In recent years, several researches are being done to improve the means by which human to machine interaction. With the development of input devices like keyboard, mouse and pen are not sufficient due to this limitation direct use of hand gesture as an input device to provide natural human to machine interaction. The objective of this paper is to implement the vision based hand gesture recognition system to control the movement of robot. We can use of Scale invariant feature transform (SIFT) for extract the keypoint from the gesture image capture by single sensing device. Space incompatibility of SIFT keypoint causes bag of feature approach was introduced. Then use the vector quantization will map the keypoint extracted from SIFT into unified dimensional histogram vector after the K-mean clustering. The histogram vectors as an input to multiclass SVM classifier for recognize the gesture. Generate the grammar apply to the robot to control the movements (Left, Right, Straight ward, Backward, stop) of robot.Keywords-Bag-of-features, Human to machine interaction, Kmean, scale invariant feature transform (SIFT), support vector machine (SVM).

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.