Psychological distress detection plays a critical role in modern healthcare, especially in ambient environments where continuous monitoring is essential for timely intervention. Advances in sensor technology and artificial intelligence (AI) have enabled the development of systems capable of mental health monitoring using multi-modal data. However, existing models often struggle with contextual adaptation and real-time decision-making in dynamic settings. This paper addresses these challenges by proposing TRANS-HEALTH, a hybrid framework that integrates transformer-based inference with Belief-Desire-Intention (BDI) reasoning for real-time psychological distress detection. The framework utilizes a multimodal dataset containing EEG, GSR, heart rate, and activity data to predict distress while adapting to individual contexts. The methodology combines deep learning for robust pattern recognition and symbolic BDI reasoning to enable adaptive decision-making. The novelty of the approach lies in its seamless integration of transformer models with BDI reasoning, providing both high accuracy and contextual relevance in real time. Performance metrics such as accuracy, precision, recall, and F1-score are employed to evaluate the system’s performance. The results show that TRANS-HEALTH outperforms existing models, achieving 96.1% accuracy with 4.78 ms latency and significantly reducing false alerts, with an enhanced ability to engage users, making it suitable for deployment in wearable and remote healthcare environments.

Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information. The proposed Auto-Stack ID in the study is a stacked ensemble of encoder-enhanced auctions that can be used to improve intrusion detection in healthcare networks. The WUSTL-EHMS 2020 dataset trains and evaluates the model, constituting an imbalanced class distribution (87.46% normal traffic and 12.53% intrusion attacks). To address this imbalance, the study balances the effect of training Bias through Stratified K-fold cross-validation (K = 5), so that each class is represented similarly on training and validation splits. Second, the Auto-Stack ID method combines many base classifiers such as TabNet, LightGBM, Gaussian Naive Bayes, Histogram-Based Gradient Boosting (HGB), and Logistic Regression. We apply a two-stage training process based on the first stage, where we have base classifiers that predict out-of-fold (OOF) predictions, which we use as inputs for the second-stage meta-learner XGBoost. The meta-learner learns to refine predictions to capture complicated interactions between base models, thus improving detection accuracy without introducing bias, overfitting, or requiring domain knowledge of the meta-data. In addition, the auto-stack ID model got 98.41% accuracy and 93.45% F1 score, better than individual classifiers. It can identify intrusions due to its 90.55% recall and 96.53% precision with minimal false positives. These findings identify its suitability in ensuring healthcare networks’ security through ensemble learning. Ongoing efforts will be deployed in real time to improve response to evolving threats.

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.