Sustainable intelligent outbreak with self-directed learning system and feature extraction approach in technology

The COVID-19 epidemic is one of the deadliest viruses in recent history, but it is an infodemic that infects students and the community with false information, exacerbating the existing ailment. We attempt to identify and classify bogus news on the internet to identify false information about epidemics and coronavirus. Real comments were gathered from legitimate Twitter usernames, while phoney comments, publications, and events were obtained through realisation websites like news sources. Methods of deep learning, such as convolutional and recurrent neural networks, were combined in this process. Conventional classification techniques such as Binary Classification, K-nearest, Asymmetric Boost, and Random Forest were used up until they were exhausted in the random mutimodal deep learning (RMDL). Examining these tactics and gaining a deeper understanding of the dataset, including the information on COVID-19 that can be found on the internet, required the application of the high-frequency sub and clothing word convolutional extracted features.

Forecasting the Growth in Covid-19 Infection Rates

The recent times have seen the global rise in infection rates from the virus Covid-19, leading to a pandemic. The exponential rise in infections and deaths lead to panic and nation-wide lockdowns across the globe. Advancements in biotechnical and medical research have paved the way for the development and mass distribution of vaccines. To build an understanding of the current situation we did a comparative analysis of the rise in infection rates among citizens across the countries and also the growth in vaccinations in the pre-vaccination phase and the post-vaccination phase of the on-going pandemic to determine whether the rate of vaccination is more than the rate of infection or otherwise. Then, a comparison is done among two prediction models we built, one using polynomial regression and other using SVM to determine which model provides better prediction results of infection rates in a country. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Real-Time Surveillance System for Detection of Social Distancing

As the corona virus can mutate and due to other scientific factor associated to it, experts believe that COVID-19 will remain with us for decades. Therefore, one has to keep social distancing measures. Accepting the pandemic situation, the paper presents a mechanism for detecting violations of social distancing using deep learning to estimate the distance between individuals to diminish the influence of COVID-19. The focus of this paper is to understand the effect of social distancing on the spread of COVID-19 by using YOLOv3 and Faster-RCNN and proposes IFRCNN (improved faster region convolution neural network). The proposed method IFRCNN is checked on a live streaming video of pedestrians walking on the street. This paper keeps the live updates of the recorded video along with social distancing violation records on a location, so how many people in a location are maintaining social distancing. Updates will be stored in a cloud-based storage system and any organization or firm can get live updates of that location in their digital devices.

The effects of blood group type on COVID-19 recovery in patients

Background: The arising Covid infection 2019 is rapidly spreading over the world and has been named a pandemic by the World Health Organization (WHO). The ABO blood group has been linked to the susceptibility to viral infection in the past. According to research, blood groups A and O are associated with a higher and lower risk of coronavirus disease 2019 positivity. Objectives: The goal of the study was to see if there was a link between ABO blood type and COVID-19 susceptibility in patients recovering in Baghdad and Hilla hospitals, and if the former could be used as a biomarker for the latter.

Forecasting the Growth in Covid-19 Infection Rates

The recent times have seen the global rise in infection rates from the virus Covid-19, leading to a pandemic. The exponential rise in infections and deaths lead to panic and nation-wide lockdowns across the globe. Advancements in biotechnical and medical research have paved the way for the development and mass distribution of vaccines. To build an understanding of the current situation we did a comparative analysis of the rise in infection rates among citizens across the countries and also the growth in vaccinations in the pre-vaccination phase and the post-vaccination phase of the on-going pandemic to determine whether the rate of vaccination is more than the rate of infection or otherwise. Then, a comparison is done among two prediction models we built, one using polynomial regression and other using SVM to determine which model provides better prediction results of infection rates in a country. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Design and Development of IoT Wearable Device for Early Detection of COVID-19 and Monitoring Through Efficient Data Management Framework in Pre-pandemic Life

COVID-19 virus named CORONA is a vigorous disease spread all over the world very quickly and creates a pandemic situation to the human beings normal life. As per the doctors and researchers from the laboratory point of view, it will spread to a huge volume when humans are not followed certain principles. Moreover, this disease is easily transferred to neighbors and others in a short period which leads to death. To rectify the remedy for this virus, various spread countries and research peoples are creating the vaccines and some precautionary methods for living hood situation. Recent techniques are used to detect and monitoring the COVID-19- affected person's lifestyle and insisting they take precaution steps for early pre-pandemic life. IoT is a framework that is used to generate data from the human body from the sensors opted for human conditions. Wearable devices have been created with these sensors and communicated with human bodies directly or indirectly. The generated data will send through the server using any connectivity techniques such as Bluetooth or Wi-Fi. Analytics will be done at the server side for taking actions like the human body is affected by the COVID-19 virus or not. Finally, the generated data from a human can continuously store in real time in a cloud server which will be managed as a framework efficiently. This research work proposes a framework for data management in the early detection and monitoring of COVID-19 persons through IoT wearable devices in a pre-pandemic life. The experiments have been done at different zones, and the results are shown symptoms of COVID-19 disease. Parallel work reveals the data management in a cloud server since data have generated continuously in real time and tracking details also stored genuinely. Data management is the typical process in this research because all the data were generated in real time and analytics will be done whenever required. For that large amount of space and effective retrieval technique is required for data extraction. This research work data set is derived from various Internet sources like government web sites and mobile applications, and then, results have displayed the COVID-19 disease details accurately in real time.

An Effective Diagnostic Framework for COVID-19 Using an Integrated Approach

The coronavirus, one of the deadliest virus erupted in Wuhan, China in December and has claimed millions of lives worldwide and infected too. This virus has off-late demonstrated mutations thus making it difficult for the health professionals to adopt a uniform means of cure. Many people due to lack of support have confined themselves at home. The hospitals too are running short of equipment and support systems. Thus, computational connectivity between the patients at home and the hospitals needs to be established. The objective of this paper is to propose a framework/model that connects all the stakeholders so that either in regular monitoring or in emergency cases help can be provided to them. It has been well established through research and case studies that critical factors associated with this disease are oxygen level (SPO2), pulse rate, fever, chest infection, cough causing choking, and breathlessness. Data shall be collected, stored, and analyzed for the above symptoms and for this cloud storage and blockchain technology would be used. It has been established through various studies that non-clinical techniques like AI and machine learning prove to be effective for the prediction and diagnosis of COVID-19. Using this theory as the standard basis, machine learning models like SVM, Naive Bayes, and decision trees can be used for the analysis, diagnosis, and prediction. Using IoT and its variants, remote monitoring of patient, and consultation can be provided to the patient. Appropriate action would be taken. In addition, a mobile application would enable the patients to gather or read about experiences of other patients. Thus, it would be established through the proposed framework, that an integrated approach of technologies has a great potential in such applications and offers several advantages.

Adoption Social Media-Movie Based Learning Project (SMMBL) To Engage Students' Online Environment

Developing ICT as English Language Teaching (ELT) materials in Indonesia is still being studied in higher education contexts. Nowadays, amid covid 19 there are many tools, apps and technologies are being integration in virtual classroom. ICT can be used as media for teaching and learning process it should be related today’s fact by integrating ICT easy enough to share it and easy to access by the students’ technologies devices (Muthmainnah, et.all, 2021 and Apriani et.al, 2021 The purposes of this study is to investigate the impact of development instructional design incorporating social media-movie based learning project (SMMBLP) in Computer Science Faculty in learning English to engagement student’s online environment. The method of this study is quantitative research and used pre-experimental design to investigates the impact of SMMBLP on students’ English skill and student’s motivation in ELT. The participants in this study were the first semester students of Computer Science Faculty academic year 2020-2021 at Universitas Al Asyariah Mandar consisted 40 students. The data were collected by test;pre-test, post-test and questionnaire. The result of the study implied the impact on integration social media-movie based learning project (SMMBLP) that very effective to enhancing students’ English skill and motivation. The study suggests SMMBL can be integration in online or Hybrid/ Blended environment for the next education teaching model in ELT for EFL students. © 2022, Pegem Akademi Yayincilik Egitim Danismanlik Hizmetleri Ticaret A.S.. All rights reserved.

Cloud based iot smart healthcare system for remote patient monitoring

INTRODUCTION: Covid-19 has exposed the necessitate for the rapid acceptance of increasingly pioneering digital health technologies, especially remote health monitoring. The digital revolution in Healthcare is dynamic ease of use of inexpensive concern solutions, enhancing patient care, reducing complications, improving effectiveness, and authorizing healthcare decision-makers with intelligence insight at the point of care. OBJECTIVES: The primary objective of this work is to depict the need to recognize wearable sensors as a prerequisite for supporting paradigms in monitoring patients in real-time and enabling access information from the cloud. METHODS: Internet of Things (IoT) is the association of substantial objects where information and communication tools connect various embedded devices to the internet for gathering and switching over data. The combinations of embedded devices with cloud servers recommend extensive pertinence of IoT to several areas of human life. This paper has proposed a method through cloud-based IoT healthcare sensors to formulate patient monitoring remotely. In combination with the implementation of various inbuilt capabilities, internet-enabled heterogeneous wearable sensors can be used for the collection of biomedical data to transmit patient data directly to cloud severe systems to monitor health remotely. RESULTS: Smart healthcare monitoring includes channels of communication, embedded internal and external sensors, IoT server, and cloud storage. The health parameters activities are done at various levels of refining named application layer, management layer, network layer and layer of device. Different data sensors have been collected by wireless media from nodes. It is saved as an unstructured dataset in the cloud. For security with username and password, a patient database is created. Authorized individuals have access to the cloud in order to monitor cloud sensor data in data log, analogue log, digital input and digital output. CONCLUSION: Patient physical parameters like heart beat respiration,high temperature and stress are calculated via sensors and can be progressed by WIFI unit in the cloud. From this healthcare practitioner will analyze medical to have effective medication. For face-to-face consultation between doctors and patients, the video feature can be added in the future work. © 2021 G.Jaya Lakshmi et al.,.