In this paper, a discretization of a three-dimensional fractional-order prey-predator model has been investigated with Holling type III functional response. All its fixed points are determined; also, their local stability is investigated. We extend the discretized system to an optimal control problem to get the optimal harvesting amount. For this, the discrete-time Pontryagin’s maximum principle is used. Finally, numerical simulation results are given to confirm the theoretical outputs as well as to solve the optimality problem.

This paper proposes and studies an ecotoxicant system with Lotka-Volterra functional response for predation including prey protective region. The equilibrium points and the stability of this model have been investigated analytically both locally and globally. Finally, numerical simulations and graphical representations have been utilized to support our analytical findings

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

Object tracking is one of the most important topics in the fields of image processing and computer vision. Object tracking is the process of finding interesting moving objects and following them from frame to frame. In this research, Active models–based object tracking algorithm is introduced. Active models are curves placed in an image domain and can evolve to segment the object of interest. Adaptive Diffusion Flow Active Model (ADFAM) is one the most famous types of Active Models. It overcomes the drawbacks of all previous versions of the Active Models specially the leakage problem, noise sensitivity, and long narrow hols or concavities. The ADFAM is well known for its very good capabilities in the segmentation process. In this

Gender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer lea

In unpredicted industrial environment, being able to adapt quickly and effectively to the changing is key in gaining a competitive advantage in the global market. Agile manufacturing evolves new ways of running factories to react quickly and effectively to changing markets, driven by customized requirement. Agility in manufacturing can be successfully achieved via integration of information system, people, technologies, and business processes. This article presents the conceptual model of agility in three dimensions named: driving factor, enabling technologies and evaluation of agility in manufacturing system. The conceptual model was developed based on a review of the literature. Then, the paper demonstrates the agility

The rise in the general level of prices in Iraq makes the local commodity less able to compete with other commodities, which leads to an increase in the amount of imports and a decrease in the amount of exports, since it raises demand for foreign currencies while decreasing demand for the local currency, which leads to a decrease in the exchange rate of the local currency in exchange for an increase in the exchange rate of currencies. This is one of the most important factors affecting the determination of the exchange rate and its fluctuations. This research deals with the currency of the European Euro and its impact against the Iraqi dinar. To make an accurate prediction for any process, modern methods can be used through which

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi

A 2D geological model for Mauddud Formation in the Badra oil field is built using Rockworks 16 software. Mauddud Formation produces oil from limestone units; it represents the main reservoir in the Badra oil field. Six wells (BD-1, BD-2, BD-4, BD-5, P-15, and P-19) are selected to build facies and petrophysical (Porosity and Water saturation) models. Wells data are taken from the core and cutting samples and studied through the microscopic. The petrophysical data (effective porosity and water saturation) are derived from computer processes interpretation results that are calculated by using Interactive Petrophysics software. The 2D models are built to illustrate the vertical and horizontal distribution of petrophysical properties between we