OVERLAPPING OPTIMIZATION WITH PARSING THROUGH METAGRAMMARS

ABSTRACT This paper describes techniques for improving the performance of meta framework developed by combining C++ and Java language segments through reducing the number of bytecodes generated. Augmented versions of existing languages can be developed by combining good properties of those languages. It increases the flexibility of programmers in using language constructs of those languages. The framework identifies and parses source code with C++ and Java language statements using metagrammar developed and create a unified AST for the hybrid source code. Bytecodes are generated for AST and interpreted. The performance of Bytecodes can be improved through optimization techniques associated with metagrammars, like constant propagation which identifies constant values for variables and propagate it to the place where the variable occurs and replace it with corresponding value. Function inlining and exception optimization greatly improves the execution time performance of Bytecodes. Optimization through metagrammars eliminates rigorous analysis of bytecodes to identify hot spots and optimize them

Convolution Neural Network Based Brain Tumour Detection Using Efficient Classification Technique – A Robotics Approach

Medical image processing has become an important and essential element in the fields of biomedical and biological research such as tumor recognition and detection process is automatically determining the volume of a heart chamber and screening the brain scans for probable damages and diseases. Various techniques and methods for automatic detection and recognition of brain tumor which involved many steps viz. image acquisition through scan, segmentation of images, classification of images using neural network, optimization of developed images and identification of exact tumor category. This research paper dealt with a novel approach to identify and segment brain related tumors. The recognition and detection followed by segmentation of brain tumors can be formulized as novelty detection by using a new methodology of Hybrid probability based segmentation model which is straightened and bound. The main purpose and objective of this proposed novel method is to use precisely to identify the existence of tumour cells in brain images as an premature and early indication of malignant cells that may cause life threat and fatal to human beings

Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019

10.1016/s0140-6736(21)01169-7The Lancet397102922337-236