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1.
12th Annual IEEE Global Humanitarian Technology Conference, GHTC 2022 ; : 106-113, 2022.
Article in English | Scopus | ID: covidwho-2136182

ABSTRACT

In this paper, we analyze social media data (e.g., tweets) related to coronavirus disease 2019 (COVID-19) and COVID-19 vaccines. The main objective is to explore daily COVID-19 cases and vaccine rates in addition to analyzing sentiments and discussions related to COVID-19 vaccination on social media, e.g., Twitter. During the early days of the pandemic, there were rapid developments of vaccines that can prevent the novel COVID-19. However, the potential hurdles of developing COVID-19 vaccines faster than any other conventional vaccine has made some people apprehensive about taking the COVID-19 vaccine. Since social media keeps individuals connected locally and globally, Twitter as a social networking platform is a great way to collect information on tweets related to the coronavirus vaccine. Specifically, this paper studies various data analytic tools that can help study the changes in users' opinions and emotions related to coronavirus vaccines, as well as studying the coronavirus cases and vaccine rates globally. Furthermore, this study will enable individuals to get real-time insights into the sentiments of COVID-19 vaccines based on social media tweets. © 2022 IEEE.

2.
12th Annual IEEE Global Humanitarian Technology Conference, GHTC 2022 ; : 154-161, 2022.
Article in English | Scopus | ID: covidwho-2136178

ABSTRACT

The sudden exodus of healthcare worker has left many healthcare organizations with limited capabilities to provide efficient and continuous care for long-term patients. When the COVID-19 pandemic arose, society has witnessed the lack of sophisticated medical systems. An abundance of acute patients in long term care needing consistent monitoring has left the healthcare workforce to dwindle due to strict monitoring protocols and fatigue. Due to these global events, it has become apparent that more advanced methods of automated e-health processes should be implemented to relieve the distress within the workforce. As Internet of Things (IoT) becomes prominent within major commercial and military sectors, the interoperability between devices is increasing significantly. Intelligent medical devices and network connectivity in major healthcare organizations have been using IoT and cloud computing to maximize productivity and minimize workforce fatigue. Currently, the limitations of health monitoring systems and smart devices are data interoperability and human intervention errors. In this survey, we will review the selected Finite State Automata that are able to be implemented for real-time update to assist the major issues based on human intervention errors and a framework for updating e-health records. © 2022 IEEE.

3.
Pandemic Risk, Response, and Resilience: COVID-19 Responses in Cities around the World ; : 231-259, 2022.
Article in English | Scopus | ID: covidwho-2035594

ABSTRACT

Globalization has facilitated fast spread of COVID-19 cutting across political boundaries and even the remote locations have not been spared. Spread of the contagion is studied in the Himalayan province of Uttarakhand in India that is generally visited by pilgrims and tourists in large numbers from across the country and abroad. Despite restrictions on travel, the virus has spread even to the remote locations of the province. The study analyzes the efforts put in by the provincial government and the pace of spread as a function of geographical remoteness, together with the constraints faced by the administration and disaster managers in restricting the spread. The study highlights the important lessons which would help the management of future pandemics. The chapter at the same time highlights limited involvement of medical community in disaster management which is generally limited to post-disaster casualty management, triage, first aid, medical care, and psychosocial support. © 2022 Elsevier Inc. All rights reserved.

4.
7th EAI International Conference on Science and Technologies for Smart Cities, SmartCity360° 2021 ; 442 LNICST:422-433, 2022.
Article in English | Scopus | ID: covidwho-1930337

ABSTRACT

The transportation problem is a very applicable and relevant logistic problem. In this paper, to test meta-heuristics on the transportation problem and also improve initial feasible solutions in few number of iterations, four recent and effective meta-heuristic algorithms are used to solve transportation problems. Laying Chicken Algorithm (LCA), Volcano Eruption Algorithm (VEA), COVID-19 Optimizer Algorithm (CVA), and Multiverse Algorithm (MVA) are implemented to solve different sizes of the transportation problem. Computational results show that CVA is the most efficient optimizer for large size cases and LCA is the best algorithm for the others. Finally, convergence of algorithms will be discussed and rate of convergence will be compared. The advantage of these heuristics are that they can be easily adapted to more challenging versions of the transportation problem which are not solveable by the Simplex method. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

5.
IEEE International Conference on Communications (ICC) ; 2021.
Article in English | Web of Science | ID: covidwho-1562334

ABSTRACT

Lately people are using social media for sharing their thoughts, insights about different topics and issues. The main aim of social media is to connect users and update their statuses/thoughts. One of the most used online social networking sites to share information is Twitter with roughly 330 million uses globally and 4835 million US users where they share their opinions and thoughts. Recently, the world faced a serious pandemic, Corona virus disease (COVID-19) outbreak and the World Health Organization (WHO) declared the virus as a global health emergency. The COVID-19 started in late December of 2019 in Wuhan City, Hubei Province, China. Around the world during this time, individuals use social media to share their opinions about the pandemic. Because of the lack of information about the virus, people switched to micro-blogging platforms such as Twitter. In this study, we utilize natural language processing (NLP) techniques for opinion mining to extract negative and positive sentiments/tweets on COVID-19. We investigate NLP based sentiment analysis using Recurrent Neural Network (RNN) model with Long-Short Term Memory networks (LSTMs). Predicted sentiment using LSTM-RNN, which gives high accuracy, can be used to educate people about the virus.

6.
Ieee Consumer Electronics Magazine ; 10(4):18-27, 2021.
Article in English | Web of Science | ID: covidwho-1307643

ABSTRACT

Without an effective vaccine, treatment, or therapy, the Coronavirus Disease 2019 (COVID-19) is spreading like fire and claiming lives. Countries began to adopt various strategies such as lockdown, mass testing, tracing, quarantine, sanitization, isolation, and treatment to contain COVID-19. However, it was soon realized that we need to take the help of powerful technologies to combat the spread of deadly COVID-19 until a vaccine or a drug is discovered. In this article, we discuss how the use of cutting edge technologies such as the Internet of Things (IoT), Big data, artificial intelligence (AI), unmanned aerial vehicles (UAVs)/drones, blockchain, robotics, autonomous ground vehicles, communication technologies in screening, testing, contact tracing, spread analysis, sanitization, and protocol enforcements can help prevent the COVID-19 spread.

7.
21st IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2020 ; : 180-187, 2020.
Article in English | Scopus | ID: covidwho-860074

ABSTRACT

Coronaviruses are a famous family of viruses that cause illness in both humans and animals. The new type of coronavirus COVID-19 was firstly discovered in Wuhan, China. However, recently, the virus has widely spread in most of the world and causing a pandemic according to the World Health Organization (WHO). Further, nowadays, all the world countries are striving to control the COVID-19. There are many mechanisms to detect coronavirus including clinical analysis of chest CT scan images and blood test results. The confirmed COVID-19 patient manifests as fever, tiredness, and dry cough. Particularly, several techniques can be used to detect the initial results of the virus such as medical detection Kits. However, such devices are incurring huge cost, taking time to install them and use. Therefore, in this paper, a new framework is proposed to detect COVID-19 using built-in smartphone sensors. The proposal provides a low-cost solution, since most of radiologists have already held smartphones for different daily-purposes. Not only that but also ordinary people can use the framework on their smartphones for the virus detection purposes. Today's smartphones are powerful with existing computation-rich processors, memory space, and large number of sensors including cameras, microphone, temperature sensor, inertial sensors, proximity, colour-sensor, humidity-sensor, and wireless chipsets/sensors. The designed Artificial Intelligence (AI) enabled framework reads the smartphone sensors' signal measurements to predict the grade of severity of the pneumonia as well as predicting the result of the disease. © 2020 IEEE.

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