Search Results - (( java applications testing algorithm ) OR ( structural testing clustering algorithm ))
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1
An investigation of structural breaks on spot and futures crude palm oil returns
Published 2011“…Based on the Bai and Perron test results, we found two structural changes in tested returns mean series located in late 1998 and 1999. …”
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2
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 -
3
Spatial Clustering Algorithm for Time Series Rainfall Data Using X-Means Data Splitting
Published 2017“…The performance of the clustering algorithm gets even worse if it relies on actual data and many clustering algorithms are often faced with conflict in handling high dimensional data. …”
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A multi-view clustering algorithm for attributed weighted multi-edge directed networks
Published 2022“…The performance of the proposed algorithm is tested on a synthetic, provided for a three-view attributed weighted directed network including 50 nodes, and two real-world datasets: UK faculty and Seventh graders. …”
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5
Evaluation of the effectiveness of clustering algorithm in retrieving Malay documents / Aminah Mahmood
Published 2004“…This study has evaluated and identified the effectiveness of clustering algorithm in Malay document retrieval system using Hadith test collections, which consists of Hadith documents, relevant judgments and one set of queries. …”
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6
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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7
Support Vector Machines (SVM) in Test Extraction
Published 2006“…This project's objective is to create a summarizer, or extractor, based on machine learning algorithms, which are namely SVM and K-Means. Each word in the particular document is processed by both algorithms to determine its actual occurrence in the document by which it will first be clustered or grouped into categories based on parts of speech (verb, noun, adjective) which is done by K-Means, then later processed by SVM to determine the actual occurrence of each word in each of the cluster, taking into account whether the words have similar meanings with otherwords in the subsequent cluster. …”
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Final Year Project -
8
Support Vector Machines (SVM) in Test Extraction
Published 2006“…This project's objective is to create a summarizer, or extractor, based on machine learning algorithms, which are namely SVM and K-Means. Each word in the particular document is processed by both algorithms to determine its actual occurrence in the document by which it will first be clustered or grouped into categories based on parts of speech (verb, noun, adjective) which is done by K-Means, then later processed by SVM to determine the actual occurrence of each word in each of the cluster, taking into account whether the words have similar meanings with otherwords in the subsequent cluster. …”
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Final Year Project -
9
Quantification and segmentation of breast cancer diagnosis: efficient hardware accelerator approach
Published 2022“…Segmentation clustering algorithms have setbacks on overlapping clusters, proportion, and multidimensional scaling to map and leverage the data. …”
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10
A maximal-clique-based clustering approach for multi-observer multi-view data by using k-nearest neighbor with S-pseudo-ultrametric induced by a fuzzy similarity
Published 2024“…The proposed clustering algorithm is programmed and tested on synthetic and four real-world datasets using the R software. …”
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Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed
Published 2012“…In conventional hard clustering approach, the number of clusters was determined by hierarchical clustering and two-step cluster analysis; then the sites were allocated to the appropriate cluster by k-means clustering method. …”
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12
Clustering the addiction levels of drug users using fuzzy c-mean
Published 2025“…The Silhouette Coefficient, Calinski-Harabasz Index, and Davies-Bouldin Index algorithms reveal high potential values to indicate the proper achievement of this clustering structural test. …”
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13
Application of clustering in managing unstructured textual data in relational database / Wael Mohamed Shaher Yafooz
Published 2014“…Their name entities and top most frequent terms are selected for the factors used in a cluster assignment. The model is tested and evaluated by embedding it in a component-based package of a relational databases internal structure. …”
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14
Reassembly and clustering bifragmented intertwined jpeg images using genetic algorithm and extreme learning machine
Published 2019“…The RX_myKarve is a framework that contains both structure-based carving and content-based carving approaches. …”
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15
Designing new chaotic and hyperchaotic systems for chaos-based cryptography
Published 2019“…That means, the generated sequences are either chaotic or coexistence of chaotic attractors. Randomness test results show that the generated pseudo-random sequences by the new algorithm can pass all the statistical tests.…”
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16
Enhancing clustering algorithm with initial centroids in tool wear region recognition
Published 2020“…It is conflicting with the standard K-means that return different clustering structure in each run, while K-means + + replicates several times to achieve minimum objective function. …”
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Implementation of (AES) Advanced Encryption Standard algorithm in communication application
Published 2014“…The concept of ABS algorithm was firstly studied, including the definition, historical background, and a brief comparison was made between the ABS algorithm with other types of algorithm. …”
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Undergraduates Project Papers -
19
Exploring clusters of rare events using unsupervised random forests
Published 2022“…The proximity plot seems to show there could be cluster tendency as Hopkins’s statistics test value was H = 0.8735 and k=2 was identified to be the optimal number of clusters. …”
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Conference or Workshop Item -
20
Classification System for Heart Disease Using Bayesian Classifier
Published 2007“…This system was developing base on to three main part which is data processing, testing and implementation of the algorithm. In this system a Bayesian algorithm was used in order to implement the system. …”
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