Search Results - (( developing interface machine algorithm ) OR ( java implementation clustering algorithm ))

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  1. 1

    Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly by Zulkifly, Ahmad Zuladzlan

    Published 2019
    “…This project will use fuzzy k-means clustering algorithm to cluster the data because it is easy to implement and have many advantages. …”
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    Thesis
  2. 2

    Image clustering comparison of two color segmentation techniques by Subramaniam, Kavitha Pichaiyan

    Published 2010
    “…The clustering research is regarding the area of data mining and implementation of the clustering algorithms. …”
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    Thesis
  3. 3

    Brain Machine Interface Controlled Robot Chair by Hema Chengalvarayan, Radhakrishnamurthy

    Published 2010
    “…Brain Machine Interface Controlled Robot Chair: Brain Machine Interface is a device that links the human brain directly to devices such as computer, wheelchairs and prosthetic arms. …”
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    Thesis
  4. 4

    A Toolkit for Simulation of Desktop Grid Environment by FOROUSHAN, PAYAM CHINI

    Published 2014
    “…A simulator for desktop grid environment has been developed using Java as the implementation language due to its wide popularity. …”
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    Final Year Project
  5. 5

    Development Of Machine Learning User Interface For Pump Diagnostics by Lee, Zhao Yang

    Published 2022
    “…The main objectives for this project are focusing on the development of user interface that can connect with the machine learning build in Microsoft Azure for pump diagnostic purpose. …”
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    Monograph
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    A web-based implementation of k-means algorithms by Lee, Quan

    Published 2022
    “…Firstly, k-luster could incorporate additional clustering algorithms, or even classification algorithms in the future. …”
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    Final Year Project / Dissertation / Thesis
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    Graphical user interface for surface roughness prediction of CNC milling machine / Muhammad Qayyum Nor Asffan by Nor Asffan, Muhammad Qayyum

    Published 2020
    “…In this research, MATLAB Graphical User Interface (GUI) is used to develop a user- friendly program that can predict surface roughness of Aluminium 6061 in CNC milling machine. …”
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    Student Project
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    Mathematical simulation for 3-dimensional temperature visualization on open source-based grid computing platform by Alias, Norma, Satam, Noriza, Abd. Ghaffar, Zarith Safiza, Darwis, Roziha, Hamzah, Norhafiza, Islam, Md. Rajibul

    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
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    Development of interactive application for classification of Artocarpus Species by Abdul Ghapar, Nadia

    Published 2020
    “…This study capable to provide the results for current accuracy data representation of the selected Artocarpus species. The development of Graphical User Interface (GUI) for classification of Artocarpus species help user to identify and differentiate the species in faster and easier way especially botanist, taxonomist, and researcher. …”
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    Undergraduate Final Project Report
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    Particle swarm optimization (PSO) for CNC route problem by Nur Azia Azwani, Ismail

    Published 2002
    “…The purpose of this study is to develop the application of Particle Swarm Optimization (PSO) which applicable to CNC machine route problem. …”
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    Undergraduates Project Papers
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    System Interface Design For CAD/CAM-Simulink Data Exchange System Using MATLAB by Mat Seman, Norhidayah, Jamaludin, Zamberi, Minhat, Mohamad

    Published 2018
    “…The proposed CS-DES system enhances data automation through formation of the graphical user input reference interface of the machine tools control system using NC manufacturing codes data as the reference input signal. …”
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    Article
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    A New Mobile Botnet Classification based on Permission and API Calls by Yusof, M, Saudi, MM, Ridzuan, F

    Published 2024
    “…As a result, 16 permissions and 31 API calls that are most related with mobile botnet have been extracted using feature selection and later classified and tested using machine learning algorithms. The experimental result shows that the Random Forest Algorithm has achieved the highest detection accuracy of 99.4% with the lowest false positive rate of 16.1% as compared to other machine learning algorithms. …”
    Proceedings Paper