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Big Data Analytics and Information Science for Business and Biomedical Applications: Third Edition

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 3872

Special Issue Editors

Prof. Dr. S. Ejaz Ahmed

E-Mail Website
Guest Editor
Department of Mathematics and Statistics, Brock University, St. Catharines, ON L2S 3A1, Canada
Interests: model selection; post-estimation and prediction; shrinkage and empirical Bayes; Bayesian data analysis; machine learning; business; information science; statistical genetics; image analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Farouk Nathoo

E-Mail Website
Guest Editor
Department of Mathematics and Statistics, University of Victoria, Victoria, BC V8W 3P4, Canada
Interests: Bayesian methods; statistical computing; spatial statistics; high-dimensional data; statistical modeling; neuroimaging statistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The world that we live in today is rather data-centric. We encounter data in every walk of life, and the information they contain can be used to improve society, business, health, and medicine. However, making sense of data and extracting meaningful information from it may not be an easy task. In recent years, the rapid growth in the size and scope of datasets in a host of disciplines has created the need for innovative statistical strategies for analyzing and visualizing such data.

An enormous trove of digital data has been produced by biomedicine researchers worldwide, including genetic variants genotyped or sequenced at genome-wide scales, gene expression measured under different experimental conditions, biomedical imaging data such as neuroimaging data, electronic medical records (EMRs) of patients, and many more.

The rise of ‘Big Data’ will not only broaden our understanding of complex human traits and diseases, but will also shed light on disease prevention, diagnosis, and treatment. Undoubtedly, comprehensive analysis of Big Data in genomics and neuroimaging calls for statistically rigorous methods. Various statistical methods have been developed to accommodate the features of genomic studies as well as studies examining the functions and structure of the brain. Simultaneously, statistical theories have also been developed.

Alongside biomedical applications, there has been a growing interest in the use of Big Data in business and financial applications. Financial time series analysis and prediction problems present many challenges for the development of statistical methodology and computational strategies for streaming data.

The analysis of Big Data in biomedical as well as business and financial research has garnered much attention from researchers worldwide. This Special Issue will be the third volume in a series that aims to provide a platform for comprehensive discussions on novel statistical methods developed for the analysis of Big Data in these areas. Both applied and theoretical research, emphasizing varying statistical problems with special emphasis on data analytics and statistical methodology, will be highlighted.

As with the previous two volumes, this Special Issue invites new and original research pertaining to statistical methods and applications in biomedical and business research, including the recent state-of-the-art developments.

Prof. Dr. S. Ejaz Ahmed
Dr. Farouk Nathoo
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. Entropy is an international peer-reviewed open access monthly 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.

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Published Papers (3 papers)

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Research

20 pages, 4184 KiB  
Article
Air Quality Prediction Based on Singular Spectrum Analysis and Artificial Neural Networks
by Javier Linkolk López-Gonzales, Rodrigo Salas, Daira Velandia and Paulo Canas Rodrigues
Entropy 2024, 26(12), 1062; https://doi.org/10.3390/e26121062 - 6 Dec 2024
Viewed by 452
Abstract
Singular spectrum analysis is a powerful nonparametric technique used to decompose the original time series into a set of components that can be interpreted as trend, seasonal, and noise. For their part, neural networks are a family of information-processing techniques capable of approximating [...] Read more.
Singular spectrum analysis is a powerful nonparametric technique used to decompose the original time series into a set of components that can be interpreted as trend, seasonal, and noise. For their part, neural networks are a family of information-processing techniques capable of approximating highly nonlinear functions. This study proposes to improve the precision in the prediction of air quality. For this purpose, a hybrid adaptation is considered. It is based on an integration of the singular spectrum analysis and the recurrent neural network long short-term memory; the SSA is applied to the original time series to split signal and noise, which are then predicted separately and added together to obtain the final forecasts. This hybrid method provided better performance when compared with other methods. Full article
(This article belongs to the Special Issue Big Data Analytics and Information Science for Business and Biomedical Applications: Third Edition)
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16 pages, 724 KiB  
Article
Fast Fusion Clustering via Double Random Projection
by Hongni Wang, Na Li, Yanqiu Zhou, Jingxin Yan, Bei Jiang, Linglong Kong and Xiaodong Yan
Entropy 2024, 26(5), 376; https://doi.org/10.3390/e26050376 - 28 Apr 2024
Viewed by 1415
Abstract
In unsupervised learning, clustering is a common starting point for data processing. The convex or concave fusion clustering method is a novel approach that is more stable and accurate than traditional methods such as k-means and hierarchical clustering. However, the optimization algorithm [...] Read more.
In unsupervised learning, clustering is a common starting point for data processing. The convex or concave fusion clustering method is a novel approach that is more stable and accurate than traditional methods such as k-means and hierarchical clustering. However, the optimization algorithm used with this method can be slowed down significantly by the complexity of the fusion penalty, which increases the computational burden. This paper introduces a random projection ADMM algorithm based on the Bernoulli distribution and develops a double random projection ADMM method for high-dimensional fusion clustering. These new approaches significantly outperform the classical ADMM algorithm due to their ability to significantly increase computational speed by reducing complexity and improving clustering accuracy by using multiple random projections under a new evaluation criterion. We also demonstrate the convergence of our new algorithm and test its performance on both simulated and real data examples. Full article
(This article belongs to the Special Issue Big Data Analytics and Information Science for Business and Biomedical Applications: Third Edition)
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19 pages, 4301 KiB  
Article
Prediction Consistency Regularization for Learning with Noise Labels Based on Contrastive Clustering
by Xinkai Sun, Sanguo Zhang and Shuangge Ma
Entropy 2024, 26(4), 308; https://doi.org/10.3390/e26040308 - 30 Mar 2024
Cited by 1 | Viewed by 1445
Abstract
In the classification task, label noise has a significant impact on models’ performance, primarily manifested in the disruption of prediction consistency, thereby reducing the classification accuracy. This work introduces a novel prediction consistency regularization that mitigates the impact of label noise on neural [...] Read more.
In the classification task, label noise has a significant impact on models’ performance, primarily manifested in the disruption of prediction consistency, thereby reducing the classification accuracy. This work introduces a novel prediction consistency regularization that mitigates the impact of label noise on neural networks by imposing constraints on the prediction consistency of similar samples. However, determining which samples should be similar is a primary challenge. We formalize the similar sample identification as a clustering problem and employ twin contrastive clustering (TCC) to address this issue. To ensure similarity between samples within each cluster, we enhance TCC by adjusting clustering prior to distribution using label information. Based on the adjusted TCC’s clustering results, we first construct the prototype for each cluster and then formulate a prototype-based regularization term to enhance prediction consistency for the prototype within each cluster and counteract the adverse effects of label noise. We conducted comprehensive experiments using benchmark datasets to evaluate the effectiveness of our method under various scenarios with different noise rates. The results explicitly demonstrate the enhancement in classification accuracy. Subsequent analytical experiments confirm that the proposed regularization term effectively mitigates noise and that the adjusted TCC enhances the quality of similar sample recognition. Full article
(This article belongs to the Special Issue Big Data Analytics and Information Science for Business and Biomedical Applications: Third Edition)
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