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

This research aims to understand complexity management and its impact on the use of the dynamic capabilities of a sample of private colleges.  Private colleges are currently facing many crises, changes, unrest and high competitive pressures.  Which is sometimes difficult or even impossible to predict.  The recruitment of dynamic capabilities is also one of the challenges facing senior management at private colleges to help them survive and survive.  Thus, the problem of research was (there is a clear insufficiency of interest in Complexity Management and trying to employ it in improving the dynamic capabilities of Colleges that have been discussed?). A group of private colleges was selected as a

Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a

In This paper, sky radio emission background level associated with radio storm burst for the Sun and Jupiter is determined at frequency (20.1 MHz). The observation data for radio Jove telescope for the Sun and Jupiter radio storm observations data are loaded from NASA radio Jove telescope website, the data of Sunspot number are loaded from National Geophysical Data Center, (NGDC). Two radio Jove stations [(Sula, MT), (Lamy, NM)] are chose from data website for these huge observations data. For the Sun, twelve figures are used to determine the relation between radio background emission, and the daily Sunspot number. For Jupiter a twenty four figures are used to determine the relation between radio background emission and diffraction betwe

Mass transfer was examined at a stationary rectangular copper electrode (cathode) by using the reduction of cupric ions as the electrochemical reaction. The influence of electrolyte temperature (25, 45, and 65 ^oC), and cupric ions concentration (4, 8, and 12 mM) on mass transfer coefficient were investigated by using limiting current technique. The mass transfer coefficient and hence the Sherwood number was correlated as Sh =

Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep lea

<p>In combinatorial testing development, the fabrication of covering arrays is the key challenge by the multiple aspects that influence it. A wide range of combinatorial problems can be solved using metaheuristic and greedy techniques. Combining the greedy technique utilizing a metaheuristic search technique like hill climbing (HC), can produce feasible results for combinatorial tests. Methods based on metaheuristics are used to deal with tuples that may be left after redundancy using greedy strategies; then the result utilization is assured to be near-optimal using a metaheuristic algorithm. As a result, the use of both greedy and HC algorithms in a single test generation system is a good candidate if constructed correctly. T

Low-Density-Parity-Check (LDPC) codes are a cornerstone for achieving robust error correction capabilities in 5G New Radio applications, significantly improving the reliability of data transmission across noisy and unpredictable wireless channels. Since an evaluation and discussion of the performance with channel coding is significantly absent in two-dimensional Index Modulation (IM)-Differential Chaos Shift Keying (DCSK) schemes. Therefore, in this study, the 5G new radio LDPC codes based generalized joint subcarrier-time index modulation DCSK system (5G NR-LDPC-GJSTIM-DCSK) is proposed, where 5G NR-LDPC codes are used as channel coding. The aim is to improve the system’s performance specifically across AWGN (additive white gauss