Explore MAKE’s Hot Papers From 2022

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IMPACT FACTOR 3.9 Explore MAKE’s Hot Papers from 2022 Machine Learning Based Restaurant Sales Forecasting Austin Schmidt et al.   Fairness and Explanation in AI-Informed Decision Making Alessa Angerschmid et al. Developing a Novel Fair-Loan Classifier through a Multi-Sensitive Debiasing Pipeline: DualFair Arashdeep Singh et al.   Machine Learning in Disaster Management: Recent Developments in Methods and Applications Vasileios Linardos et al. A Transfer Learning Evaluation of Deep Neural Networks for Image Classification Nermeen Abou Baker et al.   Hierarchical Reinforcement Learning: A Survey and Open Research Challenges Matthias Hutsebaut-Buysse et al. An Attention-Based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series Tareq Tayeh et al.   Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset Scarlet Stadtler et al. Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication Nyle Siddiqui et al.   Robust Reinforcement Learning: A Review of Foundations and Recent Advances Janosch Moos et al. Call for Papers         Advances in Explainable Artificial Intelligence (XAI): 2nd Edition edited by Luca Longo submission deadline 29 February 2024   Transparency of Deep Neural Networks and Complex Tree Ensembles edited by Yoichi Hayashi submission deadline 31 March 2024   Why Submit to MAKE?   ⮚ Open Access Unlimited and free access for readers   ⮚ No Copyright Constraints Retain copyright of your work and free use of your article   ⮚ Thorough Peer-Review   ⮚ Rapid Publication Manuscripts are peer-reviewed and a first decision is provided to authors approximately 19.2 days after submission; acceptance to publication is undertaken in 3.7 days (median values for papers published in this journal in the first half of 2023)   ⮚ Coverage by Leading Indexing Services Scopus, ESCI (Web of Science), dblp and other databases   ⮚ No Space Constraints, No Extra Space or Color Charges No restriction on the maximum length of the papers, number of figures or colors   ⮚ Recognition of Reviewers Reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done   ⮚ Discounts on Article Processing Charges (APC) If you belong to an institute that participates with the MDPI Institutional Open Access Program (IOAP) Journal Information Machine Learning and Knowledge Extraction (ISSN 2504-4990) provides an advanced forum for studies related to all areas of machine learning and knowledge extraction. To submit to the journal click here. Unsubscribe Manage your subscriptions MDPI www.mdpi.com St. Alban-Anlage 66, 4052 Basel, Switzerland Tel. +41 61 683 77 34 Disclaimer: MDPI recognizes the importance of data privacy and protection. We treat personal data in line with the General Data Protection Regulation (GDPR) and with what the community expects of us. The information contained in this message is confidential and intended solely for the use of the individual or entity to whom they are addressed. If you have received this message in error, please notify me and delete this message from your system. You may not copy this message in its entirety or in part, or disclose its contents to anyone.