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A machine learning algorithm for high throughput identification of FTIR spectra: Application on microplastics collected in the Mediterranean Sea ArchiMer
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
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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 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 ArchiMer
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
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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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Comparative analysis of decision tree algorithms on quality of water contaminated with soil Ciência Rural
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
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Computação científica na agricultura. Repositório Alice
TERNES, S.; MOURA, M. F.; SOUZA, K. X. S. de; VAZ, G. J.; OLIVEIRA, S. R. de M.; HIGA, R. H.; LIMA, H. P. de; TAKEMURA, C. M.; COELHO, E. A.; BARBOSA, F. F. L.; VISOLI, M. C.; MENEZES, G. R. de O.; SILVA, L. O. C. da; SANTOS, S. A.; MASSRUHÁ, S. M. F. S.; ABREU, U. G. P. de; SORIANO, B. M. A.; SALIS, S. M.; OLIVEIRA, M. D. de; TOMAS, W. M..
Introdução. Inteligência artificial. Classificação automática de solos. Sistema especialista baseado no SiBCS. Sistema inteligente de classificação de solos. Mineração de textos em publicações técnico-científicas. Modelagem matemática e estatística. Modelagem da dinâmica de dispersão do "HLB do citros". Avaliação genética de animais. Fazenda Pantaneira Sustentável (FPS). O software FPS. Considerações finais.
Tipo: Capítulo em livro científico (ALICE) Palavras-chave: Agricultura digital; Computação científica; Transformação digital na agricultura; Inteligência Artificial; Aprendizado de máquina; Mineração de textos; Modelagem matemática; Machine learning; Text mining; Digital agriculture; Agricultura; Análise Estatística; Agriculture; Artificial intelligence; Statistical analysis; Mathematical models.
Ano: 2020 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1126229
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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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Data‐Driven Modeling of the Distribution of Diazotrophs in the Global Ocean ArchiMer
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
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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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Detecting and classifying pests in crops using proximal images and machine learning: a review. Repositório Alice
BARBEDO, J. G. A..
Abstract: Pest management is among the most important activities in a farm. Monitoring all different species visually may not be effective, especially in large properties. Accordingly, considerable research effort has been spent towards the development of effective ways to remotely monitor potential infestations. A growing number of solutions combine proximal digital images with machine learning techniques, but since species and conditions associated to each study vary considerably, it is difficult to draw a realistic picture of the actual state of the art on the subject. In this context, the objectives of this article are (1) to briefly describe some of the most relevant investigations on the subject of automatic pest detection using proximal digital...
Tipo: Artigo em periódico indexado (ALICE) Palavras-chave: Aprendizado de máquina; Imagem digital; Imagens digitais; Monitoramento de pragas; Pest detection; Machine learning; Agricultural crops; Infestação; Inseto; Pest monitoring; Insects; Digital images.
Ano: 2020 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1125314
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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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From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy. Repositório Alice
BOCK, C. H.; BARBEDO, J. G. A.; DEL PONTE, E. M.; BOHNENKAMP, D.; MAHLEIN, A. K..
Abstract. The severity of plant diseases, traditionally the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases and is prone to error. Good quality disease severity data should be accurate (close to the true value). Earliest quantification of disease severity was by visual estimates. Sensor-based image analysis including visible spectrum and hyperspectral and multispectral sensors are established technologies that promise to substitute, or complement visual ratings. Indeed, these technologies have measured disease severity accurately under controlled conditions but are yet to demonstrate their full potential for accurate measurement under field conditions. Sensor technology is advancing rapidly, and...
Tipo: Artigo em periódico indexado (ALICE) Palavras-chave: Inteligência artificial; Aprendizado de máquina; Dispositivo móvel; Tecnologias digitais; Aprendizado profundo; Precisão; Acurácia; Severidade da doença; Machine learning; Assessment; Sensor; Mobile device; Digital technologies; Deep learning; Phenotyping; Doença de Planta; Precision agriculture; Plant diseases and disorders; Artificial intelligence; Disease severity; Accuracy; Precision.
Ano: 2020 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1122199
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Generalization of Parameter Selection of SVM and LS-SVM for Regression ArchiMer
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
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