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Ozone Depletion Identification in Stratosphere Through Faster Region-Based Convolutional Neural Network

Computers, Materials & Continua 2021年第8期68卷 2159-2178页

作者： Bakhtawar Aslam Ziyad Awadh Alrowaili Bushra Khaliq jaweria manzoor Saira Raqeeb Fahad Ahmad Department of Computer Sciences Kinnaird College for WomenLahore54000Pakistan Physics Department College of ScienceJouf UniversitySakakaAljouf72341Saudi Arabia Department of Basic Sciences Deanship of Common First YearJouf UniversitySakakaAljouf72341Saudi Arabia

The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place in physical systems over time and effect *** study has made ozone depletion identification through classification using Faster Region-Based Convolutional Neural Network(F-RCNN).The main advantage of F-RCNN is to accumulate the bounding boxes on images to differentiate the depleted and non-depleted ***,image classification’s primary goal is to accurately predict each minutely varied case’s targeted classes in the dataset based on ozone *** permanent changes in climate are of serious *** leading causes beyond these destructive variations are ozone layer depletion,greenhouse gas release,deforestation,pollution,water resources contamination,and UV *** research focuses on the prediction by identifying the ozone layer depletion because it causes many health issues,e.g.,skin cancer,damage to marine life,crops damage,and impacts on living being’s immune *** have tried to classify the ozone images dataset into two major classes,depleted and non-depleted regions,to extract the required persuading features through ***,CNN has been used for feature extraction in the existing literature,and those extricated diverse RoIs are passed on to the CNN for grouping *** is difficult to manage and differentiate those RoIs after grouping that negatively affects the gathered *** classification outcomes through F-RCNN approach are proficient and demonstrate that general accuracy lies between 91%to 93%in identifying climate variation through ozone concentration classification,whether the region in the image under consideration is depleted or *** proposed model presented 93%accuracy,and it outperforms the prevailing techniques.

关键词： Deep learning image processing classification climate variation ozone layer depleted region non-depleted region UV radiation faster region-based convolutional neural network

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