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: CiteScore - Q1 (Communication)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 25.5 days after submission; acceptance to publication is undertaken in 4.4 days (median values for papers published in this journal in the first half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.1 (2022);
5-Year Impact Factor:
2.7 (2022)
Latest Articles
Exploring How Healthcare Organizations Use Twitter: A Discourse Analysis
Informatics 2023, 10(3), 65; https://doi.org/10.3390/informatics10030065 - 08 Aug 2023
Abstract
The use of Twitter by healthcare organizations is an effective means of disseminating medical information to the public. However, the content of tweets can be influenced by various factors, such as health emergencies and medical breakthroughs. In this study, we conducted a discourse
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The use of Twitter by healthcare organizations is an effective means of disseminating medical information to the public. However, the content of tweets can be influenced by various factors, such as health emergencies and medical breakthroughs. In this study, we conducted a discourse analysis to better understand how public and private healthcare organizations use Twitter and the factors that influence the content of their tweets. Data were collected from the Twitter accounts of five private pharmaceutical companies, two US and two Canadian public health agencies, and the World Health Organization from 1 January 2020, to 31 December 2022. The study applied topic modeling and association rule mining to identify text patterns that influence the content of tweets across different Twitter accounts. The findings revealed that building a reputation on Twitter goes beyond just evaluating the popularity of a tweet in the online sphere. Topic modeling, when applied synchronously with hashtag and tagging analysis can provide an increase in tweet popularity. Additionally, the study showed differences in language use and style across the Twitter accounts’ categories and discussed how the impact of popular association rules could translate to significantly more user engagement. Overall, the results of this study provide insights into natural language processing for health literacy and present a way for organizations to structure their future content to ensure maximum public engagement.
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(This article belongs to the Special Issue Novel Informatics Algorithms and Applications to Biomedicine)
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Reinforcement Learning for Reducing the Interruptions and Increasing Fault Tolerance in the Cloud Environment
Informatics 2023, 10(3), 64; https://doi.org/10.3390/informatics10030064 - 02 Aug 2023
Abstract
Cloud computing delivers robust computational services by processing tasks on its virtual machines (VMs) using resource-scheduling algorithms. The cloud’s existing algorithms provide limited results due to inappropriate resource scheduling. Additionally, these algorithms cannot process tasks generating faults while being computed. The primary reason
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Cloud computing delivers robust computational services by processing tasks on its virtual machines (VMs) using resource-scheduling algorithms. The cloud’s existing algorithms provide limited results due to inappropriate resource scheduling. Additionally, these algorithms cannot process tasks generating faults while being computed. The primary reason for this is that these existing algorithms need an intelligence mechanism to enhance their abilities. To provide an intelligence mechanism to improve the resource-scheduling process and provision the fault-tolerance mechanism, an algorithm named reinforcement learning-shortest job first (RL-SJF) has been implemented by integrating the RL technique with the existing SJF algorithm. An experiment was conducted in a simulation platform to compare the working of RL-SJF with SJF, and challenging tasks were computed in multiple scenarios. The experimental results convey that the RL-SJF algorithm enhances the resource-scheduling process by improving the aggregate cost by 14.88% compared to the SJF algorithm. Additionally, the RL-SJF algorithm provided a fault-tolerance mechanism by computing 55.52% of the total tasks compared to 11.11% of the SJF algorithm. Thus, the RL-SJF algorithm improves the overall cloud performance and provides the ideal quality of service (QoS).
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(This article belongs to the Topic Theory and Applications of High Performance Computing)
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Finding Good Attribute Subsets for Improved Decision Trees Using a Genetic Algorithm Wrapper; a Supervised Learning Application in the Food Business Sector for Wine Type Classification
Informatics 2023, 10(3), 63; https://doi.org/10.3390/informatics10030063 - 21 Jul 2023
Abstract
This study aims to provide a method that will assist decision makers in managing large datasets, eliminating the decision risk and highlighting significant subsets of data with certain weight. Thus, binary decision tree (BDT) and genetic algorithm (GA) methods are combined using a
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This study aims to provide a method that will assist decision makers in managing large datasets, eliminating the decision risk and highlighting significant subsets of data with certain weight. Thus, binary decision tree (BDT) and genetic algorithm (GA) methods are combined using a wrapping technique. The BDT algorithm is used to classify data in a tree structure, while the GA is used to identify the best attribute combinations from a set of possible combinations, referred to as generations. The study seeks to address the problem of overfitting that may occur when classifying large datasets by reducing the number of attributes used in classification. Using the GA, the number of selected attributes is minimized, reducing the risk of overfitting. The algorithm produces many attribute sets that are classified using the BDT algorithm and are assigned a fitness number based on their accuracy. The fittest set of attributes, or chromosomes, as well as the BDTs, are then selected for further analysis. The training process uses the data of a chemical analysis of wines grown in the same region but derived from three different cultivars. The results demonstrate the effectiveness of this innovative approach in defining certain ingredients and weights of wine’s origin.
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(This article belongs to the Special Issue Applications of Machine Learning and Deep Learning in Agriculture)
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Biologically Plausible Boltzmann Machine
Informatics 2023, 10(3), 62; https://doi.org/10.3390/informatics10030062 - 14 Jul 2023
Abstract
The dichotomy in power consumption between digital and biological information processing systems is an intriguing open question related at its core with the necessity for a more thorough understanding of the thermodynamics of the logic of computing. To contribute in this regard, we
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The dichotomy in power consumption between digital and biological information processing systems is an intriguing open question related at its core with the necessity for a more thorough understanding of the thermodynamics of the logic of computing. To contribute in this regard, we put forward a model that implements the Boltzmann machine (BM) approach to computation through an electric substrate under thermal fluctuations and dissipation. The resulting network has precisely defined statistical properties, which are consistent with the data that are accessible to the BM. It is shown that by the proposed model, it is possible to design neural-inspired logic gates capable of universal Turing computation under similar thermal conditions to those found in biological neural networks and with information processing and storage electric potentials at comparable scales.
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(This article belongs to the Section Machine Learning)
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Poverty Traps in Online Knowledge-Based Peer-Production Communities
Informatics 2023, 10(3), 61; https://doi.org/10.3390/informatics10030061 - 13 Jul 2023
Abstract
Online knowledge-based peer-production communities, like question and answer sites (Q&A), often rely on gamification, e.g., through reputation points, to incentivize users to contribute frequently and effectively. These gamification techniques are important for achieving the critical mass that sustains a community and enticing new
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Online knowledge-based peer-production communities, like question and answer sites (Q&A), often rely on gamification, e.g., through reputation points, to incentivize users to contribute frequently and effectively. These gamification techniques are important for achieving the critical mass that sustains a community and enticing new users to join. However, aging communities tend to build “poverty traps” that act as barriers for new users. In this paper, we present our investigation of 32 domain communities from Stack Exchange and our analysis of how different subjects impact the development of early user advantage. Our results raise important questions about the accessibility of knowledge-based peer-production communities. We consider the analysis results in the context of changing information needs and the relevance of Q&A in the future. Our findings inform policy design for building more equitable knowledge-based peer-production communities and increasing the accessibility to existing ones.
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(This article belongs to the Section Human-Computer Interaction)
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Towards a Universal Privacy Model for Electronic Health Record Systems: An Ontology and Machine Learning Approach
Informatics 2023, 10(3), 60; https://doi.org/10.3390/informatics10030060 - 11 Jul 2023
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This paper proposed a novel privacy model for Electronic Health Records (EHR) systems utilizing a conceptual privacy ontology and Machine Learning (ML) methodologies. It underscores the challenges currently faced by EHR systems such as balancing privacy and accessibility, user-friendliness, and legal compliance. To
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This paper proposed a novel privacy model for Electronic Health Records (EHR) systems utilizing a conceptual privacy ontology and Machine Learning (ML) methodologies. It underscores the challenges currently faced by EHR systems such as balancing privacy and accessibility, user-friendliness, and legal compliance. To address these challenges, the study developed a universal privacy model designed to efficiently manage and share patients’ personal and sensitive data across different platforms, such as MHR and NHS systems. The research employed various BERT techniques to differentiate between legitimate and illegitimate privacy policies. Among them, Distil BERT emerged as the most accurate, demonstrating the potential of our ML-based approach to effectively identify inadequate privacy policies. This paper outlines future research directions, emphasizing the need for comprehensive evaluations, testing in real-world case studies, the investigation of adaptive frameworks, ethical implications, and fostering stakeholder collaboration. This research offers a pioneering approach towards enhancing healthcare information privacy, providing an innovative foundation for future work in this field.
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A Machine-Learning-Based Motor and Cognitive Assessment Tool Using In-Game Data from the GAME2AWE Platform
Informatics 2023, 10(3), 59; https://doi.org/10.3390/informatics10030059 - 09 Jul 2023
Abstract
With age, a decline in motor and cognitive functionality is inevitable, and it greatly affects the quality of life of the elderly and their ability to live independently. Early detection of these types of decline can enable timely interventions and support for maintaining
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With age, a decline in motor and cognitive functionality is inevitable, and it greatly affects the quality of life of the elderly and their ability to live independently. Early detection of these types of decline can enable timely interventions and support for maintaining functional independence and improving overall well-being. This paper explores the potential of the GAME2AWE platform in assessing the motor and cognitive condition of seniors based on their in-game performance data. The proposed methodology involves developing machine learning models to explore the predictive power of features that are derived from the data collected during gameplay on the GAME2AWE platform. Through a study involving fifteen elderly participants, we demonstrate that utilizing in-game data can achieve a high classification performance when predicting the motor and cognitive states. Various machine learning techniques were used but Random Forest outperformed the other models, achieving a classification accuracy ranging from 93.6% for cognitive screening to 95.6% for motor assessment. These results highlight the potential of using exergames within a technology-rich environment as an effective means of capturing the health status of seniors. This approach opens up new possibilities for objective and non-invasive health assessment, facilitating early detections and interventions to improve the well-being of seniors.
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(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)
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Digital Citizenship and the Big Five Personality Traits
Informatics 2023, 10(3), 58; https://doi.org/10.3390/informatics10030058 - 07 Jul 2023
Abstract
Over the past two decades, the internet has become an increasingly important venue for political expression, community building, and social activism. Scholars in a wide range of disciplines have endeavored to understand and measure how these transformations have affected individuals’ civic attitudes and
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Over the past two decades, the internet has become an increasingly important venue for political expression, community building, and social activism. Scholars in a wide range of disciplines have endeavored to understand and measure how these transformations have affected individuals’ civic attitudes and behaviors. The Digital Citizenship Scale (original and revised form) has become one of the most widely used instruments for measuring and evaluating these changes, but to date, no study has investigated how digital citizenship behaviors relate to exogenous variables. Using the classic Big Five Factor model of personality (Openness to experience, Conscientiousness, Extroversion, Agreeableness, and Neuroticism), this study investigated how personality traits relate to the key components of digital citizenship. Survey results were gathered across three countries (n = 1820), and analysis revealed that personality traits map uniquely on to digital citizenship in comparison to traditional forms of civic engagement. The implications of these findings are discussed.
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(This article belongs to the Section Human-Computer Interaction)
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Information and Communication Technologies in Primary Education: Teachers’ Perceptions in Greece
Informatics 2023, 10(3), 57; https://doi.org/10.3390/informatics10030057 - 07 Jul 2023
Abstract
Innovative learning methods including the increasing use of Information and Communication Technologies (ICT) applications are transforming the contemporary educational process. Teachers’ perceptions of ICT, self-efficacy on computers and demographics are some of the factors that have been found to impact the use of
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Innovative learning methods including the increasing use of Information and Communication Technologies (ICT) applications are transforming the contemporary educational process. Teachers’ perceptions of ICT, self-efficacy on computers and demographics are some of the factors that have been found to impact the use of ICT in the educational process. The aim of the present research is to analyze the perceptions of primary school teachers about ICT and how they affect their use in the educational process, through the case of Greece. To do so, primary research was carried out. Data from 285 valid questionnaires were statistically analyzed using descriptive statistics, principal components analysis, correlation and regression analysis. The main results were in accordance with the relevant literature, indicating the impact of teachers’ self-efficacy, perceptions and demographics on ICT use in the educational process. These results provide useful insights for the achievement of a successful implementation of ICT in education.
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(This article belongs to the Section Human-Computer Interaction)
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FOXS-GSC—Fast Offset Xpath Service with HexagonS Communication
Informatics 2023, 10(3), 56; https://doi.org/10.3390/informatics10030056 - 04 Jul 2023
Abstract
Congestion in large cities is widely recognized as a problem that impacts various aspects of society, including the economy and public health. To support the urban traffic system and to mitigate traffic congestion and the damage it causes, in this article we propose
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Congestion in large cities is widely recognized as a problem that impacts various aspects of society, including the economy and public health. To support the urban traffic system and to mitigate traffic congestion and the damage it causes, in this article we propose an assistant Intelligent Transport Systems (ITS) service for traffic management in Vehicular Networks (VANET), which we name FOXS-GSC, for Fast Offset Xpath Service with hexaGonS Communication. FOXS-GSC uses a VANET communication and fog computing paradigm to detect and recommend an alternative vehicle route to avoid traffic jams. Unlike the previous solutions in the literature, the proposed service offers a versatile approach in which traffic road classification and route suggestions can be made by infrastructure or by the vehicle itself without compromising the quality of the route service. To achieve this, the service operates in a decentralized way, and the components of the service (vehicles/infrastructure) exchange messages containing vehicle information and regional traffic information. For communication, the proposed approach uses a new dedicated multi-hop protocol that has been specifically designed based on the characteristics and requirements of a vehicle routing service. Therefore, by adapting to the inherent characteristics of a vehicle routing service, such as the density of regions, the proposed communication protocol both enhances reliability and improves the overall efficiency of the vehicle routing service. Simulation results comparing FOXS-GSC with baseline solutions and other proposals from the literature demonstrate its significant impact, reducing network congestion by up to 95% while maintaining a coverage of 97% across various scenery characteristics. Concerning road traffic efficiency, the traffic quality is increasing by 29%, for a reduction in carbon emissions of 10%.
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(This article belongs to the Special Issue The Smart Cities Continuum via Machine Learning and Artificial Intelligence)
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Classification of Benign and Malignant Renal Tumors Based on CT Scans and Clinical Data Using Machine Learning Methods
Informatics 2023, 10(3), 55; https://doi.org/10.3390/informatics10030055 - 03 Jul 2023
Abstract
Up to 20% of renal masses ≤4 cm is found to be benign at the time of surgical excision, raising concern for overtreatment. However, the risk of malignancy is currently unable to be accurately predicted prior to surgery using imaging alone. The objective
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Up to 20% of renal masses ≤4 cm is found to be benign at the time of surgical excision, raising concern for overtreatment. However, the risk of malignancy is currently unable to be accurately predicted prior to surgery using imaging alone. The objective of this study is to propose a machine learning (ML) framework for pre-operative renal tumor classification using readily available clinical and CT imaging data. We tested both traditional ML methods (i.e., XGBoost, random forest (RF)) and deep learning (DL) methods (i.e., multilayer perceptron (MLP), 3D convolutional neural network (3DCNN)) to build the classification model. We discovered that the combination of clinical and radiomics features produced the best results (i.e., AUC [95% CI] of 0.719 [0.712–0.726], a precision [95% CI] of 0.976 [0.975–0.978], a recall [95% CI] of 0.683 [0.675–0.691], and a specificity [95% CI] of 0.827 [0.817–0.837]). Our analysis revealed that employing ML models with CT scans and clinical data holds promise for classifying the risk of renal malignancy. Future work should focus on externally validating the proposed model and features to better support clinical decision-making in renal cancer diagnosis.
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(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)
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Risk-Based Approach for Selecting Company Key Performance Indicator in an Example of Financial Services
Informatics 2023, 10(2), 54; https://doi.org/10.3390/informatics10020054 - 19 Jun 2023
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Risk management is a highly important issue for Fintech companies; moreover, it is very specific and puts forward the serious requirements toward the top management of any financial institution. This study was devoted to specifying the risk factors affecting the finance and capital
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Risk management is a highly important issue for Fintech companies; moreover, it is very specific and puts forward the serious requirements toward the top management of any financial institution. This study was devoted to specifying the risk factors affecting the finance and capital adequacy of financial institutions. The authors considered the different types of risks in combination, whereas other scholars usually analyze risks in isolation; however, the authors believe that it is necessary to consider their mutual impact. The risks were estimated using the PLS-SEM method in Smart PLS-4 software. The quality of the obtained model is very high according to all indicators. Five hypotheses related to finance and five hypotheses related to capital adequacy were considered. The impact of AML, cyber, and governance risks on capital adequacy was confirmed; the effect of governance and operational risks on finance was also confirmed. Other risks have no impact on finance and capital adequacy. It is interesting that risks associated with staff have no impact on finance and capital adequacy. The findings of this study can be easily applied by any financial institution for risk analysis. Moreover, this study can serve toward a better collaboration of scholars investigating the Fintech activities and practitioners working in this sphere. The authors present a novel approach for enhancing key performance indicators (KPIs) for Fintech companies, proposing utilizing metrics that are derived from the company’s specific risks, thereby introducing an innovative method for selecting KPIs based on the inherent risks associated with the Fintech’s business model. This model aligns the KPIs with the unique risk profile of the company, fostering a fresh perspective on performance measurement within the Fintech industry.
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The Smart Governance Framework and Enterprise System’s Capability for Improving Bio-Business Licensing Services
Informatics 2023, 10(2), 53; https://doi.org/10.3390/informatics10020053 - 16 Jun 2023
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One way to improve Indonesia’s ranking in terms of ease of conducting business is by taking a closer look at the business licensing process. This study aims to carry out an assessment using a smart governance framework and recommendation capabilities from the Enterprise
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One way to improve Indonesia’s ranking in terms of ease of conducting business is by taking a closer look at the business licensing process. This study aims to carry out an assessment using a smart governance framework and recommendation capabilities from the Enterprise System (ES). As a result, the recommendations for improvement with the expected priority are generated. The stages of this research are observing the process of making bio-business permits, followed by interviews related to several Enterprise Architecture (EA) capabilities, and providing recommendations based on the results of the maturity level of IT governance. These recommendations are then mapped into an impact—effort matrix for program prioritization. The recommendations for bio-business licenses can also be used to improve the process for other business licenses. Implementation of the EA framework has been proven to align technology, organization, and processes so that it can support continuous improvement processes.
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Detection of Abnormal Patterns in Children’s Handwriting by Using an Artificial-Intelligence-Based Method
Informatics 2023, 10(2), 52; https://doi.org/10.3390/informatics10020052 - 14 Jun 2023
Abstract
Using camera-based algorithms to detect abnormal patterns in children’s handwriting has become a promising tool in education and occupational therapy. This study analyzes the performance of a camera- and tablet-based handwriting verification algorithm to detect abnormal patterns in handwriting samples processed from 71
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Using camera-based algorithms to detect abnormal patterns in children’s handwriting has become a promising tool in education and occupational therapy. This study analyzes the performance of a camera- and tablet-based handwriting verification algorithm to detect abnormal patterns in handwriting samples processed from 71 students of different grades. The study results revealed that the algorithm saw abnormal patterns in 20% of the handwriting samples processed, which included practices such as delayed typing speed, excessive pen pressure, irregular slant, and lack of word spacing. In addition, it was observed that the detection accuracy of the algorithm was 95% when comparing the camera data with the abnormal patterns detected, which indicates a high reliability in the results obtained. The highlight of the study was the feedback provided to children and teachers on the camera data and any abnormal patterns detected. This can significantly impact students’ awareness and improvement of writing skills by providing real-time feedback on their writing and allowing them to adjust to correct detected abnormal patterns.
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(This article belongs to the Special Issue Digital Humanities and Visualization)
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Compact-Fusion Feature Framework for Ethnicity Classification
Informatics 2023, 10(2), 51; https://doi.org/10.3390/informatics10020051 - 12 Jun 2023
Abstract
In computer vision, ethnicity classification tasks utilize images containing human faces to extract ethnicity labels. Ethnicity is one of the soft biometric feature categories useful in data analysis for commercial, public, and health sectors. Ethnicity classification begins with face detection as a preprocessing
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In computer vision, ethnicity classification tasks utilize images containing human faces to extract ethnicity labels. Ethnicity is one of the soft biometric feature categories useful in data analysis for commercial, public, and health sectors. Ethnicity classification begins with face detection as a preprocessing process to determine a human’s presence; then, the feature representation is extracted from the isolated facial image to predict the ethnicity class. This study utilized four handcrafted features (multi-local binary pattern (MLBP), histogram of gradient (HOG), color histogram, and speeded-up-robust-features-based (SURF-based)) as the basis for the generation of a compact-fusion feature. The compact-fusion framework involves optimal feature selection, compact feature extraction, and compact-fusion feature representation. The final feature representation was trained and tested with the SVM One Versus All classifier for ethnicity classification. When it was evaluated in two large datasets, UTKFace and Fair Face, the proposed framework achieved accuracy levels of 89.14%, 82.19%, and 73.87%, respectively, for the UTKFace dataset with four or five classes and the Fair Face dataset with four classes. Furthermore, the compact-fusion feature with a small number of features at 4790, constructed based on conventional handcrafted features, achieved competitive results compared with state-of-the-art methods using a deep-learning-based approach.
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(This article belongs to the Section Machine Learning)
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Low-Code Machine Learning Platforms: A Fastlane to Digitalization
Informatics 2023, 10(2), 50; https://doi.org/10.3390/informatics10020050 - 12 Jun 2023
Abstract
In the context of developing machine learning models, until and unless we have the required data engineering and machine learning development competencies as well as the time to train and test different machine learning models and tune their hyperparameters, it is worth trying
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In the context of developing machine learning models, until and unless we have the required data engineering and machine learning development competencies as well as the time to train and test different machine learning models and tune their hyperparameters, it is worth trying out the automatic machine learning features provided by several cloud-based and cloud-agnostic platforms. This paper explores the possibility of generating automatic machine learning models with low-code experience. We developed criteria to compare different machine learning platforms for generating automatic machine learning models and presenting their results. Thereafter, lessons learned by developing automatic machine learning models from a sample dataset across four different machine learning platforms were elucidated. We also interviewed machine learning experts to conceptualize their domain-specific problems that automatic machine learning platforms can address. Results showed that automatic machine learning platforms can provide a fast track for organizations seeking the digitalization of their businesses. Automatic machine learning platforms help produce results, especially for time-constrained projects where resources are lacking. The contribution of this paper is in the form of a lab experiment in which we demonstrate how low-code platforms can provide a viable option to many business cases and, henceforth, provide a lane that is faster than the usual hiring and training of already scarce data scientists and to analytics projects that suffer from overruns.
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(This article belongs to the Section Machine Learning)
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Open AccessEditorial
AI Chatbots: Threat or Opportunity?
Informatics 2023, 10(2), 49; https://doi.org/10.3390/informatics10020049 - 12 Jun 2023
Abstract
In November 2022, OpenAI launched ChatGPT, an AI chatbot that gained over 100 million users by February 2023 [...]
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(This article belongs to the Topic AI Chatbots: Threat or Opportunity?)
Open AccessArticle
The Design and Development of a Foot-Detection Approach Based on Seven-Foot Dimensions: A Case Study of a Virtual Try-On Shoe System Using Augmented Reality Techniques
Informatics 2023, 10(2), 48; https://doi.org/10.3390/informatics10020048 - 05 Jun 2023
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Unsuitable shoe shapes and sizes are a critical reason for unhealthy feet, may severely contribute to chronic injuries such as foot ulcers in susceptible people (e.g., diabetes patients), and thus need accurate measurements in the manner of expert-based procedures. However, the manual measure
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Unsuitable shoe shapes and sizes are a critical reason for unhealthy feet, may severely contribute to chronic injuries such as foot ulcers in susceptible people (e.g., diabetes patients), and thus need accurate measurements in the manner of expert-based procedures. However, the manual measure of such accurate shapes and sizes is labor-intensive, time-consuming, and impractical to apply in a real-time system. This research proposes a foot-detection approach using expert-like measurements to address this concern. It combines the seven-foot dimensions model and the light detection and ranging sensor to encode foot shapes and sizes and detect the dimension surfaces. The graph-based algorithms are developed to present seven-foot dimensions and visualize the shoe’s model based on the augmented reality (AR) technique. The results show that our approach can detect shapes and sizes more effectively than the traditional approach, helps the system imitate expert-like measurements accurately, and can be employed in intelligent applications for susceptible people-based feet measurements.
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Open AccessArticle
Meeting Ourselves or Other Sides of Us?—Meta-Analysis of the Metaverse
Informatics 2023, 10(2), 47; https://doi.org/10.3390/informatics10020047 - 02 Jun 2023
Abstract
We were promised that the Metaverse would revolutionize our lives, social interactions, work, and business. However, how and when will this happen? We have seen the growth and development of technology, but there is no agreement or prediction about a specific time, and
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We were promised that the Metaverse would revolutionize our lives, social interactions, work, and business. However, how and when will this happen? We have seen the growth and development of technology, but there is no agreement or prediction about a specific time, and we can only follow the how question. To investigate more leads about this concept, we considered a main research question: How is the Metaverse actually being perceived? This question is connected with three objectives: to verify how the Metaverse is being represented and characterized, identify the main dimensions that facilitate or influence the acceptance of the Metaverse, and identify the leading technologies that suit the Metaverse concept. This study consisted of a documental analysis—or meta-analysis—of fifty of the most relevant scientific papers (taking into account some inclusion criteria) published in the last three years, using the Leximancer software to create concept maps to illustrate the main concepts and themes extracted from the articles to understand their associations or relations with the Metaverse concept. This study provided us with essential findings about how this concept has been perceived and allowed us to answer our objectives, contributing to a scientific discussion on the topic, and provided some valid suggestions for future research, which is already in progress. It also provided new leads on approaching this concept in development.
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(This article belongs to the Special Issue Feature Papers in Human-Computer Interaction)
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Open AccessFeature PaperArticle
Predicting the Risk of Alzheimer’s Disease and Related Dementia in Patients with Mild Cognitive Impairment Using a Semi-Competing Risk Approach
Informatics 2023, 10(2), 46; https://doi.org/10.3390/informatics10020046 - 30 May 2023
Abstract
Alzheimer’s disease (AD) and AD-related dementias (AD/ADRD) are a group of progressive neurodegenerative diseases. The progression of AD can be conceptualized as a continuum in which patients progress from normal cognition to preclinical AD (i.e., no symptoms but biological changes in the brain)
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Alzheimer’s disease (AD) and AD-related dementias (AD/ADRD) are a group of progressive neurodegenerative diseases. The progression of AD can be conceptualized as a continuum in which patients progress from normal cognition to preclinical AD (i.e., no symptoms but biological changes in the brain) to mild cognitive impairment (MCI) due to AD (i.e., mild symptoms but not interfere with daily activities), followed by increasing severity of dementia due to AD. Early detection and prediction models for the transition of MCI to AD/ADRD are needed, and efforts have been made to build predictions of MCI conversion to AD/ADRD. However, most existing studies developing such prediction models did not consider the competing risks of death, which may result in biased risk estimates. In this study, we aim to develop a prediction model for AD/ADRD among patients with MCI considering the competing risks of death using a semi-competing risk approach.
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(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)
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Informatics
Software Engineering Practices, Challenges and Trends
Guest Editors: Fernando Reinaldo Ribeiro, José Metrôlho, Javier Berrocal, Luis L. Fernández-SanzDeadline: 30 November 2023
Special Issue in
Informatics
Information Technology for Agri-Food
Guest Editors: Remo Pareschi, Karl Presser, Claudia ZoaniDeadline: 15 December 2023
Topical Collections
Topical Collection in
Informatics
Promotion of Computational Thinking and Informatics Education in Pre-University Studies
Collection Editor: Francisco José García-Peñalvo
Topical Collection in
Informatics
Uncertainty in Digital Humanities
Collection Editors: Roberto Theron, Eveline Wandl-Vogt, Jennifer Cizik Edmond, Cezary Mazurek