Facts reveal that numerous road accidents worldwide occur due to fatigue, drowsiness, and distraction while driving. Few works on the automated drowsiness detection problem, propose to extract physiological signals of the driver including ECG, EEG, heart variability rate, blood pressure, etc. which make those solutions non-ideal. While recent ones propose computer vision-based solutions but show limited performances as either they use hand-crafted features with conventional techniques like Naïve Bayes and SVM or use excessively bulky deep learning models which are still low on performances. Hence in this work, we propose an ensemble deep learning architecture that operates over incorporated features of eyes and mouth subsamples along with a decision structure to determine the fitness of the driver. The proposed ensemble model consists of only two InceptionV3 modules that help in containing the parameter space of the network. These two modules respectively and exclusively perform feature extraction of eyes and mouth subsamples extracted using the MTCNN from the face images. Their respective output is passed to the ensemble boundary using the weighted average method whose weights are tuned using the ensemble algorithm. The output of this system determines whether the driver is drowsy or non-drowsy. The benchmark NTHU-DDD video dataset is used for effective training and evaluation of the proposed model. The model established a train and validation accuracy of 99.65% and 98.5% respectively with an accuracy of 97.1% on the evaluation dataset which is significantly higher than those achieved by models proposed in recent works on this dataset.

A plethora of studies have shown that heart diseases have become the number one killer in the world. Congenital Heart Defect (CHD) is one of the most commonly observed heart defect in an increasing number of newborns. Various research work has been done for CHD detection based on clinical data, however very less work has been with non-clinical data of the patient. Hence in this work, we explore the best suitable approach for non-clinical information, which is prominently no invasive, of the pregnant women in diagnosing the chances of CHD in a child that lives in their womb. For this purpose, a variety of machine learning algorithms including the Naïve Bayes (NB) and the Feedforward Artificial Neural Network (ANN) have been explored. Few relevant researches done previously on the dataset of this problem, used the weighted Support Vector Machine (WSVM) and the weighted Random Forest (WRF) models, however, the state of the art neural network model, used in this paper, significantly improved the CHD detection performance. The results show that the ANN model yields a high accuracy of 99.6% and an impressive weighted accuracy of 90.9% on the selected dataset. Hence it may be strongly suggested to build an ANN-based model for CHD detection in yet to be born babies using such noninvasive nonclinical datasets.