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Image segmentation is a critical process in computer vision. It involves dividing a visible input into segments to simplify image analysis. Segments represent objects or parts of objects and comprise sets of super-pixels. Image segmentation sorts pixels into larger components, eliminating the need to believe individual pixels as units of observation. Brain tumour segmentation is a crucial task in medical image segmentation. Early diagnosis of brain tumours plays a crucial role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumours for cancer diagnosis, from great deal of MRI images generated in clinical routine, may be a difficult and time-consuming task. There is a requirement for automatic brain tumour image segmentation. The method is proposed to segment normal tissues like substantial alba, grey matter, spinal fluid, and abnormal tissue like tumour part from a resonance Imaging (MRI) automatically. The system also uses to segment the tumour cells along the morphological filtering are going to be wont to remove background noises for smoothening of region. The project results will be presented as segmented tissues and classification using, Convolutional Neural Network (CNN) classifier.

The most leading applications of Artificial Intelligence that seems to witness an immense Progression in the digital era are the Machine Learning (ML) Techniques. It learns itself from the past experiences and attempts at the best prediction of future instances or trends. Such progressive learning does not demand any explicit programming structures. Machine learning finds a wide range of application areas, and out of which accurate real time weather prediction gains importance. An interactive neural network based classification model for better prediction of rainfall has been put forth in this paper; we have proposed an interactive model for predicting rainfall using neural classification. The model is premeditated in a way, that it fetches feature extraction from a database including information about previous rainfalls in a specific area. The features were then pre-processed and further segmented by employing the random forest. The segmented outputs are then classified using neural networks. A comparison of spatial interpolation scheme is done with existing systems by deploying the hybrid classifier. The efficiency of the proposed model is evaluated and is compared with the traditional Deep Learning process and it is observed that the Random forest based interactive model provides better performance. Results of the model seem to be more accurate as the model uses an iterative approach for feature extraction.

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