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Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following:  Fuzziness in smart grids computing  Emerging techniques for renewable energy forecasting  Robust and proactive solution of optimal smart grids operation  Fuzzy-based smart grids monitoring and control frameworks  Granular computing for uncertainty management in smart grids  Self-organizing and decentralized paradigms for information processing

Information technology industries --- voltage regulation --- smart grid --- decentralized control architecture --- multi-agent systems --- t-SNE algorithm --- numerical weather prediction --- data preprocessing --- data visualization --- wind power generation --- partial discharge --- gas insulated switchgear --- case-based reasoning --- data matching --- variational autoencoder --- DSHW --- TBATS --- NN-AR --- time-series clustering --- decentral smart grid control (DSGC) --- interpretable and accurate DSGC-stability prediction --- data mining --- computational intelligence --- fuzzy rule-based classifiers --- multi-objective evolutionary optimization --- power systems resilience --- dynamic Bayesian network --- Markov model --- probabilistic modeling --- resilience models

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This Special Issue focused on novel vision-based approaches, mainly related to computer vision and machine learning, for the automatic analysis of human behaviour. We solicited submissions on the following topics: information theory-based pattern classification, biometric recognition, multimodal human analysis, low resolution human activity analysis, face analysis, abnormal behaviour analysis, unsupervised human analysis scenarios, 3D/4D human pose and shape estimation, human analysis in virtual/augmented reality, affective computing, social signal processing, personality computing, activity recognition, human tracking in the wild, and application of information-theoretic concepts for human behaviour analysis. In the end, 15 papers were accepted for this special issue. These papers, that are reviewed in this editorial, analyse human behaviour from the aforementioned perspectives, defining in most of the cases the state of the art in their corresponding field.

History of engineering & technology --- multi-objective evolutionary algorithms --- rule-based classifiers --- interpretable machine learning --- categorical data --- hand sign language --- deep learning --- restricted Boltzmann machine (RBM) --- multi-modal --- profoundly deaf --- noisy image --- ensemble methods --- adaptive classifiers --- recurrent concepts --- concept drift --- stock price direction prediction --- toe-off detection --- gait event --- silhouettes difference --- convolutional neural network --- saliency detection --- foggy image --- spatial domain --- frequency domain --- object contour detection --- discrete stationary wavelet transform --- attention allocation --- attention behavior --- hybrid entropy --- information entropy --- single pixel single photon image acquisition --- time-of-flight --- action recognition --- fibromyalgia --- Learning Using Concave and Convex Kernels --- Empatica E4 --- self-reported survey --- speech emotion recognition --- 3D convolutional neural networks --- k-means clustering --- spectrograms --- context-aware framework --- accuracy --- false negative rate --- individual behavior estimation --- statistical-based time-frequency domain and crowd condition --- emotion recognition --- gestures --- body movements --- Kinect sensor --- neural networks --- face analysis --- face segmentation --- head pose estimation --- age classification --- gender classification --- singular point detection --- boundary segmentation --- blurring detection --- fingerprint image enhancement --- fingerprint quality --- speech --- committee of classifiers --- biometric recognition --- multimodal-based human identification --- privacy --- privacy-aware --- multi-objective evolutionary algorithms --- rule-based classifiers --- interpretable machine learning --- categorical data --- hand sign language --- deep learning --- restricted Boltzmann machine (RBM) --- multi-modal --- profoundly deaf --- noisy image --- ensemble methods --- adaptive classifiers --- recurrent concepts --- concept drift --- stock price direction prediction --- toe-off detection --- gait event --- silhouettes difference --- convolutional neural network --- saliency detection --- foggy image --- spatial domain --- frequency domain --- object contour detection --- discrete stationary wavelet transform --- attention allocation --- attention behavior --- hybrid entropy --- information entropy --- single pixel single photon image acquisition --- time-of-flight --- action recognition --- fibromyalgia --- Learning Using Concave and Convex Kernels --- Empatica E4 --- self-reported survey --- speech emotion recognition --- 3D convolutional neural networks --- k-means clustering --- spectrograms --- context-aware framework --- accuracy --- false negative rate --- individual behavior estimation --- statistical-based time-frequency domain and crowd condition --- emotion recognition --- gestures --- body movements --- Kinect sensor --- neural networks --- face analysis --- face segmentation --- head pose estimation --- age classification --- gender classification --- singular point detection --- boundary segmentation --- blurring detection --- fingerprint image enhancement --- fingerprint quality --- speech --- committee of classifiers --- biometric recognition --- multimodal-based human identification --- privacy --- privacy-aware

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• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This Special Issue focused on novel vision-based approaches, mainly related to computer vision and machine learning, for the automatic analysis of human behaviour. We solicited submissions on the following topics: information theory-based pattern classification, biometric recognition, multimodal human analysis, low resolution human activity analysis, face analysis, abnormal behaviour analysis, unsupervised human analysis scenarios, 3D/4D human pose and shape estimation, human analysis in virtual/augmented reality, affective computing, social signal processing, personality computing, activity recognition, human tracking in the wild, and application of information-theoretic concepts for human behaviour analysis. In the end, 15 papers were accepted for this special issue. These papers, that are reviewed in this editorial, analyse human behaviour from the aforementioned perspectives, defining in most of the cases the state of the art in their corresponding field.

multi-objective evolutionary algorithms --- rule-based classifiers --- interpretable machine learning --- categorical data --- hand sign language --- deep learning --- restricted Boltzmann machine (RBM) --- multi-modal --- profoundly deaf --- noisy image --- ensemble methods --- adaptive classifiers --- recurrent concepts --- concept drift --- stock price direction prediction --- toe-off detection --- gait event --- silhouettes difference --- convolutional neural network --- saliency detection --- foggy image --- spatial domain --- frequency domain --- object contour detection --- discrete stationary wavelet transform --- attention allocation --- attention behavior --- hybrid entropy --- information entropy --- single pixel single photon image acquisition --- time-of-flight --- action recognition --- fibromyalgia --- Learning Using Concave and Convex Kernels --- Empatica E4 --- self-reported survey --- speech emotion recognition --- 3D convolutional neural networks --- k-means clustering --- spectrograms --- context-aware framework --- accuracy --- false negative rate --- individual behavior estimation --- statistical-based time-frequency domain and crowd condition --- emotion recognition --- gestures --- body movements --- Kinect sensor --- neural networks --- face analysis --- face segmentation --- head pose estimation --- age classification --- gender classification --- singular point detection --- boundary segmentation --- blurring detection --- fingerprint image enhancement --- fingerprint quality --- speech --- committee of classifiers --- biometric recognition --- multimodal-based human identification --- privacy --- privacy-aware