Researchers around the world are applying various prediction models for COVID-19 to make informed decisions and impose appropriate control measures. Because of a high degree of uncertainty and lack of necessary data, the traditional models showed low accuracy over the long term forecast. Although the literature contains several attempts to address this issue, there is a need to improve the essential prediction capability of existing models. Therefore, this study focuses on modelling and forecasting of COVID-19 spread in the top 5 worst-hit countries as per the reports on 10th July 2020. They are Brazil, India, Peru, Russia and the USA. For this purpose, the popular and powerful random vector functional link (RVFL) network is hybridized with 1-D discrete wavelet transform and a wavelet-coupled RVFL (WCRVFL) network is proposed. The prediction performance of the proposed model is compared with the state-of-the-art support vector regression (SVR) model and the conventional RVFL model. A 60 day ahead daily forecasting is also shown for the proposed model. Experimental results indicate the potential of the WCRVFL model for COVID-19 spread forecasting.

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Quantum key distribution (QKD) provides a way for distribution of secure key in at least two parties which they initially share. And there are many protocols for providing a secure key i.e. BB84 protocol, SARG04 protocol, E91 protocol and many more. In this paper all the concerned protocols that share a secret key is explained and comparative study of all protocols shown.

The celiac plexus block is an approved method for the relief of upper abdominal cancer pain. Classically, fluoroscopy-guided posterior approach to the celiac plexus block has been used. Computed tomography-guided anterior approach and endoscopic ultrasound-guided approach have also been utilized. An ultrasound-guided anterior approach to celiac plexus neurolysis with median plane single-needle entry technique has been described that targets the preaortic area between the origins of celiac trunk and superior mesenteric artery. We describe our experience with and decision to use the bedside ultrasound-guided anterior approach to celiac plexus neurolysis using bilateral paramedian needle entry technique.

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Advancements in information technology have benefited the healthcare industry by providing it with distinct methods of managing medical data which improve the quality of medical services. The Internet of Things (IoT) and artificial intelligence are the foundations for innovative sustainable computing technologies in e-healthcare applications. In the IoT-enabled sustainable healthcare system, the IoT devices normally record the patient data and transfer it to the cloud for further processing. Security is considered an important issue in the design of IoT networks in the healthcare environment. To resolve this issue, this article presents a novel blockchain and artificial intelligence-enabled secure medical data transmission (BAISMDT) for IoT networks. The goal of the BAIS-MDT model is to achieve security and privacy in reliable data transmission of the IoT networks. The proposed model involves a signcryption technique for secure and reliable IoT data transmission. The blockchain-enabled secure medical data transmission process takes place among the IoT gadgets and service providers. The blockchain technique is applied to generate a viable environment to securely and reliably transmit data among different data providers. Next to the decryption process, the modified discrete particle swarm optimization algorithm with wavelet kernel extreme learning machine model is applied to determine the presence of disease. An extensive set of simulations were carried out on a benchmark medical dataset. The experimental results analysis pointed out the superior performance of the proposed BAISMDT model with the accuracy of 97.54% and 98.13% on the applied Heart Statlog and WBC dataset, respectively.

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