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The potential of the Internet of Health Things (IoHT), also identified in the literature as the Internet of Medical Things (IoMT), is enormous, since it can generate expressive impacts on healthcare devices, such as the capnograph. When applied to mechanical ventilation, it provides essential healthcare to the patient and helps save lives. This survey elaborates on a deep review of related literature about the most robust and effective innovative healthcare solutions using modern technologies, such as the Internet of Things (IoT), cloud computing, Blynk, Bluetooth Low Energy, Robotics, and embedded systems. It emphasizes that IoT-based wearable and smart devices that work as integrated systems can be a faster response to other pandemic crises, respiratory diseases, and other problems that may occur in the future. It may also extend the performance of e-Health platforms used as monitoring systems. Therefore, this paper considers the state of the art to substantiate research about sensors, highlighting the relevance of new studies, strategies, approaches, and novelties in the field.
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The fifth generation (5G) aims to connect massive amounts of devices with higher reliability, lower latency, and faster transmission speed, which are vital for implementing e-health systems. However, the current efforts on 5G e-health systems are still not enough to accomplish its full blueprint. In this article, we first discuss the related technologies from the physical layer, upper layer, and cross-layer perspectives on designing 5G e-health systems. We then elaborate two use cases according to our implementations (i.e., 5G e-health systems for remote health and 5G e-health systems for Covid-19 pandemic containment). We finally envision the future research trends and challenges of 5G e-health systems.
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The aim of this work is to present the numerical results of the influenza disease nonlinear system using the feed forward artificial neural networks (ANNs) along with the optimization of the combination of global and local search schemes. The genetic algorithm (GA) and active-set method (ASM), i.e., GA-ASM, are implemented as global and local search schemes. The mathematical nonlinear influenza disease system is dependent of four classes, susceptible S(u), infected I(u), recovered R(u) and cross-immune individuals C(u). For the solutions of these classes based on influenza disease system, the design of an objective function is presented using these differential system equations and its corresponding initial conditions. The optimization of this objective function is using the hybrid computing combination of GA-ASM for solving all classes of the influenza disease nonlinear system. The obtained numerical results will be compared by the Adams numerical results to check the authenticity of the designed ANN-GA-ASM. In addition, the designed approach through statistical based operators shows the consistency and stability for solving the influenza disease nonlinear system.
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Fifth generation (5G) aims to connect massive devices with even higher reliability, lower latency and even faster transmission speed, which are vital for implementing the e-health systems. However, the current efforts on 5G e-health systems are still not enough to accomplish its full blueprint. In this article, we first discuss the related technologies from physical layer, upper layer and cross layer perspectives on designing the 5G e-health systems. We afterwards elaborate two use cases according to our implementations, i.e., 5G e-health systems for remote health and 5G e-health systems for Covid-19 pandemic containment. We finally envision the future research trends and challenges of 5G e-health systems.
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COVID-19RESUMEN
Amidst the global pandemic and catastrophe created by ‘COVID-19’, every research institution and scientists are doing their best efforts to invent or find the vaccine or medicine for the disease. The objective of this research is to design and develop a deep learning model for finding the degree of similarity of the genome of the Severe Acute Respiratory Syndrome-Coronavirus 2 (‘SARS-CoV-2’) with a given genome. This research also aims at detecting the genome of ‘SARS-CoV-2’ in the host human beings. The experimental results on the dataset publicly available at National Centre for Biotechnology Information, show that the model is effective in predicting the similarity score of the genomic sequence of ‘SARS-CoV-2’ and other prevalent viruses such as Severe Acute Respiratory Syndrome-Coronavirus, Middle East Respiratory Syndrome Coronavirus, Human Immunodeficiency Virus, and Human T- cell Leukaemia Virus. This is successful in detecting the genome of ‘SARS-CoV-2’ in the host genome with an accuracy of 99.27%. It may prove a useful tool for doctors to quickly classify the infected and non-infected genomes. It can also be useful in finding the most effective drug from the available drugs for the treatment of ‘COVID-19’.