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First Semester Computer Science Students’ Academic Performances Analysis by Using Data Mining Classification Algorithms
Published 2014“…From the experiment, the models develop using Rule Based and Decision Tree algorithm shows the best result compared to the model develop from the Naïve Bayes algorithm. …”
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Data mining for structural damage identification using hybrid artificial neural network based algorithm for beam and slab girder / Meisam Gordan
Published 2020“…In the modeling phase, amongst all DM algorithms, the applicability of machine learning, artificial intelligence and statistical data mining techniques were examined using Support Vector Machine (SVM), Artificial Neural Network (ANN) and Classification and Regression Tree (CART) to detect the hidden patterns in vibration data. …”
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Partitional clustering algorithms for highly similar and sparseness y-short tandem repeat data / Ali Seman
Published 2013“…For the overall performances which were based on the six data sets, the &-AMH algorithm recorded the highest mean accuracy scores of 0.93 as compared to the other algorithms: the ^-Population (0.91), the &-Modes-RVF (0.81), the New Fuzzy &-Modes (0.80), A:-Modes (0.76), &-Modes-HI (0.76), £-Modes- HII (0.75), Fuzzy £-Modes (0.74) and £-Modes-UAVM (0.70). …”
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Data Mining Analysis Of Chronic Kidney Disease (CKD) Level
Published 2022“…Adding the uncertain class the best accuracy obtained was 98.5% using the SMO algorithm. A predictive classification model that determines the accuracy for three classification classes was developed accordingly using the SMO algorithm.…”
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Monograph -
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A novel association rule mining approach using TID intermediate itemset
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Analysis Of Failure In Offline English Alphabet Recognition With Data Mining Approach
Published 2019“…Classification analysis was initially performed on all seven classifier’s algorithms at 10-fold dross validation mode. At phase one, Stroke and Curve are added into the dataset and classified respectively. …”
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Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection
Published 2020“…The second improvement includes the development and use of new Local Search Algorithm with SSA to improve its exploitation. …”
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A new model for iris data set classification based on linear support vector machine parameter's optimization
Published 2020“…The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary problems of: regression, classification, and clustering in diverse domains such as gene expression, web text mining. In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.…”
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A new text-based w-distance metric to find the perfect match between words
Published 2020“…The k-NN algorithm is an instance-based learning algorithm which is widely used in the data mining applications. …”
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A Predictive Classification Model For Running Injury
Published 2022“…The J48, SMO, Random Forest, and Simple Logistic algorithms were used for 10-fold cross validation mode classification benchmarked on the ZeroR baseline algorithm. …”
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Monograph -
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Fuzzy Soft Set Clustering for Categorical Data
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Dynamic and adaptive execution models for data stream mining applications in mobile edge cloud computing systems / Muhammad Habib Ur Rehman
Published 2016“…The critical factors of complexity at application level include data size and data rate of continuously streaming data, the selection of data fusion and data preprocessing methods, the choice of learning models, learning rates and learning modes, and the adoption of data mining algorithms. …”
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Enhancement of new smooth support vector machines for classification problems
Published 2011“…In this research, an alternative algorithm, k-mode RSVM (KMo-RSVM) that combines RSVM and k-mode clustering technique to handle classification problems on categorical large dataset and k-prototype RSVM (KPro-RSVM) which combine k-prototype and RSVM to classify large dataset with mixed attributes were proposed. …”
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