Search Results - (( java implementation mining algorithm ) OR ( using adaptive batching algorithm ))
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1
Direct approach for mining association rules from structured XML data
Published 2012“…The thesis also provides a two different implementation of the modified FLEX algorithm using a java based parsers and XQuery implementation. …”
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Thesis -
2
Study and Implementation of Data Mining in Urban Gardening
Published 2019“…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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3
Scalable approach for mining association rules from structured XML data
Published 2009“…Many techniques have been proposed to tackle the problem of mining XML data we study the various techniques to mine XML data and yet We presented a java based implementation of FLEX algorithm for mining XML data.…”
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4
Mining association rules from structured XML data
Published 2009“…Many techniques have been proposed to tackle the problem of mining XML data. We study the various techniques to mine XML data and yet We presented a java based implementation of FLEX algorithm for mining XML data.…”
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5
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 -
6
Optimization of fed-batch fermentation processes using the Backtracking Search Algorithm
Published 2018“…DE traditionally performs better than other evolutionary algorithms and swarm intelligence techniques in optimization of fed-batch fermentation. …”
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Application of genetic algorithms to model parameter identification of a recombinant e.coli high-cell density fed-batch fermentation / Kamaruddin Mamat and Farida Zuraina Mohd Yuso...
Published 2008“…In this work, a genetic algorithm was used to estimate both yield and kinetic coefficients of an unstructured model representing a fed-batch high-cell density fermentation of Escherichia coli (E. coli). …”
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8
Image clustering comparison of two color segmentation techniques
Published 2010“…The clustering research is regarding the area of data mining and implementation of the clustering algorithms. …”
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9
Model predictive control on fed-batch penicillin fermentation process
Published 2009“…In this research study the development of optimization strategies for a fed-batch penicillin fermentation process using model predictive controller was simulated using MATLAB 7.1 software. …”
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Undergraduates Project Papers -
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Application of swarm intelligence optimization on bio-process problems / Mohamad Zihin Mohd Zain
Published 2018“…Two multi-objective fed-batch models are also used as case studies to verify the performance of the proposed algorithm. …”
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11
Genetic algorithm for control and optimisation of exothermic batch process
Published 2013“…As a result, improved multivariable genetic algorithm (IMGA) with adaptable fitness function ability is introduced in this work. …”
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12
Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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Hybrid neural network - prior knowledge model in temperature control of a semi-batch polymerization process
Published 2004“…The simulation results show the advantages and robustness of utilizing the neural network in this hybrid strategy especially when an adaptive algorithm is implemented.…”
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15
SLIDING WINDOW TRAINING ALGORITHMS USING MLP-NETWORK FOR CORRELATED AND LOST PACKET DATA
Published 2012“…This thesis gives a systematic investigation of various MLP learning mainly Sliding Window (SW) learning mode which is treated as the adaptation of offline algorithms into online application Consequently this thesis reviews various offline algorithms including: batch backpropagation, nonlinear conjugate gradient, limited memory and full-memory Broyden, Fletcher, Goldfarb and Shanno algorithms and different forms of the latest proposed bimary ensemble learning. …”
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16
Application of genetic algorithms to model parameter identification of a recombinant e.coli high-cell density fedbatch fermentation / Kamaruddin Mamat and Farida Zuraina Mohd Yusof
Published 2008“…In this work, a genetic algorithm was used to estimate both yield and kinetic coefficients of an unstructured model representing a fed-batch high-cell density fermentation of Escherichia coli (E.coli). …”
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17
Impatient task mapping in elastic cloud using genetic algorithm
Published 2011“…Conclusion: Batch mapping via genetic algorithms with throughput as a fitness function can be used to map jobs to cloud resources.…”
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18
Increasing the speed of convergence of an artificial neural network based ARMA coefficients determination technique
Published 2008“…The results obtained by introducing sequential and batch method of weight initialization, batch method of weight and coefficient update, adaptive momentum and learning rate technique gives more accurate result and significant reduction in convergence time when compared t the traditional method of back propagation algorithm, thereby making FFBPNN an appropriate technique for online ARMA coefficient determination.…”
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Proceeding Paper -
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Car dealership web application
Published 2022“…In the web service, adaptive random forest regressor and classifier, which were implemented by third-party River Python library, were used to train models that automatically detected and adapted to drift over time. …”
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Final Year Project / Dissertation / Thesis -
20
Multi objective bee colony optimization framework for grid job scheduling
Published 2013“…Grid computing is the infrastructure that involves a large number of resources like computers, networks and databases which are owned by many organizations.Job scheduling problem is one of the key issues because of high heterogeneous and dynamic nature of resources and applications in the grid computing environment.Bee colony approach has been used to solve this problem because it can be easily adapted to the grid scheduling environment.The bee algorithms have shown encouraging results in terms of time and co st.In this paper a framework for multi objective bee colony optimization is proposed to schedule batch jobs to available resources where the number of jobs is greater than the number of resources.Pareto analysis and k-means analysis are integrated in the bee colony optimization algorithm to facilitate the scheduling of jobs to resources.…”
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