K-means clustering is a machine learning algorithm used to group the data based on the similarity between the data. Functional distances like squared Euclidean distance, city block, cosine, correlation and hamming are considered as a similarity parameter here. Test vectors are grouped based on the functional distance using the K-means algorithm. A simple reordering algorithm is proposed and is applied to each group of data before 'X' bit filing to minimize the test power. Experimental results on ISCAS 89 benchmark circuit shows that the proposed methods diminish the power effectively.

Brain tumor is the main threat among the people. But currently, it become more advanced because of the many Machine Learning techniques. Magnetic Resonance Imaging is the greatest technique among all the image processing techniques which scans the human body and gives  a  clear  resolution  of the tumors in an  improved  quality  image.  The fundamentals of MRI are to develop images based on magnetic field and radio waves of the anatomy of the body. The major area of segmentation of images is medical image processing. Better results are provided by MRI images than CT scan, X-rays etc. Nowadays the automatic tumor detection in large spatial and structural variability. Recently Convolutional Neural Network plays an important role in medical field and computer vision. One of its application is the identification of brain tumor. Here, the pre-processing technique is used to convert normal images to grayscale values because it contains equal intensity but in MRI, RGB content is included. Then filtering is used to remove the unwanted noises using median and high pass filter for better quality of images. The deeper architecture design in CNN is performed using small kernels. Finally, the effect of using this network for segmentation of tumor from MRI images is evaluated with better results.

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