Schiff bases (Sh1-Sh3) have been synthesized (p-aminophenol) was condensed with different aromatic aldehyde in ethanol inthe presence of glacial acetic acid as catalyst. These Schiff bases on treatment with monochloroacetyl choride gave 3-chloro-1-(4-hydroxyphenyl)-4-(substituted)azetidin-2-one(Az4-Az6), with αmercaptoacetic acid gave 3-(4-hydroxyphenyl)-2-( substituted)thiazolidin-4-one (Th7-Th9) and with anthranilic acid gave 3-(4-hydroxyphenyl)-2(substituted)-2,3-dihydroquinazolin-4(1H)-one (Qu10-Qu12). The purity of the derivatives was confirmed by TLC. The some compoundsidentify by (FT-IR and1H, 13C-NMR) data. Some of derivatives were evaluated activity against several microbesto determine ability to inhibit bacterial in some h

Capital Adequacy is on of the most importance of financial position element because its role is enhance against varions risks of Banking procese . Which this risks is a result of the nature of its operations , investment , Financing tools in addition , in order to  Faced any clients withdraws un expected .

So This study aim to measure The Capital Adequacy according to basel II and explin the different risk in  the Islamic Bank as aresult to nature and different investment and financing tools as participation speculation , murabah and natures of its assets and liabilities related to commercial banks .

Find out we concludes that the Islamic Banks needs to increase the ca

The study of biopolymers and their derivative materials had received a considerable degree of attention from researchers in the preparation of novel material. Biopolymers and their derivatives have a wide range of applications as a result of their bio-compatibility, bio-degradability and non-toxicity. In this paper, chitosan reacted with different aldehydes(2,4 –dichloro- benzaldehyde or 2-methyl benzaldehyde), different ketones (4-bromoacetophenone or 3-aminoacetophenone) to produce chitosan schiff base (1-4) . Chitosan schiff base (1-4) reacted with glutaric acid or adipic acid in acidic media in distilled water according to the steps of Fischer and Speier to produce compounds (5-12)

The 2-D Resistivity imaging survey conducted within the desert area to meet the region's need of groundwater, due to the absence of any surface water, in addition, there are no any geophysical studies carried out previously. Ten point survey was performed to study the ground water aquifers in the southwest of Samawah city, Iraq. The length of the survey line in each point survey is 1200 meters by using 120 electrodes with 10 meters electrode spacing by applying Wenner-Schlumberger array. There are three main resistivity zones in the study area. The first resistivity zone ranging between 2.00-10.1 ohm.m, locate at shallow depth, represented Quaternary deposits. The second resistivity zone ranging between 22.8-51.3 ohm.m, it represents bea

Nine new compounds of 2-amino-5-chlorobenzothiazole derivatives were synthesized. These new compounds were formed through the reaction of 2-amino-5-chlorobenzothiazole 1 with ethyl chloroacetate and KOH, which gave an ester derivative 2, followed by refluxing compound 2 with hydrazine hydrate to afford hydrazide derivative 3. The reaction of compound 3 with CS₂ and KOH gave 1,3,4-oxadiazole-2-thiol derivative 4, and then the reaction of compound 2 with thiosemicarbazide to produce compound 5  then treated it with 4%NaOH led to ring closure to provide 1,2,4-triazole-3-thiol derivative

Background/Objectives: The purpose of current research aims to a modified image representation framework for Content-Based Image Retrieval (CBIR) through gray scale input image, Zernike Moments (ZMs) properties, Local Binary Pattern (LBP), Y Color Space, Slantlet Transform (SLT), and Discrete Wavelet Transform (DWT). Methods/Statistical analysis: This study surveyed and analysed three standard datasets WANG V1.0, WANG V2.0, and Caltech 101. The features an image of objects in this sets that belong to 101 classes-with approximately 40-800 images for every category. The suggested infrastructure within the study seeks to present a description and operationalization of the CBIR system through automated attribute extraction system premised on CN

Universal image stego-analytic has become an important issue due to the natural images features curse of dimensionality. Deep neural networks, especially deep convolution networks, have been widely used for the problem of universal image stegoanalytic design. This paper describes the effect of selecting suitable value for number of levels during image pre-processing with Dual Tree Complex Wavelet Transform. This value may significantly affect the detection accuracy which is obtained to evaluate the performance of the proposed system. The proposed system is evaluated using three content-adaptive methods, named Highly Undetetable steGO (HUGO), Wavelet Obtained Weights (WOW) and UNIversal WAvelet Relative Distortion (UNIWARD).
The obtain

Human posture estimation is a crucial topic in the computer vision field and has become a hotspot for research in many human behaviors related work. Human pose estimation can be understood as the human key point recognition and connection problem. The paper presents an optimized symmetric spatial transformation network designed to connect with single-person pose estimation network to propose high-quality human target frames from inaccurate human bounding boxes, and introduces parametric pose non-maximal suppression to eliminate redundant pose estimation, and applies an elimination rule to eliminate similar pose to obtain unique human pose estimation results. The exploratory outcomes demonstrate the way that the proposed technique can pre

<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c