Search results for "machine learning."
showing 10 items of 1455 documents
Structuration spatiale des principaux poissons démersaux autour de l’île de la Réunion à partir de la formeexterne de leurs otolithes
2022
L’identification et la connaissance de la structuration spatiale de stocks sont essentielles pour étudier la dynamique des populations de poissons et ainsi gérer les pêcheries. Dans cette étude, la forme des otolithes a été employée pour comprendre la structuration des stocks des populations des principales espèces commerciales capturées à l’île de La Réunion. Un total de 1091 individus, appartenant à 9 espèces de poissons osseux bentho-pélagiques de différents compartiments d’habitats coralliens et profonds (Aphareus rutilans, Cephalopholis aurantia, Epinephelus fasciatus, Etelis carbunculus, Lutjanus kasmira, Lutjanus notatus, Pristipomoides argyrogrammicus, Pristipomoides filamentosus, V…
Estimation de l’indice foliaire et de la biomasse du blé et des adventices par imagerie visible et machine learning : vers un nouvel indicateur non d…
2019
National audience; Cette étude propose d’estimer précocement par imagerie deux variables clés dans la gestion des cultures et dans la compétition culture-adventices : l’indice foliaire (LAI) et la biomasse aérienne sèche (BM). Une expérimentation a été conduite au champ pendant la phase végétative d’une culture de blé. Pour chaque peuplement (culture de blé, adventices), les taux de couverture du sol par la végétation (TCc, TCw) ont été déduits du traitement d’image basé sur une technique de machine learning. LAI et BM ont été mesurés de façon destructive. Puis, une calibration a été réalisée entre TC et LAI d’une part et entre TC et BM d’autre part. Ce travail pourrait, à terme, faciliter …
Analyse des réseaux trophiques et quantification des interactions
2017
Prod 2017-344e SPE équipe EA GESTAD INRA; National audience; L’importante littérature consacrée au sujet suggère une relation positive entre la biodiversité en milieu agricole et la fourniture de services écosystémiques, notamment le service de contrôle des ravageurs par leurs ennemis naturels. Cependant, cette relation n’est que statistique et de nombreux contre-exemples peuvent être trouvés. L’une des raisons principales de l’absence d’additivité des effets des ennemis naturels réside dans la complexité des réseaux d’interactions qui se mettent en place dans les communautés diversifiées. Ainsi, par exemple, des phénomènes de compétition, voire de prédation intra-guilde peuvent conduire à …
Accelerometry - Simple, but challenging
2017
Analisi di test di Immunofluorescenze indiretta per il supporto alla diagnosi di Malattie Autoimmuni basata su Deep Learning.
2019
La diagnosi delle malattie autoimmuni rappresenta un problema molto importante in medicina. Il test più utilizzato a questo scopo è il test anticorpo antinucleo, un test indiretto di immunofluorescenza. Il metodo proposto affronta tale problema sfruttando le metodologie del Machine Learning. In particolare, fa uso di reti neurali pre-addestrate in grado di classificare i pattern auto anticorpali collegati alle patologie autoimmuni. Gli strati delle reti pre-addestrate e vari parametri di sistema sono stati valutati al fine di ottimizzare il processo. Le prestazioni del sistema sono state valutate in termini di accuratezza in un processo di cross validation, mostrando efficienza e robustezza.
Discriminating and simulating actions with the associative self-organising map
2015
We propose a system able to represent others’ actions as well as to internally simulate their likely continuation from a partial observation. The approach presented here is the first step towards a more ambitious goal of endowing an artificial agent with the ability to recognise and predict others’ intentions. Our approach is based on the associative self-organising map, a variant of the self-organising map capable of learning to associate its activity with different inputs over time, where inputs are processed observations of others’ actions. We have evaluated our system in two different experimental scenarios obtaining promising results: the system demonstrated an ability to learn discrim…
A Deep Learning Model for Automatic Sleep Scoring using Multimodality Time Series
2021
Sleep scoring is a fundamental but time-consuming process in any sleep laboratory. Automatic sleep scoring is crucial and urgent to help address the increasing unmet need for sleep research. Therefore, this paper aims to develop an end-to-end deep learning architecture using raw polysomnographic recordings to automate sleep scoring. The proposed model adopts two-dimensional convolutional neural networks (2D-CNN) to automatically learn features from multi-modality signals, together with a "squeeze and excitation" block for recalibrating channel-wise feature responses. The learnt representations are finally fed to a softmax classifier to generate predictions for each sleep stage. The model pe…
Endotyping allergic rhinitis in children: A machine learning approach.
2021
Introduction: The diversity of allergic rhinitis (AR) phenotypes is particularly evident in childhood, suggesting the need to analyze and identify new approaches to capture such clinical heterogeneity. Nasal cytology (NC) is a very useful diagnostic tool for identifying and quantifying nasal inflammation. Data-driven approaches such as latent class analysis (LCA) assign subjects to classes based on their characteristics. We hypothesized that LCA based on NC, including the assessment of neutrophils, eosinophils, and mast cells, may be helpful for identifying AR endotypes in children. Methods: A total of 168 children were enrolled. Sociodemographic characteristics and detailed medical history…
A novel pilot study of automatic identification of EMF radiation effect on brain using computer vision and machine learning
2020
Abstract Electromagnetic field (EMF) radiations from mobile phones and cell tower affect brain of humans and other organisms in many ways. Exposure to EMF could lead to neurological changes causing morphological or chemical changes in the brain and other internal organs. Cellular level analysis to measure and identify the effect of mobile radiations is an expensive and long process as it requires preparing the cell suspension for the analysis. This paper presents a novel pilot study to identify changes in brain morphology under EMF exposure considering drosophila melanogaster as a specimen. The brain is automatically segmented, obtaining microscopic images from which discriminatory geometri…
Robust Automated Assessment of Human Blastocyst Quality using Deep Learning
2018
AbstractMorphology assessment has become the standard method for evaluation of embryo quality and selecting human blastocysts for transfer inin vitro fertilization(IVF). This process is highly subjective for some embryos and thus prone to human bias. As a result, morphological assessment results may vary extensively between embryologists and in some cases may fail to accurately predict embryo implantation and live birth potential. Here we postulated that an artificial intelligence (AI) approach trained on thousands of embryos can reliably predict embryo quality without human intervention.To test this hypothesis, we implemented an AI approach based on deep neural networks (DNNs). Our approac…