Wisconsin Breast Cancer Dataset (WBCD) was employed to show the performance of the Adaptive Resonance Theory (ART), specifically the supervised ART-I Artificial Neural Network (ANN), to build a breast cancer diagnosis smart system. It was fed with different learning parameters and sets. The best result was achieved when the model was trained with 50% of the data and tested with the remaining 50%. Classification accuracy was compared to other artificial intelligence algorithms, which included fuzzy classifier, MLP-ANN, and SVM. We achieved the highest accuracy with such low learning/testing ratio.
The EMERGE application from Hampsson-Russell suite programs was used in the present study. It is an interesting domain for seismic attributes that predict some of reservoir three dimensional or two dimensional properties, as well as their combination. The objective of this study is to differentiate reservoir/non reservoir units with well data in the Yamama Formation by using seismic tools. P-impedance volume (density x velocity of P-wave) was used in this research to perform a three dimensional seismic model on the oilfield of Nasiriya by using post-stack data of 5 wells. The data (training and application) were utilized in the EMERGE analysis for estimating the reservoir properties of P-wave ve
... Show MoreThe current study was conducted on 100 females who were divided into two main groups; 60 with breast cancer and 40 healthy controls. Blood samples were collected from both premenopausal and postmenopausal breast cancer and healthy women. The samples were appropriately processed for the analysis of trace elements (zinc, copper, and lead) by using flame atomic absorption spectrophotometry (FAAS). The results showed a highly significant decrease (p< 0.01) in the mean serum level of zinc of in both pre- and postmenopausal breast cancer women (71.7 + 5.1 and 70.4 + 5.4 µg/dL, respectively) compared with healthy controls (89.7 + 10.2 and 97.5 + 13.2 µg/dL, respectively) . Also, a highly significant
... Show MoreBackground: Breast cancer composed of several biologic subtypes that have response to hormonal therapy. Tamoxifen is hormonal therapy with tissue- pecific antagonistic or agonist effects, the latter being responsible for multiple effects on lipid metabolism in women .
Objective: This study was designed to determine the impact of Tamoxifen on the serum lipid profile in breast cancer women.
Patients and methods: Prospective observation cohort study conducted at Oncology Teaching Hospital, Medical city complx.starting from October 2015 to October 2016. A total number of 40 premenopausal women with breast cancer were enrolled in this
... Show MoreBackground: Obesity rates are increasing day by day affecting all populations at different ages. The most prevalent kind of cancer among women worldwide is breast cancer, with increasing rates in the present time and in the future. Substantial connections between obesity and breast cancer are demonstrated. Elevated circulating levels of insulin and interleukin-6 have a substantial link to obesity and breast cancer development.
Objective: This study aimed to examine the serum levels of insulin and interleukin-6 in postmenopausal breast cancer patients and to study the connection between these biomarkers and breast cancer development.
Method: In this research
... Show MoreIn this paper, the memorization capability of a multilayer interpolative neural network is exploited to estimate a mobile position based on three angles of arrival. The neural network is trained with ideal angles-position patterns distributed uniformly throughout the region. This approach is compared with two other analytical methods, the average-position method which relies on finding the average position of the vertices of the uncertainty triangular region and the optimal position method which relies on finding the nearest ideal angles-position pattern to the measured angles. Simulation results based on estimations of the mobile position of particles moving along a nonlinear path show that the interpolative neural network approach outperf
... Show MoreThe present study develops an artificial neural network (ANN) to model an analysis and a simulation of the correlation between the average corrosion rate carbon steel and the effective parameter Reynolds number (Re), water concentration (Wc) % temperature (T o) with constant of PH 7 . The water, produced fom oil in Kirkuk oil field in Iraq from well no. k184-Depth2200ft., has been used as a corrosive media and specimen area (400 mm2) for the materials that were used as low carbon steel pipe. The pipes are supplied by Doura Refinery . The used flow system is all made of Q.V.F glass, and the circulation of the two –phase (liquid – liquid ) is affected using a Q.V.F pump .The input parameters of the model consists of Reynolds number , w
... Show MoreBackground: Breast cancer is the most frequently diagnosed malignancy and the second leading cause of mortality among women in Iraq forming 23% of cancer related deaths. The low survival from the disease is a direct consequence to the advanced stages at diagnoses. Aim: To document the composite stage of breast cancer among Iraqi patients at the time of diagnosis; correlating the observed findings with other clinical and pathological parameters at presentation. Patients and Methods: A retrospective study enrolling the clinical and pathological characteristics of 603 Iraqi female patients diagnosed with breast cancer. The composite stage of breast cancer was determined according to UICC TNM Classification System of Breast Cancer and the Ameri
... Show MoreThe study aims to predict Total Dissolved Solids (TDS) as a water quality indicator parameter at spatial and temporal distribution of the Tigris River, Iraq by using Artificial Neural Network (ANN) model. This study was conducted on this river between Mosul and Amarah in Iraq on five positions stretching along the river for the period from 2001to 2011. In the ANNs model calibration, a computer program of multiple linear regressions is used to obtain a set of coefficient for a linear model. The input parameters of the ANNs model were the discharge of the Tigris River, the year, the month and the distance of the sampling stations from upstream of the river. The sensitivity analysis indicated that the distance and discharge
... Show MoreIn this paper, first we refom1Ulated the finite element model
(FEM) into a neural network structure using a simple two - dimensional problem. The structure of this neural network is described
, followed by its application to solving the forward and inverse problems. This model is then extended to the general case and the advantages and di sadvantages of this approach are descri bed along with an analysis of the sensi tivity of
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