The current world is observing huge developments in presenting the opportunity for organizations and administrative units to use information and communication technology and their adoption by administrative work due to its importance in the achievement of work with higher efficiency, speed, and facility of communication with all individuals and companies using various means of communication Depending on the Internet networks. Therefore, the research dealt with the study of electronic systems designed and adopted in the creation or construction of a database for archiving data, which is the main method in organizations and administrative units in developed countries. Where this system works to convert documents, and manual processes and t

In this study, the preparation and characterization of hyacinth plant /chitosan composite, as a heavy metal removal, were done. Water hyacinth plant (Eichhorniacrasspes) was collected from Tigris river in Baghdad. The root and shoot parts of plant were ground to powder. Composite materials were prepared at different ratios of plant part (from 2.9% to 30.3%, wt /wt) which corresponds to (30-500mg) of hyacinth plant (root and shoot) and chitosan. The results showed that all examined ratios of plant parts have an excellent absorption to copper (Cu (II)). Moreover, it was observed that 2.9% corresponds (30mg) of plant root revealed highest removal (82.7%) of Pb (II), while 20.23% of shoot removed 61% of Cd (II) within 24 hr

Text based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering.

Calcifying epithelial odontogenic tumour (CEOT) is a benign odontogenic neoplasm of epithelial origin that secretes an amyloid‐like protein tending towards calcification. This study aims to describe a case series from Iraq of one of the rarest odontogenic tumours.

An optical video communication system is designed and constructed using pulse frequency modulation (PFM) technique. In this work PFM pulses are generated at the transmitter using voltage control oscillator (VCO) of width 50 ns for each pulse. Double frequency, equal width and narrow pulses are produced in the receiver be for demodulation. The use of the frequency doubling technique in such a system results in a narrow transmission bandwidth (25 ns) and high receiver sensitivity.

Background: Few updated retrospective histopathological-based studies in Iraq evaluate a comprehensive spectrum of oro-maxillofacial lesions. Also, there was a need for a systematic way of categorizing the diseases and reporting results in codes according to the WHO classification that helps occupational health professionals in the clinical-epidemiological approach.

Objectives: to establish an electronic archiving database according to the ICD-10 that encompasses oro-maxillofacial lesions in Sulaimani city for the last 12 years, then to study the prevalence trend and correlation with clinicopathological parameters.

Subjects and Methods:  A descri

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