Mobile Wireless sensor networks have acquired a great interest recently due to their capability to provide good solutions and low-priced in multiple fields. Internet of Things (IoT) connects different technologies such as sensing, communication, networking, and cloud computing. It can be used in monitoring, health care and smart cities. The most suitable infrastructure for IoT application is wireless sensor networks. One of the main defiance of WSNs is the power limitation of the sensor node. Clustering model is an actual way to eliminate the inspired power during the transmission of the sensed data to a central point called a Base Station (BS). In this paper, efficient clustering protocols are offered to prolong network lifetime. A kern

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi

Seventy four Iraqi breast cancer paraffin blocks were collected from patients were attended to center health laboratory, histopathology department, Bagdad, Iraq. The patients information’s which included: name, age, and the pathological stage, grade, tumor size were obtained from the clinical records of the patients also relation with sex hormones was recorded. The cases which has been taken included invasive ductal and invasive lobular carcinoma type Women age were ranged from 24-80 years peak age frequency of tumor occurred in the category of more than 40 years old. Immunohistochemical expression of her-2/neu was from total 74 cases of infiltrative ductal carcinoma cases, 27(36.49%)were positive for Her-2/neu expression, 47(63.51%) were

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A common field development task is the object of the present research by specifying the best location of new horizontal re-entry wells within AB unit of South Rumaila Oil Field. One of the key parameters in the success of a new well is the well location in the reservoir, especially when there are several wells are planned to be drilled from the existing wells. This paper demonstrates an application of neural network with reservoir simulation technique as decision tool. A fully trained predictive artificial feed forward neural network (FFNNW) with efficient selection of horizontal re-entry wells location in AB unit has been carried out with maintaining a reasonable accuracy. Sets of available input data were collected from the exploited g

Breast cancer is the commonest cause of cancer related death in women worldwide. Amplification or over-expression of the ERBB2 (HER/neu) gene occurs in approximately 15-30% of breast cancer cases and it is strongly associated with an increased disease recurrence and a poor prognosis. Determination of HER2/neu status is crucial in the treatment plan as that positive cases will respond to trastuzumab therapy. It has been used to test for HER2/neu by immunohistochemistry as a first step and then to study only the equivocal positive cases (score 2+) by in situ hybridization technique. The aim of our study is to compare between immunohistochemistry and silver in situ hybridization (SISH) in assessment of human epidermal growth factor (HER2/neu)