Consulta: subjectFacets:"Machine learning"
Registros recuperados: 25
Data/hora: 12/06/2024 15:28:31

THE USE OF ARTIFICIAL INTELLIGENCE FOR ESTIMATING SOIL RESISTANCE TO PENETRATION
Provedor de dados: 70
Autores: Pereira,Tonismar dos S.; Robaina,Adroaldo D.; Peiter,Marcia X.; Torres,Rogerio R.; Bruning,Jhosefe.
ABSTRACT The aim of this study was to present and to evaluate methodologies for the estimation of soil resistance to penetration (RP) using artificial intelligence prediction techniques. In order to do so, a data base with values of physical-water characteristics of the soils available in the literature was used, and the performances of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) were evaluated. The models generated from the ANNs were implemented through the multilayer perceptron with backpropagation algorithm of Matlab software, varying the number of neurons in the input and intermediate layers. For the procedure from SVM, the RapidMiner software was used, varying input variables, the kernel function and the coefficients of these...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Soil compaction; Machine learning; Support vector machines; Artificial neural networks.
Ano: 2018
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162018000100142

Evaluation of noise reduction techniques in the splice junction recognition problem
Provedor de dados: 74
Autores: Lorena,Ana C.; Carvalho,André C. P. L. F. de.
The Human Genome Project has generated a large amount of sequence data. A number of works are currently concerned with analyzing these data. One of the analyses carried out is the identification of genes' structures on the sequences obtained. As such, one can search for particular signals associated with gene expression. Splice junctions represent a type of signal present on eukaryote genes. Many studies have applied Machine Learning techniques in the recognition of such regions. However, most of the genetic databases are characterized by the presence of noisy data, which can affect the performance of the learning techniques. This paper evaluates the effectiveness of five data pre-processing algorithms in the elimination of noisy instances from two splice...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Pre-processing; Machine learning; Splice junction recognition.
Ano: 2004
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572004000400031

Evaluation of gene selection metrics for tumor cell classification
Provedor de dados: 74
Autores: Faceli,Katti; Carvalho,André C.P.L.F. de; Silva Jr,Wilson A.
Gene expression profiles contain the expression level of thousands of genes. Depending on the issue under investigation, this large amount of data makes analysis impractical. Thus, it is important to select subsets of relevant genes to work with. This paper investigates different metrics for gene selection. The metrics are evaluated based on their ability in selecting genes whose expression profile provides information to distinguish between tumor and normal tissues. This evaluation is made by constructing classifiers using the genes selected by each metric and then comparing the performance of these classifiers. The performance of the classifiers is evaluated using the error rate in the classification of new tissues. As the dataset has few tissue samples,...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Gene selection; Machine learning; Gene expression; Sage.
Ano: 2004
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572004000400029

Metodologia para processamento de imagens digitais do sistema radicular de milho e sorgo utilizando a plataforma Digital Imaging of Root Traits (DIRT).
Provedor de dados: 14
Autores: SANTOS, T. T.; SOUSA, S. M. de; CAMPOLINO, M. L.; LANA, U. G. de P.; COELHO, A. M.
Neste trabalho, apresentamos uma nova metodologia para segmentação e identificação de itens em imagens, baseada em aprendizado de máquina, que é mais robusta que a metodologia de pré-processamento de imagens originalmente proposta para o DIRT.
Tipo: Boletim de Pesquisa e Desenvolvimento (INFOTECA-E)
Palavras-chave: Metodologia; Processamento de imagem digital; Digital Imaging of Root Traits; Raiz de planta; Aprendizado de máquina; Shovelomics; Árvore de decisão; Image processing; Machine learning; Decision tree; Fósforo; Digital images; Phosphorus; Roots; Image analysis.
Ano: 2019
URL: http://www.infoteca.cnptia.embrapa.br/infoteca/handle/doc/1117049

Exploring Machine Learning to Correct Satellite-Derived Sea Surface Temperatures
Provedor de dados: 5
Autores: Picart, Stephane Saux; Tandeo, Pierre; Autret, Emmanuelle; Gausset, Blandine.
Machine learning techniques are attractive tools to establish statistical models with a high degree of non linearity. They require a large amount of data to be trained and are therefore particularly suited to analysing remote sensing data. This work is an attempt at using advanced statistical methods of machine learning to predict the bias between Sea Surface Temperature (SST) derived from infrared remote sensing and ground “truth” from drifting buoy measurements. A large dataset of collocation between satellite SST and in situ SST is explored. Four regression models are used: Simple multi-linear regression, Least Square Shrinkage and Selection Operator (LASSO), Generalised Additive Model (GAM) and random forest. In the case of geostationary satellites for...
Tipo: Text
Palavras-chave: Machine learning; Systematic error; Sea surface temperature; Random forest.
Ano: 2018
URL: https://archimer.ifremer.fr/doc/00426/53797/54721.pdf

Water Mass and Biogeochemical Variability in the Kerguelen Sector of the Southern Ocean: A Machine Learning Approach for a Mixing Hotspot
Provedor de dados: 5
Autores: Rosso, Isabella; Mazloff, Matthew R.; Talley, Lynne D.; Purkey, Sarah G.; Freeman, Natalie M.; Maze, Guillaume.
The Southern Ocean (SO) is one of the most energetic regions in the world, where strong air‐sea fluxes, oceanic instabilities, and flow‐topography interactions yield complex dynamics. The Kerguelen Plateau (KP) region in the Indian sector of the SO is a hotspot for these energetic dynamics, which result in large spatio‐temporal variability of physical and biogeochemical (BGC) properties throughout the water column. Data from Argo floats (including biogeochemical) are used to investigate the spatial variability of intermediate and deep water physical and BGC properties. An unsupervised machine learning classification approach is used to organize the float profiles into five SO frontal zones based on their temperature and salinity structure between 300 and...
Tipo: Text
Palavras-chave: Southern Ocean; Kerguelen Plateau; Argo; Unsupervised clustering; Machine learning.
Ano: 2020
URL: https://archimer.ifremer.fr/doc/00613/72471/71438.pdf

Utilisation conjointe de FlowCAM / ZooPhytoImage et de la cytométrie en flux. Premiers résultats et perspectives. Action 9. FlowCam ZooPhytoImage. Livrable n° 4. Rapport final, 23 septembre 2014
Provedor de dados: 5
Autores: Ali, Nour; Wacquet, Guillaume; Didry, Morgane; Hamad, Denis; Artigas, Luis Felipe; Grosjean, Philippe.
The goal of this study is to investigate about the possibility of coupling measurements made by image analysis from the FlowCAM with Zoo/PhytoImage with data obtained with a flux cytometer (pulse-shape-recording Scanning Flow Cytometry) on the same samples gathered in current monitoring networks in the eastern Channel and southern North Sea. In this preliminary study, we collected a series of samples off Boulogne-sur-Mer (SRN-REPHY monitoring system run by IFREMER) and along a transect in the Baie St-Jean (Wimereux-Slack) run by LOG. All these samples were digitized with a FlowCAM and measured with a scanning flow cytometer (CytoSense). The complete analysis with the FlowCAM and Zoo/PhytoImage is detailed in the present report. In order to get a better...
Tipo: Text
Palavras-chave: Manche – Mer du Nord; Phytoplancton; Analyse d'image; Classification supervisée; Cytométrie en flux; Eastern English Channel and southern North Sea; Phytoplankton; Image analysis; Machine learning; Scanning Flow Cytometry.
Ano: 2014
URL: http://archimer.ifremer.fr/doc/00363/47442/47470.pdf

Generalization of Parameter Selection of SVM and LS-SVM for Regression
Provedor de dados: 5
Autores: Zeng, J; Tan, Zh; Matsunaga, T; Shirai, T.
A Support Vector Machine (SVM) for regression is a popular machine learning model that aims to solve nonlinear function approximation problems wherein explicit model equations are difficult to formulate. The performance of an SVM depends largely on the selection of its parameters. Choosing between an SVM that solves an optimization problem with inequality constrains and one that solves the least square of errors (LS-SVM) adds to the complexity. Various methods have been proposed for tuning parameters, but no article puts the SVM and LS-SVM side by side to discuss the issue using a large dataset from the real world, which could be problematic for existing parameter tuning methods. We investigated both the SVM and LS-SVM with an artificial dataset and a...
Tipo: Text
Palavras-chave: Support vector machine for regression; SVM; LS-SVM; Machine learning; Parameter optimization; Global ocean CO2.
Ano: 2019
URL: https://archimer.ifremer.fr/doc/00676/78774/80949.pdf

Construction of Multi-Year Time-Series Profiles of Suspended Particulate Inorganic Matter Concentrations Using Machine Learning Approach
Provedor de dados: 5
Autores: Renosh, Pannimpullath R.; Jourdin, Frederic; Charantonis, Anastase A.; Yala, Khalil; Rivier, Aurelie; Badran, Fouad; Thiria, Sylvie; Guillou, Nicolas; Leckler, Fabien; Gohin, Francis; Garlan, Thierry.
Hydro-sedimentary numerical models have been widely employed to derive suspended particulate matter (SPM) concentrations in coastal and estuarine waters. These hydro-sedimentary models are computationally and technically expensive in nature. Here we have used a computationally less-expensive, well-established methodology of self-organizing maps (SOMs) along with a hidden Markov model (HMM) to derive profiles of suspended particulate inorganic matter (SPIM). The concept of the proposed work is to benefit from all available data sets through the use of fusion methods and machine learning approaches that are able to process a growing amount of available data. This approach is applied to two different data sets entitled “Hidden” and “Observable”. The hidden...
Tipo: Text
Palavras-chave: Suspended particulate inorganic matter; Self-organizing maps; Hidden Markov Model; Machine learning; English Channel; ROMS.
Ano: 2017
URL: http://archimer.ifremer.fr/doc/00415/52653/53511.pdf

Reconstructing Global Chlorophyll-a Variations Using a Non-linear Statistical Approach
Provedor de dados: 5
Autores: Martinez, Elodie; Gorgues, Thomas; Lengaigne, Matthieu; Fontana, Clement; Sauzède, Raphaëlle; Menkes, Christophe; Uitz, Julia; Di Lorenzo, Emanuele; Fablet, Ronan.
Monitoring the spatio-temporal variations of surface chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) greatly benefited from the availability of continuous and global ocean color satellite measurements from 1997 onward. These two decades of satellite observations are however still too short to provide a comprehensive description of Chl variations at decadal to multi-decadal timescales. This paper investigates the ability of a machine learning approach (a non-linear statistical approach based on Support Vector Regression, hereafter SVR) to reconstruct global spatio-temporal Chl variations from selected surface oceanic and atmospheric physical parameters. With a limited training period (13 years), we first demonstrate that Chl variability...
Tipo: Text
Palavras-chave: Machine learning; Phytoplankton variability; Satellite ocean color; Decadel variability; Global scale.
Ano: 2020
URL: https://archimer.ifremer.fr/doc/00641/75314/75810.pdf

Data‐Driven Modeling of the Distribution of Diazotrophs in the Global Ocean
Provedor de dados: 5
Autores: Tang, Weiyi; Cassar, Nicolas.
Diazotrophs play a critical role in the biogeochemical cycling of nitrogen, carbon, and other elements in the global ocean. Despite their well‐recognized role, the diversity, abundance, and distribution of diazotrophs in the world's ocean remain poorly characterized largely due to limited observations. Here we update the database of diazotroph nifH gene abundances and assess how environmental factors may regulate diazotrophs at the global scale. Our meta‐analysis more than doubles the number of observations in the previous database. Using linear and nonlinear regressions, we find that the abundances of Trichodesmium, UCYN‐A, UCYN‐B, and Richelia relate differently to temperature, light, and nutrients. We further apply a random forest algorithm to estimate...
Tipo: Text
Palavras-chave: Diazotrophs; Marine nitrogen fixation; Meta-analysis; Machine learning.
Ano: 2019
URL: https://archimer.ifremer.fr/doc/00591/70322/68359.pdf

Global patterns and predictors of trophic position, body size and jaw size in fishes
Provedor de dados: 5
Autores: Kopf, R. Keller; Yen, Jian D. L.; Nimmo, Dale G.; Brosse, Sébastien; Villeger, Sébastien; Tittensor, Derek.
Aim The aim of this study was test whether maximum body mass and jaw length are reliable predictors of trophic position (TP) in fishes, and to compare linear and nonlinear machine‐learning (ML) models incorporating biogeography, habitat and other morphological traits. Location Global. Time period Modern. Major taxa studied Fishes. Methods We compiled a global database of TP (2.0–4.5), maximum body mass, jaw length, order, ecoregion, habitat and other morphological traits of freshwater, estuarine and diadromous fishes (n = 1,991). We used Bayesian linear mixed effects and ML, with r2 analogues and 10‐fold cross‐validation, to explain and predict TP. Results Random forest models outperformed Bayesian models in all comparisons. Jaw length was the most...
Tipo: Text
Palavras-chave: Allometric trophic network models; Allometry; Body mass; Gape limitation; Machine learning; Predator&amp;#8211; Prey; Random forest; Trophic network theory.
Ano: 2021
URL: https://archimer.ifremer.fr/doc/00661/77349/78823.pdf

Guide méthodologique. Version actualisée de ZooPhytoImage avec refonte de l’interface graphique. Action 9. FlowCam ZooPhytoImage. Livrable n°1. Rapport final
Provedor de dados: 5
Autores: Grosjean, Philippe.
Zoo/PhytoImage 4 is an « open source » software based on R and ImageJ. It processes numerical images of plankton particles digitized using a FlowCAM, a flat-bed scanner, microor macrophotos, etc. The general concept consists in the individual outlining of particles on the pictures, followed by their measurements (so-called « attributes ») such the size, the shape, transparency, textures, etc. These attributes are then used by a classification tool to automatically predict the taxonomic group the particles belong to. The classifier is obtained after a learning stage using a machine learning algorithm and a training set of pre-identified particles. The algorithm learns to recognize the taxonomic group from the set of attributes measured on the picture. The...
Tipo: Text
Palavras-chave: Océanographie biologique; Plancton; Surveillance côtière; Analyse automatisée; Analyse d'image; Classification supervisée; Biological oceanography; Plankton; Costal survey; Automated analysis; Image analysis; Machine learning.
Ano: 2014
URL: http://archimer.ifremer.fr/doc/00363/47436/47461.pdf

A machine learning algorithm for high throughput identification of FTIR spectra: Application on microplastics collected in the Mediterranean Sea
Provedor de dados: 5
Autores: Kedzierski, Mikaël; Falcou-préfol, Mathilde; Kerros, Marie Emmanuelle; Henry, Maryvonne; Pedrotti, Maria Luiza; Bruzaud, Stéphane.
The development of methods to automatically determine the chemical nature of microplastics by FTIR-ATR spectra is an important challenge. A machine learning method, named k-nearest neighbors classification, has been applied on spectra of microplastics collected during Tara Expedition in the Mediterranean Sea (2014). To realize these tests, a learning database composed of 969 microplastic spectra has been created. Results show that the machine learning process is very efficient to identify spectra of classical polymers such as poly(ethylene), but also that the learning database must be enhanced with less common microplastic spectra. Finally, this method has been applied on more than 4000 spectra of unidentified microplastics. The verification protocol...
Tipo: Text
Palavras-chave: Microplastic; Tara mediterranean campaign; FTIR spectra; Machine learning; K-nearest neighbor classification.
Ano: 2019
URL: https://archimer.ifremer.fr/doc/00501/61247/64825.pdf

An Alternative to Static Climatologies: Robust Estimation of Open Ocean CO2 Variables and Nutrient Concentrations From T, S, and O-2 Data Using Bayesian Neural Networks
Provedor de dados: 5
Autores: Bittig, Henry C.; Steinhoff, Tobias; Claustre, Harve; Fiedler, Bjoern; Williams, Nancy L.; Sauzede, Raphaelle; Koertzinger, Arne; Gattuso, Jean-pierre.
This work presents two new methods to estimate oceanic alkalinity (A(T)), dissolved inorganic carbon (C-T), pH, and pCO(2) from temperature, salinity, oxygen, and geolocation data. "CANYON-B" is a Bayesian neural network mapping that accurately reproduces GLODAPv2 bottle data and the biogeochemical relations contained therein. "CONTENT" combines and refines the four carbonate system variables to be consistent with carbonate chemistry. Both methods come with a robust uncertainty estimate that incorporates information from the local conditions. They are validated against independent GO-SHIP bottle and sensor data, and compare favorably to other state-of-the-art mapping methods. As "dynamic climatologies" they show comparable performance to classical...
Tipo: Text
Palavras-chave: Carbon cycle; GLODAP; Marine carbonate system; Surface pCO(2) climatology; Revelle buffer factor increase; Machine learning; Nutrient estimation.
Ano: 2018
URL: https://archimer.ifremer.fr/doc/00675/78681/80879.pdf

"M2B" package in R: Deriving multiple variables from movement data to predict behavioural states with random forests
Provedor de dados: 5
Autores: Thiebault, Andrea; Dubroca, Laurent; Mullers, Ralf H. E.; Tremblay, Yann; Pistorius, Pierre A.
1. The behaviour of individuals affect their distributions and is therefore fundamental in determining ecological patterns. While, the direct observation of behaviour is often limited due to logistical constraints, collection of movement data has been greatly facilitated through the development of bio-logging. Movement data obtained through tracking instrumentation may potentially constitute a relevant proxy to infer behaviour. 2. To infer behaviour from movement data is a key focus within the "movement ecology" discipline. Statistical learning constitutes a number of methods that can be used to assess the link between given variables from a fully informed training dataset and then predict the values on a non-informed variable. We chose the random forest...
Tipo: Text
Palavras-chave: Cape gannet; Fisheries; GPS; Local enhancement; Machine learning; Onboard observers; Social interactions; Video cameras.
Ano: 2018
URL: https://archimer.ifremer.fr/doc/00445/55683/57354.pdf

Optimisation de l’identification et du dénombrement du microphytoplancton avec le système couplé de numérisation et d’analyse d’images FlowCAM – Zoo/PhytoImage (système innovant)
Provedor de dados: 5
Autores: Grosjean, Philippe; Wacquet, Guillaume.
This report details the work accomplished to enhance the Zoo/PhytoImage software to optimize its use for the analysis of phytoplankton samples in general, but more particularly, in the framework of an operational survey of coastal seawater (REPHY, IFREMER). Zoo/PhytoImage allows to analyze “numerically recorded” plankton samples, that is, by using digital images gathered with specialized devices such as the FlowCAM, or the FastCAM (see report 3). A machine learning approach allows to automatically classify the digitized particles into various taxonomic groups. Once this is done, global statistics are calculated on each sample, including the number of particles, the biomass, and the size spectrum per taxonomic group. Two major changes are introduced in the...
Tipo: Text
Palavras-chave: Phytoplancton; REPHY; Analyse d'image; Classification supervisée; Dénombrement de cellules; Apprentissage actif; Manche; Atlantique.; Phytoplankton; REPHY; Image analysis; Machine learning; Cells enumeration; Active learning; The Channel; Atlantic Ocean.
Ano: 2016
URL: http://archimer.ifremer.fr/doc/00389/49990/50578.pdf

TEXT CATEGORIZATION USING ONLY FRAGMENTS OF DOCUMENTS
Provedor de dados: 31
Autores: Pilaszy,   Istvan; Dobrowiecki,   Tadeusz.
In this paper we presented a lot of experiments that examine how the particular parts of the documents do contribute to the performance of a classifier. We evaluated text classifiers on two very different text corpora. We conclude that some parts of the text are more important from the point of text classification performance. Giving higher weights to more important parts can increase the performance of the classifier. The question, that which parts are more or less important depends on the nature of the documents in the corpora. Some tasks that remains to be done: − More text corpora should be investigated. − In section 6.4 we optimized the number of features to be kept independent from the section. However, it could be optimized for each section. −...
Tipo: Journal Article
Palavras-chave: Machine learning; Text categorization; Classifier ensembles; Research and Development/Tech Change/Emerging Technologies.
Ano: 2007
URL: http://purl.umn.edu/58927

Digital Soil Mapping Using Machine Learning Algorithms in a Tropical Mountainous Area
Provedor de dados: 90
Autores: Meier,Martin; Souza,Eliana de; Francelino,Marcio Rocha; Fernandes Filho,Elpídio Inácio; Schaefer,Carlos Ernesto Gonçalves Reynaud.
ABSTRACT: Increasingly, applications of machine learning techniques for digital soil mapping (DSM) are being used for different soil mapping purposes. Considering the variety of models available, it is important to know their performance in relation to soil data and environmental variables involved in soil mapping. This paper investigated the performance of eight machine learning algorithms for soil mapping in a tropical mountainous area of an official rural settlement in the Zona da Mata region in Brazil. Morphometric maps generated from a digital elevation model, together with Landsat-8 satellite imagery, and climatic maps, were among the set of covariates to be selected by the Recursive Feature Elimination algorithm to predict soil types using machine...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Soil classification; Machine learning; Pedometrics; Land use planning; Agrarian reform.
Ano: 2018
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832018000100313

Prediction of Girolando cattle weight by means of body measurements extracted from images
Provedor de dados: 96
Autores: Weber,Vanessa Aparecida de Moraes; Weber,Fabricio de Lima; Gomes,Rodrigo da Costa; Oliveira Junior,Adair da Silva; Menezes,Geazy Vilharva; Abreu,Urbano Gomes Pinto de; Belete,Nícolas Alessandro de Souza; Pistori,Hemerson.
Abstract The objective with this study was to analyze the body measurements of Girolando cattle, as well as measurements extracted from their images, to generate a model to understand which measures further explain the cattle body weight. Therefore, the experiment physically measured 34 Girolando cattle (two males and 32 females), for the following traits: heart girth (HGP), circumference of the abdomen, body length, occipito-ischial length, wither height, and hip height. In addition, images of the dorsum and the body lateral area of these animals allowed measurements of hip width (HWI), body length, tail distance to the neck, dorsum area (DAI), dorsum perimeter, wither height, hip height, body lateral area, perimeter of the lateral area, and rib height....
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Cattle; Computer vision; Livestock precision; Machine learning; Mass estimation.
Ano: 2020
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-35982020000100800

Evaluation of imputed genomic data in discrete traits using Random forest and Bayesian threshold methodsb
Provedor de dados: 2
Autores: Sadeghi, Saadat; Rafat, Seyed Abbas; Alijani, Sadegh.
The objectives of this study were (1) to quantify imputation accuracy and to assess the factors affecting it; and (2) to evaluate the accuracy of threshold BayesA (TBA), Bayesian threshold LASSO (BTL) and random forest (RF) algorithms to analyze discrete traits. Genomic data were simulated to reflect variations in heritability (h2 = 0.30 and 0.10), number of QTL (QTL = 81 and 810), number of SNP (10 K and 50 K) and linkage disequilibrium (LD=low and high) for 27 chromosomes. For real condition simulating, we randomly masked markers with 90% missing rate for each scenario; afterwards, hidden markers were imputed using FImpute software. In imputed genotypes, a wide range of accuracy was observed for RF (0.164-0.512) compared to TBA (0.283-0.469) and BTL...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: PhD candidate of animal breeding accuracy; Genomic architecture; Linkage disequilibrium; Machine learning; Masked genotypes.
Ano: 2018
URL: http://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/39007

Breast Cancer Prediction Using Dominance-based Feature Filtering Approach: A Comparative Investigation in Machine Learning Archetype
Provedor de dados: 52
Autores: Atrey,Kushangi; Sharma,Yogesh; Bodhey,Narendra K.; Singh,Bikesh Kumar.
Abstract Breast cancer is the most commonly witnessed cancer amongst women around the world. Computer aided diagnosis (CAD) have been playing a significant role in early detection of breast tumors hence to curb the overall mortality rate. This work presents an enhanced empirical study of impact of dominance-based filtering approach on performances of various state-of-the-art classifiers. The feature dominance level is proportional to the difference in means of benign and malignant tumors. The experiments were done on original Wisconsin Breast Cancer Dataset (WBCD) with total nine features. It is found that the classifiers’ performances for top 4 and top 5 dominant-based features are almost equivalent to performances for all nine features. Artificial neural...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Breast cancer; Computer aided diagnosis; Dominance-based filtering; Machine learning.
Ano: 2019
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132019000100611

Comparative analysis of decision tree algorithms on quality of water contaminated with soil
Provedor de dados: 65
Autores: Dota,Mara Andrea; Cugnasca,Carlos Eduardo; Barbosa,Domingos Sávio.
Agriculture, roads, animal farms and other land uses may modify the water quality from rivers, dams and other surface freshwaters. In the control of the ecological process and for environmental management, it is necessary to quickly and accurately identify surface water contamination (in areas such as rivers and dams) with contaminated runoff waters coming, for example, from cultivation and urban areas. This paper presents a comparative analysis of different classification algorithms applied to the data collected from a sample of soil-contaminated water aiming to identify if the water quality classification proposed in this research agrees with reality. The sample was part of a laboratory experiment, which began with a sample of treated water added with...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Environmentalcontrol; Runoff; Wireless sensor networks; Machine learning; Data mining.
Ano: 2015
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782015000200267

Measurement of nitrogen content in rice by inversion of hyperspectral reflectance data from an unmanned aerial vehicle
Provedor de dados: 65
Autores: Wen,Du; Tongyu,Xu; Fenghua,Yu; Chunling,Chen.
ABSTRACT: The Nitrogen content of rice leaves has a significant effect on growth quality and crop yield. We proposed and demonstrated a non-invasive method for the quantitative inversion of rice nitrogen content based on hyperspectral remote sensing data collected by an unmanned aerial vehicle (UAV). Rice canopy albedo images were acquired by a hyperspectral imager onboard an M600-UAV platform. The radiation calibration method was then used to process these data and the reflectance of canopy leaves was acquired. Experimental validation was conducted using the rice field of Shenyang Agricultural University, which was classified into 4 fertilizer levels: zero nitrogen, low nitrogen, normal nitrogen, and high nitrogen. Gaussian process regression (GPR) was...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: UAV; Hyperspectral remote sensing; Machine learning; Nitrogen content.
Ano: 2018
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782018000600352

Heuristic methods applied in reference evapotranspiration modeling
Provedor de dados: 64
Autores: Althoff,Daniel; Bazame,Helizani Couto; Filgueiras,Roberto; Dias,Santos Henrique Brant.
ABSTRACT The importance of the precise estimation of evapotranspiration is directly related to sustainable water usage. Since agriculture represents 70% of Brazil’s water consumption, adequate and efficient application of water may reduce the conflicts over the use of water among the multiple users. Considering the importance of accurate estimation of evapotranspiration, the objective of the present study was to model and compare the reference evapotranspiration from different heuristic methodologies. The standard Penman-Monteith method was used as reference for evapotranspiration, however, to evaluate the heuristic methodologies with scarce data, two widely known methods had their performances assessed in relation to Penman-Monteith. The methods used to...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Machine learning; Model comparison; Water management.
Ano: 2018
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1413-70542018000300314