Analyzing Arabic Handwriting Style through Hand Kinematics.

Handwriting style is an important aspect affecting the quality of handwriting. Adhering to one style is crucial for languages that follow cursive orthography and possess multiple handwriting styles, such as Arabic. The majority of available studies analyze Arabic handwriting style from static documents, focusing only on pure styles.

Effective connectivity of working memory performance: a DCM study of MEG data.

Visual working memory (WM) engages several nodes of a large-scale network that includes frontal, parietal, and visual regions; however, little is understood about how these regions interact to support WM behavior. In particular, it is unclear whether network dynamics during WM maintenance primarily represent feedforward or feedback connections. This question has important implications for current debates about the relative roles of frontoparietal and visual regions in WM maintenance.

High BMI and the risk for incident type 1 Diabetes Mellitus: a systematic review and meta-analysis of aggregated cohort studies.

Background: There is uncertainty regarding the role of obesity in type 1 diabetes development. The aim of this systematic review and meta-analysis was to collect and synthesize evidence regarding BMI and the risk of developing type 1 diabetes.

Methods: A systematic review and meta-analysis were conducted to assess the association between BMI and incident type 1 diabetes.

Assessing handwriting task difficulty levels through kinematic features: a deep-learning approach.

Handwriting is a complex task that requires coordination of motor, sensory, cognitive, memory, and linguistic skills to master. The extent these processes are involved depends on the complexity of the handwriting task. Evaluating the difficulty of a handwriting task is a challenging problem since it relies on subjective judgment of experts.

Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach.

Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.