Search results for "Deep Learning"
showing 10 items of 337 documents
Discovering human mobility from mobile data : probabilistic models and learning algorithms
2020
Smartphone usage data can be used to study human indoor and outdoor mobility. In our work, we investigate both aspects in proposing machine learning-based algorithms adapted to the different information sources that can be collected.In terms of outdoor mobility, we use the collected GPS coordinate data to discover the daily mobility patterns of the users. To this end, we propose an automatic clustering algorithm using the Dirichlet process Gaussian mixture model (DPGMM) so as to cluster the daily GPS trajectories. This clustering method is based on estimating probability densities of the trajectories, which alleviate the problems caused by the data noise.By contrast, we utilize the collecte…
Exploring architectural traits and ecophysiological responses in soybean under heat and water stress: implications for climate change adaptation
2023
In the context of climate change, characterized by increasingly frequent droughts and heat waves, it is anticipated that the global soybean yields, the most extensively grown legume, will experience a significant decline in the foreseeable future.. There is thus an urgent need to improve its ability to maintain growth and productivity under such conditions. The objective of this study was to explore which plant traits make soybeans more resilient to heat and/or water stress, with a focus on plant architecture. For this purpose, two soybean genotypes, already shown to have contrasted root architecture (Maslard et al., 2021) were grown under controlledconditions in the high-throughput phenoty…
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.
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…
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…
Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization
2019
AbstractVisual morphology assessment is routinely used for evaluating of embryo quality and selecting human blastocysts for transfer after in vitro fertilization (IVF). However, the assessment produces different results between embryologists and as a result, the success rate of IVF remains low. To overcome uncertainties in embryo quality, multiple embryos are often implanted resulting in undesired multiple pregnancies and complications. Unlike in other imaging fields, human embryology and IVF have not yet leveraged artificial intelligence (AI) for unbiased, automated embryo assessment. We postulated that an AI approach trained on thousands of embryos can reliably predict embryo quality with…
Estimating Tree Health Decline Caused by Ips typographus L. from UAS RGB Images Using a Deep One-Stage Object Detection Neural Network
2022
Various biotic and abiotic stresses are causing decline in forest health globally. Presently, one of the major biotic stress agents in Europe is the European spruce bark beetle (Ips typographus L.) which is increasingly causing widespread tree mortality in northern latitudes as a consequence of the warming climate. Remote sensing using unoccupied aerial systems (UAS) together with evolving machine learning techniques provide a powerful tool for fast-response monitoring of forest health. The aim of this study was to investigate the performance of a deep one-stage object detection neural network in the detection of damage by I. typographus in Norway spruce trees using UAS RGB images. A Scaled…
Domain‐specific neural networks improve automated bird sound recognition already with small amount of local data
2022
1. An automatic bird sound recognition system is a useful tool for collecting data of different bird species for ecological analysis. Together with autonomous recording units (ARUs), such a system provides a possibility to collect bird observations on a scale that no human observer could ever match. During the last decades, progress has been made in the field of automatic bird sound recognition, but recognizing bird species from untargeted soundscape recordings remains a challenge. 2. In this article, we demonstrate the workflow for building a global identification model and adjusting it to perform well on the data of autonomous recorders from a specific region. We show how data augmentatio…
Convolutional Long Short-Term Memory Network for Multitemporal Cloud Detection Over Landmarks
2019
In this work, we propose to exploit both the temporal and spatial correlations in Earth observation satellite images through deep learning methods. In particular, the combination of a U-Net convolutional neural network together with a convolutional long short-term memory (LSTM) layer is proposed. This model is applied for cloud detection on MSG/SEVIRI image time series over selected landmarks. Implementation details are provided and our proposal is compared against a standard SVM and a U-Net without the convolutional LSTM layer but including temporal information too. Experimental results show that this combination of networks exploits both the spatial and temporal dependence and provides st…
Infantile Hemangioma Detection using Deep Learning
2020
Infantile hemangiomas are the most common type of benign tumor which appear in the first weeks of life. As currently there is no robust protocol to monitor and assess the hemangioma status, this study proposes a preliminary method to detect the lesion. Therefore, in this paper we describe a hemangiomas classifier based on a linear convolutional neural network architecture. The challenge was to achieve a good classification using a relatively small internal database of 240 images from 40 different patients. The results are promising as the CNN performance evaluation showed a level of accuracy on the test set of 93.84%. Five metrics were calculated to assess the proposed model performances: a…