Cerebral palsy is a prevalent neurodevelopmental syndrome that disrupts motor development in children, making early detection vital for effective intervention. Traditional clinical assessments rely on subjective observations, often missing minor motor abnormalities until they become severe, typically after 12 months of age. This article presents a novel deep learning model, TransCP-Net (Transformer-based Cerebral Palsy Network), designed for early detection of infant cerebral palsy through spatiotemporal pose representation learning. The architecture employs hierarchical spatial and temporal attention to analyze complex motion patterns in video sequences, integrating multi-modal data for improved accuracy. TransCP-Net incorporates specialized preprocessing, including temporal smoothing and trajectory encoding, to enhance feature learning. Tests on 1370 infant movement videos yielded impressive results: 94.7% sensitivity, 92.3% specificity, and an AUC-ROC of 0.968, outperforming ten state-of-the-art methods. Notably, it achieved a sensitivity of 96.3% within the critical 9–15 weeks range of fidgety movements, enabling timely interventions. Attention visualization highlights key areas such as the hips and shoulders, reinforcing clinical relevance. TransCP-Net demonstrates effectiveness across diverse clinical settings, serving as a viable, non-invasive tool for early cerebral palsy detection.

Sustainable energy systems will entail a change in the carbon intensity projections, which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions. The present article outlines the TransCarbonNet, a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory (Bi-LSTM) network to forecast the carbon intensity of the grid several days. The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data; hence, it is able to give suitable forecasts over a period of seven days. TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections, which means the Bi-LSTM element calculates sequential dependencies in both directions. Massive tests on two actual data sets indicate much improved results in comparison with the existing results, with mean relative errors of 15.3 percent and 12.7 percent, respectively. The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations, and informed decisions on the management of carbon sustainability. The effectiveness of the proposed solution has been validated in numerous cases of operations, and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.

Dementia is a neurological disorder that affects the brain and its functioning, and women experience its effects more than men do. Preventive care often requires non-invasive and rapid tests, yet conventional diagnostic techniques are time-consuming and invasive. One of the most effective ways to diagnose dementia is by analyzing a patient’s speech, which is cheap and does not require surgery. This research aims to determine the effectiveness of deep learning (DL) and machine learning (ML) structures in diagnosing dementia based on women’s speech patterns. The study analyzes data drawn from the Pitt Corpus, which contains 298 dementia files and 238 control files from the Dementia Bank database. Deep learning models and SVM classifiers were used to analyze the available audio samples in the dataset. Our methodology used two methods: a DL-ML model and a single DL model for the classification of diabetics and a single DL model. The deep learning model achieved an astronomic level of accuracy of 99.99% with an F1 score of 0.9998, Precision of 0.9997, and recall of 0.9998. The proposed DL-ML fusion model was equally impressive, with an accuracy of 99.99%, F1 score of 0.9995, Precision of 0.9998, and recall of 0.9997. Also, the study reveals how to apply deep learning and machine learning models for dementia detection from speech with high accuracy and low computational complexity. This research work, therefore, concludes by showing the possibility of using speech-based dementia detection as a possibly helpful early diagnosis mode. For even further enhanced model performance and better generalization, future studies may explore real-time applications and the inclusion of other components of speech.