Optimum Median Filter Based on Crow Optimization Algorithm

يُقترح مرشح متوسط ​​جديد يعتمد على خوارزميات تحسين الغراب (OMF) لتقليل ضوضاء الملح والفلفل العشوائية وتحسين جودة الصور ذات اللون الرمادي والملونة . الفكرة الرئيسية لهذا النهج هي أن أولاً ، تقوم خوارزمية تحسين الأداء بالكشف عن وحدات البكسل الخاصة بالضوضاء ، واستبدالها بقيمة وسيطة مثالية تبعًا لدالة الأداء. أخيرًا ، تم استخدام نسبة القياس القصوى لنسبة الإشارة إلى الضوضاء (PSNR) ، والتشابه الهيكلي والخطأ المربع المطلق والخطأ التربيعي المتوسط ​​لاختبار أداء المرشحات المقترحة (المرشح الوسيط الأصلي والمحسّن) المستخدمة في الكشف عن الضوضاء وإزالتها من الصور. يحقق المحاكاة استنادًا إلى MATLAB R2019b والنتائج الحالية التي تفيد بأن المرشح المتوسط ​​المحسّن مع خوارزمية تحسين الغراب أكثر فعالية من خوارزمية المرشح المتوسط ​​الأصلية ومرشحات لطرق حديثة ؛ أنها تبين أن العملية المقترحة قوية للحد من مشكلة الخطأ وإزالة الضوضاء بسبب مرشح عامل التصفية المتوسط ​​؛ ستظهر النتائج عن طريق تقليل الخطأ التربيعي المتوسط ​​إلى أدنى أو أقل من (1.5) ، والخطأ المطلق للتساوي (0.22) ,والتشابه الهيكلي اكثر من ( 95%) والحصول على PSNR أكثر من 45dB).) وبنسبة تحسين ( 25%) .          A novel median filter based on crow optimization algorithms (OMF) is suggested to reduce the random salt and pepper noise and improve the quality of the RGB-colored and gray images. The fundamental idea of the approach is that first, the crow optimization algorithm detects noise pixels, and that replacing them with an optimum median value depending on a criterion of maximization fitness function. Finally, the standard measure peak signal-to-noise ratio (PSNR), Structural Similarity, absolute square error and mean square error have been used to test the performance of suggested filters (original and improved median filter) used to removed noise from images. It achieves the simulation based on MATLAB R2019b and the results present that the improved median filter with crow optimization algorithm is more effective than the original median filter algorithm and some recently methods; they show that the suggested process is robust to reduce the error problem and remove noise because of a candidate of the median filter; the results will show by the minimized mean square error to equal or less than (1.38), absolute error to equal or less than (0.22) ,Structural Similarity (SSIM) to equal (0.9856) and getting PSNR more than (46 dB). Thus, the percentage of improvement in work is (25%)

Intelligent Tutoring System Effects on the Learning Process

The traditional education systems that have been used for several centuries have evolved very slowly and might be ineffective for addressing diverse learning styles and levels of preparation. This system is characterized by many students interacting with a single teacher, who is unable to address the individual needs of every student. Therefore, some students can become frustrated and fail to reach their educational potential. An Intelligent Tutoring System (ITS), which is a computer application used to provide students with one-to-one supplemental tutoring tailored to the student\u27s learning style and pace, is of interest to educators for improving student learning. To evaluate the effectiveness of ITS, a systematic review of the recent literature was performed using a carefully crafted protocol designed to provide data to support a meta-study of the effectiveness of ITS. The research question guiding this study is: Does an intelligent tutoring system improve students\u27 learning abilities more than traditional learning? A t-test, one-way ANOVA test, and KNIME program that does Latent Dirichlet allocation were performed. The results support the conclusion that ITS causes a significant improvement in learning over traditional instructional methods

A REVIEW PAPER: ANALYSIS OF WEKA DATA MINING TECHNIQUES FOR HEART DISEASE PREDICTION SYSTEM

Data mining is characterized as searching for useful information through very large data sets. Some of the key and most common techniques for data mining are association rules, classification, clustering, prediction, and sequential models. For a wide range of applications, data mining techniques are used. Data mining plays a significant role in disease detection in the health care industry. The patient should be needed to detect a number of tests for the disease. However, the number of tests should be reduced by using data mining techniques. In time and performance, this reduced test plays an important role. Heart disease is a cardiovascular disease that causes death. Health problems are enormous in this recent situation because of the prediction and the classification of health problems in different situations. The data mining area included the prediction and identification of abnormality and its risk rate in these domains. Today the health industry holds hidden information essential for decision-making. For predicting heart problems, data extraction algorithms like K-star, J48, SMO, Naïve Bayes, MLP, Random Forest, Bayes Net, and REPTREE are used for this study (Weka 3.8.3) software. The results of the predictive accuracy, the ROC curve, and the AUC value are combined using a standard set of data and a collected dataset. By applying different data mining algorithms, the patient data can be used for diagnosis as training samples. The main drawbacks of the previous studies are that they need accurate and the number of features. This paper surveys recent data mining techniques applied to predict heart diseases. And Identifying the major risk factors of Heart Disease categorizing the risk factors in an order which causes damages to the heart such as high blood cholesterol, diabetes, smoking, poor diet, obesity, hypertension, stress, etc. Data mining functions and techniques are used to identify the level of risk factors to help the patients in taking precautions in advance to save their life

A REVIEW PAPER: ANALYSIS OF WEKA DATA MINING TECHNIQUES FOR HEART DISEASE PREDICTION SYSTEM

Data mining is characterized as searching for useful information through very large data sets. Some of the key and most common techniques for data mining are association rules, classification, clustering, prediction, and sequential models. For a wide range of applications, data mining techniques are used. Data mining plays a significant role in disease detection in the health care industry. The patient should be needed to detect a number of tests for the disease. However, the number of tests should be reduced by using data mining techniques. In time and performance, this reduced test plays an important role. Heart disease is a cardiovascular disease that causes death. Health problems are enormous in this recent situation because of the prediction and the classification of health problems in different situations. The data mining area included the prediction and identification of abnormality and its risk rate in these domains. Today the health industry holds hidden information essential for decision-making. For predicting heart problems, data extraction algorithms like K-star, J48, SMO, Naïve Bayes, MLP, Random Forest, Bayes Net, and REPTREE are used for this study (Weka 3.8.3) software. The results of the predictive accuracy, the ROC curve, and the AUC value are combined using a standard set of data and a collected dataset. By applying different data mining algorithms, the patient data can be used for diagnosis as training samples. The main drawbacks of the previous studies are that they need accurate and the number of features. This paper surveys recent data mining techniques applied to predict heart diseases. And Identifying the major risk factors of Heart Disease categorizing the risk factors in an order which causes damages to the heart such as high blood cholesterol, diabetes, smoking, poor diet, obesity, hypertension, stress, etc. Data mining functions and techniques are used to identify the level of risk factors to help the patients in taking precautions in advance to save their life