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COVID-19 has stunned the global economy and threatened human life. Due to rapidly emerging fatalities and enormous cases appearing every day, researchers across the globe are producing significant contributions to mitigate this pandemic. Besides the race for discovering a vaccine and treatment for COVID-19, there is utmost focus on flattening the curve by undertaking appropriate measures. The remarkable role of frontline medical practitioners, who are eagerly treating the affected people will be penned in the history books. The efforts of scientists and technologists will be remembered for their extraordinary contributions to assist healthcare professionals and governments in mitigating the threats of COVID-19. Leading technology firms have formed consortiums and research groups, which provide funding and free access to supercomputers for solving complex computational problems to eliminate COVID-19. In this research, we have unveiled five state-of-the-art technologies and their remarkable applications that can be used to mitigate and eliminate the problems of COVID-19. These technologies include Artificial Intelligence (AI), 3D Printing Technology (3DPT), Big Data Analytics (BDA), High Performance Computing (HPC) and Telecommunication Technology (TT). This research investigates the use of technology to encounter COVID-19 and aims to serve as the primary reference for promoting future research as well as developments to produce solutions for COVID-19 using AI, 3DPT, BDA, HPC and TT.

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Cyber-security, as an emerging field of research, involves the development and management of techniques and technologies for protection of data, information and devices. Protection of network devices from attacks, threats and vulnerabilities both internally and externally had led to the development of ceaseless research into Network Intrusion Detection System (NIDS). Therefore, an empirical study was conducted on the effectiveness of deep learning and ensemble methods in NIDS, thereby contributing to knowledge by developing a NIDS through the implementation of machine and deep-learning algorithms in various forms on recent network datasets that contains more recent attacks types and attackers' behaviours (UNSW-NB15 dataset). This research involves the implementation of a deep-learning algorithm-Long Short-Term Memory (LSTM)-and two ensemble methods (a homogeneous method-using optimised bagged Random-Forest algorithm, and a heterogeneous method-an Averaged Probability method of Voting ensemble). The heterogeneous ensemble was based on four (4) standard classifiers with different computational characteristics (Naïve Bayes, kNN, RIPPER and Decision Tree). The respective model implementations were applied on the UNSW_NB15 datasets in two forms: as a two-classed attack dataset and as a multi-attack dataset. LSTM achieved a detection accuracy rate of 80% on the two-classed attack dataset and 72% detection accuracy rate on the multi-attack dataset. The homogeneous method had an accuracy rate of 98% and 87.4% on the two-class attack dataset and the multi-attack dataset, respectively. Moreover, the heterogeneous model had 97% and 85.23% detection accuracy rate on the two-class attack dataset and the multi-attack dataset, respectively.

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Background/objectives: This study presents a secure and energy-efficient scheme of patient's data transmission from wearable IoT sensors (WIS) to base station (BS). IoT sensors are widely used in the healthcare domain for realtime data collection and transmission. However, these sensors are resourceconstrained in terms of computational power and storage due to which chances of security breaches and threats increase. Moreover, with time the energy level of IoT sensors also degrade that sometimes leads towards loss of sensitive patient data. Purpose: The purpose of this study is to provide secure data transmission between wearable sensors and base stations and increasing energy efficiency of resource-constrained wearable IoT sensors. Method: The proposed scheme was tested by creating a mathematical model and then creating a simulation setup using the Cooja Contiki simulator. Findings: The results show that the proposed scheme provides secure and energy-efficient data transmission from WISs to BS as compared to the existing approaches. Further, the proposed scheme addresses some key issues including availability, reliability, scalability, and limited patient mobility. Novelty: Our research presented a unique approach for e-health applications using the IoT, where we considered the lightweight and secure scheme providing multiple group node concept.

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