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Magnetic resonance image (MRI) is an important tool to diagnose human diseases effectively. It is very important for research and clinical application to classify the normal and abnormal human brain MRI images automatically. In this paper, an accurate and efficient technique is proposed to extract features of MRIs and classify these images into normal and abnormal categories. We use two-dimensional multifractal detrended fluctuation analysis (2D MF-DFA) to obtain image features. These features are the local generalized Hurst exponents calculated by 2D MF-DFA. In this regard, the values of Hurst exponents are given as the training input vector and are taken to the classifiers. We use $k$-nearest neighbor ($k$-NN) and support vector machine (SVM) to classify a specific brain MRI as normal or glioma affected. For SVM, we apply the leave-one-out cross-validation method for experimental verification. The 2D MF-DFA-SVM system achieved accuracy, sensitivity, and specificity of $99.82\%$ ±$0.07$, $100\%$, and $99.81\%$ ±$0.09$, respectively. The 2D MF-DFA-$k$-NN system achieved accuracy, sensitivity, and specificity of $96\%$, $92.59\%$, and $100\%$, respectively. We find that when performing binary classification for brain MRIs, the SVM is superior to $k$-NN. In addition, our experimental results indicate that the proposed 2D MF-DFA-SVM achieved excellent outcomes compared to those of the previous works. The proposed system is a promising system to clinical use.

As various genome sequencing projects have already been completed or are near completion, genome researchers are shifting their focus to functional genomics. Functional genomics represents the next phase, that expands the biological investigation to studying the functionality of genes of a single organism as well as studying and correlating the functionality of genes across many different organisms. Recently developed methods for monitoring genome-wide mRNA expression changes hold the promise of allowing us to inexpensively gain insights into the function of unknown genes. In this paper we focus on evaluating the feasibility of using supervised machine learning methods for determining the function of genes based solely on their expression profiles. We experimentally evaluate the performance of traditional classification algorithms such as support vector machines and k-nearest neighbors on the yeast genome, and present new approaches for classification that improve the overall recall with moderate reductions in precision. Our experiments show that the accuracies achieved for different classes varies dramatically. In analyzing these results we show that the achieved accuracy is highly dependent on whether or not the genes of that class were significantly active during the various experimental conditions, suggesting that gene expression profiles can become a viable alternative to sequence similarity searches provided that the genes are observed under a wide range of experimental conditions.