To evaluate the Interaction of Mn(II), Fe(II), Co(II), Ni(II),Cu(II), Zn(II) And Cd(II) Mixed- Ligand Complexes of cephalexin mono hydrate (antibiotics) And Furan-2-Carboxylic Acid To The Different DNA Sources. All the metal complexes were observed to cleave the DNA. A difference in the bands of complexes .The cleavage efficiency of the complexes compared with that of the control is due to their efficient DNA-binding ability and the other factors like solubility and bond length between the metal and ligand may also increase the DNA-binding ability. The ligands (Cephalexin mono hydrate (antibiotics) and Furan-2- Carboxylic acid and there newly synthesized metal complexes shows good antimicrobial activities and Binding DNA , thus, can be used

Azo ligand 4-((2-hydroxy-3,5-dimethylphenyl)diazenyl) benzoic acid was synthesized from 4-aminobenzoic acid and 2,4- dimethylphenol. Azo dye compounds have been characterized by different techniques (1H-NMR, UV-Vis and FT-IR). Metal chelates of (ZnII, CdII and HgII) have been synthesized with azo ligand (L). Produced compounds have been identified by using spectral studies, elemental analysis(C.H.N.) and conductivity. Produced metal chelates were studied using mole ratio as well sequences contrast types. Rate of concentration(1×10-4-3×10-4 Mole/L) sequence Beer's law. Compound solutions have been noticed height molar absorptivity. The addendum of ligand and compounds has applied as disperse dyes on cotton fabrics for antibacterial activit

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

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 new Schiff base ligand [L] [3-methyl-9,10 phenyl -6,7 dihydro-5,8 –dioxo-1,2 diazo –cyclo dodecu 2,11-diene ,4-one ] and its complexes with (Co(II), Ni(II), Cu (II), Zn(II) and Cd(II)) were synthesis.This ligand was prepared in three steps, in the first step a solution of salicyladehyed in methanol reacted under refluxed with hydrazine monohydrate to give an (intermediate compound 1) which reacted in the second step with sodium pyruvate to give an (intermediate compound 2) which gave the ligand [L] in the three step when it reacted with 1,2- dichloro ethane.The complexes were synthesized by direct reaction of the corresponding metal chloride with the ligand. The ligand and complexes were characterized by spectroscopic methods [IR, UV-

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.

The purpose of this work is to concurrently estimate the UVvisible spectra of binary combinations of piroxicam and mefenamic acid using the chemometric approach. To create the model, spectral data from 73 samples (with wavelengths between 200 and 400 nm) were employed. A two-layer artificial neural network model was created, with two neurons in the output layer and fourteen neurons in the hidden layer. The model was trained to simulate the concentrations and spectra of piroxicam and mefenamic acid. For piroxicam and mefenamic acid, respectively, the Levenberg-Marquardt algorithm with feed-forward back-propagation learning produced root mean square errors of prediction of 0.1679 μg/mL and 0.1154 μg/mL, with coefficients of determination of