Interactive Visual Representations Research Articles

The Importance of Technology-Based Learning Design to Increase Student Involvement

This research examines the use of animation as a technology-based learning medium to increase student learning motivation. In the rapid development of information and communication technology, education requires innovative transformations in learning methods. Animation has been recognized as an effective tool for enriching students' learning experiences through clear and interactive visual representations. This research aims to analyze literature related to the use of animation as a learning medium, explore the advantages and challenges of its use, and convey the contribution of this research to the development of innovative learning practices. The method used in this research is a literature study, where data is obtained through analysis of theoretical sources and related research. The results of the literature study analysis show that animation has great potential to increase student learning motivation, involvement, creativity, and personalized learning. However, educators and researchers need to pay attention to good animation design and the role of educators in facilitating the effective use of animation. This research contributes to the development of innovative learning practices and enriches understanding of the use of animation as an effective learning medium in increasing student learning motivation.

Analyzing the impact of data visualization applications for diagnosing the health conditions through hesitant fuzzy-based hybrid medical expert system

Effectively managing healthcare data is crucial for accurate diagnosis and personalized patient care. As the utilization of healthcare data grows for personalized care, concerns about reliability, privacy, and security have emerged. To address these issues, this research explores the fusion of analytical techniques with interactive visual representations, known as visual analytics, as a promising solution. The focus is on evaluating the trustworthiness of healthcare data in Kingdom of Saudi Arabia, particularly the capability of visual analytics tools in facilitating accurate and secure healthcare data analysis. This study tackles challenges such as the absence of specific evaluation criteria, the need to process vast healthcare datasets, the establishment of trust, and the necessity for automation. In response, a hybrid medical decision support system is introduced, leveraging hesitant fuzzy decision systems. The primary objective is to evaluate trustworthiness of visual analytics tools for disease diagnosis within healthcare data. Within the framework of hesitant fuzzy logic, the paper employs a medical multi-criteria decision-making system that integrates the analytic network process and the technique for order of preference by similarity to an ideal solution. Rigorous validation ensures the accuracy and reliability of the findings. The research not only provides valuable insights but also conducts comparative analyses of the proposed models against existing ones, demonstrating the practicality of optimal decision-making in Saudi Arabia environment of healthcare scenarios. Several popular alternatives of healthcare based tools have been used in this study such as Tableau, JupyteR, Zoho Reports, QlikView, Visual.ly, DOMO BI, SAS Visual Analytics. From the results achieved DOMO BI visual analytics tool is found to be most secure and robust tool for healthcare professionals. This effort aims to enhance patient care and outcomes, ultimately contributing to the improvement of the overall healthcare landscape in Saudi Arabia.

Analisis Peran Animasi Menggunakan Aplikasi Macromedia Flash dalam Media Pembelajaran TIK di Kelas 11 SMA 1 Sungai Pua

In the midst of the rapid development of information and communication technology, education requires innovative transformations in learning methods. Animation has been recognized as an effective tool in enriching students' learning experiences through clear and interactive visual representations. The aim of this research is to analyze literature related to the use of animation as a learning medium, explore the advantages and challenges of its use, and convey the contribution of this research in the development of innovative learning practices. The method used in this research is a literature study, where data is obtained through analysis of theoretical sources and related research. The results of the literature study analysis show that animation has great potential to increase student learning motivation, involvement, creativity and personalized learning. However, it is important for educators and researchers to pay attention to good animation design and the role of educators in facilitating the effective use of animation. This research contributes to the development of innovative learning practices and enriches understanding of the use of animation as an effective learning medium in increasing student learning motivation

Pemanfaatan Animasi sebagai Media Pembelajaran Berbasis Teknologi untuk Meningkatkan Motivasi Belajar

This study examines the utilization of animation as a technology-based learning media to enhance student motivation. In the midst of rapid advancements in information and communication technology, education requires innovative transformation in learning methods. Animation has been recognized as an effective tool to enrich student learning experiences through clear and interactive visual representations. The aim of this research is to analyze literature related to the utilization of animation as a learning media, explore its benefits and challenges, and convey the contribution of this study to the development of innovative learning practices. The method employed in this research is literature review, where data is obtained through analysis of theoretical sources and relevant studies. The results of the literature analysis indicate that animation holds great potential in enhancing student motivation, engagement, creativity, and personalized learning. However, it is crucial for educators and researchers to pay attention to good animation design and the role of educators in facilitating effective animation utilization. This research contributes to the development of innovative learning practices and enriches the understanding of the utilization of animation as an effective learning media in enhancing student motivation.

Leveraging H3Africa Scholarly Publications for Technology-Enhanced Personalized Bioinformatics Education

The Coronavirus Disease 2019 (COVID-19) pandemic has catalyzed the expectations for technology-enhanced interactions with personalized educational materials. Adjusting the content of educational materials to the geographical location of a learner is a customization feature of personalized education and is used to develop the interest of a learner in the content. The educational content of interest in this report is bioinformatics, in which the knowledge spans biological science and applied mathematics disciplines. The Human Heredity and Health in Africa (H3Africa) Initiative is a resource suitable for use when obtaining data and peer-reviewed scholarly articles, which are geographically relevant and focus on authentic problem solving in the human health domain. We developed a computerized platform of interactive visual representations of curated bioinformatics datasets from H3Africa projects, which also supports customization, individualization and adaptation features of personalized education. We obtained evidence for the positive effect size and acceptable usability of a visual analytics resource designed for the retrieval-based learning of facts on functional impacts of genomic sequence variants. We conclude that technology-enhanced personalized bioinformatics educational interventions have implications in (1) the meaningful learning of bioinformatics; (2) stimulating additional student interest in bioinformatics; and (2) improving the accessibility of bioinformatics education to non-bioinformaticians.

COVID-19 epidemic: analysis and prediction

“Novel Coronavirus”, commonly known as COVID-19 has spread nearly to the entire world. The number of impacted cases and deaths has increased significantly in each country, posing a challenge for the world’s health organizations. The goal of this paper was to better comprehend and analyze the growth of the disease in India, including confirmed, recovered, fatalities, and active cases of COVID-19. Data analysis affects an organization’s decision-making process with interactive visual representation. The proposed model was an ensemble model that was built using linear regression, polynomial regression, and support vector machine (SVM) regression models. The model predicted the number of confirmed cases from 30 th May 2021 to 15 th June 2021 based on the data available from 22 January 2020 to 29 May 2021 and improved accuracy was obtained when compared with the actual data. Forecasting the confirmed cases might assist health organizations in planning medical facilities. Following that, an appropriate machine leraning (ML) model must be found that can predict the number of new cases in the future.

Visual Analytics for Predicting Disease Outcomes Using Laboratory Test Results

Laboratory tests play an essential role in the early and accurate diagnosis of diseases. In this paper, we propose SUNRISE, a visual analytics system that allows the user to interactively explore the relationships between laboratory test results and a disease outcome. SUNRISE integrates frequent itemset mining (i.e., Eclat algorithm) with extreme gradient boosting (XGBoost) to develop more specialized and accurate prediction models. It also includes interactive visualizations to allow the user to interact with the model and track the decision process. SUNRISE helps the user probe the prediction model by generating input examples and observing how the model responds. Furthermore, it improves the user’s confidence in the generated predictions and provides them the means to validate the model’s response by illustrating the underlying working mechanism of the prediction models through visualization representations. SUNRISE offers a balanced distribution of processing load through the seamless integration of analytical methods with interactive visual representations to support the user’s cognitive tasks. We demonstrate the usefulness of SUNRISE through a usage scenario of exploring the association between laboratory test results and acute kidney injury, using large provincial healthcare databases from Ontario, Canada.

A Review of COVID-19 Outbreak Analysis Using Visual Analytics Approach

The unexpected outbreak of the new infectious disease called the novel coronavirus 2019 has a great impact on public health worldwide. The virus has challenged the healthcare systems globally, and it remains a mystery in front of them. Thus, there is a need to study how this deadly disease has been spread across the world in such a rapid phase. This study is beneficial to the industries that are affected by the current situation by making certain observations while considering the COVID-19 outbreak cases. It also helps these industries in taking the needed precautionary measures in the future if such a global pandemic situation arises. An interactive visual representation of this task efficiently and effectively done is more understandable and presentable. This study gives you a clear idea of COVID-19, Visual Analytics and its application in the field of public health, especially in the case of COVID-19. The main scope of this review is to create awareness among the community about the outbreak and spread of this contagious disease using the visual analytics approach.

Evaluation of the Level-of-Detail Generator for Visual Analysis of the ATLAS Computing Metadata

The ATLAS experiment at the LHC processes, analyses and stores vast amounts of data, which is either recorded by the detector or simulated worldwide using Monte Carlo methods. ATLAS Computing metadata is generated at very high rates and volumes. The necessity to analyze this metadata is constantly increasing, since the heterogeneous, distributed and dynamically changing computing infrastructure requires sophisticated optimization decisions, made by human or/and by machines. Visual analytics is one of the methods facilitating the analysis of massive amounts of data (structured, semi-structured, and unstructured) which leverages human judgement by means of interactive visual representations. Given the huge number of ATLAS computing jobs that need to be visualized simultaneously for error investigations or other optimization processes, resources of the client application responsible for such visualization may reach its limits. Data objects that share similar feature values can be represented and visualized as a single group, thus initial large data sample would be represented at different levels of detail. This approach will also avoid client overload. In this paper we evaluate implementations of k-means-based Level-of-Detail generator method applied to the metadata of ATLAS jobs. This method is used in the visual analytics application InVEx (Interactive Visual Explorer) that is under development, and which is based on 3-dimensional interactive visualization of multidimensional data.

Interactive three-dimensional visualization of network intrusion detection data for machine learning

The threat of cyber-attacks is on the rise in the digital world today. As such, effective cybersecurity solutions are becoming increasingly important for detecting and combating cyber-attacks. The use of machine learning techniques for network intrusion detection is a growing area of research, as these techniques can potentially provide a means for automating the detection of attacks and abnormal traffic patterns in real-time. However, misclassification is a common problem in machine learning for intrusion detection, and the improvement of machine learning models is hindered by a lack of insight into the reasons behind such misclassification. This paper presents an interactive method of visualizing network intrusion detection data in three-dimensions. The objective is to facilitate the understanding of network intrusion detection data using a visual representation to reflect the geometric relationship between various categories of network traffic. This interactive visual representation can potentially provide useful insight to aid the understanding of machine learning results. To demonstrate the usefulness of the proposed visualization approach, this paper presents results of experiments on commonly used network intrusion detection datasets.

Real-time and archival data visualisation techniques in city dashboards

ABSTRACTCity dashboards have become a common smart city technology, emerging as a key means of sharing and visualising urban data for the benefit of the public and city administrations. Operating as the front-end of many cities’ data stores, dashboards display and benchmark indicators relating to city operations, characteristics, and trends, displayed through interactive visual representations of spatial and temporal patterns. Many dashboards collect, archive, and present data collected in real-time, as well as more traditional time-sliced administrative data. In this paper, we evaluate the techniques that dashboards employ to present real-time data to dashboard users. Our analysis identifies two factors that shape and differentiate real-time visual analytic tools: the dynamic nature of the data, how they are refreshed, and how the realtimeness of the data is communicated to the user; and how the tool enables archival comparison. We assess dashboard design according to the strategies used to address specific challenges associated with each factor, specifically change blindness and temporal pattern detection. We conclude by proposing effective techniques for city dashboard design.

Visual Representations of Risk Enhance Long-Term Retention of Risk Information: A Randomized Trial.

People often overestimate their risk of developing cancer, which can cause undue worry and unwarranted risk-reducing actions. Standard counseling has a limited and short-lived effect on correcting these misperceptions. We conducted a randomized study to evaluate whether incorporation of visual depictions of risk improves the efficacy and durability of cancer risk counseling. Sixty-six individuals seen in the Familial Cancer Program were randomized to receive standard counseling or counseling supplemented with 2 interactive visual representations of their 10-year risk of developing the cancer type of greatest concern (enhanced counseling). The primary outcome was accuracy of self-perceived risk (ratio of perceived to objective risk) 2 weeks and 6 months after counseling. Prior to counseling, 80% of participants overestimated their risk. Improvement in self-perception of risk was greater among those individuals randomized to receive enhanced counseling. At the 2-week follow-up, the percentage of participants who continued to overestimate their risk by 5-fold or more was 3 to 4 times lower in those who received enhanced counseling, compared to the standard counseling group. At the 6-month follow-up, sustained improvement in risk perception was most evident among those exposed to visual depictions of their risk. Statistical significance was achieved in chi-square analysis at P < 0.05, grouping participants' risk estimate as approximately accurate (<2-fold) or different from objective risk to varying degrees. Overestimation of cancer risk among people with a family history of cancer is common. Counseling can improve risk perception, but individuals tend to revert back to their prior misperception 6 months after counseling. By including visual representations of risk during counseling, correction of risk perception was of greater magnitude and more durable.

NAPOLI: A Graph-Based Strategy to Detect and Visualize Conserved Protein-Ligand Interactions in Large-Scale.

Essential roles in biological systems depend on protein-ligand recognition, which is mostly driven by specific non-covalent interactions. Consequently, investigating these interactions contributes to understanding how molecular recognition occurs. Nowadays, a large-scale data set of protein-ligand complexes is available in the Protein Data Bank, what led several tools to be proposed as an effort to elucidate protein-ligand interactions. Nonetheless, there is not an all-in-one tool that couples large-scale statistical, visual, and interactive analysis of conserved protein-ligand interactions. Therefore, we propose nAPOLI (Analysis of PrOtein-Ligand Interactions), a web server that combines large-scale analysis of conserved interactions in protein-ligand complexes at the atomic-level, interactive visual representations, and comprehensive reports of the interacting residues/atoms to detect and explore conserved non-covalent interactions. We demonstrate the potential of nAPOLI in detecting important conserved interacting residues through four case studies: two involving a human cyclin-dependent kinase 2 (CDK2), one related to ricin, and other to the human nuclear receptor subfamily 3 (hNR3). nAPOLI proved to be suitable to identify conserved interactions according to literature, as well as highlight additional interactions. Finally, we illustrate, with a virtual screening ligand selection, how nAPOLI can be widely applied in structural biology and drug design. nAPOLI is freely available at bioinfo.dcc.ufmg.br/napoli/.

Transcription Direction Patterns of Adjacent Genes in Mycobacterium Tuberculosis Using GENAVIS

The understanding of the relationship of a gene with other genes in its neighbourhood and the implication of this relationship on the biochemical activities of the entire genome need an efficient computational tool to unfold the relationship. GENAVIS: (GENe Adjacency VIsualization Software) is an open source, platform independent web-based software for modeling neighborhood genes as binary codes. We also incorporated the feature of having an interactive visual representation of patterns of the binary code for a specific gene family in multiple microbial genomes. The concept of using binary code for representation is derived from computational thinking techniques which models problems using computer logic of applying abstraction and pattern matching to extract hidden patterns aimed at knowledge discovery. The result provides an insight into the analysis of transcriptional unit with more than one gene and genes encoding for universal stress protein, which also allows for a comparative analysis of multiple genomes as the basis for biosynthetic pathways and multi-gene function prediction.

Sequana coverage: detection and characterization of genomic variations using running median and mixture models.

BackgroundIn addition to mapping quality information, the Genome coverage contains valuable biological information such as the presence of repetitive regions, deleted genes, or copy number variations (CNVs). It is essential to take into consideration atypical regions, trends (e.g., origin of replication), or known and unknown biases that influence coverage. It is also important that reported events have robust statistics (e.g. z-score) associated with their detections as well as precise location.ResultsWe provide a stand-alone application, sequana_coverage, that reports genomic regions of interest (ROIs) that are significantly over- or underrepresented in high-throughput sequencing data. Significance is associated with the events as well as characteristics such as length of the regions. The algorithm first detrends the data using an efficient running median algorithm. It then estimates the distribution of the normalized genome coverage with a Gaussian mixture model. Finally, a z-score statistic is assigned to each base position and used to separate the central distribution from the ROIs (i.e., under- and overcovered regions). A double thresholds mechanism is used to cluster the genomic ROIs. HTML reports provide a summary with interactive visual representations of the genomic ROIs with standard plots and metrics. Genomic variations such as single-nucleotide variants or CNVs can be effectively identified at the same time.

Interactive Visual Graph Mining and Learning

This article presents a platform for interactive graph mining and relational machine learning called GraphVis. The platform combines interactive visual representations with state-of-the-art graph mining and relational machine learning techniques to aid in revealing important insights quickly as well as learning an appropriate and highly predictive model for a particular task (e.g., classification, link prediction, discovering the roles of nodes, and finding influential nodes). Visual representations and interaction techniques and tools are developed for simple, fast, and intuitive real-time interactive exploration, mining, and modeling of graph data. In particular, we propose techniques for interactive relational learning (e.g., node/link classification), interactive link prediction and weighting, role discovery and community detection, higher-order network analysis (via graphlets, network motifs), among others. GraphVis also allows for the refinement and tuning of graph mining and relational learning methods for specific application domains and constraints via an end-to-end interactive visual analytic pipeline that learns, infers, and provides rapid interactive visualization with immediate feedback at each change/prediction in real-time. Other key aspects include interactive filtering, querying, ranking, manipulating, exporting, as well as tools for dynamic network analysis and visualization, interactive graph generators (including new block model approaches), and a variety of multi-level network analysis techniques.

PanACEA: a bioinformatics tool for the exploration and visualization of bacterial pan-chromosomes

BackgroundBacterial pan-genomes, comprised of conserved and variable genes across multiple sequenced bacterial genomes, allow for identification of genomic regions that are phylogenetically discriminating or functionally important. Pan-genomes consist of large amounts of data, which can restrict researchers ability to locate and analyze these regions. Multiple software packages are available to visualize pan-genomes, but currently their ability to address these concerns are limited by using only pre-computed data sets, prioritizing core over variable gene clusters, or by not accounting for pan-chromosome positioning in the viewer.ResultsWe introduce PanACEA (Pan-genome Atlas with Chromosome Explorer and Analyzer), which utilizes locally-computed interactive web-pages to view ordered pan-genome data. It consists of multi-tiered, hierarchical display pages that extend from pan-chromosomes to both core and variable regions to single genes. Regions and genes are functionally annotated to allow for rapid searching and visual identification of regions of interest with the option that user-supplied genomic phylogenies and metadata can be incorporated. PanACEA’s memory and time requirements are within the capacities of standard laptops. The capability of PanACEA as a research tool is demonstrated by highlighting a variable region important in differentiating strains of Enterobacter hormaechei.ConclusionsPanACEA can rapidly translate the results of pan-chromosome programs into an intuitive and interactive visual representation. It will empower researchers to visually explore and identify regions of the pan-chromosome that are most biologically interesting, and to obtain publication quality images of these regions.

Promoting Student Understanding through Virtual Manipulatives

The number of collections and the capabilities of virtual manipulatives (VMs) has grown considerably during the past twenty-plus years. A VM is “an interactive Web-based visual representation of a dynamic object that presents opportunities for constructing mathematical knowledge” (Moyer, Bolyard, and Spikell 2002). VMs come in a variety of platforms (e.g., java, html5, flash, cdf, etc.) and as apps for tablets (e.g., iPad and Android). Additionally, I consider preconstructed dynamic geometry objects (e.g., dynamic worksheets constructed and published using GeoGebra) as VMs due to the interactive nature of these dynamic objects.

Visual Analysis of Brain Networks Using Sparse Regression Models

Studies of the human brain network are becoming increasingly popular in the fields of neuroscience, computer science, and neurology. Despite this rapidly growing line of research, gaps remain on the intersection of data analytics, interactive visual representation, and the human intelligence—all needed to advance our understanding of human brain networks. This article tackles this challenge by exploring the design space of visual analytics. We propose an integrated framework to orchestrate computational models with comprehensive data visualizations on the human brain network. The framework targets two fundamental tasks: the visual exploration of multi-label brain networks and the visual comparison among brain networks across different subject groups. During the first task, we propose a novel interactive user interface to visualize sets of labeled brain networks; in our second task, we introduce sparse regression models to select discriminative features from the brain network to facilitate the comparison. Through user studies and quantitative experiments, both methods are shown to greatly improve the visual comparison performance. Finally, real-world case studies with domain experts demonstrate the utility and effectiveness of our framework to analyze reconstructions of human brain connectivity maps. The perceptually optimized visualization design and the feature selection model calibration are shown to be the key to our significant findings.

EvoluSon: Walking through an Interactive History of Music

Abstract The EvoluSon project proposes an immersive experience where the spectator explores an interactive visual and musical representation of the main periods of the history of Western music. The musical content is constituted of original musical compositions based on the theme of Bach’s Art of Fugue to illustrate the eight main musical eras from Antiquity to the contemporary epoch. The EvoluSon project contributes at the same time to the usage of VR for intangible culture representation and to interactive digital art that puts the user at the center of the experience. The EvoluSon project focuses on music through a presentation of the history of Western music, and uses virtual reality to valorize the different pieces through the ages. The user is immersed in a coherent visual and sound environment and can interact with both modalities. This project is the result of collaboration between a computer science research laboratory and a research laboratory on art and music. It was first presented to a public event on science and music organized by the computer science research laboratory.