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Data Science and Big Data in Biology, Physical Science and...
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Data Science and Big Data in Biology, Physical Science and Engineering—3rd Edition

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Information and Communication Technologies".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 702

Special Issue Editor

Dr. Mohammed Mahmoud

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Guest Editor
Department of Computing, Informatics and Data Science, College of Science and Engineering, St. Cloud State University, St. Cloud, MN, USA
Interests: data science; big data; machine learning; deep learning; artificial intelligence (AI); cybersecurity; software engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Big Data analysis is one of the most contemporary areas of development and research in today's world. Tremendous amounts of data are generated daily from digital technologies and modern information systems, including cloud computing and Internet of Things (IoT) devices. Analysis of these enormous amounts of data has become a crucial need and requires a lot of effort to extract valuable knowledge for decision-making, which in turn will help in both academia and industry.

Big Data and Data Science have appeared due to the significant need for generating, storing, organising, and processing immense amounts of data. Data Scientists strive to utilize Artificial Intelligence (AI) and Machine Learning (ML) approaches and models, enabling computers to detect and identify the data's meaning and detect patterns more quickly, efficiently, and reliably than humans.

The goal of this Special Issue is to explore and discuss various principles, tools, and models in the context of Data Science, as well as diverse and varied concepts and techniques in Big Data, including those from Biology, Chemistry, Biomedical Engineering, Physics, Mathematics, and other areas that utilize Big Data.

Dr. Mohammed Mahmoud
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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. Technologies 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 1800 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

  • data science 
  • big data 
  • machine learning 
  • deep learning 
  • artificial intelligence

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Published Papers (1 paper)

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Research

41 pages, 80556 KB  
Article
Why ROC-AUC Is Misleading for Highly Imbalanced Data: In-Depth Evaluation of MCC, F2-Score, H-Measure, and AUC-Based Metrics Across Diverse Classifiers
by Mehdi Imani, Majid Joudaki, Ayoub Bagheri and Hamid R. Arabnia
Technologies 2026, 14(1), 54; https://doi.org/10.3390/technologies14010054 - 10 Jan 2026
Viewed by 467
Abstract
This study re-evaluates ROC-AUC for binary classification under severe class imbalance (<3% positives). Despite its widespread use, ROC-AUC can mask operationally salient differences among classifiers when the costs of false positives and false negatives are asymmetric. Using three benchmarks, credit-card fraud detection (0.17%), [...] Read more.
This study re-evaluates ROC-AUC for binary classification under severe class imbalance (<3% positives). Despite its widespread use, ROC-AUC can mask operationally salient differences among classifiers when the costs of false positives and false negatives are asymmetric. Using three benchmarks, credit-card fraud detection (0.17%), yeast protein localization (1.35%), and ozone level detection (2.9%), we compare ROC-AUC with Matthews Correlation Coefficient, F2-score, H-measure, and PR-AUC. Our empirical analyses span 20 classifier–sampler configurations per dataset, combined with four classifiers (Logistic Regression, Random Forest, XGBoost, and CatBoost) and four oversampling methods plus a no-resampling baseline (no resampling, SMOTE, Borderline-SMOTE, SVM-SMOTE, ADASYN). ROC-AUC exhibits pronounced ceiling effects, yielding high scores even for underperforming models. In contrast, MCC and F2 align more closely with deployment-relevant costs and achieve the highest Kendall’s τ rank concordance across datasets; PR-AUC provides threshold-independent ranking, and H-measure integrates cost sensitivity. We quantify uncertainty and differences using stratified bootstrap confidence intervals, DeLong’s test for ROC-AUC, and Friedman–Nemenyi critical-difference diagrams, which collectively underscore the limited discriminative value of ROC-AUC in rare-event settings. The findings recommend a shift to a multi-metric evaluation framework: ROC-AUC should not be used as the primary metric in ultra-imbalanced settings; instead, MCC and F2 are recommended as primary indicators, supplemented by PR-AUC and H-measure where ranking granularity and principled cost integration are required. This evidence encourages researchers and practitioners to move beyond sole reliance on ROC-AUC when evaluating classifiers in highly imbalanced data. Full article
(This article belongs to the Special Issue Data Science and Big Data in Biology, Physical Science and Engineering—3rd Edition)
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