Search Results - (( yield selection method 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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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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Mining Sequential Patterns using I-PrefixSpan
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Opposition-Based Learning Binary Bat Algorithm as Feature Selection Approach in Taguchi's T-Method
Published 2024“…Thus, this study proposed an Opposition-based Learning Binary Bat Algorithm as the feature selection technique in the T-method. …”
Conference Paper -
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A partition based feature selection approach for mixed data clustering / Ashish Dutt
Published 2020“…One such pre-processing algorithm in EDM is clustering. It is a widely used method in data mining to discover unique patterns in underlying data. …”
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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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Development of genetic algorithm for optimization of yield models in oil palm production
Published 2018“…This research concludes that the GA method is a user-friendly variable selection tool with excellent results because it can choose variables correctly.…”
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Taguchi?s T-method with Normalization-Based Binary Bat Algorithm
Published 2025“…In conclusion, the proposed method successfully yields better prediction accuracy as compared to conventional approaches. …”
Conference paper -
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Predicting crop yield and field energy output for oil palm using genetic algorithm and neural network models
Published 2019“…This research presents the development of a GA and SW as a variables selection method in ANN and NARX models for predicting oil palm yield and output energy. …”
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Hybrid binary grey Wolf with Harris hawks optimizer for feature selection
Published 2021“…The proposed hybrid method outperforms the BGWO algorithm in terms of accuracy, selected feature size, and computational time. …”
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Hybrid Binary Grey Wolf with Harris Hawks Optimizer for Feature Selection
Published 2021“…The proposed hybrid method outperforms the BGWO algorithm in terms of accuracy, selected feature size, and computational time. …”
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Hybrid binary grey Wolf with Harris hawks optimizer for feature selection
Published 2021“…The proposed hybrid method outperforms the BGWO algorithm in terms of accuracy, selected feature size, and computational time. …”
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Hybrid Artificial Bees Colony Algorithms For Optimizing Carbon Nanotubes Characteristics
Published 2018“…Chemical Vapor Deposition (CVD) is the most efficient method for CNTs production.However,using CVD method encounters crucial issues such as customization,time and cost.Therefore,Response Surface Methodology (RSM) is proposed for modeling and the ABC-βHC is proposed for optimization purpose to address such issues.The selected CNTs characteristics are CNTs yield and quality represented by the ratio of the relative intensity of the D and G-bands (ID/IG).Six case studies are generated from collected dataset including four cases of CNTs yield and one case of ID/IG as single objective optimization problems,while the sixth case represents multi-objective problem.The input parameters of each case are a subset from the set of input parameters including reaction temperature,duration,carbon dioxide flow rate,methane partial pressure,catalyst loading,polymer weight and catalyst weight.The models for the first three case studies were mentioned in the original work.RSM is proposed to develop polynomial models for the output responses in the other three cases and to identi significant process parameters and interactions that could affect the CNTs output responses.The developed models are validated using t-test,correlation and pattern matching.The predictive results have a good agreement with the actual experimental data.The models are used as objective functions in optimization techniques.For multi-objective optimization,this study proposes Desirability Function Approach (DFA) to be integrated with other proposed algorithms to form hybrid techniques namely RSM-DFA,ABC-DFA and ABC-βHC-DFA.The proposed algorithms and other selected well-known algorithms are evaluated and compared on their CNTs yield and quality.The optimization results reveal that ABC-βHC and ABC-βHC-DFA obtained significant results in terms of success rate,required time,iterations,and function evaluations number compared to other well-known algorithms.Significantly,the optimization results from this study are better than the results from the original work of the collected dataset.…”
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Sensitivity-based fuzzy multi-objective portfolio model with Value-at-Risk
Published 2019“…The results show that the fuzzy VaR multi-objective portfolio model based on the sensitivity analysis method can effectively identify and quantitatively analyze the sensitivity of individual stocks to yield changes, i.e. that can identify which stock has more stable yields and that can calculate the degree of stability, then to obtain stable portfolio solutions. …”
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Feature fusion using a modified genetic algorithm for face and signature recognition system
Published 2015“…Results show that the proposed method yield optimal recognition with the highest accuracy of 97.50%. …”
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Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms
Published 2005“…The comparison results between BBSI and JM-distance showed that, both algorithms accurately selected the best four-band combination that yielded the highest overall accuracy classification map with value of 91% in the Landsat TM dataset. …”
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