Search Results - (( automatic classification parallel algorithm ) OR ( java evaluation modified algorithm ))

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  1. 1
    Automated plant recognition system based on multi-objective parallel genetic algorithm and neural network

    Automated plant recognition system based on multi-objective parallel genetic algorithm and neural network by Sefidgar, Seyed Mohammad Hossein

    Published 2014
    “…This method resulted around 99% of classification rate. To conclude, multi objective parallel genetic algorithm can automatically tune feed forward neural network to classify the dataset with a good classification rate.…”
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  2. 2
    Design and analysis of management platform based on financial big data

    Design and analysis of management platform based on financial big data by Chen, Yuhua, Mustafa, Hasri, Zhang, Xuandong, Liu, Jing

    Published 2023
    “…In order to make financial big data generate business value and improve the information application level of financial management, aiming at the high error rate of current financial data classification system, this article adopts the fuzzy clustering algorithm to classify financial data automatically, and adopts the local outlier factor algorithm with neighborhood relation (NLOF) to detect abnormal data. …”
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  3. 3
    Projecting named entity tags from a resource rich language to a resource poor language

    Projecting named entity tags from a resource rich language to a resource poor language by Zamin, Norshuhani, Oxley, Alan, Abu Bakar, Zainab

    Published 2012
    “…Named Entities (NE) are the prominent entities appearing in textual documents.Automatic classification of NE in a textual corpus is a vital process in Information Extraction and Information Retrieval research. …”
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  4. 4
    Projecting named entity tags from a resource rich language to a resource poor language

    Projecting named entity tags from a resource rich language to a resource poor language by Zamin, N., Oxley, A., Bakar, Z.A.

    Published 2013
    “…Named Entities (NE) are the prominent entities appearing in textual documents. Automatic classification of NE in a textual corpus is a vital process in Information Extraction and Information Retrieval research. …”
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  5. 5
    Projecting named entity tags from a resource rich language to a resource poor language

    Projecting named entity tags from a resource rich language to a resource poor language by Zamin, N., Oxley, A., Bakar, Z.A.

    Published 2013
    “…Named Entities (NE) are the prominent entities appearing in textual documents. Automatic classification of NE in a textual corpus is a vital process in Information Extraction and Information Retrieval research. …”
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  6. 6
    Projecting named entity tags from a resource rich language to a resource poor language

    Projecting named entity tags from a resource rich language to a resource poor language by Zamin, N., Oxley, A., Bakar, Z.A.

    Published 2013
    “…Named Entities (NE) are the prominent entities appearing in textual documents. Automatic classification of NE in a textual corpus is a vital process in Information Extraction and Information Retrieval research. …”
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  7. 7
    A framework of test case prioritisation in regression testing using particle swarm-artificial bee colony algorithm

    A framework of test case prioritisation in regression testing using particle swarm-artificial bee colony algorithm by Ba-Quttayyan, Bakr Salim Abdullah

    Published 2024
    “…This research employed a modified version of the Design Science Research Methodology (DSRM), streamlined into five stages: problem identification, theoretical study, framework development, evaluation, and reporting. …”
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  8. 8
    An ensemble learning method for spam email detection system based on metaheuristic algorithms

    An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

    Published 2015
    “…In order to address the challenges that mentioned above in this study, in the first phase, a novel architecture based on ensemble feature selection techniques include Modified Binary Bat Algorithm (NBBA), Binary Quantum Particle Swarm Optimization (QBPSO) Algorithm and Binary Quantum Gravita tional Search Algorithm (QBGSA) is hybridized with the Multi-layer Perceptron (MLP) classifier in order to select relevant feature subsets and improve classification accuracy. …”
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  9. 9
    An automated multimodal white matter hyperintensities identification in MRI brain images using image processing / Iza Sazanita Isa

    An automated multimodal white matter hyperintensities identification in MRI brain images using image processing / Iza Sazanita Isa by Isa, Iza Sazanita

    Published 2018
    “…This research proposes a method for automatic detection of WMH in white matter (WM) tissue for MRI images. …”
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  10. 10
    An automated multimodal white matter hyperintensities identification in MRI brain images using image processing / Iza Sazanita Isa

    An automated multimodal white matter hyperintensities identification in MRI brain images using image processing / Iza Sazanita Isa by Isa, Iza Sazanita

    Published 2018
    “…This research proposes a method for automatic detection of WMH in white matter (WM) tissue for MRI images. …”
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  11. 11
    Fuzzy-based multi-agent approach for reliability assessment and improvement of power system protection

    Fuzzy-based multi-agent approach for reliability assessment and improvement of power system protection by Nadheer Abdulridha, Shalash

    Published 2015
    “…The second agent is a reliability evaluation agent that uses a recursive algorithm to predict the suitability generator based on the frequency and duration reliability indices in each state while the third agent is the storage and transfer of data between the other two agents. …”
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  12. 12
    Evolutionary cost-cognizant regression test case prioritization for object-oriented programs

    Evolutionary cost-cognizant regression test case prioritization for object-oriented programs by Bello, AbdulKarim

    Published 2019
    “…Afterward evolutionary algorithm (EA) was employed to prioritize test cases based on the rate severity of fault detection per unit test cost. …”
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