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Adversarial XAI Methods in Cybersecurity

Aditya Kuppa, Nhien‐An Le‐Khac • IEEE Transactions on Information Forensics and Security (2021)

Machine Learning methods are playing a vital role in combating ever-evolving threats in the cybersecurity domain. Explanation methods that shed light on the decision process of black-box classifiers are one of the biggest drivers in the suc...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1109/tifs.2021.3117075

Deep hybrid neural net (DHN-Net) for minute-level day-ahead solar and wind power forecast in a decarbonized power system

Olusola Bamisile, Dongsheng Cai, Humphrey Adun, Chukwuebuka Ejiyi et al. • Energy Reports (2023)

The need to reduce global carbon emissions has led to a significant increase in clean energy globally. While renewable energy penetration into energy grids and power systems is increasing in many countries, the intermittency and stochastic ...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.egyr.2023.05.229

Prediction of cardiorrespiratory fitness by screen time in schoolchildrens

Amanda de Oliveira, Flávio Ricardo Guilherme, Stevan Ricardo dos Santos, Maria Teresa Martins Fávero et al. • Revista da Educação Física (2020)

The time spent in front of the screens, results in accumulations of sedentary behavior, which is related to health damages to adolescents, such as a low cardiorespiratory fitness. In this sense, this study aimed to predict cardiorespiratory...

DOAJ Open Access (DOAJ) DOI: 10.4025/jphyseduc.v31i1.3139

Identification of wild-caught phlebotomine sand flies from Crete and Cyprus using DNA barcoding

Emmanouil Dokianakis, Nikolaos Tsirigotakis, Vasiliki Christodoulou, Nikos Poulakakis et al. • Parasites & Vectors (2018)

Abstract Background Phlebotomine sand flies (Diptera: Psychodidae) are vectors of Leishmania spp., protozoan parasites responsible for a group of neglected diseases called leishmaniases. Two sand fly genera, Phlebotomus and Sergentomyia, co...

DOAJ Open Access (DOAJ) DOI: 10.1186/s13071-018-2676-0

Predicting Microbial Protein Synthesis in Cattle: Evaluation of Extant Equations and Steps Needed to Improve Accuracy and Precision of Future Equations

Michael L. Galyean, Luis O. Tedeschi • Animals (2024)

Predictions of microbial crude protein (MCP) synthesis for beef cattle generally rely on empirical regression equations, with intakes of energy and protein as key variables. Using a database from published literature, we developed new equat...

DOAJ Open Access (DOAJ) DOI: 10.3390/ani14192903

Seroprevalence of dengue, yellow fever, and related flaviviruses among the rural human population in Nguruman and Kerio Valley, Kenya

Mercy Hokah Kibathi, Mercy Hokah Kibathi, Edith Chepkorir, Sepha Nyatichi Mabeya et al. • Frontiers in Virology (2024)

BackgroundYellow fever virus (YFV) and dengue virus (DENV) are among the major re-emerging arboviruses that pose a significant threat to public health. Their associated burden and prevalence can be substantially underestimated due to insuff...

DOAJ Open Access (DOAJ) DOI: 10.3389/fviro.2024.1459021

Modelling the trends of the healthcare funding in the EU countries

Nadiya Dubrovina, Russell Gerrard, Stanislav Filip, Vira Dubrovina • Przegląd Europejski (2021)

Healthcare is one of the most important sectors of the public economy in the EU countries. An important task in the analysis and prediction of the values for healthcare funding is the development and application of quantitative models based...

DOAJ Open Access (DOAJ) DOI: 10.31338/1641-2478pe.1.21.4

Research on Personalized Recommendation Algorithm Integrating Cross-Grained Sentiment and Rating Interaction Features

Bing Li, Jiangtao Dong, Xile Wang, Yuqi Hou et al. • IEEE Access (2025)

To investigate the impact of cross-grained sentiments on user feature representation and address the issue of data sparsity, this paper proposes a Personalized Recommendation Algorithm Integrating Cross-Grained Sentiment and Rating Interact...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2025.3530431

Microstructure-based variational deep material networks for robust uncertainty quantification in materials digital twins

Andreas E. Robertson, Samuel B. Inman, Ashley T. Lenau, Ricardo A. Lebensohn et al. • npj Computational Materials (2026)

Abstract Aleatoric uncertainties – irremovable variability in microstructure morphology, constituent behavior, and processing conditions – pose a major challenge to developing uncertainty-robust digital twins. We introduce the Variational D...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41524-026-02054-5

Proposing New Data Whitening Method through Application of LSTM Method for Fault Condition Prediction in Bearing Data

Nasrin Rezaei, Andreas Rehkopf • Advances in Engineering and Intelligence Systems (2026)

In the context of big data, data preprocessing, especially data whitening, plays a pivotal role in improving the effectiveness of AI models. While traditional whitening methods suffer from severe computational bottlenecks, arising from the ...

DOAJ Open Access (DOAJ) DOI: 10.22034/aeis.2026.575941.1510

The JWST Resolved Stellar Populations Early Release Science Program. VIII. The Spatially Resolved Star Formation History of WLM

Roger E. Cohen, Kristen B. W. McQuinn, Alessandro Savino, Max J. B. Newman et al. • The Astrophysical Journal (2025)

Radial stellar population gradients within dwarf galaxies provide a promising avenue for disentangling the drivers of galaxy evolution, including environment. Within the Local Volume, radial stellar age gradient slopes correlate with intera...

DOAJ Open Access (DOAJ) DOI: 10.3847/1538-4357/adb61d

Classifying Characteristics of Opioid Use Disorder From Hospital Discharge Summaries Using Natural Language Processing

Melissa N. Poulsen, Philip J. Freda, Vanessa Troiani, Anahita Davoudi et al. • Frontiers in Public Health (2022)

BackgroundOpioid use disorder (OUD) is underdiagnosed in health system settings, limiting research on OUD using electronic health records (EHRs). Medical encounter notes can enrich structured EHR data with documented signs and symptoms of O...

DOAJ Open Access (DOAJ) DOI: 10.3389/fpubh.2022.850619

Bias and Fairness in Natural Language Processing

Kai-Wei Chang, Vinod M. Prabhakaran, Vicente Ordóñez • Conference on Empirical Methods in Natural Language Processing (EMNLP) (2019)

Recent advances in data-driven machine learning techniques (e.g., deep neural networks) have revolutionized many natural language processing applications. These approaches automatically learn how to make decisions based on the statistics an...

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Emergency department triaging using ChatGPT based on emergency severity index principles: a cross-sectional study

Cansu Colakca, Mehmet Ergın, Habibe Selmin Ozensoy, Alp Sener et al. • Scientific Reports (2024)

Abstract Erroneous and delayed triage in an increasingly crowded emergency department (ED). ChatGPT is an artificial intelligence model developed by OpenAI® and is being trained for use in natural language processing tasks. Our study aims t...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-024-73229-7

User‐Based Evaluation of Explainability Techniques for Misogyny Detection in Code‐Mixed Hindi–English

Sargam Yadav, Abhishek Kaushik, Kevin McDaid • Applied AI Letters (2026)

ABSTRACT Although artificial intelligence (AI) models have demonstrated great success and efficiency in automatically countering online hate speech and misogyny, they suffer from a lack of explainability and transparency. Explainable artifi...

DOAJ Open Access (DOAJ) DOI: 10.1002/ail2.70028

Developing Clinical Risk Prediction Models for Worsening Heart Failure Events and Death by Left Ventricular Ejection Fraction

Rishi V. Parikh, Alan S. Go, Ankeet S. Bhatt, Thida C. Tan et al. • Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease (2023)

Background There is a need to develop electronic health record–based predictive models for worsening heart failure (WHF) events across clinical settings and across the spectrum of left ventricular ejection fraction (LVEF). Methods and Resul...

DOAJ Open Access (DOAJ) DOI: 10.1161/JAHA.122.029736