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  • machine learning  (232)
  • MDPI - Multidisciplinary Digital Publishing Institute  (232)
  • 2020-2024  (232)
  • 2020-2023
  • 2022  (232)
  • 2022  (232)
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  • 2020-2024  (232)
  • 2020-2023
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  • 1
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-11
    Description: This Special Issue was intended as a forum to advance research and apply machine-learning and data-mining methods to facilitate the development of modern electric power systems, grids and devices, and smart grids and protection devices, as well as to develop tools for more accurate and efficient power system analysis. Conventional signal processing is no longer adequate to extract all the relevant information from distorted signals through filtering, estimation, and detection to facilitate decision-making and control actions. Machine learning algorithms, optimization techniques and efficient numerical algorithms, distributed signal processing, machine learning, data-mining statistical signal detection, and estimation may help to solve contemporary challenges in modern power systems. The increased use of digital information and control technology can improve the grid’s reliability, security, and efficiency; the dynamic optimization of grid operations; demand response; the incorporation of demand-side resources and integration of energy-efficient resources; distribution automation; and the integration of smart appliances and consumer devices. Signal processing offers the tools needed to convert measurement data to information, and to transform information into actionable intelligence. This Special Issue includes fifteen articles, authored by international research teams from several countries.
    Keywords: virtual power plant (VPP) ; power quality (PQ) ; global index ; distributed energy resources (DER) ; energy storage systems (ESS) ; power systems ; long-term assessment ; battery energy storage systems (BESS) ; smart grids ; conducted disturbances ; power quality ; supraharmonics ; 2–150 kHz ; Power Line Communications (PLC) ; intentional emission ; non-intentional emission ; mains signalling ; virtual power plant ; data mining ; clustering ; distributed energy resources ; energy storage systems ; short term conditions ; cluster analysis (CA) ; nonlinear loads ; harmonics, cancellation, and attenuation of harmonics ; waveform distortion ; THDi ; low-voltage networks ; optimization techniques ; different batteries ; off-grid microgrid ; integrated renewable energy system ; cluster analysis ; K-means ; agglomerative ; ANFIS ; fuzzy logic ; induction generator ; MPPT ; neural network ; renewable energy ; variable speed WECS ; wind energy conversion system ; wind energy ; frequency estimation ; spectrum interpolation ; power network disturbances ; COVID-19 ; time-varying reproduction number ; social distancing ; load profile ; demographic characteristic ; household energy consumption ; demand-side management ; energy management ; time series ; Hidden Markov Model ; short-term forecast ; sparse signal decomposition ; supervised dictionary learning ; dictionary impulsion ; singular value decomposition ; discrete cosine transform ; discrete Haar transform ; discrete wavelet transform ; transient stability assessment ; home energy management ; binary-coded genetic algorithms ; optimal power scheduling ; demand response ; Data Injection Attack ; machine learning ; critical infrastructure ; smart grid ; water treatment plant ; power system ; n/a ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology ; thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNB Energy industries and utilities
    Language: English
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  • 2
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-03-21
    Description: This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners.
    Keywords: truck dispatching ; mining equipment uncertainties ; orebody uncertainty ; discrete event simulation ; Q-learning ; grinding circuits ; minerals processing ; random forest ; decision trees ; machine learning ; knowledge discovery ; variable importance ; mineral prospectivity mapping ; random forest algorithm ; epithermal gold ; unstructured data ; blast impact ; empirical model ; mining ; fragmentation ; mine worker fatigue ; random forest model ; health and safety management ; stockpiles ; operational data ; mine-to-mill ; geostatistics ; ore control ; mine optimization ; digital twin ; modes of operation ; geological uncertainty ; multivariate statistics ; partial least squares regression ; oil sands ; bitumen extraction ; bitumen processability ; mine safety and health ; accidents ; narratives ; natural language processing ; random forest classification ; hyperspectral imaging ; multispectral imaging ; dimensionality reduction ; neighbourhood component analysis ; artificial intelligence ; mining exploitation ; masonry buildings ; damage risk analysis ; Bayesian network ; Naive Bayes ; Bayesian Network Structure Learning (BNSL) ; rock type ; mining geology ; bluetooth beacon ; classification and regression tree ; gaussian naïve bayes ; k-nearest neighbors ; support vector machine ; transport route ; transport time ; underground mine ; tactical geometallurgy ; data analytics in mining ; ball mill throughput ; measurement while drilling ; non-additivity ; coal ; petrographic analysis ; macerals ; image analysis ; semantic segmentation ; convolutional neural networks ; point cloud scaling ; fragmentation size analysis ; structure from motion ; n/a ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
    Language: English
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  • 3
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-09
    Description: Third millennium engineering address new challenges in materials sciences and engineering. In particular, the advances in materials engineering combined with the advances in data acquisition, processing and mining as well as artificial intelligence allow for new ways of thinking in designing new materials and products. Additionally, this gives rise to new paradigms in bridging raw material data and processing to the induced properties and performance. This present topical issue is a compilation of contributions on novel ideas and concepts, addressing several key challenges using data and artificial intelligence, such as:- proposing new techniques for data generation and data mining;- proposing new techniques for visualizing, classifying, modeling, extracting knowledge, explaining and certifying data and data-driven models;- processing data to create data-driven models from scratch when other models are absent, too complex or too poor for making valuable predictions;- processing data to enhance existing physic-based models to improve the quality of the prediction capabilities and, at the same time, to enable data to be smarter; and- processing data to create data-driven enrichment of existing models when physics-based models exhibit limits within a hybrid paradigm.
    Keywords: plasticity ; machine learning ; constitutive modeling ; manifold learning ; topological data analysis ; GENERIC ; soft living tissues ; hyperelasticity ; computational modeling ; data-driven mechanics ; TDA ; Code2Vect ; nonlinear regression ; effective properties ; microstructures ; model calibration ; sensitivity analysis ; elasto-visco-plasticity ; Gaussian process ; high-throughput experimentation ; additive manufacturing ; Ti–Mn alloys ; spherical indentation ; statistical analysis ; Gaussian process regression ; nanoporous metals ; open-pore foams ; FE-beam model ; data mining ; mechanical properties ; hardness ; principal component analysis ; structure–property relationship ; microcompression ; nanoindentation ; analytical model ; finite element model ; artificial neural networks ; model correction ; feature engineering ; physics based ; data driven ; laser shock peening ; residual stresses ; data-driven ; multiscale ; nonlinear ; stochastics ; neural networks ; n/a ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
    Language: English
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  • 4
    Publication Date: 2024-03-27
    Description: This Special Issue provides an insight, collated from 26 articles, focusing on various aspects of the Fit-For-Purpose Land Administration (FFPLA) concept and its application. It presents some influential and innovative trends and recommendations for designing, implementing, maintaining and further developing Fit-For-Purpose solutions for providing secure land rights at scale. The first group of 14 articles is published in Volume One and discusses various conceptual innovations related to spatial, legal and institutional aspects and its wider applications within land use management. The second group of 12 articles is published in Volume Two and focuses on case studies from various countries throughout the world, providing evidence and lessons learned from the FFPLA implementation process.
    Keywords: complete cadastre ; legal element ; fixing boundary ; eligible landowner ; agreement ; boundary marker ; fit-for-purpose land administration (FFP LA) ; violent conflict ; United Nations ; extra-legal ; transitional justice ; peace building ; land governance ; power relations ; securing land rights ; land registration ; development impacts ; fit-for-purpose land administration ; land administration ; decentralization ; India ; fit-for-purpose ; institutions ; governance ; politics ; Amazon ; deforestation ; Fit-For-Purpose land administration ; participatory mapping ; indigenous land conflict ; Cumaribo ; Colombia ; community-based land adjudication ; components of adjudication ; land tenure ; land rights ; good practices ; updating land records ; systematic land registration ; unconventional approach ; case study ; Benin ; cadaster ; land administration domain model ; LADM ; cadastre ; FFPLA ; customary tenure ; land inventory ; land management ; mobile-based applications ; pro-poor ; land surveying ; tenure security ; land rights and tenure ; fit-for-purpose approach ; human rights ; design science research ; design thinking ; fit for purpose ; spatial data quality ; spatial data quality assurance ; maintenance ; update ; upgrade ; upkeep ; renewal ; data quality ; spatial framework ; STDM ; technology ; UAV ; feature extraction ; rapid urbanization ; climate change ; pandemic ; urban resilience ; spatial ; legal ; and institutional frameworks ; land tenure security ; pro-poor land recordation ; land governance reform ; cost effectiveness ; innovative technology ; case studies ; Uganda ; customary land tenure ; land recordation tools ; semantic technologies ; land information system ; fit-for-purpose land management ; aerial and street level imagery ; machine learning ; integrated land programs ; land policy ; pilot study ; informal settlements ; urban development ; Brazil ; community-based crowdsourcing ; SiGIT ; Ecuador ; land and resources rights ; public-private partnerships ; corporate social responsibility ; poverty reduction ; business driven solutions ; social enterprises ; n/a ; thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
    Language: English
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  • 5
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-05-06
    Description: Today, the flow of electricity is bidirectional, and not all electricity is centrally produced in large power plants. With the growing emergence of prosumers and microgrids, the amount of electricity produced by sources other than large, traditional power plants is ever-increasing. These alternative sources include photovoltaic (PV), wind turbine (WT), geothermal, and biomass renewable generation plants. Some renewable energy resources (solar PV and wind turbine generation) are highly dependent on natural processes and parameters (wind speed, wind direction, temperature, solar irradiation, humidity, etc.). Thus, the outputs are so stochastic in nature. New data-science-inspired real-time solutions are needed in order to co-develop digital twins of large intermittent renewable plants whose services can be globally delivered.
    Keywords: self-healing grid ; machine-learning ; feature extraction ; event detection ; optimization techniques ; manta ray foraging optimization algorithm ; multi-objective function ; radial networks ; optimal power flow ; automatic P2P energy trading ; Markov decision process ; deep reinforcement learning ; deep Q-network ; long short-term delayed reward ; inter-area oscillations ; modal analysis ; reduced order modeling ; dynamic mode decomposition ; machine learning ; artificial neural networks ; steady-state security assessment ; situation awareness ; cellular computational networks ; load flow prediction ; contingency ; fuzzy system ; change detection ; data analytics ; data mining ; filtering ; optimization ; power quality ; signal processing ; total variation smoothing ; n/a ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues ; bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
    Language: English
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  • 6
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2022-05-06
    Description: In recent years, new and emerging digital technologies applied to food science have been gaining attention and increased interest from researchers and the food/beverage industries. In particular, those digital technologies that can be used throughout the food value chain are accurate, easy to implement, affordable, and user-friendly. Hence, this Special Issue (SI) is dedicated to novel technology based on sensor technology and machine/deep learning modeling strategies to implement artificial intelligence (AI) into food and beverage production and for consumer assessment. This SI published quality papers from researchers in Australia, New Zealand, the United States, Spain, and Mexico, including food and beverage products, such as grapes and wine, chocolate, honey, whiskey, avocado pulp, and a variety of other food products.
    Keywords: sensory ; physicochemical measurements ; artificial neural networks ; near infra-red spectroscopy ; wine quality ; machine learning modeling ; weather ; consumer acceptance prediction ; data fusion ; emotion recognition ; facial expression recognition ; galvanic skin response ; machine learning ; neural networks ; sensory analysis ; avocado ; cultivars ; preference mapping ; sensory evaluation ; sensory descriptive analysis ; consumer science ; unifloral honeys ; botanical origin ; physicochemical parameters ; classification ; natural language processing ; deep learning ; sensory science ; flavor lexicon ; long short-term memory ; n/a ; bic Book Industry Communication::G Reference, information & interdisciplinary subjects::GP Research & information: general ; bic Book Industry Communication::P Mathematics & science::PS Biology, life sciences ; bic Book Industry Communication::T Technology, engineering, agriculture
    Language: English
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  • 7
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-09
    Description: This book mainly deals with recent advances in biomedical sensing and imaging. More recently, wearable/smart biosensors and devices, which facilitate diagnostics in a non-clinical setting, have become a hot topic. Combined with machine learning and artificial intelligence, they could revolutionize the biomedical diagnostic field. The aim of this book is to provide a research forum in biomedical sensing and imaging and extend the scientific frontier of this very important and significant biomedical endeavor.
    Keywords: finite element method ; thin shell model ; β dispersion ; Maxwell–Wagner effect ; bio-impedance spectroscopy ; multisensory ; electromyography ; pattern recognition ; rehabilitation ; blood coagulation ; image sensing ; image classification ; electrical impedance tomography ; frequency difference ; time difference ; lung imaging ; electromagnetic detection and biosensors ; electromagnetic biological theory ; biomedical application ; frequency ; machine learning ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
    Language: English
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  • 8
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-03-30
    Description: Personalised medicine is the next step in healthcare, especially when applied to genetically diverse diseases such as cancers. Naturally, a host of methods need to evolve alongside this, in order to allow the practice and implementation of individual treatment regimens. One of the major tasks for the development of personalised treatment of cancer is the identification and validation of a comprehensive, robust, and reliable panel of biomarkers that guide the clinicians to provide the best treatment to patients. This is indeed important with regards to radiotherapy; not only do biomarkers allow for the assessment of treatability, tumour response, and the radiosensitivity of healthy tissue of the treated patient. Furthermore, biomarkers should allow for the evaluation of the risks of developing adverse late effects as a result of radiotherapy such as second cancers and non-cancer effects, for example cardiovascular injury and cataract formation. Knowledge of all of these factors would allow for the development of a tailored radiation therapy regime. This Special Issue of the Journal of Personalised Medicine covers the topic of Radiation Response Biomarkers in the context of individualised cancer treatments, and offers an insight into some of the further evolution of radiation response biomarkers, their usefulness in guiding clinicians, and their application in radiation therapy.
    Keywords: carbon-ion radiotherapy ; head-and-neck tumors ; squamous cell carcinoma ; radiosensitivity ; relative biological effectiveness ; lung cancer ; radiotherapy ; radiotherapy monitoring ; radiation-induced lung injury ; RILI ; pneumonitis ; radiation-induced lung fibrosis ; RILF ; circulating biomarkers ; microRNA ; micronuclei ; uterine cervical cancer ; cGAS ; STING ; abscopal effect ; immunotherapy ; PBMCS ; micronucleus assay ; biological dosimetry ; human blood ; genotoxicity tests ; ionizing radiation ; biomarkers ; dicentric assay ; gamma H2AX foci assay ; health surveillance analyses ; clonogenic assays ; methods ; plating ; cancer ; radiation ; head and neck cancer ; exosomes ; serum ; metabolomics ; GC/MS ; biodosimetry ; chromosome aberrations ; normal tissue toxicity ; predictive tests ; normal tissue ; biomarker ; protein ; immune infiltrate ; stroma ; tumour microenvironment ; proteomics ; telomeres ; chromosomal instability ; inversions ; prostate cancer ; IMRT ; machine learning ; individual radiosensitivity ; late effects ; personalized medicine ; liquid biopsy ; circulating tumour cells ; extracellular vesicles ; microRNAs ; immune system ; inflammation ; n/a
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  • 9
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-11
    Description: In recent years, the implementation of sustainable concrete systems has been a topic of great interest in the field of construction engineering worldwide, as a result of the large and rapid increase in carbon emissions and environmental problems resulting from traditional concrete production and industry. For example, the uses of supplementary cementitious materials, geopolymer binder, recycled aggregate and industrial/agricultural wastes in concrete are all approaches to building a sustainable concrete system. However, such materials have inherent flaws due to their variety of sources, and exhibit very different properties compared with traditional concrete. Therefore, they require specific modifications in preprocessing, design, and evaluation before use in concrete. This reprint, entitled “Advances in Sustainable Concrete System”, covers a broad range of advanced concrete research in environmentally friendly concretes, cost-effective admixtures, and waste recycling, specifically including the design methods, mechanical properties, durability, microstructure, various models, hydration mechanisms, and practical applications of solid wastes in concrete systems.
    Keywords: high-strength concrete ; energy evolution ; elastic strain energy ; brittleness evaluation index ; concrete ; humidity ; moisture absorption ; moisture desorption ; numerical simulation ; acoustic emission ; AE rate process theory ; corrosion rate ; damage evolution ; axial load ; precast concrete structure ; lattice girder semi-precast slabs ; bending resistance ; FE modelling ; concrete damage ; GSP ; high strength ; hydration ; strength ; penetrability ; rice husk ash ; sustainable concrete ; artificial neural networks ; multiple linear regression ; eco-friendly concrete ; green concrete ; sustainable development ; artificial intelligence ; data science ; machine learning ; bagasse ash ; mechanical properties ; natural coarse aggregate ; recycled coarse aggregate ; two-stage concrete ; materials design ; recycled concrete ; crumb rubber concrete ; crumb rubber ; NaOH treatment ; lime treatment ; water treatment ; detergent treatment ; compressive strength ; materials ; adhesively-bonded joint ; temperature aging ; residual strength ; mechanical behavior ; failure criterion ; steel slag powder ; compound activator ; mortar strength ; orthogonal experiment ; GM (0, N) model ; ultrafine metakaolin ; silica fume ; durability ; fiber-reinforced concrete ; damage mechanism ; uniaxial tension ; cracked concrete ; crack width ; crack depth ; tortuosity ; sustainability ; concrete composites ; sulfate and acid attacks ; WPFT fibers ; coal gangue ; gradation ; cement content ; unconfined compressive strength ; freeze–thaw cycle ; minimum energy dissipation principle ; three-shear energy yield criterion ; damage variable ; constitutive model ; phosphorus slag ; limestone ; sulphate-corrosion resistance ; volume deformation ; blast furnace ferronickel slag ; alkali-activated material ; dosage of activator ; reactive powder concrete ; beam-column joint ; FE modeling ; crack ; cementitious gravel ; fly ash ; age ; optimal dosage ; bamboo ; sawdust ; pretreatment ; bio-based material ; mechanical property ; self-compacting concrete ; supplementary cementitious materials ; hydration mechanisms ; microstructure ; fresh properties ; synthetic polymer ; high temperature ; bentonite-free drilling fluid ; rheology ; filtration ; FRP reinforced concrete slab ; punching shear strength ; SHAP ; n/a ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TN Civil engineering, surveying and building::TNK Building construction and materials::TNKX Conservation of buildings and building materials
    Language: English
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  • 10
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    MDPI - Multidisciplinary Digital Publishing Institute
    Publication Date: 2024-04-09
    Description: This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective.
    Keywords: subject-dependent emotion recognition ; subject-independent emotion recognition ; electrodermal activity (EDA) ; deep learning ; convolutional neural networks ; automatic facial emotion recognition ; intensity of emotion recognition ; behavioral biometrical systems ; machine learning ; artificial intelligence ; driving stress ; electrodermal activity ; road traffic ; road types ; Viola-Jones ; facial emotion recognition ; facial expression recognition ; facial detection ; facial landmarks ; infrared thermal imaging ; homography matrix ; socially assistive robot ; EEG ; arousal detection ; valence detection ; data transformation ; normalization ; mental stress detection ; electrocardiogram ; respiration ; in-ear EEG ; emotion classification ; emotion monitoring ; elderly caring ; outpatient caring ; stress detection ; deep neural network ; convolutional neural network ; wearable sensors ; psychophysiology ; sensor data analysis ; time series analysis ; signal analysis ; similarity measures ; correlation statistics ; quantitative analysis ; benchmarking ; boredom ; emotion ; GSR ; classification ; sensor ; face landmark detection ; fully convolutional DenseNets ; skip-connections ; dilated convolutions ; emotion recognition ; physiological sensing ; multimodal sensing ; flight simulation ; activity recognition ; physiological signals ; thoracic electrical bioimpedance ; smart band ; stress recognition ; physiological signal processing ; long short-term memory recurrent neural networks ; information fusion ; pain recognition ; long-term stress ; electroencephalography ; perceived stress scale ; expert evaluation ; affective corpus ; multimodal sensors ; overload ; underload ; interest ; frustration ; cognitive load ; stress research ; affective computing ; human-computer interaction ; deep convolutional neural network ; transfer learning ; auxiliary loss ; weighted loss ; class center ; stress sensing ; smart insoles ; smart shoes ; unobtrusive sensing ; stress ; center of pressure ; regression ; signal processing ; arousal ; aging adults ; musical genres ; emotion elicitation ; dataset ; emotion representation ; feature selection ; feature extraction ; computer science ; virtual reality ; head-mounted display ; n/a ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
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