Search results for "Machine learning techniques"

showing 4 items of 4 documents

Automatic early detection of decay in citrus fruit using optical technologies and machine learning techniques

2015

Los cítricos representan el cultivo frutal de mayor valor en términos de comercio internacional, siendo España el primer exportador mundial de cítricos para consumo en fresco. Sin embargo, la presencia de podredumbres causadas por hongos del género Penicillium se encuentra entre los principales problemas que afectan la postcosecha y comercialización de cítricos. Un número reducido de frutas infectadas puede contaminar una partida completa de cítricos durante el almacenamiento de la fruta por largos períodos de tiempo o en el transporte al extranjero, lo que conlleva grandes pérdidas económicas y el desprestigio de los productores de cítricos. Por lo tanto, la detección temprana de infeccion…

Fruit inspection:MATEMÁTICAS::Estadística ::Análisis de datos [UNESCO]:MATEMÁTICAS::Ciencia de los ordenadores::Inteligencia artificial [UNESCO]Decay in citrus fruit:MATEMÁTICAS::Ciencia de los ordenadores::Sistemas automatizados de control de calidad [UNESCO]Machine learning techniquesUNESCO::MATEMÁTICAS::Ciencia de los ordenadores::Sistemas automatizados de control de calidadUNESCO::MATEMÁTICAS::Estadística ::Análisis de datosOptical technologiesUNESCO::MATEMÁTICAS::Ciencia de los ordenadores::Inteligencia artificial
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Predicting sediment deposition rate in check-dams using machine learning techniques and high-resolution DEMs

2021

Sediments accumulated in check dams are a valuable measure to estimate soil erosion rates. Here, geographic information systems (GIS) and three machine learning techniques (MARS-multivariate adaptive regression splines, RF-random forest and SVM-support vector machine) were used, for the first time, to predict sediment deposition rate (SR) in check-dams located in six watersheds in SW Spain. There, 160 dry-stone check dams (~ 77.8 check-dams km−2), accumulated sediments during a period that varied from 11 to 23 years. The SR was estimated in former research using a topographical method and a high-resolution Digital Elevation Model (DEM) (average of 0.14 m3 ha−1 year−1). Nine environmental-to…

Mean squared error0208 environmental biotechnologyMean absolute errorSoil ScienceHigh resolution02 engineering and technology010501 environmental sciencesMachine learningcomputer.software_genre01 natural sciencesEnvironmental ChemistryDigital elevation model0105 earth and related environmental sciencesEarth-Surface ProcessesWater Science and TechnologyGlobal and Planetary ChangeMultivariate adaptive regression splinesbusiness.industryGeologyMars Exploration ProgramPollution020801 environmental engineeringCheck dam Machine learning techniques Sediment deposition rate (SR) Structure-from-motion (SfM) Unmanned aerial vehicle (UAV)Support vector machineArtificial intelligencebusinesscomputerCheck dam
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Simulation and anticipation as tools for coordinating with the future

2013

A key goal in designing an artificial intelligence capable of performing complex tasks is a mechanism that allows it to efficiently choose appropriate and relevant actions in a variety of situations and contexts. Nowhere is this more obvious than in the case of building a general intelligence, where the contextual choice and application of actions must be done in the presence of large numbers of alternatives, both subtly and obviously distinct from each other. We present a framework for action selection based on the concurrent activity of multiple forward and inverse models. A key characteristic of the proposed system is the use of simulation to choose an action: the system continuously sim…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniMechanism (biology)Computer sciencebusiness.industryAction selectionOutcome (game theory)AnticipationVariety (cybernetics)Domain (software engineering)Action SelectionAction (philosophy)Anticipation (artificial intelligence)Key (cryptography)Artificial intelligencebusinessMachine learning techniquesSimulation
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Investigating Novice Developers’ Code Commenting Trends Using Machine Learning Techniques

2023

Code comments are considered an efficient way to document the functionality of a particular block of code. Code commenting is a common practice among developers to explain the purpose of the code in order to improve code comprehension and readability. Researchers investigated the effect of code comments on software development tasks and demonstrated the use of comments in several ways, including maintenance, reusability, bug detection, etc. Given the importance of code comments, it becomes vital for novice developers to brush up on their code commenting skills. In this study, we initially investigated what types of comments novice students document in their source code and further categoriz…

luokitus (toiminta)Numerical Analysismachine learning techniquesohjelmistokehittäjätvasta-alkajatTheoretical Computer Sciencesource code commentsComputational MathematicskoneoppiminenclassificationComputational Theory and Mathematicssource code comments; classification; machine learning techniqueslähdekooditohjelmointiohjelmistokehitysAlgorithms; Volume 16; Issue 1; Pages: 53
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