142 results on 'LN cat08778a'
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2. Clean Ruby: A Guide to Crafting Better Code for Rubyists#
- Author
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Carleton DiLeo
- Subjects
Internet of things ,Machine learning ,Electronic books - Published
- 2020
3. Python machine learning cookbook : over 100 recipes to progress from smart data analytics to deep learning using real-world datasets.
- Author
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Ciaburro, Giuseppe and Joshi, Prateek
- Subjects
Python (Computer program language) ,Machine learning ,Electronic books - Published
- 2019
4. Business analytics.
- Author
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Sahay, Amar
- Subjects
Management -- Statistical methods ,Decision making -- Statistical methods ,Business planning ,Strategic planning ,Business intelligence ,BUSINESS & ECONOMICS -- Industrial Management ,BUSINESS & ECONOMICS -- Management ,BUSINESS & ECONOMICS -- Management Science ,BUSINESS & ECONOMICS -- Organizational Behavior ,Electronic books ,analytics ,business analytics ,business intelligence ,data analysis ,data mining ,decision making ,descriptive analytics ,machine learning ,modeling ,neural networks ,optimization ,predictive analytics ,predictive modeling ,prescriptive analytics ,quantitative techniques ,regression analysis ,simulation ,statistical analysis ,time-series forecasting - Abstract
Abstract: This book is about Business Analytics (BA)--an emerging area in modern business decision making. The first part provides an overview of the field of Business Intelligence (BI) that looks into historical data to better understand business performance thereby improving performance, and creating new strategic opportunities for growth. Business analytics (BA) is about anticipated future trends of the key performance indicators used to automate and optimize business processes. The three major categories of business analytics--the descriptive, predictive, and prescriptive analytics along with advanced analytics tools are explained. The flow diagrams outlining the tools of each of the descriptive, predictive, and prescriptive analytics are presented. We also describe a number of terms related to business analytics. The second part of the book is about descriptive analytics and its applications. The topics discussed are--Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically--Concepts and Computer Applications. Finally, an overview and a case on descriptive statistics with applications and notes on implementation are presented. The concluding remarks provide information on becoming a certified analytics professional (CAP) and an overview of the second volume of this book which is a continuation of this first volume. It is about predictive analytics which is the application of predictive models to predict future trends. The second volume discusses Prerequisites for Predictive Modeling; Most Widely used Predictive Analytics Models, Linear and Non-linear regression, Forecasting Techniques, Data mining, Simulation, and Data Mining.
- Published
- 2018
5. Business analytics.
- Author
-
Sahay, Amar
- Subjects
Management -- Statistical methods ,Decision making -- Statistical methods ,Business planning ,Strategic planning ,Business intelligence ,BUSINESS & ECONOMICS -- Industrial Management ,BUSINESS & ECONOMICS -- Management ,BUSINESS & ECONOMICS -- Management Science ,BUSINESS & ECONOMICS -- Organizational Behavior ,Electronic books ,analytics ,business analytics ,business intelligence ,data analysis ,data mining ,decision making ,descriptive analytics ,machine learning ,modeling ,neural networks ,optimization ,predictive analytics ,predictive modeling ,prescriptive analytics ,quantitative techniques ,regression analysis ,simulation ,statistical analysis ,time-series forecasting - Abstract
Abstract: This book is about Business Analytics (BA)--an emerging area in modern business decision making. The first part provides an overview of the field of Business Intelligence (BI) that looks into historical data to better understand business performance thereby improving performance, and creating new strategic opportunities for growth. Business analytics (BA) is about anticipated future trends of the key performance indicators used to automate and optimize business processes. The three major categories of business analytics--the descriptive, predictive, and prescriptive analytics along with advanced analytics tools are explained. The flow diagrams outlining the tools of each of the descriptive, predictive, and prescriptive analytics are presented. We also describe a number of terms related to business analytics. The second part of the book is about descriptive analytics and its applications. The topics discussed are--Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically--Concepts and Computer Applications. Finally, an overview and a case on descriptive statistics with applications and notes on implementation are presented. The concluding remarks provide information on becoming a certified analytics professional (CAP) and an overview of the second volume of this book which is a continuation of this first volume. It is about predictive analytics which is the application of predictive models to predict future trends. The second volume discusses Prerequisites for Predictive Modeling; Most Widely used Predictive Analytics Models, Linear and Non-linear regression, Forecasting Techniques, Data mining, Simulation, and Data Mining.
- Published
- 2018
6. Hands-on supervised machine learning with Python. [electronic resource]
- Subjects
Python (Computer program language) ,Machine learning ,Instructional films - Abstract
Summary: Teach your machine to think for itself! About This Video: Take a deep dive into supervised learning and grasp how a machine "learns" from data. Follow detailed and thorough coding examples to implement popular machine learning algorithms from scratch, developing a deep understanding along the way. Work your Python muscle! This course will help you grow as a developer by heavily relying on some of the most popular scientific and mathematical libraries in the Python language. In Detail: Supervised machine learning is used in a wide range of industries across sectors such as finance, online advertising, and analytics, and it's here to stay. Supervised learning allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more, while allowing the system to self-adjust and make decisions on its own. This makes it crucial to know how a machine "learns" under the hood.This course will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You'll embark on this journey with a quick course overview and see how supervised machine learning differs from unsupervised learning. Next, we'll explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you'll wrap up with a brief foray into neural networks and transfer learning. By the end of the video course, you'll be equipped with hands-on techniques to gain the practical know-how needed to quickly and powerfully apply these algorithms to new problems. All the codes of the course are uploaded on GitHub.
- Published
- 2018
7. Fundamentals of deep learning : designing next-generation machine intelligence algorithms.
- Author
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Buduma, Nikhil and Locascio, Nicholas
- Subjects
Artificial intelligence ,Machine learning ,Neural networks (Computer science) ,Deep learning ,Künstliche Intelligenz ,Maschinelles Lernen ,Electronic books - Published
- 2017
8. A First Course in Machine Learning.
- Author
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Rogers, Simon and Girolami, Mark
- Subjects
Machine learning ,COMPUTERS -- General ,Data Mining ,Maschinelles Lernen ,Machine Learning ,Electronic books - Published
- 2016
9. Learning Spark : lightening fast data analysis.
- Author
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Karau, Holden
- Subjects
ApacheSpark ,Big data ,Machine learning ,Electronic books - Abstract
Summary: This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. You'll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning.
- Published
- 2015
10. Kernel methods and machine learning.
- Author
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Kung, S. Y.
- Subjects
Support vector machines ,Machine learning ,Kernel functions ,COMPUTERS / Computer Vision & Pattern Recognition - Abstract
Summary: "Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors"-- Provided by publisher.
- Published
- 2014
11. Machine learning in action.
- Author
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Harrington, Peter
- Subjects
Machine learning ,Machine learning -- Handbooks, manuals, etc ,Engineering & Applied Sciences ,Computer Science ,Electronic book ,Electronic books ,Handbooks and manuals - Abstract
Summary: Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About this Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos About the Author Peter Harrington is a professional developer and data scientist. He holds five US patents and his work has been published in numerous academic journals.
- Published
- 2012
12. Machine learning in action.
- Author
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Harrington, Peter
- Subjects
Machine learning ,Machine learning -- Handbooks, manuals, etc ,Engineering & Applied Sciences ,Computer Science ,Electronic book ,Electronic books ,Handbooks and manuals - Abstract
Summary: Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About this Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos About the Author Peter Harrington is a professional developer and data scientist. He holds five US patents and his work has been published in numerous academic journals.
- Published
- 2012
13. Ensembles in machine learning applications.
- Author
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Okun, Oleg, Valentini, Giorgio, and Re, Matteo
- Subjects
Machine learning ,Set theory - Published
- 2011
14. (Mass Spectrometric) Non Target Screening-Techniques and Strategies.
- Author
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Letzel, Thomas and Letzel, Thomas
- Subjects
Ganoderma lingzhi ,developmental stages ,untargeted metabolomics ,GC/MS ,LC/IT-TOF-MS ,α-glucosidase inhibitory activity ,azoxystrobin ,glutathione ,glutathione conjugate ,tea ,metabolomics ,software ,database ,MS subtraction ,spectral deconvolution ,2DGC ,volatilomics ,amino acids ,equation ,HPLC ,MS/MS ,NTS techniques (separation, ionization, and detection) ,nucleosides ,open access software ,target gas ,triple quadrupole ,mass spectrometry ,non-target screening ,ultraviolet photodissociation ,higher-energy collisional dissociation ,organic micropollutants ,water quality ,small molecule fragmentation ,cheminformatics ,data analysis ,furan ,2-methylfuran ,UPLC-qToF ,untargeted analysis ,urinary metabolites ,Ionization ,quantification ,decision making ,NTS strategies ,gas chromatography ion mobility spectroscopy (GC-IMS) ,volatile organic compounds (VOCs) ,non-targeted screening (NTS) using machine learning ,GC-API ,GC-APCI ,GC-APLI ,GC-APPI ,GC-MS ,persistent organic pollutants ,nontargeted screening ,computational mass spectrometry ,emerging contaminants ,high-resolution mass spectrometry ,micropollutant fingerprint ,solid phase extraction ,statistical analysis ,urban waters ,glycomics ,glycoproteomics ,glycosylation ,proteomics ,in silico docking ,network pharmacology ,non-small cell lung cancer ,marker compounds ,non-targeted screening ,pharmaceutical and personal care products ,plant-derived food ,collision cross section ,ion mobility spectrometry ,machine learning ,lipidomics ,review ,analytical ,corticosteroids ,NSAIDs - Abstract
Summary: (Mass spectrometric) non-target screening is a preferably comprehensive and untargeted (predominantly organic molecules detecting) approach combining (robust) analytical measurements with adapted data evaluation concepts, systematic compound identification workflows, and statistical data interpretation. It is well suitable for the identification of new, unexpected and/or unknown organic compounds as well as monitoring 'molecular fingerprints' and profiling 'process-relevant' molecules via statistical methods. In recent years, 14 articles in various disciplines were published and presented in this Special Issue, whereby it contains 4 peer-reviewed review articles and 10 peer-reviewed research articles dealing with non-target screening strategies and solutions.
15. Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments.
- Author
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Woźniak, Marcin and Woźniak, Marcin
- Subjects
Information technology industries ,Traffic sign detection and tracking (TSDR) ,advanced driver assistance system (ADAS) ,computer vision ,3D convolutional neural networks ,machine learning ,CT brain ,brain hemorrhage ,visual inspection ,one-class classifier ,grow-when-required neural network ,evolving connectionist systems ,automatic design ,bio-inspired techniques ,artificial bee colony ,image analysis ,feature extraction ,ship classification ,marine systems ,citrus ,pests and diseases identification ,convolutional neural network ,parameter efficiency ,vehicle detection ,YOLOv2 ,focal loss ,anchor box ,multi-scale ,deep learning ,neural network ,generative adversarial network ,synthetic images ,tool wear monitoring ,superalloy tool ,image recognition ,object detection ,UAV imagery ,vehicular traffic flow detection ,vehicular traffic flow classification ,vehicular traffic congestion ,video classification ,benchmark ,semantic segmentation ,atrous convolution ,spatial pooling ,ship radiated noise ,underwater acoustics ,surface electromyography (sEMG) ,convolution neural networks (CNNs) ,hand gesture recognition ,fabric defect ,mixed kernels ,cross-scale ,cascaded center-ness ,deformable localization ,continuous casting ,surface defects ,3D imaging ,defect detection ,object detector ,object tracking ,activity measure ,Yolo ,deep sort ,Hungarian algorithm ,optical flows ,spatiotemporal interest points ,sports scene ,CT images ,convolutional neural networks ,hepatic cancer ,visual question answering ,three-dimensional (3D) vision ,reinforcement learning ,human-robot interaction ,few shot learning ,SVM ,CNN ,cascade classifier ,video surveillance ,RFI ,artefacts ,InSAR ,image processing ,pixel convolution ,thresholding ,nearest neighbor filtering ,data acquisition ,augmented reality ,pose estimation ,industrial environments ,information retriever sensor ,multi-hop reasoning ,evidence chains ,complex search request ,high-speed trains ,hunting ,non-stationary ,feature fusion ,multi-sensor fusion ,unmanned aerial vehicles ,drone detection ,UAV detection ,visual detection ,n/a - Abstract
Summary: Recent years have seen a vast development in various methodologies for object detection and feature extraction and recognition, both in theory and in practice. When processing images, videos, or other types of multimedia, one needs efficient solutions to perform fast and reliable processing. Computational intelligence is used for medical screening where the detection of disease symptoms is carried out, in prevention monitoring to detect suspicious behavior, in agriculture systems to help with growing plants and animal breeding, in transportation systems for the control of incoming and outgoing transportation, for unmanned vehicles to detect obstacles and avoid collisions, in optics and materials for the detection of surface damage, etc. In many cases, we use developed techniques which help us to recognize some special features. In the context of this innovative research on computational intelligence, the Special Issue "Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments" present an excellent opportunity for the dissemination of recent results and achievements for further innovations and development. It is my pleasure to present this collection of excellent contributions to the research community. - Prof. Marcin Woźniak, Silesian University of Technology, Poland -
16. Advanced Signal Processing in Wearable Sensors for Health Monitoring.
- Author
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Abbod, Maysam, Shieh, Jiann-Shing, and Abbod, Maysam
- Subjects
Technology: general issues ,History of engineering & technology ,automated dietary monitoring ,eating detection ,eating timing error analysis ,biomedical signal processing ,smart eyeglasses ,wearable health monitoring ,artificial neural network ,joint moment prediction ,extreme learning machine ,Hill muscle model ,online input variables ,Review ,ECG ,Signal Processing ,Machine Learning ,Cardiovascular Disease ,Anomaly Detection ,photoplethysmography ,motion artifact ,independent component analysis ,multi-wavelength ,continuous arterial blood pressure ,systolic blood pressure ,diastolic blood pressure ,deep convolutional autoencoder ,genetic algorithm ,electrocardiography ,vectorcardiography ,myocardial infarction ,long short-term memory ,spline ,multilayer perceptron ,pain detection ,stress detection ,wearable sensor ,physiological signals ,behavioral signals ,non-invasive system ,hemodynamics ,arterial blood pressure ,central venous pressure ,pulmonary arterial pressure ,intracranial pressure ,heart rate measurement ,remote HR ,remote PPG ,remote BCG ,blind source separation ,drowsiness detection ,EEG ,frequency-domain features ,multicriteria optimization ,machine learning ,n/a - Abstract
Summary: Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods.
17. Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics.
- Author
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Gou, Jianping, Ou, Weihua, Zeng, Shaoning, Du, Lan, and Gou, Jianping
- Subjects
Information technology industries ,Computer science ,head detection ,YoloV4 ,NMS ,soft-NMS ,people counting ,vehicle re-identification ,license plate recognition ,video surveillance ,feature extraction ,pedestrian detection ,machine learning ,end-to-end ,anchor-free ,feature reuse ,correlation filters ,second-order fitting ,visual tracking ,DCNN-BiLSTM ,domain adaptation ,MMD ,fine-tuning ,C-MAPSS ,cross-working ,small sample ,blind image deblurring ,image prior ,sparse channel ,sparsity ,multi-output ,kNN ,metric learning ,cost-weighted ,geometric mean metric ,motion deblurring ,image super-resolution ,multi-order attention ,gated learning ,decoupling ,face recognition ,second-order gradient ,image gradient orientations ,collaborative-representation-based classification ,image aesthetic assessment ,semi-supervised learning ,label propagation ,deep learning ,computer vision ,garbage quantity identification ,YOLOX ,Soft-NMS ,stability ,switched system ,state-dependent switching ,time delay ,multi-source domain adaptation ,Dempster-Shafer evidence theory ,cross-domain classification ,3D reconstruction ,multi-view stereo ,structure from motion ,background matting ,adversarial example ,feature transformation ,black-box attack ,ensemble attack ,deep neural network ,intelligent design ,data analysis ,models and algorithms ,extension theory ,scheme design ,adversarial learning ,adversarial equilibrium ,transferability quantification ,power load forecasting ,routing, modulation and spectrum assignment ,elastic optical networks ,deep reinforcement learning ,knowledge distillation ,aspect-based sentiment analysis ,graph neural networks ,dependency trees ,dependency types ,graph attention mechanism ,syntactic ,semantic ,vehicle color recognition ,low-high level joint task ,object detection ,joint semantic learning ,rainy image recovery ,XSS attack ,traffic detection ,payloads ,fusion verification ,hypergraph matching ,similarity metric ,information-theoretic metric learning ,mixed noise removal ,matrix nuclear norm ,logarithm norm ,ADMM ,plug-and-play ,aspect-level sentiment classification ,external knowledge ,KGE ,GCN ,discriminative feature learning ,multidimensional scaling ,fuzzy k-means ,pairwise constraint propagation ,iterative majorization algorithm ,Aspect Level Sentiment Classification ,Contrasitve Learning ,Graph Convolutional Networks ,graph convolutional networks ,commonsense knowledge graph ,anomaly detection ,cyber-physical ,industrial control systems ,image classification ,large-margin technique ,robustness ,anti-noise performance ,cross-domain sentiment classification ,word embedding ,GAT ,hate speech detection ,contrastive learning ,multi-task learning ,attention mechanism ,state reconstruction ,gait adjustment ,uncertain temporal knowledge graph ,temporal knowledge graph ,knowledge graph embedding ,confidence score ,n/a - Abstract
Summary: The present reprint contains 33 articles accepted and published in the Special Issue entitled "Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics, 2022" in the MDPI journal, Mathematics, which covers a wide range of topics connected to the theory and applications of feature representation learning for image processing, artificial intelligence, data mining and robotics. These topics include, among others, elements from image blurring, image aesthetic quality assessment, pedestrian detection, visual tracking, vehicle re-identification, face recognition, 3D reconstruction, the stability of switched systems, domain adaption, deep reinforcement, sentiment analysis, graph convolutional networks, knowledge graphs, geometric metric learning, etc. It is hoped that this reprint will be interesting and useful for those working in the area of image processing, computer vision, machine learning, natural language processing and robotics, as well as for those with backgrounds in machine learning who are willing to become familiar with recent advancements in artificial intelligence, which, today, is present in almost all aspects of human life and activities.
18. Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education.
- Author
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Lacave, Carmen, Molina, Ana Isabel, and Lacave, Carmen
- Subjects
Research & information: general ,Mathematics & science ,Alzheimer's disease ,dementia ,functional data analysis ,functional depth ,statistical data depth ,symmetry ,elemental information matrix ,gamma distribution ,poisson distribution ,D-optimization ,misspecification ,machine learning ,modeling ,programming ,text analysis ,remote rehabilitation ,recommender system ,stroke ,fuzzy logic ,telemedicine ,collaborative learning ,collaborative work ,genetic algorithms ,group formation ,personality traits ,computer-supported cooperative learning ,non-parametric statistics ,predictive methods ,supervised classification ,random methods ,after-school exercise ,academic performance ,structural relationship ,quantile regression ,instrumental variable quantile regression ,vitamin D ,decision making ,anthropometric parameters ,optimal experimental design ,bioimpedance ,impedance spectroscopy ,algorithm ,competency-based model ,didactic planning ,ontology ,natural language processing ,Bloom's taxonomy ,retina ,fundus image ,retinal vasculature ,retinal disorders ,semantic segmentation ,learning behavior ,student performance prediction ,deep neural network (DNN) ,recurrent neural network (RNN) ,educational data mining (EDM) ,probabilistic graphical models ,bayesian networks ,value-based potentials ,approximate inference ,medical applications ,electrocardiogram signal ,discriminative convolutional sparse coding ,dictionary filter learning ,linear SVM ,student dropout ,Feature Selection ,Artificial Neural Networks ,Support Vector Machines ,decision trees ,logistic regression ,ECG ,mental fatigue ,signal analysis ,classification ,OpenMarkov ,Bayesian Networks ,d-separation ,inference ,Learning Bayesian Networks ,continuous assessment ,Bayesian networks ,artificial neural networks ,influenza-like illness ,COVID-19 ,Arabic sentiment analysis ,disease classification ,Facebook ,Algerian dialect ,mobile computing ,dual tasking ,cognitive decline ,human motion tracking ,gait analysis ,n/a - Abstract
Summary: The present reprint contains all of the articles accepted and published in the Special Issue " Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education" from the MDPI journal Mathematics. This Special Issue aims to develop more efficient and effective approaches to healthcare and education, leveraging the increasing availability of big data and advancements in artificial intelligence. By sharing new methods, applications, and case studies, this reprint is dedicated to the development of innovative solutions that improve healthcare and education for all. The topics addressed in this Special Issue cover a wide range of areas, including data mining, machine learning, learning analytics, prediction methods, pattern recognition, decision analysis, probabilistic reasoning, fuzzy systems, student or patient modelling, adaptive systems, collaborative systems, recommendation systems, experimental design, and empirical study cases. We hope that this reprint will enable the scientific community in both medicine and education to leverage the techniques from statistics and artificial intelligence to drive significant advances in their respective fields. These approaches hold promise for improving patient outcomes and enhancing the quality of education for students around the world.
19. Advances in Asphalt Pavement Technologies and Practices.
- Author
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Tabakovic, Amir, Valentin, Jan, He, Liang, and Tabakovic, Amir
- Subjects
Technology: general issues ,History of engineering & technology ,n/a ,steel slag aggregate ,warm mix asphalt ,RSM ,stiffness modulus ,dynamic creep ,moisture sensitivity ,pavement ,porous asphalt mixture ,low temperature performance ,orthogonal experimental design ,porosity ,cigarette filters ,recycled cigarette butts ,asphalt concrete ,SMA ,fibers ,Trinidad lake asphalt ,aged asphalt ,hot-mix recycled asphalt mixture ,reclaimed asphalt pavement ,high-modulus asphalt mixture ,Grouted Semi-flexible Pavement ,influential factor ,evaluation method ,performance ,laboratory test ,hot mix asphalt ,compaction quality control ,artificial neural networks ,degree of compaction ,vibration ,reflection crack ,numerical analysis ,extended finite element method ,J-integral ,stress intensity factors ,multiple aging and rejuvenation cycles ,SBS modified asphalt ,morphological properties ,DSR ,FTIR ,AFM ,moisture damage ,random forest ,machine learning ,factor importance ,prediction ,high and low temperature performance ,infrared spectroscopy ,additives ,orthogonal test ,bituminous mixtures ,neural network ,Bayesian optimization ,asphalt mixture ,master curve ,characteristic parameter ,relaxation characteristics ,cold recycling ,foamed asphalt ,emulsified asphalt ,asphalt pavements ,self-healing ,hybrid self-healing system ,induction heating ,indirect tensile strength ,water sensitivity ,wheel tracking rutting resistance ,International Roughness Index ,pavement condition evaluation ,correlation ,Saudi Arabia ,flexible pavement ,pavement maintenance ,management system ,smartphone sensors ,Google Earth ,resilient modulus ,pavement design ,unbound granular material (UGM) ,cyclic triaxial test (CTT) ,non-destructive testing ,bearing capacity ,falling weight deflectometer (FWD) ,base layers ,subbase layers ,rutting performance prediction model ,long-term observation data ,full-scale pavement structure ,model accuracy evaluation ,local correction coefficient ,rutting prediction ,moisture ,interface cracking ,digital image correlation (DIC) ,molecular dynamics ,crumb rubber asphalt (CR) ,bitumen ,coarse-grained ,force field ,asphalt pavement ,construction control ,toposable set theory ,comprehensive evaluation - Abstract
Summary: Unlike other construction materials, road materials have developed minimally over the past 100 years. However, since the 1970s, the focus has been on more sustainable road construction materials such as recycled asphalt pavements. Recycling asphalt involves removing old asphalt and mixing it with new (fresh) aggregates, binders, and/or rejuvenators. Similarly, there are various efforts to use alternative modifiers and technical solutions such as crumb rubber, plastics, or various types of fibres. For the past two decades, researchers have been developing novel materials and technologies, such as self-healing materials, in order to improve road design, construction, and maintenance efficiency and reduce the financial and environmental burden of road construction. This Special Issue on "Advances in Asphalt Pavement Technologies and Practices" curates advanced/novel work on asphalt pavement design, construction, and maintenance. The Special Issue comprises 19 papers describing unique works that address the current challenges that the asphalt industry and road owners face.
20. Advances in Autism Research.
- Author
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Narzisi, Antonio and Narzisi, Antonio
- Subjects
Medicine ,Neurosciences ,autism spectrum disorder ,infants ,frontal EEG alpha asymmetry ,early detection ,autism spectrum disorders ,toddlers ,eye tracking ,joint attention ,longitudinal ,regression ,cytokines ,PAI-1 ,neuroinflammation ,gastrointestinal ,autism ,literature review ,comorbidity ,early intervention ,early intensive behavioral intervention ,behavioral intervention ,First Year Inventory ,early screening ,risk ,cross-cultural generalisability ,validity ,preschool teachers ,self-efficacy ,knowledge ,belief ,skills ,identify ,autism spectrum disorder (ASD) ,level 1 and level 2 screening tools ,systematic review ,COSMIN ,PRISMA ,screening ,infection ,prion ,meta-analysis ,motion analysis ,video signal processing ,neurodevelopmental disorders ,infant screening ,n/a ,developmental language disorder ,semantic features ,word learning ,central coherence ,biomarker ,p-cresol ,mouse social behavior ,dopamine ,ASD ,vision ,proprioception ,self-motion ,immersive virtual reality ,IVR ,HMD ,technology ,persuasive text writing ,perspective-taking ,adolescence ,intervention ,sign language ,imitation ,cognition ,language acquisition ,prevalence estimate ,predictors ,surveillance review ,Autism Spectrum Disorder (ASD) ,early intensive intervention ,developmental trajectories ,moderators and mediators of intervention ,psychosis ,schizophrenia ,psychopathology ,AQ ,accuracy ,attention to detail ,self-awareness ,insight ,preconception risk factor ,Gilles de la Tourette ,obsession ,compulsion ,social behavior ,social impairment ,sensory profile ,sensory responsiveness ,feeding problems ,short sensory profile (SSP) ,sensory experience questionnaire (SEQ) ,coronavirus ,2019-nCoV ,neurodevelopment ,child and adolescent psychiatry ,mental health prevention ,Asperger syndrome ,adults ,cerebrospinal fluid ,antibodies ,blood-brain barrier ,GAD65 ,Early Start Denver Model ,high-risk infants ,motor development ,high-functioning autism ,language ,experience ,communication ,autonomic nervous system ,wearable technologies ,EEG ,theory of mind ,adults and adolescents ,human figure drawings ,Draw-a-Man ,drawings maturity ,social perception ,ERP ,reward response ,RewP ,sensitization ,social skills intervention ,PEERS® ,adulthood ,diagnosis ,autistic traits ,action observation ,action prediction ,context ,priors ,hypothalamus ,amygdala ,oxytocin ,social cognition ,social interaction ,affiliative behavior ,neuroimaging ,COVID-19 ,challenging behavior ,dental care ,oral health ,medical procedures ,ICT ,wearable sensors ,migration ,Europe ,health system ,autism in adulthood ,intellectual disability ,regressive autism ,epilepsy ,challenging behaviors ,empathy ,executive functions ,attention deficit and hyperactivity disorder ,disruptive behavior disorders ,alexithymia ,anxiety ,depression ,TAS-20 ,TSIA ,parents ,broader autism phenotype ,autistic-like features ,social-cognitive development ,stereotypical behaviors ,visual impairment ,language profiles ,grammatical comprehension ,cannabinoids ,cannabidiol ,cannabidivarin ,THC ,problem behaviors ,sleep ,hyperactivity ,side effects ,motor performance skills ,Gulf ,BOT-2 ,machine learning ,employment ,telehealth ,ABA ,RCT ,cortisol ,group activity ,stress ,art ,assessment ,sensorimotor integration ,postural balance ,false positive report probability (FPRP) ,Bayesian false-discovery probability (BFDP) ,Genome-Wide Association Studies (GWAS) ,microbiome ,metabolomics ,study design ,biomarker discovery ,precise medicine ,bipolar disorder ,suicidal ideation ,suicidal attempts - Abstract
Summary: This book represents one of the most up-to-date collections of articles on clinical practice and research in the field of Autism Spectrum Disorders (ASD). The scholars who contributed to this book are experts in their field, carrying out cutting edge research in prestigious institutes worldwide (e.g., Harvard Medical School, University of California, MIND Institute, King's College, Karolinska Institute, and many others). The book addressed many topics, including (1) The COVID-19 pandemic; (2) Epidemiology and prevalence; (3) Screening and early behavioral markers; (4) Diagnostic and phenotypic profile; (5) Treatment and intervention; (6) Etiopathogenesis (biomarkers, biology, and genetic, epigenetic, and risk factors); (7) Comorbidity; (8) Adulthood; and (9) Broader Autism Phenotype (BAP). This book testifies to the complexity of performing research in the field of ASD. The published contributions underline areas of progress and ongoing challenges in which more certain data is expected in the coming years. It would be desirable that experts, clinicians, researchers, and trainees could have the opportunity to read this updated text describing the challenging heterogeneity of Autism Spectrum Disorder.
21. Advances in Computer-Aided Technology.
- Author
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Kočiško, Marek, Pollák, Martin, and Kočiško, Marek
- Subjects
Language ,tensor glyph ,golden section ,vector space ,sandwich ,springback ,Vegter yield criterion ,numerical simulation ,PAM-STAMP 2G ,isotropic hardening law ,kinematic hardening law ,bending ,Bauschinger effect ,machine learning ,artificial neural network ,additive manufacturing ,high precision metrology ,CAD ,predictive model ,ship hull structure ,computer-aided design of structure ,database ,function soft block ,gun drill tool ,deep-drilling technology ,optimization ,tool life ,angle ,digital implant impression ,interimplant distance ,intraoral scanner ,trueness ,sewing machine ,needle bar ,floating needle ,electromagnet ,electromagnetic simulation ,noise reduction ,cycloidal gearbox ,friction ,actuator ,servomotor ,permanent magnet synchronous machine ,fixture design ,machining ,sustainable manufacturing ,process innovation ,complex-shape part ,signal processing ,monitoring system ,laser profiler ,surface roughness ,quality assessment ,non-contact method ,vision-based method ,frequency analysis ,abrasive water jet ,wood plastic composite ,natural reinforcement ,knitting machine ,stroke ,drive ,simulation ,cylinder ,dynamic modeling ,load spectrum reconstruction ,fatigue test ,hydraulic excavator ,n/a - Abstract
Summary: This book reprints articles from the Special Issue "Advances in Computer-Aided Technology" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of thirteen published articles. This Special Issue belongs to the "Mechatronic and Intelligent Machines" section. Industry 4.0 is characterized by the integration of advanced technologies, such as artificial intelligence, the Internet of Things, and cloud computing, into traditional manufacturing and production processes. CAx (Computer-Aided Systems) systems are a set of computer software tools used in engineering and product design, covering various stages of the product development cycle. Advanced CAx tools combine many different aspects of product lifecycle management (PLM), including design, finite element analysis (FEA), manufacturing, production planning and product. In connection with the transition to Industry 4.0 concepts, the concept of the digital twin comes to the fore, and existing CAx systems must adapt to this trend. The Special Issue deals with a number of research areas, such as: - New trends in CAx systems; Digital manufacturing; Internet of Things in manufacturing; Simulation of production systems and processes; Systems for advanced finite element analysis; Material engineering; Digitization and 3D scanning.
22. Advances in Intelligent Vehicle Control.
- Author
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Cabrera, Juan A. and Cabrera, Juan A.
- Subjects
Technology: general issues ,History of engineering & technology ,nonlinear height control ,active air suspension ,output constraints ,random road excitation ,disturbance observer design ,electric vehicles ,in-vehicle network ,controller area network ,cybersecurity ,intrusion detection ,deep learning ,transfer learning ,model-based control ,vehicle dynamic potential ,tyre thermodynamics ,tyre wear ,weather influence ,vehicle safety ,double lane change ,safety optimization ,noninverting buck-boost converter ,high efficiency ,wide bandwidth control ,discrete-time sliding-mode current control (DSMCC) ,electric vehicle (EV) ,driver vehicle system ,energy management ,vehicle localization ,GNSS receivers ,RTK corrections ,sensor redundancy ,VMS ,machine learning ,ADAS ,image processing ,environment perception ,semantics ,3D multiple object detection ,multiple object tracking ,dynamic SLAM ,roll angle estimator ,Kalman filter ,LQR controller ,inertial sensors ,motorcycle lean angle ,electrical vehicles ,EV charging scheduling ,binary linear programming ,binary quadratic programming ,vehicle control ,reinforcement learning ,curriculum learning ,sim-to-real world ,intelligent mobility ,heterogeneous vehicular communication ,Internet of connected vehicles ,vehicular ad hoc networks ,heterogeneous networking ,Internet of Things ,n/a - Abstract
Summary: This book is a printed edition of the Special Issue Advances in Intelligent Vehicle Control that was published in the journal Sensors. It presents a collection of eleven papers that covers a range of topics, such as the development of intelligent control algorithms for active safety systems, smart sensors, and intelligent and efficient driving. The contributions presented in these papers can serve as useful tools for researchers who are interested in new vehicle technology and in the improvement of vehicle control systems.
23. Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems.
- Author
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Calvo, Roque, Yagüe-Fabra, José A., Tosello, Guido, and Calvo, Roque
- Subjects
Technology: general issues ,History of engineering & technology ,Industry 4.0 ,continuous improvement ,lean manufacturing ,lean production systems ,lean 4.0 ,systematic literature review ,tiny RFID ,metallic electromagnetic isolation ,analog manometer ,Ansys HFSS simulator ,passive digitization ,ontology ,production simulation ,multi-agent ,digital twin ,augmented reality ,industry 4.0 ,quality 4.0 ,metrology ,assembly ,turbine blades ,re-manufacturing ,uncertainty ,robust scheduling ,case-based reasoning ,failure mode ,effect and criticality analysis ,knowledge-based system ,nearest neighbor ,reliability-centered maintenance ,virtual reality ,cyber physical system ,OPC UA ,CAD ,industrial IoT ,prototyping ,retrofitting solutions ,embedded solutions ,low-cost ,value chain ,additive manufacturing ,subtractive manufacturing ,cost comparison ,plant simulation ,technology comparison ,hybrid dependability modelling ,production scheduling ,dynamic failure rate ,discrete event simulation ,time-driven simulation ,machine learning ,manufacturing ,artificial intelligence ,smart manufacturing ,digitization ,smart surface ,friction force field ,under-actuation ,feeding ,simulation ,material flow handling ,intralogistics ,robotic bin-picking ,simulation model ,ADAMS ,pick-point determination ,MATLAB/Simulink ,2-F robotic gripper ,performance analysis ,plastic injection molding ,design of experiments ,process optimization ,facility layout problem ,evolutionary algorithms ,numerical modeling ,multi-objective optimization ,n/a - Abstract
Summary: The book is the reprint of the Special Issue 'Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems' published in the journal Applied Science (MDPI). It contains 17 articles, including 1 Editorial, 14 Research Papers, and 2 Reviews.
24. AI and Financial Markets.
- Author
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Hamori, Shigeyuki, Takiguchi, Tetsuya, and Hamori, Shigeyuki
- Subjects
Economics, finance, business & management ,algorithmic trading ,Stop Loss ,Turtle ,ATR ,community finances ,fiscal flexibility ,individualized financial arrangements ,sustainable financial services ,price momentum ,hidden markov model ,asset allocation ,blockchain ,BlockCloud ,Artificial Intelligence ,consensus algorithms ,exchange rates ,fundamentals ,prediction ,random forest ,support vector machine ,neural network ,deep reinforcement learning ,financial market simulation ,agent based simulation ,artificial market ,simulation ,CAR regulation ,portfolio ,contract for difference ,CfD ,reinforcement learning ,RL ,neural networks ,long short-term memory ,LSTM ,Q-learning ,deep learning ,uncertainty ,economic policy ,text mining ,topic model ,yield curve ,term structure of interest rates ,machine learning ,autoencoder ,interpretability - Abstract
Summary: Artificial intelligence (AI) is regarded as the science and technology for producing an intelligent machine, particularly, an intelligent computer program. Machine learning is an approach to realizing AI comprising a collection of statistical algorithms, of which deep learning is one such example. Due to the rapid development of computer technology, AI has been actively explored for a variety of academic and practical purposes in the context of financial markets. This book focuses on the broad topic of "AI and Financial Markets", and includes novel research associated with this topic. The book includes contributions on the application of machine learning, agent-based artificial market simulation, and other related skills to the analysis of various aspects of financial markets.
25. AI and Financial Technology.
- Author
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Giudici, Paolo, Papenbrock, Jochen, Schwendner, Peter, Hochreiter, Ronald, Osterrieder, Joerg, and Giudici, Paolo
- Subjects
Science: general issues ,Computer science ,FinTech ,SupTech ,RegTech ,AI ,machine learning ,P2P lending ,Blockchain - Abstract
Summary: 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
26. AI AND THE SINGULARITY. A FALLACY OR A GREAT OPPORTUNITY?
- Author
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Logan, Robert K., Braga, Adriana, and Logan, Robert K.
- Subjects
Information technology industries ,technological Singularity ,intelligence ,emotion ,artificial general intelligence ,artificial intelligence ,computer ,logic ,figure/ground ,computers ,consciousness ,singularity ,self ,futures ,technological singularity ,philosophy ,cosmic evolution ,anthropology ,technical singularity ,non-axiomatic reasoning system ,metasystem transitions ,patterns ,patterning ,cognition ,set theory ,language ,information ,abductive reasoning ,futurism and futurology ,hard science fiction ,models of consciousness ,intelligent machines ,machine replication ,machine evolution and optimization ,Turing test ,embodiment ,competition ,cooperation ,self-organization ,robots ,heterogeneity ,team sports ,artificial intelligence (AI) ,automated journalism ,robo-journalism ,writing algorithms ,future of news ,media ecology ,autogenous intelligence ,bootstrap fallacy ,recursive self-improvement ,self-modifying software ,superintelligence ,skepticism ,cyborg ,evolution ,love ,misinformation ,Technological Singularity ,Accelerated Change ,Artificial (General) Intelligence ,apophenia ,pareidolia ,complexity ,research focused social network ,networked minds ,complexity break ,complexity fallacy ,philosophy of information ,machine learning ,information quality ,information friction ,Artificial Intelligence (AI) ,Artificial General Intelligence (AGI) ,Artificial Social Intelligence (ASI) ,social sciences ,embodied cognition ,value alignment ,experience ,phenomenal consciousness ,access consciousness ,percept ,concept ,deep neural networks ,meaning ,understanding ,Singularity ,intuition ,wisdom - Abstract
Summary: "AI and the Technological Singularity: A Fallacy or a Great Opportunity" is a collection of essays that addresses the question of whether the technological singularity-the notion that AI-based computers can program the next generation of AI-based computers until a singularity is achieved, where an AI-based computer can exceed human intelligence-is a fallacy or a great opportunity. The group of scholars that address this question have a variety of positions on the singularity, ranging from advocates to skeptics. No conclusion can be reached, as the development of artificial intelligence is still in its infancy, and there is much wishful thinking and imagination in this issue rather than trustworthy data. The reader will find a cogent summary of the issues faced by researchers who are working to develop the field of artificial intelligence and, in particular, artificial general intelligence. The only conclusion that can be reached is that there exists a variety of well-argued positions as to where AI research is headed.
27. AI Applications to Power Systems.
- Author
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Tjing Lie, Tek and Tjing Lie, Tek
- Subjects
Technology: general issues ,History of engineering & technology ,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 - Abstract
Summary: 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.
28. Algorithms in Decision Support Systems.
- Author
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García-Díaz, Vicente and García-Díaz, Vicente
- Subjects
History of engineering & technology ,semi-supervised learning ,transfer learning ,radar emitter ,decision support systems ,population health management ,big data ,machine learning ,deep learning ,personalized patient care ,Nonlinear regression ,interactive platform ,component-based approach ,software architecture ,Eclipse-RCP (Rich Client Platform) ,spatial prediction ,rule-based expert systems ,tennis hitting technique ,computer algebra systems ,Groebner bases ,Boolean logic ,data envelopment analysis ,dimensionality reduction ,ensembles ,exhaustive state space search ,entropy ,associative classification ,class association rule ,vertical data representation ,classification ,algorithm evaluation ,parallel algorithms ,multi-objective optimization ,train rescheduling ,very large-scale decision support systems ,very large-scale data and program cores of information systems ,meta-database ,teleological meta-database ,thematic list ,indicators list ,computational methods list ,geographically dispersed systems ,external sources - Abstract
Summary: This book aims to provide a new vision of how algorithms are the core of decision support systems (DSSs), which are increasingly important information systems that help to make decisions related to unstructured and semi-unstructured decision problems that do not have a simple solution from a human point of view. It begins with a discussion of how DSSs will be vital to improving the health of the population. The following article deals with how DSSs can be applied to improve the performance of people doing a specific task, like playing tennis. It continues with a work in which authors apply DSSs to insect pest management, together with an interactive platform for fitting data and carrying out spatial visualization. The next article improves how to reschedule trains whenever disturbances occur, together with an evaluation framework. The final works focus on different relevant areas of DSSs: 1) a comparison of ensemble and dimensionality reduction models based on an entropy criterion; 2) a radar emitter identification method based on semi-supervised and transfer learning; 3) design limitations, errors, and hazards in creating very large-scale DSSs; and 4) efficient rule generation for associative classification. We hope you enjoy all the contents in the book.
29. Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation.
- Author
-
Stavrakakis, George S. and Stavrakakis, George S.
- Subjects
Technology: general issues ,History of engineering & technology ,entrained flow coal gasification ,ash melting point ,operation temperature ,Markov process ,stochastic optimization model ,genetic algorithm ,gallium nitride ,magnetic-free converters ,module-level converters ,parallel architecture ,partial shading ,photovoltaic systems ,switched capacitor converters ,hybrid energy storage system ,supercapacitor ,lead-acid battery ,energy management system ,battery degradation ,depth of discharge ,techno-economic analysis ,hybrid power station ,green island ,energy storage ,remote community ,reserves ,k-means ,probabilistic dimensioning ,dynamic dimensioning ,balancing ,wave energy converters ,deep neural networks ,renewable energy sources ,spatial planning ,sentinel satellite imagery ,permanent magnet synchronous machines ,generators ,fault detection ,demagnetization ,artificial intelligence ,data mining ,machine learning ,advanced deep learning ,windspeed forecasting ,solar irradiation forecasting ,increased RES penetration ,smart grid ,scalability ,replicability ,FLEXITRANSTORE ,Angolan economy ,diversification ,strategic alternative ,biofuels - Abstract
Summary: An economic development model analyzes the adoption of alternative strategy capable of leveraging the economy, based essentially on RES. The combination of wind turbine, PV installation with new technology battery energy storage, DSM network and RES forecasting algorithms maximizes RES integration in isolated islands. An innovative model of power system (PS) imbalances is presented, which aims to capture various features of the stochastic behavior of imbalances and to reduce in average reserve requirements and PS risk. Deep learning techniques for medium-term wind speed and solar irradiance forecasting are presented, using for first time a specific cloud index. Scalability-replicability of the FLEXITRANSTORE technology innovations integrates hardware-software solutions in all areas of the transmission system and the wholesale markets, promoting increased RES. A deep learning and GIS approach are combined for the optimal positioning of wave energy converters. An innovative methodology to hybridize battery-based energy storage using supercapacitors for smoother power profile, a new control scheme and battery degradation mechanism and their economic viability are presented. An innovative module-level photovoltaic (PV) architecture in parallel configuration is introduced maximizing power extraction under partial shading. A new method for detecting demagnetization faults in axial flux permanent magnet synchronous wind generators is presented. The stochastic operating temperature (OT) optimization integrated with Markov Chain simulation ascertains a more accurate OT for guiding the coal gasification practice.
30. Applications in Electronics Pervading Industry, Environment and Society. Sensing Systems and Pervasive Intelligence.
- Author
-
Saponara, Sergio, De Gloria, Alessandro, Bellotti, Francesco, and Saponara, Sergio
- Subjects
Technology: general issues ,model-based design ,FPGA ,HDL code generation ,wearable sensors ,embedded devices ,face recognition ,face verification ,biometric sensors ,deep learning ,distillation ,convolutional neural networks ,spatial transformer network ,video coding ,discrete cosine transform ,directional transform ,VLSI ,alternative representations to float numbers ,posit arithmetic ,Deep Neural Networks (DNNs) ,neural network activation functions ,surface electromyography ,event-driven ,functional electrical stimulation ,embedded system ,resampling ,interpolating polynomial ,polyphase filter ,digital circuit design ,ASIC ,bitmap indexing ,processing in memory ,memory wall ,big data ,internet of things ,intelligent sensors ,autonomous driving ,cyber security ,HW accelerator ,on-chip random number generator (RNG) ,SHA2 ,ASIC standard-cell ,machine learning ,edge computing ,edge analytics ,ANN ,k-NN ,SVM ,decision trees ,ARM ,X-Cube-AI ,STM32 Nucleo ,rad-hard ,PLL (phase-locked loop) ,SEE (single event effects) ,Spacefibre ,TID (total ionization dose) ,charge pump ,phase/frequency detector ,frequency divider ,ring oscillator ,LC-tank oscillator ,SpaceFibre ,rad-hard circuits ,radiation effects ,high-speed data transfer ,support attitude ,inertial measurement unit ,coal mining ,unscented Kalman filter ,quaternion ,gradient descent ,research data collection and sharing ,connected and automated driving ,deployment and field testing ,vehicular sensors ,impact assessment ,knowledge management ,collaborative project methodology ,n/a - Abstract
Summary: This book features the manuscripts accepted for the Special Issue "Applications in Electronics Pervading Industry, Environment and Society-Sensing Systems and Pervasive Intelligence" of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the "Applications in Electronics Pervading Industry, Environment and Society" (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs.
31. Applications of Internet of Things.
- Author
-
Chen, Chi-Hua, Lo, Kuen-Rong, and Chen, Chi-Hua
- Subjects
Technology: general issues ,urban traffic ,grey relational membership degree ,traffic congestion judgment ,mobile positioning ,commercial vehicle operation data ,cellular network ,cloud computing ,location-based service ,Internet of Things ,distributed system architecture ,intelligent transportation system ,cellular networks ,vehicle positioning ,speed estimation ,machine learning ,road anomaly ,avoidance ,behavior recognition ,smartphone ,opportunistic sensing ,anklet monitoring and tracking ,detection algorithms ,geoprocessing ,Law Enforcement Telecommunications Systems (LETS) ,sensor data fusion ,camera coverage estimation ,multistage grid subdivision ,line of sight ,viewshed analysis ,obstacle ,k-anonymity ,location-based services ,location privacy ,the credible chain - Abstract
Summary: This book introduces the Special Issue entitled "Applications of Internet of Things", of ISPRS International Journal of Geo-Information. Topics covered in this issue include three main parts: (I) intelligent transportation systems (ITSs), (II) location-based services (LBSs), and (III) sensing techniques and applications. Three papers on ITSs are as follows: (1) "Vehicle positioning and speed estimation based on cellular network signals for urban roads," by Lai and Kuo; (2) "A method for traffic congestion clustering judgment based on grey relational analysis," by Zhang et al.; and (3) "Smartphone-based pedestrian's avoidance behavior recognition towards opportunistic road anomaly detection," by Ishikawa and Fujinami. Three papers on LBSs are as follows: (1) "A high-efficiency method of mobile positioning based on commercial vehicle operation data," by Chen et al.; (2) "Efficient location privacy-preserving k-anonymity method based on the credible chain," by Wang et al.; and (3) "Proximity-based asynchronous messaging platform for location-based Internet of things service," by Gon Jo et al. Two papers on sensing techniques and applications are as follows: (1) "Detection of electronic anklet wearers' groupings throughout telematics monitoring," by Machado et al.; and (2) "Camera coverage estimation based on multistage grid subdivision," by Wang et al.
32. Applied and Computational Mathematics for Digital Environments.
- Author
-
Demidova, Liliya and Demidova, Liliya
- Subjects
Research & information: general ,Mathematics & science ,mathematical model for evaluating the effectiveness of integrating information technology ,digital platforms ,virtual simulation infrastructures ,experimental virtual environment ,statistics ,multiscale analysis ,data analysis ,system on chip ,increasing traffic capacity ,percolation threshold ,transport link density ,transport network ,density of transport links ,computer algebra system ,wxMaxima ,Calculus ,symbolic computation ,mobile agents ,timeouts ,knowledge as set of trees ,behavioural equivalences ,nonlinear dynamics ,processes in social systems ,Fokker-Planck equation ,power law ,monitoring ,management ,image segmentation ,complex numbers ,CNN classifier ,outdoor environments ,relaxation subgradient methods ,space dilation ,nonsmooth minimization methods ,machine learning algorithm ,control synthesis ,optimal control ,stabilization ,symbolic regression ,machine learning ,evolutionary algorithm ,mobile robot ,problem decomposition ,large-scale global optimization ,self-adaptive differential evolution ,memetic algorithm ,cooperative co-evolution ,decision-making ,oncological disease ,kNN classifier ,SVM classifier ,dataset ,features ,UMAP algorithm ,entropy ,fractal dimension ,n/a - Abstract
Summary: The present reprint contains the 11 papers that were accepted and published in the Special Issue "Applied and Computational Mathematics for Digital Environments" of the MDPI Mathematics journal. The topics of interest include, among others, scientific research, applied tasks, and problems in the following areas: The construction of mathematical and information models of intelligent computer systems for monitoring and controlling the parameters of digital environments; The development of intelligent optimization algorithms that search for optimal parameter values of mathematical and information models in digital environments; Software and mathematical technologies in the implementation of intelligent monitoring and computer control of the parameters of digital environments; The development and application of mathematical and information models, machine learning methods, and artificial intelligence for the analysis and processing of big data in digital environments. I hope that this reprint will be useful to those who are interested in the real-world applications of applied and computational mathematics for digital environments in terms of solving actual, practical problems in all spheres of human life and activity.
33. Applied Mathematics and Computational Physics.
- Author
-
Wood, Aihua and Wood, Aihua
- Subjects
Research & information: general ,Mathematics & science ,radial basis functions ,finite difference methods ,traveling waves ,non-uniform grids ,chaotic oscillator ,one-step method ,multi-step method ,computer arithmetic ,FPGA ,high strain rate impact ,modeling and simulation ,smoothed particle hydrodynamics ,finite element analysis ,hybrid nanofluid ,heat transfer ,non-isothermal ,shrinking surface ,MHD ,radiation ,multilayer perceptrons ,quaternion neural networks ,metaheuristic optimization ,genetic algorithms ,micropolar fluid ,constricted channel ,MHD pulsatile flow ,strouhal number ,flow pulsation parameter ,multiple integral finite volume method ,finite difference method ,Rosenau-KdV ,conservation ,solvability ,convergence ,transmission electron microscopy (TEM) ,convolutional neural networks (CNN) ,anomaly detection ,principal component analysis (PCA) ,machine learning ,deep learning ,neural networks ,Gallium-Arsenide (GaAs) ,radiation-based flowmeter ,two-phase flow ,feature extraction ,artificial intelligence ,time domain ,Boltzmann equation ,collision integral ,convolutional neural network ,annular regime ,scale layer-independent ,petroleum pipeline ,volume fraction ,dual energy technique ,prescribed heat flux ,similarity solutions ,dual solutions ,stability analysis ,RBF-FD ,node sampling ,lebesgue constant ,complex regions ,finite-difference methods ,data assimilation ,model order reduction ,finite elements analysis ,high dimensional data ,welding - Abstract
Summary: As faster and more efficient numerical algorithms become available, the understanding of the physics and the mathematical foundation behind these new methods will play an increasingly important role. This Special Issue provides a platform for researchers from both academia and industry to present their novel computational methods that have engineering and physics applications.
34. Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids.
- Author
-
Simões, Marcelo Godoy, Paredes, Helmo Kelis Morales, and Simões, Marcelo Godoy
- Subjects
History of engineering & technology ,droop curve ,frequency regulation ,fuzzy logic ,the rate of change of frequency ,reserve power ,smart grid ,energy Internet ,convolutional neural network ,decision optimization ,deep reinforcement learning ,electric load forecasting ,non-dominated sorting genetic algorithm II ,multi-layer perceptron ,adaptive neuro-fuzzy inference system ,meta-heuristic algorithms ,automatic generation control ,fuzzy neural network control ,thermostatically controlled loads ,back propagation algorithm ,particle swarm optimization ,load disaggregation ,artificial intelligence ,cognitive meters ,machine learning ,state machine ,NILM ,non-technical losses ,semi-supervised learning ,knowledge embed ,deep learning ,distribution network equipment ,condition assessment ,multi information source ,fuzzy iteration ,current balancing algorithm ,level-shifted SPWM ,medium-voltage applications ,multilevel current source inverter ,motor drives ,phase-shifted carrier SPWM ,STATCOM ,electricity forecasting ,CNN-LSTM ,very short-term forecasting (VSTF) ,short-term forecasting (STF) ,medium-term forecasting (MTF) ,long-term forecasting (LTF) ,asynchronous motor ,linear active disturbance rejection control ,error differentiation ,vector control ,renewable energy ,solar power plant ,Data Envelopment Analysis (DEA) ,Fuzzy Analytical Network Process (FANP) ,Fuzzy Theory - Abstract
Summary: Artificial intelligence techniques, such as expert systems, fuzzy logic, and artificial neural network techniques have become efficient tools in modeling and control applications. For example, there are several benefits in optimizing cost-effectiveness, because fuzzy logic is a methodology for the handling of inexact, imprecise, qualitative, fuzzy, and verbal information systematically and rigorously. A neuro-fuzzy controller generates or tunes the rules or membership functions of a fuzzy controller with an artificial neural network approach. There are new instantaneous power theories that may address several challenges in power quality. So, this book presents different applications of artificial intelligence techniques in advanced high-tech electronics, such as applications in power electronics, motor drives, renewable energy systems and smart grids.
35. Approximate Bayesian Inference.
- Author
-
Alquier, Pierre and Alquier, Pierre
- Subjects
Research & information: general ,Mathematics & science ,bifurcation ,dynamical systems ,Edward-Sokal coupling ,mean-field ,Kullback-Leibler divergence ,variational inference ,Bayesian statistics ,machine learning ,variational approximations ,PAC-Bayes ,expectation-propagation ,Markov chain Monte Carlo ,Langevin Monte Carlo ,sequential Monte Carlo ,Laplace approximations ,approximate Bayesian computation ,Gibbs posterior ,MCMC ,stochastic gradients ,neural networks ,Approximate Bayesian Computation ,differential evolution ,Markov kernels ,discrete state space ,ergodicity ,Markov chain ,probably approximately correct ,variational Bayes ,Bayesian inference ,Markov Chain Monte Carlo ,Sequential Monte Carlo ,Riemann Manifold Hamiltonian Monte Carlo ,integrated nested laplace approximation ,fixed-form variational Bayes ,stochastic volatility ,network modeling ,network variability ,Stiefel manifold ,MCMC-SAEM ,data imputation ,Bethe free energy ,factor graphs ,message passing ,variational free energy ,variational message passing ,approximate Bayesian computation (ABC) ,differential privacy (DP) ,sparse vector technique (SVT) ,Gaussian ,particle flow ,variable flow ,Langevin dynamics ,Hamilton Monte Carlo ,non-reversible dynamics ,control variates ,thinning ,meta-learning ,hyperparameters ,priors ,online learning ,online optimization ,gradient descent ,statistical learning theory ,PAC-Bayes theory ,deep learning ,generalisation bounds ,Bayesian sampling ,Monte Carlo integration ,no free lunch theorems ,sequential learning ,principal curves ,data streams ,regret bounds ,greedy algorithm ,sleeping experts ,entropy ,robustness ,statistical mechanics ,complex systems - Abstract
Summary: Extremely popular for statistical inference, Bayesian methods are also becoming popular in machine learning and artificial intelligence problems. Bayesian estimators are often implemented by Monte Carlo methods, such as the Metropolis-Hastings algorithm of the Gibbs sampler. These algorithms target the exact posterior distribution. However, many of the modern models in statistics are simply too complex to use such methodologies. In machine learning, the volume of the data used in practice makes Monte Carlo methods too slow to be useful. On the other hand, these applications often do not require an exact knowledge of the posterior. This has motivated the development of a new generation of algorithms that are fast enough to handle huge datasets but that often target an approximation of the posterior. This book gathers 18 research papers written by Approximate Bayesian Inference specialists and provides an overview of the recent advances in these algorithms. This includes optimization-based methods (such as variational approximations) and simulation-based methods (such as ABC or Monte Carlo algorithms). The theoretical aspects of Approximate Bayesian Inference are covered, specifically the PAC-Bayes bounds and regret analysis. Applications for challenging computational problems in astrophysics, finance, medical data analysis, and computer vision area also presented.
36. Artificial Intelligence and Ambient Intelligence.
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Gams, Matjaz, Gjoreski, Martin, and Gams, Matjaz
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Information technology industries ,robotic hand ,control ,perception ,tactile sensing ,mechatronics ,grasping ,manipulation ,PUT-Hand ,underactuated ,multi-modal fusion ,machine learning ,robotics ,perception for grasping ,effective computing ,emotion system ,emotional machine ,agent ,human-machine interface ,Wi-Fi ,CSI ,crowd counting ,Doppler spectrum ,information society ,electronics ,artificial intelligence ,ambient intelligence ,one-dimensional depth sensor ,biometrics ,identification ,affective computing ,cognitive load ,psychophysiology ,supervised learning ,n/a - Abstract
Summary: This book includes a series of scientific papers published in the Special Issue on Artificial Intelligence and Ambient Intelligence at the journal Electronics MDPI. The book starts with an opinion paper on "Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules", presenting relations between information society, electronics and artificial intelligence mainly through twenty-four IS laws. After that, the book continues with a series of technical papers that present applications of Artificial Intelligence and Ambient Intelligence in a variety of fields including affective computing, privacy and security in smart environments, and robotics. More specifically, the first part presents usage of Artificial Intelligence (AI) methods in combination with wearable devices (e.g., smartphones and wristbands) for recognizing human psychological states (e.g., emotions and cognitive load). The second part presents usage of AI methods in combination with laser sensors or Wi-Fi signals for improving security in smart buildings by identifying and counting the number of visitors. The last part presents usage of AI methods in robotics for improving robots' ability for object gripping manipulation and perception. The language of the book is rather technical, thus the intended audience are scientists and researchers who have at least some basic knowledge in computer science.
37. Artificial Intelligence and Cognitive Computing. Methods, Technologies, Systems, Applications and Policy Making.
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Lytras, Miltiadis, Visvizi, Anna, and Lytras, Miltiadis
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Information technology industries ,data mining ,decision-making system ,rough set ,mixed integer linear programming ,assembly clearance ,diesel engine quality ,Internet of things ,Wireless nodes ,Hybrid clustering ,Multi-hop routing ,Network lifetime ,Artificial intelligence ,data envelopment analysis ,decision making ,artificial intelligence ,performance ,visual analytics ,system ,air quality ,spatiotemporal ,multivariate ,dimension reduction ,clustering ,regular patterns ,anomalies ,speech recognition ,Long Short Term Memory (LSTM) ,speech output correction ,most-matching ,empirical correlations ,rheological properties ,real-time ,water-based drill-in fluid ,artificial neural network ,elastic parameters ,Poisson's ratio ,sandstone ,self-adaptive differential evolution ,total organic carbon ,barnett shale ,devonian shale ,fishbone multilateral wells ,predictive models ,well productivity ,international research ,knowledge map visualization ,policy documents quantification ,research hotspot ,policy keyword ,minimum miscibility pressure (MMP) ,CO2 flooding ,new models ,face recognition ,security ,spoofing ,histogram of oriented gradients ,smart cities ,deep learning ,LSTM ,neural networks ,location prediction ,trajectories ,smart tourism ,static Young's modulus ,sandstone formations ,machine learning ,n/a - Abstract
Summary: Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today's world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that.
38. Artificial Intelligence for Multisource Geospatial Information.
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Bordogna, Gloria, Fugazza, Cristiano, and Bordogna, Gloria
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Technology: general issues ,History of engineering & technology ,geosemantics ,implicit semantics ,formal semantics ,powerful semantics ,satellite image ,semantic segmentation ,encoder-decoder ,CNN ,TH-1 ,cloud and snow detection ,label quality ,street crime ,people on the street ,streetscape ,Baidu Street View image ,spatial lag negative binomial regression ,deep learning ,convolutional neural network ,backbone network ,target detection ,remote sensing images ,segmentation ,high resolution ,transformer ,OLAP ,fuzzy SOLAP-based framework ,fuzzy spatiotemporal queries ,fuzzy spatiotemporal predictive query ,fuzzy query visualization ,machine learning ,classification ,LiDAR ,3D point cloud ,urban trees ,image feature vector ,clustering ,Siamese Network ,automatic classification of tourist photos ,deep learning model ,Arabic tweets ,COVID-19 pandemic ,sentiment analysis ,social data mining ,spatio-temporal correlation ,off-line integration of geo-tagged data sets ,data sets about public places ,soft integration methodology ,effective soft integration through a stand-along tool ,n/a - Abstract
Summary: This reprint collects 10 original research contributions published in the Special Issue entitled "Artificial Intelligence for Multisource Geospatial Information" of the ISPRS International Journal of Geo-Information. The focus is on different methods of Geospatial Artificial Intelligence (GeoAI) based on deep learning using different network architectures, clustering, soft computing, and semantic approaches. They are proposed to deal with a variety of Geospatial Big Data (GBD), such as georeferenced texts and photos in social networks, remote sensing images, cartographic maps, multidimensional geo databases, metadata in spatial data infrastructures, and for different tasks, such as for multisource georeferenced text integration and geodata flexible querying, for social sensing by applying sentiment analysis, clustering and geo analysis, for segmentation of roads, clouds and snow, and for detection of small targets and people on the streets.
39. Assessment of Renewable Energy Resources with Remote Sensing.
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Martins, Fernando Ramos and Martins, Fernando Ramos
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Research & information: general ,metaheuristic ,parameter extraction ,solar photovoltaic ,whale optimization algorithm ,cloud detection ,digitized image processing ,artificial neural networks ,solar irradiance estimation ,solar irradiance forecasting ,solar energy ,sky camera ,remote sensing ,CSP plants ,coastal wind measurements ,scanning LiDAR ,plan position indicator ,velocity volume processing ,Hazaki Oceanographical Research Station ,cloud coverage ,image processing ,total sky imagery ,geothermal energy ,geophysical prospecting ,time domain electromagnetic method ,electrical resistivity tomography ,potential well field location ,GES-CAL software ,smart island ,solar radiation forecasting ,light gradient boosting machine ,multistep-ahead prediction ,feature importance ,voxel-design approach ,shading envelopes ,point cloud data ,computational design method ,passive design strategy ,lake breeze influence ,hydropower reservoir ,solar irradiance enhancement ,solar energy resource ,wind speed ,extreme value analysis ,scatterometer ,feature engineering ,forecasting ,graphical user interface software ,machine learning ,photovoltaic power plant ,surface solar radiation ,global radiation ,satellite ,Baltic area ,coastline ,cloud ,convection ,climate ,renewable energy resource assessment and forecasting ,remote sensing data acquisition ,data processing ,statistical analysis ,machine learning techniques - Abstract
Summary: The book "Assessment of Renewable Energy Resources with Remote Sensing" focuses on disseminating scientific knowledge and technological developments for the assessment and forecasting of renewable energy resources using remote sensing techniques. The eleven papers inside the book provide an overview of remote sensing applications on hydro, solar, wind and geothermal energy resources and their major goal is to provide state of art knowledge to contribute with the renewable energy resource deployment, especially in regions where energy demand is rapidly expanding. Renewable energy resources have an intrinsic relationship with local environmental features and the regional climate. Even small and fast environment and/or climate changes can cause significant variability in power generation at different time and space scales. Methodologies based on remote sensing are the primary source of information for the development of numerical models that aim to support the planning and operation of an electric system with a substantial contribution of intermittent energy sources. In addition, reliable data and knowledge on renewable energy resource assessment are fundamental to ensure sustainable expansion considering environmental, financial and energetic security.
40. Big Data in Dental Research and Oral Healthcare.
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Joda, Tim and Joda, Tim
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Medicine ,digital transformation ,rapid prototyping ,augmented and virtual reality (AR/VR) ,artificial intelligence (AI) ,machine learning (ML) ,personalized dental medicine ,tele-health ,patient-centered outcomes ,integrated care, medical-dental integration, simulation model, dental research ,oral medicine ,oral healthcare ,dentistry ,gerodontology ,elderly patient ,big data ,Big Data ,digital dentistry ,oral health ,ethical issues ,dental education ,augmented reality (AR) ,virtual reality (VR) ,artificial intelligence ,AI ,machine learning ,ML ,cone beam computed tomography (CBCT) ,intraoral scanning ,facial scanning ,healthcare cost ,medical healthcare cost ,dental healthcare cost ,zero-inflated model ,neural network - Abstract
Summary: Progress in information technology has fostered a global explosion of data generation. Accumulated big data are now estimated to be 4.4 zettabytes in the digital universe; and trends predict an exponential increase in the future. Health data are gathered from professional routine care and other expanded sources including the social determinants of health, such as Internet of Things. Biomedical research has recently moved through three stages in digital healthcare: (1) data collection; (2) data sharing; and (3) data analytics. With the explosion of stored health data, dental medicine is edging into its fourth stage of digitization using new technologies including augmented and virtual reality, artificial intelligence, and blockchain. Big data collaborations involve interactions between a diverse range of stakeholders with analytical, technical and political focus. In oral healthcare, data technology has many areas of application: prognostic analysis and predictive modeling, the identification of unknown correlations of diseases, clinical decision support for novel treatment concepts, public health surveys and population-based clinical research, as well as the evaluation of healthcare systems. The objective of this Special Issue is to provide an update on the current knowledge with state-of-the-art theory and practical information on human and social perspectives that determine the uptake of technological innovations in big data science in the field of dental medicine. Moreover, it will focus on the identification of future research needs to manage the continuous increase in health data and to accomplish its clinical translation for patient-centered research and personalized dentistry. This Special Issue welcomes all types of studies and reviews considering the perspectives of different stakeholders on technological innovations for big data science in all dental disciplines. Kind regards,
41. Bioinformatics Applications Based On Machine Learning.
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Chamoso, Pablo, Rodriguez, Sara, Mohamad, Mohd, González-Briones, Alfonso, and Chamoso, Pablo
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Technology: general issues ,machine learning ,metagenomics ,bioinformatics ,CTX-M ,data mining ,cluster ,clinical implications ,diabetes ,epidemiology ,forecast ,PART ,Decision table ,Weka ,real-life patients ,regression ,ear detection ,computer vision ,convolutional neural network ,image recognition ,video analysis ,gene clustering ,swarm intelligence ,biological functions detection ,informative genes ,fuel cell ,hydrogen energy ,intelligent systems ,hybrid systems ,Artificial Neural Networks ,power management ,Machine Learning ,personality assessment ,gradient boosting ,Affective Computing ,transposable elements ,metrics ,deep learning ,detection ,classification ,mitochondrial protein ,bi-directional LSTM ,plasmodium falciparum ,Particle Swarm Optimization ,Harmony Search ,parameter estimation ,Arabidopsis thaliana ,clinical data ,feature selection ,genetic programming ,evolutionary computation ,dynamic models ,evolutionary computing ,derivative-free optimization ,metabolism ,glycolysis ,yeast - Abstract
Summary: The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems.
42. Biological Efficacy of Natural and Chemically Modified Products against Oral Inflammatory Lesions.
- Author
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Sakagami, Hiroshi
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gargle ,oral lichen planus ,angiotensin II blocker ,quantitative structure-activity relationship ,metabolomics ,CCN2 ,anti-human immunodeficiency virus (HIV) ,oral cell ,arachidonic acid cascade ,Kampo medicine ,lignin-carbohydrate complex ,traditional medicine ,eugenol ,QSAR analysis ,constituent plant extract ,polyphenol ,benzaldehyde ,glucosyltransferase ,infective endocarditis ,antiviral ,periodontitis ,nutritionally variant streptococci ,Kampo ,quantitative structure-activity relationship (QSAR) analysis ,traditional Japanese herbal medicine ,technical terms ,allergic rhinitis ,nasal epithelial cell ,antimicrobial susceptibilities ,alkaline extract ,mastic ,stomatitis ,thioredoxin ,production ,oral microbiota ,Jixueteng ,oral inflammation ,random forest ,mice ,chromone ,natural products ,Chinese herbal remedies ,inflammation ,quercetin ,in vivo ,kampo formula ,glucocorticoids ,Hangeshashinto ,recurrent aphthous stomatitis ,anti-osteoclast activity ,cytotoxicity ,dental application ,tongue diagnosis ,natural product ,alkaloids ,inflammatory disease ,pathogenic factors ,increase ,machine learning ,human virus ,cepharanthin ,mucositis ,oral diseases ,Juzentaihoto ,in vitro ,herbal medicine ,tumour-specificity - Abstract
Summary: Oral health is general health. If the oral cavity is kept healthy, the whole body is always healthy. Bacteria in the oral cavity do not stay in the oral cavity, but rather they travel throughout the body and can induce various diseases. Periodontal pathogens are involved in tooth loss. The number of remaining teeth decreases with age. People with more residual teeth can bite food well and live longer with lower incidence of dementia. There are many viruses in the oral cavity that also cause various diseases. Bacteria and viruses induce and aggravate inflammation, and therefore should be removed from the oral cavity. In the natural world, there are are many as yet undiscovered antiviral, antibacterial and anti-inflammatory substances. These natural substances, as well as chemically modified derivatives, help our oral health and lead us to more fulfilling, high quality lives. This Special Issue, entitled "Biological Efficacy of Natural and Chemically Modified Products against Oral Inflammatory Lesions", was written by specialists from a diverse variety of fields. It serves to provide readers with up-to-date information on incidence rates in each age group, etiology and treatment of stomatitis, and to investigate the application of such treatments as oral care and cosmetic materials.
43. Biomechanical Spectrum of Human Sport Performance.
- Author
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TAIAR, Redha, Bernardo-Filho, Mario, and TAIAR, Redha
- Subjects
History of engineering & technology ,foot morphology ,toes function ,biomechanics ,barefoot ,jumping ,running ,jump throwing ability ,3D motion analysis ,acceleration ,kidney ,torque ,exercise ,physical fitness ,peritoneal dialysis ,sports engineering ,ground reaction forces ,swimming ,performance ,neuromuscular training ,strength ,injury prevention ,young athletes ,basketball shoe ,comfort perception ,foot loading ,plantar pressure ,maneuver ,two-dimensional video analysis ,validity ,reliability ,quantitative biomechanical parameters ,artistic gymnastics ,minimalist index ,patellofemoral contact force ,patellofemoral contact stress ,footwear ,patellofemoral joint pain syndrome ,virtual reality ,visual speed perception ,treadmill running ,self-motion perception ,optical flow ,locomotion ,forefoot ,Gait ,Heel ,TUG ,Type 2 diabetes mellitus ,metabolic syndrome ,whole body vibration exercise ,range of motion of the knees ,surface electromyographic pattern ,neuromuscular activation ,sleep quality ,whole-body vibration exercise ,Pittsburgh Sleep Quality Index ,Epworth Sleepiness Scale ,Berlin Questionnaire ,mechanical vibration ,traditional Chinese medicine ,balance ,flexibility ,muscle strength ,stabilometry ,ballet dancers ,ankle injury ,chronic ankle instability ,functional ,movement ,evaluation ,assessment ,screen ,type 2 diabetes mellitus ,blood flow velocity ,skin temperature ,vibration ,3D force modeling ,rowing biomechanics ,robot-assisted training ,individualized training ,virtual reality simulator ,training of experts ,rowing simulator ,tendon based parallel robot ,transversal vibration control ,physical training intensity ,therapeutic exercises ,work related neck pain ,musculoskeletal pain ,flight time ,vertical jump ,center of mass ,landing ,anthropometric variables ,hamstring-to-quadriceps ratio ,H/Q ratio ,isometric ,lower limb ,measurement acquisitions and techniques ,strength and conditioning ,sports training ,strength abilities ,anterior cruciate ligament ,automation ,drop jump ,injury risk ,deep learning ,machine learning ,movement screen ,OpenPose ,signal complexity ,texture analysis ,multiscale entropy analysis ,wavelet transform ,photoplethysmography ,orthosis ,tensile strength test ,robotic therapy ,posture stability ,football ,training ,slackline balancing ,dynamics reconstruction ,contact force modeling ,optimal control ,subject-specific modeling ,velocity ,technique ,overhead ,racket ,swing ,stroke ,modeling and simulation in sport science ,mechanical analyses of sports ,sport medicine ,injury in sport ,human behavior ,quality of life ,applied science in musculoskeletal disorders - Abstract
Summary: Writing or managing a scientific book, as it is known today, depends on a series of major activities, such as regrouping researchers, reviewing chapters, informing and exchanging with contributors, and at the very least, motivating them to achieve the objective of publication. The idea of this book arose from many years of work in biomechanics, health disease, and rehabilitation. Through exchanges with authors from several countries, we learned much from each other, and we decided with the publisher to transfer this knowledge to readers interested in the current understanding of the impact of biomechanics in the analysis of movement and its optimization. The main objective is to provide some interesting articles that show the scope of biomechanical analysis and technologies in human behavior tasks. Engineers, researchers, and students from biomedical engineering and health sciences, as well as industrial professionals, can benefit from this compendium of knowledge about biomechanics applied to the human body.
44. Biomedical Insights that Inform the Diagnosis of ME/CFS.
- Author
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Lidbury, Brett and Fisher, Paul
- Subjects
fatigue syndrome ,n/a ,work rehabilitation ,tryptophan metabolism ,substrate inhibition ,myalgic encephalomyelitis ,3-dioxygenase ,ME (Myalgic Encephalomyelitis) ,assessment ,myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) ,muscles ,hypoacetylation ,circadian rhythm ,immune system ,chronic ,CFS (Chronic Fatigue Syndrome) ,energy metabolism ,ME/CFS ,immunological ,cytokine ,bistability ,diagnosis ,biomarker ,symptoms ,kynurenine pathway ,inflammation and immunity ,metabolism ,neuro-inflammation ,pathology ,signaling ,exercise ,Epstein Barr virus ,methylhistidine ,hypothalamic-pituitary-adrenal axis ,potential biomarkers ,critical point ,inflammation ,histone deacetylation ,participatory research ,neurology ,neuroimmune ,activin ,mitochondria ,patient-driven questionnaire ,medical retirement ,machine learning ,prognosis ,gut microbiota ,post-exertional malaise ,chronic fatigue syndrome ,mathematical model ,reference intervals ,diagnostic biomarker ,indoleamine-2 - Abstract
Summary: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a severe chronic health condition that is often misunderstood or ignored by health establishments. The lack of definitive diagnostic markers to separate ME/CFS patients from the healthy population as well as from other chronic disorders is problematic for both health professionals and researchers. A consortium of Australian researchers gathered to systematically understand ME/CFS, ranging from a deep analysis of clinical and pathology data to metabolomic profiles and the investigation of mitochondrial function. From this broad collaboration, a number of compelling insights have arisen that may form the basis of specific serum, blood, and/or urinary biomarkers of ME/CFS. This Special Edition reports on a conference centred on these biomedical discoveries, with other contributions, with a translation focus for predictive markers for ME/CFS diagnosis. By supporting health professionals with developments in diagnostics for this condition, the patients and their families will hopefully benefit from an improved recognition of the biomedical underpinnings of the condition and will be better able to access the care that is urgently required. This Special Edition contains a mix of speaker submissions and other accepted manuscripts that contributed to our objective of advancing biomedical insights to enable the accurate diagnosis of ME/CFS.
45. Building Energy Audits-Diagnosis and Retrofitting.
- Author
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Balaras, Constantinos A. and Balaras, Constantinos A.
- Subjects
Research & information: general ,feature selection ,prediction of energy consumption ,electricity consumption ,machine learning ,non-residential buildings ,sustainability ,buildings ,neighbourhoods ,decision-making process ,key performance indicators ,KPIs ,built environment ,audit ,assessment tools ,brick 1 ,moisture 2 ,heat flow 3 ,energetic rehabilitation 4 ,non-destructive test 5 ,energy community (EC) ,renewable energy sources (RESs) ,citizen involvement ,co-ownership in renewable energies ,nonresidential buildings ,baselines ,EUI ,energy use intensities ,carbon emission intensities ,EPCs ,energy performance certificates ,building energy simulation ,school building ,field measurements ,validation ,airing ,windows and door opening ,occupancy behaviour ,energy efficiency measures ,retrofitting ,thermo-modernization ,final energy ,primary energy ,energy consumption ,home energy management system ,human comfort factor ,thermal comfort ,visual comfort ,demand response ,energy performance ,energy audits ,school buildings ,indoor climate ,HeLLo ,energy retrofit ,non-destructive test ,in situ ,hygrothermal measurement ,dynamic conditions ,hygrothermal simulation ,historic wall ,daylight ,lighting control ,lighting ,occupant preferences ,occupant satisfaction ,photosensor ,post-occupancy evaluation ,survey ,single-family houses ,embodied energy ,operational energy ,benchmarks ,renovations ,energy use intensity (EUI) ,embodied energy intensity (EEI) ,energy recovery time - Abstract
Summary: The book "Building Energy Audits-Diagnosis and Retrofitting" is a collection of twelve papers that focus on the built environment in order to systematically collect and analyze relevant data for the energy use profile of buildings and extended for the sustainability assessment of the built environment. The contributions address historic buildings, baselines for non-residential buildings from energy performance audits, and from in-situ measurements, monitoring, and analysis of data, and verification of energy saving and model calibration for various building types. The works report on how to diagnose existing problems and identify priorities, assess, and quantify the opportunities and measures that improve the overall building performance and the environmental quality and well-being of occupants in non-residential buildings and houses. Several case studies and lessons learned from the field are presented to help the readers identify, quantify, and prioritize effective energy conservation and efficiency measures. Finally, a new urban sustainability audit and rating method of the built environment addresses the complexities of the various issues involved, providing practical tools that can be adapted to match local priorities in order to diagnose and evaluate the current state and future scenarios towards meeting specific sustainable development goals and local priorities.
46. Characterization and Modelling of Composites.
- Author
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Georgantzinos, Stelios K. and Georgantzinos, Stelios K.
- Subjects
Research & information: general ,structural dynamics ,composite plastics ,stiffness ,damping ,fiber orientation ,ODF ,viscoelasticity ,geopolymer concrete ,fly-ash ,bottom-ash ,freeze-thaw ,leachability ,non-destructive test ,TCLP ,RFT ,fiber matrix interface ,finite element analysis ,characterization ,composite ,measurements ,testing ,structural monitoring ,flax-epoxy composite ,interlaminar fracture energy ,fracture toughness ,delamination ,Mode I ,Mode II and Mixed-mode I-II interlaminar fracture ,critical energy release rate ,machine learning ,mould filling simulations ,composite materials ,liquid moulding ,lattice cell structures ,InsideBCC ,equivalent solid properties ,three-dimensional printing ,nacre ,hexagonal tablets ,analytical model ,finite element simulations ,Abaqus ,fused filament fabrication ,PLA ,bamboo ,mechanical strength ,damage detection ,laminated composite plates ,modal analysis ,curvature mode shape ,strain energy ,n/a - Abstract
Summary: Composites have increasingly been used in various structural components in the aerospace, marine, automotive, and wind energy sectors. The material characterization of composites is a vital part of the product development and production process. Physical, mechanical, and chemical characterization helps developers to further their understanding of products and materials, thus ensuring quality control. Achieving an in-depth understanding and consequent improvement of the general performance of these materials, however, still requires complex material modeling and simulation tools, which are often multiscale and encompass multiphysics. This Special Issue aims to solicit papers concerning promising, recent developments in composite modeling, simulation, and characterization, in both design and manufacturing areas, including experimental as well as industrial-scale case studies. All submitted manuscripts will undergo a rigorous review process and will only be considered for publication if they meet journal standards. Selected top articles may have their processing charges waived at the recommendation of reviewers and the Guest Editor.
47. Citizen Science and Geospatial Capacity Building.
- Author
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Kocaman, Sultan, Saran, Sameer, Durmaz, Murat, Kumar, A., and Kocaman, Sultan
- Subjects
Research & information: general ,participatory toponyms ,knowledge sharing ,public participation ,citizen science ,geospatial capacity building ,volunteered geographic information ,social media ,spatiotemporal bias ,CitSci ,earthquake ,intensity mapping ,disaster mitigation ,spatial kriging ,volunteered geographic information (VGI) ,data contribution activities ,spatial and temporal patterns ,biases ,eBird ,community-based geoportal ,crowdsourced earth observation product ,remote sensing ,spatial data infrastructure (SDI) ,crowdsourced data quality ,GeoWeb ,outdoor air pollution ,symptom mapping ,data quality ,web application ,water quality ,community-based monitoring ,machine learning ,Indian monsoon ,Jacobin cuckoo ,Maxent ,species distribution model ,habitat suitability ,range expansion ,WorldClim ,CMIP ,crowdsourcing ,participatory GIS - Abstract
Summary: This book is a collection of the articles published the Special Issue of ISPRS International Journal of Geo-Information on "Citizen Science and Geospatial Capacity Building". The articles cover a wide range of topics regarding the applications of citizen science from a geospatial technology perspective. Several applications show the importance of Citizen Science (CitSci) and volunteered geographic information (VGI) in various stages of geodata collection, processing, analysis and visualization; and for demonstrating the capabilities, which are covered in the book. Particular emphasis is given to various problems encountered in the CitSci and VGI projects with a geospatial aspect, such as platform, tool and interface design, ontology development, spatial analysis and data quality assessment. The book also points out the needs and future research directions in these subjects, such as; (a) data quality issues especially in the light of big data; (b) ontology studies for geospatial data suited for diverse user backgrounds, data integration, and sharing; (c) development of machine learning and artificial intelligence based online tools for pattern recognition and object identification using existing repositories of CitSci and VGI projects; and (d) open science and open data practices for increasing the efficiency, decreasing the redundancy, and acknowledgement of all stakeholders.
48. Clinical Medicine for Healthcare and Sustainability.
- Author
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Meen, Teen-Hang, Matsumoto, Yusuke, Lee, Kuan-Han, and Meen, Teen-Hang
- Subjects
Medicine ,sarcopenia ,bioimpedance analysis ,computed tomography ,discordance ,effect ,protection ,catheter ,hemodialysis ,meta-analysis ,trial sequential analysis ,Long-term oxygen therapy ,home mechanical ventilation ,patient-reported experience measures ,quality of care ,healthcare ,sustainability ,hepatocellular carcinoma ,health-related quality of life ,minimal clinically important difference ,survival ,serum urate ,menopause ,hypertension ,xanthine dehydrogenase ,cross-sectional cohort study ,diabetic foot ulcer ,peripheral arterial disease ,incidence ,prevalence ,cost ,National Health Insurance Service data ,erythrodermic psoriasis ,secukinumab ,addiction ,smoking ,alcohol ,cannabis ,virtual reality ,musculoskeletal disorders ,randomized controlled tria ,ceftaroline ,ceftriaxone ,community-acquired pneumonia ,safety ,eravacycline ,complicated intra-abdominal infection ,efficacy ,mortality ,laparoscopic ,open surgery ,non-metastatic colorectal cancer ,surgical complication ,oncologic outcome ,single surgeon experience ,doripenem ,acute bacterial infection ,pneumonia ,intra-abdominal infection ,complicated urinary tract infection ,ultrasound-guided injection ,laser assisted ,long-axis injection ,chronic disease ,multimorbidity ,suicidal thoughts ,suicidal plans ,stroke ,post-acute care ,medical referral system ,propensity score matching ,resistance training ,arterial pressure ,disease prevention ,caffeine ,older adults ,frailty ,medication ,primary care ,white matter hyperintensity ,MRI ,healthcare quotient ,chronic ,older adults living in super-aging society ,mild cognitive impairment ,walking speed ,depression ,urinary tract infection ,rapid culture ,antibiotic susceptibility testing (AST), evidence-based prescription ,antibiotics ,antimicrobial resistance (AMR), rapid diagnostics ,prediction ,deep learning ,conventional neural network ,bariatric surgery ,hospital emergency department ,queuing theory ,decision support ,cost optimization ,health behavior ,socioeconomic status ,Korea ,cardiovascular disease ,postprandial ,hypotension ,blood pressure ,elderly ,lung cancer ,physical activity ,season ,preoperative ,wearable ,macrosomia ,large for gestational age ,machine learning ,ensemble methods ,sensitivity ,specificity ,clinical deterioration ,early medical intervention ,electronic health records ,hospital rapid response team ,intensive care units ,medical records system ,computerized ,osteoporosis screening ,artificial intelligence ,convolutional neural networks ,dental panoramic radiographs ,palliative care ,nursing homes ,symptom assessment ,drug therapy ,therapeutics ,longitudinal studies ,occupational medicine ,forensic medicine ,insurance medicine ,psychoactive substances ,clinical ,forensic ,law ,ethics ,uric acid ,risk factor ,epidemiology ,cardiometabolic diseases ,healthcare and sustainability ,therapy of internal medicine diseases - Abstract
Summary: When the domestic government, the private sector, and people in various professional fields talk about long-term care issues, they all focus on creating a warm and home-like care institution. However, we actively emphasize the importance of community-based long-term care. For "aging in place", the development of domestic non-institutional care is still in its infancy, and some long-term care needs must still be met through institutional care, and the facilitation of the extension or outreach of community-based care and respite service platforms for the development of community-based long-term care still rely on institutional care. The history of the development of long-term care in Taiwan is much shorter than that of Japan, Europe, the United States, and Canada. Despite years of hard work and rapid development, the long-term care resources needed to establish a complete system in terms of universalization, fairness, accessibility, and selectivity are not available. In the future, based on the soundness of institutional care, it hoped that outreach will move toward the goals of community care and aging in place. We hope the studies in this Special Issue will help further develop clinical medicine for healthcare and stainability.
49. Clinical Studies, Big Data, and Artificial Intelligence in Nephrology and Transplantation.
- Author
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Cheungpasitporn, Wisit, Thongprayoon, Charat, Kaewput, Wisit, and Cheungpasitporn, Wisit
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Medicine ,tacrolimus ,C/D ratio ,tacrolimus metabolism ,everolimus ,conversion ,kidney transplantation ,gut microbiome ,renal transplant recipient ,diarrhea ,immunosuppressive medication ,gut microbiota ,16S rRNA sequencing ,butyrate-producing bacteria ,Proteobacteria ,torquetenovirus ,immunosuppression ,transplantation ,immunosuppressed host ,outcome ,renal transplantation ,Goodpasture syndrome ,anti-GBM disease ,epidemiology ,hospitalization ,outcomes ,acute kidney injury ,risk prediction ,artificial intelligence ,patent ductus arteriosus ,conservative management ,blood pressure ,eradication ,interferon-free regimen ,hepatitis C infection ,kidney transplant ,allograft steatosis ,lipopeliosis ,transplant numbers ,live donors ,public awareness ,Google TrendsTM ,machine learning ,big data ,nephrology ,chronic kidney disease ,NLR ,PLR ,RPGN ,predictive value ,hemodialysis ,withdrawal ,cellular crescent ,global sclerosis ,procurement kidney biopsy ,glomerulosclerosis ,minimally-invasive donor nephrectomy ,robot-assisted surgery ,laparoscopic surgery ,organ donation ,living kidney donation ,MeltDose® ,LCPT ,renal function ,liver transplantation ,metabolism ,erythropoietin ,fibroblast growth factor 23 ,death ,weekend effect ,in-hospital mortality ,comorbidity ,dialysis ,elderly ,klotho ,α-Klotho ,FGF-23 ,kidney donor ,Nephrology ,CKD-MBD ,CKD-Mineral and Bone Disorder ,deceased donor ,Eurotransplant Senior Program ,risk stratification ,intensive care ,kidney transplant recipients ,long-term outcomes ,graft failure ,cardiovascular mortality ,lifestyle ,inflammation ,vascular calcification ,bone mineral density ,dual-energy X-ray absorptiometry ,living donation ,repeated kidney transplantation ,graft survival ,prolonged ischaemic time ,patient survival ,pre-emptive transplantation ,metabolomics ,urine ,acute rejection ,allograft ,cystatin C ,hyperfiltration ,kidney injury molecule (KIM)-1 ,tubular damage ,genetic polymorphisms ,(cardiac) surgery ,inflammatory cytokines ,clinical studies ,chronic kidney disease (CKD) ,no known kidney disease (NKD) ,ICD-10 billing codes ,phenotyping ,electronic health record (EHR) ,estimated glomerular filtration rate (eGFR) ,machine learning (ML) ,generalized linear model network (GLMnet) ,random forest (RF) ,artificial neural network (ANN), clinical natural language processing (clinical NLP) ,discharge summaries ,laboratory values ,area under the receiver operating characteristic (AUROC) ,area under the precision-recall curve (AUCPR) ,fibrosis ,extracellular matrix ,collagen type VI ,living-donor kidney transplantation ,ethnic disparity - Abstract
Summary: In recent years, artificial intelligence has increasingly been playing an essential role in diverse areas in medicine, assisting clinicians in patient management. In nephrology and transplantation, artificial intelligence can be utilized to enhance clinical care, such as through hemodialysis prescriptions and the follow-up of kidney transplant patients. Furthermore, there are rapidly expanding applications and validations of comprehensive, computerized medical records and related databases, including national registries, health insurance, and drug prescriptions. For this Special Issue, we made a call to action to stimulate researchers and clinicians to submit their invaluable works and present, here, a collection of articles covering original clinical research (single- or multi-center), database studies from registries, meta-analyses, and artificial intelligence research in nephrology including acute kidney injury, electrolytes and acid-base, chronic kidney disease, glomerular disease, dialysis, and transplantation that will provide additional knowledge and skills in the field of nephrology and transplantation toward improving patient outcomes.
50. Computation in Complex Networks.
- Author
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Pizzuti, Clara, Socievole, Annalisa, and Pizzuti, Clara
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Technology: general issues ,city interaction network ,evolution model ,preferential attachment ,WeChat ,maximum likelihood ,chimera states ,coupled map lattice ,nilpotent matrix ,community detection ,membrane algorithm ,self-organizing map network ,complex networks ,optimization ,structural balance ,minimum memory based sign adjustment ,social networks ,NW network ,convergence ,complex system simulation ,cloud computing architecture ,service-oriented modeling ,semantic search framework ,QoS-based service selection ,cascading failures ,network topology ,null models ,SciSci ,knowledge evolution ,machine learning ,bridging centrality ,disjoint nodes ,disjunct nodes ,node similarity ,overlapping nodes ,Bayesian networks ,entropy ,socio-ecological system ,complex network ,chaotic time series ,Gaussian mixture model ,maximum mean discrepancy ,angiogenesis ,network properties ,variational inference ,graph neural network ,variational autoencoder ,network embedding ,online social networks ,social media ,information spreading ,information diffusion ,cross-entropy ,cross-domain recommendation ,sentiment analysis ,latent sentiment review feature ,non-linear mapping ,dissimilarity spaces ,support vector machines ,kernel methods ,computational biology ,systems biology ,protein contact networks ,data mining ,overlapping communities ,modularity ,literary works ,genre classification ,stylistic attributes ,lemmatization ,renormalisation process ,network growth ,inverse preferential attachment ,language networks ,language development ,multilayer complex networks ,stability ,spreading control ,graph neural networks ,node classification ,active learning ,graph representation learning ,n/a - Abstract
Summary: Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue "Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicine
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