The rapid expansion of the Web of Things (WoT) and the Internet of Things (IoT) has raised security issues, with Denial of Service (DoS) attacks becoming increasingly prevalent. So, the aim of this study is to identify the security concerns in the four architectural layers of the Web of Things, particularly DoS attacks. For this study, existing literature are identified using search queries, and approximately 80 of relevant primary papers published in the recent decade are obtained after a thorough review which helps in addressing our research questions. After finding the relevant primary studies, we applied strict quality evaluation criteria to verify that all studies are evaluated. In addition, a taxonomy of deep learning (DL) techniques is presented on the basis of literature analysis conducted in this research, which is then used to characterize the various security concerns that occur in IoT and WoT systems. The study also examines which DL approaches are used to detect DoS/DDoS attacks in IoT and WoT. Our findings indicate that the optimal form of Intrusion Detection System (IDS) for dealing with DoS attacks is a hybrid IDS, which uses both the signature-based and the anomaly-based IDS. Moreover, DL techniques such as, CNNs and LSTMs, produced excellent results but are still in the development stage in terms of scalability and practical use. This review further highlights the present state of security mechanisms and sets the basis for future research, with an emphasis on refining DL-based techniques and improving the scalability and adaptability of security systems for WoT networks.

Diabetic Retinopathy (DR) is a predominant cause of visual impairment and loss. Approximately 285 million worldwide population is affected with diabetes, and one-third of these patients have symptoms of DR. Specifically, it tends to affect the patients with 20 years or more with diabetes, but it can be reduced by early detection and proper treatment. Diagnosis of DR by using manual methods is a time-consuming and expensive task which involves trained ophthalmologists to observe and evaluate DR using digital fundus images of the retina. This study aims to systematically find and analyze high-quality research work for the diagnosis of DR using deep learning approaches. This research comprehends the DR grading, staging protocols and also presents the DR taxonomy. Furthermore, identifies, compares, and investigates the deep learning-based algorithms, techniques, and, methods for classifying DR stages. Various publicly available dataset used for deep learning have also been analyzed and dispensed for descriptive and empirical understanding for real-time DR applications. Our in-depth study shows that in the last few years there has been an increasing inclination towards deep learning approaches. 35% of the studies have used Convolutional Neural Networks (CNNs), 26% implemented the Ensemble CNN (ECNN) and, 13% Deep Neural Networks (DNN) are amongst the most used algorithms for the DR classification. Thus using the deep learning algorithms for DR diagnostics have future research potential for DR early detection and prevention based solution.