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"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."--

Intro -- Preface -- Introduction -- Contents -- About the Editors -- 1 A Review on Generative Adversarial Networks -- Introduction -- Basics of GAN -- Training Data for Discriminator -- Training the Discriminator -- The Generator -- Using the Discriminator to Train the Generator -- Attentional Generative Adversarial Network -- Control GAN -- DC-GAN -- Conditional GAN -- Cycle Consistent GAN -- FM-GAN -- Stack GAN -- MirrorGAN -- Fusion GAN -- Comparison of Different GAN Based on Inception Score -- Merits of Generative Adversarial Network -- Demerits of Generative Adversarial Network -- Future Prospects of GAN -- Conclusion -- References -- 2 Integration of Machine Learning in Education: Challenges, Issues and Trends -- Introduction -- Machine Learning Overview -- Opportunities of Machine Learning in Education -- Challenges and Issues -- Explainability -- Accountability -- Cultural Bias -- Ethical Concerns -- Existing Examples -- Conclusion -- References -- 3 IoT-Based Continuous Glucose Monitoring System for Diabetic Patients Using Sensor Technology -- Introduction -- Glucose Measurement Methods -- Existing Methods -- Internet of Things (IoT) -- General IoT-Based Continuous Glucose Measurement Architecture -- Internet of Things (IoT)-Enabled Continuous Glucose Monitoring System (CGMS) -- Results and Discussion -- Conclusion -- References -- 4 Role of Machine Learning and Cloud-Driven Platform in IoT-Based Smart Farming -- Introduction -- Literature Review -- Applications of Machines Learning in a Major IoT-Based Smart Farming Environment -- IOT-AI-Cloud in Agriculture-The Need and Implementation -- User Domain -- IoT Domain -- Cloud Domain -- Smart Farming Challenges -- Conclusion -- References -- 5 Smart Airport System to Counter COVID-19 and Future Sustainability -- Introduction -- Proposed System -- Passenger Registration Using Blockchain.

"A jaw-dropping exploration of everything that goes wrong when we build AI systems-and the movement to fix them. Today's "machine-learning" systems, trained by data, are so effective that we've invited them to see and hear for us-and to make decisions on our behalf. But alarm bells are ringing. Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole-and appear to assess black and white defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And autonomous vehicles on our streets can injure or kill. When systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem. In best-selling author Brian Christian's riveting account, we meet the alignment problem's "first-responders," and learn their ambitious plan to solve it before our hands are completely off the wheel"--

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. Ideal for academics, public and private organizations, managers, and the wider public, the book speaks to the urgency of adopting AI and ML in disaster management. Providing a comprehensive overview of research in the field serves as a catalyst for further studies, especially among postgraduate students interested in the convergence of AI and ML in predicting and managing natural disasters

In spite of intense but traditional academic effort, a unique formal framework to study civil conflict has been elusive. This book uses predictive machine learning to highlight a framework to identify potential causes of civil conflict. Machine learning also improves the human ability to predict and therefore prevent conflict.

This book introduces machine learning and its applications in smart environments/cities. At this stage, a comprehensive understanding of smart environment/city applications is critical for supporting future research. This book includes chapters written by researchers from different countries across the globe and identifies critical threads in research and also gaps that open up new and challenging lines of research for the future. Recent advances are discussed, and thorough reviews introduce readers to critical domains. The discussion on key research topics presented in this book accelerates smart city and smart environment implementations based on IoT technologies. Consequently, this book supports future research activities aimed at developing future IoT architectures for smart environments/cities

In the age of data-driven problem-solving, applying sophisticated computational tools for explaining substantive phenomena is a valuable skill. Yet, application of methods assumes an understanding of the data, structure, and patterns that influence the broader research program. This Element offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered in this Element, in addition to R code and real data to facilitate interaction with the concepts. Upon setting the stage for clustering, the following algorithms are detailed: agglomerative hierarchical clustering, k-means clustering, Gaussian mixture models, and at a higher-level, fuzzy C-means clustering, DBSCAN, and partitioning around medoids (k-medoids) clustering.

This book is a comprehensive guide for agricultural and meteorological predictions. It presents advanced models for predicting target variables. The different details and conceptions in the modelling process are explained in this book. The models of the current book help better agriculture and irrigation management. The models of the current book are valuable for meteorological organizations. Meteorological and agricultural variables can be accurately estimated with this book's advanced models. Modelers, researchers, farmers, students, and scholars can use the new optimization algorithms and evolutionary machine learning to better plan and manage agriculture fields. Water companies and universities can use this book to develop agricultural and meteorological sciences. The details of the modeling process are explained in this book for modelers. Also this book introduces new and advanced models for predicting hydrological variables. Predicting hydrological variables help water resource planning and management. These models can monitor droughts to avoid water shortage. And this contents can be related to SDG6, clean water and sanitation. The book explains how modelers use evolutionary algorithms to develop machine learning models. The book presents the uncertainty concept in the modeling process. New methods are presented for comparing machine learning models in this book. Models presented in this book can be applied in different fields. Effective strategies are presented for agricultural and water management. The models presented in the book can be applied worldwide and used in any region of the world. The models of the current books are new and advanced. Also, the new optimization algorithms of the current book can be used for solving different and complex problems. This book can be used as a comprehensive handbook in the agricultural and meteorological sciences. This book explains the different levels of the modeling process for scholars

This book highlights recent advance in the area of Machine Learning and IoT, and their applications to solve societal issues/problems or useful for various users in the society. It is known that many smart devices are interconnected and the data generated is being analyzed and processed with machine learning models for prediction, classification, etc., to solve human needs in various sectors like health, road safety, agriculture, and education. This contributed book puts together chapters concerning the use of intelligent techniques in various aspects related to the IoT domain from protocols to applications, to give the reader an up-to-date picture of the state-of-the-art on the connection between computational intelligence, machine learning, and IoT

In this paper, we investigate how technology has contributed to experimental economics in the past and illustrate how experimental economics can contribute to technological progress in the future. We argue that with machine learning (ML) a new technology is at hand, where for the first time experimental economics can contribute to enabling substantial improvement of technology. At the same time, ML opens up new questions for experimental research because it can generate observations that were previously impossible. To demonstrate this, we focus on algorithms trained to detect lies. Such algorithms are of high relevance for research in economics as they deal with the ability to retrieve otherwise private information. We deduce that most of the commonly applied data sets for the training of lie detection algorithms could be improved by applying the toolbox of experimental economics. To illustrate this, we replicate the "lies in disguise-experiment" (Fischbacher & Föllmi-Heusi, 2013) with a modification regarding monitoring. The modified setup guarantees a certain level of privacy from the experimenter yet allows to record the subjects as they lie to the camera. Our results indicate the same lying behavior as in the original experiment despite monitoring. Yet, our experiment allows for an individual-level analysis and provides a video data set that can be used for lie detection algorithms.

Intro -- Preface -- Contents -- Chapter 1: Smart Technologies for COVID-19: The Strategic Approaches in Combating the Virus -- 1.1 Introduction -- 1.1.1 Scope of the Study -- 1.2 Related Works -- 1.2.1 Radio Frequency Identification -- 1.2.2 Wireless Sensor Network -- 1.2.3 Contact Tracing -- 1.2.4 COVID-19 Laboratory Tests -- 1.2.5 Thoracic Imaging -- 1.3 Smart Technology Applications -- 1.3.1 The Strategic Approaches -- 1.3.1.1 Prepare and Be Ready -- 1.3.1.2 Protect and Reduce Transmission -- 1.3.1.3 Identify and Treat -- 1.3.1.4 Innovate and Learn -- 1.3.2 Smart Technologies for COVID-19 -- 1.3.2.1 Smart HandWashing and Sanitizer -- 1.3.2.2 Non-Contact Infrared Thermometer -- 1.3.2.3 Smart Wireless Biosensors -- 1.3.2.4 VivaLnk Temperature Sensor -- 1.3.2.5 Kinsa Smart Thermometer -- 1.3.2.6 EarlySense -- 1.3.2.7 Autonomous Vehicle Technology (AVT) -- 1.3.2.8 Robots -- 1.3.2.9 Artificial Intelligent -- 1.3.2.10 Drones -- 1.3.3 Importance and Benefit -- 1.3.3.1 Social Media and Wireless Communication Technology -- 1.3.3.2 Digital Health Technology -- 1.3.3.3 Autonomous Vehicle Technology -- 1.3.4 Challenges and Limitations -- 1.3.4.1 Social Media -- 1.3.4.2 Contact Tracing -- 1.3.4.3 Drones -- 1.3.4.4 AVT -- 1.4 Conclusion -- References -- Chapter 2: A Review on COVID-19 -- 2.1 Introduction -- 2.1.1 Origin -- 2.2 Research on Safety Precautions -- 2.2.1 Law and Limit of Quarantine [5] -- 2.2.2 A Mathematical Framework to Optimize Border Control to Stop the Global Spread [7] -- 2.2.3 Result -- 2.2.4 H1N1 Case Study Model Calibration -- 2.2.5 Shortcomings -- 2.3 Testing -- 2.3.1 Viral Test -- 2.3.2 Antibody Test -- 2.3.2.1 Detection of COVID-19 Using Chest Radiography Images [9] -- 2.3.2.2 Computational Prediction of Protein Structure Associated with COVID-19 -- 2.3.3 AlphaFold -- 2.3.4 Using a Neural Network to Predict Physical Properties [11].