Search results for "Deep Learning"
showing 10 items of 337 documents
Gravitational-wave parameter inference using Deep Learning
2021
We explore machine learning methods to detect gravitational waves (GW) from binary black hole (BBH) mergers using deep learning (DL) algorithms. The DL networks are trained with gravitational waveforms obtained from BBH mergers with component masses randomly sampled in the range from 5 to 100 solar masses and luminosity distances from 100 Mpc to, at least, 2000 Mpc. The GW signal waveforms are injected in public data from the O2 run of the Advanced LIGO and Advanced Virgo detectors, in time windows that do not coincide with those of known detected signals, and the data from each detector in the Advanced LIGO and Advanced Virgo network is combined into a unique RGB image. We show that a clas…
Automatic Segmentation Using a Hybrid Dense Network Integrated With an 3D-Atrous Spatial Pyramid Pooling Module for Computed Tomography (CT) Imaging
2020
Computed tomography (CT) with a contrast-enhanced imaging technique is extensively proposed for the assessment and segmentation of multiple organs, especially organs at risk. It is an important factor involved in the decision making in clinical applications. Automatic segmentation and extraction of abdominal organs, such as thoracic organs at risk, from CT images are challenging tasks due to the low contrast of pixel values surrounding other organs. Various deep learning models based on 2D and 3D convolutional neural networks have been proposed for the segmentation of medical images because of their automatic feature extraction capability based on large labeled datasets. In this paper, we p…
The Usage of Quadtree in Deep Neural Networks to Represent Data For Navigation From a Monocular Camera
2022
Depth acquisition represents a key element for navigation tasks. It is, therefore, one of the major research topics in computer vision. Many approaches have been developed to address this problem by constructing the depth from series of images. However, there is a minimal case proposing a prediction from a single image, made possible with the emergence of deep learning approaches. The latter makes it possible to consider a reduction in both hardware and computing time costs, which is beneficial for embedded systems. However, network architecture remains a heavy process requiring a lot of GPU memory. Few approaches have proposed addressing this problem by developing lightweight architectures…
Neural networks for animal science applications: Two case studies
2006
Abstract Artificial neural networks have shown to be a powerful tool for system modelling in a wide range of applications. In this paper, we focus on neural network applications to intelligent data analysis in the field of animal science. Two classical applications of neural networks are proposed: time series prediction and clustering. The first task is related to the prediction of weekly milk production in goat flocks, which includes a knowledge discovery stage in order to analyse the relative relevance of the different variables. The second task is the clustering of goat flocks; it is used to analyse different livestock surveys by using self-organizing maps and the adaptive resonance theo…
Semi-supervised deep learning-driven anomaly detection schemes for cyber-attack detection in smart grids
2022
Modern power systems are continuously exposed to malicious cyber-attacks. Analyzing industrial control system (ICS) traffic data plays a central role in detecting and defending against cyber-attacks. Detection approaches based on system modeling require effectively modeling the complex behavior of the critical infrastructures, which remains a challenge, especially for large-scale systems. Alternatively, data-driven approaches which rely on data collected from the inspected system have become appealing due to the availability of big data that supports machine learning methods to achieve outstanding performance. This chapter presents an enhanced cyber-attack detection strategy using unlabeled…
Classification of Sequences with Deep Artificial Neural Networks: Representation and Architectural Issues
2021
DNA sequences are the basic data type that is processed to perform a generic study of biological data analysis. One key component of the biological analysis is represented by sequence classification, a methodology that is widely used to analyze sequential data of different nature. However, its application to DNA sequences requires a proper representation of such sequences, which is still an open research problem. Machine Learning (ML) methodologies have given a fundamental contribution to the solution of the problem. Among them, recently, also Deep Neural Network (DNN) models have shown strongly encouraging results. In this chapter, we deal with specific classification problems related to t…
An Overview of the Application of Deep Learning in Short Read Sequence Classification
2020
AbstractAdvances in sequencing technology have led to an ever increasing amount of available short read sequencing data. This has, consequently, exacerbated the need for efficient and precise classification tools that can be used in the analysis of this data. As it stands, recent years have shown that massive leaps in performance can be achieved when it comes to approaches that are based in heuristics, and alongside these improvements there has been an ever increasing interest in applying deep learning techniques to revolutionize this classification task. We attempt to gather up these approaches and to evaluate their performance in a reproducible fashion to get a better perspective on the c…
Deep learning for agricultural land use classification from Sentinel-2
2020
[ES] En el campo de la teledetección se ha producido recientemente un incremento del uso de técnicas de aprendizaje profundo (deep learning). Estos algoritmos se utilizan con éxito principalmente en la estimación de parámetros y en la clasificación de imágenes. Sin embargo, se han realizado pocos esfuerzos encaminados a su comprensión, lo que lleva a ejecutarlos como si fueran “cajas negras”. Este trabajo pretende evaluar el rendimiento y acercarnos al entendimiento de un algoritmo de aprendizaje profundo, basado en una red recurrente bidireccional de memoria corta a largo plazo (2-BiLSTM), a través de un ejemplo de clasificación de usos de suelo agrícola de la Comunidad Valenciana dentro d…
Smart Data Blending Framework to Enhance Precipitation Estimation through Interconnected Atmospheric, Satellite, and Surface Variables
2023
Accurate precipitation estimation remains a challenge, though it is fundamental for most hydrological analyses. In this regard, this study aims to achieve two objectives. Firstly, we evaluate the performance of two precipitation products from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG) for Sicily, Italy, from 2016 to 2020 by a set of categorical indicators and statistical indices. Analyses indicate the favorable performance of daily estimates, while half-hourly estimates exhibited poorer performance, revealing larger discrepancies between satellite and ground-based measurements at sub-hourly timescales. Secondly, we propose four multi-source me…
EMBER—Embedding Multiple Molecular Fingerprints for Virtual Screening
2022
In recent years, the debate in the field of applications of Deep Learning to Virtual Screening has focused on the use of neural embeddings with respect to classical descriptors in order to encode both structural and physical properties of ligands and/or targets. The attention on embeddings with the increasing use of Graph Neural Networks aimed at overcoming molecular fingerprints that are short range embeddings for atomic neighborhoods. Here, we present EMBER, a novel molecular embedding made by seven molecular fingerprints arranged as different “spectra” to describe the same molecule, and we prove its effectiveness by using deep convolutional architecture that assesses ligands&…