Physiological measures, such as heart rate variability (HRV) and beats per minute (BPM), can be powerful health indicators of respiratory infections. HRV and BPM can be acquired through widely available wrist-worn biometric wearables and smartphones. Successive abnormal changes in these indicators could potentially be an early sign of respiratory infections such as COVID-19. Thus, wearables and smartphones should play a significant role in combating COVID-19 through the early detection supported by other contextual data and artificial intelligence (AI) techniques. In this paper, we investigate the role of the heart measurements (i.e., HRV and BPM) collected from wearables and smartphones in demonstrating early onsets of the inflammatory response to the COVID-19. The AI framework consists of two blocks: an interpretable prediction model to classify the HRV measurements status (as normal or affected by inflammation) and a recurrent neural network (RNN) to analyze users’ daily status (i.e., textual logs in a mobile application). Both classification decisions are integrated to generate the final decision as either “potentially COVID-19 infected” or “no evident signs of infection”. We used a publicly available dataset, which comprises 186 patients with more than 3200 HRV readings and numerous user textual logs. The first evaluation of the approach showed an accuracy of 83.34 ± 1.68% with 0.91, 0.88, 0.89 precision, recall, and F1-Score, respectively, in predicting the infection two days before the onset of the symptoms supported by a model interpretation using the local interpretable model-agnostic explanations (LIME).

Early diagnosis of medical conditions in infants is crucial for ensuring timely and effective treatment. However, infants are unable to verbalize their symptoms, making it difficult for healthcare professionals to accurately diagnose their conditions. Crying is often the only way for infants to communicate their needs and discomfort. In this paper, we propose a medical diagnostic system for interpreting infants’ cry audio signals (CAS) using a combination of different audio domain features and deep learning (DL) algorithms. The proposed system utilizes a dataset of labeled audio signals from infants with specific pathologies. The dataset includes two infant pathologies with high mortality rates, neonatal respiratory distress syndrome (RDS), sepsis, and crying. The system employed the harmonic ratio (HR) as a prosodic feature, the Gammatone frequency cepstral coefficients (GFCCs) as a cepstral feature, and image-based features through the spectrogram which are extracted using a convolution neural network (CNN) pretrained model and fused with the other features to benefit multiple domains in improving the classification rate and the accuracy of the model. The different combination of the fused features is then fed into multiple machine learning algorithms including random forest (RF), support vector machine (SVM), and deep neural network (DNN) models. The evaluation of the system using the accuracy, precision, recall, F1-score, confusion matrix, and receiver operating characteristic (ROC) curve, showed promising results for the early diagnosis of medical conditions in infants based on the crying signals only, where the system achieved the highest accuracy of 97.50% using the combination of the spectrogram, HR, and GFCC through the deep learning process. The finding demonstrated the importance of fusing different audio features, especially the spectrogram, through the learning process rather than a simple concatenation and the use of deep learning algorithms in extracting sparsely represented features that can be used later on in the classification problem, which improves the separation between different infants’ pathologies. The results outperformed the published benchmark paper by improving the classification problem to be multiclassification (RDS, sepsis, and healthy), investigating a new type of feature, which is the spectrogram, using a new feature fusion technique, which is fusion, through the learning process using the deep learning model.

Crying is the only means of communication for a newborn baby with its surrounding environment, but it also provides significant information about the newborn’s health, emotions, and needs. The cries of newborn babies have long been known as a biomarker for the diagnosis of pathologies. However, to the best of our knowledge, exploring the discrimination of two pathology groups by means of cry signals is unprecedented. Therefore, this study aimed to identify septic newborns with Neonatal Respiratory Distress Syndrome (RDS) by employing the Machine Learning (ML) methods of Multilayer Perceptron (MLP) and Support Vector Machine (SVM). Furthermore, the cry signal was analyzed from the following two different perspectives: 1) the musical perspective by studying the spectral feature set of Harmonic Ratio (HR), and 2) the speech processing perspective using the short-term feature set of Gammatone Frequency Cepstral Coefficients (GFCCs). In order to assess the role of employing features from both short-term and spectral modalities in distinguishing the two pathology groups, they were fused in one feature set named the combined features. The hyperparameters (HPs) of the implemented ML approaches were fine-tuned to fit each experiment. Finally, by normalizing and fusing the features originating from the two modalities, the overall performance of the proposed design was improved across all evaluation measures, achieving accuracies of 92.49% and 95.3% by the MLP and SVM classifiers, respectively. The MLP classifier was outperformed in terms of all evaluation measures presented in this study, except for the Area Under Curve of Receiver Operator Characteristics (AUC-ROC), which signifies the ability of the proposed design in class separation. The achieved results highlighted the role of combining features from different levels and modalities for a more powerful analysis of the cry signals, as well as including a neural network (NN)-based classifier. Consequently, attaining a 95.3% accuracy for the separation of two entangled pathology groups of RDS and sepsis elucidated the promising potential for further studies with larger datasets and more pathology groups.

Selecting the appropriate undergraduate program is a critical decision for students. Many elements influence this choice for secondary students, including financial, social, demographic, and cultural factors. If a student makes a poor choice, it will have implications for their academic life as well as their professional life. These implications may include having to change their major, which will cause a delay in their graduation, having a low grade-point average (GPA) in their chosen major, which will cause difficulties in finding a job, or even dropping out of university. In this paper, various supervised machine learning techniques, including Decision Tree, Random Forest, and Support Vector Machine, were investigated to predict undergraduate majors. The input features were related to the student’s academic history and the job market. We were able to recommend the program that guarantees both a high academic degree and employment, depending on previous data and experience, for Master of Business Administration (MBA) students. This research was conducted based on a published research and using the same dataset and aimed to improve the results by applying hyper-tuning, which was absent in previous research. The obtained results showed that our work outperformed the work of the published research, where the random forest exceeded the other classification techniques and reached an accuracy of 97.70% compared to 75.00% on the published research. The importance of features was also investigated, and it was found that the degree percentage, MBA percentage, and entry test result were the top contributing features to the model.

Early detection of infant pathologies by non-invasive means is a critical aspect of pediatric healthcare. Audio analysis of infant crying has emerged as a promising method to identify various health conditions without direct medical intervention. In this study, we present a cutting-edge machine learning model that employs audio spectrograms and transformer-based algorithms to classify infant crying into distinct pathological categories. Our innovative model bypasses the extensive preprocessing typically associated with audio data by exploiting the self-attention mechanisms of the transformer, thereby preserving the integrity of the audio’s diagnostic features. When benchmarked against established machine learning and deep learning models, our approach demonstrated a remarkable 98.69% accuracy, 98.73% precision, 98.71% recall, and an F1 score of 98.71%, surpassing the performance of both traditional machine learning and convolutional neural network models. This research not only provides a novel diagnostic tool that is scalable and efficient but also opens avenues for improving pediatric care through early and accurate detection of pathologies.

Work zone areas are commonly known as crash-prone areas. Thus, they usually receive high priority by road operators as drivers and workers have higher chances of being involved in road crashes. The paper aims to investigate driving behavior in work zones using unsupervised machine learning and vehicle kinematic data. A dataset of 67 participants was gathered through an experiment using a driving simulator located at the Qatar Transportation and Traffic Safety Center (QTTSC). The study considered two different work zone scenarios where the leftmost lane was closed for maintenance. In the first scenario, drivers drove on the leftmost lane (Drive 1), while in the second, they drove on the second leftmost lane (Drive 2). The results show that the number of aggressive and conservative drivers was surprisingly more than normal drivers, as most participants either cautiously drove through or failed to drive without being aggressive. The results also show that drivers acted more aggressively in the leftmost lane rather than in the second leftmost lane. We also found that female drivers and drivers with relatively little driving experience were more likely to be aggressive as they drove through a work zone. The framework was found to be promising and can help policymakers take optimal safety countermeasures in work zones during construction.