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Explainable machine learning in materials science

Xiaoting Zhong, Brian J. Gallagher, Shusen Liu, Bhavya Kailkhura et al. • npj Computational Materials (2022)

Abstract Machine learning models are increasingly used in materials studies because of their exceptional accuracy. However, the most accurate machine learning models are usually difficult to explain. Remedies to this problem lie in explaina...

OPENALEX cc-by DOI: https://doi.org/10.1038/s41524-022-00884-7

Argumentation approaches for explanaible AI in medical informatics

Luciano Caroprese, Eugenio Vocaturo, Ester Zumpano • Intelligent Systems with Applications (2022)

Artificial Intelligence algorithms are powerful in performing accurate predictions, but they are often considered black boxes as they do not provide any explanation about how outputs are derived from inputs or why a decision is taken. There...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.iswa.2022.200109

Coalition of explainable artificial intelligence and quantum computing in precision medicine

Soumyadeep Ray, Pronaya Bhattacharya, Ebrahim A. Mattar, Abhishek Mukhopadhyay • Computational and Structural Biotechnology Journal (2025)

This survey examines the convergence of Explainable Artificial Intelligence (XAI) and Quantum Computing (QC) toward precision medicine. We review developments from 2018 to 2025, summarizing quantum algorithms, quantum-machine-learning model...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.csbj.2025.11.031

An Explainable AI-Driven Zero-Day Attack Detection Framework for Securing Edge Devices in Smart Cities

Santhiyakumari N., Sabarinathan S., Veerakumar S., Chandraman M. et al. • Journal of Cognitive Human-Computer Interaction (2025)

The rapid proliferation of edge computing in smart cities has enhanced real-time data processing capabilities, but it has also exposed critical vulnerabilities to sophisticated cyber threats such as zero-day attacks. Traditional signature-b...

DOAJ Open Access (DOAJ) DOI: 10.54216/JCHCI.100201

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

A Survey on Explainable Artificial Intelligence for Cybersecurity

Gaith Rjoub, Jamal Bentahar, Omar Abdel Wahab, Rabeb Mizouni et al. • IEEE Transactions on Network and Service Management (2023)

The “black-box” nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create mac...

OPENALEX Open Access DOI: https://doi.org/10.1109/tnsm.2023.3282740

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

Evaluation of Similarity of Image Explanations Produced by SHAP, LIME and Grad-CAM

Vladyslav Yavtukhovskyi, Violeta Tretynyk • Кібернетика та комп'ютерні технології (2025)

Introduction. Convolutional neural networks (CNNs) are a subtype of neural networks developed specifically to work with images [1]. They have achieved great success both in research and in practical applications in recent years, however, on...

DOAJ Open Access (DOAJ) DOI: 10.34229/2707-451X.25.2.6

Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions

Shahin Atakishiyev, Mohammad Salameh, Hengshuai Yao, Randy Goebel • IEEE Access (2024)

Autonomous driving has achieved significant milestones in research and development over the last two decades. There is increasing interest in the field as the deployment of autonomous vehicles (AVs) promises safer and more ecologically frie...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2024.3431437

Explainable artificial intelligence: A survey

Filip Karlo Došilović, Mario Brčić, Nikica Hlupić • International Convention on Information and Communication Technology, Electronics and Microelectronics (2018)

In the last decade, with availability of large datasets and more computing power, machine learning systems have achieved (super)human performance in a wide variety of tasks. Examples of this rapid development can be seen in image recognitio...

OPENALEX DOI: https://doi.org/10.23919/mipro.2018.8400040

Explainable and perturbation-resilient model for cyber-threat detection in industrial control systems Networks

Urslla Uchechi Izuazu, Cosmas Ifeanyi Nwakanma, Dong-Seong Kim, Jae Min Lee • Discover Internet of Things (2025)

Abstract Deep learning-based intrusion detection systems (DL-IDS) have proven effective in detecting cyber threats. However, their vulnerability to adversarial attacks and environmental noise, particularly in industrial settings, limits pra...

DOAJ Open Access (DOAJ) DOI: 10.1007/s43926-025-00100-0