Search Results - (( variable validation clustering algorithm ) OR ( java data visualization algorithm ))
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1
Optimized clustering with modified K-means algorithm
Published 2021“…In order to obtain the optimum number of clusters and at the same time could deal with correlated variables in huge data, modified k-means algorithm was proposed. …”
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Thesis -
2
Efficient genetic partitioning-around-medoid algorithm for clustering
Published 2019“…These algorithms mostly built upon the partitioning k-means clustering algorithm. …”
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Thesis -
3
Statistical performance of agglomerative hierarchical clustering technique via pairing of correlation-based distances and linkage methods
Published 2025“…To validate the clustering model on real data, the Spearman-average algorithm was applied to cluster Juru river basin data based on five water quality parameters. …”
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4
Prediction of Machine Failure by Using Machine Learning Algorithm
Published 2019“…Validation for the model is analyzed by using validation testing data and cross validation. …”
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Final Year Project -
5
Seed disperser ant algorithm for optimization / Chang Wen Liang
Published 2018“…In this research, we applied SDAA to solve the constrained engineering problems and introduce an efficient data clustering algorithm which is hybrid of K-means and SDAA. …”
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6
Application of Optimization Methods for Solving Clustering and Classification Problems
Published 2011“…Intuitively, samples within a valid cluster are more similar to each other than they are to a sample belonging to a different cluster. …”
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7
Enhanced bibliographic data retrieval and visualization using query optimization and spectral centrality measure
Published 2019“…This study proposing an enhance bibliographic data retrieval and visualization using hybrid clustering method consists of K-harmonic mean (KHM) and Spectral Algorithm and eigenvector centrality measure. …”
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Article -
8
Web-based RIG performance reporting system using interactive visualization techniques / Amir Hambaly Nasaruddin
Published 2019“…The result of this project is all algorithm for visualizing data is functioning and the output produced from the system is correct and effective. …”
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9
Dynamic force-directed graph with weighted nodes for scholar network visualization
Published 2022“…The approach is realized by creating a web-based interface using D3 JavaScript algorithm that allows the visualization to focus on how data are connected to each other more accurately than the conventional lines of data seen in traditional data representation. …”
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10
Design Of Robot Motion Planning Algorithm For Wall Following Robot
Published 2006“…Computer A will be sent the data to computer B through the internet/LAN using JAVA program. …”
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Monograph -
11
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
Centre based evolving clustering framework with extended mobility features for vehicular ad-hoc networks
Published 2021“…This framework uses an evolving data clustering algorithm by adopting the concept of grid granularity to capture the features of a cluster more efficiently. …”
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Thesis -
13
3D surface roughness measurement for scaliness scoring of psoriasis lesions
Published 2013“…The algorithm has been validated with an accuracy of 94.12. …”
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Article -
14
Independent And Dependent Job Scheduling Algorithms Based On Weighting Model For Grid Environment
Published 2018“…This thesis presents job weighting model using a Twostep clustering to assign the categorical and continuous variables of jobs into classes for both independent and dependent job scheduling. …”
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15
Mathematical simulation for 3-dimensional temperature visualization on open source-based grid computing platform
Published 2009“…The development of this architecture is based on several programming language as it involves algorithm implementation on C, parallelization using Parallel Virtual Machine (PVM) and Java for web services development. …”
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Conference or Workshop Item -
16
Development Of Machine Learning User Interface For Pump Diagnostics
Published 2022“…Build up a user interface by using Visual Studio Code (VSC) to run the coding of Cascading Style Sheet (CSS), Hyper Text Markup Language (HTML) and JavaScript (JS) as a webpage and connect to Azure Machine Learning Model and this will allow the user from using the model from a webpage when they have active internet with any devices.…”
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Monograph -
17
Tacit knowledge for business intelligence framework using cognitive-based approach
Published 2022“…The framework was tested on 23 librarians from several university libraries in West Java and Yogyakarta, Indonesia. The algorithm starts with a content targeted interview to identify the list of problems faced by librarians. …”
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18
Landslide susceptibility mapping using decision-tree based chi-squared automatic interaction detection (CHAID) and logistic regression (LR) integration
Published 2014“…LR model was used to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. A cluster pattern of landslide locations was extracted in previous study using nearest neighbor index (NNI), which were then used to identify the clustered landslide locations range. …”
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19
Predicting 30-day mortality after an acute coronary syndrome (ACS) using machine learning methods for feature selection, classification and visualization
Published 2021“…Feature selection methods such as Boruta, Random Forest (RF), Elastic Net (EN), Recursive Feature Elimination (RFE), learning vector quantization (LVQ), Genetic Algorithm (GA), Cluster Dendrogram (CD), Support Vector Machine (SVM) and Logistic Regression (LR) were combined with RF, SVM, LR, and EN classifiers for 30-day mortality prediction. …”
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Article -
20
A novel AI-driven EEG images emotion recognition generalized classification model for cross-subject analysis
Published 2025“…However, the significant inter-individual variability in EEG signals complicates cross-subject analysis, thereby limiting their generalizability. …”
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