ABSTRACT
Due to advancements in information and communication technology, the Internet of Things has gained popularity in a variety of academic fields. In IoT-based healthcare systems, numerous wearable sensors are employed to collect various data from patients. The healthcare system has been challenged by the increase in the number of people living with chronic and infectious diseases. There are several existing IoT-based healthcare systems and ontology-based methods to judiciously diagnose, and monitor patients with chronic diseases in real-time and for a very long term. This was done to drastically minimize the vast manual labor in healthcare monitoring and recommendation systems. The current monitoring and recommendation systems generally utilised Type-1 Fuzzy Logic (T1FL) or ontology that is unsuitable owing to uncertainty and inconsistency in the processing, and analysis of observed data. Due to the expansion of risk and unpredictable factors in chronic and infectious patients such as diabetes, heart attacks, and COVID-19, these healthcare systems cannot be utilized to collect thorough physiological data about patients. Furthermore, utilizing the current T1FL ontology-based method to extract the ideal membership value of risk factors becomes challenging and problematic, resulting in unsatisfactory outcomes. Therefore, this chapter discusses the applicability of IoT-based enabled Type-2 Fuzzy Logic (T2FL) in the healthcare system, and the challenges and prospects of their applications were also reviewed. The chapter proposes an IoT-based enabled T2FL system for monitoring patients with diabetes by extracting the physiological factors from patients' bodies. The wearable sensors were used to capture the physiological factors of the patients, and the data capture was used for the monitoring of patients. The results from the experiment reveal that the model is very efficient and effective for diabetes patient monitoring, using patient risk factors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Vaccinations are critical and effective in resolving the current pandemic. With the highly transmissible and deadly SARS-CoV-2 virus (COVID-19), a delay in acceptance, or refusal of vaccines despite the availability of vaccine services poses a significant public health threat. Moreover, vaccine-related hesitancy, mis/disinformation, and anti-vaccination discourse are hindering the rapid uptake of the COVID-19 vaccine. It is urgent to examine how anti-vaccine sentiment and behavior spread online to influence vaccine acceptance. Therefore, this study aimed to investigate the COVID-19 vaccine hesitancy diffusion networks in an online Reddit community within the initial phase of the COVID-19 pandemic. We also sought to assess the anti-vaccine discourse evolution in language content and style. Overall, our study findings could help facilitate and promote efficient messaging strategies/campaigns to improve vaccination rates. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Vaccinations are critical and effective in resolving the current pandemic. With the highly transmissible and deadly SARS-CoV-2 virus (COVID-19), a delay in acceptance, or refusal of vaccines despite the availability of vaccine services poses a significant public health threat. Moreover, vaccine-related hesitancy, mis/disinformation, and anti-vaccination discourse are hindering the rapid uptake of the COVID-19 vaccine. It is urgent to examine how anti-vaccine sentiment and behavior spread online to influence vaccine acceptance. Therefore, this study aimed to investigate the COVID-19 vaccine hesitancy diffusion networks in an online Reddit community within the initial phase of the COVID-19 pandemic. We also sought to assess the anti-vaccine discourse evolution in language content and style. Overall, our study findings could help facilitate and promote efficient messaging strategies/campaigns to improve vaccination rates. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.