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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
    “…The thesis also provides a two different implementation of the modified FLEX algorithm using a java based parsers and XQuery implementation. …”
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  2. 2
    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
    “…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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  3. 3
    Scalable approach for mining association rules from structured XML data

    Scalable approach for mining association rules from structured XML data by Abazeed, Ashraf Riad, Mamat, Ali, Sulaiman, Md. Nasir, Ibrahim, Hamidah

    Published 2009
    “…Many techniques have been proposed to tackle the problem of mining XML data we study the various techniques to mine XML data and yet We presented a java based implementation of FLEX algorithm for mining XML data.…”
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  4. 4
    Mining association rules from structured XML data

    Mining association rules from structured XML data by Abazeed, Ashraf Riad, Mamat, Ali, Sulaiman, Md. Nasir, Ibrahim, Hamidah

    Published 2009
    “…Many techniques have been proposed to tackle the problem of mining XML data. We study the various techniques to mine XML data and yet We presented a java based implementation of FLEX algorithm for mining XML data.…”
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  5. 5
    Multilingual Ontology Learning Algorithm for Emails

    Multilingual Ontology Learning Algorithm for Emails by Beseiso M., Ahmad A.R., Ismail R., Taher M.

    Published 2023
    “…In this research, we have explained the necessity for utilizing multilingual ontology learning processes to solve this problem. We have proposed the multilingual ontology learning algorithm for semantic email. …”
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  6. 6
    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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  7. 7
    A Reinforced Active Learning Algorithm for Semantic Segmentation in Complex Imaging

    A Reinforced Active Learning Algorithm for Semantic Segmentation in Complex Imaging by Usmani, U.A., Watada, J., Jaafar, J., Aziz, I.A., Roy, A.

    Published 2021
    “…We propose a new reinforced active learning strategy based on a deep reinforcement learning algorithm. …”
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  8. 8
    Visual and semantic context modeling for scene-centric image annotation

    Visual and semantic context modeling for scene-centric image annotation by Zand, Mohsen, Doraisamy, Shyamala, Abdul Halin, Alfian, Mustaffa, Mas Rina

    Published 2015
    “…The sub-visual distributions are discovered through a clustering algorithm, and probabilistically associated with semantic classes using mixture models. …”
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  9. 9
    Image clustering comparison of two color segmentation techniques

    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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  10. 10
    Inference Algorithms in Latent Dirichlet Allocation for Semantic Classification

    Inference Algorithms in Latent Dirichlet Allocation for Semantic Classification by Mohammad Zubir, W.M.A., Abdul Aziz, I., Jaafar, J., Hasan, M.H.

    Published 2018
    “…The inference algorithm used in this research work is Gibbs sampling. …”
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  11. 11
    Inference Algorithms in Latent Dirichlet Allocation for Semantic Classification

    Inference Algorithms in Latent Dirichlet Allocation for Semantic Classification by Mohammad Zubir, W.M.A., Abdul Aziz, I., Jaafar, J., Hasan, M.H.

    Published 2018
    “…The inference algorithm used in this research work is Gibbs sampling. …”
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  12. 12
    Adaptive learning for lemmatization in morphology analysis

    Adaptive learning for lemmatization in morphology analysis by Ting, Mary, Abdul Kadir, Rabiah, Tengku Sembok, Tengku Mohd, Ahmad, Fatimah, Azman, Azreen

    Published 2014
    “…Morphological analysis is used to study the internal structure words by reducing the number of vocabularies used while retaining the semantic meaning of the knowledge in NLP system. …”
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  13. 13
    A Study of Automated Essay Scoring Frameworks on Evaluating Malaysian University English Test Essays Based on Syntactic and Semantic Features

    A Study of Automated Essay Scoring Frameworks on Evaluating Malaysian University English Test Essays Based on Syntactic and Semantic Features by Chun Then, Lim

    Published 2023
    “…Based on the results, we found that syntactic features of an essay have a higher effect than semantic features towards essay grades. Besides, we also found that the differences between machine learning and deep learning algorithms were not obvious, and neither algorithm's performance can be considered excellent because the quadratically weighted Kappa (QWK) scores were less than 0.75. …”
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  14. 14
    Classification of machine learning engines using latent semantic indexing

    Classification of machine learning engines using latent semantic indexing by Yusof, Yuhanis, Alhersh, Taha, Mahmuddin, Massudi, Mohamed Din, Aniza

    Published 2012
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  15. 15
    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
    “…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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  16. 16
    Source code classification using latent semantic indexing with structural and frequency term weighting

    Source code classification using latent semantic indexing with structural and frequency term weighting by Yusof, Yuhanis, Alhersh, Taha, Mahmuddin, Massudi, Mohamed Din, Aniza

    Published 2012
    “…Based on the undertaken experiment the LSI classifier is noted to generate a higher precision and recall compared to the C4.5 algorithm. Furthermore,it is also learned that the use of structural information in the weighting scheme contribute to a better classification.…”
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  17. 17
    Place recognition using semantic concepts of visual words

    Place recognition using semantic concepts of visual words by Rostami, Vahid, Ramli, Abdul Rahman, Samsudin, Khairulmizam, Saripan, M. Iqbal

    Published 2011
    “…This paper presents a supervised learning framework for place recognition using the semantic concepts of visual words. …”
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  18. 18
    Learning a deeply supervised multi-modal RGB-D embedding for semantic scene and object category recognition

    Learning a deeply supervised multi-modal RGB-D embedding for semantic scene and object category recognition by Mohd Zaki, Hasan Firdaus, Shafait, Faisal, Mian, Ajmal

    Published 2017
    “…Recognizing semantic category of objects and scenes captured using vision-based sensors is a challenging yet essential capability for mobile robots and UAVs to perform high-level tasks such as long-term autonomous navigation. …”
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  19. 19
    An automated learner for extracting new ontology relations

    An automated learner for extracting new ontology relations by Amaal Saleh Hassan, Al Hashimy, Narayanan, Kulathuramaiyer

    Published 2013
    “…As this task 'automatic recognition of semantic relations between pairs of words in text' can be used in many NLP applications, its implementation are demanding and may include many potential methodologies. …”
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  20. 20
    Semantic focus fusion based on deep learning for deblurring effect

    Semantic focus fusion based on deep learning for deblurring effect by Ismail, .

    Published 2024
    “…The method is termed semantic focus fusion for deblurring effect. It employs deep learning architecture to extract focus and blurred features. …”
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