Geographic Information Systems (GIS) has been prominently working for the designed to sculpt the world. With the growth and data and increasing sophistication of analysis and processing techniques the traditional sequential methods of performing GIS processing on desktop computers is insufficient. This paper is based on analysis and the performance of 3D convex hull algorithm for the three flavors of parallel architecture considering spatial scatter point data using parallel programming. As GIS use huge set of scatter data for processing and development of many product , a Convex Hull of planner scattered point set will useful in the area of planning and grafting the satellite image in GIS. Analysis is based on the parallel algorithm on OpenMP, MPI and Hybrid of HPC (High Performance Computing) architecture also improvement strategy for the huge data point available for computing such as GIS spatial data with respective OpenMP, MPI and Hybrid is stated.

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Image processing is essential for the success of image-based authentication. Included in multiple ”Multimodal image classification” subheadings. In this research, we will investigate three methods that have been shown to improve the precision of image classification. Pre-processing refers to the subsequent phase of extracting and classifying features. Gaussian filters are used for the pre-processing step, while the PSO algorithm is responsible for the feature extraction. Incorporating categorization algorithms is made possible by employing the ECNN. Finally, we evaluate our proposal by contrasting it with state-of-the-art scientific findings.

Class-incremental learning (CIL) is a revolutionary framework we develop in this study to address multi-class problems with support vector machines (SVM). Text classifiers built with support for support vector machines (SVMs) can be kept up-to-date with the help of CIL’s two incremental processes. Reusing previously learned classifier models, the CIL only needs to train a single binary sub-classifier and an extra step for feature assortmentonce a new class is introduced. The projections of the vectors onto the relevant subspaces are analyzed using the present classifier. Any text classification method based on binary classification can use CIL as a universal framework for implementation. We found that the CIL-based SVM not only outperformed well-known batch SVM learning strategies like 1-against-rest, 1-against-1, and divide-by-2, but also required much less time to train.