Auto-detection of diseases has become a prime issue in medical sciences as population density is fast growing. An intelligent framework for disease detection helps physicians identify illnesses, give reliable and consistent results, and reduce death rates. Coronavirus (Covid-19) has recently been one of the most severe and acute diseases in the world. An automatic detection framework should therefore be introduced as the fastest diagnostic alternative to avoid Covid-19 spread. In this paper, an automatic Covid-19 identification in the CT scan and chest X-ray is obtained with the help of a combined deep learning and multi-level feature extraction methodology. In this method, the multi-level feature extraction approach comprises GIST, Scale Invariant Feature Transform (SIFT), and Convolutional Neural Network (CNN) extract features from CT scans and chest X-rays. The objective of multi-level feature extraction is to reduce the training complexity of CNN network, which significantly assists in accurate and robust Covid-19 identification. Finally, Long Short-Term Memory (LSTM) along the CNN network is used to detect the extracted Covid-19 features. The Kaggle SARS-CoV-2 CT scan dataset and the Italian SIRM Covid-19 CT scan and chest X-ray dataset were employed for testing purposes. Experimental outcomes show that proposed approach obtained 98.94% accuracy with the SARS-CoV-2 CT scan dataset and 83.03% accuracy with the SIRM Covid-19 CT scan and chest X-ray dataset. The proposed approach helps radiologists and practitioners to detect and treat Covid-19 cases effectively over the pandemic.

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The notion of Information-Centric Networking (ICN) specifies a new communication model that emphasis on the content exchanged rather than the devices connected. ICN architectures like Content Centric Network (CCN) and Named Data Networking (NDN) have been proposed to shift from host-centric based to content-centric based communication along and provide benefits to users in addressing the challenges of traditional IP networks. It differs from host-centric standard Internet Protocol (IP) networking in of naming, routing, forwarding, and caching characteristics. Naming features used in NDN use global unique names provided by content-based security and encryption. It ensures content integrity and authenticity as part of its design. In this paper, we survey on security aspects of NDN/CCN, discussing three integrity attacks such as replay attacks, Man-in-The-Middle (MiTM) attacks, and Content Poisoning Attack (CPA) with countermeasure against them. In addition, we highlight an open challenge and offer future research directions in the context of security.

Since the end of 2019, a COVID-19 outbreak has put healthcare systems worldwide on edge. In rural areas, where traditional testing is unfeasible, innovative computer-aided diagnostic approaches must deliver speedy and cost-effective screenings. Conducting a full scoping review is essential for academics despite several studies on the use of Deep Learning (DL) to combat COVID-19. This review examines the application of DL techniques in CT and ULS images for the early detection of COVID-19. In this review, the PRISMA literature review approach was followed. All studies are retrieved from IEEE, ACM, Medline, and Science Direct. Performance metrics were highlighted for each study to measure the proposed solutions' performance and conceptualization; A set of publicly available datasets were appointed; DL architectures based on more than one image modality such as CT and ULS are explored. Out of 32 studies, the combined U-Net segmentation and 3D classification VGG19 network had the best F1 score (98%) on ultrasound images, while ResNet-101 had the best accuracy (99.51%) on CT images for COVID-19 detection. Hence, data augmentation techniques such as rotation, flipping, and shifting were frequently used. Grad-CAM was used in eight studies to identify anomalies on the lung surface. Our research found that transfer learning outperformed all other AI-based prediction approaches. Using a UNET with a predefined backbone, like VGG19, a practical computer-assisted COVID-19 screening approach can be developed. More collaboration is required from healthcare professionals and the computer science community to provide an efficient deep learning framework for the early detection of COVID-19.