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

 In this paper we study the effect of the number of training samples for  Artificial neural networks ( ANN ) which is necessary for training process of feed forward neural network  .Also we design 5 Ann's and train 41 Ann's which illustrate how good the training samples that represent the actual function for Ann's.

The aim of this paper, is to design multilayer Feed Forward Neural Network(FFNN)to find the approximate solution of the second order linear Volterraintegro-differential equations with boundary conditions. The designer utilized to reduce the computation of solution, computationally attractive, and the applications are demonstrated through illustrative examples.

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

Background: Autism spectrum disorder (ASD) is characterized by impairments in social interaction and communication, restricted patterns of behavior, and unusual sensory sensitivities. Saliva may provide an easily accessible sample for analysis. Some salivary constituents levels altered in adolescents with ASD including antioxidants . This study aimed to investigate salivary physicochemical characteristic in relation to oral health status among adolescent with ASD. Materials and methods: Two groups were included in this study: forty institutionalized autistic adolescents and forty apparently healthy school adolescents with age range (12-15 years old, only males) selected randomly from Baghdad. Each group subdivided into two groups according

Background: The symptoms of Parkinson's disease (PD) can lead to problems in movement and coordination that lead to difficulty in maintaining well oral cleaning which can then negatively affect dental status of those Patients. The aim of present study: To evaluate prosthetic status in relation to weight status and occupation by age and gender among Parkinson's disease Patients in Baghdad-Iraq. Methods: The sample consisted of 104 patients with Parkinson disease attended to the Neurosciences Hospital in Baghdad city / Iraq, aged 60-79 years Prosthetic Status was recorded according to WHO(1997). Weight status was recorded according to Trowbridge 1988 and occupation was recorded according to Erikson and Goldthorpe (1992) and Ganzeboom et al (