Early screening is a key component of intensive intervention therapy and rehabilitation for children with autism spectrum disorder (ASD). Electroencephalogram (EEG) signals provide real-time, high-sensitivity monitoring of pathological activities in children with ASD. This study used a dataset of 52 samples with 19-channel sleep data and proposed a new approach to diagnosing ASD based on energy differences between the left and right hemispheres of the brain. The preprocessing stage included decimation, band-pass filtering to remove unwanted frequencies, artifact subspace reconstruction to eliminate artifacts, and amplitude normalization to preserve the relative relationships between the signal features, which were crucial for the subsequent analysis and classification. Five band-pass filters were applied to decomposing the EEG signals. For each decomposed band, the signals from each brain hemisphere were analyzed separately by calculating the peak sensor’s envelope and obtaining the mean envelope for each hemisphere, resulting in two mean signals (right and left). Features were extracted using a sliding window approach applied to the mean signals of each hemisphere, with varying overlap ratios (12.5% to 87.5%, in 12.5% steps). The maximum, mean, and minimum energy values were used individually as features. Three types of SVM kernels—linear (L), the radial basis function (RBF), and quadratic—were employed for classification. The proposed method achieved the highest accuracy, sensitivity, and F1-score of 91.7%, 91.4%, and 91.6%, respectively, in the Theta band using SVM-L with the maximum energy features and the maximum overlap ratio.

Sports image classification using neural networks and machine vision is a rapidly expanding field, with applications in highlight reel creation, performance analysis, and illegal play detection. We present an innovative structure for sports image classification using convolutional neural networks...

Journal of Applied Mathematics and Computational Mechanics, Prace Naukowe Instytutu Matematyki i Informatyki, Politechnika Częstochowska, Scientific Research of the Institute of Mathematics and Computer Science, Czestochowa University of Technology

The last and most important procedure during fruit or vegetable cultivation is harvesting. One of the basic challenges during grape growing is the use of agriculture 4.0 machines (including robots) during harvesting which is associated with the need for quick identification of berries or grape...