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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

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Monitoring of vegetation structure and functioning is critical to modeling terrestrial ecosystems and energy cycles. In particular, leaf area index (LAI) is an important structural property of vegetation used in many land surface vegetation, climate, and crop production models. Canopy structure (LAI, fCover, plant height, and biomass) and biochemical parameters (leaf pigmentation and water content) directly influence the radiative transfer process of sunlight in vegetation, determining the amount of radiation measured by passive sensors in the visible and infrared portions of the electromagnetic spectrum. Optical remote sensing (RS) methods build relationships exploiting in situ measurements and/or as outputs of physical canopy radiative transfer models. The increased availability of passive (radar and LiDAR) RS data has fostered their use in many applications for the analysis of land surface properties and processes, thanks also to their insensitivity to weather conditions and the capability to exploit rich structural and textural information. Data fusion and multi-sensor integration techniques are pressing topics to fully exploit the information conveyed by both optical and microwave bands.

artificial neural network --- downscaling --- simulation --- 3D point cloud --- European beech --- consistency --- adaptive threshold --- evaluation --- photosynthesis --- geographic information system --- P-band PolInSAR --- validation --- density-based clustering --- structure from motion (SfM) --- EPIC --- Tanzania --- signal attenuation --- trunk --- canopy closure --- REDD+ --- unmanned aerial vehicle (UAV) --- forest --- recursive feature elimination --- Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) --- aboveground biomass --- random forest --- uncertainty --- household survey --- spectral information --- forests biomass --- root biomass --- biomass --- unmanned aerial vehicle --- Brazilian Amazon --- VIIRS --- global positioning system --- LAI --- photochemical reflectance index (PRI) --- allometric scaling and resource limitation --- R690/R630 --- modelling aboveground biomass --- leaf area index --- forest degradation --- spectral analyses --- terrestrial laser scanning --- BAAPA --- leaf area index (LAI) --- stem volume estimation --- tomographic profiles --- polarization coherence tomography (PCT) --- canopy gap fraction --- automated classification --- HemiView --- remote sensing --- multisource remote sensing --- Pléiades imagery --- photogrammetric point cloud --- farm types --- terrestrial LiDAR --- altitude --- RapidEye --- forest aboveground biomass --- recovery --- southern U.S. forests --- NDVI --- machine-learning --- conifer forest --- satellite --- chlorophyll fluorescence (ChlF) --- tree heights --- phenology --- point cloud --- local maxima --- clumping index --- MODIS --- digital aerial photograph --- Mediterranean --- hemispherical sky-oriented photo --- managed temperate coniferous forests --- fixed tree window size --- drought --- GLAS --- smartphone-based method --- forest above ground biomass (AGB) --- forest inventory --- over and understory cover --- sampling design

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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.

Information technology industries --- 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

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Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible.

artificial neural network (ANN) --- Grain weevil identification --- neural modelling classification --- winter wheat --- grain --- artificial neural network --- ferulic acid --- deoxynivalenol --- nivalenol --- MLP network --- sensitivity analysis --- precision agriculture --- machine learning --- similarity --- metric --- memory --- deep learning --- plant growth --- dynamic response --- root zone temperature --- dynamic model --- NARX neural networks --- hydroponics --- vegetation indices --- UAV --- neural network --- corn plant density --- corn canopy cover --- yield prediction --- CLQ --- GA-BPNN --- GPP-driven spectral model --- rice phenology --- EBK --- correlation filter --- crop yield prediction --- hybrid feature extraction --- recursive feature elimination wrapper --- artificial neural networks --- big data --- classification --- high-throughput phenotyping --- modeling --- predicting --- time series forecasting --- soybean --- food production --- paddy rice mapping --- dynamic time warping --- LSTM --- weakly supervised learning --- cropland mapping --- apparent soil electrical conductivity (ECa) --- magnetic susceptibility (MS) --- EM38 --- neural networks --- Phoenix dactylifera L. --- Medjool dates --- image classification --- convolutional neural networks --- transfer learning --- average degree of coverage --- coverage unevenness coefficient --- optimization --- high-resolution imagery --- oil palm tree --- CNN --- Faster-RCNN --- image identification --- agroecology --- weeds --- yield gap --- environment --- health --- crop models --- soil and plant nutrition --- automated harvesting --- model application for sustainable agriculture --- remote sensing for agriculture --- decision supporting systems --- neural image analysis

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Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible.

Research & information: general --- Biology, life sciences --- Technology, engineering, agriculture --- artificial neural network (ANN) --- Grain weevil identification --- neural modelling classification --- winter wheat --- grain --- artificial neural network --- ferulic acid --- deoxynivalenol --- nivalenol --- MLP network --- sensitivity analysis --- precision agriculture --- machine learning --- similarity --- metric --- memory --- deep learning --- plant growth --- dynamic response --- root zone temperature --- dynamic model --- NARX neural networks --- hydroponics --- vegetation indices --- UAV --- neural network --- corn plant density --- corn canopy cover --- yield prediction --- CLQ --- GA-BPNN --- GPP-driven spectral model --- rice phenology --- EBK --- correlation filter --- crop yield prediction --- hybrid feature extraction --- recursive feature elimination wrapper --- artificial neural networks --- big data --- classification --- high-throughput phenotyping --- modeling --- predicting --- time series forecasting --- soybean --- food production --- paddy rice mapping --- dynamic time warping --- LSTM --- weakly supervised learning --- cropland mapping --- apparent soil electrical conductivity (ECa) --- magnetic susceptibility (MS) --- EM38 --- neural networks --- Phoenix dactylifera L. --- Medjool dates --- image classification --- convolutional neural networks --- transfer learning --- average degree of coverage --- coverage unevenness coefficient --- optimization --- high-resolution imagery --- oil palm tree --- CNN --- Faster-RCNN --- image identification --- agroecology --- weeds --- yield gap --- environment --- health --- crop models --- soil and plant nutrition --- automated harvesting --- model application for sustainable agriculture --- remote sensing for agriculture --- decision supporting systems --- neural image analysis