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Effect of Successive Convolution Layers to Detect Gender
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Image classification can be defined as one of the most important tasks in the area of machine learning. Recently, deep neural networks, especially deep convolution networks, have participated greatly in end-to-end learning which reduce need for human designed features in the image recognition like Convolution Neural Network. It is offers the computation models which are made up of several processing layers for learning data representations with several abstraction levels. In this work, a pre-trained deep CNN is utilized according to some parameters like filter size, no of convolution, pooling, fully connected and type of activation function which includes 300 images for training and predict 100 image gender using probability measures. Results in Classification and precision accuracy equal to 0.68 and 0.3225 respectively.

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Publication Date
Sat Dec 01 2012
Journal Name
Journal Of Economics And Administrative Sciences
Using Benford’s Law to detect Financial Fraud
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Fraud Includes acts involving the exercise of deception by multiple parties inside and outside companies in order to obtain economic benefits against the harm to those companies, as they are to commit fraud upon the availability of three factors which represented by the existence of opportunities, motivation, and rationalization. Fraud detecting require necessity of indications the possibility of its existence. Here, Benford’s law can play an important role in direct the light towards the possibility of the existence of financial fraud in the accounting records of the company, which provides the required effort and time for detect fraud and prevent it.

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Publication Date
Sat Apr 15 2023
Journal Name
Iraqi Journal Of Science
Best Way to Detect Breast Cancer by UsingMachine Learning Algorithms
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Breast cancer is the second deadliest disease infected women worldwide. For this
reason the early detection is one of the most essential stop to overcomeit dependingon
automatic devices like artificial intelligent. Medical applications of machine learning
algorithmsare mostly based on their ability to handle classification problems,
including classifications of illnesses or to estimate prognosis. Before machine
learningis applied for diagnosis, it must be trained first. The research methodology
which isdetermines differentofmachine learning algorithms,such as Random tree,
ID3, CART, SMO, C4.5 and Naive Bayesto finds the best training algorithm result.
The contribution of this research is test the data set with mis

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Publication Date
Sat Jul 31 2021
Journal Name
Brain Sciences
Robust EEG Based Biomarkers to Detect Alzheimer’s Disease
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Biomarkers to detect Alzheimer’s disease (AD) would enable patients to gain access to appropriate services and may facilitate the development of new therapies. Given the large numbers of people affected by AD, there is a need for a low-cost, easy to use method to detect AD patients. Potentially, the electroencephalogram (EEG) can play a valuable role in this, but at present no single EEG biomarker is robust enough for use in practice. This study aims to provide a methodological framework for the development of robust EEG biomarkers to detect AD with a clinically acceptable performance by exploiting the combined strengths of key biomarkers. A large number of existing and novel EEG biomarkers associated with slowing of EEG, reductio

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Publication Date
Tue May 30 2023
Journal Name
Iraqi Journal Of Science
Deep Convolutional Neural Network Architecture to Detect COVID-19 from Chest X-Ray Images
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      Today, the world is living in a time of epidemic diseases that spread unnaturally and infect and kill millions of people worldwide. The COVID-19 virus, which is one of the most well-known epidemic diseases currently spreading, has killed more than six million people as of May 2022. The World Health Organization (WHO) declared the 2019 coronavirus disease (COVID-19) after an outbreak of SARS-CoV-2 infection. COVID-19 is a severe and potentially fatal respiratory disease caused by the SARS-CoV-2 virus, which was first noticed at the end of 2019 in Wuhan city. Artificial intelligence plays a meaningful role in analyzing medical images and giving accurate results that serve healthcare workers, especially X-ray images, which are co

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Publication Date
Sat Jul 22 2023
Journal Name
Journal Of Engineering
Multiwavelet based-approach to detect shared congestion in computer networks
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Internet paths sharing the same congested link can be identified using several shared congestion detection techniques. The new detection technique which is proposed in this paper depends on the previous novel technique (delay correlation with wavelet denoising (DCW) with new denoising method called Discrete Multiwavelet Transform (DMWT) as signal denoising to separate between queuing delay caused by network congestion and delay caused by various other delay variations. The new detection technique provides faster convergence (3 to 5 seconds less than previous novel technique) while using fewer probe packets approximately half numbers than the previous novel technique, so it will reduce the overload on the network caused by probe packets.

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Publication Date
Tue Oct 01 2013
Journal Name
Journal Of Economics And Administrative Sciences
The use of analytical procedures to detect Earning Management Practices
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            That analytical procedures are of analytical tools important because it gives assurance to the auditor-free financial statements of the economic units replace the audit of cases offraud and errors and distortions, and thereby to increase the effectiveness of the audit process and confirm the possibility oftrust and reliance on the financial statements that Adfgaha auditor.
Inspite of identify evidence of proof necessary to enhance the auditor's opinion the results reached in the audit p

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Publication Date
Sat Apr 30 2022
Journal Name
Iraqi Journal Of Science
A Review on Face Detection Based on Convolution Neural Network Techniques
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     Face detection is one of the important applications of biometric technology and image processing. Convolutional neural networks (CNN) have been successfully used with great results in the areas of image processing as well as pattern recognition. In the recent years, deep learning techniques specifically CNN techniques have achieved marvellous accuracy rates on face detection field. Therefore, this study provides a comprehensive analysis of face detection research and applications that use various CNN methods and algorithms. This paper presents ten of the most recent studies and illustrate the achieved performance of each method. 

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Publication Date
Fri Jul 21 2023
Journal Name
Journal Of Engineering
The Effect of Laminated Layers on the Flutter Speed of Composite Wing
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The paper presents an investigation to the flutter speed of composite wing for different ply orientation. Structurally the composite wing was idealized as a composite beam load carrying structure. Theodorsen’s expression was used to get the 2- dimension unsteady lifting force and pitching moment in the limit of incompressible flow and subsonic speed which were integrated over the wing span. A free vibration analysis was first carried out to get the natural frequencies and mode shapes .The velocity-damping (V-g) method was used to calculate the flutter speed and the flutter frequency. A wing of unmanned aerial vehicle was manufactured from woven glass and polyester resin where the flutter speed was calculated experimentally by the wind

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Publication Date
Thu Apr 18 2019
Journal Name
Iraqi Journal Of Science
Simple Colorimetric Method Using Aqueous Solution to Detect Heavy Metal
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Simple method has been used to determine the absence of heavy metals in an aqueous solution. Fluorescein was used as the base colorimetric materialThis was doped with CuCl2 and the final solution showeda clear change in color. This change was correlated with the change in both pH and electrical conductivity of the solution. The optical property as an obvious change of the spectra was observed. Therefore, this simple method could be proposed as a method to detectheavy metals in any solution.

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Publication Date
Wed Jul 01 2009
Journal Name
Journal Of The Faculty Of Medicine Baghdad
ELFA and IFAT Techniques to Detect Chlamydial Infections in Baghdad Women and Its Effect on the Immunoglobulins Level.
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Background: Chlamydia trachomatis is one of the most common human pathogens and considered as one of the causative agents of STDs. This organism cause acute and recurrent pelvic infections and infertility.
Patients and Methods: Two hundred and seventy three females were included in the present study, attending infertility department, AL-Elwiya hospital, AL-Jarah private hospital, central public health laboratory and STDs clinic to whom IFAT, ELAF and immunoglobulins concentration were done.
Results: Females were divided into three age groups <20; 20-39 and ≥40 years. Single and repeated abortions were 44.9%, 55.1% respectively. Primary and secondary infertility were 55.6% and 44.4%. Higher abortio

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