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Classification of brain tumors using the multilayer perceptron artificial neural network
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Information from 54 Magnetic Resonance Imaging (MRI) brain tumor images (27 benign and 27 malignant) were collected and subjected to multilayer perceptron artificial neural network available on the well know software of IBM SPSS 17 (Statistical Package for the Social Sciences). After many attempts, automatic architecture was decided to be adopted in this research work. Thirteen shape and statistical characteristics of images were considered. The neural network revealed an 89.1 % of correct classification for the training sample and 100 % of correct classification for the test sample. The normalized importance of the considered characteristics showed that kurtosis accounted for 100 % which means that this variable has a substantial effect on how the network perform when predicting cases of brain tumor, contrast accounted for 64.3 %, correlation accounted for 56.7 %, and entropy accounted for 54.8 %. All remaining characteristics accounted for 21.3-46.8 % of normalized importance. The output of the neural networks showed that sensitivity and specificity were scored remarkably high level of probability as it approached % 96.

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Publication Date
Mon Jan 01 2024
Journal Name
Journal Of Engineering
Face-based Gender Classification Using Deep Learning Model
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Gender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer lea

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Publication Date
Fri Jan 01 2016
Journal Name
Middle-east Journal Of Scientific Research
Question Classification Using Different Approach: A Whole Review
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Publication Date
Thu Mar 01 2018
Journal Name
Journal Of Oral And Maxillofacial Surgery, Medicine, And Pathology
Assessment of the extracapsular dissection of the benign parotid tumors, extending the literature
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Publication Date
Fri Sep 27 2019
Journal Name
Journal Of Baghdad College Of Dentistry
Surgical Treatment Outcome of Salivary Gland Tumors
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Background: Salivary gland neoplasms constitute a group of heterogeneous lesions with complex clinicopathologic characteristics and distinct biological behavior. Numerous studies have suggested geographical variation, therefore the aims of this study were to analyze the characteristics of salivary gland neoplasms in two Iraqi centers and to analyze the postoperative complications that are encountered after surgical treatment of these tumors. Materials and Methods: A retrospective study of the patients who were treated for major and minor epithelial salivary gland tumors was conducted. The analyzed data included; demographic information (age and gender), the site of the tumor, the clinical manifestations, the histological type of the tumo

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Publication Date
Sat Jul 20 2024
Journal Name
Sumer Journal For Pure Science
Classify the Nutritional Status of Iraqi children under Five Years Using Fuzzy Classification
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Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
Effects of ethanol on the hippocampus of rat Brain
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ten albino male rates were orally treated daily 20% and 30% ethanol for 30 days treatment with 30%ethanol caused of hippocampuse of darckness google hospital patients

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Publication Date
Sat Sep 30 2017
Journal Name
Al-khwarizmi Engineering Journal
Study the Effect of Multilayer Single Point Incremental Forming on Residual Stresses for Bottom Plates
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Abstract

 

Knowing the amount of residual stresses and find technological solutions to minimize and control them during the production operation are an important task because great levels of deformation which occurs in single point incremental forming (SPIF), this induce highly non-uniform residual stresses. In this papera propose of a method for multilayer single point incremental forming with change in thickness of the top plate (0.5, 0.7, 0.9) mm and lubrication or material between two plates(polymer, grease, grease with graphite, mos2) to knowing an effect of this method  and parameters on residual stresses for the bottom plates. Also compare these results for the

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Publication Date
Tue Sep 01 2020
Journal Name
Journal Of Engineering
An Adaptive Digital Neural Network-Like-PID Control Law Design for Fuel Cell System Based on FPGA Technique
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This paper proposes an on-line adaptive digital Proportional Integral Derivative (PID) control algorithm based on Field Programmable Gate Array (FPGA) for Proton Exchange Membrane Fuel Cell (PEMFC) Model. This research aims to design and implement Neural Network like a digital PID using FPGA in order to generate the best value of the hydrogen partial pressure action (PH2) to control the stack terminal output voltage of the (PEMFC) model during a variable load current applied. The on-line Particle Swarm Optimization (PSO) algorithm is used for finding and tuning the optimal value of the digital PID-NN controller (kp, ki, and kd) parameters that improve the dynamic behavior of the closed-loop digital control fue

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Publication Date
Sat Dec 02 2017
Journal Name
Al-khwarizmi Engineering Journal
Human Face Recognition Using Wavelet Network
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            This paper presents a study of wavelet self-organizing maps (WSOM) for face recognition. The WSOM is a feed forward network that estimates optimized wavelet based for the discrete wavelet transform (DWT) on the basis of the distribution of the input data, where wavelet basis transforms are used as activation function.

 

 

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Publication Date
Thu Sep 01 2016
Journal Name
Journal Of Engineering
Calculating the Transport Density Index from Some of the Productivity Indicators for Railway Lines by Using Neural Networks
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The efficiency evaluation of the railway lines performance is done through a set of indicators and criteria, the most important are transport density, the productivity of enrollee, passenger vehicle production, the productivity of freight wagon, and the productivity of locomotives. This study includes an attempt to calculate the most important of these indicators which transport density index from productivity during the four indicators, using artificial neural network technology. Two neural networks software are used in this study, (Simulnet) and (Neuframe), the results of second program has been adopted. Training results and test to the neural network data used in the study, which are obtained from the international in

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