Search results for "Artificial Intelligence"
showing 10 items of 6122 documents
Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran
2021
The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses. In this study, we applied two novel deep learning algorithms, the recurrent neural network (RNN) and convolutional neural network (CNN), for national-scale landslide susceptibility mapping of Iran. We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors (altitude, slope degree, profile curvature, distance to river, aspect, plan curvature, distance to road, distance to fault, rainfall, geology and land-sue) to construct a geospatial database and divided the data into the training and the testing dataset. We then d…
Modelling Complex Volume Shape Using Ellipsoid: Application to Pore Space Representation
2017
Natural shapes have complex volume forms that are usually difficult to model using simple analytical equations. The complexity of the representation is due to the heterogeneity of the physical environment and the variety of phenomena involved. In this study we consider the representation of the porous media. Thanks to the technological advances in Computed Topography scanners, the acquisition of images of complex shapes becomes possible. However, and unfortunately, the image data is not directly usable for simulation purposes. In this paper, we investigate the modeling of such shapes using a piece wise approximation of image data by ellipsoids. We propose to use a split-merge strategy and a…
Predicting year of plantation with hyperspectral and lidar data
2017
This paper introduces a methodology for predicting the year of plantation (YOP) from remote sensing data. The application has important implications in forestry management and inventorying. We exploit hyperspectral and LiDAR data in combination with state-of-the-art machine learning classifiers. In particular, we present a complete processing chain to extract spectral, textural and morphological features from both sensory data. Features are then combined and fed a Gaussian Process Classifier (GPC) trained to predict YOP in a forest area in North Carolina (US). The GPC algorithm provides accurate YOP estimates, reports spatially explicit maps and associated confidence maps, and provides sens…
The paramount power of selection: From Darwin to Kauffman
1995
For approximately two decades now, the Darwinian interpretation of evolution has now been challenged in many ways. Modern criticisms make it difficult, even for the staunchest Darwinians, not to take a distance from Darwin’s bold phrases on the “power” of natural selection. Let me remind you of some famous declarations of Darwin on the subject: “It may be said that natural selection is daily and hourly scrutinising, throughout the world, every variation, even the slightest; rejecting that which is bad, preserving and adding up all that is good; silently and insensibly working, whenever and wherever opportunity offers, at the improvement of each organic being in relation to its organic and i…
Stage boundaries, global stratigraphy, and the time scale: towards a simplification
2004
International audience; This paper examines four facets of stratigraphic terminology and usage considered faulty and proposes corrective measures. The four perfectible areas are: (1) The system of dual nomenclature requiring discrete terminologies for the superpositional and temporal aspects of rock units. (2) The premise that a GSSP establishes the base of a stage as being coincident with the top of the preceding stage rather than simply defining it as the boundary between stages. (3) The rejection of supplementary (auxiliary) sections that would broaden the knowledge of a GSSP and enlarge the area in which it is easily usable. (4) The current dual system of nomenclature for Precambrian an…
Experimental evidence suggests that specular reflectance and glossy appearance help amplify warning signals
2017
AbstractSpecular reflection appears as a bright spot or highlight on any smooth glossy convex surface and is caused by a near mirror-like reflectance off the surface. Convex shapes always provide the ideal geometry for highlights, areas of very strong reflectance, regardless of the orientation of the surface or position of the receiver. Despite highlights and glossy appearance being common in chemically defended insects, their potential signalling function is unknown. We tested the role of highlights in warning colouration of a chemically defended, alpine leaf beetle, Oreina cacaliae. We reduced the beetles’ glossiness, hence their highlights, by applying a clear matt finish varnish on thei…
Potential use of machine learning methods in assessment of Fusarium culmorum and Fusariumproliferatum growth and mycotoxin production in treatments w…
2021
Abstract The use of Fusarium-controlling fungicides is necessary to limit crop loss. Little is known about the effect of commercial antifungal formulations at sub-lethal doses, and their interaction with abiotic factors, on Fusarium culmorum and F. proliferatum development and on zearalenone and fumonisin biosynthesis, respectively. In the present study different treatments based on sulfur, trifloxystrobin and demethylation inhibitor fungicides (cyproconazole, tebuconazole and prothioconazole) under different environmental conditions, in Maize Extract Medium (MEM), are assayed in vitro. Then, several machine learning methods (neural networks, random forest and extreme gradient boosted trees…
Evaluation of image processing technique as an expert system in mulberry fruit grading based on ripeness level using artificial neural networks (ANNs…
2020
Abstract Image processing and artificial intelligence (AI) techniques have been applied to analyze, evaluate and classify mulberry fruit according to their ripeness (unripe, ripe, and overripe). A total of 577 mulberries were graded by an expert and the images were captured by an imaging system. Then, the geometrical properties, color, and texture characteristics of each segmented mulberry was extracted using two feature reduction methods: Correlation-based Feature Selection subset (CFS) and Consistency subset (CONS). Artificial Neural Networks (ANN) and Support Vector Machine (SVM) were applied to classify mulberry fruit. ANN classification with the CFS subset feature extraction method res…
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation
2019
Our understanding and ability to effectively monitor and manage coastal ecosystems are severely limited by observation methods. Automatic recognition of species in natural environment is a promising tool which would revolutionize video and image analysis for a wide range of applications in marine ecology. However, classifying fish from images captured by underwater cameras is in general very challenging due to noise and illumination variations in water. Previous classification methods in the literature relies on filtering the images to separate the fish from the background or sharpening the images by removing background noise. This pre-filtering process may negatively impact the classificat…
Calibrating Expert Assessments Using Hierarchical Gaussian Process Models
2020
Expert assessments are routinely used to inform management and other decision making. However, often these assessments contain considerable biases and uncertainties for which reason they should be calibrated if possible. Moreover, coherently combining multiple expert assessments into one estimate poses a long-standing problem in statistics since modeling expert knowledge is often difficult. Here, we present a hierarchical Bayesian model for expert calibration in a task of estimating a continuous univariate parameter. The model allows experts' biases to vary as a function of the true value of the parameter and according to the expert's background. We follow the fully Bayesian approach (the s…