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

Sheep are considered as an important part of livestock in the worldwide, particularly in Iraq, as they provide meat, milk, leather, wool, and manure. The present study aim is isolation and identification of staphylococci, enteric bacteria and Pseudomonas spp. Totally, 115 samples were collected from sheep (100 samples were collected from the nasal cavity of local sheep suffering from respiratory infections, and 15 samples were collected from apparently healthy local sheep). All the samples were collected from seven flocks located in Abu Ghraib and Al-Radwaniyah, Baghdad governorate, Iraq. The samples were taken during the period from October 2020 to February 2021. Staphylococcus spp., Pseudomonas spp., and enteric bacteria were detected fi

In this research Artificial Neural Network (ANN) technique was applied to study the filtration process in water treatment. Eight models have been developed and tested using data from a pilot filtration plant, working under different process design criteria; influent turbidity, bed depth, grain size, filtration rate and running time (length of the filtration run), recording effluent turbidity and head losses. The ANN models were constructed for the prediction of different performance criteria in the filtration process: effluent turbidity, head losses and running time. The results indicate that it is quite possible to use artificial neural networks in predicting effluent turbidity, head losses and running time in the filtration process, wi

Semliki Forest Virus (SFV), a member of the Alphavirus genus in the Togaviridae family, is a small-enveloped, positive-sense single-stranded RNA (+ssRNA) virus. The virus is spread by mosquitos and can infect humans, resulting in mild febrile disease with symptoms that include fever, myalgia, arthralgia, persistent headaches and asthenia.  Virulent strains of SFV in mice cause lethal encephalitis by infecting neurons in the central nervous system. In on-going experiments in the research group using a focused siRNA screen we have investigated the role of deubiquitylases (DUBs) during SFV infection (as a model alphavirus) and monitored the effect of DUB depletion on cell viability after infection. We identified a group of DUBs that h

The deterioration of buried sewers during their lifetime can be affected by several factors leading to bad performance and can damage the infrastructure similar to other engineering structures. The Hydraulic deterioration of the buried sewers caused by sewer blockages while the structural deterioration caused by sewer collapses due to sewer specifications and the surrounding soil characteristics and the groundwater level. The main objective of this research is to develop deterioration models, which are used to predict changes in sewer condition that can provide assessment tools for determining the serviceability of sewer networks in Baghdad city. Two deterioration models were developed and tested using statistical software SPSS, the

In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database

Visceral leishmaniosis is one of the most fatal old-world neglected disease with estimated 90 thousand worldwide cases emerge each year. In Iraq, the cutaneous and visceral form are endemic but available chemotherapies are either toxic with diverse side effects, expensive available drugs or parasite …