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Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that.

Information technology industries --- data mining --- decision-making system --- rough set --- mixed integer linear programming --- assembly clearance --- diesel engine quality --- Internet of things --- Wireless nodes --- Hybrid clustering --- Multi-hop routing --- Network lifetime --- Artificial intelligence --- data envelopment analysis --- decision making --- artificial intelligence --- performance --- visual analytics --- system --- air quality --- spatiotemporal --- multivariate --- dimension reduction --- clustering --- regular patterns --- anomalies --- speech recognition --- Long Short Term Memory (LSTM) --- speech output correction --- most-matching --- empirical correlations --- rheological properties --- real-time --- water-based drill-in fluid --- artificial neural network --- elastic parameters --- Poisson’s ratio --- sandstone --- self-adaptive differential evolution --- total organic carbon --- barnett shale --- devonian shale --- fishbone multilateral wells --- predictive models --- well productivity --- international research --- knowledge map visualization --- policy documents quantification --- research hotspot --- policy keyword --- minimum miscibility pressure (MMP) --- CO2 flooding --- new models --- face recognition --- security --- spoofing --- histogram of oriented gradients --- smart cities --- deep learning --- LSTM --- neural networks --- location prediction --- trajectories --- smart tourism --- static Young’s modulus --- sandstone formations --- machine learning --- n/a --- Poisson's ratio --- static Young's modulus

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This special issue aims to contribute to the climate actions which called for the need to address Greenhouse Gas (GHG) emissions, keeping global warming to well below 2°C through various means, including accelerating renewables, clean fuels, and clean technologies into the entire energy system. As long as fossil fuels (coal, gas and oil) are still used in the foreseeable future, it is vital to ensure that these fossil fuels are used cleanly through abated technologies. Financing the clean and energy transition technologies is vital to ensure the smooth transition towards net zero emission by 2050 or beyond. The lack of long‐term financing, the low rate of return, the existence of various risks, and the lack of capacity of market players are major challenges to developing sustainable energy systems.This special collected 17 high-quality empirical studies that assess the challenges for developing secure and sustainable energy systems and provide practical policy recommendations. The editors of this special issue wish to thank the Economic Research Institute for ASEAN and East Asia (ERIA) for funding several papers that were published in this special issue.

Research & information: general --- Physics --- industrial energy intensity --- pollution emission intensity --- quantile DID method --- Beijing–Tianjin–Hebei coordinated development --- China --- environmental Kuznets curve --- CO2 emission --- energy efficiency --- economic growth --- panel ARDL --- DEA --- energy transition --- renewables --- hydrogen --- fossil fuels --- emissions --- FDIA --- blockchain --- data exchanging --- under-operating agents --- ISO --- electricity market --- Saudi Arabia --- energy sustainability --- world energy trilemma index --- Bayesian Belief Network --- green technology --- sustainability --- climate change --- Southeast Asia --- energy policy --- high-efficiency --- low-emission --- carbon dioxide emissions --- carbon pricing --- subcritical --- desulphurization --- denitrification --- cost–benefit analysis --- levelized cost of electricity --- energy supply security --- energy dependence --- energy diversity --- business as usual (BAU) --- Alternative Policy Scenarios (APSs) --- clean technologies --- and resiliency --- multi plant firms --- environmental assessment --- local-global performance --- wind energy --- power trade --- counterfactual scenario --- ASEAN --- natural gas --- multi-objective --- goal programming --- optimization --- allocation --- connectivity --- energy infrastructure --- Mekong Subregion --- green bonds --- post-COVID-19 era --- Asia and the Pacific --- green finance --- sustainable development --- thermal energy storage (TES) --- latent heat thermal energy storage (LHTES) --- circular economy --- environmental sustainability --- life cycle assessment (LCA) --- physico-chemical characterization --- Coats–Redfern model --- flammability --- integral model --- iso-conversional --- wind farm site selection --- multi-criteria decision-making system --- Analytic Hierarchy Process --- Semnan province --- ArcGIS

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With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.

metadata --- image classification --- sensitivity analysis --- ROI detection --- residual learning --- image alignment --- adaptive convolutional kernels --- Hough transform --- class imbalance --- land surface temperature --- inundation mapping --- multiscale representation --- object-based --- convolutional neural networks --- scene classification --- morphological profiles --- hyperedge weight estimation --- hyperparameter sparse representation --- semantic segmentation --- vehicle classification --- flood --- Landsat imagery --- target detection --- multi-sensor --- building damage detection --- optimized kernel minimum noise fraction (OKMNF) --- sea-land segmentation --- nonlinear classification --- land use --- SAR imagery --- anti-noise transfer network --- sub-pixel change detection --- Radon transform --- segmentation --- remote sensing image retrieval --- TensorFlow --- convolutional neural network --- particle swarm optimization --- optical sensors --- machine learning --- mixed pixel --- optical remotely sensed images --- object-based image analysis --- very high resolution images --- single stream optimization --- ship detection --- ice concentration --- online learning --- manifold ranking --- dictionary learning --- urban surface water extraction --- saliency detection --- spatial attraction model (SAM) --- quality assessment --- Fuzzy-GA decision making system --- land cover change --- multi-view canonical correlation analysis ensemble --- land cover --- semantic labeling --- sparse representation --- dimensionality expansion --- speckle filters --- hyperspectral imagery --- fully convolutional network --- infrared image --- Siamese neural network --- Random Forests (RF) --- feature matching --- color matching --- geostationary satellite remote sensing image --- change feature analysis --- road detection --- deep learning --- aerial images --- image segmentation --- aerial image --- multi-sensor image matching --- HJ-1A/B CCD --- endmember extraction --- high resolution --- multi-scale clustering --- heterogeneous domain adaptation --- hard classification --- regional land cover --- hypergraph learning --- automatic cluster number determination --- dilated convolution --- MSER --- semi-supervised learning --- gate --- Synthetic Aperture Radar (SAR) --- downscaling --- conditional random fields --- urban heat island --- hyperspectral image --- remote sensing image correction --- skip connection --- ISPRS --- spatial distribution --- geo-referencing --- Support Vector Machine (SVM) --- very high resolution (VHR) satellite image --- classification --- ensemble learning --- synthetic aperture radar --- conservation --- convolutional neural network (CNN) --- THEOS --- visible light and infrared integrated camera --- vehicle localization --- structured sparsity --- texture analysis --- DSFATN --- CNN --- image registration --- UAV --- unsupervised classification --- SVMs --- SAR image --- fuzzy neural network --- dimensionality reduction --- GeoEye-1 --- feature extraction --- sub-pixel --- energy distribution optimizing --- saliency analysis --- deep convolutional neural networks --- sparse and low-rank graph --- hyperspectral remote sensing --- tensor low-rank approximation --- optimal transport --- SELF --- spatiotemporal context learning --- Modest AdaBoost --- topic modelling --- multi-seasonal --- Segment-Tree Filtering --- locality information --- GF-4 PMS --- image fusion --- wavelet transform --- hashing --- machine learning techniques --- satellite images --- climate change --- road segmentation --- remote sensing --- tensor sparse decomposition --- Convolutional Neural Network (CNN) --- multi-task learning --- deep salient feature --- speckle --- canonical correlation weighted voting --- fully convolutional network (FCN) --- despeckling --- multispectral imagery --- ratio images --- linear spectral unmixing --- hyperspectral image classification --- multispectral images --- high resolution image --- multi-objective --- convolution neural network --- transfer learning --- 1-dimensional (1-D) --- threshold stability --- Landsat --- kernel method --- phase congruency --- subpixel mapping (SPM) --- tensor --- MODIS --- GSHHG database --- compressive sensing