Identifying breast cancer utilizing artificial intelligence technologies is valuable and has a great influence on the early detection of diseases. It also can save humanity by giving them a better chance to be treated in the earlier stages of cancer. During the last decade, deep neural networks (DNN) and machine learning (ML) systems have been widely used by almost every segment in medical centers due to their accurate identification and recognition of diseases, especially when trained using many datasets/samples. in this paper, a proposed two hidden layers DNN with a reduction in the number of additions and multiplications in each neuron. The number of bits and binary points of inputs and weights can be changed using the mask configuration

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

One study whose importance has significantly grown in recent years is lip-reading, particularly with the widespread of using deep learning techniques. Lip reading is essential for speech recognition in noisy environments or for those with hearing impairments. It refers to recognizing spoken sentences using visual information acquired from lip movements. Also, the lip area, especially for males, suffers from several problems, such as the mouth area containing the mustache and beard, which may cover the lip area. This paper proposes an automatic lip-reading system to recognize and classify short English sentences spoken by speakers using deep learning networks. The input video extracts frames and each frame is passed to the Viola-Jone

Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural

The water scarcity that Iraq suffers from and the low irrigation efficiency in irrigation projects, therefore, it was necessary to evaluate the performance of the irrigation system of the western canal for the Ishaqi irrigation project in Salah al-Din Governorate to determine the water management strategies that can be used to improve the irrigation efficiency in the project. The performance of the field irrigation system was evaluated on two fields of different crops and irrigation methods according to the agricultural reality of the study area in the Western canal for the Al-Ishaqi Irrigation Project in Salah Al-Din Governorate. The fieldwork included measurements of the moisture content before and after irrigation, fi

Background: Different diagnostic definition and criteria have been recommended by different expert groups for the diagnosis of metabolic syndrome, however, it’s prevalence in the same population could differ depending on the definition used yielding different results. In Iraq, there is a lack of research comparing these different diagnostic definitions. Objective: To find out the most suitable metabolic syndrome definition to be used for Iraqi people.  Methods: 320 participants were recruited for this study, 53.4% men and 46.6% women, aged between 25-85 years, visiting Baghdad Teaching Hospital, the prevalence of metabolic syndrome according to different definitions were compared and the agreement was assessed by the Kappa st

Background: Different diagnostic definition and criteria have been recommended by different expert groups for the diagnosis of metabolic syndrome, however, it’s prevalence in the same population could differ depending on the definition used yielding different results. In Iraq, there is a lack of research comparing these different diagnostic definitions.

Objective: To find out the most suitable metabolic syndrome definition to be used for Iraqi people.

 Methods: 320 participants were recruited for this study, 53.4% men and 46.6% women, aged between 25-85 years, visiting Baghdad Teaching Hospital, the prevalence of metabolic syndrome according to different definition