Search Results - (( data replication scheduling algorithm ) OR ( code classification mining algorithm ))
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Dynamic replication aware load blanced scheduling in distributed environment / Said Bakhshad
Published 2018“…Several algorithms have been proposed and studied for scheduling and data replication, however a little research has been done so far on capturing and minimizing the migration rate of data from an existing available replica site to a next site on the basis of data scheduling in order to minimize the transfers and deletion rate. …”
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A dynamic replication aware load balanced scheduling for data grids in distributed environments of internet of things
Published 2018“…We propose a novel dynamic Replication Aware Load Balanced Scheduling (DRALBS) algorithm, that considers the replica location dynamically at the time of scheduling of the job. …”
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Evaluations of oil palm fresh fruit bunches maturity degree using multiband spectrometer
Published 2017“…Furthermore, the Lazy-IBK algorithm have been validated to produce the best classifier model, with the machine learning algorithm performance of 65.26%, recall of 65.3%, and 65.4% F-measured as compared to other evaluated machine learning classifier algorithms proposed within the WEKA data mining algorithm. …”
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Comparative analysis for topic classification in juz Al-Baqarah
Published 2018“…The SVM performance is then compared against other classification algorithms such as Naive Bayes, J48 Decision Tree and K-Nearest Neighbours. …”
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Classification of metamorphic virus using n-grams signatures
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Learner’s emotion prediction using production rules classification algorithm through brain computer interface tool
Published 2018“…No EEG studies in Malaysia has been done on school children to study their emotional behaviour while learning. Classification and prediction are the functions provided by the data mining techniques that suit in EEG signal processing. …”
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7
An enhanced android botnet detection approach using feature refinement
Published 2019“…The experimental and statistical tests show that 97.28% accuracy achieved by Random Forest machine classifier, it performs well as compared to other classification algorithms. Based on the test results, various open research issues which need to be addressed in future studies are highlighted.…”
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