این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
مدیریت فناوری اطلاعات، جلد ۱۵، شماره Special Issue، صفحات ۱۰۲-۱۱۹
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Real-Time Deep Intelligence Analysis and Visualization of COVID-19 Using FCNN Mechanism
چکیده انگلیسی مقاله The Analytic visualization suggests representing knowledge during a visual type that may be charts, graphs, lists, or maps. The COVID 19 detection and analysis of spreading is very important for countries. Database management with respect to virus deep analysis is a critical task to the researcher through conventional algorithms. The RNA, DNA, and biological data are helping to the bio-inspired algorithm but its implementation can be complex by software tools. Therefore, an effective technique is required to cross over the above limitations. So that covid 19 pandemic data analysis is performed through FCNN (Fully conventional Neural Network) pre-training network. The dataset is collected from social media, Kaggle, and GitHub databases. At 1st stage, the auto stack encoding process is applied later same data is processed with FCNN deep learning classifier. In this research work, covid-pandemic affects parameters like infected persons, deaths, active cases, and recovering cases. The FCNN is take care of feature extraction, training, testing, and classification. Finally using a confusion matrix accuracy of 98.34%, sensitivity 97.63%, Recall 98.26%, and F measure 98.83% had been estimated.
کلیدواژه‌های انگلیسی مقاله DNA RNA sequence,Covid-19,SARS-CoV-2,coronavirus,Pandemic
نویسندگان مقاله Cherukuri Triveni |
Department of Computer Science and Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India.

K. Suvarna Vani |
Department of Computer Science and Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India.

M. Likhitha |
Department of Computer Science and Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India.

نشانی اینترنتی https://jitm.ut.ac.ir/article_89414_e3b8556e08f7ceae11271e4e8cebbb88.pdf
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات