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<p class="0abstract"> </p><p class="0abstract">This paper researches the evolution process of what is called two-factor authentication technique and its adaptation related to the educational system through the Internet. This technique is a measure of security employed, particularly in scopes which have valuable information like bank services. It witnesses developments so far as today, in parallel with the developments occurring in technology. Since this technique consists of two phases, the security is going to be developed. Today, bank services, devices using the Internet of things, tickets of public transportation and lots of other scopes are utilized. In the information field, the researchers and scientists always update the techniques of two-factor authentication to resist the attacks related to security. Last years, the researchers studied novel technologies like behavioral biometric or biometrics. The training through the Internet may become much more useful than going to someplace to study a specific course. Mostly, the participants in the trainings through the Internet get many certificates for success, participation, etc. The principal problem is how to certify the truthiness of the participant who desires to get the certification. In this paper, and by researching the techniques of two-factor authentication, the Mimic Control Method with Sound Intensity (MCMSI) is proposed to be used for the training through the Internet.</p>

<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121, InceptionV3, and Inception-ResNetV2. RNN was used to classify data after extracting complicated characteristics from them using CNN. The VGG19-RNN design had the greatest accuracy of all of the networks with 97.8% accuracy. Gradient-weighted the class activation mapping (Grad-CAM) method was then used to show the decision-making areas of pictures that are distinctive to each class. In comparison to other current systems, the system produced promising findings, and it may be confirmed as additional samples become available in the future. For medical personnel, the examination revealed an excellent alternative way of diagnosing COVID-19.</p>