Results 11 to 20 of about 528 (150)

Identificación de las principales enfermedades de la planta del café (Coffea arabica) a través de visión artificial

Ciencia Ergo Sum, 2023
Se utilizan técnicas de reconocimiento de patrones para identificar hojas sanas y cuatro enfermedades de la planta del café Coffeea arabica. Las enfermedades son la roya del café, el minador de la hoja, phoma quema y Cercospora coffeicola. Para lograrlo,
Oscar Eder Flores Colorado, Jair Cervantes Canales, Farid García-Lamont, José Sergio Ruiz Castilla   +3 more
doaj   +1 more source

Application of a deep learning image classifier for identification of Amazonian fishes

Ecology and Evolution, Volume 13, Issue 5, May 2023., 2023
To address the growing prevalence of large ecological datasets combined with a need for rapid species identification tools, we describe a novel application of a computer vision model, which is built to rapidly identify Amazonian fishes from captured image data.
Alexander J. Robillard, Michael G. Trizna, Morgan Ruiz‐Tafur, Edgard Leonardo Dávila Panduro, C. David de Santana, Alexander E. White, Rebecca B. Dikow, Jessica L. Deichmann   +7 more
wiley   +1 more source

Redes neuronales artificiales en el pronóstico de la producción de leche bovina

Revista Colombiana de Computación, 2022
Los pronósticos facilitan la toma de decisiones en granjas productoras de leche y contribuyen a mejorar la cadena productiva de este alimento. En la literatura se identificó que las redes neuronales artificiales poseen un ajuste aceptable al pronóstico ...
Rudibel Perdigón-Llanes, Neilys González-Benítez   +1 more
doaj   +1 more source

Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation

Methods in Ecology and Evolution, Volume 13, Issue 11, Page 2622-2634, November 2022., 2022
Abstract Tropical forests are subject to diverse deforestation pressures while their conservation is essential to achieve global climate goals. Predicting the location of deforestation is challenging due to the complexity of the natural and human systems involved but accurate and timely forecasts could enable effective planning and on‐the‐ground ...
James G. C. Ball, Katerina Petrova, David A. Coomes, Seth Flaxman   +3 more
wiley   +1 more source

Automated identification of diverse Neotropical pollen samples using convolutional neural networks

Methods in Ecology and Evolution, Volume 13, Issue 9, Page 2049-2064, September 2022., 2022
Abstract Pollen is used to investigate a diverse range of ecological problems, from identifying plant–pollinator relationships to tracking flowering phenology. Pollen types are identified according to a set of distinctive morphological characters which are understood to capture taxonomic differences and phylogenetic relationships among taxa.
Surangi W. Punyasena, Derek S. Haselhorst, Shu Kong, Charless C. Fowlkes, J. Enrique Moreno   +4 more
wiley   +1 more source

Artificial intelligence to improve efficiency of administration of gross motor function assessment in children with cerebral palsy

Developmental Medicine &Child Neurology, Volume 64, Issue 2, Page 228-234, February 2022., 2022
Aim To create a reduced version of the 66‐item Gross Motor Function Measure (rGMFM‐66) using innovative artificial intelligence methods to improve efficiency of administration of the GMFM‐66. Method This study was undertaken using information from an existing data set of children with cerebral palsy participating in a rehabilitation programme ...
Ibrahim Duran, Christina Stark, Ahmet Saglam, Alexandra Semmelweis, Heidrun Lioba Wunram, Karoline Spiess, Eckhard Schoenau   +6 more
wiley   +1 more source

Microglia modulate hippocampal synaptic transmission and sleep duration along the light/dark cycle

Glia, Volume 70, Issue 1, Page 89-105, January 2022., 2022
Main Points Neuronal spontaneous activity and microglial CX3CR1 increase in the dark. Microglia alterations affect sleep and synaptic transmission in a phase‐dependent manner. CX3CR1 mediates microglial effects on neuronal activity in the light/dark cycle.
Giorgio Corsi, Katherine Picard, Maria Amalia di Castro, Stefano Garofalo, Federico Tucci, Giuseppina Chece, Claudio del Percio, Maria Teresa Golia, Marcello Raspa, Ferdinando Scavizzi, Fanny Decoeur, Clotilde Lauro, Mara Rigamonti, Fabio Iannello, Davide Antonio Ragozzino, Eleonora Russo, Giovanni Bernardini, Agnès Nadjar, Marie Eve Tremblay, Claudio Babiloni, Laura Maggi, Cristina Limatola   +21 more
wiley   +1 more source

Big Data, AI und die Freude am Ingenieurwesen Big Data, AI, and the Pleasures of Engineering

Chemie Ingenieur Technik, Volume 93, Issue 3, Page 364-372, March 2021., 2021
Gegen die allzu einmütige Begeisterung gegenüber Big Data und AI soll in diesem Beitrag ein kleiner Kontrapunkt gesetzt werden. Konkret wird nach den Auswirkungen dieser Techniken auf die Ingenieurwissenschaften gefragt. Die provokative Schlussfolgerung lautet: Big Data und AI verderben – mittelfristig – den Spaß am Ingenieurwesen.
Michael Kuhn
wiley   +1 more source

Use of Neural network to predict the peak ground accelerations and pseudo spectral accelerations for Mexican Inslab and Interplate Earthquakes

Geofísica Internacional, 2014
El uso de redes neuronales artificiales es explorado para predecir aceleraciones máximas del terreno y pseudoaceleraciones para sismos de tipo intraslabe interplaca.
Adrián Pozos-Estrada, Roberto Gómez, H.P. Hong   +2 more
doaj   +1 more source

Elaboración de un modelo neuronal artificial para la estimación de la demanda bioquímica de oxígeno en aguas marinas

Revista de Investigación Agraria y Ambiental, 2020
Contextualización: los modelos neuronales artificiales son modelos diseñados a partir de métodos numéricos denominados Redes Neuronales Artificiales. El uso de estos, como herramienta de predicción de la Demanda Bioquímica de Oxígeno (DBO), ha demostrado
Reynaldo Meza Castellar, Luis Gonzalez Salcedo   +1 more
doaj   +1 more source