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Amidst impending climate change and enhanced pollution levels around the globe, the need of the hour is to develop bio-based materials that are sustainable and possess comparable performance properties to their synthetic counterparts. In light of the aforementioned, numerous investigations are being conducted to identify, process, and create materials that are concurrently innocuous towards the environment and have superior properties. This book is a collection of such scientific articles that propagate novel ideas for the development of polymeric composite materials, which have application potential in numerous fields such as medicine, automobile, aviation, construction, etc. It also contains a pedagogical article that proposes some strategies to continue experimental research during pandemics. This book will provide readers a quick glance into recent developments regarding polymeric materials and will encourage them to propagate these research ideas further.

History of engineering & technology --- solid urban waste --- formaldehyde --- durability --- electrical properties --- mechanical properties --- recycling --- epoxy resin --- flammability --- heat release rate --- microscale combustion calorimetry --- multiple linear regression --- adaptive neuro-fuzzy inference system --- polyvinyl alcohol (PVA) --- bionanocomposites --- nanomechanical behaviour --- thin films --- particle size --- model free --- model fitting --- avrami–eroféev --- DAEM --- superhydrophobic surfaces --- self-healing --- natural hierarchical microstructures --- wood --- bio-composite --- linear low density polyethylene --- performance --- straws --- biocomposites --- nanofibers --- electrospinning --- cell culture --- graphene oxide --- barrier properties --- poly(lactic acid) --- clay --- nanocomposite --- permeability --- bacterial cellulose --- metal organic framework --- adsorption --- chitosan --- composite nanofibers --- silk fibroin --- polycaprolactone --- Taguchi --- rheological properties --- DMA --- injection molding --- thermal properties --- natural fibers --- biochar --- carbon fillers --- nanocomposites --- flame retardants --- fire --- n/a --- PHB --- natural fiber --- compatibilizer --- cellulose --- biocomposite --- avrami-eroféev

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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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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

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The rapid development of advanced, arguably, intelligent sensors and their massive deployment provide a foundation for new paradigms to combat the challenges that arise in significant tasks such as positioning, tracking, navigation, and smart sensing in various environments. Relevant advances in artificial intelligence (AI) and machine learning (ML) are also finding rapid adoption by industry and fan the fire. Consequently, research on intelligent sensing systems and technologies has attracted considerable attention during the past decade, leading to a variety of effective applications related to intelligent transportation, autonomous vehicles, wearable computing, wireless sensor networks (WSN), and the internet of things (IoT). In particular, the sensors community has a great interest in novel, intelligent information fusion, and data mining methods coupling AI and ML for substantial performance enhancement, especially for the challenging scenarios that make traditional approaches inappropriate. This reprint book has collected 14 excellent papers that represent state-of-the-art achievements in the relevant topics and provides cutting-edge coverage of recent advances in sensor signal and data mining techniques, algorithms, and approaches, particularly applied for positioning, tracking, navigation, and smart sensing.

History of engineering & technology --- clustering --- data fusion --- target detection --- Grey Wolf Optimizer --- Fireworks Algorithm --- hybrid algorithm --- exploitation and exploration --- GNSS --- MIMU --- odometer --- state constraints --- simultaneous localization and mapping (SLAM) --- range-only SLAM --- sum of Gaussian (SoG) filter --- cooperative approach --- automatic fare collection system --- passenger flow forecasting --- time series decomposition --- singular spectrum analysis --- ensemble learning --- extreme learning machine --- wheeled mobile robot --- path panning --- laser simulator --- fuzzy logic --- laser range finder --- Wi-Fi camera --- sensor fusion --- local map --- odometry --- deep learning --- softmax --- decision-making --- classification --- sensor data --- Internet of Things --- extended target tracking --- gamma-Gaussian-inverse Wishart --- Poisson multi-Bernoulli mixture --- 5G IoT --- indoor positioning --- tracking --- localization --- navigation --- positioning accuracy --- single access point positioning --- fuzzy inference --- calibration --- car-following --- Takagi–Sugeno --- Kalman filter --- microscopic traffic model --- continuous-time model --- LoRa --- positioning --- LoRaWAN --- TDoA --- map matching --- compass --- automotive LFMCW radar --- radial velocity --- lateral velocity --- Doppler-frequency estimation --- waveform --- signal model --- tensor modeling --- smart community system --- power efficiency --- object-detection coprocessor --- histogram of oriented gradient --- support vector machine --- block-level once sliding detection window --- multi-shape detection-window

Choose an application

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

The rapid development of advanced, arguably, intelligent sensors and their massive deployment provide a foundation for new paradigms to combat the challenges that arise in significant tasks such as positioning, tracking, navigation, and smart sensing in various environments. Relevant advances in artificial intelligence (AI) and machine learning (ML) are also finding rapid adoption by industry and fan the fire. Consequently, research on intelligent sensing systems and technologies has attracted considerable attention during the past decade, leading to a variety of effective applications related to intelligent transportation, autonomous vehicles, wearable computing, wireless sensor networks (WSN), and the internet of things (IoT). In particular, the sensors community has a great interest in novel, intelligent information fusion, and data mining methods coupling AI and ML for substantial performance enhancement, especially for the challenging scenarios that make traditional approaches inappropriate. This reprint book has collected 14 excellent papers that represent state-of-the-art achievements in the relevant topics and provides cutting-edge coverage of recent advances in sensor signal and data mining techniques, algorithms, and approaches, particularly applied for positioning, tracking, navigation, and smart sensing.

History of engineering & technology --- clustering --- data fusion --- target detection --- Grey Wolf Optimizer --- Fireworks Algorithm --- hybrid algorithm --- exploitation and exploration --- GNSS --- MIMU --- odometer --- state constraints --- simultaneous localization and mapping (SLAM) --- range-only SLAM --- sum of Gaussian (SoG) filter --- cooperative approach --- automatic fare collection system --- passenger flow forecasting --- time series decomposition --- singular spectrum analysis --- ensemble learning --- extreme learning machine --- wheeled mobile robot --- path panning --- laser simulator --- fuzzy logic --- laser range finder --- Wi-Fi camera --- sensor fusion --- local map --- odometry --- deep learning --- softmax --- decision-making --- classification --- sensor data --- Internet of Things --- extended target tracking --- gamma-Gaussian-inverse Wishart --- Poisson multi-Bernoulli mixture --- 5G IoT --- indoor positioning --- tracking --- localization --- navigation --- positioning accuracy --- single access point positioning --- fuzzy inference --- calibration --- car-following --- Takagi–Sugeno --- Kalman filter --- microscopic traffic model --- continuous-time model --- LoRa --- positioning --- LoRaWAN --- TDoA --- map matching --- compass --- automotive LFMCW radar --- radial velocity --- lateral velocity --- Doppler-frequency estimation --- waveform --- signal model --- tensor modeling --- smart community system --- power efficiency --- object-detection coprocessor --- histogram of oriented gradient --- support vector machine --- block-level once sliding detection window --- multi-shape detection-window