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Auswahl der wissenschaftlichen Literatur zum Thema „E-boosting“
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Zeitschriftenartikel zum Thema "E-boosting"
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Sangrulkar, Mrs Surekha A. „E-Banking- ICT Plus Banking for Boosting Business“. International Journal of Trend in Scientific Research and Development Special Issue, Special Issue-FIIIIPM2019 (20.03.2019): 98–100. http://dx.doi.org/10.31142/ijtsrd23074.
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Méndez, José R., M. Reboiro-Jato, Fernando Díaz, Eduardo Díaz und Florentino Fdez-Riverola. „Grindstone4Spam: An optimization toolkit for boosting e-mail classification“. Journal of Systems and Software 85, Nr. 12 (Dezember 2012): 2909–20. http://dx.doi.org/10.1016/j.jss.2012.06.027.
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Hamim, Touria, Faouzia Benabbou und Nawal Sael. „Student Profile Modeling Using Boosting Algorithms“. International Journal of Web-Based Learning and Teaching Technologies 17, Nr. 5 (September 2022): 1–13. http://dx.doi.org/10.4018/ijwltt.20220901.oa4.
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The student profile has become an important component of education systems. Many systems objectives, as e-recommendation, e-orientation, e-recruitment and dropout prediction are essentially based on the profile for decision support. Machine learning plays an important role in this context and several studies have been carried out either for classification, prediction or clustering purpose. In this paper, the authors present a comparative study between different boosting algorithms which have been used successfully in many fields and for many purposes. In addition, the authors applied feature selection methods Fisher Score, Information Gain combined with Recursive Feature Elimination to enhance the preprocessing task and models’ performances. Using multi-label dataset predict the class of the student performance in mathematics, this article results show that the Light Gradient Boosting Machine (LightGBM) algorithm achieved the best performance when using Information gain with Recursive Feature Elimination method compared to the other boosting algorithms.
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Larkin, Marilynn. „Robert E W Hancock–boosting innate immunity to combat infection“. Lancet Infectious Diseases 3, Nr. 11 (November 2003): 736–39. http://dx.doi.org/10.1016/s1473-3099(03)00799-0.
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Monti, Luciano, Gianfrancesco Rizzuti und Erica Pepe. „E-government and Open Data Boosting Economic Growth: A New Index“. Journal of Business and Economics 6, Nr. 12 (20.12.2015): 2080–88. http://dx.doi.org/10.15341/jbe(2155-7950)/12.06.2015/009.
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Al-Adwan, Ahmad Samed, und Maher Ahmad Al-Horani. „Boosting Customer E-Loyalty: An Extended Scale of Online Service Quality“. Information 10, Nr. 12 (03.12.2019): 380. http://dx.doi.org/10.3390/info10120380.
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The Customer trust, satisfaction and loyalty with regard to the provision of e-commerce services is expected to be critical factors for the assessment of the success of online businesses. Service quality and high-quality product settings are closely linked to these factors. However, despite the rapid advancement of e-commerce applications, especially in the context of business to consumer (B2C), prior research has confirmed that e-retailers face difficulties when it comes to maintaining customer loyalty. Several e-service quality frameworks have been employed to boost service quality by targeting customer loyalty. Among these prominent frameworks is the scale of online etail quality (eTailQ). This scale has been under criticism as it was developed before the emergence of Web 2.0 technologies. Consequently, this paper aims to fill this gap by offering empirically-tested and conceptually-derived measurement model specifications for an extended eTailQ scale. In addition, it investigates the potential effects of the extended scale on e-trust and e-satisfaction, and subsequently e-loyalty. The practical and theoretical implications are highlighted to help businesses to design effective business strategies based on quality in order to achieve enhanced customer loyalty, and to direct future research in the field of e-commerce.
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Hu, Bo, Chunlin Chen, Zhangsong Zhan, Xueying Su, Tiegang Hu, Guangyong Zheng und Zhiyong Yang. „Progress and recent trends in 48 V hybridisation and e-boosting technology on passenger vehicles – a review“. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 232, Nr. 11 (24.10.2017): 1543–61. http://dx.doi.org/10.1177/0954407017729950.
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Electrification of the powertrain system will play an important role in reducing fuel consumption and engine-out emissions in the next few decades. Compared to the pure electric and full hybrid concept, 48 V mild hybridisation and the accompanying 48 V e-boosting concept, due to their superior cost–benefit performance, may become mainstream for the next generation of fuel reduction measures. The mild hybrid system can realise advanced stop–start, active and passive engine-off coasting, braking recuperation, boost assistance, e-creeping and torque vectoring functions, and is thus deemed to give approximately 10–15% fuel consumption benefits in the New European Driving Cycle (NEDC); and the e-boosting concept, due to its capability for further downsizing and down-speeding, allows the engine operating points to be shifted into a more efficient area. A strong synergy between 48 V mild hybridisation and the 48 V e-boosting concept has been found after reviewing both technologies, and the trends for developing such a combined electrical system are also discussed. Together with more engine and vehicle component electrification, fuel efficiency could be further improved, although the interactions between these technologies are highly complex and need to be optimised at a system level.
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Al-Qudah, Dana A., Ala' M. Al-Zoubi, Pedro A. Castillo-Valdivieso und Hossam Faris. „Sentiment Analysis for e-Payment Service Providers Using Evolutionary eXtreme Gradient Boosting“. IEEE Access 8 (2020): 189930–44. http://dx.doi.org/10.1109/access.2020.3032216.
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Chen, Liang, Lei Zhang, Yan Wang und Zhiping Yu. „A Compact E-Band Power Amplifier With Gain-Boosting and Efficiency Enhancement“. IEEE Transactions on Microwave Theory and Techniques 68, Nr. 11 (November 2020): 4620–30. http://dx.doi.org/10.1109/tmtt.2020.3017728.
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Vieira, Fábio D., Stanley R. de M. Oliveira und Samuel R. Paiva. „Metodologia baseada em técnicas de mineração de dados para suporte à certificação de raças de ovinos“. Engenharia Agrícola 35, Nr. 6 (Dezember 2015): 1172–86. http://dx.doi.org/10.1590/1809-4430-eng.agric.v35n6p1172-1186/2015.
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RESUMO O objetivo deste trabalho foi desenvolver uma metodologia baseada em técnicas de mineração de dados para selecionar os principais marcadores SNP (Single Nucleotide Polymorphism) para as raças de ovinos: Crioula, Morada Nova e Santa Inês. Os dados utilizados foram obtidos do Consórcio Internacional de Ovinos e são compostos por 72 animais das raças citadas, e cada animal possui 49.034 marcadores SNP. Considerando que o número de atributos (marcadores) é muito maior que o de observações (animais), foram aplicadas as técnicas de predição LASSO (Least Absolute Shrinkage and Selection Operator), Random Forest e Boosting para a geração de modelos preditivos que incorporam métodos de seleção de atributos. Os resultados revelaram que os modelos preditivos selecionaram os principais marcadores SNP para identificação das raças estudadas. O modelo LASSO selecionou um total de 29 marcadores relevantes. A partir dos modelos Random Forest e Boosting, foram obtidos 27 e 20 marcadores importantes, respectivamente. Por meio da intersecção dos modelos gerados, identificou-se um subconjunto de 18 marcadores com maior potencial de identificação das raças.
Dissertationen zum Thema "E-boosting"
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TEIXEIRA, JÚNIOR Talisman Cláudio de Queiroz. „Classificação fonética utilizando Boosting e SVM“. Universidade Federal do Pará, 2006. http://repositorio.ufpa.br/jspui/2011/2533.
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Para compor um sistema de Reconhecimento Automático de Voz, pode ser utilizada uma tarefa chamada Classificação Fonética, onde a partir de uma amostra de voz decide-se qual fonema foi emitido por um interlocutor. Para facilitar a classificação e realçar as características mais marcantes dos fonemas, normalmente, as amostras de voz são pré- processadas através de um fronl-en'L Um fron:-end, geralmente, extrai um conjunto de parâmetros para cada amostra de voz. Após este processamento, estes parâmetros são insendos em um algoritmo classificador que (já devidamente treinado) procurará decidir qual o fonema emitido. Existe uma tendência de que quanto maior a quantidade de parâmetros utilizados no sistema, melhor será a taxa de acertos na classificação. A contrapartida para esta tendência é o maior custo computacional envolvido. A técnica de Seleção de Parâmetros tem como função mostrar quais os parâmetros mais relevantes (ou mais utilizados) em uma tarefa de classificação, possibilitando, assim, descobrir quais os parâmetros redundantes, que trazem pouca (ou nenhuma) contribuição à tarefa de classificação. A proposta deste trabalho é aplicar o classificador SVM à classificação fonética, utilizando a base de dados TIMIT, e descobrir os parâmetros mais relevantes na classificação, aplicando a técnica Boosting de Seleção de Parâmetros.
With the aim of setting up a Automatic Speech Recognition (ASR) system, a task named Phonetic Classification can be used. That task consists in, from a speech sample, deciding which phoneme was pronounced by a speaker. To ease the classification task and to enhance the most marked characteristics of the phonemes, the speech samples are usually pre-processed by a front-end. A front-end, as a general rule, extracts a set of features to each speech sample. After that, these features are inserted in a classification algorithm, that (already properly trained) will try to decide which phoneme was pronounced. There is a rule of thumb which says that the more features the system uses, the smaller the classification error rate will be. The disadvantage to that is the larger computational cost. Feature Selection task aims to show which are the most relevant (or more used) features in a classification task. Therefore, it is possible to discover which are the redundant features, that make little (or no) contribution to the classification task. The aim of this work is to apply SVM classificator in Phonetic Classification task, using TIMIT database, and discover the most relevant features in this classification using Boosting approach to implement Feature Selection.
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Rodrigo, Portela Ferreira Marcelo. „Análise discriminante clássica e de núcleo: avaliação e algumas contribuições relativas aos métodos Boosting e Bootstrap“. Universidade Federal de Pernambuco, 2007. https://repositorio.ufpe.br/handle/123456789/6288.
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Previous issue date: 2007Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Desde que tecnologia da informação tornou-se essencial para muitas atividades da vida moderna e grandes conjuntos de dados surgiram junto com ela, mineração de dados tornou-se uma das mais importantes áreas de pesquisa na ciência estatística. Apesar de existirem muitos campos relacionados a mineração de dados, a tarefa de classificação ainda figura como uma das mais comuns na literatura estatística. Esta dissertação faz uma revisão de dois métodos clássicos de classificação, análise discriminante linear e quadrática, e um método não-paramétrico, a análise discriminante de núcleo. Experimentos de simulação e conjuntos de dados reais são utilizados para avaliar e comparar os três métodos de classificação. Também apresenta algumas contribuições relacionadas aos métodos boosting e bootstrap no contexto de classificação. A primeira contribuição trata-se de uma nova formulação para o método boosting em análise discriminante linear. Os resultados numéricos mostram que esta nova formulação tem desempenho similar à formulação usual. Entretanto, a nova formulação do método boosting é conceitualmente mais adequada. Dois métodos bootstrap para problemas de classificação são introduzidos e avaliados. O primeiro método bootstrap é utilizado para obter uma fronteira de classificação. O conceito de fronteira de classificação pode ser entendido como uma região onde é difícil alocar uma observação para uma das populações existentes. O segundo método bootstrap é um intervalo de confiança para a taxa de erro de classificação. Intervalos de confiança podem ser utilizados para comparar dois ou mais métodos de classificação na estrutura de inferência
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Nascimento, Diego Silveira Costa. „Configuração heterogênea de ensembles de classificadores : investigação em bagging, boosting e multiboosting“. Universidade de Fortaleza, 2009. http://dspace.unifor.br/handle/tede/83562.
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Previous issue date: 2009-12-21This work presents a study on the characterization and evaluation of six new heterogeneous committees machines algorithms, which are aimed at solving problems of pattern classification. These algorithms are extensions of models which are already found in the literature and have been successfully applied in different fields of research. Following two approaches, evolutionary and constructive, different machine learning algorithms (inductors) can be used for induction of components of the ensemble to be trained by standard Bagging, Boosting or MultiBoosting on the resampled data, aiming at the increasing of the diversity of the resulting composite model. As a means of automatic configuration of different types of components, we adopt a customized genetic algorithm for the first approach and greedy search for the second approach. For purposes of validation of the proposal, an empirical study has been conducted involving 10 different types of inductors and 18 classification problems taken from the UCI repository. The acuity values obtained by the evolutionary and constructive heterogeneous ensembles are analyzed based on those produced by models of homogeneous ensembles composed of the 10 types of inductors we have utilized, and the majority of the results evidence a gain in performance from both approaches. Keywords: Machine learning, Committee machines, Bagging, Wagging, Boosting, MultiBoosting, Genetic algorithm.
Este trabalho apresenta um estudo quanto à caracterização e avaliação de seis novos algoritmos de comitês de máquinas heterogêneos, sendo estes destinados à resolução de problemas de classificação de padrões. Esses algoritmos são extensões de modelos já encontrados na literatura e que vêm sendo aplicados com sucesso em diferentes domínios de pesquisa. Seguindo duas abordagens, uma evolutiva e outra construtiva, diferentes algoritmos de aprendizado de máquina (indutores) podem ser utilizados para fins de indução dos componentes do ensemble a serem treinados por Bagging, Boosting ou MultiBoosting padrão sobre os dados reamostrados, almejando-se o incremento da diversidade do modelo composto resultante. Como meio de configuração automática dos diferentes tipos de componentes, adota-se um algoritmo genético customizado para a primeira abordagem e uma busca de natureza gulosa para a segunda abordagem. Para fins de validação da proposta, foi conduzido um estudo empírico envolvendo 10 diferentes tipos de indutores e 18 problemas de classificação extraídos do repositório UCI. Os valores de acuidade obtidos via ensembles heterogêneos evolutivos e construtivos são analisados com base naqueles produzidos por modelos de ensembles homogêneos compostos pelos 10 tipos de indutores utilizados, sendo que em grande parte dos casos os resultados evidenciam ganhos de desempenho de ambas as abordagens. Palavras-chave: Aprendizado de máquina, Comitês de máquinas, Bagging, Wagging, Boosting, MultiBoosting, Algoritmo genético.
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Rubesam, Alexandre. „Estimação não parametrica aplicada a problemas de classificação via Bagging e Boosting“. [s.n.], 2004. http://repositorio.unicamp.br/jspui/handle/REPOSIP/306510.
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Orientador: Ronaldo DiasDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica
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Resumo: Alguns dos métodos mais modernos e bem sucedidos de classificação são bagging, boosting e SVM (Support Vector M achines ). B agging funciona combinando classificadores ajustados em amostras bootstrap dos dados; boosting funciona aplicando-se seqüencialmente um algoritmo de classificação a versões reponderadas do conjunto de dados de treinamento, dando maior peso às observações classificadas erroneamente no passo anterior, e SVM é um método que transforma os dados originais de maneira não linear para um espaço de dimensão maior, e procura um hiperplano separador neste espaço transformado. N este trabalho estudamos os métodos descritos acima, e propusemos dois métodos de classificação, um baseado em regressão não paramétrica por Hsplines (também proposto aqui) e boosting, e outro que é uma modificação de um algoritmo de boosting baseado no algoritmo MARS. Os métodos foram aplicados em dados simulados e em dados reais
Abstract: Some of the most modern and well succeeded classification methods are bagging, boosting and SVM (Support Vector Machines). Bagging combines classifiers fitted to bootstrap samples of the training data; boosting sequentially applies a classification algorithm to reweighted versions of the training data, increasing in each step the weights of the observations that were misclassified in the previous step, and SVM is a method that transforms the data in a nonlinear way to a space of greater dimension than that of the original data, and searches for a separating hyperplane in this transformed space. In this work we have studied the methods described above. We propose two classification methods: one of them is based on a nonparametric regression method via H-splines (also proposed here) and boosting, and the other is a modification of a boosting algorithm, based on the MARS algorithm. The methods were applied to both simulated and real data
Mestrado
Mestre em Estatística
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Lopes, Neilson Soares. „Modelos de classificação de risco de crédito para financiamentos imobiliários: regressão logística, análise discriminante, árvores de decisão, bagging e boosting“. Universidade Presbiteriana Mackenzie, 2011. http://tede.mackenzie.br/jspui/handle/tede/527.
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Previous issue date: 2011-08-08Fundo Mackenzie de Pesquisa
This study applied the techniques of traditional parametric discriminant analysis and logistic regression analysis of credit real estate financing transactions where borrowers may or may not have a payroll loan transaction. It was the hit rate compared these methods with the non-parametric techniques based on classification trees, and the methods of meta-learning bagging and boosting that combine classifiers for improved accuracy in the algorithms.In a context of high housing deficit, especially in Brazil, the financing of real estate can still be very encouraged. The impacts of sustainable growth in the mortgage not only bring economic benefits and social. The house is, for most individuals, the largest source of expenditure and the most valuable asset that will have during her lifetime.At the end of the study concluded that the computational techniques of decision trees are more effective for the prediction of payers (94.2% correct), followed by bagging (80.7%) and boosting (or arcing , 75.2%). For the prediction of bad debtors in mortgages, the techniques of logistic regression and discriminant analysis showed the worst results (74.6% and 70.7%, respectively). For the good payers, the decision tree also showed the best predictive power (75.8%), followed by discriminant analysis (75.3%) and boosting (72.9%). For the good paying mortgages, bagging and logistic regression showed the worst results (72.1% and 71.7%, respectively). Logistic regression shows that for a borrower with payroll loans, the chance to be a bad credit is 2.19 higher than if the borrower does not have such type of loan.The presence of credit between the payroll operations of mortgage borrowers also has relevance in the discriminant analysis.
Neste estudo foram aplicadas as técnicas paramétricas tradicionais de análise discriminante e regressão logística para análise de crédito de operações de financiamento imobiliário. Foi comparada a taxa de acertos destes métodos com as técnicas não-paramétricas baseadas em árvores de classificação, além dos métodos de meta-aprendizagem BAGGING e BOOSTING, que combinam classificadores para obter uma melhor precisão nos algoritmos.Em um contexto de alto déficit de moradias, em especial no caso brasileiro, o financiamento de imóveis ainda pode ser bastante fomentado. Os impactos de um crescimento sustentável no crédito imobiliário trazem benefícios não só econômicos como sociais. A moradia é, para grande parte dos indivíduos, a maior fonte de despesas e o ativo mais valioso que terão durante sua vida. Ao final do estudo, concluiu-se que as técnicas computacionais de árvores de decisão se mostram mais efetivas para a predição de maus pagadores (94,2% de acerto), seguida do BAGGING (80,7%) e do BOOSTING (ou ARCING, 75,2%). Para a predição de maus pagadores em financiamentos imobiliários, as técnicas de regressão logística e análise discriminante apresentaram os piores resultados (74,6% e 70,7%, respectivamente). Para os bons pagadores, a árvore de decisão também apresentou o melhor poder preditivo (75,8%), seguida da análise discriminante (75,3%) e do BOOSTING (72,9%). Para os bons pagadores de financiamentos imobiliários, BAGGING e regressão logística apresentaram os piores resultados (72,1% e 71,7%, respectivamente).A regressão logística mostra que, para um tomador com crédito consignado, a chance se ser um mau pagador é 2,19 maior do que se este tomador não tivesse tal modalidade de empréstimo. A presença de crédito consignado entre as operações dos tomadores de financiamento imobiliário também apresenta relevância na análise discriminante.
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Mayrink, Victor Teixeira de Melo. „Avaliação do algoritmo Gradient Boosting em aplicações de previsão de carga elétrica a curto prazo“. Universidade Federal de Juiz de Fora (UFJF), 2016. https://repositorio.ufjf.br/jspui/handle/ufjf/3563.
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FAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerais
O armazenamento de energia elétrica em larga escala ainda não é viável devido a restrições técnicas e econômicas. Portanto, toda energia consumida deve ser produzida instantaneamente; não é possível armazenar o excesso de produção, ou tampouco cobrir eventuais faltas de oferta com estoques de segurança, mesmo que por um curto período de tempo. Consequentemente, um dos principais desafios do planejamento energético consiste em realizar previsões acuradas para as demandas futuras. Neste trabalho, apresentamos um modelo de previsão para o consumo de energia elétrica a curto prazo. A metodologia utilizada compreende a construção de um comitê de previsão, por meio da aplicação do algoritmo Gradient Boosting em combinação com modelos de árvores de decisão e a técnica de amortecimento exponencial. Esta estratégia compreende um método de aprendizado supervisionado que ajusta o modelo de previsão com base em dados históricos do consumo de energia, das temperaturas registradas e de variáveis de calendário. Os modelos propostos foram testados em duas bases de dados distintas e demonstraram um ótimo desempenho quando comparados com resultados publicados em outros trabalhos recentes.
The storage of electrical energy is still not feasible on a large scale due to technical and economic issues. Therefore, all energy to be consumed must be produced instantly; it is not possible to store the production leftover, or either to cover any supply shortages with safety stocks, even for a short period of time. Thus, one of the main challenges of energy planning consists in computing accurate forecasts for the future demand. In this paper, we present a model for short-term load forecasting. The methodology consists in composing a prediction comitee by applying the Gradient Boosting algorithm in combination with decision tree models and the exponential smoothing technique. This strategy comprises a supervised learning method that adjusts the forecasting model based on historical energy consumption data, the recorded temperatures and calendar variables. The proposed models were tested in two di erent datasets and showed a good performance when compared with results published in recent papers.
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Sousa, Ithalo Coelho de. „Predição genômica da resistência à ferrugem alaranjada em café arábica via algoritmos de aprendizagem de máquina“. Universidade Federal de Viçosa, 2018. http://www.locus.ufv.br/handle/123456789/20584.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
A seleção genômica (SG) foi proposta como uma forma de aumentar a eficiência e acelerar o melhoramento genético. A SG enfatiza a predição simultânea dos efeitos genéticos de milhares de marcadores dispersos em todo o genoma de um organismo. Algumas metodologias estatísticas têm sido utilizadas em SG para a predição do mérito genético, como por exemplo a Ridge Regression Best Linear Unbiased Prediction (RR- BLUP), Bayesian Lasso (BLASSO). Porém tais metodologias exigem algumas pressuposições a respeito dos dados tais como normalidade da distribuição dos valores fenotípicos. Além disto, a presença de fatores complicadores tais como epistasia e dominância atrapalham a utilização destes modelos, uma vez que exigem que tais efeitos sejam estabelecidos à priori pelo pesquisador. Visando contornar a não normalidade dos valores fenotípicos a literatura sugere o uso dos modelos lineares generalizados sob o enfoque bayesiano (BGLR). Outra alternativa são os modelos baseados em aprendizagem de máquina (AM), representados por metodologias tais como Redes Neurais (RNA), Árvores de Decisão (AD) e seus possíveis refinamentos (Bagging, Random Forest e Boosting) as quais podem incorporar a epistasia e a dominância no modelo além de não exigirem pressuposições quanto ao modelo e a distribuição dos valores fenotípicos. Diante disso, o objetivo deste trabalho foi utilizar AD e seus refinamentos Bagging, Random Forest e Boosting para predição da resistência a ferrugem alaranjada no café arábica. Além disso, AD e seus refinamentos foram utilizadas para identificar a importância dos marcadores relacionados a característica de interesse. Os resultados foram comparados com aqueles provenientes do GBLASSO (Lasso Bayesiano Generalizado) e RNA. Foram utilizados dados da resistência a ferrugem do café de 245 plantas derivadas do cruzamento do Híbrido de Timor e do Catuaí Amarelo, genotipados para 137 marcadores. A AD e seus refinamentos obtiveram resultados satisfatórios, visto que apresentaram valores iguais ou inferiores de Taxa de Erro Aparente comparados com aqueles obtidos pelo GBLASSO e RNA. Ademais, os refinamentos da AD demonstraram ser capazes de identificar marcadores importantes para característica de interesse, visto que dentre os 10 marcadores mais importantes analisados em cada metodologia, 3-4 marcadores estavam próximos a QTL’s relacionados a resistência a doença listados na literatura. Por fim, a AD e seus refinamentos mostraram um melhor desempenho em relação ao GBLASSO e a RNA quanto ao custo computacional.
Genomic selection (GS) has been proposed as a way to increase efficiency and accelerate genetic improvement. GS emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers throughout an organism's genome. Some statistical methodologies have been used in GS for the prediction of genetic merit, such as Ridge Regression Best Linear Unbiased Prediction (RR-BLUP), Bayesian Lasso (BLASSO). However such methodologies require some assumptions about the data such as normality of the distribution of phenotypic values. In addition, the presence of complicating factors such as epistasis and dominance hinder the use of these models, since they require that such effects be established a priori by the researcher. In order to avoid the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (BGLR). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and their possible refinements such as Bagging, Random Forest and Boosting, which can incorporate epistasis and dominance in the model, besides not requiring assumptions about the model and the distribution of phenotypic values. The aim of this work was to use DT and its refinements Bagging, Random Forest and Boosting for prediction of resistance to orange rust in arabica coffee. In addition, DT and its refinements were used to identify the importance of markers related to the characteristic of interest. The results were compared with those from GBLASSO (Generalized Bayesian Lasso) and ANN. Data from the coffee rust resistance of 245 plants derived from the hybrid of the Timor Hybrid and the Yellow Catuaí, genotyped for 137 markers were used. The DT and its refinements obtained satisfactory results, since they presented equal or inferior values of Apparent Error Rate compared to those obtained by GBLASSO and RNA. In addition, DT refinements seem to be able to identify important markers for characteristic of interest, since among the 10 most important markers analyzed in each methodology, 3-4 markers were close to QTLs related to resistance to disease listed in the literature. Finally, the Decision Tree and its refinements showed a better performance in relation to the GBLASSO and RNA regarding computational cost.
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Ippoliti, Pierpaola. „Ricerca dell'emissione alle alte energie da parte delle radio galassie fri e frii“. Master's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/6575/.
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Lo scenario di unificazione degli AGN caratterizza le molteplici proprietà di questi oggetti in termini del differente angolo di vista rispetto ad un sistema costituito da un toro oscurante, un disco di accrescimento che alimenta il SMBH e nubi di gas che circondano il buco nero. Circa il 10% degli AGN sono forti sorgenti radio. Questi oggetti, detti AGN Radio-Loud, sono caratterizzati da getti relativistici emessi trasversalmente rispetto al disco di accrescimento e comprendono le radio galassie e i blazar. In accordo con il modello unificato, le radio galassie (MAGN), rappresentano i blazar visti a grandi angoli di inclinazione del getto rispetto alla linea di vista. Nei blazar la radiazione emessa dai getti su scale del pc viene amplificata da effetti relativistici dando origine a spettri piatti con elevata polarizzazione ottica e forte variabilità. Questi oggetti rappresentano le sorgenti più brillanti identificate nel cielo gamma extragalattico. I MAGN, a differenza dei blazar, mostrano spettri ripidi e strutture radio quasi simmetriche. In queste sorgenti, l'effetto del Doppler boosting è meno evidente a causa del grande angolo di inclinazione del getto. In soli 3 mesi di osservazioni scientifiche effettuate con il satellite Fermi è stata rivelata emissione gamma da parte delle radio galassie NGC 1275 e Cen A. I MAGN rappresentano una nuova classe di sorgenti gamma. Tuttavia, il numero di radio galassie rivelate è sorprendentemente piccolo ponendo degli interrogativi sui meccanismi di emissione alle alte energie di questi oggetti. Nel presente lavoro di tesi, si analizzeranno i dati gamma raccolti dal LAT durante i primi 5 anni di osservazioni scientifiche per un campione di 10 radio galassie più brillanti selezionate dai cataloghi B2 e BCS. L'obiettivo principale sarà migliorare la statistica e cercare di comprendere la natura dell'emissione alle alte energie da parte delle radio galassie.
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Teixeira, Filipe. „Boosting compression-based classifiers for authorship attribution“. Master's thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/18375.
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Mestrado em Engenharia de Computadores e TelemáticaAtribuição de autoria é o ato de atribuir um autor a documento anónimo. Apesar de esta tarefa ser tradicionalmente feita por especialistas, muitos novos métodos foram apresentados desde o aparecimento de computadores, em meados do século XX, alguns deles recorrendo a compressores para encontrar padrões recorrentes nos dados. Neste trabalho vamos apresentar os resultados que podem ser alcançados ao utilizar mais do que um compressor, utilizando um meta-algoritmo conhecido como Boosting.
Authorship attribution is the task of assigning an author to an anonymous document. Although the task was traditionally performed by expert linguists, many new techniques have been suggested since the appearance of computers, in the middle of the XX century, some of them using compressors to find repeating patterns in the data. This work will present the results that can be achieved by a collaboration of more than one compressor using a meta-algorithm known as Boosting.
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Le?es, Neto Ant?nio do Nascimento. „Classifica??o com algoritmo AdaBoost.M1 : o mito do limiar de erro de treinamento“. Pontif?cia Universidade Cat?lica do Rio Grande do Sul, 2017. http://tede2.pucrs.br/tede2/handle/tede/7854.
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The accelerated growth of data repositories, in the different areas of activity, opens space for research in the area of data mining, in particular, with the methods of classification and combination of classifiers. The Boosting method is one of them, which combines the results of several classifiers in order to obtain better results. The main purpose of this dissertation is the experimentation of alternatives to increase the effectiveness and performance of the algorithm AdaBoost.M1, which is the implementation often employed by the Boosting method. An empirical study was perfered taking into account stochastic aspects trying to shed some light on an obscure internal parameter, in which algorithm creators and other researchers assumed that the training error threshold should be correlated with the number of classes in the target data set and logically, most data sets should use a value of 0.5. In this paper, we present an empirical evidence that this is not a fact, but probably a myth originated by the mistaken application of the theoretical assumption of the joint effect. To achieve this goal, adaptations were proposed for the algorithm, focusing on finding a better suggestion to define this threshold in a general case.
O crescimento acelerado dos reposit?rios de dados, nas diversas ?reas de atua??o, abre espa?o para pesquisas na ?rea da minera??o de dados, em espec?fico, com os m?todos de classifica??o e de combina??o de classificadores. O Boosting ? um desses m?todos, e combina os resultados de diversos classificadores com intuito de obter melhores resultados. O prop?sito central desta disserta??o ? responder a quest?o de pesquisa com a experimenta??o de alternativas para aumentar a efic?cia e o desempenho do algoritmo AdaBoost.M1 que ? a implementa??o frequentemente empregada pelo Boosting. Foi feito um estudo emp?rico levando em considera??o aspectos estoc?sticos tentando lan?ar alguma luz sobre um par?metro interno obscuro em que criadores do algoritmo e outros pesquisadores assumiram que o limiar de erro de treinamento deve ser correlacionado com o n?mero de classes no conjunto de dados de destino e, logicamente, a maioria dos conjuntos de dados deve usar um valor de 0.5. Neste trabalho, apresentamos evid?ncias emp?ricas de que isso n?o ? um fato, mas provavelmente um mito originado pela aplica??o da primeira defini??o do algoritmo. Para alcan?ar esse objetivo, foram propostas adapta??es para o algoritmo, focando em encontrar uma sugest?o melhor para definir esse limiar em um caso geral.
Bücher zum Thema "E-boosting"
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Young, Bill. Boosting cereal exports through a more equitable grain trade, market driven and aided by e-commerce. Market Harborough: Nuffield Farming Scholarships Trust, 2003.
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Tartaglia, Andrea, Roberto Bolici und Matteo Gambaro, Hrsg. La ricerca tra innovazione, creatività e progetto / Research among Innovation, Creativity and Design. Florence: Firenze University Press, 2012. http://dx.doi.org/10.36253/978-88-6655-160-7.
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In the current socio-cultural scenario, the implementation of the university reform aimed at boosting third-level education calls for meditation within the discipline of Architectural Technology (ICAR 12). This review must address the research topics and academic profiles of PhD courses in the Technological Area, also in terms of fostering actions consistent with European strategic lines for the promotion of a knowledge society. Research, innovation, creativity and design are the keywords of this scenario that PhD students and lecturers must bear in mind when considering three fields of study: environmental design and landscape, building production and construction and works and services strategic for the community. This book "Research among innovation, creativity and design" develops the topics addressed during the VII OSDOTTA workshop (the network of PhD courses in the field of Architectural Technology) held at the Mantua campus of Milan Polytechnic on 15th-16th-17th September 2011.
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Organisation for economic co-operation and development. Oecd Employment Outlook 2006: Boosting Jobs and Incomes (O E C D Employment Outlook). Organization for Economic Cooperation & Devel, 2006.
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How to Win Sales & Influence Spiders: Boosting Your Business & Buzz on the Web (Voices That Matter). New Riders Press, 2007.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "E-boosting"
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Sousa, Cristóvão, Mariana Carvalho und Carla Pereira. „Boosting E-Auditing Process Through E-Files Semantic Enrichment“. In Advances in Intelligent Systems and Computing, 449–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72651-5_43.
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Valle-Cruz, David, und Rodrigo Sandoval-Almazan. „Boosting E-Participation“. In Optimizing E-Participation Initiatives Through Social Media, 103–25. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5326-7.ch005.
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In this chapter, the authors show two case studies of the use of social media in municipal governments: Lerma, a small municipality with a significant growth, and Metepec, an important municipality of the State of México. The purpose of this chapter is to provide empirical evidence of how social media improves government to citizen relationship and promotes e-participation in municipal governments. The results are based on semi-structured interviews applied to public servants and a survey to evaluate e-government services by citizens. So, the citizen perception is contrasted with public servants' interviews. Citizens consider that electronic procedures and services implemented by their municipalities do not generate value. The efforts of governments should focus on avoiding corruption, making governments transparent, opening data, and properly managing the privacy of information.
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Chung, Ik Jae. „Toward E-Government Sustainability“. In Handbook of Research on Strategies for Local E-Government Adoption and Implementation, 773–93. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-282-4.ch041.
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As a nationwide e-government project in South Korea, the Information Network Village project was launched in 2001. It was designed to increase accessibility to e-government services by reducing the digital divide in rural areas and to improve the income level of local residents by boosting regional economy through e-commerce. As of 2007, more than 300 villages are working on- and off-line. Information centers, networked to high-speed Internet, were built in each village. Personal computers were distributed to most households and a website of each village was created to facilitate e-commerce activities. Government reports and evaluation research highlight the performance of the six-year-old project. This chapter revisits, not the performance, but the governance structure, the implementation process, managerial capability, and the administrative value of the project for the purpose of further strengthening its enabler-role in reducing the digital divide and expediting regional development. Policy lessons are summarized to foster the sustainability of e-government projects with consideration of diverse perspectives.
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Kowsalya, Mariyappan, Mohan Prasanna Rajeshkumar, Thangavel Velmurugan, Kattakgounder Govindaraj Sudha und Saheb Ali. „Role of Vitamin E in Boosting the Immunity from Neonates to Elderly“. In Vitamin E in Health and Disease - Interactions, Diseases and Health Aspects [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.98553.
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The vitamin E is a fat-soluble vitamin which occurs as a tocopherol component abundant in humans. The vitamin E supplements in humans and animals have provided numerous health benefits. The vitamin E is rich in antioxidants which slow the aging process and reduce the free radical damage. Vitamin E isoforms play an important role in respiratory health. It is also important in health and well-being of preterm neonates. Vitamin E deficiency in new born includes hemolytic anemia, disease of retina, bronchopulmonary dysplasia. Further, in vitro studies, vitamin E has increased the oxidative resistance and prevents the atherosclerotic plaque. The consumption of vitamin E rich foods reduces coronary heart diseases. This chapter focuses on the treatment of vitamin E deficiency in preterm babies and the role of vitamin E in preventing coronary heart diseases.
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Muñoz, Laura Alcaide, und Raquel Garde Sánchez. „Implementation of E-Government and Reforms in Public Administrations in Crisis Periods“. In Public Affairs and Administration, 2028–45. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8358-7.ch104.
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The importance of e-Government in the reform of public administrations has made it an essential element on political agendas and an important question to be addressed in the current economic crisis that we are witnessing. The consequent drastic reduction in public revenue has made e-Government a key element in the promotion of renewed and sustainable growth with the aim of increasing efficiency and effectiveness in the management of procedures and boosting service provision. In this respect, the considerable amount of research that currently exists in academic literature requires a comprehensive review that allows for improvement in this field of knowledge, offering a broad vision of the current situation and the research possibilities for the future. The authors believe that their findings will allow public managers to be more aware of the need for a cost-benefit analysis of the new technological initiatives proposed.
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Musso, Fabio, und Roxana Adam. „Retailing 4.0 and Technology-Driven Innovation“. In Handbook of Research on Retailing Techniques for Optimal Consumer Engagement and Experiences, 338–54. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1412-2.ch015.
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The chapter analyzes the contribution of technology for boosting innovation within the retail industry. The study focuses on the main areas of innovation for retailers, both in the relationships with suppliers and the final demand. With reference to vertical relationships (for supplying activities), the key innovation areas are those of technology-based interaction tools, joint management of supplying activities, and E-sourcing. In the relations with consumers, technology is stimulating innovation on checkout technologies, dynamic in-store pricing, electronic and mobile payments, augmented reality, artificial intelligence-supported devices, and self-service technologies.
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Vrshek-Schallhorn, Suzanne, Bradley M. Avery und Vaibhav Sapuram. „Gene–environment interactions in humans across multiple units of analyses“. In Genes, brain, and emotions, 18–31. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198793014.003.0003.
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Gene–environment interaction (G×E) research in humans seeks to answer how specific genetic variation contributes to marked individual differences in responding to life experiences, primarily in regard to psychological functioning. In this chapter, we highlight theoretical models underlying G×E research, aspects of its history and controversies, the current state of G×E knowledge, and emerging and future directions for G×E research. Throughout this discussion, we show how this work has emerged across multiple units or levels of analyses, ranging from those closer to the biological functioning of the genes involved, such as neural activity in functional imaging, to more distal outcomes such as diagnoses of psychopathology. Important future directions for G×E research are transitioning from single variant to multiple variant approaches, and more carefully conceptualizing and measuring risk environments while also boosting sample sizes. Ultimately, by attending to these issues, G×E research can not only contribute to early detection of individuals with risky genetic and environmental profiles, but can also aid in revealing etiological pathways, thereby elucidating novel treatment approaches to mental illnesses.
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Santos Kucharski, Marcus Vinicius, Isaac Woungang und Moses Nyongwa. „A Pliant-Based Software Tool for Courseware Development“. In Software Applications, 1404–24. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-060-8.ch081.
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The increasing importance of e-learning has been a boosting element for the emergence of Internet-based educational tools. As we move into the information age, tremendous efforts are made in the development of new information and communication technologies for educational purposes. The ultimate goal is to facilitate elearning methodologies and acquisition. The chapter’s contribution is in the area of open source software for technology-enhanced learning. First, we report on the capabilities of Pliant, a novel software framework for Web-based courseware development. Pliant’ design features upon which e-learning capabilities are built are presented, showing that Pliant has some advantages over existing software, including flexibility, efficiency, and universal usability. A case study of the use of Pliant in the project “Multilanguage Database for Localization” developed at the CUSB School of Translation is presented. Second, we present Academia,3 a Pliant-based courseware development Web portal, and its use in translation studies at CUSB.
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Sen, Saikat, und Raja Chakraborty. „Food in Health Preservation and Promotion“. In Food Science and Nutrition, 392–426. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5207-9.ch017.
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Association between food and health is complex. Healthy food can promote and maintain good human health. Healthy food and nutrition is a key regulating factor for boosting the immunity and therapeutic effectiveness of a treatment strategy. Oxidative stress is well involved in the pathogenesis of diverse diseases and aging. Food always considered as good source of nutrients, protein, fat, carbohydrates, vitamins, minerals and antioxidants. Consumed as part of a normal diet, phytochemicals present in food like vitamins (vitamin C & E), minerals (like, zinc, selenium), phytoconstituents (phenolic compounds, flavonoids, carotenoids) confer additional health benefits, by virtue of their antioxidant property. A diet rich that rich in antioxidant molecule reduces the risk of several oxidative stress related diseases. Numerous antioxidant molecules isolated from food showed the curative and health promotion effect. This chapter majorly deals with the role antioxidant/pro-oxidant substances present in different foods on human body.
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Sen, Saikat, und Raja Chakraborty. „Food in Health Preservation and Promotion“. In Advances in Environmental Engineering and Green Technologies, 265–300. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0591-4.ch013.
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Association between food and health is complex. Healthy food can promote and maintain good human health. Healthy food and nutrition is a key regulating factor for boosting the immunity and therapeutic effectiveness of a treatment strategy. Oxidative stress is well involved in the pathogenesis of diverse diseases and aging. Food always considered as good source of nutrients, protein, fat, carbohydrates, vitamins, minerals and antioxidants. Consumed as part of a normal diet, phytochemicals present in food like vitamins (vitamin C & E), minerals (like, zinc, selenium), phytoconstituents (phenolic compounds, flavonoids, carotenoids) confer additional health benefits, by virtue of their antioxidant property. A diet rich that rich in antioxidant molecule reduces the risk of several oxidative stress related diseases. Numerous antioxidant molecules isolated from food showed the curative and health promotion effect. This chapter majorly deals with the role antioxidant/pro-oxidant substances present in different foods on human body.
Konferenzberichte zum Thema "E-boosting"
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Kotsiuba, Igor, Artem Velvkzhanin, Yury Yanovich, Iuna Skarga Bandurova, Yuriy Dyachenko und Viacheslav Zhygulin. „Decentralized e-Health Architecture for Boosting Healthcare Analytics“. In 2018 Second World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). IEEE, 2018. http://dx.doi.org/10.1109/worlds4.2018.8611621.
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Ahmed, Waleed K., und Ali H. Al Marzouqi. „Boosting students' proficiency in thermodynamics via e-learning“. In 2015 International Conference on Industrial Engineering and Operations Management (IEOM). IEEE, 2015. http://dx.doi.org/10.1109/ieom.2015.7093778.
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Hasan, Raza, Salman Mahmood, Mohammad Sohail Hayat und Syed Imran Ali. „Role of financial institutions in boosting e-banking in Pakistan“. In 2015 2nd World Symposium on Web Applications and Networking (WSWAN). IEEE, 2015. http://dx.doi.org/10.1109/wswan.2015.7210355.
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Dias Júnior, Domingos A., Luana B. da Cruz, João O. B. Diniz, Geraldo Braz Júnior und Aristófanes C. Silva. „Classificação automática de glóbulos brancos usando descritores de forma e textura e eXtreme Gradient Boosting“. In Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/sbcas.2021.16056.
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O diagnóstico de doenças sanguíneas envolve a identificação e caracterização de amostras de sangue de pacientes pela contagem e classificação de glóbulos brancos. Métodos automatizados têm importantes aplicações para auxiliar médicos. O objetivo deste trabalho é desenvolver um método para classificação automática de glóbulos brancos utilizando técnicas de realce, Threshold Adjacency Statistics (TAS) para extração de características e eXtreme Gradient Boosting (XGBoost) para classificação. Os resultados são promissores comparados a outras técnicas e trabalhos da literatura, alcançado 93,27% de acurácia e 90% de F-Measure. Com isto, acredita-se que o método possa auxiliar especialista nesta tarefa importante.
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Kucina Softic, Sandra, und Jadranka Lasić Lazić. „THE E-LEARNING AWARD AS A WAY FOR BOOSTING TEACHERS’ MOTIVATION FOR E-LEARNING IMPLEMENTATION“. In 10th annual International Conference of Education, Research and Innovation. IATED, 2017. http://dx.doi.org/10.21125/iceri.2017.0694.
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Kosasi, Sandy, Vedyanto und I. Dewa Ayu Eka Yuliani. „Boosting E-Service Quality through IT Service Management of Online Stores“. In 2019 6th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI). IEEE, 2019. http://dx.doi.org/10.23919/eecsi48112.2019.8976950.
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Huijing, Jiao, Yang Xuefeng, Pang Wenxue, Guo Longwei, Fu Linfeng, Shi Yongbo und Wu ping. „Practical Exploration of Rural E-commerce Boosting Rural Revitalization Based on 4C Model“. In 2021 2nd International Conference on E-Commerce and Internet Technology (ECIT). IEEE, 2021. http://dx.doi.org/10.1109/ecit52743.2021.00023.
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Souza, Vanessa C. O., Erick T. A. Silva, Rafael M. D. und Melise M. V. Paula. „Análise de diferentes técnicas de pré-processamento em algoritmos de Aprendizado de Máquina na detecção de SQL Injection“. In Seminário Integrado de Software e Hardware. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/semish.2021.15830.
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Atualmente, a SQL Injection é uma das maiores ameaças à segurança das aplicações WEB. Desta forma, diversas abordagens vêm sendo analisadas para tentar resolver esse problema. O objetivo deste trabalho foi utilizar algoritmos de aprendizado de máquina para detectar SQL Injection a partir do tratamento dos dados de entrada de cinco formas diferentes, variando a tokenização, transformação e extração de atributos das bases de SQL Padrão e Injection. Os algoritmos utilizados foram Naive Bayes, SVM, Gradient Boosting Tree (GBT) e Random Forest (RF). O melhor resultado foi obtido com o GBT com as métricas G-Test e Entropia, calculadas sobre tokenização e transformação com expressão regular, apresentando acurácia de 98.46%.
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Yan, Zhang-Fa, Yu-Lin Shen, Wei-Jun Liu, Jie-Min Long und Qingyang Wei. „An E-Commerce Coupon Target Population Positioning Model Based on Random Forest and eXtreme Gradient Boosting“. In 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2018. http://dx.doi.org/10.1109/cisp-bmei.2018.8633247.
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Shu, Yiyang, Huizhen Jenny Qian und Xun Luo. „17.4 A 18.6-to-40.1GHz 201.7dBc/Hz FoMT Multi-Core Oscillator Using E-M Mixed-Coupling Resonance Boosting“. In 2020 IEEE International Solid- State Circuits Conference - (ISSCC). IEEE, 2020. http://dx.doi.org/10.1109/isscc19947.2020.9063100.
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