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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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Energy has been a crucial element for human beings and sustainable development. The issues of global warming and non-green energy have yet to be resolved. This book is a collection of twelve articles that provide strong evidence for the success of artificial intelligence deployment in energy research, particularly research devoted to non-intrusive load monitoring, network, and grid, as well as other emerging topics. The presented artificial intelligence algorithms may provide insight into how to apply similar approaches, subject to fine-tuning and customization, to other unexplored energy research. The ultimate goal is to fully apply artificial intelligence to the energy sector. This book may serve as a guide for professionals, researchers, and data scientists—namely, how to share opinions and exchange ideas so as to facilitate a better fusion of energy, academic, and industry research, and improve in the quality of people's daily life activities.

artificial neural network --- home energy management systems --- conditional random fields --- LR --- ELR --- energy disaggregation --- artificial intelligence --- genetic algorithm --- decision tree --- static young’s modulus --- price --- scheduling --- self-adaptive differential evolution algorithm --- Marsh funnel --- energy --- yield point --- non-intrusive load monitoring --- mud rheology --- distributed genetic algorithm --- MCP39F511 --- Jetson TX2 --- sustainable development --- artificial neural networks --- transient signature --- load disaggregation --- smart villages --- ambient assisted living --- smart cities --- demand side management --- smart city --- CNN --- wireless sensor networks --- object detection --- drill-in fluid --- ERELM --- sandstone reservoirs --- RPN --- deep learning --- RELM --- smart grids --- multiple kernel learning --- load --- feature extraction --- NILM --- energy management --- energy efficient coverage --- insulator --- Faster R-CNN --- home energy management --- smart grid --- LSTM --- smart metering --- optimization algorithms --- forecasting --- plastic viscosity --- machine learning --- computational intelligence --- policy making --- support vector machine --- internet of things --- sensor network --- nonintrusive load monitoring --- demand response