Nanotechnology Based Smart Remote Sensing Networks For Disaster Prevention
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Nanotechnology-Based Smart Remote Sensing Networks for Disaster Prevention
Nanotechnology-Based Smart Remote Sensing Networks for Disaster Prevention outlines how nanotechnology and space technology could be applied for the detection of disaster risks in early stages, using cheap sensors, cheap constellations of low Earth orbit (LEO) satellites, and smart wireless networks with artificial intelligence (AI) tools. Nanomaterial-based sensors (nanosensors) can offer several advantages over their micro-counterparts, such as lower power or self-powered consumption, high sensitivity, lower concentration of analytes, and smaller interaction distances between the object and the sensor. Besides this, with the support of AI tools, such as fuzzy logic, genetic algorithms, neural networks, and ambient intelligence, sensor systems are becoming smarter when a large number of sensors are used. This book is an important reference source for materials scientists, engineers, and environmental scientists who are seeking to understand how nanotechnology-based solutions can help mitigate natural disasters. - Shows how nanotechnology-based solutions can be combined with space technology to provide more effective disaster management solutions - Explores the best materials for manufacturing different types of nanotechnology-based remote sensing devices - Assesses the challenges of creating a nanotechnology-based disaster mitigation system in a cost-effective way
Sensor Networks for Smart Hospitals
Sensor Networks for Smart Hospitals shows how the use of sensors to gather data on a patient's condition and the environment in which their care takes place can allow healthcare professionals to monitor well-being and make informed decisions about treatment. Written by experts in the field, this book is an invaluable resource for researchers and healthcare practitioners in their drive to use technology to improve the lives of patients. Data from sensor networks via the smart hospital framework is comprised of three main layers: data, insights, and access.Medical data is collected in real-time from an array of intelligent devices/systems deployed within the hospital. This data offers insight from the analytics or machine learning software that is accessible to healthcare staff via a smartphone or mobile device to facilitate swifter decisions and greater efficiency. - Describes the fundamentals of sensors, biosensors, and smart hospitals - Explains how sensors and implanted nanodevices can be used in smart healthcare - Discusses how intelligent wireless medical sensor networks can be used for healthcare in the future - Companion volume to Advanced Sensors for Smart Healthcare
Predicting Natural Disasters With AI and Machine Learning
In a world where the relentless force of natural and man-made disasters threatens societies, the need for effective disaster management has never been more critical. Predicting Natural Disasters With AI and Machine Learning addresses the challenges of disasters and charts a path toward proactive solutions by applying artificial intelligence (AI) and machine learning (ML). This book begins by interpreting the nature of disasters, clearly distinguishing between natural and man-made hazards. It delves into the intricacies of disaster risk reduction (DRR), emphasizing the human contribution to most disasters. Recognizing the necessity for a multifaceted approach, the book advocates the four ‘R’s - Risk Mitigation, Response Readiness, Response Execution, and Recovery - as integral components of comprehensive disaster management. This book explores various AI and ML applications designed to predict, manage, and mitigate the impact of natural disasters, focusing on natural language processing, and early warning systems. The contrast between weak AI, simulating human intelligence for specific tasks, and strong AI, capable of autonomous problem-solving, is thoroughly examined in the context of disaster management. Its chapters systematically address critical issues, including real-world data handling, challenges related to data accessibility, completeness, security, privacy, and ethical considerations.