In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial

This paper presents an improved technique on Ant Colony Optimization (ACO) algorithm. The procedure is applied on Single Machine with Infinite Bus (SMIB) system with power system stabilizer (PSS) at three different loading regimes. The simulations are made by using MATLAB software. The results show that by using Improved Ant Colony Optimization (IACO) the system will give better performance with less number of iterations as it compared with a previous modification on ACO. In addition, the probability of selecting the arc depends on the best ant performance and the evaporation rate.

This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm n

<p>Generally, The sending process of secret information via the transmission channel or any carrier medium is not secured. For this reason, the techniques of information hiding are needed. Therefore, steganography must take place before transmission. To embed a secret message at optimal positions of the cover image under spatial domain, using the developed particle swarm optimization algorithm (Dev.-PSO) to do that purpose in this paper based on Least Significant Bits (LSB) using LSB substitution. The main aim of (Dev. -PSO) algorithm is determining an optimal paths to reach a required goals in the specified search space based on disposal of them, using (Dev.-PSO) algorithm produces the paths of a required goals with most effi

Variable-Length Subnet Masks (VLSM), often referred to as "subnetting a subnet", is used to maximize addressing efficiency. The network administrator is able to use a long mask on networks with few hosts, and a short mask on subnets with many hosts. This addressing scheme allows growth and does not involve wasting addresses. VLSM gives a way of subnetting a network with minimal loses of IP addresses for a specific range. Unfortunately, the network administrator has to perform several mathematical steps (or use charts) to get the required results from VLSM. In this paper, a simple graph simulator is proposed (using Visual Basic 6.0 Language) to perform all the required mathematical steps and to display the obtained required information (the

      In developing countries, individual students and researchers are not able to afford the high price of the subscription to the international publishers, like JSTOR, ELSEVIER,…; therefore the governments and/or universities of those countries aim to purchase one global subscription to the international publishers to provide their educational resources at a cheaper price, or even freely, to all students and researchers of those institutions. For realizing this concept, we must build a system that sits between the publishers and the users (students or researchers) and act as a gatekeeper and a director of information: this system must register its users and must have an adequate security to e

Variable-Length Subnet Masks (VLSM), often referred to as "subnetting a subnet", is used to maximize addressing efficiency. The network administrator is able to use a long mask on networks with few hosts, and a short mask on subnets with many hosts. This addressing scheme allows growth and does not involve wasting addresses. VLSM gives a way of subnetting a network with
minimal loses of IP addresses for a specific range. Unfortunately, the network administrator has to perform several mathematical steps (or use charts) to get the required results from VLSM. In this paper, a simple graph simulator is proposed (using Visual Basic 6.0 Language) to perform all the required mathematical steps and to display the obtained required informatio

The ability of the human brain to communicate with its environment has become a reality through the use of a Brain-Computer Interface (BCI)-based mechanism. Electroencephalography (EEG) has gained popularity as a non-invasive way of brain connection. Traditionally, the devices were used in clinical settings to detect various brain diseases. However, as technology advances, companies such as Emotiv and NeuroSky are developing low-cost, easily portable EEG-based consumer-grade devices that can be used in various application domains such as gaming, education. This article discusses the parts in which the EEG has been applied and how it has proven beneficial for those with severe motor disorders, rehabilitation, and as a form of communi

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with