Mohammed Elamine Moumene

Cardiovascular diseases remain one of the primary causes of death worldwide, highlighting the importance of early detection. PhonoCardioGraphy (PCG) provides a non-invasive approach to identify cardiac abnormalities. However, its effectiveness is limited by scarce datasets and signal variability. Deep learning (DL) models have shown promising results, but their performance strongly depends on the availability of sufficient training data. In this study, PCG signals were converted into spectrogram representations, and four data augmentation techniques were applied individually and in combination, followed by a systematic evaluation. A baseline convolutional neural network (CNN) and several transfer learning models were trained on both original and augmented datasets. The results demonstrate that combining augmentation techniques yields the best performance. The proposed CNN achieved an accuracy of 94%, representing a 5.6% improvement over its performance without data augmentation. In contrast, transfer learning such as VGG16, ResNet50, and DenseNet121 showed improvements of 3.4%, 3.4%, and 2.2%, respectively. These findings indicate that the proposed augmentation strategy effectively improves the classification performance and generalization capability of DL models for heart sound classification.

The detection of human skin color has been studied extensively during the past two decades. It is an essential task for various computer vision applications such as biometric authentication, face/hands tracking and gesture analysis. New machine learning methods are effective for skin color detection. However, they are not suitable for real time applications since they are computationally heavy. A lightweight approach for skin color detection consists of using segmentation rules extracted by an investigation on skin color distribution. The kin appearance varies with diversity of image types, acquisition parameters and scene illumination. There are no general segmentation rules that provide effective skin segmentation for different scene conditions. In this paper we present a real-time skin color detector which adapts itself according to tracked human parts. First, initial thresholds are calculated using two popular skin datasets. Those thresholds can also be calculated quickly using small training sets. The proposed skin color detector showed comparable skin segmentation to DeepLabV3++ application and an improvement in term of F1 measure when compared to methods that relies on static rules.

There are several studies on road lane detection but very few address adverse conditions for acquisition such as sun glare. Loss of details in underexposed images captured facing a low sun leads to misleading road lane detection. High Dynamic Range Imaging methods are used to acquire most details in such scenes. Unfortunately, these techniques are heavy on computations and therefore unsuitable for real time road lane detection. In this paper, we propose a machine learning solution that avoids High Dynamic Range Imaging computations that are the radiance map estimation, tone-mapping algorithms and quality measures calculation. We train a neural network on a High Dynamic Range Imaging dataset. The resulting model produces suitable images for road lane detection facing sun glare, in real time. Subjective and objective comparisons with the most popular High Dynamic Range Imaging method, Mertens Algorithm, are conducted to prove the effectiveness of the proposed Neural Network. The delivered images demonstrated an improvement in road lane detection.