19c96fd51791d8d23b96418 20211112071153599 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 11 30 2021 11 1 10.35940/ijitee.11.1 https://www.ijitee.org/download/volume-11-issue-1/ Department of Systems and Computer Engineering, Faculty of Engineering, Al-Azhar University, Cairo, Egypt.. Ahmed Hashem El Fiky The COVID-19 will take place for the first time in December 2019 in Wuhan, China. After that, the virus spread all over the world, with over 4.7 million confirmed cases and over 315000 deaths as of the time of writing this report. Radiologists can employ machine learning algorithms developed on radiography pictures as a decision support mechanism to help them speed up the diagnostic process. The goal of this study is to conduct a quantitative evaluation of six off-the-shelf convolutional neural networks (CNNs) for COVID-19 X-ray image analysis. Due to the limited amount of images available for analysis, the CNN transfer learning approach was used. We also developed a simple CNN architecture with a modest number of parameters that does a good job of differentiating COVID-19 from regular X-rays. in this paper, we are used large dataset which contained CXR images of normal patients and patients with COVID-19. the number of CXR images for normal patients are 10,192 image and the number of CXR images for COVID-19 patients are 3,616 images. The results of experiments show the effectiveness and robustness of Deep-COVID-19 and pretrained models like VGG16, VGG19, and MobileNets. Our proposed Model Deep-COVID-19 achieved over 94.5% accuracy. 11 30 2021 1 6 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.A9589.1111121 https://www.ijitee.org/wp-content/uploads/papers/v11i1/A95891111121.pdf Department of Civil Engineering, Sarvajanik College of Engineering and Technology Surat, India. Abdullah Lala Toshif Patel Engineer, Varjlal V. Ambaliya Structural Engineer and Consultant, Surat, India. Ravi Karkar Engineer, Varjlal V. Ambaliya Structural Engineer and Consultant, Surat, India. Dr. Jigar Sevalia Professor & Head, Department of Civil Engineering, Sarvajanik college of Engineering and Technology Surat, India. In modern construction, there is a trend to go deeper below the grade level in terms of basements which can be utilized for parking, shopping malls or a combination of both. In such cases, dynamic soil properties have a significant effect of activating dynamic soil structure interaction phenomenon during earthquake. Here in present study an effort is made to study the behavior of a building by varying five and three number of basements considering dynamic soil structure interaction. Issues like influence zone to be considered for dynamic soil structure interaction, behavior of building with basements under different water level conditions for two different types of layered soil and their comparison with fixed based structure for a real-life structure is dealt with. It is observed that dynamic soil structure interaction can significantly change the behavior and also the failure pattern of the building and hence it is recommended to perform dynamic soil structure interaction for building with multiple basements. 11 30 2021 7 27 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.A9585.1111121 https://www.ijitee.org/wp-content/uploads/papers/v11i1/A95851111121.pdf