Search results for "Machine learning"

showing 10 items of 1464 documents

Modern Multispectral Sensors Help Track Explosive Eruptions

2013

Due to its massive air traffic impact, the 2010 eruption of Eyjafjallajokull was felt by millions of people and cost airlines more than U.S. $1.7 billion. The event has, thus, become widely cited in renewed efforts to improve real-time tracking of volcanic plumes, as witnessed by special sections published last year in Journal of Geophysical Research, (117, issues D20 and B9).

geographyExplosive eruptiongeography.geographical_feature_category010504 meteorology & atmospheric sciencesMeteorologyStrombolian Eruptions Multi-sensor field surveyMultispectral imageAir traffic control010502 geochemistry & geophysicsTrack (rail transport)01 natural sciencesAeronauticsVolcano[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG][INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing13. Climate action[SDU]Sciences of the Universe [physics]General Earth and Planetary SciencesGeologyComputingMilieux_MISCELLANEOUS0105 earth and related environmental sciences
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Mapping lava flows at Etna Volcano using Google Earth Engine, open-access satellite data, and machine learning

2021

Estimating eruptive parameters is fundamental to assess the volcanic hazards posed to the community living at the edge of active volcanoes. Here, we analyzed satellite remote sensing data by using machine learning unsupervised and supervised techniques and analytical approaches, i.e., mathematical-physics and statistics formulations, to map lava flows emitted during the long sequences of short-lived, violent eruptions occurred at Etna volcano between December 2020 and March 2021. Satellite observations allowed to follow the evolution of eruptions thanks to their capability to survey large areas with frequent revisit time and accurate spatial resolution. We quantified the areal coverage of l…

geographyVolcanic hazardsgeography.geographical_feature_categoryLearning classifier systembusiness.industryLavaMachine learningcomputer.software_genrelaw.inventionEtna volcanoVolcanolawSatelliteArtificial intelligenceRadarbusinesscomputerImage resolutionGeology2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)
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Description of movement sensor dataset for dog behavior classification

2022

The description and results of the original investigation are found in: Dog behaviour classification with movement sensors placed on the harness and the collar, Kumpulainen, P., Valldeoriola Cardó, A., Somppi, S., Törnqvist, H., Väätäjä, H., Majaranta, P., Gizatdinova, Y., Antink, C. H., Surakka, V., V. Kujala, M., Vainio, O. & Vehkaoja, A., Aug 2021, In: Applied Animal Behaviour Science. 241, 7 p., 105393. Movement sensor data from seven static and dynamic dog behaviors (sitting, standing, lying down, trotting, walking, playing, and (treat) searching i.e. sniffing) was collected from 45 middle to large sized dogs with six degree-of-freedom movement sensors attached to the collar and the ha…

gyroscopeScience (General)Computer applications to medicine. Medical informaticsR858-859.7behavior classificationliikkeenkaappaus413 Veterinary scienceeläinten käyttäytyminenkoiradog activity classificationQ1-390mittauslaitteetMachine learningDog activity classificationData Articleluokitus (toiminta)MultidisciplinaryMovement sensor113 Computer and information sciencesGyroscopeAccelerometermovement sensoraccelerometermachine learningkoneoppiminenBehavior classificationData in Brief
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Time Unification on Local Binary Patterns Three Orthogonal Planes for Facial Expression Recognition

2019

International audience; Machine learning has known a tremendous growth within the last years, and lately, thanks to that, some computer vision algorithms started to access what is difficult or even impossible to perceive by the human eye. While deep learning based computer vision algorithms have made themselves more and more present in the recent years, more classical feature extraction methods, such as the ones based on Local Binary Patterns (LBP), still present a non negligible interest, especially when dealing with small datasets. Furthermore, this operator has proven to be quite useful for facial emotions and human gestures recognition in general. Micro-Expression (ME) classification is…

human eyeHistogramsgeometryUnificationComputer scienceLocal binary patternsoptimisationFeature extraction02 engineering and technologyhuman gestures recognitionFacial recognition systemcomputer visionVideos[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]time unification method03 medical and health sciences0302 clinical medicineMathematical modelLBPemotion recognition0202 electrical engineering electronic engineering information engineeringfacial emotionsfacial expression recognitionlocal binary patternsFace recognitionContextual image classificationArtificial neural networkbusiness.industryDeep learningdeep learning[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Pattern recognitionComputational modelingmicroexpression classificationInterpolationorthogonal planesneural netsmachine learning[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Micro expressionFeature extraction020201 artificial intelligence & image processinglearning (artificial intelligence)Artificial intelligencebusiness030217 neurology & neurosurgeryGestureimage classification
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A method for anomaly detection in hyperspectral images, using deep convolutional autoencoders

2017

Menetelmä poikkeavuuksien havaitsemiseen hyperspektrikuvista käyttäen syviä konvolutiivisia autoenkoodereita. Poikkeavuuksien havaitseminen kuvista, erityisesti hyperspektraalisista kuvista, on hankalaa. Kun ongelmaan yhdistetään ennalta tuntematon data ja poikkeavuudet, muodostuu ongelma vielä laajemmaksi. Spektraalisten poikkeavuuksien havaitsemiseen on kehitetty useita eri menetelmiä, mutta spatiaalisten poikkeavuuksien havaitseminen on huomattavasti hankalempaa. Tässä työssä esitellään uudenkaltainen menetelmä sekä spatiaalisten että spektraalisten poikkeavuuksien samanaikaiseen havaitsemiseen. Menetelmä on suunniteltu erityisesti spektraaliselle datalle, mutta soveltuu myös perinteisil…

hyperspectral imagesautoencoderautoenkooderithdbscanSCAEconvolutional neural networkdeep learninghavaitseminenneuroverkotanomaly detectionconvolutional autoencodermachine learningkoneoppiminenpoikkeavuuskonvoluutioälytekniikkaCAEhyperspektrikuvatautoenkooderi
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Can Individual Movement Characteristics Across Different Throwing Disciplines Be Identified in High-Performance Decathletes?

2020

Although the individuality of whole-body movements has been suspected for years, the scientific proof and systematic investigation that individuals possess unique movement patterns did not manifest until the introduction of the criteria of uniqueness and persistence from the field of forensic science. Applying the criteria of uniqueness and persistence to the individuality of motor learning processes requires complex strategies due to the problem of persistence in the learning processes. One approach is to examine the learning process of different movements. For this purpose, it is necessary to differentiate between two components of movement patterns: the individual-specific component and …

individuality796 Sportlcsh:BF1-990pattern recognition796 Athletic and outdoor sports and gameslcsh:Psychologymachine learningtransdisciplinary individualityPsychologyhigh-performance sportssupport vector machinemotor learningGeneral PsychologyOriginal ResearchFrontiers in Psychology
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Desafíos y oportunidades de Sentinel-2 en la monitorización de las aguas continentales

2023

En los ecosistemas de agua dulce, la escasez y la contaminación de este recurso está promoviendo que los organismos gubernamentales incluyan en sus agendas estrategias para mitigar esta situación a través de una gestión sostenible. La Directiva Marco del Agua establece entre sus requerimientos la monitorización del estado ecológico de las aguas continentales para determinar su calidad. Las imágenes satelitales ofrecen una visión sinóptica y continua a partir de la que es posible derivar métricas del estado ecológico. Esas métricas son un complemento a los tradicionales muestreos ya que se incrementa la cobertura espacial y la periodicidad en la monitorización. Sentinel-2, con su sensor Mult…

inland waterssupervised classificationsentinel-2UNESCO::FÍSICAoptical water typesremote sensingmachine learningtipos ópticos de aguasaprendizaje automáticoteledeteccióncalidad de las aguasaguas continentalesdisco de secchiclasificación supervisadacorrección atmosféricaUNESCO::CIENCIAS DE LA TIERRA Y DEL ESPACIOconcentración de clorofilaUNESCO::GEOGRAFÍA
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Large-scale nonlinear dimensionality reduction for network intrusion detection

2017

International audience; Network intrusion detection (NID) is a complex classification problem. In this paper, we combine classification with recent and scalable nonlinear dimensionality reduction (NLDR) methods. Classification and DR are not necessarily adversarial, provided adequate cluster magnification occurring in NLDR methods like $t$-SNE: DR mitigates the curse of dimensionality, while cluster magnification can maintain class separability. We demonstrate experimentally the effectiveness of the approach by analyzing and comparing results on the big KDD99 dataset, using both NLDR quality assessment and classification rate for SVMs and random forests. Since data involves features of mixe…

intrusion detection[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG][ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][ INFO.INFO-LG ] Computer Science [cs]/Machine Learning [cs.LG][STAT.ML] Statistics [stat]/Machine Learning [stat.ML][INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]ComputingMethodologies_PATTERNRECOGNITION[STAT.ML]Statistics [stat]/Machine Learning [stat.ML][INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]Gower[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML][SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingdimensionality reduction
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Towards a Great Design of Conceptual Modelling

2020

Humankind faces a most crucial mission; we must endeavour, on a global scale, to restore and improve our natural and social environments. This is a big challenge for global information systems development and for their modelling. In this paper, we discuss on different aspects of conceptual modelling in global environmental context. The paper is the summary of the panel session “The Future of Conceptual Modelling” in the 29th International Conference on Information Modelling and Knowledge Bases. peerReviewed

järjestelmäsuunnitteluympäristöteknologiaglobalisaatiocontext computingconceptual modellingdata miningtekoälyartificial intelligence113 Computer and information sciencesmodel suitesenvironmental ICTmachine learningkoneoppiminenmulti-agent systemsemantic computing5D world map systemtiedonlouhintaglobalizationkonseptisuunnittelu
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Unstable feature relevance in classification tasks

2011

knowledge discoveryaineistottiedonhallintatekoälyfeature relevancefeature weightingrelevanssifeature selectionmachine learningkoneoppiminenclassificationanalyysiensemble learningtietokannattiedonlouhintaData miningtiedonhakuclusteringluokitus
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