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 work, new Schiff bases of quinazolinone derivatives (Q1-Q5) were synthesized from methyl anthranilate. The synthesis involved three steps. In the first step, methyl anthranilate was reacted with isothiocyanatobenzene, producing the thiourea derivative K1. The second step entailed reacting K1 with hydrazine hydrate, synthesizing 3-amino-2-(phenylamino) quinazolin-4(3H)-one (K2). The third step involved reaction of K2 with various aromatic aldehydes, yielding the Schiff bases derivatives Q1-Q5. The chemical structures of these compounds were identified by FT-IR,1H NMR and 13C NMR spectroscopy. The newly synthesized derivatives (Q1-Q5) were subjected to rigorous evaluation to assess their efficacy as corrosion inhibitors for ca

Recently, numerous the generalizations of Hurwitz-Lerch zeta functions are investigated and introduced. In this paper, by using the extended generalized Hurwitz-Lerch zeta function, a new Salagean’s differential operator is studied. Based on this new operator, a new geometric class and yielded coefficient bounds, growth and distortion result, radii of convexity, star-likeness, close-to-convexity, as well as extreme points are discussed.

         Allopurinol derivative were prepared by reacting the (1-chloroacetyl)-2-Hydropyrazolo{3,4-d}pyrimidine-4-oneiwith 5- methoxy- 2-aminoibenzothiazoleiunder certain conditions to obtain new compound  ( N- (2-aminoacetyl (5-methoxy) benzothiazole -2yl) (A₄), Reaction of 5-(P-dimethyl amine benzene)-2-amino-1,3,4- oxadiazole in the presence of potassium carbonate anhydrous to yield new  compound (N-(2- aminoacetyl-5-(P-dimethyl amine benzene )-1,3,4-oxadiazoles-2-yl)(A₃₀) and Azo compound (N-(5-(Azo-2-hydroxy-5-amino benzene)-1,3-Diazol-2yl)Allopurinol(A₄₆). The structure of prepared compounds were confirmed by (FT-IR)

Chloroacetamide derivatives (2a-g) have been prepared through reaction of chloroacetyl chloride(1) (which prepared by the reaction of chloroacetic acid with thionyl chloride) with primary aromatic amines and sulfa compounds to afford compounds (2a-g) which then reacted with p-hydroxy benzaldehyde via Williamson reaction to obtaine the new compounds 2-(4-formyl phenoxy)-N-aryl acetamide (3a-g). Finally , compounds (3a-g) will be use as a good synthon to prepare the Schiff bases represented by compounds 2-(4-aryliminophenoxy)-N-arylacetamide (4a-g). through , reaction with some primary aromatic amine. All the prepared compounds were investigated by the available physical and spectroscopic methods.

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

As one type of heating furnaces, the electric heating furnace (EHF) typically suffers from time delay, non-linearity, time-varying parameters, system uncertainties, and harsh en-vironment of the furnace, which significantly deteriorate the temperature control process of the EHF system. In order to achieve accurate and robust temperature tracking performance, an integration of robust state feedback control (RSFC) and a novel sliding mode-based disturbance observer (SMDO) is proposed in this paper, where modeling errors and external disturbances are lumped as a lumped disturbance. To describe the characteristics of the EHF, by using convection laws, an integrated dynamic model is established and identified as an uncertain nonlinear second ord