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Machine Learning and Knowledge Extraction is a peer-reviewed journal published by MDPI, covering Explainable Artificial Intelligence (XAI), Topic Modeling, Adversarial Robustness in Machine Learning. Over its lifetime it has published 819 papers, which have received 13,990 citations.
| Publisher | MDPI |
|---|---|
| Reference style | Numbered (MDPI) Numbered — [1], [2] in the text 1. Smith, A.; Jones, B.; Lee, C. A Representative Article Title. Machine Learning and Knowledge Extraction 2023, 12, 45–58.
Formats any DOI in Machine Learning and Knowledge Extraction style. No sign-up. |
| Abbreviation (ISO-4) | Mach. Learn. Knowl. Extr. |
| Publishes research in | Explainable Artificial Intelligence (XAI) Topic Modeling Adversarial Robustness in Machine Learning Anomaly Detection Techniques and Applications Advanced Neural Network Applications |
| ISSN | 2504-4990 |
| Citation impact (2-yr) | 5.16 |
| h-index | 51 |
| i10-index | 229 |
| Article processing charge | $1,515 |
| Open access | Yes — indexed in DOAJ |
| Top institutions publishing here | Tampere University |
| Journal website | www.mdpi.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. We present a comprehensive analysis of YOLO’s evolution, examining the innovations and contributions in each iteration from the original YOLO up to YOLOv8, YOLO-NAS, and YOLO with transformers. We start by describing the standard metrics and postprocessing; then,…
Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems that cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social co-operation of birds and…
Deep Learning is a state-of-the-art technique to make inference on extensive or complex data. As a black box model due to their multilayer nonlinear structure, Deep Neural Networks are often criticized as being non-transparent and their predictions not traceable by humans. Furthermore, the models learn from artificially generated datasets, which often do not reflect reality.…
Local Interpretable Model-Agnostic Explanations (LIME) is a popular technique used to increase the interpretability and explainability of black box Machine Learning (ML) algorithms. LIME typically creates an explanation for a single prediction by any ML model by learning a simpler interpretable model (e.g., linear classifier) around the prediction through generating simulated data around the instance…
Document-level sentiment analysis is a challenging task given the large size of the text, which leads to an abundance of words and opinions, at times contradictory, in the same document. This analysis is particularly useful in analyzing press articles and blog posts about a particular product or company, and it requires a high concentration, especially…
