Multipoint Relay Selection based on Stability of Spatial Relation in Mobile Ad hoc Networks

Increasing stability is one of the main objectives in designing routing protocols for Mobile Ad-Hoc Network (MANETS). Various research schemes have been addressed to this challenge and to support it. In fact, some papers have considered modifications to MPRs selection mechanism in OLSR. In this paper, the author proposes a new mechanism to elect stable and sustainable nodes relay between all nodes in MANETs. In this mechanism, a mobility function is used as the main selection criterion based on the calculation of the spatial relation of a node relative to its neighbor. This mechanism is applied in OLSR protocol to choose stable and supportable MPRs nodes. This mechanism significantly finds more stable MPRs and it promises QoS metrics such as lost packets and delay. Simulation results reveals a significant performance gains and it motivates further examinations to develop the mechanism in order to improve the routing protocol requirements. Performances are evaluated based on Random Waypoint model and network simulator ns3

Effective Detection of Parkinson’s Disease at Different Stages using Measurements of Dysphonia

This paper addressees the problem of multiclass of Parkinson’s disease by the characteristic features of person’s voice. So we computed 22 dysphonia measures from 375 voice samples of healthy and people suffer from PD. We used the particle swarm optimization (PSO) feature selection method, with random forest and the linear discriminant analysis (LDA) along with the 4-fold cross validation analysis to classify the subjects in 4 classes according to the severity of symptoms. With a classification accuracy score of 95.2%. Promisingly, the proposed diagnosis system might serve as a powerful tool for diagnosing PD, and could also extended for other voice pathologies

Voice Assessments for Detecting Patients with Parkinson’s Diseases in Different Stages

Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to detect patients with Parkinson’s disease (PD). So we have computed 19 dysphonia measures from sustained vowels collected from 375 voice samples from healthy and people suffer from PD. All the features are analysed and the more relevant ones are selected by the Principal component analysis (PCA) to classify the subjects in 4 classes according to the UPDRS (unified Parkinson’s disease Rating Scale) score. We used k-folds cross validation method with (k=4) validation scheme; 75% for training and 25% for testing, along with the Support Vector Machines (SVM) with its different types of kernels. The best result obtained was 92.5% using the PCA and the linear SVM

A cough-based Covid-19 detection with gammatone and mel-frequency cepstral coefficients

Many countries have adopted a public health approach that aims to address the particular challenges faced during the pandemic Coronavirus disease 2019 (COVID-19). Researchers mobilized to manage and limit the spread of the virus, and multiple artificial intelligence-based systems are designed to automatically detect the disease. Among these systems, voice-based ones since the virus have a major impact on voice production due to the respiratory system's dysfunction. In this paper, we investigate and analyze the effectiveness of cough analysis to accurately detect COVID-19. To do so, we distinguished positive COVID patients from healthy controls. After the gammatone cepstral coefficients (GTCC) and the Mel-frequency cepstral coefficients (MFCC) extraction, we have done the feature selection (FS) and classification with multiple machine learning algorithms. By combining all features and the 3-nearest neighbor (3NN) classifier, we achieved the highest classification results. The model is able to detect COVID-19 patients with accuracy and an f1-score above 98 percent. When applying FS, the higher accuracy and F1-score were achieved by the same model and the ReliefF algorithm, we lose 1 percent of accuracy by mapping only 12 features instead of the original 53