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Ensemble machine learning trains a diverse group of machine learning models to work together, aggregating their output to deliver richer results than a single model. Now in Ensemble Methods for Machine Learning you'll discover core ensemble methods that have proven records in both data science competitions and real-world applications. Hands-on case studies show you how each algorithm works in production. By the time you're done, you'll know the benefits, limitations, and practical methods of applying ensemble machine learning to real-world data, and be ready to build more explainable ML systems. Automatically compare, contrast, and blend the output from multiple models to squeeze the best results from your data. Ensemble machine learning applies a "wisdom of crowds" method that dodges the inaccuracies and limitations of a single model. By basing responses on multiple perspectives, this innovative approach can deliver robust predictions even without massive datasets. Ensemble Methods for Machine Learning teaches you practical techniques for applying multiple ML approaches simultaneously. Each chapter contains a unique case study that demonstrates a fully functional ensemble method, with examples including medical diagnosis, sentiment analysis, handwriting classification, and more. There's no complex math or theory--you'll learn in a visuals-first manner, with ample code for easy experimentation!

Ensemble learning (Machine learning)

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heavily Environmental mathematical models represent one of the key aids for scientists to forecast, create, and evaluate complex scenarios. These models rely on the data collected by direct field observations. However, assembly of a functional and comprehensive dataset for any environmental variable is difficult, mainly because of i) the high cost of the monitoring campaigns and ii) the low reliability of measurements (e.g., due to occurrences of equipment malfunctions and/or issues related to equipment location). The lack of a sufficient amount of Earth science data may induce an inadequate representation of the response’s complexity in any environmental system to any type of input/change, both natural and human-induced. In such a case, before undertaking expensive studies to gather and analyze additional data, it is reasonable to first understand what enhancement in estimates of system performance would result if all the available data could be well exploited. Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. Different approaches are available to deal with missing data. Traditional statistical data completion methods are used in different domains to deal with single and multiple imputation problems. More recently, machine learning techniques, such as clustering and classification, have been proposed to complete missing data. This book showcases the body of knowledge that is aimed at improving the capacity to exploit the available data to better represent, understand, predict, and manage the behavior of environmental systems at all practical scales.

geophysical monitoring --- data scarcity --- missing data --- climate extreme indices (CEIs) --- rule extraction --- Dataset Licensedatabase --- data assimilation --- data imputation --- support vector machines --- environmental observations --- multi-class classification --- earth-science data --- remote sensing --- magnetotelluric monitoring --- soil texture calculator --- machine learning --- ClimPACT --- invasive species --- species distribution modeling --- 3D-Var --- ensemble learning --- data quality --- water quality --- microhabitat --- k-Nearest Neighbors --- Expert Team on Climate Change Detection and Indices (ETCCDI) --- decision trees --- processing --- attribute reduction --- Expert Team on Sector-specific Climate Indices (ET-SCI) --- core attribute --- rough set theory --- GLDAS --- arthropod vector --- environmental modeling --- statistical methods

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This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section.

Technology: general issues --- History of engineering & technology --- induction machines --- electrical machines --- vector control --- SVPWM modulation --- frequency inverter --- artificial intelligence --- photovoltaics --- fault detection --- machine learning --- operation and maintenance --- renewable energy --- water-in-crude oil emulsion --- water content --- ultrasound --- propagation velocity --- exoskeletons --- test bench --- industry --- benchmarking --- microgrid model-based systems engineering --- service systems --- goal-oriented requirements engineering --- safety instrumented system --- ventricular assist device Bayesian network --- Petri net --- control strategy --- UAV --- fuzzy --- PID controller --- ROS --- Industry 4.0 --- database --- data models --- big data and analytics --- asset administration shell --- MLOps --- digital twin --- IoT --- prediction --- coordinate metrology --- optical scanning --- noise reduction --- digital manufacturing --- integrated inspection system --- data analytics --- uncertainty --- convolutional neural networks --- warehouse management --- image classification --- ensemble learning --- synthetic data --- depth image --- electrical maintenance --- COVID-19 --- thermography --- fever --- computer vision --- intelligent systems

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This book includes 13 papers published in Special Issue ("Visual and Camera Sensors") of the journal Sensors. The goal of this Special Issue was to invite high-quality, state-of-the-art research papers dealing with challenging issues in visual and camera sensors.

self-assembly device --- 3D point clouds --- accuracy analysis --- VSLAM-photogrammetric algorithm --- portable mobile mapping system --- low-cost device --- BIM --- camera calibration --- DLT --- PnP --- weighted DLT --- uncertainty --- covariance --- robustness --- visual-inertial --- semi-direct SLAM --- multi-sensor fusion --- side-rear-view monitoring system --- automatic online calibration --- Hough-space --- unmanned aerial vehicle --- autonomous landing --- deep-learning-based motion deblurring and marker detection --- network slimming --- pruning model --- convolutional neural network --- convolutional filter --- classification --- multimodal human recognition --- blur image restoration --- DeblurGAN --- CNN --- facial expression recognition system --- computer vision --- multi-scale featured local binary pattern --- unsharp masking --- machine learning --- lens distortion --- DoF-dependent --- distortion partition --- vision measurement --- pathological site classification --- in vivo endoscopy --- computer-aided diagnosis --- artificial intelligence --- ensemble learning --- convolutional auto-encoders --- local image patch --- point pair feature --- plank recognition --- robotic grasping --- flying object detection --- drone --- image processing --- camera networks --- open-pit mine slope monitoring --- optimum deployment --- close range photogrammetry --- three-dimensional reconstruction --- OCD4M

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The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications.

sEMG --- deep learning --- neural networks --- gait phase --- classification --- everyday walking --- convolutional neural network --- CRISPR --- leukemia nucleus image --- segmentation --- soft covering rough set --- clustering --- machine learning algorithm --- soft computing --- multistage support vector machine model --- multiple imputation by chained equations --- SVM-based recursive feature elimination --- unipolar depression --- diabetic retinopathy (DR) --- pre-trained deep ConvNet --- uni-modal deep features --- multi-modal deep features --- transfer learning --- 1D pooling --- cross pooling --- IMU --- gait analysis --- long-term monitoring --- multi-unit --- multi-sensor --- time synchronization --- Internet of Medical Things --- body area network --- MIMU --- early detection --- sepsis --- evaluation metrics --- machine learning --- medical informatics --- feature extraction --- physionet challenge --- electrocardiogram --- Premature ventricular contraction --- sparse autoencoder --- unsupervised learning --- Softmax regression --- medical diagnosis --- artificial neural network --- e-health --- Tri-Fog Health System --- fault data elimination --- health status prediction --- health status detection --- health off --- diffusion tensor imaging --- ensemble learning --- decision support systems --- healthcare --- computational intelligence --- Alzheimer’s disease --- fuzzy inference systems --- genetic algorithms --- next-generation sequencing --- ovarian cancer --- interpretable models --- n/a --- Alzheimer's disease