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This book is about JIDOKA, a Japanese management technique coined by Toyota that consists of imbuing machines with human intelligence. The purpose of this compilation of research articles is to show industrial leaders innovative cases of digitization of value creation processes that have allowed them to improve their performance in a sustainable way. This book shows several applications of JIDOKA in the quest towards an integration of human and AI within Industry 4.0 Cyber Physical Manufacturing Systems. From the use of artificial intelligence to advanced mathematical models or quantum computing, all paths are valid to advance in the process of human–machine integration.

Technology: general issues --- History of engineering & technology --- healthy operator 4.0 --- human–cyber–physical system --- industrial internet of things --- industry 4.0 --- smart workplaces --- EEG sensors --- manufacturing systems --- shopfloor management --- machine learning --- deep learning --- reference architecture model --- interoperability --- digital twin --- distributed ledger technology --- GDPR --- RAMI 4.0 --- LASFA --- quantum computing --- strategic organizational design --- Industry 4.0 --- complex networks --- cyber-physical systems --- lean management systems --- quantum strategic organizational design --- quantum circuits --- quantum simulation --- JIDOKA --- Operator 4.0 --- process variability --- integration explaining variability --- quantum approximate optimization algorithm --- value–stream networks --- optimization --- maintenance interval --- maintenance model --- semi-Markov process --- right-censored data --- finite horizon --- maintenance cost --- Cyber-Physical Systems --- Lean Manufacturing --- Directed Acyclic Graphs --- scikit-learn --- pipegraph --- machine learning models --- healthy operator 4.0 --- human–cyber–physical system --- industrial internet of things --- industry 4.0 --- smart workplaces --- EEG sensors --- manufacturing systems --- shopfloor management --- machine learning --- deep learning --- reference architecture model --- interoperability --- digital twin --- distributed ledger technology --- GDPR --- RAMI 4.0 --- LASFA --- quantum computing --- strategic organizational design --- Industry 4.0 --- complex networks --- cyber-physical systems --- lean management systems --- quantum strategic organizational design --- quantum circuits --- quantum simulation --- JIDOKA --- Operator 4.0 --- process variability --- integration explaining variability --- quantum approximate optimization algorithm --- value–stream networks --- optimization --- maintenance interval --- maintenance model --- semi-Markov process --- right-censored data --- finite horizon --- maintenance cost --- Cyber-Physical Systems --- Lean Manufacturing --- Directed Acyclic Graphs --- scikit-learn --- pipegraph --- machine learning models

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Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches.

Research & information: general --- Geography --- sequential estimation --- InSAR time series --- groundwater --- land subsidence and rebound --- earthquake --- rapid mapping --- damage assessment --- deep learning --- convolutional neural networks --- ordinal regression --- aerial image --- landslide --- machine learning models --- remote sensing --- ensemble models --- validation --- ice storm --- forest ecosystems --- disaster impact --- post-disaster recovery --- ice jam --- snowmelt --- flood mapping --- monitoring and prediction --- VIIRS --- ABI --- NUAE --- flash flood --- BRT --- CART --- naive Bayes tree --- geohydrological model --- landslide susceptibility --- Bangladesh --- digital elevation model --- random forest --- modified frequency ratio --- logistic regression --- automatic landslide detection --- OBIA --- PBA --- random forests --- supervised classification --- landslides --- uncertainty --- K-Nearest Neighbor --- Multi-Layer Perceptron --- Random Forest --- Support Vector Machine --- agriculture --- drought --- NDVI --- MODIS --- landslide deformation --- InSAR --- reservoir water level --- Sentinel-1 --- Three Gorges Reservoir area (China) --- peri-urbanization --- urban growth boundary demarcation --- climate change --- climate migrants --- natural hazards --- flooding --- land use and land cover --- night-time light data --- Dhaka

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Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches.

sequential estimation --- InSAR time series --- groundwater --- land subsidence and rebound --- earthquake --- rapid mapping --- damage assessment --- deep learning --- convolutional neural networks --- ordinal regression --- aerial image --- landslide --- machine learning models --- remote sensing --- ensemble models --- validation --- ice storm --- forest ecosystems --- disaster impact --- post-disaster recovery --- ice jam --- snowmelt --- flood mapping --- monitoring and prediction --- VIIRS --- ABI --- NUAE --- flash flood --- BRT --- CART --- naive Bayes tree --- geohydrological model --- landslide susceptibility --- Bangladesh --- digital elevation model --- random forest --- modified frequency ratio --- logistic regression --- automatic landslide detection --- OBIA --- PBA --- random forests --- supervised classification --- landslides --- uncertainty --- K-Nearest Neighbor --- Multi-Layer Perceptron --- Random Forest --- Support Vector Machine --- agriculture --- drought --- NDVI --- MODIS --- landslide deformation --- InSAR --- reservoir water level --- Sentinel-1 --- Three Gorges Reservoir area (China) --- peri-urbanization --- urban growth boundary demarcation --- climate change --- climate migrants --- natural hazards --- flooding --- land use and land cover --- night-time light data --- Dhaka

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Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches.

Research & information: general --- Geography --- sequential estimation --- InSAR time series --- groundwater --- land subsidence and rebound --- earthquake --- rapid mapping --- damage assessment --- deep learning --- convolutional neural networks --- ordinal regression --- aerial image --- landslide --- machine learning models --- remote sensing --- ensemble models --- validation --- ice storm --- forest ecosystems --- disaster impact --- post-disaster recovery --- ice jam --- snowmelt --- flood mapping --- monitoring and prediction --- VIIRS --- ABI --- NUAE --- flash flood --- BRT --- CART --- naive Bayes tree --- geohydrological model --- landslide susceptibility --- Bangladesh --- digital elevation model --- random forest --- modified frequency ratio --- logistic regression --- automatic landslide detection --- OBIA --- PBA --- random forests --- supervised classification --- landslides --- uncertainty --- K-Nearest Neighbor --- Multi-Layer Perceptron --- Random Forest --- Support Vector Machine --- agriculture --- drought --- NDVI --- MODIS --- landslide deformation --- InSAR --- reservoir water level --- Sentinel-1 --- Three Gorges Reservoir area (China) --- peri-urbanization --- urban growth boundary demarcation --- climate change --- climate migrants --- natural hazards --- flooding --- land use and land cover --- night-time light data --- Dhaka --- sequential estimation --- InSAR time series --- groundwater --- land subsidence and rebound --- earthquake --- rapid mapping --- damage assessment --- deep learning --- convolutional neural networks --- ordinal regression --- aerial image --- landslide --- machine learning models --- remote sensing --- ensemble models --- validation --- ice storm --- forest ecosystems --- disaster impact --- post-disaster recovery --- ice jam --- snowmelt --- flood mapping --- monitoring and prediction --- VIIRS --- ABI --- NUAE --- flash flood --- BRT --- CART --- naive Bayes tree --- geohydrological model --- landslide susceptibility --- Bangladesh --- digital elevation model --- random forest --- modified frequency ratio --- logistic regression --- automatic landslide detection --- OBIA --- PBA --- random forests --- supervised classification --- landslides --- uncertainty --- K-Nearest Neighbor --- Multi-Layer Perceptron --- Random Forest --- Support Vector Machine --- agriculture --- drought --- NDVI --- MODIS --- landslide deformation --- InSAR --- reservoir water level --- Sentinel-1 --- Three Gorges Reservoir area (China) --- peri-urbanization --- urban growth boundary demarcation --- climate change --- climate migrants --- natural hazards --- flooding --- land use and land cover --- night-time light data --- Dhaka

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The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis.

interpretable/explainable machine learning --- image classification --- image processing --- machine learning models --- white box --- black box --- cancer prediction --- deep learning --- multimodal learning --- convolutional neural networks --- autism --- fMRI --- texture analysis --- melanoma --- glcm matrix --- machine learning --- classifiers --- explainability --- explainable AI --- XAI --- medical imaging --- diagnosis --- ARMD --- change detection --- unsupervised learning --- microwave breast imaging --- image reconstruction --- tumor detection --- digital pathology --- whole slide image processing --- multiple instance learning --- deep learning classification --- HER2 --- medical images --- transfer learning --- optimizers --- neo-adjuvant treatment --- tumour cellularity --- cancer --- breast cancer --- diagnostics --- imaging --- computation --- artificial intelligence --- 3D segmentation --- active surface --- discriminant analysis --- PET imaging --- medical image analysis --- brain tumor --- cervical cancer --- colon cancer --- lung cancer --- computer vision --- musculoskeletal images --- lung disease detection --- taxonomy --- convolutional neural network --- CycleGAN --- data augmentation --- dermoscopic images --- domain transfer --- macroscopic images --- skin lesion segmentation --- infection detection --- COVID-19 --- X-ray images --- bayesian inference --- shifted-scaled dirichlet distribution --- MCMC --- gibbs sampling --- object detection --- surgical tools --- open surgery --- egocentric camera --- computers in medicine --- segmentation --- MRI --- ECG signal detection --- portable monitoring devices --- 1D-convolutional neural network --- medical image segmentation --- domain adaptation --- meta-learning --- U-Net --- computed tomography (CT) --- magnetic resonance imaging (MRI) --- low-dose --- sparse-angle --- quantitative comparison --- interpretable/explainable machine learning --- image classification --- image processing --- machine learning models --- white box --- black box --- cancer prediction --- deep learning --- multimodal learning --- convolutional neural networks --- autism --- fMRI --- texture analysis --- melanoma --- glcm matrix --- machine learning --- classifiers --- explainability --- explainable AI --- XAI --- medical imaging --- diagnosis --- ARMD --- change detection --- unsupervised learning --- microwave breast imaging --- image reconstruction --- tumor detection --- digital pathology --- whole slide image processing --- multiple instance learning --- deep learning classification --- HER2 --- medical images --- transfer learning --- optimizers --- neo-adjuvant treatment --- tumour cellularity --- cancer --- breast cancer --- diagnostics --- imaging --- computation --- artificial intelligence --- 3D segmentation --- active surface --- discriminant analysis --- PET imaging --- medical image analysis --- brain tumor --- cervical cancer --- colon cancer --- lung cancer --- computer vision --- musculoskeletal images --- lung disease detection --- taxonomy --- convolutional neural network --- CycleGAN --- data augmentation --- dermoscopic images --- domain transfer --- macroscopic images --- skin lesion segmentation --- infection detection --- COVID-19 --- X-ray images --- bayesian inference --- shifted-scaled dirichlet distribution --- MCMC --- gibbs sampling --- object detection --- surgical tools --- open surgery --- egocentric camera --- computers in medicine --- segmentation --- MRI --- ECG signal detection --- portable monitoring devices --- 1D-convolutional neural network --- medical image segmentation --- domain adaptation --- meta-learning --- U-Net --- computed tomography (CT) --- magnetic resonance imaging (MRI) --- low-dose --- sparse-angle --- quantitative comparison