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Mathematics
Special Issues
Data-Driven Visual Analytics
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Data-Driven Visual Analytics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 1 July 2024 | Viewed by 704

Special Issue Editors

Dr. Mohamed A. Sharaf

E-Mail Website
Guest Editor
Department of Computer Science, United Arab Emirates University, Al Ain, United Arab Emirates
Interests: the general area of data science, with a special emphasis on large-scale big data analytics, interactive human-in-the-loop data exploration, and scalable data visualization
Dr. Kostas Stefanidis

E-Mail Website
Guest Editor
Faculty of Information Technology and Communication Sciences (ITC), Tampere University, Kalevantie 4, 33100 Tampere, Finland
Interests: big data management; personalization; recommender systems; entity resolution; data exploration; data analytics; responsible data management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Visual data analysis is ubiquitous in many applications, including the Internet of Things, financial analysis, and health monitoring. Data visualization is an essential step in the data science pipeline, whereby analysts examine datasets up-close to extract valuable insights. However, in the era of Big Data, most analyzed datasets are typically large, dynamic, noisy, and heterogeneous. Thus, it is necessary to investigate advanced visual data analytic techniques that address those challenges and to enable efficient and effective visual analytics.

Dr. Mohamed A. Sharaf
Dr. Kostas Stefanidis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • visual data exploration
  • data analytics
  • human-in-the-loop data processing
  • scalable and interactive visualization
  • visualization recommendation
  • visual representation and summarization
  • storytelling with data
  • explanations via visualization
  • guidelines for data-driven visual analytics
  • visualization for stakeholders
  • benchmarks for data visualization
  • case and user studies
  • systems and tools
  • mathematical optimization
  • data compression and summarization
  • dimensionality reduction
  • feature selection
  • scientific visualization
  • topological methods in data visualization
  • visualization tools for simulation and modeling

Published Papers (1 paper)

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Research

32 pages, 5699 KiB  
Article
Explainable Deep Learning: A Visual Analytics Approach with Transition Matrices
by Pavlo Radiuk, Olexander Barmak, Eduard Manziuk and Iurii Krak
Mathematics 2024, 12(7), 1024; https://doi.org/10.3390/math12071024 - 29 Mar 2024
Viewed by 513
Abstract
The non-transparency of artificial intelligence (AI) systems, particularly in deep learning (DL), poses significant challenges to their comprehensibility and trustworthiness. This study aims to enhance the explainability of DL models through visual analytics (VA) and human-in-the-loop (HITL) principles, making these systems more transparent [...] Read more.
The non-transparency of artificial intelligence (AI) systems, particularly in deep learning (DL), poses significant challenges to their comprehensibility and trustworthiness. This study aims to enhance the explainability of DL models through visual analytics (VA) and human-in-the-loop (HITL) principles, making these systems more transparent and understandable to end users. In this work, we propose a novel approach that utilizes a transition matrix to interpret results from DL models through more comprehensible machine learning (ML) models. The methodology involves constructing a transition matrix between the feature spaces of DL and ML models as formal and mental models, respectively, improving the explainability for classification tasks. We validated our approach with computational experiments on the MNIST, FNC-1, and Iris datasets using a qualitative and quantitative comparison criterion, that is, how different the results obtained by our approach are from the ground truth of the training and testing samples. The proposed approach significantly enhanced model clarity and understanding in the MNIST dataset, with SSIM and PSNR values of 0.697 and 17.94, respectively, showcasing high-fidelity reconstructions. Moreover, achieving an F1m score of 77.76% and a weighted accuracy of 89.38%, our approach proved its effectiveness in stance detection with the FNC-1 dataset, complemented by its ability to explain key textual nuances. For the Iris dataset, the separating hyperplane constructed based on the proposed approach allowed for enhancing classification accuracy. Overall, using VA, HITL principles, and a transition matrix, our approach significantly improves the explainability of DL models without compromising their performance, marking a step forward in developing more transparent and trustworthy AI systems. Full article
(This article belongs to the Special Issue Data-Driven Visual Analytics)
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