Developing a new adaptive satellite images classification technique, based on a new way of merging between regression line of best fit and new empirical conditions methods. They are supervised methods to recognize different land cover types on Al habbinya region. These methods should be stand on physical ground that represents the reflection of land surface features.      The first method has separated the arid lands and plants. Empirical thresholds of different TM combination bands; TM3, TM4, and TM5 were studied in the second method, to detect and separate water regions (shallow, bottomless, and very bottomless). The Optimum Index Factor (OIF) is computed for these combination bands, which realized

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

Theligand4-[5-(2-hydoxy-phenyl)- [1,3,4- thiadiazole-2- ylimino methyl]-1,5-dimethyl -2-phenyl-1,2-dihydro-pyrazol-3-one [HL1] is prepared and characterized. It is reacted with poly(vinyl chloride) (PVC) in THF to form the PVC-L compounds ,PVC-L interacted with ions of transition metals to form PVC-L-MII complexes .All prepared compounds are characterized by FTIR spectroscopy, u.v-visible spectroscopy, C.H.N.S. analysis and some of them by 1HNMR

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

This paper present a simple and sensitive method for the determination of DL-Histidine using FIA-Chemiluminometric measurement resulted from oxidation of luminol molecule by hydrogen peroxide in alkaline medium in the presence of DL-Histidine. Using 70?l. sample linear plot with a coefficient of determination 95.79% for (5-60) mmol.L-1 while for a quadratic relation C.O.D = 96.44% for (5-80) mmol.L-1 and found that guadratic plot in more representative. Limit of detection was 31.93 ?g DL-Histidine (S/N = 3), repeatability of measurement was less that 5% (n=6). Positive and negative ion interferances was removed by using minicolume containing ion exchange resin located after injection valve position.

A new ligand [N-(acetyl amino) thioxomethyl] valine was prepared from the reaction of acetyl iso thiocyanate with valine. The ligand was characterized by FT-IR, UV- vis and 1HNMR spectrum, The complexes with some metal ions (M +2 =Co,Ni,Cu,Zn,Cd,Hg) have been prepared and characterized. The structural diagnosis were established by IR,UV-Vis  spectrum, flame atomic absorption spectroscopy conductivity and magnetic susceptibility ,the complexes showed tetrahedral geometry around the metal   l.

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

The present work involved two steps: the first step include Mannich reaction was carried out on 2- mercaptobenzimidazole using formaldehyde and different secondary amine or amide to gives the compounds(2-16). The secnd step include preparation of (Ethylbenzimidazoly-2-mercaptoacetate)(17) from the reaction of 2- mercaptobenzimidazole with ethylchloroacetate than prepared hydrazide derivative[18]from reaction of compound(17) with hydrazinehydrate. Followed Preparation of shiff bases(19-24) and there reaction with mercaptoacetic acid to give a new compounds containing thiazolidinderivetives(25-30).Structure confirmation of all prepared compound were proved using FTIR and element analysis (C.H.N.S) and mesurmentedmelting poi