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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
Fri Oct 02 2015
Journal Name
American Journal Of Applied Sciences
Advances in Document Clustering with Evolutionary-Based Algorithms
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Document clustering is the process of organizing a particular electronic corpus of documents into subgroups of similar text features. Formerly, a number of conventional algorithms had been applied to perform document clustering. There are current endeavors to enhance clustering performance by employing evolutionary algorithms. Thus, such endeavors became an emerging topic gaining more attention in recent years. The aim of this paper is to present an up-to-date and self-contained review fully devoted to document clustering via evolutionary algorithms. It firstly provides a comprehensive inspection to the document clustering model revealing its various components with its related concepts. Then it shows and analyzes the principle research wor

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Publication Date
Fri May 21 2021
Journal Name
International Journal Of Pavement Research And Technology
Developing Resilient Modulus Prediction Models Based on Experimental Results of Crushed Hornfels Mixes with Different Gradations and Plasticity
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Publication Date
Wed Mar 01 2017
Journal Name
Neural Computing And Applications
The potential of nonparametric model in foundation bearing capacity prediction
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Publication Date
Tue Mar 31 2015
Journal Name
Al-khwarizmi Engineering Journal
Study on Flow Characteristics and Heat Transfer Behavior Around Different Geometrical Corrugated Extended Surfaces
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Abstract

The current study presents numerical investigation of the fluid (air) flow characteristics and convection heat transfer around different corrugated surfaces geometry in the low Reynolds number region (Re<1000). The geometries are included wavy, triangle, and rectangular. The effect of different geometry parameters such as aspect ratio and number of cycles per unit length on flow field characteristics and heat transfer was estimated and compared with each other. The computerized fluid dynamics package (ANSYS 14) is used to simulate the flow field and heat transfer, solve the governing equations, and extract the results. It is found that the turbulence intensity for rectangular extended surface was larg

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Publication Date
Wed Sep 01 2004
Journal Name
Faculty Of Engineering/mustansiriyah University
Studying and Analyzing Actual Safety Situation of Construction Factories in Iraq
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Safety programmes are considered one of the means to protect workers from different accidents and injuries and also to protect all assets including machines equipment and various materials. In spite of world state interests in occupational safety, this subject didn't get the required attention by senior staff of management at most of our country construction factories and the application of safety programmes still limited and in active. In order to a achieve the goal of the study, scientific method has been pursued to obtain the necessary information related to this study through tours to related companies and their construction factories and review literature that deal with occupational safety subject and their programmes and cost, in a

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Publication Date
Tue Dec 05 2023
Journal Name
Baghdad Science Journal
Classification of Diseases in Oil Palm Leaves Using the GoogLeNet Model
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The general health of palm trees, encompassing the roots, stems, and leaves, significantly impacts palm oil production, therefore, meticulous attention is needed to achieve optimal yield. One of the challenges encountered in sustaining productive crops is the prevalence of pests and diseases afflicting oil palm plants. These diseases can detrimentally influence growth and development, leading to decreased productivity. Oil palm productivity is closely related to the conditions of its leaves, which play a vital role in photosynthesis. This research employed a comprehensive dataset of 1,230 images, consisting of 410 showing leaves, another 410 depicting bagworm infestations, and an additional 410 displaying caterpillar infestations. Furthe

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Publication Date
Sat Dec 31 2022
Journal Name
Mathematical Modelling Of Engineering Problems
Investigation of Energy Efficient Clustering Algorithms in WSNs: A Review
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In recent years, Wireless Sensor Networks (WSNs) are attracting more attention in many fields as they are extensively used in a wide range of applications, such as environment monitoring, the Internet of Things, industrial operation control, electric distribution, and the oil industry. One of the major concerns in these networks is the limited energy sources. Clustering and routing algorithms represent one of the critical issues that directly contribute to power consumption in WSNs. Therefore, optimization techniques and routing protocols for such networks have to be studied and developed. This paper focuses on the most recent studies and algorithms that handle energy-efficiency clustering and routing in WSNs. In addition, the prime

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Publication Date
Sun Apr 02 2023
Journal Name
Journal Of Survey In Fisheries Sciences
Evaluation of the Serum Asprosin Levels in Patients with Double Diabetes
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Background : Double diabetes (DD) is the term used to describe situations in which a patient exhibits characteristics that are a combination of type 1 diabetes mellitus(T1DM) and type 2 Diabetes Mellitus (T2DM) a large epidemiological study found that 25.5% of people with T1D also had the metabolic syndrome. A new protein hormone called asprosin is predominantly released by white adipose tissue. It was initially discovered in 2016 . Asprosin is important diagnoses marker for insulin resistant in diabetes patients ,additionally is very important denotation about early diagnoses of type 2 diabetes. Objectives: The current study aims to find predictive significance of diagnosis a double diabetes by evaluating the asprosin in the blood serum of

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Publication Date
Thu Apr 13 2023
Journal Name
Journal Of Survey In Fisheries Sciences
The Elevation of Serum Subfatin Levels in Patients with Double Diabetes
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Background: Hybrid diabetes (or double diabetes, DD) occur when the patient which exhibits characteristics that combine type 1 diabetes (T1DM) and type 2 diabetes (T2DM). Formerly epidemiological studies found that quarter of people with T1D also had the metabolic syndrome. Subfatin, Also called cometin, it is a small (~27kDa) cytokine secreted by protein encoded by a gene called METRNL (simeler of meteorin). is much expressed in skin in the mucosal tissues and activated macrophages. Subfatin has also been described as a hormone that effected in some diseases such as metabolic diseases (including dyslipidemia), type 2 diabetes and obesity. Objectives: The current study objective is evaluating the subfatin in the blood serum of double diabet

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Publication Date
Thu Dec 01 2011
Journal Name
Iraqi Journal Of Physics
Multilayer Perceptron for analyzing satellite data
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Different ANN architectures of MLP have been trained by BP and used to analyze Landsat TM images. Two different approaches have been applied for training: an ordinary approach (for one hidden layer M-H1-L & two hidden layers M-H1-H2-L) and one-against-all strategy (for one hidden layer (M-H1-1)xL, & two hidden layers (M-H1-H2-1)xL). Classification accuracy up to 90% has been achieved using one-against-all strategy with two hidden layers architecture. The performance of one-against-all approach is slightly better than the ordinary approach

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