CNN-based Transfer Learning for Covid-19 Diagnosis

CNN-based transfer learning method plays a significant role in the detection of various objects such as cars, dogs, motorcycles, face and human detection in nighttime images by using visible light camera sensors. This method mainly depends on the images captured by cameras in order to detect the mentioned objects in a variety of environments based on convolutional neural networks (CNNs). In this study, we utilized the same method to detect coronavirus phenomena by using chest X-ray images that have been collected from three different open-source datasets with the aim of rapid detection of the infected patients and speed up the diagnostic process. We used one of the deep learning architectures in a Transfer Learning mode and modified its final layers to adapt to the number of classes in our investigation. The deep learning architecture that we used for the purpose of COVID-19 detection from X-ray images is a CNN designed to detect human in nighttime. We also modified the CNN architecture in three different scenarios named (Model 1, Model 2 and Model 3) in order to improve the classification results. Compared to model one and two, the result improved in model three and the number of misclassified cases reduced particularly in detecting Abnormal and COVID-19 cases. Although our CNN-based method shows high performance in COVID-19 detection, CNN decisions should not to be taken into consideration until clinical tests confirms symptoms of the infected patients. © 2021 IEEE.

Built-in Encrypted Health Cloud Environment for Sharing COVID-19 Data

During the COVID-19 pandemic, the increasing amount of personal and sensitive data collected in the healthcare inistiutions, make it necessary to use the cloud not only to store the data, but also to process it in the cloud. Obviously, most people would like to get a secure information related to their health. Therefore, with the recent growth of cloud computing privacy and data protection requirements have been evolving toto protect individuals from surveillance and data leakage. However, due to security concerns about frequent data breaches and recently upgraded legal data protection requirements (such as the EU's General Data Protection Regulation), it is recommended not to outsourcing unprotected sensitive data to public clouds. Cloud computing offers on-demand services including platforms, infrastructure and software that have been developed. To ensure confidentiality, the encryption algorithm is the most commonly used technique. However, Using a single algorithm is not effective for the high-level security of data in cloud computing. To fix this issue, we have introduced a new security mechanism using a built-in encrypted cloud storage system, which makes the proposed system strong against vulnerabilities. Various symmetric and asymmetric encryption techniques have been used to protect data from external threats. These techniques can help restrict the access to the stored data in order to be available for authorized users only. Combining Advanced Encryption Standard (AES) and Cryptographic Curve Cryptography (ECC), the sensitive files are stored in client side and uploaded to the server. The results show that the impact of this built-in approach is more important than the other secure algorithms. In addition, it also showed that in the built-in algorithm, adding additional layers of security did not cause an increase in the download and upload time from and to the cloud. © 2021 IEEE.

SAARSNet: A Deep Neural Network for COVID-19 Cases Diagnosis

The global spread of the COVID-19 is a continuously evolving situation and it is still a major risk on the health of people around the world. A huge number of people are infected by this deadly virus and the number is still getting increased day by day. At this time, no specific vaccines or treatments of COVID-19 are found. Numerous ways are offered to detect COVID-19 such as swab test, CDC and RT-PCR tests. All of them can detect corona virus in different ways but they are not recommended by the reason of their limited availability, inaccurate results, high false-negative rate predicates, high cost and time consuming. Hence, medical radiography and Computer Tomography (CT) images were suggested as the next best alternative of RT -PCR and other tests for detecting Covid-19 cases. Recent studies found that patients with COVID-19 cases are present abnormalities in chest X-Ray images. Motivated by this, many researchers propose deep learning systems for COVID-19 detection. Although, these developed AI systems have shown quite promising results in terms of accuracy, they are closed source and unavailable to the research community. Therefore, in the present work, we introduced a deep convolutional neural network design (SAARSNet) designed to detect COVID-19 cases from chest X-Ray images. 1292 X-Ray images have been used to train and test the proposed model. the images have been collected from two open-source datasets. The input images are progressively resized into (220 by 150 by 3) in order to decrease the training time of the system and improve the performance of the SAARSNet architecture. Furthermore, we also investigate how SAARSNet makes predictions under three different scenarios with the aim of distinguishing COVID-19 class from both Normal and Abnormal classes as well as gaining deeper perceptions into critical factors related to COVID-19 cases. We also used the confusion metrics for evaluating the performance of SAARSNet CNN in an attempt to measure the true and false identifications of the classes from the tested images. With the proposed architecture promising results has been achieved in all of the three different scenarios. Although, there are some misclassified cases of COVID-19, the corresponding performance was best in detecting both Normal and Abnormal cases correctly. Furthermore, in the three classes scenario, normal class has been achieved 100% positive predictive value while optimistic results have been investigated in detecting COVID-19 and abnormal classes. © 2020 IEEE.

CBES: Cloud Based Learning management System for Educational Institutions

Recent technologies like the internet, coupled with the software applications, have lately assisted to enrich our contemporary lives. Consequently, countless educational institutions can see the benefit of embracing these apps to attain the edge over. Cloud computing is taking the center stage in academia due to its abundant benefits. It is a growing technology that enables the conventional IT systems to be modified. Dissimilar learning institutions can apply various cloud-based services offered by service providers to guarantee that their students and other consumers can efficiently apply all the instructional activities. This paper emphasizes on the various technologies that have been applied to support the education process such as learning management systems. In the current work, we introduced Cloud-Based Educational System (CBES), which presents a conceptual model of cloud-based learning management system for educational institutions. Furthermore, the paper will demonstrate the expected results of the proposed model, which is aimed to provide a conducive environment to the educators, academic staffs and the administrators as well as dealing with more vital tasks required for e-learning process such as requesting for learning management system (LMS) navigations, text and media learning contents, and video learning contents. © 2021 IEEE.