ABSTRACT
Globally, millions of people were affected by the Corona-virus disease-2019 (COVID-19) causing loads of deaths. Most COVID-19 affected people recover in a few spans of weeks. However, certain people even those with a milder variant of the disease persist in experiencing symptoms subsequent to their initial recuperation. Here, a novel Block-Chain (BC)-assisted optimized deep learning algorithm, explicitly improved dragonfly algorithm based Deep Neural Network (IDA-DNN), is proposed for detecting the different diseases of the COVID-19 patients. Initially, the input data of the COVID-19 recovered patients are gathered centered on their post symptoms and their data is amassed as a BC for rendering security to the patient's data. After that, the disease identification of the patient's data is performed with the aid of system training. The training includes '4' disparate datasets for data collection, and then, performs preprocessing, Feature Extraction (FE), Feature Reduction (FR), along with classification utilizing ID-DNN on the gathered inputted data. The IDA-DNN classifies '2' classes (presence of disease and absence of disease) for every type of data. The proposed method's outcomes are examined as well as contrasted with the other prevailing techniques to corroborate that the proposed IDA-DNN detects the COVID-19 more efficiently.
ABSTRACT
BACKGROUND: Physical health is vital to the improvement of our skills and the enhancement of eye movements. The coordination of good body movement helps to establish a safe position of the body. The challenging characteristics of physical education include insufficient time allocation, inadequately trained teachers, and inadequate provision of the equipment is considered as an important factor. OBJECTIVE: In this paper, IoT-based Computational Narrowband Physical Health Framework (IoT-CNPHF) has been proposed to strengthen adequate time allocation, appropriately qualified teachers, and sustainable provision in the physical education system. METHOD: Massive extended range analysis is introduced to enhance the duration and time allotted for physical activity that helps in creating awareness about the importance of physical activities and sports in our daily life. The multimodal supervised technique is incorporated with IoT-CNPHF to improve the knowledge of physical education for the teachers and to provide suitable provision for students in the physical education system. RESULTS: The simulation analysis is performed based on accuracy, performance, and its efficiency proves the reliability of the proposed framework.
Subject(s)
Internet of Things , Physical Education and Training , Exercise , Humans , Reproducibility of ResultsABSTRACT
BACKGROUND: In recent years, social media have filtered our life both in the professional and personal aspects. Currently, most of us suffer from poor quality of thinking, which is due to the impact of social media towards our lives, particularly in the health care arena. OBJECTIVES: In this article, cultural tension due to social media creates an unwanted risk to the youngsters and others with sleep deprivation. They become dependent on staying dynamic via social networking sites media all the time. As indicated by an ongoing report, there is a reliable connection between the measure of time spent via web-based networking media and depression among youthful grown-ups, which creates unprofessional problems and potential healthcare risk in individuals due to the usage of social media. RESULTS: This article speaks about the research gap and possible risks reforming strategies on healthcare communication in social media through statistical analysis. CONCLUSION: The experimental validation of case studies shows prominent solutions that have not been addressed in traditional methods.
