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Showing 20 of 127 academic articles

Quantum transfer learning for cross-domain cybersecurity threat detection and categorization

Shtwai Alsubai, Mohamed Ayari, Natalia Kryvinska, Ahmad Almadhor et al. • Scientific Reports (2026)

Abstract Cybersecurity challenges have become increasingly complex and widespread, and the risks associated with these problems are substantial, affecting thousands of individuals and organisations and being crucial to national security. As...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-026-40634-z

Machine learning in cybersecurity: A review

Anand Handa, Ashu Sharma, Sandeep K. Shukla • Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (2019)

Machine learning technology has become mainstream in a large number of domains, and cybersecurity applications of machine learning techniques are plenty. Examples include malware analysis, especially for zero‐day malware detection, threat a...

OPENALEX DOI: https://doi.org/10.1002/widm.1306

A neural network approach to cloud classification

J. Lee, Ronald C. Weger, S.K. Sengupta, Ronald M. Welch • IEEE Transactions on Geoscience and Remote Sensing (1990)

It is shown that, using high-spatial-resolution data, very high cloud classification accuracies can be obtained with a neural network approach. A texture-based neural network classifier using only single-channel visible Landsat MSS imagery ...

OPENALEX DOI: https://doi.org/10.1109/36.58972

Neural networks for classification: a survey

Gongbo Zhang • IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) (2000)

Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. This paper summarizes some of the most important developments in neural network classification research. Specifi...

OPENALEX DOI: https://doi.org/10.1109/5326.897072

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

Enhancing sentiment classification on small datasets through data augmentation and transfer learning

Mahmoud S. Mayaleh, Samer A. Mayaleh • Discover Artificial Intelligence (2026)

Abstract Small-scale sentiment classification often suffers from data scarcity, which limits the generalization ability of the models. This study evaluates and compares the effectiveness of three data augmentation strategies: Easy Data Augm...

DOAJ Open Access (DOAJ) DOI: 10.1007/s44163-025-00813-9

Neither Telling nor Describing. Reflective Passages and Perceived Reflectiveness 1700–1945

Anke Holler, Anna Mareike Weimer, Benjamin Gittel, Caroline Sporleder et al. • Journal of Computational Literary Studies (2024)

The paper analyses within-fiction reflections in 250 years of literary history. To this end, we formalised the concept of “reflective passage”, demonstrate how our annotation categories are deduced from literary theory and derive three subp...

DOAJ Open Access (DOAJ) DOI: 10.48694/jcls.3861

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

Machine Learning and Natural Language Processing in Mental Health: Systematic Review

Aziliz Le Glaz, Yannis Haralambous, Deok-Hee Kim-Dufor, Philippe Lenca et al. • Journal of Medical Internet Research (2020)

BACKGROUND: Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides ...

OPENALEX cc-by DOI: https://doi.org/10.2196/15708 PMC: 8132982

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Seyedmostafa Sheikhalishahi, Riccardo Miotto, Joel T. Dudley, Alberto Lavelli et al. • JMIR Medical Informatics (2019)

BACKGROUND: Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the seconda...

OPENALEX cc-by DOI: https://doi.org/10.2196/12239 PMC: 6528438

SAM-trace: a fuzzy logic approach in identifying interaction effects in the exacerbation of childhood malnutrition severity

S Shruthi, Priya Govindarajan, S. R. Shalini, Pavan John Antony • Discover Artificial Intelligence (2026)

Abstract Pediatric malnutrition remains a major global health challenge. Conventional WHO-based binary classification (Severe Acute Malnutrition (SAM) / Moderate Acute Malnutrition (MAM) relies solely on anthropometric thresholds. It fails ...

DOAJ Open Access (DOAJ) DOI: 10.1007/s44163-026-01450-6

Tracking discussions of complementary, alternative, and integrative medicine in the context of the COVID-19 pandemic: a month-by-month sentiment analysis of Twitter data

Jeremy Y. Ng, Wael Abdelkader, Cynthia Lokker • BMC Complementary Medicine and Therapies (2022)

Abstract Background Coronavirus disease 2019 (COVID-19) is a novel infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Despite the paucity of evidence, various complementary, alternative and integr...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12906-022-03586-1