This research aims to developing new method for breast tumors extraction and
features detection in breast magnetic resonance images by depending on clusteringand
image processing algorithms. At the beginning, one of clustering algorithms was used f
or
image segmentation and grouping pixels by their gray scale values. Then morphological
operations were implemented in order to remove noise and undesired regions, after that
suspected areas were extracted. Finally some shape features for extracted area were
detected, this features could be very useful for tumors diagnosis. A database consisted of
96breast magnetic resonance images were used and proposed approach was appliedby
MATLAB program, and we obtainedbreast tumors extraction and its features and
compared them with the doctor's opinion .
Educational data mining aims to study the available data in the educational field and extract the hidden knowledge from it in order to benefit from this knowledge in enhancing the education process and making successful decisions that will improve th
e student’s academic performance. This study proposes the use of data mining techniques to improve student performance prediction. Three classification algorithms (Naïve Bayes,J48, Support Vector Machine) were applied to the student performance database, and then a new classifier was designed to combine the results of those individual classifiers using Voting Method. The WEKA tool was used, which supports a lot of data mining algorithms and methods. The results show that the ensemble classifier has the highest accuracy for predicting students' levels compared to other classifiers, as it has achieved a recognition accuracy of 74.8084%. The simple k-means clustering algorithm was useful in grouping similar students into separate groups, thus understanding the characteristics of each group, which helps to lead and direct each group separately.
