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1
Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model
Published 2018“…EM and K-means clustering algorithms are used to cluster the multi-class classification attribute according to its relevance criteria and afterward, the clustered attributes are classified using an ensemble random forest classifier model. …”
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2
Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms
Published 2005“…The comparison showed that, the accuracy of the unsupervised classification map with value of 88.4% that was generated by using the cluster labelling algorithm was slightly more than the maximum-likelihood supervised classification map with value of 87.5%. …”
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Thesis -
3
RSA Encryption & Decryption using JAVA
Published 2006“…The implementation of this project will be based on Rapid Application Design Methodology (RAD) and will be more focusing on research and finding, ideas and the implementation of the algorithm, and finally running and testing the algorithm. …”
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Final Year Project -
4
Improving the tool for analyzing Malaysia’s demographic change: data standardization analysis to form geo-demographics classification profiles using k-means algorithms
Published 2016“…K-means is one of the methods commonly used in cluster algorithm because it is more significant. …”
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5
Identify texture of MRI human brain using Adaptive Fuzzy C-Means (AFCM) Algorithm / Faridatul Akma Mohd Noor
Published 2010“…The main objective of this research is to develop a prototype that use Adaptive Fuzzy C-Means (AFCM) algorithm to identify texture of human brain.…”
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6
Pelvic classification based on deep learning algorithm on clinical CT scans in Malaysian population
Published 2023“…This research concludes that using Google Teachable Machine provide high accuracy and precision for sex estimation but not useful for age classification for sample below 20 years old.…”
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7
Gas Identi cation by Using a Cluster-k-Nearest-Neighbor
Published 2009“…We find 98.7% of accuracy in the classification of 6 different types of Gas by using K-means cluster algorithm and we find almost the same by using the new clustering algorithm.…”
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8
Pattern Classification of Human Epithelial Images
Published 2016“…This project shows an important role to diagnosis autoimmune disorder which is by a comparative analysis on the most appropriate clustering technique for the segmentation and also to develop algorithm for positivity classification. In this project, there are four stages will be used to analyze pattern classification in human epithelial (HEp-2) images. …”
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Final Year Project -
9
Use of hybrid classification algorithm for land use and land cover analysis in data scarce environment
Published 2013“…In conclusion, hybrid classification as a combination of k-means and support vector machine algorithms and post-classification comparison change detection technique can be used to monitor land cover changes in Halabja city, Iraq. …”
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10
Fuzzy C-Mean And Genetic Algorithms Based Scheduling For Independent Jobs In Computational Grid
Published 2006“…In this paper, we combine Fuzzy C-Mean and Genetic Algorithms which are popular algorithms, the Grid can be used for scheduling. …”
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11
An Analysis Of Various Algorithms For Text Spam Classification And Clustering Using Rapidminer And Weka
Published 2024“…By using the same dataset, which is downloaded from UCI, Machine Learning Repository, various algorithms used in classification and clustering in this simulation has been analysed comparatively. …”
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12
An Improved Wavelet Neural Network For Classification And Function Approximation
Published 2011“…Second, the proposed enhanced fuzzy c-means clustering algorithm—specifically, the modified point symmetry-based fuzzy c-means (MPSDFCM) algorithm—was employed in selecting the locations of the translation vectors of the WNN. …”
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13
Reliable multiclass cancer classification of microarray gene expression profiles using an improved wavelet neural network
Published 2011“…Second, the proposed enhanced fuzzy c-means clustering algorithm—specifically, the modified point symmetry-based fuzzy c-means (MSFCM) algorithm—was employed in selecting the locations of the translation vectors of the WNN. …”
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14
MINING CUSTOMER DATA FOR DECISION MAKING USING NEW HYBRID CLASSIFICATION ALGORITHM
Published 2011“…The experimental result shows that the proposed hybrid k-mean plus MFP algorithm can generate more useful pattern from large stock data.…”
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Citation Index Journal -
15
Determining the preprocessing clustering algorithm in radial basis function neural network
Published 2008“…Three types of method used in this study to find the centres include random selections, K-means clustering algorithm and also K-median clustering algorithm. …”
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16
Extremal region selection for MSER detection in food recognition
Published 2021“…Therefore, this research proposes an Extremal Region Selection (ERS) algorithm to improve MSER detection by reducing the number of irrelevant extremal regions by using unsupervised learning based on the k-means algorithm. …”
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17
Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition
Published 2017“…Phase 1 is mainly to evaluate the performance of clustering algorithm (K-Means and FCM). Phase 2 is to study the performance of proposed integration system which using the data clustered to be used as train data for Naïve Bayes classifier. …”
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18
Efficient classifying and indexing for large iris database based on enhanced clustering method
Published 2018“…In the current work, the new Weighted K-means algorithm based on the Improved Firefly Algorithm (WKIFA) has been used to overcome the shortcomings in using the Fireflies Algorithm (FA). …”
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Comparison of Search Algorithms in Javanese-Indonesian Dictionary Application
Published 2020“…Performance Testing is used to test the performance of algorithm implementations in applications. …”
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Journal -
20
K-gen phishguard: an ensemble approach for phishing detection with k-means and genetic algorithm
Published 2025“…This research presents a two-phase phishing detection system by employing unsupervised feature selection and supervised classification. In the first phase, the best set of features is identified by the Genetic algorithm and is utilised by the K-means clustering algorithm to divide the dataset into groups with similar traits. …”
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