A Completely Automated Public Turing Test to tell Computers and Humans Apart (CAPTCHA) is used in web systems to secure authentication purposes; it may break using Optical Character Recognition (OCR) type methods. CAPTCHA breakers make web systems highly insecure. However, several techniques to break CAPTCHA suggest CAPTCHA designers about their designed CAPTCHA’s need improvement to prevent computer vision-based malicious attacks. This research primarily used deep learning methods to break state-of-the-art CAPTCHA codes; however, the validation scheme and conventional Convolutional Neural Network (CNN) design still need more confident validation and multi-aspect covering feature schemes. Several public datasets are available of text-based CAPTCHa, including Kaggle and other dataset repositories where self-generation of CAPTCHA datasets are available. The previous studies are dataset-specific only and cannot perform well on other CAPTCHA’s. Therefore, the proposed study uses two publicly available datasets of 4- and 5-character text-based CAPTCHA images to propose a CAPTCHA solver. Furthermore, the proposed study used a skip-connection-based CNN model to solve a CAPTCHA. The proposed research employed 5-folds on data that delivers 10 different CNN models on two datasets with promising results compared to the other studies.
With the emergence of demand for massive computing tasks in the edge cloud of the city, the disordered computing in the edge cloud leads to the high energy consumption of CPU computing and the problem of too long time delay caused by the blockage of computing tasks. This has become the first technical difficulty to solve in the construction of edge cloud. The virtual machine placement method is optimized by computing energy consumption and computing delay. First, the LRR physical host screening model is constructed to detect the status of the physical hosts and form a list of migrated physical hosts; secondly, the MMT time scale model is constructed to generate the list of migrated virtual machines; finally, the GA algorithm is used to place the virtual machines. The simulation results show that this algorithm can reduce the CPU energy consumption of the edge cloud center by 20. 21% and the time delay by 16. 11%. This optimization method has a good theoretical guidance effect on the construction of cloud computing.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.