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Informatics, Volume 9, Issue 4 (December 2022) – 27 articles

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Article
Smartphone Usage before and during COVID-19: A Comparative Study Based on Objective Recording of Usage Data
Informatics 2022, 9(4), 98; https://doi.org/10.3390/informatics9040098 - 07 Dec 2022
Abstract
Most studies that claimed changes in smartphone usage during COVID-19 were based on self-reported usage data, e.g., that collected through a questionnaire. These studies were also limited to reporting the overall smartphone usage, with no detailed investigation of distinct types of apps. The [...] Read more.
Most studies that claimed changes in smartphone usage during COVID-19 were based on self-reported usage data, e.g., that collected through a questionnaire. These studies were also limited to reporting the overall smartphone usage, with no detailed investigation of distinct types of apps. The current study investigated smartphone usage before and during COVID-19. Our study used a dataset from a smartphone app that objectively logged users’ activities, including apps accessed and each app session start and end time. These were collected during two periods: pre-COVID-19 (161 individuals with 77 females) and during COVID-19 (251 individuals with 159 females). We report on the top 15 apps used in both periods. The Mann–Whitney U test was used for the inferential analysis. The results revealed that the time spent on smartphones has increased since COVID-19. During both periods, emerging adults were found to spend more time on smartphones compared to adults. The time spent on social media apps has also increased since COVID-19. Females were found to spend more time on social media than males. Females were also found to be more likely to launch social media apps than males. There has also been an increase in the number of people who use gaming apps since the pandemic. The use of objectively collected data is a methodological strength of our study. Additionally, we draw parallels with the usage of smartphones in contexts similar to the COVID-19 period, especially concerning the limitations on social gatherings, including working from home for extended periods. Our dataset is made available to other researchers for benchmarking and future comparisons. Full article
Article
OA-Pain-Sense: Machine Learning Prediction of Hip and Knee Osteoarthritis Pain from IMU Data
Informatics 2022, 9(4), 97; https://doi.org/10.3390/informatics9040097 - 06 Dec 2022
Viewed by 158
Abstract
Joint pain is a prominent symptom of Hip and Knee Osteoarthritis (OA), impairing patients’ movements and affecting the joint mechanics of walking. Self-report questionnaires are currently the gold standard for Hip OA and Knee OA pain assessment, presenting several problems, including the fact [...] Read more.
Joint pain is a prominent symptom of Hip and Knee Osteoarthritis (OA), impairing patients’ movements and affecting the joint mechanics of walking. Self-report questionnaires are currently the gold standard for Hip OA and Knee OA pain assessment, presenting several problems, including the fact that older individuals often fail to provide accurate self-pain reports. Passive methods to assess pain are desirable. This study aims to explore the feasibility of OA-Pain-Sense, a passive, automatic Machine Learning-based approach that predicts patients’ self-reported pain levels using SpatioTemporal Gait features extracted from the accelerometer signal gathered from an anterior-posterior wearable sensor. To mitigate inter-subject variability, we investigated two types of data rescaling: subject-level and dataset-level. We explored six different binary machine learning classification models for discriminating pain in patients with Hip OA or Knee OA from healthy controls. In rigorous evaluation, OA-Pain-Sense achieved an average accuracy of 86.79% using the Decision Tree and 83.57% using Support Vector Machine classifiers for distinguishing Hip OA and Knee OA patients from healthy subjects, respectively. Our results demonstrate that OA-Pain-Sense is feasible, paving the way for the development of a pain assessment algorithm that can support clinical decision-making and be used on any wearable device, such as smartphones. Full article
(This article belongs to the Section Health Informatics)
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Article
CerealNet: A Hybrid Deep Learning Architecture for Cereal Crop Mapping Using Sentinel-2 Time-Series
Informatics 2022, 9(4), 96; https://doi.org/10.3390/informatics9040096 - 30 Nov 2022
Viewed by 362
Abstract
Remote sensing-based crop mapping has continued to grow in economic importance over the last two decades. Given the ever-increasing rate of population growth and the implications of multiplying global food production, the necessity for timely, accurate, and reliable agricultural data is of the [...] Read more.
Remote sensing-based crop mapping has continued to grow in economic importance over the last two decades. Given the ever-increasing rate of population growth and the implications of multiplying global food production, the necessity for timely, accurate, and reliable agricultural data is of the utmost importance. When it comes to ensuring high accuracy in crop maps, spectral similarities between crops represent serious limiting factors. Crops that display similar spectral responses are notorious for being nearly impossible to discriminate using classical multi-spectral imagery analysis. Chief among these crops are soft wheat, durum wheat, oats, and barley. In this paper, we propose a unique multi-input deep learning approach for cereal crop mapping, called “CerealNet”. Two time-series used as input, from the Sentinel-2 bands and NDVI (Normalized Difference Vegetation Index), were fed into separate branches of the LSTM-Conv1D (Long Short-Term Memory Convolutional Neural Networks) model to extract the temporal and spectral features necessary for the pixel-based crop mapping. The approach was evaluated using ground-truth data collected in the Gharb region (northwest of Morocco). We noted a categorical accuracy and an F1-score of 95% and 94%, respectively, with minimal confusion between the four cereal classes. CerealNet proved insensitive to sample size, as the least-represented crop, oats, had the highest F1-score. This model was compared with several state-of-the-art crop mapping classifiers and was found to outperform them. The modularity of CerealNet could possibly allow for injecting additional data such as Synthetic Aperture Radar (SAR) bands, especially when optical imagery is not available. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Deep Learning in Agriculture)
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Article
A Benefit Dependency Network for Shadow Information Technology Adoption, Based on Practitioners’ Viewpoints
Informatics 2022, 9(4), 95; https://doi.org/10.3390/informatics9040095 - 24 Nov 2022
Viewed by 313
Abstract
Shadow information technology (SIT) revolves around systems that are hidden but are still managed by the same business entities. It consists of the use of devices, software, systems and applications without the information technology (IT) department’s approval. Employees use IT without the knowledge [...] Read more.
Shadow information technology (SIT) revolves around systems that are hidden but are still managed by the same business entities. It consists of the use of devices, software, systems and applications without the information technology (IT) department’s approval. Employees use IT without the knowledge of the IT department, and it creates a gap in communications, as the IT department loses the knowledge of the reality within the company. However, there are benefits involved. In order to take advantage of these benefits, changes have to be implemented in the way that business activities are handled. The benefits should be a direct result of the changes, of the difference between the ongoing and the suggested way that activities should be undertaken, and the levels of efficiency and effectiveness to which people deliver their daily tasks. The objective of this study was to propose a benefit dependency network (BDN) for SIT, and, through its concepts, to synthetize our findings and specify the connections between SIT practices and their benefits. This research was conducted a systematic literature review (SLR) and used a design science research methodology, adopting semi-structured interviews with fourteen interactions to propose a BDN for SIT. We proposed a model with five dimensions related to a BDN for SIT. By understanding the BDN and the benefits of SIT, it is easier to have a better notion of the implications and the factors involved in order to assist the decision-making process. Whether an organization wants to reach innovation, increase revenue or retain clients, the BDN helps with analysis and selection, and is something that organizations should take seriously, as it is essential to have knowledge about what the benefits are and how they can be reached. To the best of the authors’ knowledge, this research included and replaced several processes in the BDN for SIT, in a topic that is still underexplored. Full article
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Article
Do NFTs Sound Good? An Exploratory Study on Audio NFTs and Possible Avenues
Informatics 2022, 9(4), 94; https://doi.org/10.3390/informatics9040094 - 18 Nov 2022
Viewed by 465
Abstract
Crypto, non-fungible tokens (NFTs), and the metaverse have taken a massive place in our daily conversations and are highly valued. Moreover, NFTs range from luxury fashion to art, and sound is no exception, although it still needs to be explored. Could this be [...] Read more.
Crypto, non-fungible tokens (NFTs), and the metaverse have taken a massive place in our daily conversations and are highly valued. Moreover, NFTs range from luxury fashion to art, and sound is no exception, although it still needs to be explored. Could this be a unique opportunity to go digital from creation to distribution? This study looks at sound NFTs and new avenues for digital audio communication. During the pandemic, podcasts have been exhausted, leaving space for new digital media business opportunities. We look at how sound has grown, assuming a prominent role in the new media ecosystem due to podcasts consumption. We explore NFTs and their evolution in different markets over the years, highlighting the space that sound content has gained on these platforms as new possibilities for dissemination, promotion, and sale. We use content analysis on marketplaces that provide sound NFTs to understand what audio content consumers can find and future opportunities. Full article
(This article belongs to the Section Digital Humanities)
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Article
Can Citizenship Education Benefit Computing?
Informatics 2022, 9(4), 93; https://doi.org/10.3390/informatics9040093 - 18 Nov 2022
Viewed by 442
Abstract
A recurring motif in recent scholarship in the computing ethics and society studies (CESS) subfield within computing have been the calls for a wider recognition of the social and political nature of computing work. These calls have highlighted the limitations of an ethics-only [...] Read more.
A recurring motif in recent scholarship in the computing ethics and society studies (CESS) subfield within computing have been the calls for a wider recognition of the social and political nature of computing work. These calls have highlighted the limitations of an ethics-only approach to covering social and political topics such as bias, fairness, equality, and justice within computing curricula. However, given the technically focused background of most computing educators, it is not necessarily clear how political topics should best be addressed in computing courses. This paper proposes that one helpful way to do so is via the well-established pedagogy of citizenship education, and as such it endeavors to introduce the discourse of citizenship education to an audience of computing educators. In particular, the change within citizenship education away from its early focus on personal responsibility and duty to its current twin focus on engendering civic participation in one’s community along with catalyzing critical attitudes to the realities of today’s social, political, and technical worlds, is especially relevant to computing educators in light of computing’s new-found interest in the political education of its students. Related work in digital literacy education is also discussed. Full article
(This article belongs to the Section Digital Humanities)
Article
Examining the Factors Influencing E-Tax Declaration Usage among Academics’ Taxpayers in Jordan
Informatics 2022, 9(4), 92; https://doi.org/10.3390/informatics9040092 - 14 Nov 2022
Viewed by 477
Abstract
Purpose: This research attempts to profoundly understand the factors influencing the usage of e-tax declarations. Design/methodology/approach: In a cross-sectional survey, partial least square-structural equation modeling (PLS-SEM) is used to examine the hypotheses on 182 academic taxpayers working in Public Universities in Jordan. Findings: [...] Read more.
Purpose: This research attempts to profoundly understand the factors influencing the usage of e-tax declarations. Design/methodology/approach: In a cross-sectional survey, partial least square-structural equation modeling (PLS-SEM) is used to examine the hypotheses on 182 academic taxpayers working in Public Universities in Jordan. Findings: The findings indicate that knowledge, subjective norms, and attitude play a vital role in taxpayers’ usage of e-tax declarations. Moreover, knowledge confirms the power of the Theory of Planned Behavior (TPB), which helps predict people’s behavior. However, the results reveal that awareness does not moderate the previously mentioned relationship. Research limitations/implications: The sample size is limited, and the participants were academics who work at public universities. Therefore, it is advisable to study larger sample size to confirm the study’s results. Moreover, further research could diversify the sample in terms of occupation, digital divide, and e-literacy, as these factors may significantly impact e-tax declaration usage. A comparison across various groups would be beneficial in gaining a better understanding of the demographics and variables that impact the use of e-tax declarations. The second limitation is the collection of mainly quantitative data; collecting qualitative data to further understand the main factors that could affect the usage of e-services would play a role in supporting the study’s findings. Practical implications: This study provides strategic guidance for Jordanian policymakers in improving citizens’ acceptance of mandatory e-services usage by affecting their knowledge, attitude, and subjective norms. As a result, these practical suggestions positively influence taxpayers’ usage of e-services, which contributes to their usage of optional ones. E-service adoption rates may rise by emphasizing their benefits, such as improving equity, efficiency, life quality, and limiting adverse environmental effects. Originality/value: This study expands the scope of mandatory public e-services research. Full article
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Article
Breast Cancer Tumor Classification Using a Bag of Deep Multi-Resolution Convolutional Features
Informatics 2022, 9(4), 91; https://doi.org/10.3390/informatics9040091 - 28 Oct 2022
Viewed by 653
Abstract
Breast cancer accounts for 30% of all female cancers. Accurately distinguishing dangerous malignant tumors from benign harmless ones is key to ensuring patients receive lifesaving treatments on time. However, as doctors currently do not identify 10% to 30% of breast cancers during regular [...] Read more.
Breast cancer accounts for 30% of all female cancers. Accurately distinguishing dangerous malignant tumors from benign harmless ones is key to ensuring patients receive lifesaving treatments on time. However, as doctors currently do not identify 10% to 30% of breast cancers during regular assessment, automated methods to detect malignant tumors are desirable. Although several computerized methods for breast cancer classification have been proposed, convolutional neural networks (CNNs) have demonstrably outperformed other approaches. In this paper, we propose an automated method for the binary classification of breast cancer tumors as either malignant or benign that utilizes a bag of deep multi-resolution convolutional features (BoDMCF) extracted from histopathological images at four resolutions (40×, 100×, 200× and 400×) by three pre-trained state-of-the-art deep CNN models: ResNet-50, EfficientNetb0, and Inception-v3. The BoDMCF extracted by the pre-trained CNNs were pooled using global average pooling and classified using the support vector machine (SVM) classifier. While some prior work has utilized CNNs for breast cancer classification, they did not explore using CNNs to extract and pool a bag of deep multi-resolution features. Other prior work utilized CNNs for deep multi-resolution feature extraction from chest X-ray radiographs to detect other conditions such as pneumoconiosis but not for breast cancer detection from histopathological images. In rigorous evaluation experiments, our deep BoDMCF feature approach with global pooling achieved an average accuracy of 99.92%, sensitivity of 0.9987, specificity (or recall) of 0.9797, positive prediction value (PPV) or precision of 0.99870, F1-Score of 0.9987, MCC of 0.9980, Kappa of 0.8368, and AUC of 0.9990 on the publicly available BreaKHis breast cancer image dataset. The proposed approach outperforms the prior state of the art for histopathological breast cancer classification as well as a comprehensive set of CNN baselines, including ResNet18, InceptionV3, DenseNet201, EfficientNetb0, SqueezeNet, and ShuffleNet, when classifying images at any individual resolutions (40×, 100×, 200× or 400×) or when SVM is used to classify a BoDMCF extracted using any single pre-trained CNN model. We also demonstrate through a carefully constructed set of experiments that each component of our approach contributes non-trivially to its superior performance including transfer learning (pre-training and fine-tuning), deep feature extraction at multiple resolutions, global pooling of deep multiresolution features into a powerful BoDMCF representation, and classification using SVM. Full article
(This article belongs to the Section Health Informatics)
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Article
Evaluation of the Efficiency of Regional Airports Using Data Envelopment Analysis
Informatics 2022, 9(4), 90; https://doi.org/10.3390/informatics9040090 - 28 Oct 2022
Viewed by 391
Abstract
Background: Small regional airports provide the necessary assistance to enable mobility in isolated areas where access is critical and costly due to poor road infrastructure and geographic constraints. Colombia’s air transportation industry has grown astonishingly quickly and dynamically over the past fifteen years. [...] Read more.
Background: Small regional airports provide the necessary assistance to enable mobility in isolated areas where access is critical and costly due to poor road infrastructure and geographic constraints. Colombia’s air transportation industry has grown astonishingly quickly and dynamically over the past fifteen years. This period was coincident with the establishment and continued implementation of a public policy intended exclusively for the aviation industry and airports. However, there are currently no methods available to measure the efficiency of airports in Colombia, especially small regional airports. Methods: The research presented in this article aims to evaluate the technical efficiency of small regional airports in Colombia, using data envelopment analysis. This efficiency is achieved by considering the minimum infrastructure required to provide services and the administrative forms or properties that provide appropriate levels of this. Results: The study’s input and output data are identified, a non-parametric data envelopment analysis methodology is used, and the findings are assessed. Conclusions: The factors directly identified in the research affect the airport administration and, in the options, are available to help citizens transport optimally. Full article
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Review
Smart City Applications to Promote Citizen Participation in City Management and Governance: A Systematic Review
Informatics 2022, 9(4), 89; https://doi.org/10.3390/informatics9040089 - 28 Oct 2022
Viewed by 419
Abstract
This systematic review aimed to provide a comprehensive view of (1) the purposes of research studies using smart city infrastructures to promote citizen participation in the cities’ management and governance, (2) the characteristics of the proposed solutions in terms of data sources, data [...] Read more.
This systematic review aimed to provide a comprehensive view of (1) the purposes of research studies using smart city infrastructures to promote citizen participation in the cities’ management and governance, (2) the characteristics of the proposed solutions in terms of data sources, data quality, and data security and privacy mechanisms, as well, as strategies to incentivize citizen participation, and (3) the development stages of the applications being reported. An electronic search was conducted combining relevant databases and keywords, and 76 studies were included after a selection process. The results show a current interest in developing applications to promote citizen participation to identify urban problems and contribute to decision-making processes. Most of the included studies considered citizens as agents able to report issues (e.g., issues related to the maintenance of urban infrastructures or the mobility in urban spaces), monitor certain environmental parameters (e.g., air or acoustic pollution), and share opinions (e.g., opinions about the performance of local authorities) to support city management. Moreover, a minority of the included studies developed collaborative applications to involve citizens in decision-making processes in urban planning, the selection of development projects, and deepening democratic values. It is possible to conclude about the existence of significant research related to the topic of this systematic review, but also about the need to deepen mechanisms to guarantee data quality and data security and privacy, to develop strategies to incentivize citizen participation, and to implement robust experimental set-ups to evaluate the impact of the developed applications in daily contexts. Full article
(This article belongs to the Special Issue Building Smart Cities and Infrastructures for a Sustainable Future)
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Article
Development of a Chatbot for Pregnant Women on a Posyandu Application in Indonesia: From Qualitative Approach to Decision Tree Method
Informatics 2022, 9(4), 88; https://doi.org/10.3390/informatics9040088 - 27 Oct 2022
Viewed by 587
Abstract
With the widespread application of digital healthcare, mobile health (mHealth) services are also developing in maternal and child health, primarily through community-based services, such as Posyandu in Indonesia. Patients need media for consultation and decision-making, while health workers are constrained in responding quickly. [...] Read more.
With the widespread application of digital healthcare, mobile health (mHealth) services are also developing in maternal and child health, primarily through community-based services, such as Posyandu in Indonesia. Patients need media for consultation and decision-making, while health workers are constrained in responding quickly. This study aimed to obtain information from pregnant women and midwives in developing a decision tree model as material for building a semi-automated chatbot. Using an exploratory qualitative approach, semi-structured interviews were conducted through focus group discussions (FGD) with pregnant women (n = 10) and midwives (n = 12) in March 2022. The results showed 38 codes, 15 categories, and 7 subthemes that generated 3 major themes: maternal health education, information on maternal health services, and health monitoring. The decision tree method was applied from these themes based on the needs of users, evidence, and expert sources to ensure quality. In summary, the need to use a semi-automated chatbot can be applied to education about maternal health and monitoring, where severe cases should be provided with non-automated communication with midwives. Applying the decision tree method ensured quality content, supported a clinical decision, and assisted in early detection. Furthermore, future research needs to measure user evaluation. Full article
(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)
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Article
A Novel Framework to Detect Irrelevant Software Requirements Based on MultiPhiLDA as the Topic Model
Informatics 2022, 9(4), 87; https://doi.org/10.3390/informatics9040087 - 27 Oct 2022
Viewed by 447
Abstract
Noise in requirements has been known to be a defect in software requirements specifications (SRS). Detecting defects at an early stage is crucial in the process of software development. Noise can be in the form of irrelevant requirements that are included within an [...] Read more.
Noise in requirements has been known to be a defect in software requirements specifications (SRS). Detecting defects at an early stage is crucial in the process of software development. Noise can be in the form of irrelevant requirements that are included within an SRS. A previous study had attempted to detect noise in SRS, in which noise was considered as an outlier. However, the resulting method only demonstrated a moderate reliability due to the overshadowing of unique actor words by unique action words in the topic–word distribution. In this study, we propose a framework to identify irrelevant requirements based on the MultiPhiLDA method. The proposed framework distinguishes the topic–word distribution of actor words and action words as two separate topic–word distributions with two multinomial probability functions. Weights are used to maintain a proportional contribution of actor and action words. We also explore the use of two outlier detection methods, namely percentile-based outlier detection (PBOD) and angle-based outlier detection (ABOD), to distinguish irrelevant requirements from relevant requirements. The experimental results show that the proposed framework was able to exhibit better performance than previous methods. Furthermore, the use of the combination of ABOD as the outlier detection method and topic coherence as the estimation approach to determine the optimal number of topics and iterations in the proposed framework outperformed the other combinations and obtained sensitivity, specificity, F1-score, and G-mean values of 0.59, 0.65, 0.62, and 0.62, respectively. Full article
(This article belongs to the Topic Software Engineering and Applications)
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Article
Factors Influencing the Use of Digital Marketing by Small and Medium-Sized Enterprises during COVID-19
Informatics 2022, 9(4), 86; https://doi.org/10.3390/informatics9040086 - 27 Oct 2022
Viewed by 669
Abstract
This study aims to identify the factors that influence the use of digital marketing by SMEs in Medellín during COVID-19, proposing five factors that influence the use of these digital tools by 120 SMEs in Medellín, Colombia. The research was carried out under [...] Read more.
This study aims to identify the factors that influence the use of digital marketing by SMEs in Medellín during COVID-19, proposing five factors that influence the use of these digital tools by 120 SMEs in Medellín, Colombia. The research was carried out under an exploratory factorial analysis, with a quantitative approach and an exploratory-descriptive scope. For data analysis, the levels of association between the constructs of the conceptual model and the intention to use these virtual tools were estimated, using Cramer’s V coefficient. The results identify the benefits perceived by customers, the perceived advantages of using digital tools, and business optimization as the key factors in predicting their acceptance and use. It is concluded that the most used digital marketing strategies are the content and web sites of Instagram and Facebook. As the main implications, the study contributes to understanding the behavior of companies regarding technological change which could help to identify needs and successful strategies that ensure the continuity and sustainability of this business sector. As limitations, the hypotheses were tested in a single context, so it is necessary to compare these results in developed countries to contribute to a global approach. In addition, only marketing professionals who held operational positions were surveyed, so future managers and leaders of the marketing area should be included in the studies. Full article
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Article
Using Random Ordering in User Experience Testing to Predict Final User Satisfaction
Informatics 2022, 9(4), 85; https://doi.org/10.3390/informatics9040085 - 26 Oct 2022
Viewed by 439
Abstract
In user experience evaluation (UXE), it is generally accepted that the order in which users perform tasks when using a product is often random rather than fixed. UXE based on these so-called randomly ordered tasks is challenging. Although several articles have been published [...] Read more.
In user experience evaluation (UXE), it is generally accepted that the order in which users perform tasks when using a product is often random rather than fixed. UXE based on these so-called randomly ordered tasks is challenging. Although several articles have been published on UXE, none have proposed a technique to evaluate the significance of randomly ordered tasks. In this study, we propose a new approach to predict final user satisfaction based on UX related to randomly ordered tasks. We aimed to study the importance of task order in the UX. In the main experiment, 60 participants completed questionnaires about satisfaction while performing a series of tasks on a travel agency website. Among the machine learning models tested, we found that accounting for the order or sequence of actions actually performed by users in a support vector machine (SVM) algorithm with a polynomial kernel produced the most accurate predictions of final user satisfaction (97%). These findings indicate that some machine learning techniques can comprehend participants’ randomly ordered UX data. Moreover, using random ordering, which accounts for the actual order of actions performed by users, can significantly impact the prediction of final user satisfaction. Full article
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Article
EduTubers’s Pedagogical Best Practices and Their Theoretical Foundation
Informatics 2022, 9(4), 84; https://doi.org/10.3390/informatics9040084 - 22 Oct 2022
Viewed by 593
Abstract
(1) Background: The COVID-19 pandemic forced educational institutions to radically change their teaching and learning methods. Many institutions found it a challenge when they opted to deliver classes online. Given the success of several EduTubers, this study aimed to identify their best practices. [...] Read more.
(1) Background: The COVID-19 pandemic forced educational institutions to radically change their teaching and learning methods. Many institutions found it a challenge when they opted to deliver classes online. Given the success of several EduTubers, this study aimed to identify their best practices. (2) Method: The research was qualitative in nature and descriptive in scope and analyzed three angles, namely the perspective of EduTubers, their videos, and the assessment of the audience. Moreover, the results demonstrated that the level of awareness of these practices varies among actors. (3) Results: The study identified 12 practices divided into four categories, namely (a) resource management, (b) communication strategies, (c) content management, and (d) pedagogical strategies. (4) Conclusions: Followers were aware of the most evident practices, such as tone of voice and length of explanation, whereas EduTubers clearly convey practices such as anchoring in prior knowledge and use of associations. However, when analyzing the videos of EduTubers, the study found that they used other practices with low levels of awareness, such as mental representations, balanced use of different resources, or management of topics in a logical and hierarchical order. Full article
Article
Decision Tree Modeling for Osteoporosis Screening in Postmenopausal Thai Women
Informatics 2022, 9(4), 83; https://doi.org/10.3390/informatics9040083 - 19 Oct 2022
Viewed by 627
Abstract
Osteoporosis is still a serious public health issue in Thailand, particularly in postmenopausal women; meanwhile, new effective screening tools are required for rapid diagnosis. This study constructs and confirms an osteoporosis screening tool-based decision tree (DT) model. Four DT algorithms, namely, classification and [...] Read more.
Osteoporosis is still a serious public health issue in Thailand, particularly in postmenopausal women; meanwhile, new effective screening tools are required for rapid diagnosis. This study constructs and confirms an osteoporosis screening tool-based decision tree (DT) model. Four DT algorithms, namely, classification and regression tree; chi-squared automatic interaction detection (CHAID); quick, unbiased, efficient statistical tree; and C4.5, were implemented on 356 patients, of whom 266 were abnormal and 90 normal. The investigation revealed that the DT algorithms have insignificantly different performances regarding the accuracy, sensitivity, specificity, and area under the curve. Each algorithm possesses its characteristic performance. The optimal model is selected according to the performance of blind data testing and compared with traditional screening tools: Osteoporosis Self-Assessment for Asians and the Khon Kaen Osteoporosis Study. The Decision Tree for Postmenopausal Osteoporosis Screening (DTPOS) tool was developed from the best performance of CHAID’s algorithms. The age of 58 years and weight at a cutoff of 57.8 kg were the essential predictors of our tool. DTPOS provides a sensitivity of 92.3% and a positive predictive value of 82.8%, which might be used to rule in subjects at risk of osteopenia and osteoporosis in a community-based screening as it is simple to conduct. Full article
(This article belongs to the Section Health Informatics)
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Article
Types of Major League Baseball Broadcast Information and Their Impacts on Audience Experience
Informatics 2022, 9(4), 82; https://doi.org/10.3390/informatics9040082 - 10 Oct 2022
Viewed by 698
Abstract
Baseball is a sport that involves a large number of statistics, which are often displayed during broadcast events to show the players’ performance levels. With the advent of big data, the amount and types of data used in broadcasts have increased yearly. However, [...] Read more.
Baseball is a sport that involves a large number of statistics, which are often displayed during broadcast events to show the players’ performance levels. With the advent of big data, the amount and types of data used in broadcasts have increased yearly. However, the use of complex information challenges the audience’s ability to process it. This study considered data types used during broadcasts as the basis for an in-depth exploration of audiences’ experience resulting from the application of visualization. The study also examined the relationship between the contents of broadcast information and audiences’ sports participation, entertainment experience, and cognitive load. Baseball fans with varying levels of experience with handling different types of information were surveyed to understand the variations in their entertainment experiences and cognitive load levels when they watched a baseball game. The results indicated that fans with low participation levels had insufficient viewing experience, such that the use of visualized statistical information did not facilitate their understanding of the game, nor did they gain more pleasure or meaning from the game through the visualized information. Fans with high participation levels already possessed a wealth of baseball knowledge and experience, so providing visualized information did not significantly elevate their viewing experiences either. Moreover, the visualized information caused them to experience varying amounts of additional cognitive load. These results provide a reference that can be used to design sports broadcasts tailored to different information types and fan characteristics, thus improving fans’ viewing experience of sports broadcasts. Full article
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Article
Interacting with a Chatbot-Based Advising System: Understanding the Effect of Chatbot Personality and User Gender on Behavior
Informatics 2022, 9(4), 81; https://doi.org/10.3390/informatics9040081 - 10 Oct 2022
Viewed by 857
Abstract
Chatbots with personality have been shown to affect engagement and user subjective satisfaction. Yet, the design of most chatbots focuses on functionality and accuracy rather than an interpersonal communication style. Existing studies on personality-imbued chatbots have mostly assessed the effect of chatbot personality [...] Read more.
Chatbots with personality have been shown to affect engagement and user subjective satisfaction. Yet, the design of most chatbots focuses on functionality and accuracy rather than an interpersonal communication style. Existing studies on personality-imbued chatbots have mostly assessed the effect of chatbot personality on user preference and satisfaction. However, the influence of chatbot personality on behavioral qualities, such as users’ trust, engagement, and perceived authenticity of the chatbots, is largely unexplored. To bridge this gap, this study contributes: (1) A detailed design of a personality-imbued chatbot used in academic advising. (2) Empirical findings of an experiment with students who interacted with three different versions of the chatbot. Each version, vetted by psychology experts, represents one of the three dominant traits, agreeableness, conscientiousness, and extraversion. The experiment focused on the effect of chatbot personality on trust, authenticity, engagement, and intention to use the chatbot. Furthermore, we assessed whether gender plays a role in students’ perception of the personality-imbued chatbots. Our findings show a positive impact of chatbot personality on perceived chatbot authenticity and intended engagement, while student gender does not play a significant role in the students’ perception of chatbots. Full article
(This article belongs to the Section Human-Computer Interaction)
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Article
Machine Learning Applied to Tree Crop Yield Prediction Using Field Data and Satellite Imagery: A Case Study in a Citrus Orchard
Informatics 2022, 9(4), 80; https://doi.org/10.3390/informatics9040080 - 08 Oct 2022
Viewed by 681
Abstract
The overall goal of this study is to define an intelligent system for predicting citrus fruit yield before the harvest period. This system uses a machine learning algorithm trained on historical field data combined with spectral information extracted from satellite images. To this [...] Read more.
The overall goal of this study is to define an intelligent system for predicting citrus fruit yield before the harvest period. This system uses a machine learning algorithm trained on historical field data combined with spectral information extracted from satellite images. To this end, we used 5 years of historical data for a Moroccan orchard composed of 50 parcels. These data are related to climate, amount of water used for irrigation, fertilization products by dose, phytosanitary treatment dose, parcel size, and root-stock type on each parcel. Additionally, two very popular indices, the normalized difference vegetation index and normalized difference water index were extracted from Sentinel 2 and Landsat satellite images to improve prediction scores. We managed to build a total dataset composed of 250 rows, representing the 50 parcels over a period of 5 years labeled with the yield of each parcel. Several machine learning algorithms were tested with the necessary parameter optimization, while the orthonormal automatic pursuit algorithm gave good prediction scores of 0.2489 (MAE: Mean Absolute Error) and 0.0843 (MSE: Mean Squared Error). Finally, the approach followed in this study shows excellent potential for fruit yield prediction. In fact, the test was performed on a citrus orchard, but the same approach can be used on other tree crops to achieve the same goal. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Deep Learning in Agriculture)
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Article
Assessing Usability of Smartwatch Digital Health Devices for Home Blood Pressure Monitoring among Glaucoma Patients
Informatics 2022, 9(4), 79; https://doi.org/10.3390/informatics9040079 - 06 Oct 2022
Viewed by 739
Abstract
Glaucoma is a leading cause of blindness worldwide. Blood pressure (BP) dysregulation is a known risk factor, and home-based BP monitoring is increasingly used, but the usability of digital health devices to measure BP among glaucoma patients is not well studied. There may [...] Read more.
Glaucoma is a leading cause of blindness worldwide. Blood pressure (BP) dysregulation is a known risk factor, and home-based BP monitoring is increasingly used, but the usability of digital health devices to measure BP among glaucoma patients is not well studied. There may be particular usability challenges among this group, given that glaucoma disproportionately affects the elderly and can cause visual impairment. Therefore, the goal of this mixed-methods study was to assess the usability of a smart watch digital health device for home BP monitoring among glaucoma patients. Adult participants were recruited and given a smartwatch blood pressure monitor for at-home use. The eHEALS questionnaire was used to determine baseline digital health literacy. After a week of use, participants assessed the usability of the BP monitor and related mobile app using the Post-study System Usability Questionnaire (PSSUQ) and the System Usability Scale (SUS), standardized instruments to measure usability in health information technology interventions. Variations in scores were evaluated using ANOVA and open-ended responses about participants’ experience were analyzed thematically. Overall, usability scores corresponded to the 80th–84th percentile, although older patients endorsed significantly worse usability based on quantitative scores and additionally provided qualitative feedback describing some difficulty using the device. Usability for older patients should be considered in the design of digital health devices for glaucoma given their disproportionate burden of disease and challenges in navigating digital health technologies, although the overall high usability scores for the device demonstrates promise for future clinical applications in glaucoma risk stratification. Full article
(This article belongs to the Special Issue Feature Papers in Informatics in 2022)
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Article
Meteorological Data Warehousing and Analysis for Supporting Air Navigation
Informatics 2022, 9(4), 78; https://doi.org/10.3390/informatics9040078 - 04 Oct 2022
Viewed by 468
Abstract
Data analysis of weather phenomena to either predict or control human imprint on the environment requires the collection of various forms of observational data ranging from historical and longitudinal to forecast. The objective of this research paper is the development of a data [...] Read more.
Data analysis of weather phenomena to either predict or control human imprint on the environment requires the collection of various forms of observational data ranging from historical and longitudinal to forecast. The objective of this research paper is the development of a data warehouse (DW) based on a new hybrid logical schema, concerning the assimilation and maintenance of historical meteorological data from all operating airports in Greece, along with data in the Greek Flight Information Region related to flight delays and cancellations. SQL is used for querying these data and makes them easily accessible and manageable. The data from the DW are collected and used as training data for the induction of predictive models. In this study, the prediction problem is cast as a classification task, and different decision tree induction techniques are applied to build accurate models that allow flexible scheduling and planning for the minimization of waiting time and inconvenience of passengers. Full article
(This article belongs to the Section Big Data Mining and Analytics)
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Article
Predicting Future Promising Technologies Using LSTM
Informatics 2022, 9(4), 77; https://doi.org/10.3390/informatics9040077 - 27 Sep 2022
Viewed by 619
Abstract
With advances in science and technology and changes in industry, research on promising future technologies has emerged as important. Furthermore, with the advent of a ubiquitous and smart environment, governments and enterprises are required to predict future promising technologies on which new important [...] Read more.
With advances in science and technology and changes in industry, research on promising future technologies has emerged as important. Furthermore, with the advent of a ubiquitous and smart environment, governments and enterprises are required to predict future promising technologies on which new important core technologies will be developed. Therefore, this study aimed to establish science and technology development strategies and support business activities by predicting future promising technologies using big data and deep learning models. The names of the “TOP 10 Emerging Technologies” from 2018 to 2021 selected by the World Economic Forum were used as keywords. Next, patents collected from the United States Patent and Trademark Office and the Science Citation Index (SCI) papers collected from the Web of Science database were analyzed using a time-series forecast. For each technology, the number of patents and SCI papers in 2022, 2023 and 2024 were predicted using the long short-term memory model with the number of patents and SCI papers from 1980 to 2021 as input data. Promising technologies are determined based on the predicted number of patents and SCI papers for the next three years. Keywords characterizing future promising technologies are extracted by analyzing abstracts of patent data collected for each technology and the term frequency-inverse document frequency is measured for each patent abstract. The research results can help business managers make optimal decisions in the present situation and provide researchers with an understanding of the direction of technology development. Full article
(This article belongs to the Special Issue Feature Papers in Big Data)
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Article
Classification of Malaria Using Object Detection Models
Informatics 2022, 9(4), 76; https://doi.org/10.3390/informatics9040076 - 27 Sep 2022
Viewed by 789
Abstract
Malaria poses a global health problem every day, as it affects millions of lives all over the world. A traditional diagnosis requires the manual inspection of blood smears from the patient under a microscope to check for the malaria parasite. This is often [...] Read more.
Malaria poses a global health problem every day, as it affects millions of lives all over the world. A traditional diagnosis requires the manual inspection of blood smears from the patient under a microscope to check for the malaria parasite. This is often time consuming and subject to error. Thus, the automated detection and classification of the malaria type and stage of progression can provide a quicker and more accurate diagnosis for patients. In this research, we used two object detection models, YOLOv5 and scaled YOLOv4, to classify the stage of progression and type of malaria parasite. We also used two different datasets for the classification of stage and parasite type while assessing the viability of the dataset for the task. The dataset used is comprised of microscopic images of red blood cells that were either parasitized or uninfected. The infected cells were classified based on two broad categories: the type of malarial parasite causing the infection and the stage of progression of the disease. The dataset was manually annotated using the LabelImg tool. The images were then augmented to enhance model training. Both models YOLOv5 and scaled YOLOv4 proved effective in classifying the type of parasite. Scaled YOLOv4 was in the lead with an accuracy of 83% followed by YOLOv5 with an accuracy of 78.5%. The proposed models may be useful for the medical professionals in the accurate diagnosis of malaria and its stage prediction. Full article
(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)
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Review
Exploring Immersive Learning Experiences: A Survey
Informatics 2022, 9(4), 75; https://doi.org/10.3390/informatics9040075 - 26 Sep 2022
Viewed by 782
Abstract
Immersive technologies have been shown to significantly improve learning as they can simplify and simulate complicated concepts in various fields. However, there is a lack of studies that analyze the recent evidence-based immersive learning experiences applied in a classroom setting or offered to [...] Read more.
Immersive technologies have been shown to significantly improve learning as they can simplify and simulate complicated concepts in various fields. However, there is a lack of studies that analyze the recent evidence-based immersive learning experiences applied in a classroom setting or offered to the public. This study presents a systematic review of 42 papers to understand, compare, and reflect on recent attempts to integrate immersive technologies in education using seven dimensions: application field, the technology used, educational role, interaction techniques, evaluation methods, and challenges. The results show that most studies covered STEM (science, technology, engineering, math) topics and mostly used head-mounted display (HMD) virtual reality in addition to marker-based augmented reality, while mixed reality was only represented in two studies. Further, the studies mostly used a form of active learning, and highlighted touch and hardware-based interactions enabling viewpoint and select tasks. Moreover, the studies utilized experiments, questionnaires, and evaluation studies for evaluating the immersive experiences. The evaluations show improved performance and engagement, but also point to various usability issues. Finally, we discuss implications and future research directions, and compare our findings with related review studies. Full article
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Article
Posyandu Application in Indonesia: From Health Informatics Data Quality Bridging Bottom-Up and Top-Down Policy Implementation
Informatics 2022, 9(4), 74; https://doi.org/10.3390/informatics9040074 - 23 Sep 2022
Viewed by 678
Abstract
The community’s mother and child health (MCH) and nutrition problems can be overcome through evidence-based health policy. Posyandu is an implementation of community empowerment in health promotion strategies. The iPosyandu application (app) is one of the health informatics tools, in which data quality [...] Read more.
The community’s mother and child health (MCH) and nutrition problems can be overcome through evidence-based health policy. Posyandu is an implementation of community empowerment in health promotion strategies. The iPosyandu application (app) is one of the health informatics tools, in which data quality should be considered before any Posyandu health interventions are made. This study aims to describe and assess differences in data quality based on the dimensions (completeness, accuracy, and consistency) of the secondary data collected from the app in Purwakarta Regency in 2019–2021. Obstacles and suggestions for improving its implementation were explored. This research applies a mixed-method explanatory approach. Data completeness was identified as the number of reported visits of children under five per year. Data accuracy was analyzed using WHO Z-score anthropometry and implausible Z-score values. Data consistency was measured using Cronbach’s alpha coefficient, followed by qualitative research with focus group discussions, in-depth interviews, and field observation notes. The quantitative study results found that some of the data were of good quality. The qualitative research identified the obstacles experienced using the iPosyandu app, one of them being that there were no regulations governing the use of iPosyandu to bridge the needs of the government, and provided suggestions from the field to improve its implementation. Full article
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Article
A Visual Data Storytelling Framework
Informatics 2022, 9(4), 73; https://doi.org/10.3390/informatics9040073 - 23 Sep 2022
Viewed by 942
Abstract
While the consumption of visual information becomes a daily commodity integrated into our lives, data visualisation is dominated by dashboards and charts. The main contribution of this article is an advanced way to visualise data as a data story. We converged paradigms from [...] Read more.
While the consumption of visual information becomes a daily commodity integrated into our lives, data visualisation is dominated by dashboards and charts. The main contribution of this article is an advanced way to visualise data as a data story. We converged paradigms from digital storytelling, serious games, and data visualisation to turn data into useful insights. The creation, management, and analysis of data have been increasingly given more attention in industry and professional practices. However, the potential of packaging data and analytic results into easily digestible and visually explorable content intended for non-professional audiences has not yet been investigated to its full extent. We contributed towards overcoming the gap between data analytics and data presentation. By integrating a story-like environment and entertainment into data visualisation, we explore the possibilities of efficiently communicating data and insights to general audiences in a casual context. We present this modular approach to customising messages for visual data storytelling from an information and communication perspective, including a test prototype developed to illustrate our data storytelling framework. Full article
(This article belongs to the Section Digital Humanities)
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Article
Semantic Annotation of Legal Contracts with ContrattoA
Informatics 2022, 9(4), 72; https://doi.org/10.3390/informatics9040072 - 20 Sep 2022
Viewed by 622
Abstract
The aim of the research is to semi-automate the process of generating formal specifications from legal contracts in natural language text form. Towards this end, the paper presents a tool, named ContrattoA, that semi-automatically conducts semantic annotation of legal contract text using an [...] Read more.
The aim of the research is to semi-automate the process of generating formal specifications from legal contracts in natural language text form. Towards this end, the paper presents a tool, named ContrattoA, that semi-automatically conducts semantic annotation of legal contract text using an ontology for legal contracts. ContrattoA was developed through two iterations where lexical patterns were defined for legal concepts and their effectiveness was evaluated with experiments. The first iteration was based on a handful of sample contracts and resulted in defining lexical patterns for recognizing concepts in the ontology; these were evaluated with an empirical study where one group of subjects was asked to annotate legal text manually, while a second group edited the annotations generated by ContrattoA. The second iteration focused on the lexical patterns for the core contract concepts of obligation and power where results of the first iteration were mixed. On the basis of an extended set of sample contracts, new lexical patterns were derived and those were shown to substantially improve the performance of ContrattoA, nearing in quality the performance of experts. The experiments suggest that good quality annotations can be generated for a broad range of contracts with minor refinements to the lexical patterns. Full article
(This article belongs to the Topic Software Engineering and Applications)
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