Internet of medical things (IoMT): A systematic review of applications, trends, challenges, and future directions

The Internet of medical things (IoMT) is rapidly changing the healthcare sector. IoMT paired with enabling technologies like artificial intelligence, cloud computing, wireless sensor networks can effectively monitor people’s health continuously. In this chapter, the author provides a systematic review of the architectures of IoMT, applications, and trends in IoMT. IoMT applications to counter the COVID-19 pandemic are also discussed. IoMT and enabling technologies will improve the quality of human lives. This chapter addresses a few challenges while adopting IoMT in healthcare. © The Institution of Engineering and Technology 2022.

Applications of cognitive internet of medical things in modern healthcare.

The sudden outbreak of the novel coronavirus disease in 2019, known as COVID-19 has impacted the entire globe and has forced governments of various countries to a partial or full lockdown in the fear of the rapid spread of this disease. The major lesson learned from this pandemic is that there is a need to implement a robust system by using non-pharmaceutical interventions for the prevention and control of new contagious viruses. This goal can be achieved using the platform of the Internet of Things (IoT) because of its seamless connectivity and ubiquitous sensing ability. This technology-enabled healthcare sector is helpful to monitor COVID-19 patients properly by adopting an interconnected network. IoT is useful for improving patient satisfaction by reducing the rate of readmission in the hospital. The presented work discusses the applications and technologies of IoT like smart and wearable devices, drones, and robots which are used in healthcare systems to tackle the Coronavirus pandemic This paper focuses on applications of cognitive radio-based IoT for medical applications, which is referred to as "Cognitive Internet of Medical Things" (CIoMT). CIoMT is a disruptive and promising technology for dynamic monitoring, tracking, rapid diagnosis, and control of pandemics and to stop the spread of the virus. This paper explores the role of the CIoMT in the health domain, especially during pandemics, and also discusses the associated challenges and research directions.

Applications of machine learning approaches to combat COVID-19: A survey

Machine learning (ML) and artificial intelligence (AI) approaches are prominent and well established in the field of health-care informatics. Because they have a more productive ability to predict, they are successfully applied in several health-care applications. ML approaches are needed thanks to the unsatisfactory experience of the novel virus, considerable ambiguity, complicated social circumstances, and inadequate accessible data. Several approaches have been applied as a tool to combat and protect against the new diseases. The COVID-19 outbreak has rapid growth, so it is not easy to predict the patients and resources within a specified time. ML is a strong approach in the fighting against the pandemic such as COVID-19. It is found significant to predict the susceptible, infected, recovered, or exposed persons and can assist the control strategies to block the spread of infections. This study critically examines the appropriateness and contribution of AI/ML methods on COVID-19 datasets, enhancing the understanding to apply these methods for quick analysis and verification of pandemic databases. © 2022 Elsevier Inc. All rights reserved.

Symptoms Based Covid-19 Disease Diagnosis Using Machine Learning Approach

The catastrophic outbreak of SARS-CoV-2 or COVID-19 has taken the world to uncharted waters. Detecting such an outbreak at its early stages is crucial to minimize its spread but is very difficult as well. The pandemic situation is not yet under control as the virus tends to evolve and develop mutations. This further complicates the development of machine learning or AI models that can automatically detect the disease in the general public. However, researchers worldwide have been putting their incredible efforts into devising mechanisms that help analyze and control the pandemic situation. Many prediction models have been developed to predict COVID-19 infection risk that helps in mitigating the burden on the healthcare system. These models help the medical staff, especially when healthcare resources are limited. As a contribution to society's well-being, this research work deploys a machine learning prediction model that predicts COVID-19 patients with COVID-19 symptoms. Key pieces of information from RT-PCR test data results by the Israeli ministry of health publicly available have been distilled, preprocessed, and then used to train our prediction model. The model is trained on eight features, out of which five are the primary clinical symptoms of this fatal virus: cough, sore throat, fever, headache, breath shortness;and the other three features are gender, test indication, and age. Machine learning models can be considered for COVID-19 testing, especially when resources are limited. We have achieved highly accurate results in COVID-19 prediction with our prediction model. The model is best suited in urgent situations where there is a limitation of testing resources.

Machine Learning Methods for Signal, Image and Speech Processing

The signal processing (SP) landscape has been enriched by recent advances in artificial intelligence (AI) and machine learning (ML), yielding new tools for signal estimation, classification, prediction, and manipulation. Layered signal representations, nonlinear function approximation and nonlinear signal prediction are now feasible at very large scale in both dimensionality and data size. These are leading to significant performance gains in a variety of long-standing problem domains like speech and Image analysis. As well as providing the ability to construct new classes of nonlinear functions (e.g., fusion, nonlinear filtering). This book will help academics, researchers, developers, graduate and undergraduate students to comprehend complex SP data across a wide range of topical application areas such as social multimedia data collected from social media networks, medical imaging data, data from Covid tests etc. This book focuses on AI utilization in the speech, image, communications and yirtual reality domains. © 2021 River Publishers. All rights reserved.

Applications of machine learning approaches to combat COVID-19: A survey

Machine learning (ML) and artificial intelligence (AI) approaches are prominent and well established in the field of health-care informatics. Because they have a more productive ability to predict, they are successfully applied in several health-care applications. ML approaches are needed thanks to the unsatisfactory experience of the novel virus, considerable ambiguity, complicated social circumstances, and inadequate accessible data. Several approaches have been applied as a tool to combat and protect against the new diseases. The COVID-19 outbreak has rapid growth, so it is not easy to predict the patients and resources within a specified time. ML is a strong approach in the fighting against the pandemic such as COVID-19. It is found significant to predict the susceptible, infected, recovered, or exposed persons and can assist the control strategies to block the spread of infections. This study critically examines the appropriateness and contribution of AI/ML methods on COVID-19 datasets, enhancing the understanding to apply these methods for quick analysis and verification of pandemic databases.

A systematic review on AI/ML approaches against COVID-19 outbreak.

A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes. Because there is no comprehensive review study, this study systematically analyzes and summarizes related studies. A research methodology has been proposed to conduct the systematic literature review for framing the research questions, searching criteria and relevant data extraction. Finally, 264 studies were taken into account after following inclusion and exclusion criteria. This research can be regarded as a key element for epidemic and transmission prediction, diagnosis and detection, and drug/vaccine development. Six research questions are explored with 50 AI/ML approaches in COVID-19, 8 AI/ML methods for patient outcome prediction, 14 AI/ML techniques in disease predictions, along with five AI/ML methods for risk assessment of COVID-19. It also covers AI/ML method in drug development, vaccines for COVID-19, models in COVID-19, datasets and their usage and dataset applications with AI/ML.