No abstract

Abstract-

Nowadays, People share their opinions through social media. This information may be informative or non-informative. To filtering the informative information from the social media plays a challenging issue. Nevertheless, in social media especially when a disaster been occurs the peoples will interact more on that particular disaster event. They share their opinion through some textual information such as tweets or posts. In this work, we are proposing a generalized approach for categorizing the informative and non-informative on twitter media. We collected the seven natural disaster events from the crisisNLP. These datasets are different disaster events which contains the people’s opinions on that specific event. We preprocess the information which converts the tweet information into machine understandable vectors. These vectors been processed by the different machine learning algorithms. We consider the individual performance of each ML algorithm on different disaster datasets upon chosen the best five algorithms for voting techniques. We tested the performance with matrices such as accuracy, precision, recall and f1-score. We compared our results with existing models in which our proposed model performed better than other existing state of art models.

Brain MRI images that are acquired from the scanner will be having the non-brain tissues like skull, cerebrospinal fluid, Dura as the integral part of the image. All such unwanted elements does considerable impact on the estimation of the volume of the damaged region from resultant segmented image, Hence all such unwanted components from the brain MR image are be eliminated for accurate results. In this paper we had proposed a computationally efficient approach called Structural Augmentation which uses distance measures and morphological operation over a threaded bitmap image to eliminate the undesired region from the brain tissues. On applying the above said procedure well before the segmentation of the MR image, the evaluation seems to be meticulous. The end results of the proposed approach are proven to be superior in term of the accuracy and precision over conventional approaches.

No abstract