A Comprehensive State-of-the-Art Survey on Data Visualization Tools: Research Developments, Challenges and Future Domain Specific Visualization Framework

Shakeel, Hafiz Muhammad; Iram, Shamaila; Al-Aqrabi, Hussain; Alsboui, Tariq; Hill, Richard

doi:10.1109/access.2022.3205115

Cited by 14 publications

(8 citation statements)

References 135 publications

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“…In biomedical data analysis, EoAE is a promising new technique that can improve anomaly detection by combining the strengths of multiple models. However, a critical challenge lies in the domain specificity of the data [116]. Just as a physician trained in one country may at first struggle with the details of healthcare procedures in another, EoAE models trained on data from a specific hospital or region may not perform as well when exposed to data from a different source.…”

Section: F Domain Adoptationmentioning

confidence: 99%

Ensemble of Autoencoders for Anomaly Detection in Biomedical Data: A Narrative Review

Nawaz,

Khan,

Ahmad

2024

IEEE Access

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In the context of biomedical data, an anomaly could refer to a rare or new type of disease, an aberration from normal behavior, or an unexpected observation requiring immediate attention. The detection of anomalies in biomedical data has a direct impact on the health and safety of individuals. However, anomalous events are rare, diverse, and infrequent. Often, the collection of anomalous data may involve significant loss of human life and healthcare costs. Therefore, traditional supervised machine and deep learning algorithms may not be directly applicable to such problems. Biomedical data are often collected in the form of images, electronic health records, and time series. Typically, an autoencoder (AE) or its corresponding variant is trained on normal data, and an anomaly is identified as a significant deviation from these data based on reconstruction error or other metrics. An Ensemble of AEs (EoAEs) can serve as a robust approach for anomaly detection in biomedical data by combining diverse and accurate views of normal data. An EoAE can provide superior detection to a single encoder; however, its performance can depend on various factors, including the diversity of the created data, the accuracy of the individual AEs, and the combination of their outcomes. Herein, we perform a comprehensive narrative literature review on the use of EoAEs when using different types of biomedical data. Such an ensemble provides a promising approach for anomaly detection in biomedical data, offering the potential for performance improvement by leveraging the strengths of diverse AEs. However, several challenges remain, such as the need for data specification and determination of the optimal number of AEs in the ensemble. By addressing these challenges, researchers can enhance the effectiveness of EoAEs for anomaly detection in various types of biomedical data. Furthermore, through this review, we highlight the significance of evaluating and comparing the performance of an EoAE with that of single AEs by establishing agreed-upon evaluation metrics and investigating normalization techniques for anomaly scores. We conclude the review by presenting challenges and open questions in the field with for future research.

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Section: F Domain Adoptationmentioning

confidence: 99%

Ensemble of Autoencoders for Anomaly Detection in Biomedical Data: A Narrative Review

Nawaz,

Khan,

Ahmad

2024

IEEE Access

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“…This undirected, unweighted graph is built by converting all author-coauthor relationships into an edge list, resulting in 10,270 nodes V (authors) and 26,963 edges E (relation). We visualize the author-coauthor graph to represent the relationships among authors since data visualization (in our case graph visualization) is an essential tool used excessively in different domains, allowing researchers to explore large and complex datasets more effectively and gain useful insights from the data (Aljehane et al, 2015;Shakeel et al, 2022). However, to achieve the best visualization, we applied a filtering procedure by removing all authors that have no connection with other authors or the authors with connections below a specific threshold.…”

Section: Graph-based Featuresmentioning

confidence: 99%

FutureCite: Predicting Research Articles Impact using Machine Learning and Text and Graph Mining Techniques

Thafar,

Alsulami,

Albarade

2024

Preprint

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The growth rate in academic and scientific publications increased very fast. Researchers must choose a good representative and significant literature in publications, which has become challenging worldwide. Usually, the paper citation number indicates this paper's potential influence and importance. However, this standard metric of citation numbers cannot be used as a measurement to judge the popularity and significance of the recently published papers. To address this challenge, this paper presents an effective prediction method called FutureCite to predict the future citation level of research articles. FutureCite combines machine learning with text and graph mining techniques, leveraging their abilities in classification, datasets in-depth analysis, and feature extraction. FutureCite aims to predict future citation levels of research articles applying a multilabel classification approach. FutureCite can extract significant semantic features and capture the interconnection relationships found in scientific articles during feature extraction using textual content, citation networks, and metadata as feature resources. Our goal is to contribute to the advancement of substantial effective approaches impacting the citation counts in scientific publications by enhancing the precision of future citations. We conducted several experiments using a comprehensive publication dataset to evaluate our method and determine the impact of using a variety of machine learning algorithms. FutureCite demonstrated its robustness and efficiency and showed promising results based on different evaluation metrics. Using the FutureCite model has significant implications in improving the researchers' ability to determine targeted literature for their research and better understand the potential impact of research publications.

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“…One Aigner, conducted a comparative analysis of metadata visualization tools (Shakeel et al, 2022). Series of images, Xmdv Tool, Spotfire, and ILOG Exploration were evaluated in comparison, basic user activities including one or multiple parameters and innovative types of work especially in complex dilemmas were trailed, the tools' functionalities were categorized, and a sequence asserting the advantages offered by every tool was offered up.…”

Section: A Summary Of Previous Surveys and Workmentioning

confidence: 99%

A Survey of Visualization Techniques and Tools for Environmental Data

Patel

2023

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In modern time, visualizing a collection of discrete values and data is frequently required in scientific investigation. Predicting the potential fluctuation of a parameter such as heat flux, stress, or weather patterns is difficult in the case of environmental data. Numerous new visualization techniques and technologies for analyzing large datasets are common in the field of software visualization. However, selecting the right tool to meet requirements of the users for visualizing large datasets remains difficult. This paper offers the results of a survey conducted on the techniques and tools currently used for environmental data visualization by past researchers and authors. It provides an overview of several popular visualization tools and a brief assessment of their capabilities to support research involving large datasets of environmental data. A classification system of visualization techniques is also tried to present, determined by the number of different factors that can be visualized. Emerging innovations in the development of related user interfaces, as well as a variety of new visualization tools and their appropriateness, are also addressed. Ultimately, several future research directions in data visualization are proposed.

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A Comprehensive State-of-the-Art Survey on Data Visualization Tools: Research Developments, Challenges and Future Domain Specific Visualization Framework

Cited by 14 publications

References 135 publications

Ensemble of Autoencoders for Anomaly Detection in Biomedical Data: A Narrative Review

Ensemble of Autoencoders for Anomaly Detection in Biomedical Data: A Narrative Review

FutureCite: Predicting Research Articles Impact using Machine Learning and Text and Graph Mining Techniques

A Survey of Visualization Techniques and Tools for Environmental Data

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