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This paper introduces a scheme for classification of online handwritten characters based on polynomial regression of the sampled points of the sub-strokes in a character. The segmentation is done based on the velocity profile of the written character and this requires a smoothening of the velocity profile. We propose a novel scheme for smoothening the velocity profile curve and identification of the critical points to segment the character. We also porpose another method for segmentation based on the human eye perception. We then extract two sets of features for recognition of handwritten characters. Each sub-stroke is a simple curve, a part of the character, and is represented by the distance measure of each point from the first point. This forms the first set of feature vector for each character. The second feature vector are the coeficients obtained from the B-splines fitted to the control knots obtained from the segmentation algorithm. The feature vector is fed to the SVM classifier and it indicates an efficiency of 68% using the polynomial regression technique and 74% using the spline fitting method.

Medical applications have a massive footprint in human's day‐to‐day life. Among that, MRI has a significant role, as it incorporates a significant impact on a brain tumour. Segmenting the tumour from MRI is substantial, but it is a time‐consuming process. Both the normal and abnormal tissues found in the brain look similar, which increases the difficulty of the tumour detection process. The digital image needs to be processed to obtain an exact tumour detection result. The tumour detection process comprises five different stages, such as pre‐processing, segmentation, feature extraction, feature selection, and classification. In this proposed work, hybrid wavelet Hadamard transform and grey‐level co‐occurrence matrix are included for feature extraction. Feature selection utilises sequential forward selection, which is an easy greedy search algorithm. This algorithm chooses only the predominant features for classification. The classification uses a hybrid support vector machine and adaptive emperor penguin optimisation. The experimental analysis shows the efficiency of the proposed work in terms of accuracy, specificity, and sensitivity values by computing the true positive, false positive, true negative, and false negative.