Seven fish species were collected from the drainage network at Al-Madaen region, south of
Baghdad with the aid of a cast net during the period from March to August 1993. These fishes
were infected with 22 parasite species (seven sporozoans, three ciliated protozoans, seven
monogeneans, two nematodes, one acanthocephalan and two crustaceans) and one fungus
species. Among such parasites, Chloromyxum wardi and Cystidicola sp. are reported here for
the first time in Iraq. In addition, 11 new host records are added to the list of parasites of
fishes of Iraq.

Fuzzy measures are considered important tools to solve many environmental problems. Water pollution is one of the environmental problems, which has negatively effect on the health of consumers. In this paper, a mathematical model is proposed to evaluate water quality in the distribution networks of Baghdad city. Fuzzy logic and fuzzy measures have been applied to evaluate water quality with respect to chemical and microbiological contaminants. Our results are evaluate water pollution of some chemical and microbiological contaminants, which are difficult to evaluation through traditional methods.

This paper experimentally investigates the heating process of a hot water supply using a neural network implementation of a self-tuning PID controller on a microcontroller system. The Particle Swarm Optimization (PSO) algorithm employed in system tuning proved very effective, as it is simple and fast optimization algorithm. The PSO method for the PID parameters is executed on the Matlab platform in order to put these parameters in the real-time digital PID controller, which was experimented with in a pilot study on a microcontroller platform. Instead of the traditional phase angle power control (PAPC) method, the Cycle by Cycle Power Control (CBCPC) method is implemented because it yields better power factor and eliminates harmonics

In this article, we design an optimal neural network based on new LM training algorithm. The traditional algorithm of LM required high memory, storage and computational overhead because of it required the updated of Hessian approximations in each iteration. The suggested design implemented to converts the original problem into a minimization problem using feed forward type to solve non-linear 3D - PDEs. Also, optimal design is obtained by computing the parameters of learning with highly precise. Examples are provided to portray the efficiency and applicability of this technique. Comparisons with other designs are also conducted to demonstrate the accuracy of the proposed design.

Many satellite systems take cover images like QuickBird for terrain so that these images scan be used to construct 3D models likes Triangulated Irregular Network (TIN), and Digital Elevation Model (DEM). This paper presents a production of 3D TIN for Al-Karkh University of Science in Baghdad - Iraq using QuickBird image data with pixel resolution of 0.6 m. The recent generations of high-resolution satellite imaging systems open a new era of digital mapping and earth observation. It provides not only multi-spectral and high-resolution data but also the capability for stereo mapping. The result of this study is a production of 3D satellite images of the university by merging 1 m DEM with satellite image for ROI using ArcGIS package Version

Attention-Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder affecting millions of people globally, is defined by symptoms of hyperactivity, impulsivity, and inattention that can significantly affect an individual's daily life. The diagnostic process for ADHD is complex, requiring a combination of clinical assessments and subjective evaluations. However, recent advances in artificial intelligence (AI) techniques have shown promise in predicting ADHD and providing an early diagnosis. In this study, we will explore the application of two AI techniques, K-Nearest Neighbors (KNN) and Adaptive Boosting (AdaBoost), in predicting ADHD using the Python programming language. The classification accuracies obtained w

Background: The quality of drinking water is directly related to community health. Therefore, improving the quality of drinking reflects positively on the health situation in general. The studies that deal with the quality of drinking water in the city of Baghdad in terms of chemical or microbial content are very scanty. Objective: The current review highlights the most important studies and research articles that concern the quality of drinking water, both bottled water and tap water, in terms of chemical and biological contamination and chemophysical specifications for drinking water. Abstract: Studies have shown that drinking water in the city of Baghdad, especially tap water, contains certain levels of heavy metals,

A content-based image retrieval (CBIR) is a technique used to retrieve images from an image database. However, the CBIR process suffers from less accuracy to retrieve images from an extensive image database and ensure the privacy of images. This paper aims to address the issues of accuracy utilizing deep learning techniques as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon, Kim, Kim, and Song (CKKS). To achieve these aims, a system has been proposed, namely RCNN_CKKS, that includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a

Offline handwritten signature is a type of behavioral biometric-based on an image. Its problem is the accuracy of the verification because once an individual signs, he/she seldom signs the same signature. This is referred to as intra-user variability. This research aims to improve the recognition accuracy of the offline signature. The proposed method is presented by using both signature length normalization and histogram orientation gradient (HOG) for the reason of accuracy improving. In terms of verification, a deep-learning technique using a convolution neural network (CNN) is exploited for building the reference model for a future prediction. Experiments are conducted by utilizing 4,000 genuine as well as 2,000 skilled forged signatu

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet