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  1. 1
    An efficient and effective case classification method based on slicing

    An efficient and effective case classification method based on slicing by Shiba, Omar A. A., Sulaiman, Md. Nasir, Mamat, Ali, Ahmad, Fatimah

    Published 2006
    “…The algorithms are: Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5). …”
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  2. 2
    An evolutionary based features construction methods for data summarization approach

    An evolutionary based features construction methods for data summarization approach by Rayner Alfred, Suraya Alias, Chin, Kim On

    Published 2015
    “…In this work, we empirically compare the predictive accuracies of classification tasks based on the proposed feature construction methods and also the existing feature construction methods. …”
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    Research Report
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  3. 3
    Machine learning for data classification in construction project planning

    Machine learning for data classification in construction project planning by Lau, Zheng Liang

    Published 2023
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    Final Year Project / Dissertation / Thesis
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  4. 4
    Multi-label learning based on positive label correlations using predictive apriori

    Multi-label learning based on positive label correlations using predictive apriori by Al Azaidah, Raed Hasan Saleh

    Published 2019
    “…Along with that, two algorithms have been constructed by capturing the positive correlations where the first algorithm (MLR-PC) captures the positive global correlations and the second algorithm (MLCBA) proposes an adaption of AC algorithm to handle MLC based on the positive local correlations. …”
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  5. 5
    Classification of stock market index based on predictive fuzzy decision tree

    Classification of stock market index based on predictive fuzzy decision tree by Khokhar, Arashid Hafeez

    Published 2005
    “…In particular, predictive FDT algorithm is based on the concept of degree of importance of attribute contributing to the classification. …”
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    Thesis
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  6. 6
    Optimal Weighted Learning of PCA and PLS for Multicollinearity Discriminators and Imbalanced Groups in Big Data (S/O: 13224)

    Optimal Weighted Learning of PCA and PLS for Multicollinearity Discriminators and Imbalanced Groups in Big Data (S/O: 13224) by Mahat, Nor Idayu, Engku Abu Bakar, Engku Muhammad Nazri, Zakaria, Ammar, Mohd Nazir, Mohd Amril Nurman, Misiran, Masnita

    “…This study developed an algorithm for statistical classification that enable ones to classify a future data to one of predetermined groups based on the measured data which facing two major threats; (i) multicollinearity among the measured variables and (ii) imbalanced groups. …”
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    Monograph
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  7. 7
    Predicting 30-day mortality after an acute coronary syndrome (ACS) using machine learning methods for feature selection, classification and visualization

    Predicting 30-day mortality after an acute coronary syndrome (ACS) using machine learning methods for feature selection, classification and visualization by Nanyonga Aziida, Sorayya Malek, Firdaus Aziz, Khairul Shafiq Ibrahim, Sazzli Kasim

    Published 2021
    “…Hybrid combinations of feature selection, classification and visualisation using machine learning (ML) methods have the potential for enhanced understanding and 30-day mortality prediction of patients with cardiovascular disease using population-specific data. …”
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  8. 8
    Classification with degree of importance of attributes for stock market data mining

    Classification with degree of importance of attributes for stock market data mining by Khokhar, Rashid Hafeez, Md. Sap, Mohd. Noor

    Published 2004
    “…The SVM is a training algorithm for learning classification and regression rules from data [7]. …”
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  9. 9
    Review of deep convolution neural network in image classification

    Review of deep convolution neural network in image classification by Al-Saffar, Ahmed Ali Mohammed, Tao, Hai, Mohammed, Ahmed Talab

    Published 2017
    “…Then, the research status and development trend of convolution neural network model based on deep learning in image classification are reviewed, which is mainly introduced from the aspects of typical network structure construction, training method and performance. …”
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  10. 10
    A framework of modified adaptive neuro-fuzzy inference engine

    A framework of modified adaptive neuro-fuzzy inference engine by Hossen, Md. Jakir

    Published 2012
    “…Neuro-fuzzy inference engine and/or system is knowledge based data processing system and can manage the human reasoning course and create decisions based on uncertainty and imprecise situations. …”
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  11. 11
    Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach

    Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach by Qayyum A., Saeed Malik A., Saad N.M., Iqbal M., Abdullah M.F., Rasheed W., Abdullah T.A.B.R., Bin Jafaar M.Y.

    Published 2023
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  12. 12
    A systematic review of machine learning techniques and applications in soil improvement using green materials

    A systematic review of machine learning techniques and applications in soil improvement using green materials by Saad, Ahmed Hassan, Nahazanan, Haslinda, Yusuf, Badronnisa, Toha, Siti Fauziah, Alnuaim, Ahmed, El-Mouchi, Ahmed, Elseknidy, Mohamed, Mohammed, Angham Ali

    Published 2023
    “…A literature review suggests that machine learning algorithms are effective at predicting various soil characteristics, including compressive strength, deformations, bearing capacity, California bearing ratio, compaction performance, stress–strain behavior, geotextile pullout strength behavior, and soil classification. …”
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  13. 13
    A Review: Recent Automatic Algorithms for the Segmentation of Brain Tumor MRI

    A Review: Recent Automatic Algorithms for the Segmentation of Brain Tumor MRI by Rafi, A., Khan, Z., Aslam, F., Jawed, S., Shafique, A., Ali, H.

    Published 2022
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  14. 14
    Application of target detection method based on convolutional neural network in sustainable outdoor education

    Application of target detection method based on convolutional neural network in sustainable outdoor education by Yang, Xiaoming, Samsudin, Shamsulariffin, Wang, Yuxuan, Yuan, Yubin, Tengku Kamalden, Tengku Fadilah, Yaakob, Sam Shor Nahar

    Published 2023
    “…This exploration innovatively proposes a method based on the convolutional neural network (CNN) to mine the target information in underwater camera data. …”
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  15. 15
    Machine Learning based Predictive Modelling of Cybersecurity Threats Utilising Behavioural Data

    Machine Learning based Predictive Modelling of Cybersecurity Threats Utilising Behavioural Data by Ting, Tin Tin, Khiew, Jie Xin, Ali Aitizaz, Lee, Kuok Tiung, Teoh, Chong Keat, Hasan Sarwar

    Published 2023
    “…The algorithms are used to construct, test, and validate three categories of cybercrime threat (Malware, Social Engineering, and Password Attack) predictive models. …”
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  16. 16
    Case Slicing Technique for Feature Selection

    Case Slicing Technique for Feature Selection by A. Shiba, Omar A.

    Published 2004
    “…One of the problems addressed by machine learning is data classification. Finding a good classification algorithm is an important component of many data mining projects. …”
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  17. 17
    Convolutional hypercube pyramid for accurate RGB-D object category and instance recognition

    Convolutional hypercube pyramid for accurate RGB-D object category and instance recognition by Mohd Zaki, Hasan Firdaus, Shafait, Faisal, Mian, Ajmal

    Published 2016
    “…Deep learning based methods have achieved unprecedented success in solving several computer vision problems involving RGB images. …”
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    Proceeding Paper
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  18. 18
    Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition

    Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition by Leong, Shi Xiang

    Published 2017
    “…Whereas for supervised learning method, it requires teacher or prior data (i.e. large, prohibitive and labelled training data) during classification process which in real life, the cost of obtaining sufficient labelled training data is high. …”
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  19. 19
    Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model

    Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model by Sulaiman, Md. Nasir, Mohamed, Raihani, Mustapha, Norwati, Zainudin, Muhammad Noorazlan Shah

    Published 2018
    “…In data mining, classification learning is broadly categorized into two categories; supervised and unsupervised. …”
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  20. 20
    Sound quality classification of wood used for Sarawak traditional musical instrument- Sape / Wong Tee Hao

    Sound quality classification of wood used for Sarawak traditional musical instrument- Sape / Wong Tee Hao by Wong , Tee Hao

    Published 2024
    “…To address dataset imbalances, Synthetic Minority Oversampling Technique was used, enhancing dataset quality before training 40 machine learning classification algorithms. Among these, the Gaussian-kernel Support Vector Machine stood out, achieving remarkable performance with 88.18% validation and 93.37% test accuracies. …”
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