confusion matrix

AI-Powered Detection of COVID-19 and Lung Diseases from Chest X-Rays: Boosting Accuracy with CNNs and Top-K Algorithms

The COVID-19 epidemic has highlighted the need for easier and more precise diagnoses.  Traditional techniques, such as PCR tests, are helpful but can be time-consuming and laborious.  In order to further enhance picture quality, this study presents a novel method for identifying COVID-19 and other lung disorders utilizing chest X-rays, convolutional neural networks (CNNs), and histogram equalization.  The 1 823 X-ray pictures in the collection were divided into three categories: regular, COVID-19-positive, and additional lung infections.  Based on the combination of CNN

Evaluation of Classification Accuracy Using Feedforward Neural Network for Dynamic Objects

This paper investigates the impact of the number of hidden layers, the number of neurons in these layers, and the types of activation functions on the accuracy of classifying projectiles of six types (A – (artillery); A/M – (artillery/missile); A/R – (armor-piercing); A/RC – (armor-piercing- incendiary); M – (missile); R – (armor-piercing shells)) using a multi-layer neural network, evaluated by a confusion matrix.