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A prospective study on the application of Data Science in agriculture. Repositório Alice
SOUZA, K. X. S. de; TERNES, S.; OLIVEIRA, S. R. de M.; MOURA, M. F.; BARIONI, L. G.; HIGA, R. H.; FASIABEN, M. do C. R..
A quantidade e diversidade de dados disponíveis têm o potencial de causar profundas transformações na maneira que se realiza pesquisa e se propõe inovações na agricultura. Na chamada era do Petabyte, caracterizada pela ubiquidade de sensores e computadores, armazenamento quase infinito, computação em nuvem, robótica e IoT, a demanda e as oportunidades para aplicação da computação científica são extraordinárias, tanto na extração do conhecimento quanto na compreensão dos mecanismos associados a sistemas complexos. Este artigo apresenta um estudo prospectivo com base no estado da arte e enumera algumas áreas nas quais a aplicação da Ciência de Dados resultaria em grande benefício para pesquisadores, agricultores e agentes públicos.
Tipo: Artigo em anais de congresso (ALICE) Palavras-chave: Computação científica; Aprendizado de máquina; Modelagem; Redes de sensores; Simulação; Agricultura; Agriculture; Machine learning.
Ano: 2017 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1083412
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An approach based on text mining for knowledge acquisition in diagnostic systems. Repositório Alice
MASSRUHA, S. M. F. S.; MARCHI, R.; SILVA, L. M. C. da; SOUZA, K. X. S. de; OLIVEIRA, L. H. M. de; OLIVEIRA, S. R. de M.; MORANDI, M. A. B..
Introduction. Methodology. Data preparation phase. Information extraction and mining phase. A case study with corn diseases. The results and discussion.
Tipo: Artigo em anais de congresso (ALICE) Palavras-chave: Mineração de texto; Aprendizado de máquina; Doenças do milho; Text mining; Knowledge discovery; Predictions; Corn diseases; Machine learning.
Ano: 2007 URL: http://www.alice.cnptia.embrapa.br/handle/doc/2921
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An approach based on text mining for knowledge acquisition in diagnostic systems. Repositório Alice
MASSRUHÁ, S.M.F.S.; MARCHI, R.; SILVA, L.M. CUNHA da; SOUZA, K. X. S. de; OLIVEIRA, L. H. M. de; OLIVEIRA, S. R. de M.; MORANDI, M. A. B..
2007
Tipo: Artigo em anais de congresso (ALICE) Palavras-chave: Text mining; Knowledge discovery; Machine learning; Corn diseases; Predictions.
Ano: 2007 URL: http://www.alice.cnptia.embrapa.br/handle/doc/15825
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Avaliação de métodos de detecção de tópicos em pré-processamento para classificação de textos agrícolas. Repositório Alice
BARROS, F. M. M.; OLIVEIRA, S. R. de M..
Neste trabalho, buscou-se construir e comparar modelos capazes de diferenciar textos sobre a cultura da cana-de-açúcar de outros textos relacionados a outras culturas ou criações. Para criar modelos de classificação de textos, os dados são transformados em matrizes termos-documentos, de forma que os dados apresentam alta dimensionalidade. Para construir melhores modelos de classificação de textos agrícolas foram testados: a) métodos de redução de dimensionalidade utilizando LDA (Latent Dirichlet Allocation) e PCA (Principal Component Analysis); b) número de tópicos/componentes principais; c) unigrama/bigrama; e d) algoritmos Random Forest, Gradiente Boosting e SVM (Support Vector Machine), de forma a determinar os fatores que mais impactam o AUC (Area...
Tipo: Artigo em anais de congresso (ALICE) Palavras-chave: Mineração de textos; Aprendizado de máquina; Redução de dimensionalidade; Sistema de informação agrícola; Text mining; Dimensionality reduction; Agricultural information systems; Agricultura; Agriculture; Machine learning.
Ano: 2017 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1083387
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Breast Cancer Prediction Using Dominance-based Feature Filtering Approach: A Comparative Investigation in Machine Learning Archetype BABT
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
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Construction of Multi-Year Time-Series Profiles of Suspended Particulate Inorganic Matter Concentrations Using Machine Learning Approach ArchiMer
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
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Detecção automática de bagas de café em imagens de campo. Repositório Alice
SANTOS, T. T..
O presente trabalho propõe um método para detecçãao automática de bagas em imagens de cafeeiros tomadas em campo sob luz ambiente.
Tipo: Artigo em anais de congresso (ALICE) Palavras-chave: Aprendizado de máquina; Imagem digital; Machine learning; Café; Visão computacional; Fruticultura; Image analysis; Fruit growing; Artificial intelligence; Computer vision.
Ano: 2015 URL: http://www.alice.cnptia.embrapa.br/handle/doc/1027251
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Digital Soil Mapping Using Machine Learning Algorithms in a Tropical Mountainous Area Rev. Bras. Ciênc. Solo
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
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Evaluation of gene selection metrics for tumor cell classification Genet. Mol. Biol.
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
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Evaluation of imputed genomic data in discrete traits using Random forest and Bayesian threshold methodsb Animal Sciences
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
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Evaluation of noise reduction techniques in the splice junction recognition problem Genet. Mol. Biol.
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
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Exploring Machine Learning to Correct Satellite-Derived Sea Surface Temperatures ArchiMer
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
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Genome-wide association study for backfat thickness in Canchim beef cattle using Random Forest approach. Repositório Alice
MOKRY, F. B.; HIGA, R. H.; MUDADU, M. de A.; LIMA, A. O. de; MEIRELLES, S. L. C.; SILVA, M. V. G. B. da; CARDOSO, F. F.; OLIVEIRA, M. M. de; URBINATI, I.; NICIURA, S. C. M.; TULLIO, R. R.; ALENCAR, M. M. de; REGITANO, L. C. de A..
Background: Meat quality involves many traits, such as marbling, tenderness, juiciness, and backfat thickness, all of which require attention from livestock producers. Backfat thickness improvement by means of traditional selection techniques in Canchim beef cattle has been challenging due to its low heritability, and it is measured late in an animal?s life. Therefore, the implementation of new methodologies for identification of single nucleotide polymorphisms (SNPs) linked to backfat thickness are an important strategy for genetic improvement of carcass and meat quality. Results: The set of SNPs identified by the random forest approach explained as much as 50% of the deregressed estimated breeding value (dEBV) variance associated with backfat thickness,...
Tipo: Artigo em periódico indexado (ALICE) Palavras-chave: Polimorfismo de nucleotídeo único; Gado de corte; Metabolismo lipídico; Aprendizado de máquina; Inteligência artificial; Machine learning; Tecido adiposo subcutâneo; Single nucleotide polymorphism; Beef cattle; Lipid metabolism; Artificial intelligence; Subcutaneous fat.
Ano: 2013 URL: http://www.alice.cnptia.embrapa.br/handle/doc/977539
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Geração de séries temporais de dados meteorológicos utilizando algoritmos de aprendizado de máquina. Repositório Alice
OLIVEIRA, H. L. C. de; OLIVEIRA, S. R. de M.; MONTEIRO, J. E. B. de A..
RESUMO - Este trabalho objetivou desenvolver uma metodologia baseada em algoritmos de Aprendizado de Máquina para gerar séries espaço-temporais de precipitação e temperatura. Foi definida uma região de estudo de formato retangular, entre as latitudes de -18º e -22º e as longitudes de -52º e -39º, incluindo a metade norte do Estado de São Paulo e parte do sul de Goiás, a metade sul de Minas Gerais e o Estado do Espírito Santo. A região foi escolhida por ser climaticamente bastante heterogênea e por conter muitas estações meteorológicas de diversas instituições, principalmente ANA e INMET. Foram utilizadas as séries temporais de precipitação e de temperatura máxima e mínima disponíveis na região, compreendendo o período de 01/01/1999 a 31/12/2013. Também...
Tipo: Artigo em anais de congresso (ALICE) Palavras-chave: Séries temporais; Aprendizado de máquina; Aprendizado com classes desbalanceadas; Modelos preditivos; Imputação de dados; Algoritmo Random Forest; Unbalanced class learning; Predictive modeling; Data imputation; Agrometeorologia; Time series analysis; Artificial intelligence; Machine learning; Agrometeorology.
Ano: 2017 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1077534
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Guide méthodologique. Version actualisée de ZooPhytoImage avec refonte de l’interface graphique. Action 9. FlowCam ZooPhytoImage. Livrable n°1. Rapport final ArchiMer
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
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Heuristic methods applied in reference evapotranspiration modeling Ciência e Agrotecnologia
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
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Improving binding affinity prediction by using a rule-based model with physical-chemical and structural descriptors of the nano-environment for protein-ligand interactions. Repositório Alice
BORRO, L. C.; SALIM, J. A.; MAZONI, I.; YANO, I.; JARDINE, J. G.; NESHICH, G..
In order to improve binding affinity prediction, we developed a new scoring function, named STINGSF, derived from physical-chemical and structural features that describe the protein-ligand interaction nano-environment of experimentally determined structures.
Tipo: Resumo em anais de congresso (ALICE) Palavras-chave: Interação proteína-ligante; Aprendizado de máquina; Inteligência artificial; Protein-ligand interaction; Scoring functions; Machine learning; Artificial intelligence.
Ano: 2015 URL: http://www.alice.cnptia.embrapa.br/handle/doc/1032260
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"M2B" package in R: Deriving multiple variables from movement data to predict behavioural states with random forests ArchiMer
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
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Metodologia para processamento de imagens digitais do sistema radicular de milho e sorgo utilizando a plataforma Digital Imaging of Root Traits (DIRT). Infoteca-e
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.
Ano: 2019 URL: http://www.infoteca.cnptia.embrapa.br/infoteca/handle/doc/1117049
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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) ArchiMer
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
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