Search Results - (( developing selection model algorithm ) OR ( java application mining algorithm ))
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Direct approach for mining association rules from structured XML data
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Study and Implementation of Data Mining in Urban Gardening
Published 2019“…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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Article -
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Mining Sequential Patterns Using I-PrefixSpan
Published 2007“…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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A web-based implementation of k-means algorithms
Published 2022“…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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Final Year Project / Dissertation / Thesis -
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Mining Sequential Patterns using I-PrefixSpan
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Citation Index Journal -
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Model structure selection for a discrete-time non-linear system using genetic algorithm
Published 2004“…The results show that the proposed algorithm can be employed as an algorithm to select the structure of the proposed model.…”
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Model structure selection for a discrete-time non-linear system using a genetic algorithm
Published 2004“…The results show that the proposed algorithm can be employed as an algorithm to select the structure of the proposed model.…”
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Model structure selection for a discrete-time non-linear system using a genetic algorithm
Published 2004“…The results show that the proposed algorithm can be employed as an algorithm to select the structure of the proposed model.…”
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SURE-Autometrics algorithm for model selection in multiple equations
Published 2016“…Thus, this study aims to develop an algorithm for model selection in multiple equations focusing on seemingly unrelated regression equations (SURE) model. …”
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Thesis -
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Model structure selection for a discrete-time non-linear system using a genetic algorithm
Published 2004“…The results show that the proposed algorithm can be employed as an algorithm to select the structure of the proposed model.…”
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Article -
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Optimisation of Environmental Risk Assessment Architecture using Artificial Intelligence Techniques
Published 2024“…Fuzzy arithmetic operations on fuzzy numbers and artificial neural networks with a back-propagation learning algorithm were used to represent the structure of the neuro-fuzzy risk assessment model, whereas genetic algorithms were used to develop the safe path selection model. …”
thesis::doctoral thesis -
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Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.…”
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A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models
Published 2017“…These models are developed by integrating multilayer perceptron neural network and evolutionary optimization techniques. …”
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Assessing the simulation performances of multiple model selection algorithm
Published 2015“…The Autometrics is an algorithm for single equation model selection.It is a hybrid method which combines expanding and contracting search techniques.In this study, the algorithm is extended for multiple equations modelling known as SURE-Autometrics.The aim of this paper is to assess the performance of the extended algorithm using various simulation experiment conditions. …”
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Conference or Workshop Item -
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Algorithmic approaches in model selection of the air passengers flows data
Published 2015“…Algorithm is an important element in any problem solving situation.In statistical modelling strategy, the algorithm provides a step by step process in model building, model testing, choosing the ‘best’ model and even forecasting using the chosen model.Tacit knowledge has contributed to the existence of a huge variability in manual modelling process especially between expert and non-expert modellers.Many algorithms (automated model selection) have been developed to bridge the gap either through single or multiple equation modelling.This study aims to evaluate the forecasting performances of several selected algorithms on air passengers flow data based on Root Mean Square Error (RMSE) and Geometric Root Mean Square Error (GRMSE).The findings show that multiple models selection performed well in one and two step-ahead forecast but was outperformed by single model in three step-ahead forecasts.…”
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Development of decentralized data fusion algorithm with optimized kalman filter.
Published 2016“…This thesis proposes a data fusion model that facilitates selection of algorithm and recommends selected algorithm to be optimized. …”
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A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant
Published 2023“…Algorithms; Artificial intelligence; Biochemical oxygen demand; Bioinformatics; Developing countries; Effluent treatment; Effluents; Forecasting; Least squares approximations; Oxygen; Pattern recognition; Support vector machines; Water quality; Biological oxygen demand; Clonal selection algorithms; Least-square support vector machines; Sludge treatment plants; Total suspended solids; Chemical oxygen demand; oxygen; sewage; algorithm; clone; comparative study; effluent; least squares method; nonlinearity; pattern recognition; simulation; sludge; water treatment; activated sludge; algorithm; Article; biochemical oxygen demand; chemical oxygen demand; clonal selection algorithm; comparative study; computer simulation; effluent; forecasting; pattern recognition; prediction; regression analysis; septic sludge treatment plant; sludge treatment; statistical model; support vector machine; suspended particulate matter; waste water treatment plant; chemistry; procedures; sewage; theoretical model; Algorithms; Biological Oxygen Demand Analysis; Forecasting; Least-Squares Analysis; Models, Theoretical; Sewage; Support Vector Machines; Waste Disposal, Fluid…”
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LASSO-type estimations for threshold autoregressive and heteroscedastic time series models.
Published 2020“…In this thesis, we propose Least Absolute Shrinkage and Selection Operator (LASSO) type estimators to perform simultaneous parameter estimation and model selection for five specific univariate and multivariate time series models, and develop several algorithms to compute these estimators. …”
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UMK Etheses -
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Extended multiple models selection algorithms based on iterative feasible generalized least squares (IFGLS) and expectation-maximization (EM) algorithm
Published 2019“…In conclusion, SURE(IFGLS)-Autometrics and SURE(EM)-Autometrics can be used as models selection algorithms. Additionally, both algorithms are suitable in improving performance of automated models selection procedures. …”
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