A first step in this research was to synthesize Schiff's bases(1-3)using an Amoxcilline intensification reaction with different aromatic aldehydes in absolute ethanol. In benzene and refluxing conditions,Schiff's bases were cyclized with succinic and Phthalic anhydride to give a new sequence of 1,3-oxazepine derivatives(4-6) and (7-9),respectively.The last step,cyclization reactions with sodium azide in THF solvent resulted in the formation of [10 and 11], which are supposed to be biologically significant.FT.IR, 1H-NMR and 13C-NMR (for compound 4,7,9, and 11),as well as melting points reported, were used to characterize these prepared compounds ,Bacillus (G+), Staphylococcus (G+), and E.Coli (G-)were screened against these compounds. . To i

Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas

Amoxicillin 1 was treated with thiosemicarbazide and Phosphoryl chloride to obtain a new derivatives that contains 1,3,4-thiadiazole moiety 2. Schiff bases compounds were synthesized by the reaction of compound 2 with different aldehydes such as benzaldehyde and some substituted Benzaldehyde; p-hydroy, p-Chloro, p-Nitro, p-Dimethylamino, p-Methyl, p-Methoxy, p-Ethoxy to give compounds 3a-h. The obtained compounds have tested towards gram -ve and gram +ve bacteria. The compound shows good to moderate result towards the bacteria.

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

The study is concern on determine the type of Candida spp.in leukemia patients that were infected with oral candidiasis as a result to their immune suppression (weekend immune system) due to their submission to radiation and chemotherapy treatment. The result showed that the most common isolates were C. guillermondii 19 which represent 31.66% of cases, then followed by C. itermedia 11 which represent 18.3%, while the less common isolates were for C. zeylamodes 3 which represent 5%.

Silver Indium Aluminum Selenium AgIn1xAlxSe2 AIAS for x=01 thin films was deposited by thermal evaporation at RT and different︣︢︡ ︠︣1thickness 100 150 and 200 nm on the glass Substrate and p2Si wafer to produce AIAS/p3Si heterojunctionsolarcell4 Structural optical electrical and photovoltaicproperties6 are investigated for the samples XRD analysis reveals that all the deposited AIAS films show polycrystalline structure without any change due to increase of thickness Average diameter and roughness calculated from AFM images shows an increase in its value with increasing thickness The optical absorbance and transmittance for samples are measured using a spectrometer type UV Visible 1800 spectra1photometer to study the energy6gap The