Using Fuzzy Time Series Forecasting and Gaussian Mixture Model to Classify and Predict New Cases of Covid-19 in Saudi Arabia

In light of the global events resulting from the spread of the Corona pandemic and viral mutations, there is a need to examine epidemic data in terms of numbers of infected and deaths, different geographical locations, and the dynamics of disease dissemination virus. In the Kingdom of Saudi Arabia (KSA), since the spread of the virus on March 2, 2020, the number of confirmed cases has increased to 599044 cases until January 13, 2022, of which 262 are critical cases, while the number of recovery cases have reached 55035 cases, and deaths are 8901. It is a serious disease, and its spread is difficult to contain. The number of cases has continued to grow rapidly since the first cases appeared. Guess and Buck's model for forecasting time-series data is an important figure that cannot be crossed when predicting fuzzy time-series, although several modifications have been made to the model to improve the accuracy of its results. The Gaussian mixture model and the fuzzy method for modelling new cases in Saudi Arabia were used as machine learning methods to classify and predict new cases of the virus in Saudi Arabia. Foggy time series forecasting. The studied datasets from the World Health Organization from May 15 to August 12, 2020 were used.

An Experimental Approach to Diagnose Covid-19 Using Optimized CNN

The outburst of novel corona viruses aggregated worldwide and has undergone severe trials to manage medical sector all over the world. A radiologist uses x-rays and Computed Tomography (CT) scans to analyze images through which the existence of corona virus is found. Therefore, imaging and visualization systems contribute a dominant part in diagnosing process and thereby assist the medical experts to take necessary precautions and to overcome these rigorous conditions. In this research, a Multi-Objective Black Widow Optimization based Convolutional Neural Network (MBWO-CNN) method is proposed to diagnose and classify covid-19 data. The proposed method comprises of four stages, preprocess the covid-19 data, attribute selection, tune parameters, and classify cov-id-19 data. Initially, images are fed to preprocess and features are selected using Convolutional Neural Network (CNN). Next, Multi-objective Black Widow Optimization (MBWO) method is imparted to finely tune the hyper parameters of CNN. Lastly, Extreme Learning Machine Auto Encoder (ELM-AE) is used to check the existence of corona virus and further classification is done to classify the covid-19 data into respective classes. The suggested MBWO-CNN model was evaluated for effectiveness by undergoing experiments and the outcomes attained were matched with the outcome stationed by prevailing methods. The outcomes confirmed the astonishing results of the ELM-AE model to classify cov-id-19 data by achieving maximum accuracy of 97.53%. The efficacy of the proposed method is validated and observed that it has yielded outstanding outcomes and is best suitable to diagnose and classify covid-19 data. © 2022, Tech Science Press. All rights reserved.

Hybrid Model of Quantum Transfer Learning to Classify Face Images with a COVID-19 Mask

The problem of the COVID-19 disease has deter-mined that about 219 million people have contracted it, of which 4.55 million died. This importance has led to the implementation of security protocols to prevent the spread of this disease. One of the main protocols is to use protective masks that properly cover the nose and mouth. The objective of this paper was to classify images of faces using protective masks of COVID-19, in the classes identified as correct mask, incorrect mask, and no mask, with a Hybrid model of Quantum Transfer Learning. To do this, the method used has made it possible to gather a data set of 660 people of both sexes (man and woman), with ages ranging from 18 to 86 years old. The classic transfer learning model chosen was ResNet-18;the variational layers of the proposed model were built with the Basic Entangler Layers template for four qubits, and the optimization of the training was carried out with the Stochastic Gradient Descent with Nesterov Momentum. The main finding was the 99.05% accuracy in classifying the correct Protective Masks using the Pennylane quantum simulator in the tests performed. The conclusion reached is that the proposed hybrid model is an excellent option to detect the correct position of the protective mask for COVID-19.