This paper presents a hybrid energy resources (HER) system consisting of solar PV, storage, and utility grid. It is a challenge in real time to extract maximum power point (MPP) from the PV solar under variations of the irradiance strength.  This work addresses challenges in identifying global MPP, dynamic algorithm behavior, tracking speed, adaptability to changing conditions, and accuracy. Shallow Neural Networks using the deep learning NARMA-L2 controller have been proposed. It is modeled to predict the reference voltage under different irradiance. The dynamic PV solar and nonlinearity have been trained to track the maximum power drawn from the PV solar systems in real time.

Moreover, the proposed controller i

This research depends on the relationship between the reflected spectrum, the nature of each target, area and the percentage of its presence with other targets in the unity of the target area. The changes occur in Land cover have been detected for different years using satellite images based on the Modified Spectral Angle Mapper (MSAM) processing, where Landsat satellite images are utilized using two software programming (MATLAB 7.11 and ERDAS imagine 2014). The proposed supervised classification method (MSAM) using a MATLAB program with supervised classification method (Maximum likelihood Classifier) by ERDAS imagine have been used to get farthest precise results and detect environmental changes for periods. Despite using two classificatio

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature

Protecting information sent through insecure internet channels is a significant challenge facing researchers. In this paper, we present a novel method for image data encryption that combines chaotic maps with linear feedback shift registers in two stages. In the first stage, the image is divided into two parts. Then, the locations of the pixels of each part are redistributed through the random numbers key, which is generated using linear feedback shift registers. The second stage includes segmenting the image into the three primary colors red, green, and blue (RGB); then, the data for each color is encrypted through one of three keys that are generated using three-dimensional chaotic maps. Many statistical tests (entropy, peak signa

The emphasis of Master Production Scheduling (MPS) or tactic planning is on time and spatial disintegration of the cumulative planning targets and forecasts, along with the provision and forecast of the required resources. This procedure eventually becomes considerably difficult and slow as the number of resources, products and periods considered increases. A number of studies have been carried out to understand these impediments and formulate algorithms to optimise the production planning problem, or more specifically the master production scheduling (MPS) problem. These algorithms include an Evolutionary Algorithm called Genetic Algorithm, a Swarm Intelligence methodology called Gravitational Search Algorithm (GSA), Bat Algorithm (BAT), T

Correct grading of apple slices can help ensure quality and improve the marketability of the final product, which can impact the overall development of the apple slice industry post-harvest. The study intends to employ the convolutional neural network (CNN) architectures of ResNet-18 and DenseNet-201 and classical machine learning (ML) classifiers such as Wide Neural Networks (WNN), Naïve Bayes (NB), and two kernels of support vector machines (SVM) to classify apple slices into different hardness classes based on their RGB values. Our research data showed that the DenseNet-201 features classified by the SVM-Cubic kernel had the highest accuracy and lowest standard deviation (SD) among all the methods we tested, at 89.51 %  1.66 %. This

Human identification is crucial in forensics for the investigation of large-scale disasters such as fires, epidemics, earthquakes, and tsunamis. Even though biometric identification using panoramic dental radiography (PDR) has been the subject of several studies in the literature, further study remains a necessary and challenging issue. In this research, a human identification system was developed based on a convolutional neural network (CNN) and contour transform (CT). The proposed system was implemented on a total of 1540 PDR from 302 individuals. The preprocessing applied to PDRs for enhancing and taking the Region of Interest (ROI). The features were extracted using CT transform. These features were fused with features extracted

In this paper, a simulation of the electrical performance for Pentacene-based top-contact bottom-gate (TCBG) Organic Field-Effect Transistors (OFET) model with Polymethyl methacrylate (PMMA) and silicon nitride (Si3N4) as gate dielectrics was studied. The effects of gate dielectrics thickness on the device performance were investigated. The thickness of the two gate dielectric materials was in the range of 100-200nm to maintain a large current density and stable performance. MATLAB simulation demonstrated for model simulation results in terms of output and transfer characteristics for drain current and the transconductance. The layer thickness of 200nm may result in gate leakage current points to the requirement of optimizing the t

Introduction: The association between acute stroke and
renal function is well known. The aim of this study is to
know which group of patients with acute stroke is more
likely to have undiagnosed Chronic Kidney Disease and
which risk factors are more likely to be associated with.
Methods:We studied 77 patients who were diagnosed to
have an acute stroke.Patients were selected between
April2011andJune 2011 using the " 4-variable
Modification of
Diet in Renal Disease Formula " which estimates
Glomerular Filtration Rate using four variables :serum
creatinine ,age ,race and gender.
Results :The study included 38 male and 39 females
patients ,aged (35-95) years. Glomerular Filtration Rate in
patients wi