Publications by authors named "Mohamed A Bencherif"
Article Synopsis
- Sign language is crucial for communication among hearing-impaired individuals, but mastering it can be challenging for those who can hear.
- The study introduces a novel sign language recognition architecture using a convolutional graph neural network (GCN) with fewer layers to reduce over-smoothing issues.
- The architecture includes a spatial attention mechanism to improve gesture representation, and its effectiveness is demonstrated through various dataset evaluations, showing impressive results.
This study proposes using object detection techniques to recognize sequences of articulatory features (AFs) from speech utterances by treating AFs of phonemes as multi-label objects in speech spectrogram. The proposed system, called AFD-Obj, recognizes sequence of multi-label AFs in speech signal and localizes them. AFD-Obj consists of two main stages: firstly, we formulate the problem of AFs detection as an object detection problem and prepare the data to fulfill requirement of object detectors by generating a spectral three-channel image from the speech signal and creating the corresponding annotation for each utterance.
View Article and Find Full Text PDFIntrusion detection system (IDS) design for mobile adhoc networks (MANET) is a crucial component for maintaining the integrity of the network. The need for rapid deployment of IDS capability with minimal data availability for training and testing is an important requirement of such systems, especially for MANETs deployed in highly dynamic scenarios, such as battlefields. This work proposes a two-level detection scheme for detecting malicious nodes in MANETs.
View Article and Find Full Text PDFBackground And Objective: Automatic voice-pathology detection and classification systems may help clinicians to detect the existence of any voice pathologies and the type of pathology from which patients suffer in the early stages. The main aim of this paper is to investigate Multidimensional Voice Program (MDVP) parameters to automatically detect and classify the voice pathologies in multiple databases, and then to find out which parameters performed well in these two processes.
Materials And Methods: Samples of the sustained vowel /a/ of normal and pathological voices were extracted from three different databases, which have three voice pathologies in common.