This paper presents a combination of enhancement techniques for fingerprint images affected by different type of noise. These techniques were applied to improve image quality and come up with an acceptable image contrast. The proposed method included five different enhancement techniques: Normalization, Histogram Equalization, Binarization, Skeletonization and Fusion. The Normalization process standardized the pixel intensity which facilitated the processing of subsequent image enhancement stages. Subsequently, the Histogram Equalization technique increased the contrast of the images. Furthermore, the Binarization and Skeletonization techniques were implemented to differentiate between the ridge and valley structures and to obtain one

With the rapid development of smart devices, people's lives have become easier, especially for visually disabled or special-needs people. The new achievements in the fields of machine learning and deep learning let people identify and recognise the surrounding environment. In this study, the efficiency and high performance of deep learning architecture are used to build an image classification system in both indoor and outdoor environments. The proposed methodology starts with collecting two datasets (indoor and outdoor) from different separate datasets. In the second step, the collected dataset is split into training, validation, and test sets. The pre-trained GoogleNet and MobileNet-V2 models are trained using the indoor and outdoor se

The focus of this research lies in the definition of an important aspect of financial development, which is reflected on the alleviation of poverty in Iraq, namely financial inclusion and then taking the path of achieving a sustainable economy, certainly after reviewing one of the important international experiences in this regard and finally measuring the level of financial inclusion in Iraq and its impact on poverty reduction through the absolute poverty line indicator.

The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample

Background:Wilson’s disease (WD) is an inherited
disorder of copper metabolism that is characterized
by tremendous variation in the clinical presentation.
Objective: To assess demographic distribution,
clinical presentations, diagnostic evaluation, and any
association between clinical presentations and other
studied variables of a sample of Iraqi patients with
WD.
Methods: A descriptive cross sectional study with
analytic elements was conducted during 2011, from
the 1st of February till the 10th of June. The sampling
method was a convenient non-random one, carried
out through consecutive pooling of registered WD
patients. A questionnaire-form paper had been
developed for the process of data col

Dueto their ability to providefood for people, sheep and goats areimportant to the economiesofmanynations.Toxoplasmagondii,orT.gondii,isaprotozoanparasite that often infects sheep. Stillbirth, early embryonic death and resorption, neonatal mortality, fetal death and mummification, and parasite infection are examples of possible negative effects. Theconsequences aremoreseverethe earlier in gestation the infection arises. The stage of pregnancy at which the infection occurs in thesheep and goats is connected with the severity of the illness. T.gondii may infect humansandcarnivorousanimalsvia the meatofinfectedsheepandgoats.Lessthan 4%ofsheep thatareconsistently infected withT.gondiicarrytheparasitevertically to their offspring. The majority o

Osteoarthritis (OA) is recognized as a main public health difficult. It is one of the major reasons of reduced function that diminishes quality of life worldwide. Osteoarthritis is a very common disorder affecting the joint cartilage. As there is no cure for osteoarthritis, treatments currently focus on management of symptoms. Pain relief, improved joint function, and joint stability are the main goals of therapy. The muscle weakness and muscle atrophy contribute to the disease process. So, rehabilitation and physiotherapy were often prescribed with the intention to alleviate pain and increase mobility. Medical therapy provides modest benefits in pain reduction and functional improvement; however, non-steroidal anti-inflammatory dru

In this paper, integrated quantum neural network (QNN), which is a class of feedforward

neural networks (FFNN’s), is performed through emerging quantum computing (QC) with artificial neural network(ANN) classifier. It is used in data classification technique, and here iris flower data is used as a classification signals. For this purpose independent component analysis (ICA) is used as a feature extraction technique after normalization of these signals, the architecture of (QNN’s) has inherently built in fuzzy, hidden units of these networks (QNN’s) to develop quantized representations of sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that

This research aims to analyze and simulate biochemical real test data for uncovering the relationships among the tests, and how each of them impacts others. The data were acquired from Iraqi private biochemical laboratory. However, these data have many dimensions with a high rate of null values, and big patient numbers. Then, several experiments have been applied on these data beginning with unsupervised techniques such as hierarchical clustering, and k-means, but the results were not clear. Then the preprocessing step performed, to make the dataset analyzable by supervised techniques such as Linear Discriminant Analysis (LDA), Classification And Regression Tree (CART), Logistic Regression (LR), K-Nearest Neighbor (K-NN), Naïve Bays (NB