In an attempt to reduce the infection rate of the COrona VIrus Disease-19 (Covid-19) countries around the world have echoed the exigency for an economical, accessible, point-of-need diagnostic test to identify Covid-19 carriers so that they (individuals who test positive) can be advised to self isolate rather than the entire community. Availability of a quick turnaround time diagnostic test would essentially mean that life, in general, can return to normality-at-large. In this regards, studies concurrent in time with ours have investigated different respiratory sounds, including cough, to recognise potential Covid-19 carriers. However, these studies lack clinical control and rely on Internet users confirming their test results in a web questionnaire (crowdsourcing) thus rendering their analysis inadequate. We seek to evaluate the detection performance of a primary screening tool of Covid-19 solely based on the cough sound from 8,380 clinically validated samples with laboratory molecular-test (2,339 Covid-19 positive and 6,041 Covid-19 negative) under quantitative RT-PCR (qRT-PCR) from certified laboratories. All collected samples were clinically labelled, i.e. Covid-19 positive or negative, according to the results in addition to the disease severity based on the qRT-PCR threshold cycle (Ct) and lymphocytes count from the patients. Our proposed generic method is an algorithm based on Empirical Mode Decomposition (EMD) for cough sound detection with subsequent classification based on a tensor of audio sonographs and deep artificial neural network classifier with convolutional layers called 'DeepCough'. Two different versions of DeepCough based on the number of tensor dimensions, i.e. DeepCough2D and DeepCough3D, have been investigated. These methods have been deployed in a multi-platform prototype web-app 'CoughDetect'.Covid-19 recognition results rates achieved a promising AUC (Area Under Curve) of 98.80% ± 0.83%, sensitivity of 96.43% ± 1.85%, and specificity of 96.20% ± 1.74% and average AUC of 81.08% ± 5.05% for the recognition of three severity levels. Our proposed web tool as a pointof-need primary diagnostic test for Covid-19 facilitates the rapid detection of the infection. We believe it has the potential to significantly hamper the Covid-19 pandemic across the world.

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Abstract. In this paper we describe the implementation of a fuzzy relational neural network model. In the model, the input features are represented by fuzzy membership, the weights are described in terms of fuzzy relations. The output values are obtained with the max-min composition, and are given in terms of fuzzy class membership values. The learning algorithm is a modified version of back-propagation. The system is tested on an infant cry classification problem, in which the objective is to identify pathologies in recently born babies.