Switched reluctance motor is acquiring major attention because of its simple design, economic development, and reduced dependability. These attributes make switched reluctance motors superior to other variable speed machines. The major challenge associated with the development of a switched reluctance motor is its high torque ripple. Torque ripple produces noise and vibration, resulting in degradation of its performance. Various techniques are developed to cope with torque ripples. Practically, there exists not a single mature technique for the minimization of torque ripples in switched reluctance motors. In this research, a switched reluctance motor is modelled and analysed. Its speed and current control are implemented through artificial neural networks. Artificial neural network is found to be a promising technique as compared with other techniques because of its accuracy, reduced complexity, stability, and generalization. The Levenberg–Marquardt algorithm is utilized in artificial neural networks due to its fast and stable convergence for training and testing. It is found from research that artificial neural network-based improved control shows better performance of the switched reluctance motor. Realization of this technique is further validated from its mean square error analysis. Operating parameters of the switched reluctance motor are improved significantly. Simulation environment is created in Matlab/Simulink.

The brain images are indicating what condition the brain has. The objective of this research is to design a software that will automatically classifies the brain images to their associated disorders. In order to achieve the objective of this research, a database for training and testing the software of brain images must to be found. In this research we have 105 number of images in data set. In order to differentiate between the classes of those brain images, features had to be extracted from the images. Then, images will be classified into two classes normal and abnormal by using SVM and KNN classifier. The features that were extracted were used in the classification process. The classifiers performed really well, whereas the SVM classifier performed better since its accuracy is 100% on testing set. In the end, the software was successful in separating the two classes.