FHIR) profiles, it is unclear how well these can represent malignant melanoma. (2) Methods: We created a methodology pipeline to assess to what extent an oncological FHIR profile, in combination with a standard FHIR specification, can represent a real-world data set. We extracted Electronic Health Record (EHR) data from a data platform, and identified and validated relevant features. We created a melanoma data model and mapped its features to the oncological HL7 FHIR Basisprofil Onkologie [Basic Profile Oncology] and the standard FHIR specification R4. (3) Results: We identified 216 features. Mapping showed that 45 out of 216 (20.83%) features could be mapped completely or with adjustments using the Basisprofil Onkologie [Basic Profile Oncology], and 129 (60.85%) features could be mapped using the standard FHIR specification. A total of 39 (18.06%) new, non-mappable features could be identified. (4) Conclusions: Our tumor-specific real-world melanoma data could be partially mapped using a combination of an oncological FHIR profile and a standard FHIR specification. However, important data features were lost or had to be mapped with self-defined extensions, resulting in limited interoperability.
      Citation: Informatics
      PubDate: 2024-06-28
      DOI: 10.3390/informatics11030042
      Issue No: Vol. 11, No. 3 (2024)
       
  • Informatics, Vol. 11, Pages 43: Impact of Hospital Employees’
           Awareness of the EMR System Certification on Interoperability Evaluation:
           Comparison of Public and Private Hospitals

    • Authors: Choyeal Park, Jikyeong Park
      First page: 43
      Abstract: This study examined the awareness of the EMR certification system among employees of public and private hospitals that have obtained EMR certification. It also assessed how this awareness impacted the evaluation of EMR interoperability. The objective of this study is to contribute to the stable adoption and further development of EMR system certification in Korea. Data were collected through 3600 questionnaires distributed over three years from 2021 to 2023. After excluding 24 questionnaires owing to missing values or insincere responses, 3576 responses were analyzed. The analysis involved descriptive statistics, cross-tabulation, t-tests, ANOVA, and multiple regression using SPSS 26.0. The significance level (α) for statistical tests was set at 0.05. This study revealed differences in awareness of EMR system certification and interoperability among hospital employees. In both public and private hospitals, awareness of the EMR system certification positively influences the evaluation of interoperability.
      Citation: Informatics
      PubDate: 2024-07-03
      DOI: 10.3390/informatics11030043
      Issue No: Vol. 11, No. 3 (2024)
       
  • Informatics, Vol. 11, Pages 44: A Framework for Antecedents to Health
           Information Systems Uptake by Healthcare Professionals: An Exploratory
           Study of Electronic Medical Records

    • Authors: Reza Torkman, Amir Hossein Ghapanchi, Reza Ghanbarzadeh
      First page: 44
      Abstract: Health information systems (HISs) are essential information systems used by organisations and individuals for various purposes. Past research has studied different types of HIS, such as rostering systems, Electronic Medical Records (EMRs), and Personal Health Records (PHRs). Although several past confirmatory studies have quantitatively examined EMR uptake by health professionals, there is a lack of exploratory and qualitative studies that uncover various drivers of healthcare professionals’ uptake of EMRs. Applying an exploratory and qualitative approach, this study introduces various antecedents of healthcare professionals’ uptake of EMRs. This study conducted 78 semi-structured, open-ended interviews with 15 groups of healthcare professional users of EMRs in two large Australian hospitals. Data analysis of qualitative data resulted in proposing a framework comprising 23 factors impacting healthcare professionals’ uptake of EMRs, which are categorised into ten main categories: perceived benefits of EMR, perceived difficulties, hardware/software compatibility, job performance uncertainty, ease of operation, perceived risk, assistance society, user confidence, organisational support, and technological support. Our findings have important implications for various practitioner groups, such as healthcare policymakers, hospital executives, hospital middle and line managers, hospitals’ IT departments, and healthcare professionals using EMRs. Implications of the findings for researchers and practitioners are provided herein in detail.
      Citation: Informatics
      PubDate: 2024-07-09
      DOI: 10.3390/informatics11030044
      Issue No: Vol. 11, No. 3 (2024)
       
  • Informatics, Vol. 11, Pages 45: GPTs or Grim Position Threats' The
           Potential Impacts of Large Language Models on Non-Managerial Jobs and
           Certifications in Cybersecurity

    • Authors: Raza Nowrozy
      First page: 45
      Abstract: ChatGPT, a Large Language Model (LLM) utilizing Natural Language Processing (NLP), has caused concerns about its impact on job sectors, including cybersecurity. This study assesses ChatGPT’s impacts in non-managerial cybersecurity roles using the NICE Framework and Technological Displacement theory. It also explores its potential to pass top cybersecurity certification exams. Findings reveal ChatGPT’s promise to streamline some jobs, especially those requiring memorization. Moreover, this paper highlights ChatGPT’s challenges and limitations, such as ethical implications, LLM limitations, and Artificial Intelligence (AI) security. The study suggests that LLMs like ChatGPT could transform the cybersecurity landscape, causing job losses, skill obsolescence, labor market shifts, and mixed socioeconomic impacts. A shift in focus from memorization to critical thinking, and collaboration between LLM developers and cybersecurity professionals, is recommended.
      Citation: Informatics
      PubDate: 2024-07-11
      DOI: 10.3390/informatics11030045
      Issue No: Vol. 11, No. 3 (2024)
       
  • Informatics, Vol. 11, Pages 46: Evaluating and Enhancing Artificial
           Intelligence Models for Predicting Student Learning Outcomes

    • Authors: Helia Farhood, Ibrahim Joudah, Amin Beheshti, Samuel Muller
      First page: 46
      Abstract: Predicting student outcomes is an essential task and a central challenge among artificial intelligence-based personalised learning applications. Despite several studies exploring student performance prediction, there is a notable lack of comprehensive and comparative research that methodically evaluates and compares multiple machine learning models alongside deep learning architectures. In response, our research provides a comprehensive comparison to evaluate and improve ten different machine learning and deep learning models, either well-established or cutting-edge techniques, namely, random forest, decision tree, support vector machine, K-nearest neighbours classifier, logistic regression, linear regression, and state-of-the-art extreme gradient boosting (XGBoost), as well as a fully connected feed-forward neural network, a convolutional neural network, and a gradient-boosted neural network. We implemented and fine-tuned these models using Python 3.9.5. With a keen emphasis on prediction accuracy and model performance optimisation, we evaluate these methodologies across two benchmark public student datasets. We employ a dual evaluation approach, utilising both k-fold cross-validation and holdout methods, to comprehensively assess the models’ performance. Our research focuses primarily on predicting student outcomes in final examinations by determining their success or failure. Moreover, we explore the importance of feature selection using the ubiquitous Lasso for dimensionality reduction to improve model efficiency, prevent overfitting, and examine its impact on prediction accuracy for each model, both with and without Lasso. This study provides valuable guidance for selecting and deploying predictive models for tabular data classification like student outcome prediction, which seeks to utilise data-driven insights for personalised education.
      Citation: Informatics
      PubDate: 2024-07-15
      DOI: 10.3390/informatics11030046
      Issue No: Vol. 11, No. 3 (2024)
       
  • Informatics, Vol. 11, Pages 47: Healthcare and the Internet of Medical
           Things: Applications, Trends, Key Challenges, and Proposed Resolutions

    • Authors: Inas Al Khatib, Abdulrahim Shamayleh, Malick Ndiaye
      First page: 47
      Abstract: In recent years, the Internet of medical things (IoMT) has become a significant technological advancement in the healthcare sector. This systematic review aims to identify and summarize the various applications, key challenges, and proposed technical solutions within this domain, based on a comprehensive analysis of the existing literature. This review highlights diverse applications of the IoMT, including mobile health (mHealth) applications, remote biomarker detection, hybrid RFID-IoT solutions for scrub distribution in operating rooms, IoT-based disease prediction using machine learning, and the efficient sharing of personal health records through searchable symmetric encryption, blockchain, and IPFS. Other notable applications include remote healthcare management systems, non-invasive real-time blood glucose measurement devices, distributed ledger technology (DLT) platforms, ultra-wideband (UWB) radar systems, IoT-based pulse oximeters, accident and emergency informatics (A&EI), and integrated wearable smart patches. The key challenges identified include privacy protection, sustainable power sources, sensor intelligence, human adaptation to sensors, data speed, device reliability, and storage efficiency. The proposed mitigations encompass network control, cryptography, edge-fog computing, and blockchain, alongside rigorous risk planning. The review also identifies trends and advancements in the IoMT architecture, remote monitoring innovations, the integration of machine learning and AI, and enhanced security measures. This review makes several novel contributions compared to the existing literature, including (1) a comprehensive categorization of IoMT applications, extending beyond the traditional use cases to include emerging technologies such as UWB radar systems and DLT platforms; (2) an in-depth analysis of the integration of machine learning and AI in IoMT, highlighting innovative approaches in disease prediction and remote monitoring; (3) a detailed examination of privacy and security measures, proposing advanced cryptographic solutions and blockchain implementations to enhance data protection; and (4) the identification of future research directions, providing a roadmap for addressing current limitations and advancing the scientific understanding of IoMT in healthcare. By addressing current limitations and suggesting future research directions, this work aims to advance scientific understanding of the IoMT in healthcare.
      Citation: Informatics
      PubDate: 2024-07-16
      DOI: 10.3390/informatics11030047
      Issue No: Vol. 11, No. 3 (2024)
       
  • Informatics, Vol. 11, Pages 48: Machine Learning Applied to the Analysis
           of Prolonged COVID Symptoms: An Analytical Review

    • Authors: Paola Patricia Ariza-Colpas, Marlon Alberto Piñeres-Melo, Miguel Alberto Urina-Triana, Ernesto Barceló-Martinez, Camilo Barceló-Castellanos, Fabian Roman
      First page: 48
      Abstract: The COVID-19 pandemic continues to constitute a public health emergency of international importance, although the state of emergency declaration has indeed been terminated worldwide, many people continue to be infected and present different symptoms associated with the illness. Undoubtedly, solutions based on divergent technologies such as machine learning have made great contributions to the understanding, identification, and treatment of the disease. Due to the sudden appearance of this virus, many works have been carried out by the scientific community to support the detection and treatment processes, which has generated numerous publications, making it difficult to identify the status of current research and future contributions that can continue to be generated around this problem that is still valid among us. To address this problem, this article shows the result of a scientometric analysis, which allows the identification of the various contributions that have been generated from the line of automatic learning for the monitoring and treatment of symptoms associated with this pathology. The methodology for the development of this analysis was carried out through the implementation of two phases: in the first phase, a scientometric analysis was carried out, where the countries, authors, and magazines with the greatest production associated with this subject can be identified, later in the second phase, the contributions based on the use of the Tree of Knowledge metaphor are identified. The main concepts identified in this review are related to symptoms, implemented algorithms, and the impact of applications. These results provide relevant information for researchers in the field in the search for new solutions or the application of existing ones for the treatment of still-existing symptoms of COVID-19.
      Citation: Informatics
      PubDate: 2024-07-18
      DOI: 10.3390/informatics11030048
      Issue No: Vol. 11, No. 3 (2024)
       
  • Informatics, Vol. 11, Pages 49: AI Language Models: An Opportunity to
           Enhance Language Learning

    • Authors: Yan Cong
      First page: 49
      Abstract: AI language models are increasingly transforming language research in various ways. How can language educators and researchers respond to the challenge posed by these AI models' Specifically, how can we embrace this technology to inform and enhance second language learning and teaching' In order to quantitatively characterize and index second language writing, the current work proposes the use of similarities derived from contextualized meaning representations in AI language models. The computational analysis in this work is hypothesis-driven. The current work predicts how similarities should be distributed in a second language learning setting. The results suggest that similarity metrics are informative of writing proficiency assessment and interlanguage development. Statistically significant effects were found across multiple AI models. Most of the metrics could distinguish language learners’ proficiency levels. Significant correlations were also found between similarity metrics and learners’ writing test scores provided by human experts in the domain. However, not all such effects were strong or interpretable. Several results could not be consistently explained under the proposed second language learning hypotheses. Overall, the current investigation indicates that with careful configuration and systematic metrics design, AI language models can be promising tools in advancing language education.
      Citation: Informatics
      PubDate: 2024-07-19
      DOI: 10.3390/informatics11030049
      Issue No: Vol. 11, No. 3 (2024)
       
  • Informatics, Vol. 11, Pages 50: Machine Learning to Estimate Workload and
           Balance Resources with Live Migration and VM Placement

    • Authors: Taufik Hidayat, Kalamullah Ramli, Nadia Thereza, Amarudin Daulay, Rushendra Rushendra, Rahutomo Mahardiko
      First page: 50
      Abstract: Currently, utilizing virtualization technology in data centers often imposes an increasing burden on the host machine (HM), leading to a decline in VM performance. To address this issue, live virtual migration (LVM) is employed to alleviate the load on the VM. This study introduces a hybrid machine learning model designed to estimate the direct migration of pre-copied migration virtual machines within the data center. The proposed model integrates Markov Decision Process (MDP), genetic algorithm (GA), and random forest (RF) algorithms to forecast the prioritized movement of virtual machines and identify the optimal host machine target. The hybrid models achieve a 99% accuracy rate with quicker training times compared to the previous studies that utilized K-nearest neighbor, decision tree classification, support vector machines, logistic regression, and neural networks. The authors recommend further exploration of a deep learning approach (DL) to address other data center performance issues. This paper outlines promising strategies for enhancing virtual machine migration in data centers. The hybrid models demonstrate high accuracy and faster training times than previous research, indicating the potential for optimizing virtual machine placement and minimizing downtime. The authors emphasize the significance of considering data center performance and propose further investigation. Moreover, it would be beneficial to delve into the practical implementation and dissemination of the proposed model in real-world data centers.
      Citation: Informatics
      PubDate: 2024-07-19
      DOI: 10.3390/informatics11030050
      Issue No: Vol. 11, No. 3 (2024)
       
  • Informatics, Vol. 11, Pages 51: Non-Invasive Diagnostic Approach for
           Diabetes Using Pulse Wave Analysis and Deep Learning

    • Authors: Hiruni Gunathilaka, Rumesh Rajapaksha, Thosini Kumarika, Dinusha Perera, Uditha Herath, Charith Jayathilaka, Janitha Liyanage, Sudath Kalingamudali
      First page: 51
      Abstract: The surging prevalence of diabetes globally necessitates advancements in non-invasive diagnostics, particularly for the early detection of cardiovascular anomalies associated with the condition. This study explores the efficacy of Pulse Wave Analysis (PWA) for distinguishing diabetic from non-diabetic individuals through morphological examination of pressure pulse waveforms. The research unfolds in four phases: data accrual, preprocessing, Convolutional Neural Network (CNN) model construction, and performance evaluation. Data were procured using a multipara patient monitor, resulting in 2000 pulse waves equally divided between healthy individuals and those with diabetes. These were used to train, validate, and test three distinct CNN architectures: the conventional CNN, Visual Geometry Group (VGG16), and Residual Networks (ResNet18). The accuracy, precision, recall, and F1 score gauged each model’s proficiency. The CNN demonstrated a training accuracy of 82.09% and a testing accuracy of 80.6%. The VGG16, with its deeper structure, surpassed the baseline with training and testing accuracies of 90.2% and 86.57%, respectively. ResNet18 excelled, achieving a training accuracy of 92.50% and a testing accuracy of 92.00%, indicating its robustness in pattern recognition within pulse wave data. Deploying deep learning for diabetes screening marks progress, suggesting clinical use and future studies on bigger datasets for refinement.
      Citation: Informatics
      PubDate: 2024-07-19
      DOI: 10.3390/informatics11030051
      Issue No: Vol. 11, No. 3 (2024)
       
  • Informatics, Vol. 11, Pages 52: Use of Chipless Radio Frequency
           Identification Technology for Smart Food Packaging: An Economic Analysis
           for an Australian Seafood Industry

    • Authors: Parya Fathi, Mita Bhattacharya, Sankar Bhattacharya, Nemai Karmakar
      First page: 52
      Abstract: Effective monitoring of perishable food products has become increasingly important for ensuring quality, enabling smart packaging to be a key consideration for food companies. Among the promising technologies available for transforming packaging into intelligent packaging, chipless radio frequency identification (RFID) sensors stand out. Despite the high initial implementation costs associated with chipless RFID technology, the potential benefits could outweigh the costs if electrical challenges can be overcome. We examine various economic methods to analyze the economic benefits of chipless RFID technology, evaluating the benefits of using this technology for the quality monitoring of seafood products of an Australian seafood producer, Tassal. The analysis considers three primary business drivers, viz. quality monitoring, operational efficiency, and tracking and tracing, using net present value and return on investment as the key indicators to assess the feasibility of implementing the technology. Based on sensitivity analysis, we suggest chipless RFID technology is currently best suited for large firms facing significant quality monitoring and operational efficiency challenges. However, as the cost of chipless RFID sensors decreases with further development, this technology may become a more viable option for small businesses in the future.
      Citation: Informatics
      PubDate: 2024-07-22
      DOI: 10.3390/informatics11030052
      Issue No: Vol. 11, No. 3 (2024)
       
  • Informatics, Vol. 11, Pages 53: A Comparative Analysis of Virtual
           Education Technology, E-Learning Systems Research Advances, and Digital
           Divide in the Global South

    • Authors: Ikpe Justice Akpan, Onyebuchi Felix Offodile, Aloysius Chris Akpanobong, Yawo Mamoua Kobara
      First page: 53
      Abstract: This pioneering study evaluates the digital divide and advances in virtual education (VE) and e-learning research in the Global South Countries (GSCs). Using metadata from bibliographic and World Bank data on research and development (R&D), we conduct quantitative bibliometric performance analyses and evaluate the connection between R&D expenditures on VE/e-learning research advances in GSCs. The results show that ‘East Asia and the Pacific’ (EAP) spent significantly more on (R&D) and achieved the highest scientific literature publication (SLP), with significant impacts. Other GSCs’ R&D expenditure was flat until 2020 (during COVID-19), when R&D funding increased, achieving a corresponding 42% rise in SLPs. About 67% of ‘Arab States’ (AS) SLPs and 60% of citation impact came from SLPs produced from global north and other GSCs regions, indicating high dependence. Also, 51% of high-impact SLPs were ‘Multiple Country Publications’, mainly from non-GSC institutions, indicating high collaboration impact. The EAP, AS, and ‘South Asia’ (SA) regions experienced lower disparity. In contrast, the less developed countries (LDCs), including ‘Sub-Sahara Africa’, ‘Latin America and the Caribbean’, and ‘Europe (Eastern) and Central Asia’, showed few dominant countries with high SLPs and higher digital divides. We advocate for increased educational research funding to enhance innovative R&D in GSCs, especially in LDCs.
      Citation: Informatics
      PubDate: 2024-07-23
      DOI: 10.3390/informatics11030053
      Issue No: Vol. 11, No. 3 (2024)
       
  • Informatics, Vol. 11, Pages 54: AI Literacy and Intention to Use
           Text-Based GenAI for Learning: The Case of Business Students in Korea

    • Authors: Moonkyoung Jang
      First page: 54
      Abstract: With the increasing use of large-scale language model-based AI tools in modern learning environments, it is important to understand students’ motivations, experiences, and contextual influences. These tools offer new support dimensions for learners, enhancing academic achievement and providing valuable resources, but their use also raises ethical and social issues. In this context, this study aims to systematically identify factors influencing the usage intentions of text-based GenAI tools among undergraduates. By modifying the core variables of the Unified Theory of Acceptance and Use of Technology (UTAUT) with AI literacy, a survey was designed to measure GenAI users’ intentions to collect participants’ opinions. The survey, conducted among business students at a university in South Korea, gathered 239 responses during March and April 2024. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS software (Ver. 4.0.9.6). The findings reveal that performance expectancy significantly affects the intention to use GenAI, while effort expectancy does not. In addition, AI literacy and social influence significantly influence performance, effort expectancy, and the intention to use GenAI. This study provides insights into determinants affecting GenAI usage intentions, aiding the development of effective educational strategies and policies to support ethical and beneficial AI use in academic settings.
      Citation: Informatics
      PubDate: 2024-07-26
      DOI: 10.3390/informatics11030054
      Issue No: Vol. 11, No. 3 (2024)
       
  • Informatics, Vol. 11, Pages 55: Internet Use for Health-Related Purposes
           among Older People in Thailand: An Analysis of Nationwide Cross-Sectional
           Data

    • Authors: Kittisak Robru, Prasongchai Setthasuravich, Aphisit Pukdeewut, Suthiwat Wetchakama
      First page: 55
      Abstract: As the global population ages, understanding the digital health behaviors of older adults becomes increasingly crucial. In Thailand, where the elderly population is rapidly growing, examining how older individuals use the internet for health-related purposes can provide valuable insights for enhancing healthcare accessibility and engagement. This study investigates the use of the internet for health-related purposes among older adults in Thailand, focusing on the socio-demographic factors influencing this behavior. Utilizing cross-sectional data from the “Thailand Internet User Behavior Survey 2022”, which includes responses from 4652 older adults, the study employs descriptive statistics, chi-square tests, and logistic regression analysis. The results reveal that approximately 10.83% of older adults use the internet for health purposes. The analysis shows that higher income (AOR = 1.298, p = 0.030), higher level of education (degree education: AOR = 1.814, p < 0.001), skilled occupations (AOR = 2.003, p < 0.001), residence in an urban area (AOR = 3.006, p < 0.001), and greater confidence in internet use (very confident: AOR = 3.153, p < 0.001) are significantly associated with a greater likelihood of using the internet for health purposes. Gender and age did not show significant differences in health-related internet use, indicating a relatively gender-neutral and age-consistent landscape. Significant regional differences were observed, with the northeastern region showing a markedly higher propensity (AOR = 2.249, p < 0.001) for health-related internet use compared to the northern region. Meanwhile, the eastern region (AOR = 0.489, p = 0.018) showed lower odds. These findings underscore the need for targeted healthcare policies to enhance digital health engagement among older adults in Thailand, emphasizing the importance of improving digital literacy, expanding infrastructure, and addressing region-specific health initiatives.
      Citation: Informatics
      PubDate: 2024-07-28
      DOI: 10.3390/informatics11030055
      Issue No: Vol. 11, No. 3 (2024)
       
  • Informatics, Vol. 11, Pages 56: Digital Innovations in E-Commerce:
           Augmented Reality Applications in Online Fashion Retail—A
           Qualitative Study among Gen Z Consumers

    • Authors: Ildikó Kovács, Éva Réka Keresztes
      First page: 56
      Abstract: Digital innovations have significantly transformed the marketing landscape, with visual technology solutions having become mainstream in the fashion industry approximately a decade ago. Digital technology offers a range of benefits to online fashion retailers, enhancing their online shopping platforms with augmented reality features that allow customers to “try on” products digitally before making a purchase. This research aims to explore the key factors influencing the use of augmented reality applications and e-commerce sites for purchasing apparel. A qualitative study was conducted to examine the visual experience and usage of augmented reality applications among young customers. The findings highlight the most relevant factors in the online fashion purchasing process, the visual experience, and the potential future use of augmented reality applications in fashion product purchasing. These insights are crucial for developing effective marketing strategies and communication messages.
      Citation: Informatics
      PubDate: 2024-08-03
      DOI: 10.3390/informatics11030056
      Issue No: Vol. 11, No. 3 (2024)
       
  • Informatics, Vol. 11, Pages 57: Large Language Models in Healthcare and
           Medical Domain: A Review

    • Authors: Zabir Al Nazi, Wei Peng
      First page: 57
      Abstract: The deployment of large language models (LLMs) within the healthcare sector has sparked both enthusiasm and apprehension. These models exhibit the remarkable ability to provide proficient responses to free-text queries, demonstrating a nuanced understanding of professional medical knowledge. This comprehensive survey delves into the functionalities of existing LLMs designed for healthcare applications and elucidates the trajectory of their development, starting with traditional Pretrained Language Models (PLMs) and then moving to the present state of LLMs in the healthcare sector. First, we explore the potential of LLMs to amplify the efficiency and effectiveness of diverse healthcare applications, particularly focusing on clinical language understanding tasks. These tasks encompass a wide spectrum, ranging from named entity recognition and relation extraction to natural language inference, multimodal medical applications, document classification, and question-answering. Additionally, we conduct an extensive comparison of the most recent state-of-the-art LLMs in the healthcare domain, while also assessing the utilization of various open-source LLMs and highlighting their significance in healthcare applications. Furthermore, we present the essential performance metrics employed to evaluate LLMs in the biomedical domain, shedding light on their effectiveness and limitations. Finally, we summarize the prominent challenges and constraints faced by large language models in the healthcare sector by offering a holistic perspective on their potential benefits and shortcomings. This review provides a comprehensive exploration of the current landscape of LLMs in healthcare, addressing their role in transforming medical applications and the areas that warrant further research and development.
      Citation: Informatics
      PubDate: 2024-08-07
      DOI: 10.3390/informatics11030057
      Issue No: Vol. 11, No. 3 (2024)
       
  • Informatics, Vol. 11, Pages 58: Ethical Challenges and Solutions of
           Generative AI: An Interdisciplinary Perspective

    • Authors: Mousa Al-kfairy, Dheya Mustafa, Nir Kshetri, Mazen Insiew, Omar Alfandi
      First page: 58
      Abstract: This paper conducts a systematic review and interdisciplinary analysis of the ethical challenges of generative AI technologies (N = 37), highlighting significant concerns such as privacy, data protection, copyright infringement, misinformation, biases, and societal inequalities. The ability of generative AI to produce convincing deepfakes and synthetic media, which threaten the foundations of truth, trust, and democratic values, exacerbates these problems. The paper combines perspectives from various disciplines, including education, media, and healthcare, underscoring the need for AI systems that promote equity and do not perpetuate social inequalities. It advocates for a proactive approach to the ethical development of AI, emphasizing the necessity of establishing policies, guidelines, and frameworks that prioritize human rights, fairness, and transparency. The paper calls for a multidisciplinary dialogue among policymakers, technologists, and researchers to ensure responsible AI development that conforms to societal values and ethical standards. It stresses the urgency of addressing these ethical concerns and advocates for the development of generative AI in a socially beneficial and ethically sound manner, contributing significantly to the discourse on managing AI’s ethical implications in the modern digital era. The study highlights the theoretical and practical implications of these challenges and suggests a number of future research directions.
      Citation: Informatics
      PubDate: 2024-08-09
      DOI: 10.3390/informatics11030058
      Issue No: Vol. 11, No. 3 (2024)
       
  • Informatics, Vol. 11, Pages 59: AI-Based Visual Early Warning System

    • Authors: Zeena Al-Tekreeti, Jeronimo Moreno-Cuesta, Maria Isabel Madrigal Garcia, Marcos A. Rodrigues
      First page: 59
      Abstract: Facial expressions are a universally recognised means of conveying internal emotional states across diverse human cultural and ethnic groups. Recent advances in understanding people’s emotions expressed through verbal and non-verbal communication are particularly noteworthy in the clinical context for the assessment of patients’ health and well-being. Facial expression recognition (FER) plays an important and vital role in health care, providing communication with a patient’s feelings and allowing the assessment and monitoring of mental and physical health conditions. This paper shows that automatic machine learning methods can predict health deterioration accurately and robustly, independent of human subjective assessment. The prior work of this paper is to discover the early signs of deteriorating health that align with the principles of preventive reactions, improving health outcomes and human survival, and promoting overall health and well-being. Therefore, methods are developed to create a facial database mimicking the underlying muscular structure of the face, whose Action Unit motions can then be transferred to human face images, thus displaying animated expressions of interest. Then, building and developing an automatic system based on convolution neural networks (CNN) and long short-term memory (LSTM) to recognise patterns of facial expressions with a focus on patients at risk of deterioration in hospital wards. This research presents state-of-the-art results on generating and modelling synthetic database and automated deterioration prediction through FEs with 99.89% accuracy. The main contributions to knowledge from this paper can be summarized as (1) the generation of visual datasets mimicking real-life samples of facial expressions indicating health deterioration, (2) improvement of the understanding and communication with patients at risk of deterioration through facial expression analysis, and (3) development of a state-of-the-art model to recognize such facial expressions using a ConvLSTM model.
      Citation: Informatics
      PubDate: 2024-08-12
      DOI: 10.3390/informatics11030059
      Issue No: Vol. 11, No. 3 (2024)
       
  • Informatics, Vol. 11, Pages 60: Knowledge Management for Improved Digital
           

    • Authors: Younes Elgargouh, Mohammed Reda Chbihi Louhdi, El Moukhtar Zemmouri, Hicham Behja
      First page: 60
      Abstract: Knowledge Management (KM) plays a pivotal role in contemporary businesses, facilitating the identification, management, and utilization of existing knowledge for organizational benefit. This article underscores the indispensability of effective KM processes in the insurance industry, which is undergoing profound digital transformation. Through a systematic review utilizing the PRISMA framework, we meta-analyzed 85 high-quality scientific papers sourced from prominent databases spanning 2008 to 2022. Our examination centers on the diverse implementation processes of KM worldwide, emphasizing the integration of information technologies to enhance data collection, analysis, processing, and distribution within insurance companies. The objective of this review is twofold: to devise efficient methods for implementing KM systems in the insurance sector and to delineate practical research directions in this domain.
      Citation: Informatics
      PubDate: 2024-08-12
      DOI: 10.3390/informatics11030060
      Issue No: Vol. 11, No. 3 (2024)
       
  • Informatics, Vol. 11, Pages 61: TEADASH: Implementing and Evaluating a
           Teacher-Facing Dashboard Using Design Science Research

    • Authors: Ngoc Buu Cat Nguyen, Marcus Lithander, Christian Master Östlund, Thashmee Karunaratne, William Jobe
      First page: 61
      Abstract: The benefits of teacher-facing dashboards are incontestable, yet their evidence is finite in terms of long-term use, meaningful usability, and maturity level. Thus, this paper uses design science research and critical theory to design and develop TEADASH to support teachers in making decisions on teaching and learning. Three cycles of design science research and multiple small loops were implemented to develop the dashboard. The tool was then deployed and evaluated in real time with the authentic courses. Five courses from two Swedish universities were included in this study. The co-design with teachers is crucial to the applicability of this dashboard, while letting teachers use the tool during their courses is more important to help them to recognize the features they actually use and the tool’s usefulness for their teaching practices. TEADASH can address the prior matters, align with the learning design, and meet teachers’ needs. The technical and co-design aspects, as well as the advantages and challenges of applying TEADASH in practice, are also discussed here.
      Citation: Informatics
      PubDate: 2024-08-26
      DOI: 10.3390/informatics11030061
      Issue No: Vol. 11, No. 3 (2024)
       
  • Informatics, Vol. 11, Pages 13: The Research Interest in ChatGPT and Other
           Natural Language Processing Tools from a Public Health Perspective: A
           Bibliometric Analysis

    • Authors: Giuliana Favara, Martina Barchitta, Andrea Maugeri, Roberta Magnano San Magnano San Lio, Antonella Agodi
      First page: 13
      Abstract: Natural language processing, such as ChatGPT, demonstrates growing potential across numerous research scenarios, also raising interest in its applications in public health and epidemiology. Here, we applied a bibliometric analysis for a systematic assessment of the current literature related to the applications of ChatGPT in epidemiology and public health. Methods: A bibliometric analysis was conducted on the Biblioshiny web-app, by collecting original articles indexed in the Scopus database between 2010 and 2023. Results: On a total of 3431 original medical articles, “Article” and “Conference paper”, mostly constituting the total of retrieved documents, highlighting that the term “ChatGPT” becomes an interesting topic from 2023. The annual publications escalated from 39 in 2010 to 719 in 2023, with an average annual growth rate of 25.1%. In terms of country production over time, the USA led with the highest overall production from 2010 to 2023. Concerning citations, the most frequently cited countries were the USA, UK, and China. Interestingly, Harvard Medical School emerges as the leading contributor, accounting for 18% of all articles among the top ten affiliations. Conclusions: Our study provides an overall examination of the existing research interest in ChatGPT’s applications for public health by outlining pivotal themes and uncovering emerging trends.
      Citation: Informatics
      PubDate: 2024-03-22
      DOI: 10.3390/informatics11020013
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 14: Computational Ensemble Gene Co-Expression
           Networks for the Analysis of Cancer Biomarkers

    • Authors: Julia Figueroa-Martínez, Dulcenombre M. Saz-Navarro, Aurelio López-Fernández, Domingo S. Rodríguez-Baena, Francisco A. Gómez-Vela
      First page: 14
      Abstract: Gene networks have become a powerful tool for the comprehensive examination of gene expression patterns. Thanks to these networks generated by means of inference algorithms, it is possible to study different biological processes and even identify new biomarkers for such diseases. These biomarkers are essential for the discovery of new treatments for genetic diseases such as cancer. In this work, we introduce an algorithm for genetic network inference based on an ensemble method that improves the robustness of the results by combining two main steps: first, the evaluation of the relationship between pairs of genes using three different co-expression measures, and, subsequently, a voting strategy. The utility of this approach was demonstrated by applying it to a human dataset encompassing breast and prostate cancer-associated stromal cells. Two gene networks were computed using microarray data, one for breast cancer and one for prostate cancer. The results obtained revealed, on the one hand, distinct stromal cell behaviors in breast and prostate cancer and, on the other hand, a list of potential biomarkers for both diseases. In the case of breast tumor, ST6GAL2, RIPOR3, COL5A1, and DEPDC7 were found, and in the case of prostate tumor, the genes were GATA6-AS1, ARFGEF3, PRR15L, and APBA2. These results demonstrate the usefulness of the ensemble method in the field of biomarker discovery.
      Citation: Informatics
      PubDate: 2024-03-28
      DOI: 10.3390/informatics11020014
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 15: Detecting Structured Query Language
           Injections in Web Microservices Using Machine Learning

    • Authors: Edwin Peralta-Garcia, Juan Quevedo-Monsalbe, Victor Tuesta-Monteza, Juan Arcila-Diaz
      First page: 15
      Abstract: Structured Query Language (SQL) injections pose a constant threat to web services, highlighting the need for efficient detection to address this vulnerability. This study compares machine learning algorithms for detecting SQL injections in web microservices trained using a public dataset of 22,764 records. Additionally, a software architecture based on the microservices approach was implemented, in which trained models and the web application were deployed to validate requests and detect attacks. A literature review was conducted to identify types of SQL injections and machine learning algorithms. The results of random forest, decision tree, and support vector machine were compared for detecting SQL injections. The findings show that random forest outperforms with a precision and accuracy of 99%, a recall of 97%, and an F1 score of 98%. In contrast, decision tree achieved a precision of 92%, a recall of 86%, and an F1 score of 97%. Support Vector Machine (SVM) presented an accuracy, precision, and F1 score of 98%, with a recall of 97%.
      Citation: Informatics
      PubDate: 2024-04-02
      DOI: 10.3390/informatics11020015
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 16: Key Industry 4.0 Organisational Capability
           Prioritisation towards Organisational Transformation

    • Authors: Stefan Smuts, Alta van der Merwe
      First page: 16
      Abstract: Industry 4.0 aids organisational transformation powered by innovative technologies and connectivity. In addition to navigating complex Industry 4.0 concepts and characteristics, organisations must also address organisational consequences related to fast-paced organisational transformation and resource efficacy. The optimal allocation of organisational resources and capabilities to large transformational programs, as well as the significant capital investment associated with digital transformation, compel organisations to prioritize their efforts. Hence, this study investigates how key Industry 4.0 organisational capabilities could be prioritized towards organisational digital transformation. Data were collected from 49 participants who had completed a questionnaire containing 26 statement actions aligned to sensing, seizing, transforming and supporting organisational capability domains. By analysing the data, statement actions were prioritized and operationalized into a prototyped checklist. Two organisations applied the prototyped checklist, illustrating unique profiles and transformative actions. The operationalisation of the checklist highlighted its utility in establishing where an organisation operates in terms of digital transformation, as well as what additional steps might be followed to improve its capability prioritisation based on low checklist scores. By understanding the prioritisation of Industry 4.0 capabilities, organisations could ensure that resources are allocated optimally for business value creation based on organisational capabilities prioritisation.
      Citation: Informatics
      PubDate: 2024-04-02
      DOI: 10.3390/informatics11020016
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 17: Governors in the Digital Era: Analyzing
           and Predicting Social Media Engagement Using Machine Learning during the
           COVID-19 Pandemic in Japan

    • Authors: Salama Shady, Vera Paola Shoda, Takashi Kamihigashi
      First page: 17
      Abstract: This paper presents a comprehensive analysis of the social media posts of prefectural governors in Japan during the COVID-19 pandemic. It investigates the correlation between social media activity levels, governors’ characteristics, and engagement metrics. To predict citizen engagement of a specific tweet, machine learning models (MLMs) are trained using three feature sets. The first set includes variables representing profile- and tweet-related features. The second set incorporates word embeddings from three popular models, while the third set combines the first set with one of the embeddings. Additionally, seven classifiers are employed. The best-performing model utilizes the first feature set with FastText embedding and the XGBoost classifier. This study aims to collect governors’ COVID-19-related tweets, analyze engagement metrics, investigate correlations with governors’ characteristics, examine tweet-related features, and train MLMs for prediction. This paper’s main contributions are twofold. Firstly, it offers an analysis of social media engagement by prefectural governors during the COVID-19 pandemic, shedding light on their communication strategies and citizen engagement outcomes. Secondly, it explores the effectiveness of MLMs and word embeddings in predicting tweet engagement, providing practical implications for policymakers in crisis communication. The findings emphasize the importance of social media engagement for effective governance and provide insights into factors influencing citizen engagement.
      Citation: Informatics
      PubDate: 2024-04-07
      DOI: 10.3390/informatics11020017
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 18: Variations in Pattern of Social Media
           Engagement between Individuals with Chronic Conditions and Mental Health
           Conditions

    • Authors: Elizabeth Ayangunna, Gulzar Shah, Kingsley Kalu, Padmini Shankar, Bushra Shah
      First page: 18
      Abstract: The use of the internet and supported apps is at historically unprecedented levels for the exchange of health information. The increasing use of the internet and social media platforms can affect patients’ health behavior. This study aims to assess the variations in patterns of social media engagement between individuals diagnosed with either chronic diseases or mental health conditions. Data from four iterations of the Health Information National Trends Survey Cycle 4 from 2017 to 2020 were used for this study with a sample size (N) = 16,092. To analyze the association between the independent variables, reflecting the presence of chronic conditions or mental health conditions, and various levels of social media engagement, descriptive statistics and logistic regression were conducted. Respondents who had at least one chronic condition were more likely to join an internet-based support group (Adjusted Odds Ratio or AOR = 1.5; Confidence Interval, CI = 1.11–1.93) and watch a health-related video on YouTube (AOR = 1.2; CI = 1.01–1.36); respondents with a mental condition were less likely to visit and share health information on social media, join an internet-based support group, and watch a health-related video on YouTube. Race, age, and educational level also influence the choice to watch a health-related video on YouTube. Understanding the pattern of engagement with health-related content on social media and how their online behavior differs based on the patient’s medical conditions can lead to the development of more effective and tailored public health interventions that leverage social media platforms.
      Citation: Informatics
      PubDate: 2024-04-14
      DOI: 10.3390/informatics11020018
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 19: A Machine Learning as a Service (MLaaS)
           Approach to Improve Marketing Success

    • Authors: Ivo Pereira, Ana Madureira, Nuno Bettencourt, Duarte Coelho, Miguel Ângelo Rebelo, Carolina Araújo, Daniel Alves de Oliveira
      First page: 19
      Abstract: The exponential growth of data in the digital age has led to a significant demand for innovative approaches to assess data in a manner that is both effective and efficient. Machine Learning as a Service (MLaaS) is a category of services that offers considerable potential for organisations to extract valuable insights from their data while reducing the requirement for heavy technical expertise. This article explores the use of MLaaS within the realm of marketing applications. In this study, we provide a comprehensive analysis of MLaaS implementations and their benefits within the domain of marketing. Furthermore, we present a platform that possesses the capability to be customised and expanded to address marketing’s unique requirements. Three modules are introduced: Churn Prediction, One-2-One Product Recommendation, and Send Frequency Prediction. When applied to marketing, the proposed MLaaS system exhibits considerable promise for use in applications such as automated detection of client churn prior to its occurrence, individualised product recommendations, and send time optimisation. Our study revealed that AI-driven campaigns can improve both the Open Rate and Click Rate. This approach has the potential to enhance customer engagement and retention for businesses while enabling well-informed decisions by leveraging insights derived from consumer data. This work contributes to the existing body of research on MLaaS in marketing and offers practical insights for businesses seeking to utilise this approach to enhance their competitive edge in the contemporary data-oriented marketplace.
      Citation: Informatics
      PubDate: 2024-04-15
      DOI: 10.3390/informatics11020019
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 20: Artificial Intelligence Chatbots in
           Chemical Information Seeking: Narrative Educational Insights via a SWOT
           Analysis

    • Authors: Johannes Pernaa, Topias Ikävalko, Aleksi Takala, Emmi Vuorio, Reija Pesonen, Outi Haatainen
      First page: 20
      Abstract: Artificial intelligence (AI) chatbots are next-word predictors built on large language models (LLMs). There is great interest within the educational field for this new technology because AI chatbots can be used to generate information. In this theoretical article, we provide educational insights into the possibilities and challenges of using AI chatbots. These insights were produced by designing chemical information-seeking activities for chemistry teacher education which were analyzed via the SWOT approach. The analysis revealed several internal and external possibilities and challenges. The key insight is that AI chatbots will change the way learners interact with information. For example, they enable the building of personal learning environments with ubiquitous access to information and AI tutors. Their ability to support chemistry learning is impressive. However, the processing of chemical information reveals the limitations of current AI chatbots not being able to process multimodal chemical information. There are also ethical issues to address. Despite the benefits, wider educational adoption will take time. The diffusion can be supported by integrating LLMs into curricula, relying on open-source solutions, and training teachers with modern information literacy skills. This research presents theory-grounded examples of how to support the development of modern information literacy skills in the context of chemistry teacher education.
      Citation: Informatics
      PubDate: 2024-04-18
      DOI: 10.3390/informatics11020020
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 21: Digital Transformation in Omani Higher
           Education: Assessing Student Adoption of Video Communication during the
           COVID-19 Pandemic

    • Authors: Fatima Amer jid Almahri, Islam Elbayoumi Salem, Ahmed Mohamed Elbaz, Hassan Aideed, Zameer Gulzar
      First page: 21
      Abstract: The COVID-19 pandemic has influenced many fields, such as communication, commerce, and education, and pushed business entities to adopt innovative technologies to continue their business operations. Students need to do the same, so it is essential to understand their acceptance of these technologies to make them more usable for students. This paper employs the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) to identify the factors that influenced students’ acceptance and use of different online communication services as the primary tool for learning during the COVID-19 pandemic. Six factors of UTAUT2 were used to measure the acceptance and use of video communication services at the Business College of the University of Technology and Applied Sciences. Two hundred students completed our online survey. The results demonstrated that social influence, facilitating conditions, hedonic motivation, and habit affect behavioral intention positively, while performance expectancy and effort expectancy have no effect on behavioral intention.
      Citation: Informatics
      PubDate: 2024-04-19
      DOI: 10.3390/informatics11020021
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 22: Optimization of Obstructive Sleep Apnea
           Management: Novel Decision Support via Unsupervised Machine Learning

    • Authors: Arthur Pinheiro de Araújo Costa, Adilson Vilarinho Terra, Claudio de Souza Rocha Junior, Igor Pinheiro de Araújo Costa, Miguel Ângelo Lellis Moreira, Marcos dos Santos, Carlos Francisco Simões Gomes, Antonio Sergio da Silva
      First page: 22
      Abstract: This study addresses Obstructive Sleep Apnea (OSA), which impacts around 936 million adults globally. The research introduces a novel decision support method named Communalities on Ranking and Objective Weights Method (CROWM), which employs principal component analysis (PCA), unsupervised Machine Learning techniques, and Multicriteria Decision Analysis (MCDA) to calculate performance criteria weights of Continuous Positive Airway Pressure (CPAP—key in managing OSA) and to evaluate these devices. Uniquely, the CROWM incorporates non-beneficial criteria in PCA and employs communalities to accurately represent the performance evaluation of alternatives within each resulting principal factor, allowing for a more accurate and robust analysis of alternatives and variables. This article aims to employ CROWM to evaluate CPAP for effectiveness in combating OSA, considering six performance criteria: resources, warranty, noise, weight, cost, and maintenance. Validated by established tests and sensitivity analysis against traditional methods, CROWM proves its consistency, efficiency, and superiority in decision-making support. This method is poised to influence assertive decision-making significantly, aiding healthcare professionals, researchers, and patients in selecting optimal CPAP solutions, thereby advancing patient care in an interdisciplinary research context.
      Citation: Informatics
      PubDate: 2024-04-19
      DOI: 10.3390/informatics11020022
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 23: Systematic Review of English/Arabic
           Machine Translation Postediting: Implications for AI Application in
           Translation Research and Pedagogy

    • Authors: Lamis Ismail Omar, Abdelrahman Abdalla Salih
      First page: 23
      Abstract: The twenty-first century has witnessed an extensive evolution in translation practice thanks to the accelerated progress in machine translation tools and software. With the increased scalability and availability of machine translation software empowered by artificial intelligence, translation students and practitioners have continued to show an unwavering reliance on automatic translation systems. Academically, there is little recognition of the need to develop machine translation skillsets amongst translation learners in English/Arabic translation programs. This study provides a systematic review of machine translation postediting with reference to English/Arabic machine translation. Using the Preferred Reporting Items for Systematic Review and Meta-Analysis, the paper reviewed 60 studies conducted since the beginning of the twenty-first century and classified them by different metrics to identify relevant trends and research gaps. The results showed that research on the topic has been primarily prescriptive, concentrating on evaluating and developing machine translation software while neglecting aspects related to translators’ skillsets and competencies. The paper highlights the significance of postediting as an important digital literacy to be developed among Arabic translation students and the need to bridge the existing research and pedagogic gap in MT education.
      Citation: Informatics
      PubDate: 2024-04-22
      DOI: 10.3390/informatics11020023
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 24: Machine Learning and Deep Learning
           Sentiment Analysis Models: Case Study on the SENT-COVID Corpus of Tweets
           in Mexican Spanish

    • Authors: Helena Gomez-Adorno, Gemma Bel-Enguix, Gerardo Sierra, Juan-Carlos Barajas, William Álvarez
      First page: 24
      Abstract: This article presents a comprehensive evaluation of traditional machine learning and deep learning models in analyzing sentiment trends within the SENT-COVID Twitter corpus, curated during the COVID-19 pandemic. The corpus, filtered by COVID-19 related keywords and manually annotated for polarity, is a pivotal resource for conducting sentiment analysis experiments. Our study investigates various approaches, including classic vector-based systems such as word2vec, doc2vec, and diverse phrase modeling techniques, alongside Spanish pre-trained BERT models. We assess the performance of readily available sentiment analysis libraries for Python users, including TextBlob, VADER, and Pysentimiento. Additionally, we implement and evaluate traditional classification algorithms such as Logistic Regression, Naive Bayes, Support Vector Machines, and simple neural networks like Multilayer Perceptron. Throughout the research, we explore different dimensionality reduction techniques. This methodology enables a precise comparison among classification methods, with BETO-uncased achieving the highest accuracy of 0.73 on the test set. Our findings underscore the efficacy and applicability of traditional machine learning and deep learning models in analyzing sentiment trends within the context of low-resource Spanish language scenarios and emerging topics like COVID-19.
      Citation: Informatics
      PubDate: 2024-04-23
      DOI: 10.3390/informatics11020024
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 25: A Survey of Vision-Based Methods for
           Surface Defects’ Detection and Classification in Steel Products

    • Authors: Alaa Aldein M. S. Ibrahim, Jules-Raymond Tapamo
      First page: 25
      Abstract: In the competitive landscape of steel-strip production, ensuring the high quality of steel surfaces is paramount. Traditionally, human visual inspection has been the primary method for detecting defects, but it suffers from limitations such as reliability, cost, processing time, and accuracy. Visual inspection technologies, particularly automation techniques, have been introduced to address these shortcomings. This paper conducts a thorough survey examining vision-based methodologies related to detecting and classifying surface defects on steel products. These methodologies encompass statistical, spectral, texture segmentation based methods, and machine learning-driven approaches. Furthermore, various classification algorithms, categorized into supervised, semi-supervised, and unsupervised techniques, are discussed. Additionally, the paper outlines the future direction of research focus.
      Citation: Informatics
      PubDate: 2024-04-23
      DOI: 10.3390/informatics11020025
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 26: Every Thing Can Be a Hero! Narrative
           Visualization of Person, Object, and Other Biographies

    • Authors: Jakob Kusnick, Eva Mayr, Kasra Seirafi, Samuel Beck, Johannes Liem, Florian Windhager
      First page: 26
      Abstract: Knowledge communication in cultural heritage and digital humanities currently faces two challenges, which this paper addresses: On the one hand, data-driven storytelling in these fields has mainly focused on human protagonists, while other essential entities (such as artworks and artifacts, institutions, or places) have been neglected. On the other hand, storytelling tools rarely support the larger chains of data practices, which are required to generate and shape the data and visualizations needed for such stories. This paper introduces the InTaVia platform, which has been developed to bridge these gaps. It supports the practices of data retrieval, creation, curation, analysis, and communication with coherent visualization support for multiple types of entities. We illustrate the added value of this open platform for storytelling with four case studies, focusing on (a) the life of Albrecht Dürer (person biography), (b) the Saliera salt cellar by Benvenuto Cellini (object biography), (c) the artist community of Lake Tuusula (group biography), and (d) the history of the Hofburg building complex in Vienna (place biography). Numerous suggestions for future research arise from this undertaking.
      Citation: Informatics
      PubDate: 2024-04-26
      DOI: 10.3390/informatics11020026
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 27: Prompt Design through ChatGPT’s
           Zero-Shot Learning Prompts: A Case of Cost-Sensitive Learning on a Water
           Potability Dataset

    • Authors: Kokisa Phorah, Malusi Sibiya, Mbuyu Sumbwanyambe
      First page: 27
      Abstract: Datasets used in AI applications for human health require careful selection. In healthcare, machine learning (ML) models are fine-tuned to reduce errors, and our study focuses on minimizing errors by generating code snippets for cost-sensitive learning using water potability datasets. Water potability ensures safe drinking water through various scientific methods, with our approach using ML algorithms for prediction. We preprocess data with ChatGPT-generated code snippets and aim to demonstrate how zero-shot learning prompts in ChatGPT can produce reliable code snippets that cater to cost-sensitive learning. Our dataset is sourced from Kaggle. We compare model performance metrics of logistic regressors and gradient boosting classifiers without additional code fine-tuning to check the accuracy. Other classifier performance metrics are compared with results of the top 5 code authors on the Kaggle scoreboard. Cost-sensitive learning is crucial in domains like healthcare to prevent misclassifications with serious consequences, such as type II errors in water potability assessment.
      Citation: Informatics
      PubDate: 2024-04-28
      DOI: 10.3390/informatics11020027
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 28: Variations in Using Diagnosis Codes for
           Defining Age-Related Macular Degeneration Cohorts

    • Authors: Fritz Gerald Paguiligan Kalaw, Jimmy S. Chen, Sally L. Baxter
      First page: 28
      Abstract: Data harmonization is vital for secondary electronic health record data analysis, especially when combining data from multiple sources. Currently, there is a gap in knowledge as to how studies identify cohorts of patients with age-related macular degeneration (AMD), a leading cause of blindness. We hypothesize that there is variation in using medical condition codes to define cohorts of AMD patients that can lead to either the under- or overrepresentation of such cohorts. This study identified articles studying AMD using the International Classification of Diseases (ICD-9, ICD-9-CM, ICD-10, and ICD-10-CM). The data elements reviewed included the year of publication; dataset origin (Veterans Affairs, registry, national or commercial claims database, and institutional EHR); total number of subjects; and ICD codes used. A total of thirty-seven articles were reviewed. Six (16%) articles used cohort definitions from two ICD terminologies. The Medicare database was the most used dataset (14, 38%), and there was a noted increase in the use of other datasets in the last few years. We identified substantial variation in the use of ICD codes for AMD. For the studies that used ICD-10 terminologies, 7 (out of 9, 78%) defined the AMD codes correctly, whereas, for the studies that used ICD-9 and 9-CM terminologies, only 2 (out of 30, 7%) defined and utilized the appropriate AMD codes (p = 0.0001). Of the 43 cohort definitions used from 37 articles, 31 (72%) had missing or incomplete AMD codes used, and only 9 (21%) used the exact codes. Additionally, 13 articles (35%) captured ICD codes that were not within the scope of AMD diagnosis. Efforts to standardize data are needed to provide a reproducible research output.
      Citation: Informatics
      PubDate: 2024-05-01
      DOI: 10.3390/informatics11020028
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 29: Fuzzy Classification Approach to Select
           Learning Objects Based on Learning Styles in Intelligent E-Learning
           Systems

    • Authors: Ibtissam Azzi, Abdelhay Radouane, Loubna Laaouina, Adil Jeghal, Ali Yahyaouy, Hamid Tairi
      First page: 29
      Abstract: In e-learning systems, even though the automatic detection of learning styles is considered the key element in the adaptation process, it does not represent the main goal of this process at all. Indeed, to accomplish the task of adaptation, it is also necessary to be able to automatically select the learning objects according to the detected styles. The classification techniques are the most used techniques to automatically select the learning objects by processing data derived from learning object metadata. By using these classification techniques, considerable results are obtained via several approaches and consist of mapping the learning objects into different teaching strategies and then mapping these strategies into the identified learning styles. However, these approaches have some limitations related to robustness. Indeed, a common feature of these approaches is that they do not directly map learning object metadata elements to learning style dimensions. Moreover, they do not consider the fuzzy nature of learning objects. Indeed, any learning object can be suitable for different learning styles at varying degrees of suitability. This highlights the need to find a way to remedy this shortcoming. Our work is part of the automatic selection of learning objects. So, we will propose an approach that uses the fuzzy classification technique to select learning objects based on learning styles. In this approach, the metadata of each learning object that complies with the Institute of Electrical and Electronics Engineers (IEEE) standard are stored in a database as an Extensible Markup Language (XML) file. The Fuzzy C Means algorithm is used, on one hand, to assign fuzzy suitability rates to the stored learning objects and, on the other hand, to cluster them into the Felder and Silverman learning styles model categories. The experiment results show the performance of our approach.
      Citation: Informatics
      PubDate: 2024-05-15
      DOI: 10.3390/informatics11020029
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 30: Investigating User Experience of VR Art
           Exhibitions: The Impact of Immersion, Satisfaction, and Expectation
           Confirmation

    • Authors: Lin Cheng, Junping Xu, Younghwan Pan
      First page: 30
      Abstract: As an innovative form in the digital age, VR art exhibitions have attracted increasing attention. This study aims to explore the key factors that influence visitors’ continuance intention to VR art exhibitions using the expectation confirmation model and experience economy theory and to explore ways to enhance visitor immersion in virtual environments. We conducted a quantitative study of 235 art professionals and enthusiasts, conducted using the partial least squares structural equation modeling (PLS-SEM), to examine the complex relationship between confirmation (CON), Perceived Usefulness (PU), Aesthetic Experiences (AE), Escapist Experiences (EE), Satisfaction (SAT), and Continuance Intention (CI). The results show that confirmation plays a key role in shaping PU, AE, and EE, which in turn positively affect visitors’ SAT. Among these factors, AE positively impacts PU, but EE have no impact. A comprehensive theoretical model was then constructed based on the findings. This research provides empirical support for designing and improving VR art exhibitions. It also sheds light on the application of expectation confirmation theory and experience economy theory in the art field to improve user experience and provides theoretical guidance for the sustainable development of virtual digital art environment.
      Citation: Informatics
      PubDate: 2024-05-16
      DOI: 10.3390/informatics11020030
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 31: ACME: A Classification Model for
           Explaining the Risk of Preeclampsia Based on Bayesian Network Classifiers
           and a Non-Redundant Feature Selection Approach

    • Authors: Franklin Parrales-Bravo, Rosangela Caicedo-Quiroz, Elianne Rodríguez-Larraburu, Julio Barzola-Monteses
      First page: 31
      Abstract: While preeclampsia is the leading cause of maternal death in Guayas province (Ecuador), its causes have not yet been studied in depth. The objective of this research is to build a Bayesian network classifier to diagnose cases of preeclampsia while facilitating the understanding of the causes that generate this disease. Data for the years 2017 through 2023 were gathered retrospectively from medical histories of patients treated at “IESS Los Ceibos” hospital in Guayaquil, Ecuador. Naïve Bayes (NB), The Chow–Liu Tree-Augmented Naïve Bayes (TANcl), and Semi Naïve Bayes (FSSJ) algorithms have been considered for building explainable classification models. A proposed Non-Redundant Feature Selection approach (NoReFS) is proposed to perform the feature selection task. The model trained with the TANcl and NoReFS was the best of them, with an accuracy close to 90%. According to the best model, patients whose age is above 35 years, have a severe vaginal infection, live in a rural area, use tobacco, have a family history of diabetes, and have had a personal history of hypertension are those with a high risk of developing preeclampsia.
      Citation: Informatics
      PubDate: 2024-05-17
      DOI: 10.3390/informatics11020031
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 32: Performance Evaluation of Deep Learning
           Models for Classifying Cybersecurity Attacks in IoT Networks

    • Authors: Fray L. Becerra-Suarez, Victor A. Tuesta-Monteza, Heber I. Mejia-Cabrera, Juan Arcila-Diaz
      First page: 32
      Abstract: The Internet of Things (IoT) presents great potential in various fields such as home automation, healthcare, and industry, among others, but its infrastructure, the use of open source code, and lack of software updates make it vulnerable to cyberattacks that can compromise access to data and services, thus making it an attractive target for hackers. The complexity of cyberattacks has increased, posing a greater threat to public and private organizations. This study evaluated the performance of deep learning models for classifying cybersecurity attacks in IoT networks, using the CICIoT2023 dataset. Three architectures based on DNN, LSTM, and CNN were compared, highlighting their differences in layers and activation functions. The results show that the CNN architecture outperformed the others in accuracy and computational efficiency, with an accuracy rate of 99.10% for multiclass classification and 99.40% for binary classification. The importance of data standardization and proper hyperparameter selection is emphasized. These results demonstrate that the CNN-based model emerges as a promising option for detecting cyber threats in IoT environments, supporting the relevance of deep learning in IoT network security.
      Citation: Informatics
      PubDate: 2024-05-17
      DOI: 10.3390/informatics11020032
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 33: QUMA: Quantum Unified Medical Architecture
           Using Blockchain

    • Authors: Akoramurthy Balasubramaniam, B. Surendiran
      First page: 33
      Abstract: A significant increase in the demand for quality healthcare has resulted from people becoming more aware of health issues. With blockchain, healthcare providers may safely share patient information electronically, which is especially important given the sensitive nature of the data contained inside them. However, flaws in the current blockchain design have surfaced since the dawn of quantum computing systems. The study proposes a novel quantum-inspired blockchain system (Qchain) and constructs a unique entangled quantum medical record (EQMR) system with an emphasis on privacy and security. This Qchain relies on entangled states to connect its blocks. The automated production of the chronology indicator reduces storage capacity requirements by connecting entangled BloQ (blocks with quantum properties) to controlled activities. We use one qubit to store the hash value of each block. A lot of information regarding the quantum internet is included in the protocol for the entangled quantum medical record (EQMR). The EQMR can be accessed in Medical Internet of Things (M-IoT) systems that are kept private and secure, and their whereabouts can be monitored in the event of an emergency. The protocol also uses quantum authentication in place of more conventional methods like encryption and digital signatures. Mathematical research shows that the quantum converged blockchain (QCB) is highly safe against attacks such as external attacks, intercept measure -repeat attacks, and entanglement measure attacks. We present the reliability and auditability evaluations of the entangled BloQ, along with the quantum circuit design for computing the hash value. There is also a comparison between the suggested approach and several other quantum blockchain designs.
      Citation: Informatics
      PubDate: 2024-05-17
      DOI: 10.3390/informatics11020033
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 34: An Intelligent Model and Methodology for
           Predicting Length of Stay and Survival in a Critical Care Hospital Unit

    • Authors: Maldonado Belmonte, Oton-Tortosa, Gutierrez-Martinez, Castillo-Martinez
      First page: 34
      Abstract: This paper describes the design and methodology for the development and validation of an intelligent model in the healthcare domain. The generated model relies on artificial intelligence techniques, aiming to predict the length of stay and survival rate of patients admitted to a critical care hospitalization unit with better results than predictive systems using scoring. The proposed methodology is based on the following stages: preliminary data analysis, analysis of the architecture and systems integration model, the big data model approach, information structure and process development, and the application of machine learning techniques. This investigation substantiates that automated machine learning models significantly surpass traditional prediction techniques for patient outcomes within critical care settings. Specifically, the machine learning-based model attained an F1 score of 0.351 for mortality forecast and 0.615 for length of stay, in contrast to the traditional scoring model’s F1 scores of 0.112 for mortality and 0.412 for length of stay. These results strongly support the advantages of integrating advanced computational techniques in critical healthcare environments. It is also shown that the use of integration architectures allows for improving the quality of the information by providing a data repository large enough to generate intelligent models. From a clinical point of view, obtaining more accurate results in the estimation of the ICU stay and survival offers the possibility of expanding the uses of the model to the identification and prioritization of patients who are candidates for admission to the ICU, as well as the management of patients with specific conditions.
      Citation: Informatics
      PubDate: 2024-05-17
      DOI: 10.3390/informatics11020034
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 35: Improving Minority Class Recall through a
           Novel Cluster-Based Oversampling Technique

    • Authors: Takorn Prexawanprasut, Thepparit Banditwattanawong
      First page: 35
      Abstract: In this study, we propose an approach to address the pressing issue of false negative errors by enhancing minority class recall within imbalanced data sets commonly encountered in machine learning applications. Through the utilization of a cluster-based oversampling technique in conjunction with an information entropy evaluation, our approach effectively targets areas of ambiguity inherent in the data set. An extensive evaluation across a diverse range of real-world data sets characterized by inter-cluster complexity demonstrates the superior performance of our method compared to that of existing oversampling techniques. Particularly noteworthy is its significant improvement within the Delinquency Telecom data set, where it achieves a remarkable increase of up to 30.54 percent in minority class recall compared to the original data set. This notable reduction in false negative errors underscores the importance of our methodology in accurately identifying and classifying instances from underrepresented classes, thereby enhancing model performance in imbalanced data scenarios.
      Citation: Informatics
      PubDate: 2024-05-28
      DOI: 10.3390/informatics11020035
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 36: Safety of Human–Artificial
           Intelligence Systems: Applying Safety Science to Analyze Loopholes in
           Interactions between Human Organizations, Artificial Intelligence, and
           Individual People

    • Authors: Stephen Fox, Juan G. Victores
      First page: 36
      Abstract: Loopholes involve misalignments between rules about what should be done and what is actually done in practice. The focus of this paper is loopholes in interactions between human organizations’ implementations of task-specific artificial intelligence and individual people. The importance of identifying and addressing loopholes is recognized in safety science and in applications of AI. Here, an examination is provided of loophole sources in interactions between human organizations and individual people. Then, it is explained how the introduction of task-specific AI applications can introduce new sources of loopholes. Next, an analytical framework, which is well-established in safety science, is applied to analyses of loopholes in interactions between human organizations, artificial intelligence, and individual people. The example used in the analysis is human–artificial intelligence systems in gig economy delivery driving work.
      Citation: Informatics
      PubDate: 2024-05-29
      DOI: 10.3390/informatics11020036
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 37: Analysing the Impact of Generative AI in
           Arts Education: A Cross-Disciplinary Perspective of Educators and Students
           in Higher Education

    • Authors: Sara Sáez-Velasco, Mario Alaguero-Rodríguez, Vanesa Delgado-Benito, Sonia Rodríguez-Cano
      First page: 37
      Abstract: Generative AI refers specifically to a class of Artificial Intelligence models that use existing data to create new content that reflects the underlying patterns of real-world data. This contribution presents a study that aims to show what the current perception of arts educators and students of arts education is with regard to generative Artificial Intelligence. It is a qualitative research study using focus groups as a data collection technique in order to obtain an overview of the participating subjects. The research design consists of two phases: (1) generation of illustrations from prompts by students, professionals and a generative AI tool; and (2) focus groups with students (N = 5) and educators (N = 5) of artistic education. In general, the perception of educators and students coincides in the usefulness of generative AI as a tool to support the generation of illustrations. However, they agree that the human factor cannot be replaced by generative AI. The results obtained allow us to conclude that generative AI can be used as a motivating educational strategy for arts education.
      Citation: Informatics
      PubDate: 2024-06-03
      DOI: 10.3390/informatics11020037
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 38: Chatbot Technology Use and Acceptance
           Using Educational Personas

    • Authors: Fatima Ali Amer jid Almahri, David Bell, Zameer Gulzar
      First page: 38
      Abstract: Chatbots are computer programs that mimic human conversation using text or voice or both. Users’ acceptance of chatbots is highly influenced by their persona. Users develop a sense of familiarity with chatbots as they use them, so they become more approachable, and this encourages them to interact with the chatbots more readily by fostering favorable opinions of the technology. In this study, we examine the moderating effects of persona traits on students’ acceptance and use of chatbot technology at higher educational institutions in the UK. We use an Extended Unified Theory of Acceptance and Use of Technology (Extended UTAUT2). Through a self-administrated survey using a questionnaire, data were collected from 431 undergraduate and postgraduate computer science students. This study employed a Likert scale to measure the variables associated with chatbot acceptance. To evaluate the gathered data, Structural Equation Modelling (SEM) coupled with multi-group analysis (MGA) using SmartPLS3 were used. The estimated Cronbach’s alpha highlighted the accuracy and legitimacy of the findings. The results showed that the emerging factors that influence students’ adoption and use of chatbot technology were habit, effort expectancy, and performance expectancy. Additionally, it was discovered that the Extended UTAUT2 model did not require grades or educational level to moderate the correlations. These results are important for improving user experience and they have implications for academics, researchers, and organizations, especially in the context of native chatbots.
      Citation: Informatics
      PubDate: 2024-06-03
      DOI: 10.3390/informatics11020038
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 39: MSProfileR: An Open-Source Software for
           Quality Control of Matrix-Assisted Laser Desorption Ionization–Time
           of Flight Spectra

    • Authors: Refka Ben Hamouda, Bertrand Estellon, Khalil Himet, Aimen Cherif, Hugo Marthinet, Jean-Marie Loreau, Gaëtan Texier, Samuel Granjeaud, Lionel Almeras
      First page: 39
      Abstract: In the early 2000s, matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) emerged as a performant and relevant tool for identifying micro-organisms. Since then, it has become practically essential for identifying bacteria in microbiological diagnostic laboratories. In the last decade, it was successfully applied for arthropod identification, allowing researchers to distinguish vectors from non-vectors of infectious diseases. However, identification failures are not rare, hampering its wide use. Failure is generally attributed either to the absence of respective counter species MS spectra in the database or to the insufficient quality of query MS spectra (i.e., lower intensity and diversity of MS peaks detected). To avoid matching errors due to non-compliant spectra, the development of a strategy for detecting and excluding outlier MS profiles became compulsory. To this end, we created MSProfileR, an R package leading to a bioinformatics tool through a simple installation, integrating a control quality system of MS spectra and an analysis pipeline including peak detection and MS spectra comparisons. MSProfileR can also add metadata concerning the sample that the spectra are derived from. MSProfileR has been developed in the R environment and offers a user-friendly web interface using the R Shiny framework. It is available on Microsoft Windows as a web browser application by simple navigation using the link of the package on Github v.3.10.0. MSProfileR is therefore accessible to non-computer specialists and is freely available to the scientific community. We evaluated MSProfileR using two datasets including exclusively MS spectra from arthropods. In addition to coherent sample classification, outlier MS spectra were detected in each dataset confirming the value of MSProfileR.
      Citation: Informatics
      PubDate: 2024-06-06
      DOI: 10.3390/informatics11020039
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 40: Analysis of the Epidemic Curve of the
           Waves of COVID-19 Using Integration of Functions and Neural Networks in
           Peru

    • Authors: Oliver Amadeo Vilca Huayta, Adolfo Carlos Jimenez Chura, Carlos Boris Sosa Maydana, Alioska Jessica Martínez García
      First page: 40
      Abstract: The coronavirus (COVID-19) pandemic continues to claim victims. According to the World Health Organization, in the 28 days leading up to 25 February 2024 alone, the number of deaths from COVID-19 was 7141. In this work, we aimed to model the waves of COVID-19 through artificial neural networks (ANNs) and the sigmoidal–Boltzmann model. The study variable was the global cumulative number of deaths according to days, based on the Peru dataset. Additionally, the variables were adapted to determine the correlation between social isolation measures and death rates, which constitutes a novel contribution. A quantitative methodology was used that implemented a non-experimental, longitudinal, and correlational design. The study was retrospective. The results show that the sigmoidal and ANN models were reasonably representative and could help to predict the spread of COVID-19 over the course of multiple waves. Furthermore, the results were precise, with a Pearson correlation coefficient greater than 0.999. The computational sigmoidal–Boltzmann model was also time-efficient. Moreover, the Spearman correlation between social isolation measures and death rates was 0.77, which is acceptable considering that the social isolation variable is qualitative. Finally, we concluded that social isolation measures had a significant effect on reducing deaths from COVID-19.
      Citation: Informatics
      PubDate: 2024-06-07
      DOI: 10.3390/informatics11020040
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 41: Identifying Long COVID Definitions,
           Predictors, and Risk Factors in the United States: A Scoping Review of
           Data Sources Utilizing Electronic Health Records

    • Authors: Rayanne A. Luke, George Shaw, Geetha Saarunya, Abolfazl Mollalo
      First page: 41
      Abstract: This scoping review explores the potential of electronic health records (EHR)-based studies to characterize long COVID. We screened all peer-reviewed publications in the English language from PubMed/MEDLINE, Scopus, and Web of Science databases until 14 September 2023, to identify the studies that defined or characterized long COVID based on data sources that utilized EHR in the United States, regardless of study design. We identified only 17 articles meeting the inclusion criteria. Respiratory conditions were consistently significant in all studies, followed by poor well-being features (n = 14, 82%) and cardiovascular conditions (n = 12, 71%). Some articles (n = 7, 41%) used a long COVID-specific marker to define the study population, relying mainly on ICD-10 codes and clinical visits for post-COVID-19 conditions. Among studies exploring plausible long COVID (n = 10, 59%), the most common methods were RT-PCR and antigen tests. The time delay for EHR data extraction post-test varied, ranging from four weeks to more than three months; however, most studies considering plausible long COVID used a waiting period of 28 to 31 days. Our findings suggest a limited utilization of EHR-derived data sources in defining long COVID, with only 59% of these studies incorporating a validation step.
      Citation: Informatics
      PubDate: 2024-06-14
      DOI: 10.3390/informatics11020041
      Issue No: Vol. 11, No. 2 (2024)
       
  • Informatics, Vol. 11, Pages 4: Exploring the Relation between Contextual
           

    • Authors: Aokun Chen, Yunpeng Zhao, Yi Zheng, Hui Hu, Xia Hu, Jennifer N. Fishe, William R. Hogan, Elizabeth A. Shenkman, Yi Guo, Jiang Bian
      First page: 4
      Abstract: It is prudent to take a unified approach to exploring how contextual social determinants of health (SDoH) relate to COVID-19 occurrence and outcomes. Poor geographically represented data and a small number of contextual SDoH examined in most previous research studies have left a knowledge gap in the relationships between contextual SDoH and COVID-19 outcomes. In this study, we linked 199 contextual SDoH factors covering 11 domains of social and built environments with electronic health records (EHRs) from a large clinical research network (CRN) in the National Patient-Centered Clinical Research Network (PCORnet) to explore the relation between contextual SDoH and COVID-19 occurrence and hospitalization. We identified 15,890 COVID-19 patients and 63,560 matched non-COVID-19 patients in Florida between January 2020 and May 2021. We adopted a two-phase multiple linear regression approach modified from that in the exposome-wide association (ExWAS) study. After removing the highly correlated SDoH variables, 86 contextual SDoH variables were included in the data analysis. Adjusting for race, ethnicity, and comorbidities, we found six contextual SDoH variables (i.e., hospital available beds and utilization, percent of vacant property, number of golf courses, and percent of minority) related to the occurrence of COVID-19, and three variables (i.e., farmers market, low access, and religion) related to the hospitalization of COVID-19. To our best knowledge, this is the first study to explore the relationship between contextual SDoH and COVID-19 occurrence and hospitalization using EHRs in a major PCORnet CRN. As an exploratory study, the causal effect of SDoH on COVID-19 outcomes will be evaluated in future studies.
      Citation: Informatics
      PubDate: 2024-01-15
      DOI: 10.3390/informatics11010004
      Issue No: Vol. 11, No. 1 (2024)
       
  • Informatics, Vol. 11, Pages 5: Application of Augmented Reality Technology
           for Chest ECG Electrode Placement Practice

    • Authors: Charlee Kaewrat, Dollaporn Anopas, Si Thu Aung, Yunyong Punsawad
      First page: 5
      Abstract: This study presents an augmented reality application for training chest electrocardiography electrode placement. AR applications featuring augmented object displays and interactions have been developed to facilitate learning and training of electrocardiography (ECG) chest lead placement via smartphones. The AR marker-based technique was used to track the objects. The proposed AR application can project virtual ECG electrode positions onto the mannequin’s chest and provide feedback to trainees. We designed experimental tasks using the pre- and post-tests and practice sessions to verify the efficiency of the proposed AR application. The control group was assigned to learn chest ECG electrode placement using traditional methods, whereas the intervention group was introduced to the proposed AR application for ECG electrode placement. The results indicate that the proposed AR application can encourage learning outcomes, such as chest lead ECG knowledge and skills. Moreover, using AR technology can enhance students’ learning experiences. In the future, we plan to apply the proposed AR technology to improve related courses in medical science education.
      Citation: Informatics
      PubDate: 2024-01-15
      DOI: 10.3390/informatics11010005
      Issue No: Vol. 11, No. 1 (2024)
       
  • Informatics, Vol. 11, Pages 6: Exploring the Relationship between Career
           Satisfaction and University Learning Using Data Science Models

    • Authors: Sofía Ramos-Pulido, Neil Hernández-Gress, Gabriela Torres-Delgado
      First page: 6
      Abstract: Current research on the career satisfaction of graduates limits educational institutions in devising methods to attain high career satisfaction. Thus, this study aims to use data science models to understand and predict career satisfaction based on information collected from surveys of university alumni. Five machine learning (ML) algorithms were used for data analysis, including the decision tree, random forest, gradient boosting, support vector machine, and neural network models. To achieve optimal prediction performance, we utilized the Bayesian optimization method to fine-tune the parameters of the five ML algorithms. The five ML models were compared with logistic and ordinal regression. Then, to extract the most important features of the best predictive model, we employed the SHapley Additive exPlanations (SHAP), a novel methodology for extracting the significant features in ML. The results indicated that gradient boosting is a marginally superior predictive model, with 2–3% higher accuracy and area under the receiver operating characteristic curve (AUC) compared to logistic and ordinal regression. Interestingly, concerning low career satisfaction, those with the worst scores for the phrase “how frequently applied knowledge, skills, or technological tools from the academic training” were less satisfied with their careers. To summarize, career satisfaction is related to academic training, alumni satisfaction, employment status, published articles or books, and other factors.
      Citation: Informatics
      PubDate: 2024-01-24
      DOI: 10.3390/informatics11010006
      Issue No: Vol. 11, No. 1 (2024)
       
  • Informatics, Vol. 11, Pages 7: Uncovering the Limitations and Insights of
           Packet Status Prediction Models in IEEE 802.15.4-Based Wireless Networks
           and Insights from Data Science

    • Authors: Mariana Ávalos-Arce, Heráclito Pérez-Díaz, Carolina Del-Valle-Soto, Ramon A. Briseño
      First page: 7
      Abstract: Wireless networks play a pivotal role in various domains, including industrial automation, autonomous vehicles, robotics, and mobile sensor networks. This research investigates the critical issue of packet loss in modern wireless networks and aims to identify the conditions within a network’s environment that lead to such losses. We propose a packet status prediction model for data packets that travel through a wireless network based on the IEEE 802.15.4 standard and are exposed to five different types of interference in a controlled experimentation environment. The proposed model focuses on the packetization process and its impact on network robustness. This study explores the challenges posed by packet loss, particularly in the context of interference, and puts forth the hypothesis that specific environmental conditions are linked to packet loss occurrences. The contribution of this work lies in advancing our understanding of the conditions leading to packet loss in wireless networks. Data are retrieved with a single CC2531 USB Dongle Packet Sniffer, whose pieces of information on packets become the features of each packet from which the classifier model will gather the training data with the aim of predicting whether a packet will unsuccessfully arrive at its destination. We found that interference causes more packet loss than that caused by various devices using a WiFi communication protocol simultaneously. In addition, we found that the most important predictors are network strength and packet size; low network strength tends to lead to more packet loss, especially for larger packets. This study contributes to the ongoing efforts to predict and mitigate packet loss, emphasizing the need for adaptive models in dynamic wireless environments.
      Citation: Informatics
      PubDate: 2024-01-26
      DOI: 10.3390/informatics11010007
      Issue No: Vol. 11, No. 1 (2024)
       
  • Informatics, Vol. 11, Pages 8: Topic Extraction: BERTopic’s Insight
           into the 117th Congress’s Twitterverse

    • Authors: Margarida Mendonça, Álvaro Figueira
      First page: 8
      Abstract: As social media (SM) becomes increasingly prevalent, its impact on society is expected to grow accordingly. While SM has brought positive transformations, it has also amplified pre-existing issues such as misinformation, echo chambers, manipulation, and propaganda. A thorough comprehension of this impact, aided by state-of-the-art analytical tools and by an awareness of societal biases and complexities, enables us to anticipate and mitigate the potential negative effects. One such tool is BERTopic, a novel deep-learning algorithm developed for Topic Mining, which has been shown to offer significant advantages over traditional methods like Latent Dirichlet Allocation (LDA), particularly in terms of its high modularity, which allows for extensive personalization at each stage of the topic modeling process. In this study, we hypothesize that BERTopic, when optimized for Twitter data, can provide a more coherent and stable topic modeling. We began by conducting a review of the literature on topic-mining approaches for short-text data. Using this knowledge, we explored the potential for optimizing BERTopic and analyzed its effectiveness. Our focus was on Twitter data spanning the two years of the 117th US Congress. We evaluated BERTopic’s performance using coherence, perplexity, diversity, and stability scores, finding significant improvements over traditional methods and the default parameters for this tool. We discovered that improvements are possible in BERTopic’s coherence and stability. We also identified the major topics of this Congress, which include abortion, student debt, and Judge Ketanji Brown Jackson. Additionally, we describe a simple application we developed for a better visualization of Congress topics.
      Citation: Informatics
      PubDate: 2024-02-17
      DOI: 10.3390/informatics11010008
      Issue No: Vol. 11, No. 1 (2024)
       
  • Informatics, Vol. 11, Pages 9: Genealogical Data-Driven Visits of
           Historical Cemeteries

    • Authors: Angelica Lo Duca, Matteo Abrate, Andrea Marchetti, Manuela Moretti
      First page: 9
      Abstract: This paper describes the Integration of Archives and Cultural Places (IaCuP) project, which aims to integrate information about a historical cemetery, including its map and grave inventory, with genealogical and documentary knowledge extracted from relevant historical archives. The integrated data are accessible to cemetery visitors through an interactive mobile application, enabling them to navigate a graphical representation of the cemetery while exploring comprehensive visualizations of genealogical data. The basic idea stems from the desire to provide people with access to the rich context of cultural sites, which have often lost their original references over the centuries, making it challenging for individuals today to interpret the meanings embedded within them. The proposed approach leverages large language models (LLMs) to extract information from relevant documents and Web technologies to represent such information as interactive visualizations. As a practical case study, this paper focuses on the Jewish Cemetery in Pisa and the Historical Archives of the Jewish Community in Pisa, working on the genealogical tree of one of the most representative families resting in the cemetery.
      Citation: Informatics
      PubDate: 2024-02-22
      DOI: 10.3390/informatics11010009
      Issue No: Vol. 11, No. 1 (2024)
       
  • Informatics, Vol. 11, Pages 10: Unveiling Insights: A Bibliometric
           Analysis of Artificial Intelligence in Teaching

    • Authors: Malinka Ivanova, Gabriela Grosseck, Carmen Holotescu
      First page: 10
      Abstract: The penetration of intelligent applications in education is rapidly increasing, posing a number of questions of a different nature to the educational community. This paper is coming to analyze and outline the influence of artificial intelligence (AI) on teaching practice which is an essential problem considering its growing utilization and pervasion on a global scale. A bibliometric approach is applied to outdraw the “big picture” considering gathered bibliographic data from scientific databases Scopus and Web of Science. Data on relevant publications matching the query “artificial intelligence and teaching” over the past 5 years have been researched and processed through Biblioshiny in R environment in order to establish a descriptive structure of the scientific production, to determine the impact of scientific publications, to trace collaboration patterns and to identify key research areas and emerging trends. The results point out the growth in scientific production lately that is an indicator of increased interest in the investigated topic by researchers who mainly work in collaborative teams as some of them are from different countries and institutions. The identified key research areas include techniques used in educational applications, such as artificial intelligence, machine learning, and deep learning. Additionally, there is a focus on applicable technologies like ChatGPT, learning analytics, and virtual reality. The research also explores the context of application for these techniques and technologies in various educational settings, including teaching, higher education, active learning, e-learning, and online learning. Based on our findings, the trending research topics can be encapsulated by terms such as ChatGPT, chatbots, AI, generative AI, machine learning, emotion recognition, large language models, convolutional neural networks, and decision theory. These findings offer valuable insights into the current landscape of research interests in the field.
      Citation: Informatics
      PubDate: 2024-02-25
      DOI: 10.3390/informatics11010010
      Issue No: Vol. 11, No. 1 (2024)
       
  • Informatics, Vol. 11, Pages 11: Exploring Multidimensional Embeddings for
           Decision Support Using Advanced Visualization Techniques

    • Authors: Olga Kurasova, Arnoldas Budžys, Viktor Medvedev
      First page: 11
      Abstract: As artificial intelligence has evolved, deep learning models have become important in extracting and interpreting complex patterns from raw multidimensional data. These models produce multidimensional embeddings that, while containing a lot of information, are often not directly understandable. Dimensionality reduction techniques play an important role in transforming multidimensional data into interpretable formats for decision support systems. To address this problem, the paper presents an analysis of dimensionality reduction and visualization techniques that embrace complex data representations and are useful inferences for decision systems. A novel framework is proposed, utilizing a Siamese neural network with a triplet loss function to analyze multidimensional data encoded into images, thus transforming these data into multidimensional embeddings. This approach uses dimensionality reduction techniques to transform these embeddings into a lower-dimensional space. This transformation not only improves interpretability but also maintains the integrity of the complex data structures. The efficacy of this approach is demonstrated using a keystroke dynamics dataset. The results support the integration of these visualization techniques into decision support systems. The visualization process not only simplifies the complexity of the data, but also reveals deep patterns and relationships hidden in the embeddings. Thus, a comprehensive framework for visualizing and interpreting complex keystroke dynamics is described, making a significant contribution to the field of user authentication.
      Citation: Informatics
      PubDate: 2024-02-26
      DOI: 10.3390/informatics11010011
      Issue No: Vol. 11, No. 1 (2024)
       
  • Informatics, Vol. 11, Pages 12: Causes and Mitigation Practices of
           Requirement Volatility in Agile Software Development

    • Authors: Abdulghafour Mohammad, Job Mathew Kollamana
      First page: 12
      Abstract: One of the main obstacles in software development projects is requirement volatility (RV), which is defined as uncertainty or changes in software requirements during the development process. Therefore, this research tries to understand the underlying factors behind the RV and the best practices to reduce it. The methodology used for this research is based upon qualitative research using interviews with 12 participants with experience in agile software development projects. The participants hailed from Austria, Nigeria, the USA, the Philippines, Armenia, Sri Lanka, Germany, Egypt, Canada, and Turkey and held roles such as project managers, software developers, Scrum Masters, testers, business analysts, and product owners. Our findings based on our empirical data revealed six primary factors that cause RV and three main agile practices that help to mitigate it. Theoretically, this study contributes to the body of knowledge relating to RV management. Practically, this research is expected to aid software development teams in comprehending the reasons behind RV and the best practices to effectively minimize it.
      Citation: Informatics
      PubDate: 2024-03-13
      DOI: 10.3390/informatics11010012
      Issue No: Vol. 11, No. 1 (2024)
       
 
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  Subjects -> COMMUNICATIONS (Total: 518 journals)
    - COMMUNICATIONS (446 journals)
    - DIGITAL AND WIRELESS COMMUNICATION (31 journals)
    - HUMAN COMMUNICATION (19 journals)
    - MEETINGS AND CONGRESSES (7 journals)
    - RADIO, TELEVISION AND CABLE (15 journals)

COMMUNICATIONS (446 journals)                  1 2 3 | Last

Showing 1 - 200 of 480 Journals sorted by number of followers
Evidence Based Library and Information Practice     Open Access   (Followers: 592)
Information Technologies & International Development     Open Access   (Followers: 86)
Information, Communication & Society     Hybrid Journal   (Followers: 79)
Journal of Communication     Hybrid Journal   (Followers: 64)
Convergence The International Journal of Research into New Media Technologies     Hybrid Journal   (Followers: 50)
Augmentative and Alternative Communication     Hybrid Journal   (Followers: 48)
e-learning and education (eleed)     Open Access   (Followers: 40)
Communication Theory     Hybrid Journal   (Followers: 36)
Journal of Computer-Mediated Communication     Open Access   (Followers: 35)
New Media and Mass Communication     Open Access   (Followers: 33)
Communication     Open Access   (Followers: 31)
Journal of the Association for Information Systems     Open Access   (Followers: 31)
Communication, Culture & Critique     Hybrid Journal   (Followers: 30)
Electronic Journal of Knowledge Management     Open Access   (Followers: 30)
Journalism & Mass Communication Quarterly     Hybrid Journal   (Followers: 29)
Health Information Management Journal     Hybrid Journal   (Followers: 29)
Advances in Journalism and Communication     Open Access   (Followers: 29)
Journal of Medical Internet Research     Open Access   (Followers: 28)
Advances in Image and Video Processing     Open Access   (Followers: 28)
New Review of Film and Television Studies     Hybrid Journal   (Followers: 27)
Discourse, Context & Media     Open Access   (Followers: 26)
Proceedings of the American Society for Information Science and Technology     Hybrid Journal   (Followers: 26)
Communication Papers : Media Literacy & Gender Studies     Open Access   (Followers: 25)
Journal of Media and Communication Studies     Open Access   (Followers: 25)
International Journal of Advanced Media and Communication     Hybrid Journal   (Followers: 23)
Journal of Information, Communication and Ethics in Society     Hybrid Journal   (Followers: 23)
Information & Communications Technology Law     Hybrid Journal   (Followers: 22)
Framework : The Journal of Cinema and Media     Full-text available via subscription   (Followers: 22)
Quarterly Review of Film and Video     Hybrid Journal   (Followers: 21)
Journalism & Mass Communication Educator     Hybrid Journal   (Followers: 21)
Screen     Hybrid Journal   (Followers: 20)
Journal of International and Intercultural Communication     Hybrid Journal   (Followers: 20)
Global Media and Communication     Hybrid Journal   (Followers: 19)
Language and Speech     Hybrid Journal   (Followers: 19)
Journal of Media Psychology     Hybrid Journal   (Followers: 19)
Journal of Science Communication     Open Access   (Followers: 19)
Journalism & Communication Monographs     Hybrid Journal   (Followers: 19)
Human Communication Research     Hybrid Journal   (Followers: 18)
IEEE Transactions on Smart Grid     Hybrid Journal   (Followers: 18)
Speech, Language and Hearing     Hybrid Journal   (Followers: 17)
Communication Booknotes Quarterly     Hybrid Journal   (Followers: 16)
Journal of Magnetic Resonance Imaging     Hybrid Journal   (Followers: 16)
International Journal of Information Technology, Communications and Convergence     Hybrid Journal   (Followers: 16)
Journal for the History of Rhetoric     Hybrid Journal   (Followers: 16)
Journal of Media Ethics : Exploring Questions of Media Morality     Hybrid Journal   (Followers: 15)
Public Relations Review     Hybrid Journal   (Followers: 15)
Quarterly Journal of Speech     Hybrid Journal   (Followers: 15)
Communications of the Association for Information Systems     Open Access   (Followers: 15)
International Journal of Computer Science and Telecommunications     Open Access   (Followers: 15)
Media and Communication     Open Access   (Followers: 15)
Journal of Broadcasting & Electronic Media     Hybrid Journal   (Followers: 14)
Journal of the American College of Radiology     Hybrid Journal   (Followers: 14)
Global Media Journal     Open Access   (Followers: 14)
Communications in Mobile Computing     Open Access   (Followers: 14)
Celebrity Studies     Hybrid Journal   (Followers: 14)
International Journal of Information and Communication Technology Education     Full-text available via subscription   (Followers: 13)
Journal of Technical Writing and Communication     Full-text available via subscription   (Followers: 12)
Chinese Journal of Communication     Hybrid Journal   (Followers: 12)
MedieKultur. Journal of media and communication research     Open Access   (Followers: 12)
Pragmatics and Society     Hybrid Journal   (Followers: 12)
Qualitative Studies     Open Access   (Followers: 12)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 12)
IEICE - Transactions on Fundamentals of Electronics, Communications and Computer Sciences     Full-text available via subscription   (Followers: 11)
IET Communications     Open Access   (Followers: 11)
Qualitative Research Reports in Communication     Hybrid Journal   (Followers: 11)
International Journal of Business Communication     Hybrid Journal   (Followers: 10)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 10)
Informal Logic     Open Access   (Followers: 10)
Communication & Language at Work     Open Access   (Followers: 10)
C&SC - Communication & Social Change     Open Access   (Followers: 10)
Openings : Studies in Book Art     Open Access   (Followers: 10)
Creative Artist : A Journal of Theatre and Media Studies     Open Access   (Followers: 10)
Journal of Radiotherapy in Practice     Hybrid Journal   (Followers: 9)
Magnetic Resonance Imaging     Hybrid Journal   (Followers: 9)
Interaction Studies     Hybrid Journal   (Followers: 9)
Journal of Language and Politics     Hybrid Journal   (Followers: 9)
Fibreculture Journal     Open Access   (Followers: 9)
Journal of Islamic Manuscripts     Hybrid Journal   (Followers: 9)
Comedy Studies     Hybrid Journal   (Followers: 9)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 9)
Communication & Sport     Hybrid Journal   (Followers: 9)
tripleC : Communication, Capitalism & Critique. Open Access Journal for a Global Sustainable Information Society     Open Access   (Followers: 9)
International Journal of Ad Hoc and Ubiquitous Computing     Hybrid Journal   (Followers: 8)
Seminars in Interventional Radiology     Hybrid Journal   (Followers: 8)
Information Design Journal     Hybrid Journal   (Followers: 8)
Myth & Symbol     Hybrid Journal   (Followers: 8)
Black Camera     Full-text available via subscription   (Followers: 8)
Cross-cultural Communication     Open Access   (Followers: 8)
Investigative Radiology     Hybrid Journal   (Followers: 7)
Pediatric Radiology     Hybrid Journal   (Followers: 7)
Technical Communication     Full-text available via subscription   (Followers: 7)
African Journal of Information and Communication     Open Access   (Followers: 7)
Annals of Telecommunications     Hybrid Journal   (Followers: 7)
Intelligent Information Management     Open Access   (Followers: 7)
African Journal of Information Systems     Open Access   (Followers: 7)
China Communications     Full-text available via subscription   (Followers: 7)
Journal of Radio & Audio Media     Hybrid Journal   (Followers: 6)
Sign Language Studies     Full-text available via subscription   (Followers: 6)
Review of Cognitive Linguistics     Hybrid Journal   (Followers: 6)
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Journal of Professional Communication     Open Access   (Followers: 6)
Informatics     Open Access   (Followers: 6)
Porn Studies     Hybrid Journal   (Followers: 6)
Journal of Advertising Education     Hybrid Journal   (Followers: 5)
The Communication Review     Hybrid Journal   (Followers: 5)
Journal of Asian Pacific Communication     Hybrid Journal   (Followers: 5)
Journal of Graph Theory     Hybrid Journal   (Followers: 5)
Middle East Journal of Culture and Communication     Hybrid Journal   (Followers: 5)
CIC. Cuadernos de Informacion y Comunicacion     Open Access   (Followers: 5)
Women's Studies in Communication     Hybrid Journal   (Followers: 5)
Global Advances in Business Communication     Open Access   (Followers: 5)
Transactions on Emerging Telecommunications Technologies     Hybrid Journal   (Followers: 4)
Journal of Radiology Nursing     Hybrid Journal   (Followers: 4)
Neuroimaging Clinics of North America     Full-text available via subscription   (Followers: 4)
Telecommunication Systems     Hybrid Journal   (Followers: 4)
Terminology     Hybrid Journal   (Followers: 4)
Tijdschrift voor Communicatiewetenschappen     Full-text available via subscription   (Followers: 4)
Gesture     Hybrid Journal   (Followers: 4)
Communicatio : South African Journal for Communication Theory and Research     Hybrid Journal   (Followers: 4)
Media International Australia     Hybrid Journal   (Followers: 4)
International Review of Pragmatics     Hybrid Journal   (Followers: 4)
International Journal of Cooperative Information Systems     Hybrid Journal   (Followers: 4)
International Journal of Information Communication Technologies and Human Development     Full-text available via subscription   (Followers: 4)
Medical Writing     Hybrid Journal   (Followers: 4)
Journal of Media Innovations     Open Access   (Followers: 4)
Journal of Development and Communication Studies     Open Access   (Followers: 4)
International Journal of Autonomous and Adaptive Communications Systems     Hybrid Journal   (Followers: 3)
Magnetic Resonance Materials in Physics, Biology and Medicine     Hybrid Journal   (Followers: 3)
Solid State Nuclear Magnetic Resonance     Hybrid Journal   (Followers: 3)
Journal of Location Based Services     Hybrid Journal   (Followers: 3)
Etudes de communication     Open Access   (Followers: 3)
Science China Information Sciences     Hybrid Journal   (Followers: 3)
Language, Interaction and Acquisition     Hybrid Journal   (Followers: 3)
Sign Language & Linguistics     Hybrid Journal   (Followers: 3)
Kaleidoscope : A Graduate Journal of Qualitative Communication Research     Open Access   (Followers: 3)
Pacific Asia Journal of the Association for Information Systems     Open Access   (Followers: 3)
Journal of Community Informatics     Open Access   (Followers: 3)
International Journal of Intelligence Science     Open Access   (Followers: 3)
International Journal of Interdisciplinary Telecommunications and Networking     Full-text available via subscription   (Followers: 3)
Journal of International Communication     Hybrid Journal   (Followers: 3)
MediaTropes     Open Access   (Followers: 3)
Nonprofit Communications Report     Hybrid Journal   (Followers: 3)
Journal of Media Literacy Education     Open Access   (Followers: 3)
Seton Hall Journal of Sports and Entertainment Law     Open Access   (Followers: 3)
Social Networking     Open Access   (Followers: 3)
Imaging Decisions MRI     Hybrid Journal   (Followers: 2)
Journal of Cardiovascular Computed Tomography     Hybrid Journal   (Followers: 2)
Language Problems & Language Planning     Hybrid Journal   (Followers: 2)
Research Journal of Information Technology     Open Access   (Followers: 2)
Área Abierta     Open Access   (Followers: 2)
Comunicación y Medios     Open Access   (Followers: 2)
Middle East Media Educator     Open Access   (Followers: 2)
Revue française des sciences de l’information et de la communication     Open Access   (Followers: 2)
Journal of Argumentation in Context     Hybrid Journal   (Followers: 2)
Metaphor and the Social World     Hybrid Journal   (Followers: 2)
Journal of Organizational Knowledge Communication     Open Access   (Followers: 2)
GSTF Journal on Media & Communications     Open Access   (Followers: 2)
Chasqui. Revista Latinoamericana de Comunicación     Open Access   (Followers: 2)
Bioelectromagnetics     Hybrid Journal   (Followers: 1)
Radioelectronics and Communications Systems     Hybrid Journal   (Followers: 1)
McMaster Journal of Communication     Open Access   (Followers: 1)
Palabra Clave     Open Access   (Followers: 1)
International Journal of Knowledge and Systems Science     Full-text available via subscription   (Followers: 1)
Documentación de las Ciencias de la Información     Open Access   (Followers: 1)
International Journal of Trust Management in Computing and Communications     Hybrid Journal   (Followers: 1)
South African Journal of Communication Disorders     Open Access   (Followers: 1)
FLEKS : Scandinavian Journal of Intercultural Theory and Practice     Open Access   (Followers: 1)
Public Journal of Semiotics     Open Access   (Followers: 1)
Journal of Language and Sexuality     Hybrid Journal   (Followers: 1)
Language and Dialogue     Hybrid Journal   (Followers: 1)
Anuario electrónico de estudios en Comunicación Social "Disertaciones"     Open Access   (Followers: 1)
3C TIC     Open Access   (Followers: 1)
Cuadernos.info     Open Access  
Comunicación     Open Access  
De Signos y Sentidos     Open Access  
Distúrbios da Comunicação     Open Access  
Wacana : Jurnal Sosial dan Humaniora     Open Access  
Question     Open Access  
Llengua, societat i comunicació     Open Access  
Informatio. Revista del Instituto de Información de la Facultad de Información y Comunicación     Open Access  
Communiquer : Revue de communication sociale et publique     Open Access  
Cuadernos de H Ideas     Open Access  
Punto Cero     Open Access  
Netcom     Open Access  
Observatorio (OBS*)     Open Access  
Vivat Academia     Open Access  
Commons. Revista de Comunicación y Ciudadanía Digital     Open Access  
Journal of Information and Organizational Sciences     Open Access  
Questions de communication     Open Access  
Quaderni     Open Access  
Communication et organisation     Open Access  
International Journal of Telework and Telecommuting Technologies     Full-text available via subscription  
Virtualidad, Educación y Ciencia     Open Access  
Revista Contracampo     Open Access  
Mediaciones Sociales     Open Access  
Historia y Comunicación Social     Open Access  
Pixel-Bit. Revista de Medios y Educacion     Open Access  
Cuadernos de Informacion     Open Access  
Journal of Modern Periodical Studies     Full-text available via subscription  
Tic & société     Open Access  

        1 2 3 | Last

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Heriot-Watt University
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Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


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