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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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  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
    Mutable Composite Firefly Algorithm for Microarray-Based Cancer Classification

    Mutable Composite Firefly Algorithm for Microarray-Based Cancer Classification by Fajila, Fathima, Yusof, Yuhanis

    Published 2024
    “…Recently, the swarm-based hybrid algorithms have given significant performance in cancer classification. …”
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  5. 5
    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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  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
    Songket pattern classification using backpropagation neural network / Nik Aidil Syawalni Nik Mazlan

    Songket pattern classification using backpropagation neural network / Nik Aidil Syawalni Nik Mazlan by Nik Mazlan, Nik Aidil Syawalni

    Published 2024
    “…The study's outcomes underscore the capability of the BPNN-based algorithm to attain remarkable accuracy in Songket pattern classification, thus showcasing its viability for real-world applications.…”
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  8. 8
    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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  9. 9
    The Perspective Classification of Balanced Scorecard with Ontology Technique

    The Perspective Classification of Balanced Scorecard with Ontology Technique by Kaewninprasert, Kittisak, Chai-Arayalert, Supaporn, Yamaqupta, Narueban

    Published 2024
    “…This paper aims to present the Perspective Classification of Balanced Scorecard with Ontology (PCBSC-Onto) framework for automatically classifying performance indicators into BSC’s four perspectives using an ontological approach to support the dynamic capability of the management system’s performance. …”
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  10. 10
    A modified weighted support vector machine (WSVM) to reduce noise data in classification problem

    A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

    Published 2021
    “…To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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  11. 11
    A modified weighted support vector machine (WSVM) to reduce noise data in classification problem

    A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

    Published 2021
    “…To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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  12. 12
    Assessment of cognitive load using multimedia learning and resting states with deep learning perspective

    Assessment of cognitive load using multimedia learning and resting states with deep learning perspective by Qayyum, A., Faye, I., Malik, A.S., Mazher, M.

    Published 2019
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  13. 13
    Decision tree and rule-based classification for predicting online purchase behavior in Malaysia / Maslina Abdul Aziz, Nurul Ain Mustakim and Shuzlina Abdul Rahman

    Decision tree and rule-based classification for predicting online purchase behavior in Malaysia / Maslina Abdul Aziz, Nurul Ain Mustakim and Shuzlina Abdul Rahman by Abdul Aziz, Maslina, Mustakim, Nurul Ain, Abdul Rahman, Shuzlina

    Published 2024
    “…The result indicated that the highest accuracy of 89.34% was achieved by the Random Tree algorithm, while the rule-based algorithm PART reached an accuracy of 87.56%. …”
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  14. 14
    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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  15. 15
    Fault tolerance structures in Wireless Sensor Networks (WSNs): survey, classification, and future directions

    Fault tolerance structures in Wireless Sensor Networks (WSNs): survey, classification, and future directions by Adday, Ghaihab Hassan, K. Subramaniam, Shamala, Ahmad Zukarnain, Zuriati, Samian, Normalia

    Published 2022
    “…This survey provides an elaborate classification and analysis of fault tolerance structures and their essential components and categorizes errors from several perspectives. …”
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  16. 16
    Comparison between fuzzy and non-fuzzy classification methods in the prediction of residential household water leakage / Nor Aishah Md Noh, Dr. Khairul Anwar Rasmani and Nur Rasyida Mohd Rashid

    Comparison between fuzzy and non-fuzzy classification methods in the prediction of residential household water leakage / Nor Aishah Md Noh, Dr. Khairul Anwar Rasmani and Nur Rasyid... by Md Noh, Nor Aishah, Rasmani, Khairul Anwar, Mohd Rashid, Nur Rasyida

    Published 2013
    “…Hence, searching of the alternative approaches need to be done especially based on the perspective of consumer.…”
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    Research Reports
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  17. 17
    Vision based entomology: a survey

    Vision based entomology: a survey by Hassan, Siti Noorul Asiah, Abdul Rahman, Nur Nadiah Syakira, Htike@Muhammad Yusof, Zaw Zaw, Shoon , Lei Win

    Published 2014
    “…We provides discussion on the state-of-the-art and provides perspective on future research direction in insect recognition and classification problem.…”
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  18. 18
    Online sequential extreme learning machine algorithm based human activity recognition using inertial data

    Online sequential extreme learning machine algorithm based human activity recognition using inertial data by Al Jeroudi, Yazan, Ali, M. A., Latief, Marsad, Akmeliawati, Rini

    Published 2015
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  19. 19
    Advances in automatic insect classification

    Advances in automatic insect classification by Hassan, Siti Noorul Asiah, Abdul Rahman, Nur Nadiah Syakira, Htike@Muhammad Yusof, Zaw Zaw, Shoon , Lei Win

    Published 2014
    “…We provides discussion on the state-of-the-art and provides perspective on future research direction in insect recognition and classification problem.…”
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  20. 20
    A novel AI-driven EEG images emotion recognition generalized classification model for cross-subject analysis

    A novel AI-driven EEG images emotion recognition generalized classification model for cross-subject analysis by Li, Jingjing, Lee, Ching Hung, Duan, Dingna, Zhou, Yanhong, Xie, Xueguang, Wan, Xianglong, Liu, Tiange, Li, Danyang, Yu, Hao, Hasan, W. Z.W., Song, Haiqing, Wen, Dong

    Published 2025
    “…This research offers a new perspective on reducing individual differences in EEG and advancing emotion classification tasks.…”
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