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Breast Cancer Decisive Parameters for Iraqi Women via Data Mining Techniques
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Objective This research investigates Breast Cancer real data for Iraqi women, these data are acquired manually from several Iraqi Hospitals of early detection for Breast Cancer. Data mining techniques are used to discover the hidden knowledge, unexpected patterns, and new rules from the dataset, which implies a large number of attributes. Methods Data mining techniques manipulate the redundant or simply irrelevant attributes to discover interesting patterns. However, the dataset is processed via Weka (The Waikato Environment for Knowledge Analysis) platform. The OneR technique is used as a machine learning classifier to evaluate the attribute worthy according to the class value. Results The evaluation is performed using a training data rather than cross validation. The decision tree algorithm J48 is applied to detect and generate the pattern of attributes, which have the real effect on the class value. Furthermore, the experiments are performed with three machine learning algorithms J48 decision tree, simple logistic, and multilayer perceptron using 10-folds cross validation as a test option, and the percentage of correctly classified instances as a measure to determine the best one from them. As well as, this investigation used the iteration control to check the accuracy gained from the three mentioned above algorithms. Hence, it explores whether the error ratio is decreasing after several iterations of algorithm execution or not. Conclusion It is noticed that the error ratio of classified instances are decreasing after 5-10 iterations, exactly in the case of multilayer perceptron algorithm rather than simple logistic, and decision tree algorithms. This study realized that the TPS_pre is the most common effective attribute among three main classes of examined dataset. This attribute highly indicates the BC inflammation.

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Publication Date
Fri Dec 01 2023
Journal Name
Iraqi Journal Of Physics
Gas Sensor for Hazardous Nitrogen Dioxide Based on TiO2 Nanotube Synthesis via Electrochemical Method
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Because of the quick growth of electrical instruments used in noxious gas detection, the importance of gas sensors has increased. X-ray diffraction (XRD) can be used to examine the crystal phase structure of sensing materials, which affects the properties of gas sensing. This contributes to the study of the effect of electrochemical synthesis of titanium dioxide (TiO2) materials with various crystal phase shapes, such as rutile TiO2 (R-TiO2NTs) and anatase TiO2 (A-TiO2NTs). In this work, we have studied the effect of voltage on preparing TiO2 nanotube arrays via the anodization technique for gas sensor applications. The results acquired from XRD, energy dispersion spectro

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Publication Date
Fri Dec 01 2023
Journal Name
Iraqi Journal Of Physics
Gas Sensor for Hazardous Nitrogen Dioxide Based on TiO2 Nanotube Synthesis via Electrochemical Method
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Because of the quick growth of electrical instruments used in noxious gas detection, the importance of gas sensors has increased. X-ray diffraction (XRD) can be used to examine the crystal phase structure of sensing materials, which affects the properties of gas sensing. This contributes to the study of the effect of electrochemical synthesis of titanium dioxide (TiO2) materials with various crystal phase shapes, such as rutile TiO2 (R-TiO2NTs) and anatase TiO2 (A-TiO2NTs). In this work, we have studied the effect of voltage on preparing TiO2 nanotube arrays via the anodization technique for gas sensor applications. The results acquired from XRD, energy dispersion spectroscopy (EDX), and field emission scanning electron microscopy

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Publication Date
Sun Sep 01 2013
Journal Name
Baghdad Science Journal
Synthesis of substituted (oxazepine, Diazepine, tetrazde) via Schiff Bases for 2- Aminobenzo Thaizole Derivatives
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This work includs synthesis of several Schiff bases by condensation of 6- methoxy – 2- amino benzothiazole with some aldehydes and ketones (2- hydroxyl benzaldehyde, 4- hydroxyl benzaldehyde, 4- N,N –dimethy amino acetophenone, benzophenone) to abtain schiff bases (1-5). These schiff bases were found to react with phthalate anhydride to give oxazepine derivatives (6-10) that were reacted with primary aromatic amines to give Diazepine derivatives (11-15). Besides, we prepared new tetrazole derivatives (16-20) from the reaction of the prepared Schiff bases with sodium azide in the prepared compounds that were characterized by physical properties, FT-IR and some of the 1H-NMR and 13C –NMR spectroscopy.

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Publication Date
Tue Aug 31 2021
Journal Name
International Journal Of Intelligent Engineering And Systems
FDPHI: Fast Deep Packet Header Inspection for Data Traffic Classification and Management
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Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c

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Publication Date
Thu Jan 06 2022
Journal Name
Kuwait Journal Of Science
AVO analysis for high amplitude anomalies using 2D pre-stack seismic data
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Amplitude variation with offset (AVO) analysis is an 1 efficient tool for hydrocarbon detection and identification of elastic rock properties and fluid types. It has been applied in the present study using reprocessed pre-stack 2D seismic data (1992, Caulerpa) from north-west of the Bonaparte Basin, Australia. The AVO response along the 2D pre-stack seismic data in the Laminaria High NW shelf of Australia was also investigated. Three hypotheses were suggested to investigate the AVO behaviour of the amplitude anomalies in which three different factors; fluid substitution, porosity and thickness (Wedge model) were tested. The AVO models with the synthetic gathers were analysed using log information to find which of these is the

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Publication Date
Fri Jan 01 2010
Journal Name
Ibn Al- Haitham J. Fo R Pure & Appl. Sci
Evaluation of The Nuclear Data on(α,n)Reaction for Natural Molybdenum
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The cross section evaluation for (α,n) reaction was calculated according to the available International Atomic Energy Agency (IAEA) and other experimental published data . These cross section are the most recent data , while the well known international libraries like ENDF , JENDL , JEFF , etc. We considered an energy range from threshold to 25 M eV in interval (1 MeV). The average weighted cross sections for all available experimental and theoretical(JENDL) data and for all the considered isotopes was calculated . The cross section of the element is then calculated according to the cross sections of the isotopes of that element taking into account their abundance . A mathematical representative equation for each of the element

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Publication Date
Fri Jan 01 2021
Journal Name
Ieee Access
Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction
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Publication Date
Mon Jun 01 2026
Journal Name
Iraqi Journal For Computers And Informatics
Explainable Federated Learning for Brain Tumor Classification Using Multi-Source MRI Data
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Early diagnosis and clinical decision-making depend on accurate brain tumor classification using magnetic resonance imaging (MRI). However, traditional deep learning methods usually rely on centralized medical data, which raises privacy concerns and limits the use of distributed clinical data. This research proposes a privacy-preserving federated learning framework for MRI image-based binary brain tumor classification using a decentralized ResNet-18 architecture that enables collaborative training without sharing raw patient data. To reflect realistic clinical conditions, the framework integrates heterogeneous multi-source datasets in different image formats (PNG and JPG) and evaluates performance under both IID and non-IID settings

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Publication Date
Wed Dec 30 2020
Journal Name
Al-kindy College Medical Journal
The Value of Diffusion Weighted MRI in the Detection and Localization of Prostate Cancer among a Sample of Iraqi Patients
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Background: Prostatic adenocarcinoma is the most widely recognized malignancy in men and the second cause of cancer-related mortality encountered in male patients after lung cancer.

Aim of the study:  To assess the diagnostic value of diffusion weighted imaging (DWI) and its quantitative measurement, apparent diffusion coefficient (ADC), in the identification and localization of prostatic cancer compared with T2 weighted image sequence (T2WI).

Type of the study: a prospective analytic study

Patients and methods: forty-one male patients with suspected prostatic cancer were examined by pelvic MRI at the MRI department of the Oncology Teaching Hospital/Medical City in Baghdad

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Publication Date
Sun Jun 05 2011
Journal Name
Baghdad Science Journal
Correlation between Paris function parameters to crack velocity for Alumina ceramics
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The question about the existence of correlation between the parameters A and m of the Paris function is re-examined theoretically for brittle material such as alumina ceramic (Al2O3) with different grain size. Investigation about existence of the exponential function which fit a good approximation to the majority of experimental data of crack velocity versus stress intensity factor diagram. The rate theory of crack growth was applied for data of alumina ceramics samples in region I and making use of the values of the exponential function parameters the crack growth rate theory parameters were estimated.

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