Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Static Synchronous Series Compensator (SSSC) is a well known device for effectively regulating the active power flow in a power system. In this paper, the SSSC linearized power flow equations are incorporated into Newton-Raphson algorithm in a MATLAB written program to investigate the control of active poweer flow and the transient stability of a five bus and a thirty bus IEEE test systems, during abnormal conduction (three phase fault near buses). A comparison of the results obtained for the base case without SSSC and with it to investigate the effectiveness of the device on both of the active power flow and the transient stability.

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

A total of 54 out of 67 (80.59%) of burn wound swab showed growth of one, or two, or three bacterial pathogens. Pseudomonas aeruginosa was the commonest pathogen, isolated in 48.14% of swab samples, followed by Klebsiella pneumoniae (31.48%), Staphylococcus aureus (27.77%), Acinetobacter baumanii (14.81%), Escherichia coli (7.40%), and Citrobacter freundii, Providencia stuartii, Enterobacter cloacae, with 1.85% isolation percentage for each. All bacterial isolates were tested against 19 antibiotics, and showed multi-drug resistance to 10 antibiotics, or more. The most effective antibiotics were the fifth-generation cephalosporin, ceftobiprole, and and antibiotic combinations, as Ceftazidime / clavulanic acid, and Cefoperazone /sulbactam, an

          The primary objective of this paper is to improve a biometric authentication and classification model using the ear as a distinct part of the face since it is unchanged with time and unaffected by facial expressions. The proposed model is a new scenario for enhancing ear recognition accuracy via modifying the AdaBoost algorithm to optimize adaptive learning. To overcome the limitation of image illumination, occlusion, and problems of image registration, the Scale-invariant feature transform technique was used to extract features. Various consecutive phases were used to improve classification accuracy. These phases are image acquisition, preprocessing, filtering, smoothing, and feature extraction. To assess the proposed

In this paper the proton, neutron and matter density distributions and the corresponding root mean square (rms) radii of the ground states and the elastic magnetic electron scattering form factors and the magnetic dipole moments have been calculated for exotic nucleus of potassium isotopes K (A= 42, 43, 45, 47) based on the shell model using effective W0 interaction. The single-particle wave functions of harmonic-oscillator (HO) potential are used with the oscillator parameters b. According to this interaction, the valence nucleons are asummed to move in the d3f7 model space. The elastic magnetic electron scattering of the exotic nuclei 42K (J?T= 2- 2), 43K(J?T=3/2+ 5/2), 45K (J?T= 3/2+ 7/2) and 47K (J?T= 1/2+ 9/2) investigated t

Background: The present study involved the following parts, the first part is evaluation of the levels of glycated hemoglobin(HbA1c), creatinine, uric acid(UA) and albumin in patients with diabetic nephropathy comparison with the group of healthy as a control group. The second part is the measurement and evaluation of oxidative stress represented in the malondihydehyde(MDA) as a biomarker of oxidative stress as well as the identification of vitamins C and E as an antioxidant in patients with diabetic nephropathy(DN) compared with the healthy group. Objective: The objective of this study is to estimate oxidative stress by calculate malondialdehyd as biomarker and evaluate some vitamins such as vit C and vit E as antioxidants in diabetic neph