Search Results - (( process classification max algorithm ) OR ( java application mining algorithm ))
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Modern fuzzy min max neural networks for pattern classification
Published 2019“…Among these algorithms, Fuzzy Min Max (FMM) neural network algorithm has been proven to be one of the premier neural networks for undertaking the pattern classification problems. …”
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Thesis -
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Direct approach for mining association rules from structured XML data
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Thesis -
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Individual And Ensemble Pattern Classification Models Using Enhanced Fuzzy Min-Max Neural Networks
Published 2014“…EFMM and EFMM2, are proposed to address a number of limitations in the original FMM learning algorithm. In EFMM, three heuristic rules are introduced to improve the hyperbox expansion, overlap test, and contraction processes. …”
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Kernel and multi-class classifiers for multi-floor wlan localisation
Published 2016“…Unlike the classical kNN algorithm which is a regression type algorithm, the proposed localisation algorithms utilise machine learning classification for both linear and kernel types. …”
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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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Conference or Workshop Item -
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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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Final Year Project / Dissertation / Thesis -
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Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm
Published 2018“…The suggested approaches are called new approach to min-max (NAMM) and decimal scaling (NADS). The Hybrid mean algorithms which are based on spherical clusters is also proposed to remedy the most significant limitation of the K-Means and K-Midranges algorithms. …”
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Mining Sequential Patterns using I-PrefixSpan
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Citation Index Journal -
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ANALYSIS OF BIOSENSOR PHYSIOLOGICAL SIGNALS FOR ASSESSMENT OF NEUROLOGICAL STATUS
Published 2018“…All the data signals of the 20 subjects will then be processed with features extraction method using mean, maximum (Max), minimum (Min), mean absolute deviation (MAD), Standard deviation (STD), interquartile range (IQR) and summation (Sum). …”
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Final Year Project -
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Classification of compressive strength grades for lightweight aggregate concrete with palm oil fuel ash (POFA) using k-Nearest Neighbour (k-NN)
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Conference or Workshop Item -
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Dense-cluster based voting approach for license plate identification
Published 2018“…Furthermore, the recognition results from classification show that recognition rate improves significantly compared to prior classification.…”
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Article -
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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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Thesis -
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Automated plaque classification using computed tomography angiography and Gabor transformations
Published 2019“…The features were then ordered based on the F-value and input to numerous classification methods to achieve the best classification accuracy with the least number of features. …”
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Predicting STEM academic performance in secondary schools: data mining approach
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Conference or Workshop Item -
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Classification of compressive strength grades for lightweight aggregate concrete with palm oil fuel ash (POFA) using kNearest Neighbour (k-NN)
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Conference or Workshop Item -
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Malaysian license plate recognition system using Convolutional Neural Network (CNN) on web application / Nur Farahana Mahmud
Published 2022“…The obtained sample image will first undergo pre-processing and character extraction. 3 layers of a Convolutional Neural Network (CNN) model that contain convolutional, max pooling, flatten and dense were created and further trained. …”
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