With the continuous progress of image retrieval technology, the speed of searching for the required image from a large amount of image data has become an important issue. Convolutional neural networks (CNNs) have been used in image retrieval. However, many image retrieval systems based on CNNs have poor ability to express image features. Content-based Image Retrieval (CBIR) is a method of finding desired images from image databases. However, CBIR suffers from lower accuracy in retrieving images from large-scale image databases. In this paper, the proposed system is an improvement of the convolutional neural network for greater accuracy and a machine learning tool that can be used for automatic image retrieval. It includes two phases

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The Reversed Phase High Performance Liquid Chromatography (RP-HPLC) has been used for the separation of Poly aromatic hydrocarbons(PAHs) by using column Reprosil 100 C 18 which was found to be a suitable one for this purpose. The result showed that using mobile phase of (Acetonitrile-water) Reversed Phase HPLC , flow rate of (1.2 ml/min) , column temperature (30CËš) and wave length of (254nm), give a complete separation with a good resolution . The total separation time was less than 20 min. The result of the study showed that the vegetables of Baghdad city were polluted by poly aromatic hydrocarbons(PAHs) in different places where samples taken because of drainage of the heavy water ,industrial trash and trash of oil colanders. -

Among several separation processes, the air flotation distinguish as remarkably high potential separation process related to its high separation efficiency and throughput, energy-efficient, simple process, cost-effective, applicable to a wide range of oily wastewater and no by-products. The current study aimed to investigate the effect of the type and concentration of surfactant on the stability of oil-water emulsion and efficiency of the separation process. For this purpose, three types of surfactant where used (anionic SDS, mixed nonionic Span 85/Tween 80, and cationic CTAB). The results demonstrated that the Span 85/Tween 80 surfactant has the best stability, and it increases with the surfactant concentration augmentation. The removal ef

Objective(s): to assess the effectiveness of educational program on nurses' knowledge concerning the side
effects of chemotherapy among children with leukemia.
Methodology: A descriptive analytic (quasi – experimental) design study was carried out at Baghdad City from
2
nd of October to 27th of June 2015. Non-probability sample of (35) male and female nurses was selected from
the Oncology Wards in Children Welfare, Child's Central and Baghdad Teaching Hospital. The study
instruments consisted of two major parts to meet the purposes of study. The first part is related to nurses'
demographic characteristics and the second part (four domains) is related to nurses' knowledge concerning the
side effects of chemothera

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

Electrocoagulation process was employed for the treatment of river water flows in Iraq. In this study, a batch Electrocoagulation process was used to treat river water taken from Al - Qadisiyah water treatment plant. electrolysis time, voltage and inter-electrode spacing were the most important parameters to study . A statistical model was developed using the RSM model. The optimum condition after studying the parameter effect the process was 1 cm separating, 30 volts . The RSM model shows the ideal condition of removal for both the TSS and turbidity at 1 cm, 20 volts and 55 min.

Biometrics represent the most practical method for swiftly and reliably verifying and identifying individuals based on their unique biological traits. This study addresses the increasing demand for dependable biometric identification systems by introducing an efficient approach to automatically recognize ear patterns using Convolutional Neural Networks (CNNs). Despite the widespread adoption of facial recognition technologies, the distinct features and consistency inherent in ear patterns provide a compelling alternative for biometric applications. Employing CNNs in our research automates the identification process, enhancing accuracy and adaptability across various ear shapes and orientations. The ear, being visible and easily captured in