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

In this paper, a new equivalent lumped parameter model is proposed for describing the vibration of beams under the moving load effect. Also, an analytical formula for calculating such vibration for low-speed loads is presented. Furthermore, a MATLAB/Simulink model is introduced to give a simple and accurate solution that can be used to design beams subjected to any moving loads, i.e., loads of any magnitude and speed. In general, the proposed Simulink model can be used much easier than the alternative FEM software, which is usually used in designing such beams. The obtained results from the analytical formula and the proposed Simulink model were compared with those obtained from Ansys R19.0, and very good agreement has been shown. I

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

Heterocyclic polymers / silica nanocomposite one of important materials because of excellent properties such as thermal , electrical , and mechanical properties , so that hybrid nanomaterial are widely used in many fields, in this paper nanocomposite had prepared by modification of silica nanoparticals by using acrylic acid and functionalized the surface of nanoparticles, and using free Radical polymerization by AIBN as initiators and anhydrous toluene as solvent to polymerize functionalize silica nanoparticles with heterocyclic monomers to prepare heterocylic polymers / silica nanocomposite and study electrical conductivity , The nanocomposite which had prepared characterized by many analysis technique to study thermal properties such

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

New compounds of amids [IV]a-e and Schiff bases [V]f-h derived from 2-amino-1,3,4-oxadiazoles [III] were synthesized and characterized by physical and spectraldata.2-Aamino-1,3,4-oxadiazoles was prepared by the action of bromine on acorresponding semicarbazide [II]( which was prepared by reaction of dialdehyde [I]with semicarbazide hydrochloride ) in the presence of sodium acetate , followed byan intramolecular cyclization . (PDF) Synthesis of New Amides and Schiff Bases derived From 2-Amino -1,3,4- Oxadiazole. Available from: https://www.researchgate.net/publication/326679206_Synthesis_of_New_Amides_and_Schiff_Bases_derived_From_2-Amino_-134-_Oxadiazole [accessed Nov 15 2023].

Two ligand ortho-amino phenyl thio benzyl (L1) and 1,3 bis (ortho - amino phenyl thio ) acetone (L2) and their complexes have been prepared and characterized . The L1 ligand is lossing phenyl group on complexcation and forming 1,2 bis (ortho - amino phenyl thio ) ethane L3 and this tetrahedrally coordinated to the metal ion ( M+2 = Ni , Cu , Cd ) and octahedrally coordinated with mercury and cobalt ions , while the ligand L2 is behave as tridentate ligand forming octahedrally around chrome metal ion . Structural , diagnosis were established by i.r , Uv- visible , conductivity elemental analysis and (mass spectra , H nmr spectra for( L1 , L2 ) .

The Cu(II) was found using a quick and uncomplicated procedure that involved reacting it with a freshly synthesized ligand to create an orange complex that had an absorbance peak of 481.5 nm in an acidic solution. The best conditions for the formation of the complex were studied from the concentration of the ligand, medium, the eff ect of the addition sequence, the eff ect of temperature, and the time of complex formation. The results obtained are scatter plot extending from 0.1–9 ppm and a linear range from 0.1–7 ppm. Relative standard deviation (RSD%) for n = 8 is less than 0.5, recovery % (R%) within acceptable values, correlation coeffi cient (r) equal 0.9986, coeffi cient of determination (r2) equal to 0.9973, and percentage capita