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  1. 1
    Study and Implementation of Data Mining in Urban Gardening

    Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

    Published 2019
    “…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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  2. 2
    Application Of Multi-Layer Perceptron Technique To Detect And Locate The Base Of A Young Corn Plant

    Application Of Multi-Layer Perceptron Technique To Detect And Locate The Base Of A Young Corn Plant by Morshidi, Malik Arman

    Published 2007
    “…Prior to localization of the base of young corn tree, skeletonizing operation is performed to get the basic shape of the object. …”
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  3. 3
    Petri Net Algorithm for controlling and monitoring system

    Petri Net Algorithm for controlling and monitoring system by Mohamad Shaiful, Osman

    Published 2009
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  4. 4
    Robotic path planning using rapidly-exploring random trees

    Robotic path planning using rapidly-exploring random trees by Sherwani, Fahad

    Published 2013
    “…This study concerns the implementation of Rapidly-Exploring Random Trees (RRTs) algorithm for an autonomous robot path planning. …”
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  5. 5
    Web-based expert system for material selection of natural fiber- reinforced polymer composites

    Web-based expert system for material selection of natural fiber- reinforced polymer composites by Ahmed Ali, Basheer Ahmed

    Published 2015
    “…Finally, the developed expert system was deployed over the internet with central interactive interface from the server as a web-based application. As Java is platform independent and easy to be deployed in web based application and accessible through the World Wide Web (www), this expert system can be one stop application for materials selection.…”
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  6. 6
    Recovery of tree community composition across different types of anthropogenic disturbances and characterization of their effect using Landsat time series in Bornean tropical montane forest

    Recovery of tree community composition across different types of anthropogenic disturbances and characterization of their effect using Landsat time series in Bornean tropical monta... by Keiko Ioki, Daniel James, Phua, Mui How, Satoshi Tsuyuki, Nobuo Imai

    Published 2022
    “…Five LTS metrics—time since the greatest disturbance (TSD), magnitude of disturbance (MD), distance to undisturbed forests (d_UND), recovery indicator (RI), and years to recovery (Y2R) were derived and were related to field-based tree community composition. …”
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  7. 7
    Development of an Activity Recognition System Using Accelerometers

    Development of an Activity Recognition System Using Accelerometers by Hui, Dandy Lau Jing

    Published 2014
    “…With accelerometers that capture the acceleration rate of different activities and Decision Tree algorithm for classification, the system is able to predict accurately the activity performed by the wearer. …”
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  8. 8
    Automatic extraction of digital terrain model and Building Footprint from airborne LiDAR data using rule-based learning techniques

    Automatic extraction of digital terrain model and Building Footprint from airborne LiDAR data using rule-based learning techniques by Jifroudi, Hamidreza Maskani

    Published 2021
    “…Therefore, in this research an algorithm has been created which can achieve the following goals. 1) To generate DTM only with LiDAR data without the need for layers and other information from the area 2) To create a building footprint from the LiDAR data by removing the tree cover effect 3) To create an automatic system that can perform the production process of DTM and footprint without the intervention of an expert. …”
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  9. 9
    Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.

    Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining. by Saeed, Walid

    Published 2005
    “…The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
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