Aims and scope

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