The Experts below are selected from a list of 2727 Experts worldwide ranked by ideXlab platform
Rocio Millan - One of the best experts on this subject based on the ideXlab platform.
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variability and trend based generalized Rule Induction Model to ntl detection in power companies
IEEE Transactions on Power Systems, 2011Co-Authors: Carlos Leon, Felix Biscarri, Inigo Monedero, Juan Ignacio Guerrero, Jesus Biscarri, Rocio MillanAbstract:This paper proposes a comprehensive framework to detect non-technical losses (NTLs) and recover electrical energy (lost by abnormalities or fraud) by means of a data mining analysis, in the Spanish Power Electric Industry. It is divided into four section: data selection, data preprocessing, descriptive, and predictive data mining. The authors insist on the importance of the knowledge of the particular characteristics of the Power Company customer: the main features available in databases are described. The paper presents two innovative statistical estimators to attach importance to variability and trend analysis of electric consumption and offers a predictive Model, based on the Generalized Rule Induction (GRI) Model. This predictive analysis discovers association Rules in the data and it is supplemented by a binary Quest tree classification method. The quality of this framework is illustrated by a case study considering a real database, supplied by Endesa Company.
M Pratheepa - One of the best experts on this subject based on the ideXlab platform.
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study of population dynamics of soybean semi looper gesonia gemmaswinhoe by using Rule Induction Model in maharashtra india
Legume Research, 2017Co-Authors: Cruz J Antony, M PratheepaAbstract:Gesonia gemma Swinhoe (1885) is a grey semi-looper and it has emerged as a serious threat to the soybean crop. This defoliator causes heavy damage to the crop in the form of loss in grain weight. Gesonia gemma population dynamics was studied in various districts of Maharashtra. Sequential covering algorithm (CN2 Rule Induction) has been proposed for Rule Induction Model to generate a list of classification Rules with target feature (G. gemma population) and the independent abiotic features. The classification Rules have exhibited more accuracy and showed that maximum temperature and humidity with less number of rainy days has influenced the population of Gesonia gemma in Maharashtra. Hence, this Rule Induction Model can be used to study the collected evidence for prediction and it will be helpful to the farmers to take necessary pest control strategy.
Dutra, Bruna Luiza - One of the best experts on this subject based on the ideXlab platform.
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Mineração de dados para identificação de fatores de reprovação no ensino superior
'Associacao Portuguesa de Sistemas de Informacao', 2019Co-Authors: Dutra, Bruna LuizaAbstract:Brazil and several countries are facing the problem of students dropout in the universities. Particularly, dropout rates of computer science related courses are among the highest ones, with approximately one of three students receiving the diploma. For example, recent studies showed that some classes of the Information Systems course at Federal University of Uberlândia achieved dropout rates greater than 70%. Among the several reasons for students dropout, one of the most cited in the literature is students retention. In this sense, Data Mining methods can be adopted to find indicators that contribute to student dropout or retention, which can be a relevant tool to alleviate this problem. By considering a data set of technical, social and economic features of students from the Information Systems course at Monte Carmelo Campus, this work aims at evaluating data classification techniques in the problem of predicting the students final performance regarding the discipline of Introduction to Computer Programming. The present study also seeks to identify the factors that most contribute to a student's poor performance. The experiments were performed with the Weka tool. A total of six classification techniques were considered, in addition to two database situations: with and without missing attributes. To compare the results we adopted two metrics: accuracy and F1. Statistical tests attested the good results of the JRIP Rule Induction Model in comparison with the other techniques. Among the indicators that stand out are: family per capita income, average math grade in high school, distance from parents city to the university city, distance from where student lives to the campus, type of school attended in high school and with who the student resides. It is expected that such indicators can assist course managers in the early identification of students who are most likely to fail and also in the planning of actions to reduce dropout rates.Trabalho de Conclusão de Curso (Graduação)O Brasil e diversos países vêm enfrentando o problema da evasão no ensino superior. Especificamente, as taxas de evasão dos cursos da área de computação estão entre as maiores, sendo que aproximadamente um a cada três alunos que ingressam recebe o diploma. No curso noturno de Sistemas de Informação da Universidade Federal de Uberlândia, por exemplo, um estudo recente apontou que houve turmas com taxas de evasão maior do que 70%. Entre os vários motivos para a evasão, um dos mais citados na literatura é a reprovação em disciplinas do curso. Nesse sentido, métodos de Mineração de Dados podem ser adotados para encontrar os fatores que contribuem para o aluno evadir ou reprovar, o que pode ser um ferramenta relevante para a amenização desse problema. Desse modo, partindo de uma bases de dados contendo registros técnicos, sociais e econômicos de alunos do curso de Sistemas de Informação do Campus Monte Carmelo, o objetivo deste trabalho é avaliar técnicas de classificação de dados no problema de predizer o desempenho final do aluno em relação à disciplina de Introdução à Programação de Computadores. O presente estudo também busca identificar os fatores que mais contribuem para o desempenho ruim de um aluno. Os experimentos foram realizados com a ferramenta Weka. Um total de seis técnicas de classificação foram consideradas, além de duas situações da base de dados: com e sem atributos faltantes. Para a comparação dos resultados foram utilizadas as métricas acurácia e medida F1. Através de testes estatísticos, é possível afirmar que o Modelo de indução de regras JRIP se destacou dentre as demais técnicas. Entre os indicadores que se destacaram estão a renda per capita familiar, nota média em matemática no ensino médio, distância da cidade dos pais até a cidade da universidade, distância da localidade em que o aluno mora até o campus, tipo de escola que cursou no ensino médio e com quem o aluno reside. Espera-se que esses fatores auxiliem os gestores do curso na identificação precoce dos alunos com mais chances de reprovar e permita também o planejamento de ações a fim de reduzir as taxas de evasão
Carlos Leon - One of the best experts on this subject based on the ideXlab platform.
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variability and trend based generalized Rule Induction Model to ntl detection in power companies
IEEE Transactions on Power Systems, 2011Co-Authors: Carlos Leon, Felix Biscarri, Inigo Monedero, Juan Ignacio Guerrero, Jesus Biscarri, Rocio MillanAbstract:This paper proposes a comprehensive framework to detect non-technical losses (NTLs) and recover electrical energy (lost by abnormalities or fraud) by means of a data mining analysis, in the Spanish Power Electric Industry. It is divided into four section: data selection, data preprocessing, descriptive, and predictive data mining. The authors insist on the importance of the knowledge of the particular characteristics of the Power Company customer: the main features available in databases are described. The paper presents two innovative statistical estimators to attach importance to variability and trend analysis of electric consumption and offers a predictive Model, based on the Generalized Rule Induction (GRI) Model. This predictive analysis discovers association Rules in the data and it is supplemented by a binary Quest tree classification method. The quality of this framework is illustrated by a case study considering a real database, supplied by Endesa Company.
Cruz J Antony - One of the best experts on this subject based on the ideXlab platform.
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study of population dynamics of soybean semi looper gesonia gemmaswinhoe by using Rule Induction Model in maharashtra india
Legume Research, 2017Co-Authors: Cruz J Antony, M PratheepaAbstract:Gesonia gemma Swinhoe (1885) is a grey semi-looper and it has emerged as a serious threat to the soybean crop. This defoliator causes heavy damage to the crop in the form of loss in grain weight. Gesonia gemma population dynamics was studied in various districts of Maharashtra. Sequential covering algorithm (CN2 Rule Induction) has been proposed for Rule Induction Model to generate a list of classification Rules with target feature (G. gemma population) and the independent abiotic features. The classification Rules have exhibited more accuracy and showed that maximum temperature and humidity with less number of rainy days has influenced the population of Gesonia gemma in Maharashtra. Hence, this Rule Induction Model can be used to study the collected evidence for prediction and it will be helpful to the farmers to take necessary pest control strategy.
