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  1. 1
    MINING CUSTOMER DATA FOR DECISION MAKING USING NEW HYBRID CLASSIFICATION ALGORITHM

    MINING CUSTOMER DATA FOR DECISION MAKING USING NEW HYBRID CLASSIFICATION ALGORITHM by Aurangzeb, khan, Baharum, Baharudin, Khairullah, khan

    Published 2011
    “…Classification and patterns extraction from customer data is very important for business support and decision making. …”
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  2. 2
    MINING CUSTOMER DATA FOR DECISION MAKING USING NEW HYBRID CLASSIFICATION ALGORITHM

    MINING CUSTOMER DATA FOR DECISION MAKING USING NEW HYBRID CLASSIFICATION ALGORITHM by Aurangzeb, khan, Baharum, Baharudin, Khairullah, Khan

    Published 2011
    “…decision making. Timely identification of newly emerging trends is needed in business process. …”
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  3. 3
    Frequent patterns minning of stock data using hybrid clustering association algorithm

    Frequent patterns minning of stock data using hybrid clustering association algorithm by B., Baharudin, A., Khan, K., Khan

    Published 2009
    “…Patterns and classification of stock or inventory data is very important for business support and decision making. …”
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  4. 4
    Mobility Management Incorporating Pattern Recognition in the Handoff Decision

    Mobility Management Incorporating Pattern Recognition in the Handoff Decision by Kwong, C.F., Lee, S.W., Sim, M.L.

    Published 2009
    “…This paper will discuss different techniques in the traditional handover decision algorithm and explore the method of handoff decision using pattern recognition method, mainly the Adaptive Network Fuzzy Inference System (ANFIS). …”
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    Book Section
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  5. 5
    Algorithms for frequent itemset mining: a literature review

    Algorithms for frequent itemset mining: a literature review by Chee, C.-H., Jaafar, J., Aziz, I.A., Hasan, M.H., Yeoh, W.

    Published 2018
    “…Data Analytics plays an important role in the decision making process. Insights from such pattern analysis offer vast benefits, including increased revenue, cost cutting, and improved competitive advantage. …”
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  6. 6
    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
    “…In a real world, pattern recognition problems in diversified forms are ubiquitous and are critical in most human decision making tasks. …”
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  7. 7
    Pattern generation through feature values modification and decision tree ensemble construction

    Pattern generation through feature values modification and decision tree ensemble construction by Akhand, M. A. H, Rahman, M.M. Hafizur, Murase, K.

    Published 2013
    “…An ensemble method produces diverse classifiers and combines their decisions for ensemble’s decision. A number of methods have been investigated for constructing ensemble in which some of them train classifiers with the generated patterns. …”
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  8. 8
    A spatial decision support system framework for optimization of cropping pattern and water resources allocation at pasargard plains, fars province, Iran

    A spatial decision support system framework for optimization of cropping pattern and water resources allocation at pasargard plains, fars province, Iran by Ghasemi, Mohammad Mehdi

    Published 2014
    “…A unit response matrix groundwater model was coupled with a modified version of Genetic Algorithm (GA) in order to optimize cropping patterns and water allocation decisions. …”
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  9. 9
    A New Probabilistic Output Constrained Optimization Extreme Learning Machine

    A New Probabilistic Output Constrained Optimization Extreme Learning Machine by Wong S.Y., Yap K.S., Li X.C.

    Published 2023
    “…Benchmarking; Classification (of information); Constrained optimization; Decision making; Electric power systems; Iterative methods; Knowledge acquisition; Learning algorithms; Pattern recognition; Probability; Confidence threshold; Decision making process; Extreme learning machine; Machine learning approaches; Pattern classification problems; Post-processing procedure; Power system applications; Probabilistic output; Machine learning…”
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  10. 10
    An empirical study of pattern leakage impact during data preprocessing on machine learning-based intrusion detection models reliability

    An empirical study of pattern leakage impact during data preprocessing on machine learning-based intrusion detection models reliability by Bouke, Mohamed Aly, Abdullah, Azizol

    Published 2023
    “…To address this problem, we provide suggestions for mitigating data leakage in the training process and analyzing the sensitivity of different algorithms. …”
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  11. 11
    Object with symmetrical pattern recognition with dynamic size filter

    Object with symmetrical pattern recognition with dynamic size filter by Syed Mohamad Shazali, Syed Abdul Hamid, Mohd Aras, Mohd Shahrieel, Abdul Azis, Fadilah, Ali, Fara Ashikin, Anuar , Mohamed Kassim

    Published 2011
    “…This paper presents the implementation of object with symmetrical pattern recognition algorithm for 2D vision system of 3D vision-based multi sensor feedback system. …”
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  12. 12
    Data mining using genetic algorithm in finance data / A. Noor Latiffah and A. B. Nordin

    Data mining using genetic algorithm in finance data / A. Noor Latiffah and A. B. Nordin by Latiffah, A. Noor, Nordin, A. B.

    Published 2006
    “…Data mining can discover patterns or rules from a vast volume of data. This patterns or rules may help to develop better decision-making process. …”
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  13. 13
    Machine learning using robust AI techniques / Prof. Madya Dr. Nordin Abu Bakar

    Machine learning using robust AI techniques / Prof. Madya Dr. Nordin Abu Bakar by Abu Bakar, Nordin (Prof. Madya Dr.)

    Published 2012
    “…The learning process creates knowledge that guides a person to make the decisions. …”
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  14. 14
    Classification of herbs plant diseases via hierarchical dynamic artificial neural network

    Classification of herbs plant diseases via hierarchical dynamic artificial neural network by Abdullah, Lili Nurliyana, Khalid, Fatimah, Borhan, N.M.

    Published 2010
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  15. 15
    Swarm negative selection algorithm for electroencephalogram signals classification

    Swarm negative selection algorithm for electroencephalogram signals classification by Sahel Ba-Karait, Nasser Omer, Shamsuddin, Siti Mariyam, Sudirman, Rubita

    Published 2009
    “…Therefore, there is a strong need to automate this process. Such automated systems must rely on robust and effective algorithms for detection and prediction. …”
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  16. 16
    Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns

    Ensemble Classifier for Recognition of Small Variation in X-Bar Control Chart Patterns by Alwan, Waseem, Ngadiman, Nor Hasrul Akhmal, Hassan, Adnan, Saufi, Syahril Ramadhan, Mahmood, Salwa

    Published 2023
    “…This study provides an improved control chart pattern recognition (CCPR) method focusing on X-bar chart patterns of small process variations using an ensemble classifier comprised of five complementing algorithms: decision tree, artificial neural network, linear support vector machine, Gaussian support vector machine, and k-nearest neighbours. …”
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  17. 17
    Data Classification and Its Application in Credit Card Approval

    Data Classification and Its Application in Credit Card Approval by Thai , VinhTuan

    Published 2004
    “…This project is involved with identification of the available algorithms used in data classification and the implementation of C4.5 decision tree induction algorithm in solving the data classifying task. …”
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    Final Year Project
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  18. 18
    Feature selection with integrated Gaussian seahorse optimization data mining for cross-border business cooperation between the Malaysian medical industry and tourism industry

    Feature selection with integrated Gaussian seahorse optimization data mining for cross-border business cooperation between the Malaysian medical industry and tourism industry by Ma, Yuaner, Jabar, Juhaini, Abdul Aziz, Nor Azah

    Published 2023
    “…The integrated GSH-DM model then applies the Gaussian Seahorse Optimization algorithm to optimize the data mining process, enhancing the accuracy and efficiency of pattern discovery. the GSH-DM model, this study aims to uncover hidden patterns, relationships, and predictive models that can guide decision-making and strategy development for cross-border business cooperation. …”
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  19. 19
    Analyzing enrolment patterns: modified stacked ensemble statistical learning based approach to educational decision-making

    Analyzing enrolment patterns: modified stacked ensemble statistical learning based approach to educational decision-making by Zun, Liang Chuan, Nursultan Japashov, Soon, Kien Yuan, Tan, Wei Qing, Noriszura Ismail

    Published 2024
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  20. 20
    Individual And Ensemble Pattern Classification Models Using Enhanced Fuzzy Min-Max Neural Networks

    Individual And Ensemble Pattern Classification Models Using Enhanced Fuzzy Min-Max Neural Networks by F. M., Mohammed

    Published 2014
    “…Pattern classification is one of the major components for the design and development of a computerized pattern recognition system. …”
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