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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

Science: general issues --- Botany & plant sciences --- multi-locus genome-wide association study --- mixed linear model --- mrMLM --- FASTmrMLM --- FASTmrEMMA --- ISIS EM-BLASSO --- pLARmEB --- pKWmEB --- complex traits --- omics big dataset --- multi-locus genome-wide association study --- mixed linear model --- mrMLM --- FASTmrMLM --- FASTmrEMMA --- ISIS EM-BLASSO --- pLARmEB --- pKWmEB --- complex traits --- omics big dataset

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[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.]

Medicine --- melanoma detection --- deep learning --- transfer learning --- ensemble classification --- 3D-CNN --- immunotherapy --- radiomics --- self-attention --- breast imaging --- microwave imaging --- image reconstruction --- segmentation --- unsupervised machine learning --- k-means clustering --- Kolmogorov-Smirnov hypothesis test --- statistical inference --- performance metrics --- contrast source inversion --- brain tumor segmentation --- magnetic resonance imaging --- survey --- brain MRI image --- tumor region --- skull stripping --- region growing --- U-Net --- BRATS dataset --- incoherent imaging --- clutter rejection --- breast cancer detection --- MRgFUS --- proton resonance frequency shift --- temperature variations --- referenceless thermometry --- RBF neural networks --- interferometric optical fibers --- breast cancer --- risk assessment --- machine learning --- texture --- mammography --- medical imaging --- imaging biomarkers --- bone scintigraphy --- prostate cancer --- semisupervised classification --- false positives reduction --- computer-aided detection --- breast mass --- mass detection --- mass segmentation --- Mask R-CNN --- dataset partition --- brain tumor --- classification --- shallow machine learning --- breast cancer diagnosis --- Wisconsin Breast Cancer Dataset --- feature selection --- dimensionality reduction --- principal component analysis --- ensemble method --- melanoma detection --- deep learning --- transfer learning --- ensemble classification --- 3D-CNN --- immunotherapy --- radiomics --- self-attention --- breast imaging --- microwave imaging --- image reconstruction --- segmentation --- unsupervised machine learning --- k-means clustering --- Kolmogorov-Smirnov hypothesis test --- statistical inference --- performance metrics --- contrast source inversion --- brain tumor segmentation --- magnetic resonance imaging --- survey --- brain MRI image --- tumor region --- skull stripping --- region growing --- U-Net --- BRATS dataset --- incoherent imaging --- clutter rejection --- breast cancer detection --- MRgFUS --- proton resonance frequency shift --- temperature variations --- referenceless thermometry --- RBF neural networks --- interferometric optical fibers --- breast cancer --- risk assessment --- machine learning --- texture --- mammography --- medical imaging --- imaging biomarkers --- bone scintigraphy --- prostate cancer --- semisupervised classification --- false positives reduction --- computer-aided detection --- breast mass --- mass detection --- mass segmentation --- Mask R-CNN --- dataset partition --- brain tumor --- classification --- shallow machine learning --- breast cancer diagnosis --- Wisconsin Breast Cancer Dataset --- feature selection --- dimensionality reduction --- principal component analysis --- ensemble method

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In recent years, the global climate has become variable due to intensification of the greenhouse effect, and natural disasters are frequently occurring, which poses challenges to the situation awareness of intelligent distribution networks. Aside from the continuous grid connection of distributed generation, energy storage and new energy generation not only reduces the power supply pressure of distribution network to a certain extent but also brings new consumption pressure and load impact. Situation awareness is a technology based on the overall dynamic insight of environment and covering perception, understanding, and prediction. Such means have been widely used in security, intelligence, justice, intelligent transportation, and other fields and gradually become the research direction of digitization and informatization in the future. We hope this Special Issue represents a useful contribution. We present 10 interesting papers that cover a wide range of topics all focused on problems and solutions related to situation awareness for smart distribution systems. We sincerely hope the papers included in this Special Issue will inspire more researchers to further develop situation awareness for smart distribution systems. We strongly believe that there is a need for more work to be carried out, and we hope this issue provides a useful open-access platform for the dissemination of new ideas.

Technology: general issues --- History of engineering & technology --- community integrated energy system --- energy management --- user dominated demand side response --- conditional value-at-risk --- electric heating --- load forecasting --- thermal comfort --- attention mechanism --- LSTM neural network --- smart distribution network --- situation awareness --- high-quality operation and maintenance --- critical technology --- comprehensive framework --- distributionally robust optimization (DRO) --- integrated energy system (IES) --- joint chance constraints --- linear decision rules (LDRs) --- Wasserstein distance --- load disaggregation --- denoising auto-encoder --- REDD dataset --- TraceBase dataset --- machine learning --- secondary equipment --- CNN --- short text classification --- electric vehicle --- short-term load forecasting --- convolutional neural network --- temporal convolutional network --- climate factors --- correlation analysis --- sustainable wind-PV-hydrogen-storage microgrid --- power-to-hydrogen --- receding horizon optimization --- storage --- photovoltaic (PV) system --- DC series arc fault --- power spectrum estimation --- attentional mechanism --- lightweight convolutional neural network --- capacity configuration --- wind-photovoltaic-thermal power system --- carbon emission --- multi-objective optimization --- inertia security region --- community integrated energy system --- energy management --- user dominated demand side response --- conditional value-at-risk --- electric heating --- load forecasting --- thermal comfort --- attention mechanism --- LSTM neural network --- smart distribution network --- situation awareness --- high-quality operation and maintenance --- critical technology --- comprehensive framework --- distributionally robust optimization (DRO) --- integrated energy system (IES) --- joint chance constraints --- linear decision rules (LDRs) --- Wasserstein distance --- load disaggregation --- denoising auto-encoder --- REDD dataset --- TraceBase dataset --- machine learning --- secondary equipment --- CNN --- short text classification --- electric vehicle --- short-term load forecasting --- convolutional neural network --- temporal convolutional network --- climate factors --- correlation analysis --- sustainable wind-PV-hydrogen-storage microgrid --- power-to-hydrogen --- receding horizon optimization --- storage --- photovoltaic (PV) system --- DC series arc fault --- power spectrum estimation --- attentional mechanism --- lightweight convolutional neural network --- capacity configuration --- wind-photovoltaic-thermal power system --- carbon emission --- multi-objective optimization --- inertia security region

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Application of multi-sensor fusion technology has drawn a lot of industrial and academic interest in recent years. The multi-sensor fusion methods are widely used in many applications, such as autonomous systems, remote sensing, video surveillance, and the military. These methods can obtain the complementary properties of targets by considering multiple sensors. On the other hand, they can achieve a detailed environment description and accurate detection of interest targets based on the information from different sensors.This book collects novel developments in the field of multi-sensor, multi-source, and multi-process information fusion. Articles are expected to emphasize one or more of the three facets: architectures, algorithms, and applications. Published papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems.

Information technology industries --- target detection --- multi-platform imaging --- spectral matching --- terrestrial-hyperspectral imagery --- automated image analysis --- spectral library --- multi-sensor fusion --- object detection --- deep learning --- convolutional neural networks --- autonomous vehicles --- marine environment --- co-operative --- autonomous --- multi-robot --- USV --- AUV --- semantic SLAM --- YOLOv3 --- object based map --- EKF --- specular reflection detection --- specular reflection inpainting --- transparent object --- multispectral polarimetric imagery --- light field --- maritime vessel dataset --- ship detection --- convolutional neural network --- autonomous marine navigation --- machine learning --- inversion --- ocean colour --- phytoplankton --- pigment vertical profile --- deep chlorophyll maximum --- Tara Oceans --- MAREDAT --- pigments --- ITCOMP-SOM --- Self Organizing Maps --- target detection --- multi-platform imaging --- spectral matching --- terrestrial-hyperspectral imagery --- automated image analysis --- spectral library --- multi-sensor fusion --- object detection --- deep learning --- convolutional neural networks --- autonomous vehicles --- marine environment --- co-operative --- autonomous --- multi-robot --- USV --- AUV --- semantic SLAM --- YOLOv3 --- object based map --- EKF --- specular reflection detection --- specular reflection inpainting --- transparent object --- multispectral polarimetric imagery --- light field --- maritime vessel dataset --- ship detection --- convolutional neural network --- autonomous marine navigation --- machine learning --- inversion --- ocean colour --- phytoplankton --- pigment vertical profile --- deep chlorophyll maximum --- Tara Oceans --- MAREDAT --- pigments --- ITCOMP-SOM --- Self Organizing Maps

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The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section “Mathematics and Computer Science”. Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties.

Information technology industries --- mathematical competency --- assessment --- machine learning --- classification and regression tree --- CART ensembles and bagging --- ensemble model --- multivariate adaptive regression splines --- cross-validation --- dam inflow prediction --- long short-term memory --- wavelet transform --- input predictor selection --- hyper-parameter optimization --- brain-computer interface --- EEG motor imagery --- CNN-LSTM architectures --- real-time motion imagery recognition --- artificial neural networks --- banking --- hedonic prices --- housing --- quantile regression --- data quality --- citizen science --- consensus models --- clustering --- Gower's interpolation formula --- Gower's metric --- mixed data --- multidimensional scaling --- classification --- data-adaptive kernel functions --- image data --- multi-category classifier --- predictive models --- support vector machine --- stochastic gradient descent --- damped Newton --- convexity --- METABRIC dataset --- breast cancer subtyping --- deep forest --- multi-omics data --- categorical data --- similarity --- feature selection --- kernel density estimation --- non-linear optimization --- kernel clustering --- mathematical competency --- assessment --- machine learning --- classification and regression tree --- CART ensembles and bagging --- ensemble model --- multivariate adaptive regression splines --- cross-validation --- dam inflow prediction --- long short-term memory --- wavelet transform --- input predictor selection --- hyper-parameter optimization --- brain-computer interface --- EEG motor imagery --- CNN-LSTM architectures --- real-time motion imagery recognition --- artificial neural networks --- banking --- hedonic prices --- housing --- quantile regression --- data quality --- citizen science --- consensus models --- clustering --- Gower's interpolation formula --- Gower's metric --- mixed data --- multidimensional scaling --- classification --- data-adaptive kernel functions --- image data --- multi-category classifier --- predictive models --- support vector machine --- stochastic gradient descent --- damped Newton --- convexity --- METABRIC dataset --- breast cancer subtyping --- deep forest --- multi-omics data --- categorical data --- similarity --- feature selection --- kernel density estimation --- non-linear optimization --- kernel clustering