Search Results - (( code classification mining algorithm ) OR ( leave application testing algorithm ))
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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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The comparative study of model-based and appearance based gait recognition for leave bag behind
Published 2018“…Meanwhile, the accuracy and misclassification rate (MER) of Model-based approaches obtained is 97.00% and 3.00% respectively tested on SVM classifier then the accuracy and misclassification rate (MER) of Model-based approaches is 99.00% and 1.00% respectively tested on KNN algorithm. …”
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The comparative study of model-based and appearance Based gait recognition for leave bag behind
Published 2018“…Meanwhile, the accuracy and misclassification rate (MER) of Model-based approaches obtained is 97.00% and 3.00% respectively tested on SVM classifier then the accuracy and misclassification rate (MER) of Model-based approaches is 99.00% and 1.00% respectively tested on KNN algorithm. …”
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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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State of charge estimation for lithium-ion battery based on random forests technique with gravitational search algorithm
Published 2023Conference Paper -
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A Study On Gene Selection And Classification Algorithms For Classification Of Microarray Gene Expression Data
Published 2005“…The Gene Selection Techniques Include Fisher Criterion, Golub Signal-To-Noise, Traditional T-Test And Mann-Whitney Rank Sum Statistic. The Classification Algorithms Include Support Vector Machines (Svms) With Several Kernels And K-Nearest Neighbor(K-Nn). …”
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Classification model for hotspot occurrences using spatial decision tree algorithm
Published 2013“…The result is a spatial decision tree with 276 leaves with distance from target objects to the nearest river as the first test layer and the accuracy on the training set of 87.69%. …”
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Comparison of algorithm Support Vector Machine and C4.5 for identification of pests and diseases in chili plants
Published 2019“…In this study comparing the performance classification techniques of Support Vector Machine (SVM) and C4.5 algorithms. The attributes used consist of Leaves, Stems, and Fruits. …”
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Effective gene selection techniques for classification of gene expression data
Published 2005“…Various k-means clustering algorithms and model-based clustering algorithms are proposed to group the genes. …”
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Expansion power system transmission using ant colony optimization: article / Izzataswad Ibrahim
Published 2015“…The main objective this proposal to find the lowest investment total cost transmission network. A 24 bus reliability test using in transmission expansion analysis, the results show that the algorithm is capable to deliver good solutions for relatively large systems[1]. …”
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Modeling forest fires risk using spatial decision tree
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A new mobile malware classification for call log exploitation
Published 2024journal::journal article -
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Classification model for hotspot occurrences using a decision tree method
Published 2011“…This work demonstrates the application of a decision tree algorithm, namely the C4.5 algorithm, to develop a classification model from forest fire data in the Rokan Hilir district, Indonesia. …”
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