Special Issues

Machine Learning and Knowledge Extraction runs special issues to create collections of papers on specific topics. The aim is to build a community of authors and readers to discuss the latest research and develop new ideas and research directions. Special Issues are led by Guest Editors who are experts in the subject and oversee the editorial process for papers. Papers published in a Special Issue will be collected together on a dedicated page of the journal website. For any inquiries related to a Special Issue, please contact the Editorial Office.

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Future of Artificial Intelligence in Smart Cities submission deadline 30 Oct 2023 | Viewed by 352 | Submission Open
Keywords: intelligent machines; artificial intelligence; Internet of Things; smart cities; smart healthcare; smart grids; condition monitoring; energy management; smart resource management; smart wastewater treatment; cybersecurity
Fairness and Explanation for Trustworthy AI submission deadline 15 Dec 2023 | 2 articles | Viewed by 11122 | Submission Open
Keywords: role of fairness in trustworthy AI systems; role of explanation in trustworthy AI systems; role of both fairness and explanation in trustworthy AI systems; human’ s judgement of fairness and explanations in AI systems; innovative methods and technologies in presenting fairness and explanations for boosting trustworthiness of AI systems; novel applications of user experience design and evaluation methods for trustworthy AI with fairness and explanations; social; ethical and legal aspects of fairness in AI; fostering trustworthy AI
(This special issue belongs to the Section Privacy)
Selected Papers from CD-MAKE 2022–2023 and ARES 2022 submission deadline 15 Dec 2023 | Viewed by 19 | Submission Open
Keywords: data— data fusion; preprocessing; mapping; knowledge representation; environments; etc.; learning— algorithms; contextual adaptation; causal reasoning; transfer learning; etc.; visualization— intelligent interfaces; human– AI interaction; dialogue systems; explanation interfaces; etc.; privacy— data protection; safety; security; reliability; verifiability; trust; ethics and social issues; etc.; network— graphical models; graph-based machine learning; Bayesian inference; etc.; topology— geometrical machine learning; topological and manifold learning; etc.; entropy— time and machine learning; entropy-based learning; etc.
Advances in Explainable Artificial Intelligence (XAI): 2nd Edition
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submission deadline 29 Feb 2024 | Viewed by 45 | Submission Open
Keywords: explainable artificial intelligence (XAI); neuro-symbolic reasoning for XAI; interpretable deep learning; argument-based models of explanations; graph neural networks for explainability; machine learning and knowledge-graphs; human-centric explainable AI; interpretation of black-box models; human-understandable machine learning; counterfactual explanations for machine learning; natural language processing in XAI; quantitative/qualitative evaluation metrics for XAI; ante and post hoc XAI methods; rule-based systems for XAI; fuzzy systems and explainability; human-centered learning and explanations; model-dependent and model-agnostic explainability; case-based explanations for AI systems; interactive machine learning and explanations
(This special issue belongs to the Section Learning)
Transparency of Deep Neural Networks and Complex Tree Ensembles
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submission deadline 31 Mar 2024 | Viewed by 471 | Submission Open
Keywords: big data analytics using DL and CTEs; interpretability and explanation of DL and CTEs; rule extraction techniques for a new era of XAI; simplification of DNNs and CTEs into simple decision trees (e.g.; single tree); beyond AI finance for credit scoring; credit card fraud detection; peer-to-peer (P2P) social lending; business failure and bankruptcy; beyond the accuracy– interpretability dilemma in DL and CTEs; towards a new era for medicine and bioinformatics
Sustainable Applications for Machine Learning submission deadline 2 Jul 2024 | Viewed by 79 | Submission Open
Keywords: machine learning; deep learning; artificial neural networks; reinforcement learning; sustainable computing; big data analytics; optimization; data mining
(This special issue belongs to the Section Learning)
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