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The Internet of Things (IoT) has infiltrated nearly every aspect of life. The healthcare industry has become one of the most popular applications for IoT and related technologies like Artificial Intelligence, etc. IoT infrastructures and solutions are used in smart healthcare. By making use of wearable and mobile devices, IoT-based smart healthcare systems have added great value to the healthcare domain. This leads to a significant increase in the use of health data sharing for a more accurateand timely diagnosis. Its mainstream adoption, however, remains a elusive dream. The major drive behind this is ensuring the data and participating entities' security and privacy at all costs. Smart healthcare systemsare at risk to a variety of security flaws and malicious attacks, including data leakage, tampering, forgery, and so on. To address this, green blockchain technology has come to light as a practical way of enhancing data security and privacy for both users as well as data. Through the avalancheof medical information created by e-medical records and other mechanisms based on Information Communication Technology (ICT), a plethora of applications has been discovered usinggreen blockchain in the medicare industry. Conventional healthcare procedures are swiftly adopting present-day technologies in order to make the transition to smart healthcare ecosystems. The main factors driving this transition are theend-user's convenience and user-friendliness. Even with the use of Smart Healthcare Systems (SHS), there are several obstacles in the transparency, security, and safety of data and its consumers. This paper providesa comprehensive overviewof green blockchain's use in the healthcare industry. It also puts forth a green blockchain-based SmartHealthcare Securitystructure to ensure the system's inherent security and integrity. [ABSTRACT FROM AUTHOR]

The objective of the research is analyzed and help provide a prediction model for similar diseases by using MERS and SARS data. There are examples of companies like BlueDot which used AI analysis of data points from multiple resources and predicted the outbreak in December 2019 mid. Using big data will have its advantages because we can use data from multiple sources and merge them to understand the next COVID19 or similar disease outbreak and predict the possible occurrences in different regions. Contact tracing is essential to predict the outbreak of a disease with data that is collected from different sources, for examples-post with metadata, tags on social media passenger lists, smart cards used in metros, where vehicles were parked, what all places credit and debit cards were used and the travel history. The methodology used is gathering secondary data of contact of what all locations the affected person visited. Data from social media and social networks can be worked upon and reconstructed to predict the outbreak of communicable diseases. A pandemic can be mitigated depending on the ability to identify the affected areas, individuals, and potential hotspots on a global level. Analyzing data using big data analytics from the hotspots, where data is easily available with countries and cities. If this data is modeled mathematically and used with AI, important predictions could be made. Information about the movement of people like done in Taiwan where travel history of a woman suffering from cough and diarrhea was correctly used to diagnose that she had covid19. AI and Big Data will help in preparing us for emergencies. The research will help in tackling the disease in real-time and help governments and organizations to plan and respond to sudden emergencies, lift public health, identify vaccine candidates. The research paper will help researchers to uncover relationships within massive datasets and help them in decision-making. This paper will help them in integrating information from multiple resources and predicting disease outbreaks. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, the world has witnessed rampant use of digital technology in almost every facet of huma lives. However, these digital techniques like use of mobile apps have certain drawbacks and constrains. These could be addressed with the use of emerging technologies like Internet of Things (IoT). In healthcare, that is Internet of Medical Things (IoMT). A number of ongoing studies show that integrating security measures with technology will lead to the adoption of stable IoMT applications. Emerging IoMT technologies used in conjunction with Blockchain, Artificial Intelligence, and Big Data, provide feasible solutions. The paper focuses on IoMT models, innovations, and security progress made to tackle COVID-19 issues. This paper contains the bibliometric research highlighting the use of disruptive technology for the diagnosis of COVID-19. It reviews the adoption of contactless services in the post-COVID-19 period along with potential opportunities as well as challenging obstacles for healthcare agencies, policymakers, and customers. [ABSTRACT FROM AUTHOR]

Human beings do not have enough time to consult a doctor on a regular basis about their health because of their heavy workload and lack of time. It becomes difficult for the patients to visit multiple hospitals multiple times for their examination. With Healthcare 4.0 and fog computing a large quantity of information can be processed which can generate insights about the patient's health. By using fog computing real time monitoring of the patient's health can be achieved. It makes healthcare more effective, dependable, and convenient. Individuals can self-monitor their blood pressure, heart rate, and other vital signs and take preventive measures. The healthcare industry is a latency-sensitive area, as a result, fog computing deployment in this region is critical. Better care, better treatment, and higher patient satisfaction can result from proper analytics and analysis which can be done closer to end devices using Fog. This bibliometric research focusses on the current scenario, application of FC in Healthcare in India and its relevance in healthcare 4.0. This research aims to analyses how healthcare in India has integrated fog computing to enhance the quality of their services, the developments in recent times and what are the gaps in the research. A literature review will be conducted using the academic documents published in peer reviewed scientific journals, white papers, articles and secondary research will be used. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, the world has witnessed rampant use of digital technology in almost every facet of huma lives. However, these digital techniques like use of mobile apps have certain drawbacks and constrains. These could be addressed with the use of emerging technologies like Internet of Things (IoT). In healthcare, that is Internet of Medical Things (IoMT). A number of ongoing studies show that integrating security measures with technology will lead to the adoption of stable IoMT applications. Emerging IoMT technologies used in conjunction with Blockchain, Artificial Intelligence, and Big Data, provide feasible solutions. The paper focuses on IoMT models, innovations, and security progress made to tackle COVID-19 issues. This paper contains the bibliometric research highlighting the use of disruptive technology for the diagnosis of COVID-19. It reviews the adoption of contactless services in the post-COVID-19 period along with potential opportunities as well as challenging obstacles for healthcare agencies, policymakers, and customers. [ABSTRACT FROM AUTHOR]

The objective of the research is analyzed and help provide a prediction model for similar diseases by using MERS and SARS data. There are examples of companies like BlueDot which used AI analysis of data points from multiple resources and predicted the outbreak in December 2019 mid. Using big data will have its advantages because we can use data from multiple sources and merge them to understand the next COVID19 or similar disease outbreak and predict the possible occurrences in different regions. Contact tracing is essential to predict the outbreak of a disease with data that is collected from different sources, for examples-post with metadata, tags on social media passenger lists, smart cards used in metros, where vehicles were parked, what all places credit and debit cards were used and the travel history. The methodology used is gathering secondary data of contact of what all locations the affected person visited. Data from social media and social networks can be worked upon and reconstructed to predict the outbreak of communicable diseases. A pandemic can be mitigated depending on the ability to identify the affected areas, individuals, and potential hotspots on a global level. Analyzing data using big data analytics from the hotspots, where data is easily available with countries and cities. If this data is modeled mathematically and used with AI, important predictions could be made. Information about the movement of people like done in Taiwan where travel history of a woman suffering from cough and diarrhea was correctly used to diagnose that she had covid19. AI and Big Data will help in preparing us for emergencies. The research will help in tackling the disease in real-time and help governments and organizations to plan and respond to sudden emergencies, lift public health, identify vaccine candidates. The research paper will help researchers to uncover relationships within massive datasets and help them in decision-making. This paper will help them in integrating information from multiple resources and predicting disease outbreaks. [ABSTRACT FROM AUTHOR]

Human beings do not have enough time to consult a doctor on a regular basis about their health because of their heavy workload and lack of time. It becomes difficult for the patients to visit multiple hospitals multiple times for their examination. With Healthcare 4.0 and fog computing a large quantity of information can be processed which can generate insights about the patient's health. By using fog computing real time monitoring of the patient's health can be achieved. It makes healthcare more effective, dependable, and convenient. Individuals can self-monitor their blood pressure, heart rate, and other vital signs and take preventive measures. The healthcare industry is a latency-sensitive area, as a result, fog computing deployment in this region is critical. Better care, better treatment, and higher patient satisfaction can result from proper analytics and analysis which can be done closer to end devices using Fog. This bibliometric research focusses on the current scenario, application of FC in Healthcare in India and its relevance in healthcare 4.0. This research aims to analyses how healthcare in India has integrated fog computing to enhance the quality of their services, the developments in recent times and what are the gaps in the research. A literature review will be conducted using the academic documents published in peer reviewed scientific journals, white papers, articles and secondary research will be used. [ABSTRACT FROM AUTHOR]

The healthcare industry has evolved to be one of the largest industries in almost every country due to a popular trigger of Covid-19 mechanism. COVID-19 health services and education are highly relevant because they are fundamental public resources that define the form of society in which we live both before and after the pandemic. The COVID-19 pandemic has exacerbated the already-existing informality in the healthcare sector. Because of the information asymmetry, doctors are often required to identify and treat their patients using heuristics. The use of protocols and solutions that surface insights into care unit workflows ahead of time will eliminate the need for surface-level data and disarm latent biases, resulting in better patient outcomes. The AI based applications are used to monitor records that have been reported, recovered, or expired. Through AI we can track the mutation of any deadly virus or infection in a comparatively faster duration of time and help prevent potential individuals from getting the infection. [ABSTRACT FROM AUTHOR]