Search Results - (( java application mining algorithm ) OR ( pattern selection drops algorithm ))

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  1. 1
    Direct approach for mining association rules from structured XML data

    Direct approach for mining association rules from structured XML data by Abazeed, Ashraf Riad

    Published 2012
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    Thesis
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  2. 2
    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection

    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection by Saputra , Dhany, Rambli, Dayang R.A., Foong, Oi Mean

    Published 2008
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  3. 3
    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
    “…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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  4. 4
    Mining Sequential Patterns Using I-PrefixSpan

    Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

    Published 2007
    “…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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  5. 5
    A web-based implementation of k-means algorithms

    A web-based implementation of k-means algorithms by Lee, Quan

    Published 2022
    “…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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  6. 6
    Mining Sequential Patterns using I-PrefixSpan

    Mining Sequential Patterns using I-PrefixSpan by Saputra , Dhany, Dayang R.A. Rambli, Foong, Oi Mean

    Published 2008
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    Citation Index Journal
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  7. 7
    Features selection for intrusion detection system using hybridize PSO-SVM

    Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

    Published 2016
    “…The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.…”
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  8. 8
    Land Use Changes in Jeli, Kelantan

    Land Use Changes in Jeli, Kelantan by Muhammad Firdaus Abdul Karim

    Published 2013
    “…Land use change percentage and urban land expansion index (SI) are selected algorithm in this study. The overall accuracy assessment ranged from 61.39% to 92.05% which is moderate to accurate. …”
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    Undergraduate Final Project Report
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  9. 9
    A scene invariant convolutional neural network for visual crowd counting using fast-lane and sample selective methods

    A scene invariant convolutional neural network for visual crowd counting using fast-lane and sample selective methods by Teoh, Shen Khang

    Published 2023
    “…Fast lane connection and sample selective algorithms have been designed specifically to tackle the issue of vanishing gradient and enhance the quality of the model. …”
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  10. 10
    An integrated priority-based cell attenuation model for dynamic cell sizing

    An integrated priority-based cell attenuation model for dynamic cell sizing by Amphawan, Angela, Omar, Mohd Nizam, Din, Roshidi

    Published 2012
    “…Real-time predicted mobile traffic from the EFuNN structure was used to control the size of all the cells.Results obtained demonstrate the robustness of the integrated module in recognizing the temporal pattern of the mobile traffic and dynamically controlling the cell size in order to reduce the number of calls dropped.…”
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  11. 11
    Optimal placement and sizing of distributed generation in radial distribution networks using particle swarm optimization and forward backward sweep method

    Optimal placement and sizing of distributed generation in radial distribution networks using particle swarm optimization and forward backward sweep method by Lawal, Sani Mohammed

    Published 2012
    “…PSO is among the meta-heuristics search methods like Genetic Algorithm (GA) but has been found to be computationally efficient, because it uses less number of functions for evaluation compared to GA that has genetic operators (Selection, crossover and mutation) and also the computational effort (time) required by PSO to arrive at high quality solutions is less than the effort required to the same high quality solutions by other heuristic search methods. …”
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