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Sensing Tools and Techniques for COVID-19: Developments and Challenges in Analysis and Detection of Coronavirus helps readers understand the basic principles of sensor development. Sections give a brief overview of the physical and chemical properties of sensing tools and the basics of techniques. With recent advancements in sensing technology, various smart materials and techniques are now being employed for new purposes. In addition, biosensing devices can be tuned at the molecular level to perform better detection of COVID-19. This book covers the various approaches for the development and fabrication of biosensor systems for the analysis of the novel coronavirus. In addition, the book discusses the commercialization and standardization of biosensing technology, along with future perspectives on biosensor technologies used for the analysis and treatment of COVID-19. This book will serve as an up-to-date source of trusted information on biosensor tools and techniques for the analysis of COVID-19. - Provides an in-depth look at current sensing tools and devices and their applicability in healthcare - Demonstrates the different integration approaches for the development of biosensor systems, along with design and commercialization guidelines - Presents a strategic approach for the contact-less analysis of COVID-19
The book aims to outline the issues of AI and COVID-19, involving predictions,medical support decision-making, and possible impact on human life. Starting withmajor COVID-19 issues and challenges, it takes possible AI-based solutions forseveral problems, such as public health surveillance, early (epidemic) prediction,COVID-19 positive case detection, and robotics integration against COVID-19.Beside mathematical modeling, it includes the necessity of changes in innovationsand possible COVID-19 impacts. The book covers a clear understanding of AI-driven tools and techniques, where pattern recognition, anomaly detection, machinelearning, and data analytics are considered. It aims to include the wide range ofaudiences from computer science and engineering to healthcare professionals.
Detection and Analysis of SARS Coronavirus Detecting and analyzing the COVID-19 pandemic with biosensor technology The highly contagious SARS CoV-2 pathogen has challenged health systems around the world as they struggle to detect and monitor the spread of the pathogen. In Detection and Analysis of SARS Coronavirus: Advanced Biosensors for Pandemic Viruses and Related Pathogens expert chemists Chaudhery Mustansar Hussain and Sudheesh K. Shukla deliver a practical analysis of how contactless coronavirus detectors may be developed using existing biosensor technology. The editors outline current challenges in the field, the bioanalytical principles for coronavirus detection, and available biosensor technology. They then move on to how available technology might be adapted to detect coronaviruses and how commercialization of the technology might unfold. The lessons learned in this book are readily applicable to the study of other current and emerging pathogens. Readers will also enjoy: A thorough introduction to the current diagnostic approaches for COVID-19, including common challenges, technology adaptation, and future potential An exploration of bio-analytical strategies for SARS CoV-2/COVID-19, including COVID detection via nanotechnology, biosensing approaches, and the role of nanotechnology in coronavirus detection Practical discussions of biosensors for the analysis of SARS CoV-2/COVID-19, including sensor development for coronavirus and chemical sensors for coronavirus diagnosis In-depth treatments of the commercialization and standardization for analytical technologies Perfect for virologists, pharmaceutical industry professionals, and sensor developers, Detection and Analysis of SARS Coronavirus is also an indispensable resource for those working in analytical research institutes, biotechnology industry professionals, and public health agency professionals.
This book focuses on recent advances and different research areas in multi-modal data fusion under healthcare informatics and seeks out theoretical, methodological, well-established and validated empirical work dealing with these different topics. This book brings together the latest industrial and academic progress, research, and development efforts within the rapidly maturing health informatics ecosystem. Contributions highlight emerging data fusion topics that support prospective healthcare applications. The book also presents various technologies and concerns regarding energy aware and secure sensors and how they can reduce energy consumption in health care applications. It also discusses the life cycle of sensor devices and protocols with the help of energy-aware design, production, and utilization, as well as the Internet of Things technologies such as tags, sensors, sensing networks, and Internet technologies. In a nutshell, this book gives a comprehensive overview of the state-of-the-art theories and techniques for massive data handling and access in medical data and smart health in IoT, and provides useful guidelines for the design of massive Internet of Medical Things.
Developments and applications of biosensor platforms for analysis of viral infections including Coronavirus, HIV, Hepatitis, Ebola, Zika, Norovirus, Influenza, SARS etc. Embraces properties, fabrication, and recent research regarding optical, electrochemical, piezoelectric, fluorescence, thermal, magnetic and micromechanical sensor families.
Immunosensors are widely used and are particularly important for fast diagnosis of diseases in remote environments as well as point-of-care devices. In this book, expert scientists are covering a selection of high quality representative examples from the past five years explaining how this area has developed. It is a compilation of recent advances in several areas of immunosensors for multiple target analysis using laboratory based or point-of-care set-up, for example graphene-, ISFET- and nanostructure-based immunosensors, electrochemical magneto immunosensors and nanoimprinted immunosensors. Filling a gap in the literature, it showcases the multidisciplinary, innovative developments in this highly important area and provides pointers towards commercialisation. Delivering a single, comprehensive work, it appeals to graduate students and professional researchers across academia and industry.
The aim of this book is to present new computational techniques and methodologies for the analysis of the clinical, epidemiological and public health aspects of SARS-CoV-2 and COVID-19 pandemic. The book presents the use of soft computing techniques such as machine learning algorithms for analysis of the epidemiological aspects of the SARS-CoV-2. This book clearly explains novel computational image processing algorithms for the detection of COVID-19 lesions in lung CT and X-ray images. It explores various computational methods for computerized analysis of the SARS-CoV-2 infection including severity assessment. The book provides a detailed description of the algorithms which can potentially aid in mass screening of SARS-CoV-2 infected cases. Finally the book also explains the conventional epidemiological models and machine learning techniques for the prediction of the course of the COVID-19 epidemic. It also provides real life examples through case studies. The book is intended for biomedical engineers, mathematicians, postgraduate students; researchers; medical scientists working on identifying and tracking infectious diseases.
COMPUTATIONAL INTELLIGENCE and HEALTHCARE INFORMATICS The book provides the state-of-the-art innovation, research, design, and implements methodological and algorithmic solutions to data processing problems, designing and analysing evolving trends in health informatics, intelligent disease prediction, and computer-aided diagnosis. Computational intelligence (CI) refers to the ability of computers to accomplish tasks that are normally completed by intelligent beings such as humans and animals. With the rapid advance of technology, artificial intelligence (AI) techniques are being effectively used in the fields of health to improve the efficiency of treatments, avoid the risk of false diagnoses, make therapeutic decisions, and predict the outcome in many clinical scenarios. Modern health treatments are faced with the challenge of acquiring, analyzing and applying the large amount of knowledge necessary to solve complex problems. Computational intelligence in healthcare mainly uses computer techniques to perform clinical diagnoses and suggest treatments. In the present scenario of computing, CI tools present adaptive mechanisms that permit the understanding of data in difficult and changing environments. The desired results of CI technologies profit medical fields by assembling patients with the same types of diseases or fitness problems so that healthcare facilities can provide effectual treatments. This book starts with the fundamentals of computer intelligence and the techniques and procedures associated with it. Contained in this book are state-of-the-art methods of computational intelligence and other allied techniques used in the healthcare system, as well as advances in different CI methods that will confront the problem of effective data analysis and storage faced by healthcare institutions. The objective of this book is to provide researchers with a platform encompassing state-of-the-art innovations; research and design; implementation of methodological and algorithmic solutions to data processing problems; and the design and analysis of evolving trends in health informatics, intelligent disease prediction and computer-aided diagnosis. Audience The book is of interest to artificial intelligence and biomedical scientists, researchers, engineers and students in various settings such as pharmaceutical & biotechnology companies, virtual assistants developing companies, medical imaging & diagnostics centers, wearable device designers, healthcare assistance robot manufacturers, precision medicine testers, hospital management, and researchers working in healthcare system.
This volume provides various techniques and methodologies currently used in the study of MERS-CoV. Chapters are divided into four parts detailing evolution and entry of MERS-coronavirus, genetic alteration and structural determination of MERS-coronavirus proteins, quantitation of virus and anti-viral factors, and mouse models for MERS -coronavirus. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, MERS Coronavirus: Methods and Protocols aims to ensure successful results in the further study of this vital field.
Increasingly, human beings are sensors engaging directly with the mobile Internet. Individuals can now share real-time experiences at an unprecedented scale. Social Sensing: Building Reliable Systems on Unreliable Data looks at recent advances in the emerging field of social sensing, emphasizing the key problem faced by application designers: how to extract reliable information from data collected from largely unknown and possibly unreliable sources. The book explains how a myriad of societal applications can be derived from this massive amount of data collected and shared by average individuals. The title offers theoretical foundations to support emerging data-driven cyber-physical applications and touches on key issues such as privacy. The authors present solutions based on recent research and novel ideas that leverage techniques from cyber-physical systems, sensor networks, machine learning, data mining, and information fusion. Offers a unique interdisciplinary perspective bridging social networks, big data, cyber-physical systems, and reliability Presents novel theoretical foundations for assured social sensing and modeling humans as sensors Includes case studies and application examples based on real data sets Supplemental material includes sample datasets and fact-finding software that implements the main algorithms described in the book