The journal publishes high-quality articles in areas of fundamental research in pattern recognition, machine learning, and their applications in medicine, science, and engineering. The presented research should be analytically and experimentally well tested. In particular, it is essential to verify the quality of the proposed method correctly and compare it with state-of-the-art methods. It is also necessary to ensure that the methods are compared under precisely the same conditions. Thus, all articles should follow the replicable research principle, i.e., the ability for other researchers to repeat the experiment using the same methods and obtain similar results. This ensures the validity and reliability of the original findings. Therefore, providing access to the programming code (e.g., via GitHub) and the data used during the experiments is mandatory. The journal publishes original papers and, from time to time, invited survey articles, in all areas related to pattern analysis, including, but not limited to, the following suggested topics: Machine Learning Statistical Pattern Recognition Neural Networks and Deep Learning Unsupervised and Semi-supervised Machine Learning Computer Vision and Image Processing Signal Processing Data Mining and Knowledge Discovery Natural Language Processing and Text Mining Security and Authentication Systems Medical and Biological Applications Science, Engineering, and Economic Applications .