Search results for "PREDICTION"
showing 10 items of 511 documents
Modeling Chickenpox Dynamics with a Discrete Time Bayesian Stochastic Compartmental Model
2018
[EN] We present a Bayesian stochastic susceptible-exposed-infectious-recovered model in discrete time to understand chickenpox transmission in the Valencian Community, Spain. During the last decades, different strategies have been introduced in the routine immunization program in order to reduce the impact of this disease, which remains a public health's great concern. Under this scenario, a model capable of explaining closely the dynamics of chickenpox under the different vaccination strategies is of utter importance to assess their effectiveness. The proposed model takes into account both heterogeneous mixing of individuals in the population and the inherent stochasticity in the transmiss…
Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review
2022
Objective: The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of this systematic review is to evaluate the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions. Materials and Methods: Comprehensive searches were performed on three databases: Medline, Web of Science Core Collection (Indexes = SCI-EXPANDED, SSCI, A & HCI Timespan) and Scopu…
Exploiting deep learning algorithms and satellite image time series for deforestation prediction
2022
In recent years, we have witnessed the emergence of Deep Learning (DL) methods, which have led to enormous progress in various fields such as automotive driving, computer vision, medicine, finances, and remote sensing data analysis. The success of these machine learning methods is due to the ever-increasing availability of large amounts of information and the computational power of computers. In the field of remote sensing, we now have considerable volumes of satellite images thanks to the large number of Earth Observation (EO) satellites orbiting the planet. With the revisit time of satellites over an area becoming shorter and shorter, it will probably soon be possible to obtain daily imag…
Biodegradability Prediction of Fragrant Molecules by Molecular Topology
2016
Biodegradability is a key property in the development of safer fragrances. In this work we present a green methodology for its preliminary assessment. The structure of various fragrant molecules is characterized by computing a large set of topological indices. Those relevant to biodegradability are selected by means of a hybrid stepwise selection method to build a linear classifier. This model is compared with a more complex artificial neural network trained with the indices previously found. After validation, the models show promise for time and cost reduction in the development of new, safer fragrances. The methodology presented could easily be adapted to many quasi-big data problems in R…
A Review of Kernel Methods in Remote Sensing Data Analysis
2011
Kernel methods have proven effective in the analysis of images of the Earth acquired by airborne and satellite sensors. Kernel methods provide a consistent and well-founded theoretical framework for developing nonlinear techniques and have useful properties when dealing with low number of (potentially high dimensional) training samples, the presence of heterogenous multimodalities, and different noise sources in the data. These properties are particularly appropriate for remote sensing data analysis. In fact, kernel methods have improved results of parametric linear methods and neural networks in applications such as natural resource control, detection and monitoring of anthropic infrastruc…
An Application of Hybrid Models in Credit Scoring
2000
The predictive capability of parametric and non-parametric models in solving problems related to financial classification has been widely proved in empirical research carried out in the financial field, particulary in problems like bond rating, bankruptcy prediction and credit scoring. However, recently, it has been shown that a combination of different models generally reduces the prediction error, so that the best alternative to consider may not be a specific model but a combination of them. In this paper, we study hybrid systems based on the aggregation of individual (parametric and nonparametric) models. Our hybrids are built by using both parametric and non parametric models as the sys…
Recent advances in machine learning for maximal oxygen uptake (VO2 max) prediction : A review
2022
Maximal oxygen uptake (VO2 max) is the maximum amount of oxygen attainable by a person during exercise. VO2 max is used in different domains including sports and medical sciences and is usually measured during an incremental treadmill or cycle ergometer test. The drawback of directly measuring VO2 max using the maximal test is that it is expensive and requires a fixed and controlled protocol. During the last decade, various machine learning models have been developed for VO2 max prediction and numerous studies have attempted to predict VO2 max using data from submaximal and non-exercise tests. This article gives an overview of the machine learning models developed over the past five years (…
A Standard Siren Measurement of the Hubble Constant from GW170817 without the Electromagnetic Counterpart
2019
We perform a statistical standard siren analysis of GW170817. Our analysis does not utilize knowledge of NGC 4993 as the unique host galaxy of the optical counterpart to GW170817. Instead, we consider each galaxy within the GW170817 localization region as a potential host; combining the redshift from each galaxy with the distance estimate from GW170817 provides an estimate of the Hubble constant, $H_0$. We then combine the $H_0$ values from all the galaxies to provide a final measurement of $H_0$. We explore the dependence of our results on the thresholds by which galaxies are included in our sample, as well as the impact of weighting the galaxies by stellar mass and star-formation rate. Co…
Mixed-Phase Clouds: Progress and Challenges
2017
Mixed-phase clouds represent a three-phase colloidal system consisting of water vapor, ice particles, and coexisting supercooled liquid droplets. Mixed-phase clouds are ubiquitous in the troposphere, occurring at all latitudes from the polar regions to the tropics. Because of their widespread nature, mixed-phase processes play critical roles in the life cycle of clouds, precipitation formation, cloud electrification, and the radiative energy balance on both regional and global scales. Yet, in spite of many decades of observations and theoretical studies, our knowledge and understanding of mixed-phase cloud processes remains incomplete. Mixed-phase clouds are notoriously difficult to represe…
Comparative assessment of RAMS and WRF short-term forecasts over Eastern Iberian Peninsula using various in-situ observations, remote sensing product…
2018
The Regional Atmospheric Modeling System (RAMS) and the Weather Research and Forecasting (WRF) mesoscale models are being used for weather and air quality studies as well as forecasting tools in Numerical Weather Prediction (NWP) systems. In the current study, we perform a comparative assessment of these models under distinct typical atmospheric conditions, classified according to the dominant wind flow and cloudiness, over Eastern Iberian Peninsula. This study is focused on the model representation of key physical processes in terms of meteorology and surface variables during a 7-days period in summer 2011. The hourly outputs produced by these two models are compared not only with observed…