This study was carried out to investigate whether the quantitative analysis of electroencephalogram (EEG) signals of infants with hypoxic-ischemic encephalopathy (HIE) can be used for early prediction of cerebral palsy (CP). We computed sample entropy (SampEn), permutation entropy (PEn), and spectral entropy (SpEn) measures to reflect the signals’ complexity and the graph-theoretic parameters derived from weighted phase-lag index (WPLI) to measure functional brain connectivity. Both feature sets were calculated in the noise-assisted multivariate empirical mode decomposition (NA-MEMD) domain to characterize the tempo-spectral integration of information and thus provide novel insight into the brain dynamics. Statistical analysis results showed a general abnormality in the EEG of individuals with CP at the alpha-band component. Particularly, complexity measures were decreased, and graph-theoretic parameters specified by the diameter feature were increased in infants with CP compared to those with normal neurology. The proposed set of features have also been evaluated using the random under-sampling boosting (RUSBoost) classifier, which was trained and tested on the feature vectors of a cohort of 26 infants - 6 who developed CP by the age of 24 months and 20 with normal neuromotor outcome. A good performance of 84.6% classification accuracy (ACC), 83% sensitivity (SNS), 85% specificity (SPC) and 0.87 area under curve (AUC) was obtained using the entropy features extracted from the alpha-band component. A close result of 80.8% ACC, 67% SNS, 85% SPC and 0.79 AUC was also achieved using the diameter feature calculated from the same frequency range. Therefore, it was concluded that the obtained brain functions’ characteristics successfully discriminate between the two groups of infants. These characteristics could be considered potential biomarkers of cerebral cellular damage and, therefore, could be employed in practical clinical applications for early CP prediction.

Cerebral palsy (CP) is a neurological disorder that affects movement and posture, and early diagnosis is crucial for timely interventions to improve long-term outcomes. Despite advancements in diagnostic techniques, accurate early detection remains challenging. This study aimed to investigate the effectiveness of using pose estimation techniques and hybrid deep learning (DL) approaches for predicting CP in infants. Three hybrid DL models were explored: a convolutional neural network (CNN) combined with long short-term memory (LSTM) networks (CNN-LSTM), a CNN with Gated Recurrent Units (CNN-GRU), and a Graph Convolutional Network (GCN) with GRU (GCN-GRU). To further enhance prediction accuracy, an ensemble method using majority voting was employed to aggregate the predictions from these three models to maximize their strengths while minimize individual weaknesses. Performance evaluation shows that the CNN-LSTM model achieves the highest accuracy, with 100% on training and 90% on testing datasets. The CNN-GRU model achieved a training accuracy of 96% and testing 90%, and the GCN-GRU model achieved a training accuracy of 90% and testing accuracy of 90%. These findings highlight the potential of integrating pose estimation with DL techniques to enhance early CP diagnosis, offering a promising tool for clinicians to improve predictive accuracy and enable earlier, more targeted interventions.

Legal judgment prediction is an emerging application of artificial intelligence in the legal domain, offering significant potential to enhance legal decision support systems. Such systems can improve judicial efficiency, reduce burdens on legal professionals, and assist in early-stage case assessment. This study focused on predicting whether a legal case would be Accepted or Rejected using only the Fact section of court rulings. A key challenge lay in processing long legal documents, which often exceeded the input length limitations of transformer-based models. To address this, we proposed a two-step methodology: first, each document was segmented into sentence-level inputs compatible with AraBERT—a pretrained Arabic transformer model—to generate sentence-level predictions; second, these predictions were aggregated to produce a document-level decision using several methods, including Mean, Max, Confidence-Weighted, and Positional aggregation. We evaluated the approach on a dataset of 19,822 real-world cases collected from the Saudi Arabian Commercial Court. Among all aggregation methods, the Confidence-Weighted method applied to the AraBERT-based classifier achieved the highest performance, with an overall accuracy of 85.62%. The results demonstrated that combining sentence-level modeling with effective aggregation methods provides a scalable and accurate solution for Arabic legal judgment prediction, enabling full-length document processing without truncation.

Background. Major depressive disorder (MDD) is a prevalent mental health condition with profound consequences on daily functioning and quality of life. Existing studies have focused on functional connectivity (FC), with limited investigation of effective connectivity (EC) and its frequency-specific manifestations within machine learning frameworks. New Method. This study proposes an automated MDD detection framework based on graph-theoretical features derived from two complementary connectivity measures: the phase slope index (PSI), representing directed EC, and the weighted phase lag index (WPLI), representing FC. Statistically selected features were extracted across five traditional frequency bands and evaluated using multiple machine learning classifiers. Results. PSI-based features showed a marginal advantage in accuracy and area under the curve (63.4% and 0.63) compared with WPLI-based features (62.8% and 0.60). These small differences cannot be interpreted as a meaningful performance difference between the two connectivity measures. The delta and beta bands, in both connectivity methods, consistently yielded the most discriminative results across both connectivity methods, with the theta band additionally discriminative for FC. Comparison with existing methods: Most existing studies rely on FC measures, whereas the proposed framework incorporates directed EC and systematically evaluates the discriminative contribution of individual frequency bands through graph-theoretical analysis. Conclusions. Directed EC and FC achieved comparable classification performance, but frequency-specific graph-theoretical analysis of EC could provide additional interpretability by capturing the directionality of disrupted brain communication in MDD, supporting the development of more neurophysiologically meaningful diagnostic tools.