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This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners.

Technology: general issues --- History of engineering & technology --- truck dispatching --- mining equipment uncertainties --- orebody uncertainty --- discrete event simulation --- Q-learning --- grinding circuits --- minerals processing --- random forest --- decision trees --- machine learning --- knowledge discovery --- variable importance --- mineral prospectivity mapping --- random forest algorithm --- epithermal gold --- unstructured data --- blast impact --- empirical model --- mining --- fragmentation --- mine worker fatigue --- random forest model --- health and safety management --- stockpiles --- operational data --- mine-to-mill --- geostatistics --- ore control --- mine optimization --- digital twin --- modes of operation --- geological uncertainty --- multivariate statistics --- partial least squares regression --- oil sands --- bitumen extraction --- bitumen processability --- mine safety and health --- accidents --- narratives --- natural language processing --- random forest classification --- hyperspectral imaging --- multispectral imaging --- dimensionality reduction --- neighbourhood component analysis --- artificial intelligence --- mining exploitation --- masonry buildings --- damage risk analysis --- Bayesian network --- Naive Bayes --- Bayesian Network Structure Learning (BNSL) --- rock type --- mining geology --- bluetooth beacon --- classification and regression tree --- gaussian naïve bayes --- k-nearest neighbors --- support vector machine --- transport route --- transport time --- underground mine --- tactical geometallurgy --- data analytics in mining --- ball mill throughput --- measurement while drilling --- non-additivity --- coal --- petrographic analysis --- macerals --- image analysis --- semantic segmentation --- convolutional neural networks --- point cloud scaling --- fragmentation size analysis --- structure from motion --- n/a --- gaussian naïve bayes

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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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The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things.

History of engineering & technology --- atmospheric --- on-line monitoring --- LoRa --- embedded system --- smart environments --- Internet of Things --- indoor occupancy --- machine learning --- data analysis --- landslide susceptibility --- China-Nepal Highway --- LSTM --- remote sensing images --- IoT --- network traffic --- monitoring --- DDoS --- packet classification --- indoor localization --- channel state information --- device-free passive --- WiFi fingerprint --- naive Bayes classification --- feature fusion --- posture recognition --- indoor positioning --- wireless body area network --- Kalman filtering --- multi-sensor combination --- prognostic and health management --- integrative framework --- internet of things --- convolutional neural network --- conditioned-based maintenance --- IoT platform --- intelligent monitoring robot --- active CCTV --- learning model --- electrical devices --- classification --- energy management --- smart environment --- architecture --- blockchain --- communication constraints --- decentralized application --- Ethereum --- Internet of things --- sensing and control --- computational efficiency --- robotic manipulators --- hysteresis --- adaptive control --- wireless sensor network (WSN) --- energy --- ant colony optimization (ACO) --- routing algorithm --- quantum-inspired evolutionary algorithms

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Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens.

star image --- image denoising --- reinforcement learning --- maximum likelihood estimation --- mixed Poisson–Gaussian likelihood --- machine learning-based classification --- non-uniform foundation --- stochastic analysis --- vehicle–pavement–foundation interaction --- forest growing stem volume --- coniferous plantations --- variable selection --- texture feature --- random forest --- red-edge band --- on-shelf availability --- semi-supervised learning --- deep learning --- image classification --- machine learning --- explainable artificial intelligence --- wildfire --- risk assessment --- Naïve bayes --- transmission-line corridors --- image encryption --- compressive sensing --- plaintext related --- chaotic system --- convolutional neural network --- color prior model --- object detection --- piston error detection --- segmented telescope --- BP artificial neural network --- modulation transfer function --- computer vision --- intelligent vehicles --- extrinsic camera calibration --- structure from motion --- convex optimization --- temperature estimation --- BLDC --- electric machine protection --- touchscreen --- capacitive --- display --- SNR --- stylus --- laser cutting --- quality monitoring --- artificial neural network --- burr formation --- cut interruption --- fiber laser --- semi-supervised --- fuzzy --- noisy --- real-world --- plankton --- marine --- activity recognition --- wearable sensors --- imbalanced activities --- sampling methods --- path planning --- Q-learning --- neural network --- YOLO algorithm --- robot arm --- target reaching --- obstacle avoidance

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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