Brain is one of the most complex organs in the human body that works with billions of cells. A cerebral tumor occurs when there is an uncontrolled division of cells that form an abnormal group of cells around or within the brain. This cell group can affect the normal functioning of brain activity and can destroy healthy cells. Brain tumors are classified as benign or low-grade and malignant tumors or high-grade. Benign tumors are non-cancerous tumor and they do not spread to other tissues or organs. Malignant tumors are cancerous tissue and they can easily spread to other tissues or organs. Proposed system is to differentiate between normal brain and tumor brain (benign or malign). Also, the proposed system predicts brain tumor from MRI image classification system is based on extracting useful MRI features for diagnosing the medical MRI images. The benefits of using SVM is nevertheless of the image brightness or rotation of the MRI image, it also provides huge number of strong features that can be automatically prepared well to be suitable for MRI classification. Support Vector Machine (SVM) algorithm is used to predict the diseases accurately from MRI (Magnetic Resonance Imaging) scan images. SVM algorithm is the used for the purpose of classifying the image datasets and to predict the disease by itself for those matching the images to enhance a comprehensive set of quantitative measurements among several influential on various brain image databases.
A number of different classifiers have been used to improve the precision and accuracy and give better classification results. Machine learning classifiers have proven to be the most successful techniques in majority of the fields. This paper presents a comparison of the three most successful machine learning classification techniques SVM, boosting and Local SVM applied to a cancer dataset. The comparison is made on the basis of precision and accuracy along with the training time analysis. Finally, the efficacy of the classifiers is found.
For the past decade of year’s credit card holders are facing a problem that the card had been swiped and cash has been withdrawn in an ATM (Automatic Teller Machine) or it has been swiped in a shopping mall by purchasing a product as creating a fake credit card. These transactions are considered as illegal activity, it is also one of the cybercrime theft activities. Credit card fraud detection has been increasing highly in the world. Fake credit cards can be tackled by applying data science along various machine learning algorithms. This research work focus on analysis of various credit card fraud transaction using machine learning algorithms. Also, this research focuses on detecting the fake credit cards by approaching Artificial Intelligence (AI), Data mining and Big data analytics etc. by using machine learning algorithms. Machine learning algorithms are applied by training the dataset which are collected from the fake credit dataset and original credit card dataset. Computer operations are handled by the data as data’s are ruling the world. Predicted card details are stored on the server of the banker. From the server information has been passed to the cybercrime in order to catch the culprit. Different kinds of machine learning algorithms that has been used for credit card fraud detection has been studied on which it gives the more precision.
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