Search Results - (( variable making 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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Thesis -
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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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Conference or Workshop Item -
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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 -
7
Prediction of Machine Failure by Using Machine Learning Algorithm
Published 2019“…Model built resulted in variables importance’s ranking and subsequently, prediction can be made. …”
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Final Year Project -
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Taguchi?s T-method with Normalization-Based Binary Bat Algorithm
Published 2025“…s orthogonal array is used as a variable selection approach in optimizing the predictive model. …”
Conference paper -
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Development of genetic algorithm for optimization of yield models in oil palm production
Published 2018“…In the oil palm industry, modelling and selecting variables play a crucial role in apprehending different issues, i.e. decision making. …”
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Article -
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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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Thesis -
11
Development of Genetic Algorithm Procedure for Sequencing Problem in Mixed-Model Assembly Lines
Published 2009“…Due to NP-hard nature of sequencing problem in mixed model assembly line, a genetic algorithm is applied to cope with problem complexity and obtain a near optimal solution in a reasonable amount of time. …”
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Thesis -
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Comparison on queuing performance measures on customer’s flow using Queuing Theory Model and DSW Algorithm / Muhammad Izuanudin Azmi ... [et al.]
Published 2023“…In a multi-server fuzzy queuing model, the approximate technique DSW algorithm is employed to describe a membership function regarding the performance measures (Thamotharan,2016). …”
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Article -
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A hybrid model of system dynamics and genetic algorithm to increase crude palm oil production in Malaysia
Published 2018“…Furthermore, the hybrid model provides the ability of identifying the policy variables related to the objective function at any specific time line. …”
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Thesis -
15
An Intelligent Process Model for Manufacturing System Optimization
Published 2012“…The mathematical theorem is modeled with Industrial Engineering (IE) variables into spreadsheet to perform intelligent decision making. …”
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Article -
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Optimizing the placement of fire department in Kulim using greedy heuristic and simplex method / Muhammad Abu Syah Mohd Suzaly
Published 2023“…Greedy heuristics is a type of optimization algorithm that makes decisions based on locally optimal solutions. …”
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Thesis -
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Development of fault detection, diagnosis and control system identification using multivariate statistical process control (MSPC)
Published 2006“…In this research work, an FDD algorithm is developed using MSPC and correlation coefficients between process variables. …”
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Monograph -
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Automated time series forecasting
Published 2011“…While quantitative technique is based on statistical concepts and requires large amount of data in order to formulate the mathematical models.This technique can be classified into projective and causal technique.The projective technique (or univariate modelling) just involve one variable while the causal technique (or econometric modelling) suitable for multi-variables.Since forecasting involves uncertainty, several methods need to be executed on one set of time series data in order to produce accurate forecast.Hence, usually in practice forecaster need to use several softwares to obtain the forecast values.If this practice can be transformed into algorithm (well-defined rules for solving a problem) and then the algorithm can be transformed into a computer program, less time will be needed to compute the forecast values where in business world time is money.In this study, we focused on algorithm development for univariate forecasting techniques only and will expand towards econometric modelling in the future.Two set of simulated data (yearly and non-yearly) and several univariate forecasting techniques (i.e. …”
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Monograph -
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Optimization of Upstream Offshore Oilfield Production Planning under Uncertainty and Downstream Crude Oil Scheduling at Refinery Front-End
Published 2009“…The solution obtained from the LB-MILP model, i.e., the decision variables (binary variables), was used to obtain a feasible solution for model UB-NLP. …”
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Final Year Project -
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