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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
Mon May 06 2024
Journal Name
Journal Of Ecological Engineering
Using Machine Learning Algorithms to Predict the Sweetness of Bananas at Different Drying Times
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The consumption of dried bananas has increased because they contain essential nutrients. In order to preserve bananas for a longer period, a drying process is carried out, which makes them a light snack that does not spoil quickly. On the other hand, machine learning algorithms can be used to predict the sweetness of dried bananas. The article aimed to study the effect of different drying times (6, 8, and 10 hours) using an air dryer on some physical and chemical characteristics of bananas, including CIE-L*a*b, water content, carbohydrates, and sweetness. Also predicting the sweetness of dried bananas based on the CIE-L*a*b ratios using machine learn- ing algorithms RF, SVM, LDA, KNN, and CART. The results showed that increasing the drying

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Publication Date
Sat Jun 01 2024
Journal Name
Journal Of Ecological Engineering
Using Machine Learning Algorithms to Predict the Sweetness of Bananas at Different Drying Times
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Publication Date
Mon Aug 01 2016
Journal Name
2016 38th Annual International Conference Of The Ieee Engineering In Medicine And Biology Society (embc)
Selecting the optimal movement subset with different pattern recognition based EMG control algorithms
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Publication Date
Thu Mar 31 2022
Journal Name
American Journal Of Economics And Business Management
Measuring and Analyzing the Factors Affecting the Behavior of Government Spending in Iraq Using the Co-Integration Test for the Period 2000-2020
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Government spending is the tool that the state uses to achieve its various goals. The research aims to identify the most important determinants of government spending in Iraq and to indicate the type and nature of the relationship between government spending and its determinants, which will contribute to understanding the movement of government spending. The results of the co-integration test using the border test methodology showed that the variables of population growth and oil prices have a long-term effect on government spending while inflation is not significant in the long run, and that 47% of the equilibrium imbalance (short-term imbalance) in government spending in the previous period (t-) can be corrected in the current period (t)

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Publication Date
Sun Jun 28 2026
Journal Name
Tikrit Journal Of Administrative And Economic sciences
Analyzing the relationship between human resource management practices and the creative behavior of employees Analytical research in the Ministry of Science and Technology
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Publication Date
Sat Jun 01 2019
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Lung cancer classification using data mining and supervised learning algorithms on multi-dimensional data set
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These With recent developments in machine learning, data mining and computer vision, there is great potential for improvements in early detection of lung cancer using scans and data available. This paper details the methods and techniques used in our project, where the objective is to develop algorithms to determine whether a patient has or is likely to develop lung cancer using dataset images using data mining and machine learning for the classification and examination. We explore approaches to address the problem. Cancer is the most important cause of death globally. The disease diagnosis is a major process to treat the patients who are affected by cancer disease. The diagnosis process is more difficult comparatively known about t

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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
Wed Mar 12 2025
Journal Name
The Alarba'een Journal
Analyzing and tracking the data of the millions sized gatherings for the Arba'in visit and proposing alternative ways to relieve congestion using spatial analysis algorithms
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The city of Karbala is one of the most important holy places for visitors and pilgrims from the Islamic faith, especially through the Arabian visit, when crowds of millions gather to commemorate the martyrdom of Imam Hussein. Offering services and medical treatments during this time is very important, especially when the crowds head to their destination (the holy shrine of Imam Hussein (a.s)). In recent years, the Arba'in visit has witnessed an obvious growth in the number of participants. The biggest challenge is the health risks, and the preventive measures for both organizers and visitors. Researchers identified various challenges and factors to facilitating the Arba'in visit. The purpose of this research is to deal with the religious an

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Publication Date
Mon Dec 20 2021
Journal Name
Baghdad Science Journal
Recurrent Stroke Prediction using Machine Learning Algorithms with Clinical Public Datasets: An Empirical Performance Evaluation
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Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al

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Publication Date
Wed May 01 2024
Journal Name
Baghdad Science Journal
Early Prediction of Nephropathy in Iraqi Patient with Diabetes Type II by Evaluating Some Relevant Biochemical Factors
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داء السكري من النوع 2 (T2DM) هو مرض معقد يصيب العديد من الأعضاء إلى جانب البنكرياس مثل الكلى والكبد والدماغ والعين. بسبب ارتفاع السكر في الدم لفترات طويلة وعدم السيطرة على مرض السكري مع وجود عوامل خطر أخرى ، يمكن أن تحدث مضاعفات مرض السكري. تشمل مضاعفات مرض السكري مضاعفات الأوعية الدموية الدقيقة والأوعية الدموية الكبيرة التي تستهدف الكلى. تهدف الدراسة الحالية إلى التحقق من مستوى الانجوتينسن المحول للانزيم  A

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