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Analyzing the behavior of different classification algorithms in diabetes prediction
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<span lang="EN-US">Diabetes is one of the deadliest diseases in the world that can lead to stroke, blindness, organ failure, and amputation of lower limbs. Researches state that diabetes can be controlled if it is detected at an early stage. Scientists are becoming more interested in classification algorithms in diagnosing diseases. In this study, we have analyzed the performance of five classification algorithms namely naïve Bayes, support vector machine, multi layer perceptron artificial neural network, decision tree, and random forest using diabetes dataset that contains the information of 2000 female patients. Various metrics were applied in evaluating the performance of the classifiers such as precision, area under the curve (AUC), accuracy, receiver operating characteristic (ROC) curve, f-measure, and recall. Experimental results show that random forest is better than any other classifier in predicting diabetes with a 90.75% accuracy rate.</span>

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Publication Date
Sun Jun 01 2014
Journal Name
Baghdad Science Journal
Clouds Height Classification Using Texture Analysis of Meteosat Images
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In the present work, pattern recognition is carried out by the contrast and relative variance of clouds. The K-mean clustering process is then applied to classify the cloud type; also, texture analysis being adopted to extract the textural features and using them in cloud classification process. The test image used in the classification process is the Meteosat-7 image for the D3 region.The K-mean method is adopted as an unsupervised classification. This method depends on the initial chosen seeds of cluster. Since, the initial seeds are chosen randomly, the user supply a set of means, or cluster centers in the n-dimensional space.The K-mean cluster has been applied on two bands (IR2 band) and (water vapour band).The textural analysis is used

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Publication Date
Tue Jul 31 2018
Journal Name
Journal Of Theoretical And Applied Information Technology
Classification and monitoring of autism using svm and vmcm
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Autism is a lifelong developmental deficit that affects how people perceive the world and interact with each others. An estimated one in more than 100 people has autism. Autism affects almost four times as many boys than girls. The commonly used tools for analyzing the dataset of autism are FMRI, EEG, and more recently "eye tracking". A preliminary study on eye tracking trajectories of patients studied, showed a rudimentary statistical analysis (principal component analysis) provides interesting results on the statistical parameters that are studied such as the time spent in a region of interest. Another study, involving tools from Euclidean geometry and non-Euclidean, the trajectory of eye patients also showed interesting results. In this

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Publication Date
Wed Sep 01 2010
Journal Name
Al-khwarizmi Engineering Journal
Prediction of the Scale Removal Rate in Heat Exchanger Piping System Using the Analogies between Mass and Momentum Transfer
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The possibility of predicting the mass transfer controlled CaCO3 scale removal   rate has been investigated.

Experiments were carried out using chelating agents as a cleaning solution at different time and Reynolds’s number. The results of CaCO3 scale removal or (mass transfer rate) (as it is the controlling process) are compared with proposed model of prandtl’s and Taylor particularly based on the concept of analogy among momentum and mass transfer.

Correlation for the variation of Sherwood number ( or mass transfer rate ) with Reynolds’s number have been obtained .

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Publication Date
Sat Jan 01 2022
Journal Name
Proceedings Of 2nd International Multi-disciplinary Conference Theme: Integrated Sciences And Technologies, Imdc-ist 2021, 7-9 September 2021, Sakarya, Turkey
Investigation of the Effect of Diabetes on Lower Limb Muscles with Surface Electromyography (EMG)
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Publication Date
Sat Mar 02 2019
Journal Name
World Heart Journal
Assessment of the Left Ventricular Mass (LVM) among Patients with Diabetes in Relation to Age Using Doppler Echocardiography
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Background: Diabetes mellitus a major factor that has adverse effects on the vascular system and the heart. It causes an increase in cardiac muscle thickness, resulting in decreased compliance and increased peripheral arterial stiffness. This study aims to assess the left ventricular mass (LVM) and left ventricular hemodynamic changes in diabetic patients measured by Doppler echocardiography. Patients and Methods: The study included 50 diabetic patients ranging in age between 25 and 80 years, (mean age: 54.1 ± 15.10, 19 males, 31 females) and 50 healthy subjects, aged 25 to 80 years (mean age: 48.52 ± 14.45, 11 males, 39 females). Doppler echocardiography was used to assess left ventricular function. The measurements included

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Publication Date
Wed Jun 29 2022
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Studying and Analyzing Operating Conditions of Hollow Fiber Membrane Preparation Process: A Review Paper
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Polymeric hollow fiber membrane is produced by a physical process called wet or dry/wet phase inversion; a technique includes many steps and depends on different factors (starting from selecting materials, end with post-treatment of hollow fiber membrane locally manufactured). This review highlights the most significant factors that affect and control the characterization and structure of ultrafiltration hollow fiber membranes used in different applications.    

   Three different types of polymers (polysulfone PSF, polyethersulfone PES or polyvinyl chloride PVC) were considered to study morphology change and structure of hollow fiber membranes in this review. These hollow fiber membranes were manufactured with different pro

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Publication Date
Sat May 01 2021
Journal Name
Journal Of Physics: Conference Series
The Classification of Fetus Gender Based on Fuzzy C-Mean Using a Hybrid Filter
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Abstract<p>This paper proposes a new approach, of Clustering Ultrasound images using the Hybrid Filter (CUHF) to determine the gender of the fetus in the early stages. The possible advantage of CUHF, a better result can be achieved when fuzzy c-mean FCM returns incorrect clusters. The proposed approach is conducted in two steps. Firstly, a preprocessing step to decrease the noise presented in ultrasound images by applying the filters: Local Binary Pattern (LBP), median, median and discrete wavelet (DWT), (median, DWT & LBP) and (median & Laplacian) ML. Secondly, implementing Fuzzy C-Mean (FCM) for clustering the resulted images from the first step. Amongst those filters, Median & Lap</p> ... Show More
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Publication Date
Sun Jun 20 2021
Journal Name
Baghdad Science Journal
Future Scenario of Global Climate Map change according to the Köppen -Geiger Climate Classification
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Earth’s climate changes rapidly due to the increases in human demands and rapid economic growth. These changes will affect the entire biosphere, mostly in negative ways. Predicting future changes will put us in a better position to minimize their catastrophic effects and to understand how humans can cope with the new changes beforehand. In this research, previous global climate data set observations from 1961-1990 have been used to predict the future climate change scenario for 2010-2039. The data were processed with Idrisi Andes software and the final Köppen-Geiger map was created with ArcGIS software. Based on Köppen climate classification, it was found that areas of Equator, Arid Steppes, and Snow will decrease by 3.9 %, 2.96%, an

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Publication Date
Sat May 01 2021
Journal Name
Journal Of Physics: Conference Series
The classification of fetus gender based on fuzzy C-mean using a hybrid filter
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This paper proposes a new approach, of Clustering Ultrasound images using the Hybrid Filter (CUHF) to determine the gender of the fetus in the early stages. The possible advantage of CUHF, a better result can be achieved when fuzzy c-mean FCM returns incorrect clusters. The proposed approach is conducted in two steps. Firstly, a preprocessing step to decrease the noise presented in ultrasound images by applying the filters: Local Binary Pattern (LBP), median, median and discrete wavelet (DWT),(median, DWT & LBP) and (median & Laplacian) ML. Secondly, implementing Fuzzy C-Mean (FCM) for clustering the resulted images from the first step. Amongst those filters, Median & Laplace has recorded a better accuracy. Our experimental evaluation on re

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Publication Date
Sun Dec 30 2012
Journal Name
Al-kindy College Medical Journal
The Detection of Silent Celiac Disease In patients With Type 1 Diabetes Mellitus by the use of Anti Tissue Transglutaminase Antibodies
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Objective: Detection the presumptive prevalence of silent celiac disease in patients with type 1 diabetes mellitus with determination of which gender more likely to be affected.
Methods: One hundred twenty asymptomatic patients [75 male , 45 female] with type 1 diabetes mellitus with mean age ± SD of 11.25 ± 2.85 year where included in the study . All subjects were serologically screened for the presence of anti-tissue transglutaminase IgA antibodies (anti-tTG antibodies) by Enzyme-Linked Immunosorbent Assay (ELISA) & total IgA was also measured for all using radial immunodiffusion plate . Anti-tissue transglutaminase IgG was selectively done for patients who were expressing negative anti-tissue transglutaminase IgA with low tot

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