Journal Description
Informatics
Informatics
is an international, peer-reviewed, open access journal on information and communication technologies, human–computer interaction, and social informatics, and is published quarterly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), dblp, and other databases.
- Journal Rank: JCR - Q2 (Computer Science, Interdisciplinary Applications) / CiteScore - Q1 (Communication)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 33 days after submission; acceptance to publication is undertaken in 5.7 days (median values for papers published in this journal in the first half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.4 (2023);
5-Year Impact Factor:
3.1 (2023)
Latest Articles
Differential Classification of Dengue, Zika, and Chikungunya Using Machine Learning—Random Forest and Decision Tree Techniques
Informatics 2024, 11(3), 69; https://doi.org/10.3390/informatics11030069 - 20 Sep 2024
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Dengue, Zika, and chikungunya viruses pose a serious threat globally and circulate widely in America. These diseases share similar symptoms in their early stages, which can make early diagnosis difficult. In this study, two predictive models based on Decision Trees and Random Forests
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Dengue, Zika, and chikungunya viruses pose a serious threat globally and circulate widely in America. These diseases share similar symptoms in their early stages, which can make early diagnosis difficult. In this study, two predictive models based on Decision Trees and Random Forests were developed to classify dengue, Zika, and chikungunya, with the aim of being supportive and easily interpretable for the medical community. To achieve this, a dataset was collected from a clinic in Sincelejo, Colombia, including the signs, symptoms, and laboratory results of these diseases. The Pan American Health Organization (PAHO) Diagnostic Guide 2022 methodology for the differential classification of dengue and chikungunya was applied by assigning evaluative weights to symptoms in the dataset. In addition, a bootstrapping resampling technique based on the central limit theorem was used to balance the target variable, and cross-validation was used to train the models. The main results were obtained with the Random Forest technique, achieving an accuracy of 99.7% for classifying chikungunya, 99.1% for dengue, and 98.8% for Zika. This study represents a significant advance in the differential prediction of these diseases through the use of automatic learning techniques and the integration of clinical and laboratory information.
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Open AccessArticle
Enhancing Clinical Decision Support for Precision Medicine: A Data-Driven Approach
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Nasim Sadat Mosavi and Manuel Filipe Santos
Informatics 2024, 11(3), 68; https://doi.org/10.3390/informatics11030068 - 13 Sep 2024
Abstract
Precision medicine has emerged as a transformative approach aimed at tailoring treatment to individual patients, moving away from the traditional one-size-fits-all model. However, Clinical decision support systems encounter challenges, particularly in terms of data aspects. In response, our study proposes a data-driven framework
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Precision medicine has emerged as a transformative approach aimed at tailoring treatment to individual patients, moving away from the traditional one-size-fits-all model. However, Clinical decision support systems encounter challenges, particularly in terms of data aspects. In response, our study proposes a data-driven framework rooted in Simon’s decision-making model. This framework leverages advanced technologies such as artificial intelligence and data analytics to enhance clinical decision-making in precision medicine. By addressing limitations and integrating AI and analytics, our study contributes to the advancement of optimal clinical decision-making practices in precision healthcare.
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(This article belongs to the Section Medical and Clinical Informatics)
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Open AccessArticle
Pruning Policy for Image Classification Problems Based on Deep Learning
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Cesar G. Pachon, Javier O. Pinzon-Arenas and Dora Ballesteros
Informatics 2024, 11(3), 67; https://doi.org/10.3390/informatics11030067 - 12 Sep 2024
Abstract
In recent years, several methods have emerged for compressing image classification models using CNNs, for example, by applying pruning to the convolutional layers of the network. Typically, each pruning method uses a type of pruning distribution that is not necessarily the most appropriate
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In recent years, several methods have emerged for compressing image classification models using CNNs, for example, by applying pruning to the convolutional layers of the network. Typically, each pruning method uses a type of pruning distribution that is not necessarily the most appropriate for a given classification problem. Therefore, this paper proposes a methodology to select the best pruning policy (method + pruning distribution) for a specific classification problem and global pruning rate to obtain the best performance of the compressed model. This methodology was applied to several image datasets to show the influence not only of the method but also of the pruning distribution on the quality of the pruned model. It was shown that the selected pruning policy affects the performance of the pruned model to different extents, and that it depends on the classification problem to be addressed. For example, while for the Date Fruit Dataset, variations of more than 10% were obtained, for CIFAR10, variations were less than 5% for the same cases evaluated.
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(This article belongs to the Section Machine Learning)
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Open AccessArticle
Language Differences in Online Complaint Responses between Generative Artificial Intelligence and Hotel Managers
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Yau-Ni Wan
Informatics 2024, 11(3), 66; https://doi.org/10.3390/informatics11030066 - 5 Sep 2024
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Since November 2022, the use of generative artificial intelligence (GAI) technology has increased in many customer service industries. However, little is known about AI’s language choices and meaning-making resources compared to human responses from a systematic linguistic point of view. The present study
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Since November 2022, the use of generative artificial intelligence (GAI) technology has increased in many customer service industries. However, little is known about AI’s language choices and meaning-making resources compared to human responses from a systematic linguistic point of view. The present study is a discourse analysis that explores negative online guest complaints made to four luxury heritage hotels in Hong Kong that are classified as cultural heritage sites with rich interpersonal and historical values. We collected authentic guest complaints and responses from hotel managers from April 2012 to October 2022 in online travel forums, and then had GAI draft response letters on behalf of the hotel managers. Our total dataset was 65,539 words and consisted of three subcorpora: guest complaints (Text a of 115 complaints totaling 26,224 words), hotel manager responses (Text b of 115 response letters totaling 14,975 words), and AI-generated responses (Text c of 115 response letters totaling 24,340 words). This study used systemic functional linguistics to explore interpersonal meanings in texts; for example, appraisal resources, verb processes, and personal pronouns were compared between texts. First, we identified the most frequent words of the common themes across the three subcorpora and found significant differences in lexicogrammatical features between hotel managers and AI-generated responses using the log-likelihood ratio. The results suggest that AI-generated texts are able to provide a tailored and empathetic response to guests, but hotel managers may need to introduce some modifications, such as time indicators, sensory verbs used, and complimentary offers. This study explores the differences in word choices and communication strategies, which have implications and insights for the hospitality industry, especially luxury heritage hotels where caring and personalized customer service are considered important.
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Open AccessArticle
Adopting Business Intelligence Techniques in Healthcare Practice
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Hui-Chuan Huang, Hui-Kuan Wang, Hwei-Ling Chen, Jeng Wei, Wei-Hsian Yin and Kuan-Chia Lin
Informatics 2024, 11(3), 65; https://doi.org/10.3390/informatics11030065 - 4 Sep 2024
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With the rapid development of information technology, digital health technologies have become increasingly prevalent in the field of healthcare. In this study, business intelligence (BI) techniques were combined with research-based prediction models to increase the efficiency and quality of healthcare practices. A data
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With the rapid development of information technology, digital health technologies have become increasingly prevalent in the field of healthcare. In this study, business intelligence (BI) techniques were combined with research-based prediction models to increase the efficiency and quality of healthcare practices. A data scenario involving 200 older adults with various measurements, including health beliefs, social support, self-efficacy, and disease duration, was used to establish a medication adherence prediction model in a BI system. A regression model, logistic regression model, tree model, and score-based prediction model were used to predict medication adherence among older adults. The developed BI-based prediction model has visualization, real-time feedback, and data updating functionality. These features enhanced the effectiveness of prediction models in clinical practice. Healthcare professionals can incorporate the proposed system into their care practice for health assessments and management, and patients can use the system to manage themselves. The developed BI-based care system can also be used to achieve effective communication and shared decision-making between care managers and patients. Further empirical studies integrating prediction models into the proposed BI system for assessment, management, and decision-making in healthcare practice are warranted.
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Open AccessReview
Navigating Governmental Choices: A Comprehensive Review of Artificial Intelligence’s Impact on Decision-Making
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Gustavo Caiza, Verónica Sanguña, Natalia Tusa, Violeta Masaquiza, Alexandra Ortiz and Marcelo V. Garcia
Informatics 2024, 11(3), 64; https://doi.org/10.3390/informatics11030064 - 4 Sep 2024
Abstract
The integration of artificial intelligence (AI) into government decision-making is rapidly gaining traction in public administration and politics. This scoping review, guided by PRISMA protocols, examines 50 articles from reputable sources like Scopus and SpringerLink to analyze the trends, benefits, and challenges of
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The integration of artificial intelligence (AI) into government decision-making is rapidly gaining traction in public administration and politics. This scoping review, guided by PRISMA protocols, examines 50 articles from reputable sources like Scopus and SpringerLink to analyze the trends, benefits, and challenges of AI in governance. While AI offers substantial potential to enhance government efficiency and service delivery, significant barriers remain, including concerns about bias, transparency, public acceptance, and accountability. This review underscores the need for ongoing research and dialogue on the ethical, social, and practical implications of AI in government to ensure the responsible and inclusive adoption of AI-driven public services.
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(This article belongs to the Section Social Informatics and Digital Humanities)
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Open AccessArticle
Health Benefits and Adverse Effects of Kratom: A Social Media Text-Mining Approach
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Abdullah Wahbeh, Mohammad Al-Ramahi, Omar El-Gayar, Tareq Nasralah and Ahmed Elnoshokaty
Informatics 2024, 11(3), 63; https://doi.org/10.3390/informatics11030063 - 30 Aug 2024
Abstract
Background: Kratom is a substance that alters one’s mental state and is used for pain relief, mood enhancement, and opioid withdrawal, despite potential health risks. In this study, we aim to analyze the social media discourse about kratom to provide more insights about
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Background: Kratom is a substance that alters one’s mental state and is used for pain relief, mood enhancement, and opioid withdrawal, despite potential health risks. In this study, we aim to analyze the social media discourse about kratom to provide more insights about kratom’s benefits and adverse effects. Also, we aim to demonstrate how algorithmic machine learning approaches, qualitative methods, and data visualization techniques can complement each other to discern diverse reactions to kratom’s effects, thereby complementing traditional quantitative and qualitative methods. Methods: Social media data were analyzed using the latent Dirichlet allocation (LDA) algorithm, PyLDAVis, and t-distributed stochastic neighbor embedding (t-SNE) technique to identify kratom’s benefits and adverse effects. Results: The analysis showed that kratom aids in addiction recovery and managing opiate withdrawal, alleviates anxiety, depression, and chronic pain, enhances mood, energy, and overall mental well-being, and improves quality of life. Conversely, it may induce nausea, upset stomach, and constipation, elevate heart risks, affect respiratory function, and threaten liver health. Additional reported side effects include brain damage, weight loss, seizures, dry mouth, itchiness, and impacts on sexual function. Conclusion: This combined approach underscores its effectiveness in providing a comprehensive understanding of diverse reactions to kratom, complementing traditional research methodologies used to study kratom.
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(This article belongs to the Section Health Informatics)
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Open AccessReview
Artificial Intelligence and Machine Learning Technologies for Personalized Nutrition: A Review
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Dimitris Tsolakidis, Lazaros P. Gymnopoulos and Kosmas Dimitropoulos
Informatics 2024, 11(3), 62; https://doi.org/10.3390/informatics11030062 - 28 Aug 2024
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Modern lifestyle trends, such as sedentary behaviour and unhealthy diets, have been associated with obesity, a major health challenge increasing the risk of multiple pathologies. This has prompted many to reassess their routines and seek expert guidance on healthy living. In the digital
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Modern lifestyle trends, such as sedentary behaviour and unhealthy diets, have been associated with obesity, a major health challenge increasing the risk of multiple pathologies. This has prompted many to reassess their routines and seek expert guidance on healthy living. In the digital era, users quickly turn to mobile apps for support. These apps monitor various aspects of daily life, such as physical activity and calorie intake; collect extensive user data; and apply modern data-driven technologies, including artificial intelligence (AI) and machine learning (ML), to provide personalised diet and lifestyle recommendations. This work examines the state of the art in data-driven technologies for personalised nutrition, including relevant data collection technologies, and explores the research challenges in this field. A literature review, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, was conducted using three databases, covering studies from 2021 to 2024, resulting in 67 final studies. The data are presented in separate subsections for recommendation systems (43 works) and data collection technologies (17 works), with a discussion section identifying research challenges. The findings indicate that the fields of data-driven innovation and personalised nutrition are predominately amalgamated in the use of recommender systems.
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(This article belongs to the Section Health Informatics)
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TEADASH: Implementing and Evaluating a Teacher-Facing Dashboard Using Design Science Research
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Ngoc Buu Cat Nguyen, Marcus Lithander, Christian Master Östlund, Thashmee Karunaratne and William Jobe
Informatics 2024, 11(3), 61; https://doi.org/10.3390/informatics11030061 - 26 Aug 2024
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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
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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.
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Open AccessSystematic Review
Knowledge Management for Improved Digital Transformation in Insurance Companies: Systematic Review and Perspectives
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Younes Elgargouh, Mohammed Reda Chbihi Louhdi, El Moukhtar Zemmouri and Hicham Behja
Informatics 2024, 11(3), 60; https://doi.org/10.3390/informatics11030060 - 12 Aug 2024
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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
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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.
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Open AccessArticle
AI-Based Visual Early Warning System
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Zeena Al-Tekreeti, Jeronimo Moreno-Cuesta, Maria Isabel Madrigal Garcia and Marcos A. Rodrigues
Informatics 2024, 11(3), 59; https://doi.org/10.3390/informatics11030059 - 12 Aug 2024
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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’
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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.
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Open AccessSystematic Review
Ethical Challenges and Solutions of Generative AI: An Interdisciplinary Perspective
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Mousa Al-kfairy, Dheya Mustafa, Nir Kshetri, Mazen Insiew and Omar Alfandi
Informatics 2024, 11(3), 58; https://doi.org/10.3390/informatics11030058 - 9 Aug 2024
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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
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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.
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(This article belongs to the Section Social Informatics and Digital Humanities)
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Open AccessReview
Large Language Models in Healthcare and Medical Domain: A Review
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Zabir Al Nazi and Wei Peng
Informatics 2024, 11(3), 57; https://doi.org/10.3390/informatics11030057 - 7 Aug 2024
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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
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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.
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Open AccessArticle
Digital Innovations in E-Commerce: Augmented Reality Applications in Online Fashion Retail—A Qualitative Study among Gen Z Consumers
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Ildikó Kovács and Éva Réka Keresztes
Informatics 2024, 11(3), 56; https://doi.org/10.3390/informatics11030056 - 3 Aug 2024
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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
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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.
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Open AccessArticle
Internet Use for Health-Related Purposes among Older People in Thailand: An Analysis of Nationwide Cross-Sectional Data
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Kittisak Robru, Prasongchai Setthasuravich, Aphisit Pukdeewut and Suthiwat Wetchakama
Informatics 2024, 11(3), 55; https://doi.org/10.3390/informatics11030055 - 28 Jul 2024
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
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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.
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(This article belongs to the Section Health Informatics)
Open AccessArticle
AI Literacy and Intention to Use Text-Based GenAI for Learning: The Case of Business Students in Korea
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Moonkyoung Jang
Informatics 2024, 11(3), 54; https://doi.org/10.3390/informatics11030054 - 26 Jul 2024
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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
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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.
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Open AccessArticle
A Comparative Analysis of Virtual Education Technology, E-Learning Systems Research Advances, and Digital Divide in the Global South
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Ikpe Justice Akpan, Onyebuchi Felix Offodile, Aloysius Chris Akpanobong and Yawo Mamoua Kobara
Informatics 2024, 11(3), 53; https://doi.org/10.3390/informatics11030053 - 23 Jul 2024
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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
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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.
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Open AccessArticle
Use of Chipless Radio Frequency Identification Technology for Smart Food Packaging: An Economic Analysis for an Australian Seafood Industry
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Parya Fathi, Mita Bhattacharya, Sankar Bhattacharya and Nemai Karmakar
Informatics 2024, 11(3), 52; https://doi.org/10.3390/informatics11030052 - 22 Jul 2024
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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.
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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.
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Open AccessArticle
Non-Invasive Diagnostic Approach for Diabetes Using Pulse Wave Analysis and Deep Learning
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Hiruni Gunathilaka, Rumesh Rajapaksha, Thosini Kumarika, Dinusha Perera, Uditha Herath, Charith Jayathilaka, Janitha Liyanage and Sudath Kalingamudali
Informatics 2024, 11(3), 51; https://doi.org/10.3390/informatics11030051 - 19 Jul 2024
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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
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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.
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(This article belongs to the Section Medical and Clinical Informatics)
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Open AccessArticle
Machine Learning to Estimate Workload and Balance Resources with Live Migration and VM Placement
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Taufik Hidayat, Kalamullah Ramli, Nadia Thereza, Amarudin Daulay, Rushendra Rushendra and Rahutomo Mahardiko
Informatics 2024, 11(3), 50; https://doi.org/10.3390/informatics11030050 - 19 Jul 2024
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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
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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.
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