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In this paper, we have proposed a mathematical compartmental model with non-monotonic incidence and saturated treatment and we have validated the model with SARS infection in Hong Kong, 2003. We have analysed the stability of disease free and endemic equilibria as well as different bifurcations. We have shown that the epidemic disappears if the cure rate of treatment crosses a threshold value. We have obtained a necessary and sufficient condition for backward bifurcation, which shows the basic reproduction number less than unity is not sufficient to eradicate the disease completely. Saddle–node and Hopf bifurcation with respect to awareness factor have been investigated, which shows that the awareness factor is effective to change the disease dynamics. The model has been fitted to SARS cases in Hong Kong. The most effective parameters for controlling infections have been identified through sensitivity analysis. Moreover, we have investigated how the number of infected cases reduces if there was some vaccination polices in SARS infection. Finally, the model has been also used as an optimal control problem as vaccination and treatment controls are time dependent functions.

COVID-19 takes a gigantic form worldwide in a short time from December, 2019. For this reason, World Health Organization (WHO) declared COVID-19 as a pandemic outbreak. In the early days when this outbreak began, the coronavirus spread rapidly in the community due to a lack of knowledge about the virus and the unavailability of medical facilities. Therefore it becomes a significant challenge to control the influence of the disease outbreak. In this situation, mathematical models are an important tool to employ an effective strategy in order to fight against this pandemic. To study the disease dynamics and their influence among the people, we propose a deterministic mathematical model for the COVID-19 outbreak and validate the model with real data of Italy from 15th Feb 2020 to 14th July 2020. We establish the positivity and boundedness of solutions, local stability of equilibria to examine its epidemiological relevance. Sensitivity analysis has been performed to identify the highly influential parameters which have the most impact on basic reproduction number (R0). We estimate the basic reproduction number (R0) from available data in Italy and also study effective reproduction numbers based on reported data per day from 15th Feb 2020 to 14th July 2020 in Italy. Finally, the disease control policy has been summarized in the conclusion section.

Medical Imaging is the most significant technique that constitutes information needed to diagnose and make the right decisions for treatment. These images suffer from inadequate contrast and noise that occurs during image acquisition. Thus, denoising and contrast enhancement is crucial in increasing the visual quality of the images for obtaining quantitative measures. In this research, an innovative and improvised denoising technique is implemented that applies a sparse aware with convolution neural network (SA_CNN) for investigating various medical modalities. To evaluate and validate, the convolution neural network utilizes patch creation and dictionary methods for obtaining information. The proposed framework is predominant to other current approaches by employing image assessment quantitative measures like peak signal to noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE). The study also optimizes the computational time to achieve increased efficiency and better visual quality of the image. Furthermore, the widespread use of the Internet of Healthcare Things (IoHT) helps to provide security with vault and challenge schemes between IoT devices and servers.

Blockchain is attracting more and more attention to its applicability in the fields of Internet of Things (IoT). In particular, it is able to store data in unalterable blocks, associated with its secure peer-to-peer in a growing problem of transaction authorization in industrial and service provisioning applications. Moreover, it facilitates decentralized transaction (TX) validation and distributed ledger. The underneath algorithm of TX selection for validation may not be effective in terms of delay of various services of the applications. Because the existing random-based or fee-based selections are a delay insensitive that does not guarantee a minimum delay of a time-critical TX. This paper proposes a blockchain-based transaction validation protocol for a secure distributed IoT network. It includes a context-aware TX validation technique, where a TX is validated by a miner with the priority of a service. Besides, we adopt the Software Defined Networking enabled gateway as a middleware between IoT and the blockchain network in which the control operations and security of the network in a largescale are ensured. The proposed network model has evaluated and compared to the Core network. The results ensure the given priority in TX validation is more delay sensitive than the existing technique to provide quality of service of the network.

The study focuses on the artificial intelligence empowered road vehicle- train collision risk prediction assessment, which may lead to the development of a road vehicle-train collision avoidance system for unmanned railway level crossings. The study delimits itself around the road vehicle-train collisions at unmanned railway level crossings on single line rail-road sections. The first objective of the study revolves around the rail-road collision risk evaluation by the development of road vehicle-train collision frequency and severity prediction model using Poisson and Gamma-log regression techniques respectively. Another study objective is the collision modification factor implementation on predicted risk factors, to reduce the road vehicle-train collision risk at the crossings. The collision risk has been predicted to be 3.52 times higher and 77% lower in one direction while in other directions it is 2.95 times higher and 80% lower than average risk at all unmanned railway level crossings. With collision modification factor application on higher risk contributing factors i.e. `crossing angle' and `train visibility, it predicts to reduce the road vehicle-train collision risk to 85% approximately.

In this paper, we have developed and analyzed a deterministic Zika model considering both vector and sexual transmission route with the effect of human awareness and vector control in the absence of disease induce death. To formulate the model, we assume that the Zika virus is being first transmitted to human by mosquito bite, and then it is being transmitted to his or her sexual partner. The system contains at most three equilibrium points among them one is the disease free and other two are endemic equilibrium points, exists under certain conditions. The theoretical analysis shows that the diseases-free equilibrium is locally and globally asymptotically stable if the basic reproduction number is less than one. Theatrically we have established that endemic equilibrium point which is locally asymptotically stable if the basic reproduction number is greater than one. The system exhibits backward bifurcation when the transmission probability per biting of susceptible mosquito with infected humans crosses the critical value. We estimate the model parameters and validate the model by fitting the model with the reported Zika infected human data from 1 to 36 week of 2016 Zika outbreak in Colombia. Furthermore, using the normalised forward sensitivity index method we have established that the model parameter mosquito biting rate, recruitment rate of mosquito, transmission probability per biting of Susceptible (infected) humans with infected (susceptible) mosquito, rate of awareness in host population, recovery rates of infected human are most sensitive parameters of the considered Zika model. Lastly, some conclusions are given to control the spreading of the Zika disease.

In recent years, cyber-physical systems (CPSs) have gained a lot of attention from academia, industry and government agencies, considered to be the world’s third wave of information technology, following computers and the internet [...]

In this paper, we discuss virus dynamics that helps to understand the dynamics of human immunodeficiency virus (HIV), hepatitis B virus (HCB), hepatitis C virus (HCV), and some other viruses. We also discussed the model with the immune system of the hepatitis B virus model with Cytotoxic T Lymphocyte (CTL) immune responses. We obtained the basic reproduction number R0 in all models which can be used to establish the conditions for stability of the equilibrium state. Analyzing the model, we show that if the basic reproductive number of viruses is less than one then the virus-free equilibrium is asymptotically stable and if the basic reproductive ratio is greater than one then the endemic equilibrium is locally asymptotically stable. Mathematical analysis and numerical simulations show that the CTL immune responses play a significant role in eradication of the disease. Ams subject classification 2010: 37N25.34C23.49J15.92D30.

Fog computing (FC) is used to reduce the energy consumption and latency for the heterogeneous communication approaches in the smart cities’ applications of the Internet of Everything (IoE). Fog computing nodes are connected through wired or wireless medium. The goal of smart city applications is to develop the transaction relationship of real-time response applications. There are various frameworks in real-world to support the IoE in smart-cities but they face the issues like security, platform Independence, multi-application assistance, and resource management. This article is motivated from the Blockchain and Fog computing technologies and presents a secured architecture Blockchain and Fog-based Architecture Network (BFAN) for IoE applications in the smart cities. The proposed architecture secures sensitive data with encryption, authentication, and Blockchain. It assists the System-developers and Architects to deploy the applications in smart city paradigm. The goal of the proposed architecture is to reduce the latency and energy, and ensure improved security features through Blockchain technology. The simulation results demonstrate that the proposed architecture performs better than the existing frameworks for smart-cities.

In this study, the authors propose a cluster‐based trust management model that efficiently detects the malicious nodes and restricts them to be on a route in wireless ad hoc networks. In contrast to previous works, the trust of a node is calculated using various trust attributes having substantial effect on reliable routing in the networks. In the proposed model, each node periodically predicts the value of each trust attribute about other nodes using autoregression. Subsequently, the direct trust is estimated using the weighted combination of trust attributes and it is fine tuned using proportional–integral model. All these recommendation trusts, from common neighbours, are collected and combined by the clusterhead to quantify the trust, and hence the routing is reliable and secure in the proposed model. Simulation results show that the proposed trust model provides better throughput and packet delivery ratio in presence of malicious nodes compared to other existing schemes.

RSA‐based secure routing protocols are computationally expensive because of the use of large public keys and/or signatures that are disadvantageous for mobile ad‐hoc networks. In this study, the authors proposed a secure dynamic routing protocol, named SDRP, which uses identity‐based scheme with pairing on elliptic curve. It makes use of signature and ‘Message Authentication Code’ algorithms to provide end‐to‐end, hop‐to‐hop and whole‐route authentications. The proposed SDRP has several advantages over the existing RSA‐based secure routing solutions. Firstly, it requires fewer signature generations and verifications on a route. Secondly, the SDRP makes use of an identity‐based scheme which is certificate‐less, thus saving routing overhead (RO) and storage. Finally, it uses a hardware address to generate its small size of public key. Both the qualitative analysis and the simulation results show that the proposed SDRP provides secure routes and better packet delivery fraction and with significantly less RO and latency compared with previous RSA‐based secure routing protocols.

The Internet of things (IoT) is a heterogeneous network of different types of wireless networks such as wireless sensor networks (WSNs), ZigBee, Wi-Fi, mobile ad hoc networks (MANETs), and RFID. To make IoT a reality for smart environment, more attractive to end users, and economically successful, it must be compatible with WSNs and MANETs. In light of this, the present paper discusses a novel quantitative trust model for an IoT-MANET. The proposed trust model combines both direct and indirect trust opinion in order to calculate the final trust value for a node. A Beta probabilistic distribution is used to combine different trust evidences and direct trust has been calculated. The theory of ARMA/GARCH has been used to combine the recommendation trust evidences and predict the resultant trust value of each node in multi-step ahead. Further, a routing protocol has been designed to ensure the secure and reliable end-to-end delivery of packets by only considering trustworthy nodes in the path. Simulation results show that our proposed trust model outperforms similar existing trust models.