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  1. 1
    Pattern Recognition for Human Diseases Classification in Spectral Analysis

    Pattern Recognition for Human Diseases Classification in Spectral Analysis by Nur Hasshima Hasbi, Abdullah Bade, Fuei, Pien Chee, Muhammad Izzuddin Rumaling

    Published 2022
    “…On the other hand, classification methods are techniques or algorithms used to group samples into a predetermined category. …”
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  2. 2
    An extended ID3 decision tree algorithm for spatial data

    An extended ID3 decision tree algorithm for spatial data by Sitanggang, Imas Sukaesih, Yaakob, Razali, Mustapha, Norwati, Nuruddin, Ahmad Ainuddin

    Published 2011
    “…It is because spatial data mining algorithms have to consider not only objects of interest itself but also neighbours of the objects in order to extract useful and interesting patterns. …”
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  3. 3
    Evaluation of fall detection classification approaches

    Evaluation of fall detection classification approaches by Kerdegari, Hamideh, Samsudin, Khairulmizam, Ramli, Abdul Rahman, Ghotoorlar, Saeid Mokaram

    Published 2012
    “…The acceleration data with a total data of 6962 instances and 29 attributes were used to evaluate the performance of the different classification algorithm. …”
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  4. 4
    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
    “…Expectation maximization (EM) is one of the representatives clustering algorithms which have broadly applied in solving classification problems by improving the density of data using the probability density function. …”
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  5. 5
    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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  6. 6
    First Semester Computer Science Students’ Academic Performances Analysis by Using Data Mining Classification Algorithms

    First Semester Computer Science Students’ Academic Performances Analysis by Using Data Mining Classification Algorithms by Azwa, Abdul Aziz, Fadhilah, Ahmad

    Published 2014
    “…The comparative analysis is also conducted to discover the best classification model for prediction. From the experiment, the models develop using Rule Based and Decision Tree algorithm shows the best result compared to the model develop from the Naïve Bayes algorithm. …”
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  7. 7
    Comparative study of machine learning algorithms in data classification

    Comparative study of machine learning algorithms in data classification by Tan, Kai Jun

    Published 2025
    “…This research conducts a comparative study of various machine learning algorithms for dataset classification to identify the most accurate and reliable classifier. …”
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    Final Year Project / Dissertation / Thesis
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  8. 8
    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 this research, another modification of Fuzzy Decision Tree (FDT) classification techniques called predictive FDT is presented that aims to combine symbolic decision trees in data classification with approximate reasoning offered by fuzzy representation. …”
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  9. 9
    Classification Analysis Of The Badminton Five Directional Lunges

    Classification Analysis Of The Badminton Five Directional Lunges by Ho, Zhe Wei

    Published 2018
    “…Lunge type patterns were related to ID and GT. Conclusively, the identity, game reaction time and type of lunge were found being the key determinants for badminton lunge classification accounting for highest classification accuracy in REP Tree algorithm.…”
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  10. 10
    A numerical method for frequent pattern mining

    A numerical method for frequent pattern mining by Mustapha, Norwati, Nadimi-Shahraki, Mohammad-Hossein, Mamat, Ali, Sulaiman, Md. Nasir

    Published 2009
    “…The PC_Miner algorithm traverses the PC_Tree by using an efficient pruning technique. …”
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  11. 11
    A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem

    A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem by Mohd Pozi, Muhammad Syafiq

    Published 2016
    “…There are two methods in dealing with imbalanced classification problem, which are based on data or algorithmic level. …”
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  12. 12
    Classification of cervical cancer using random forest

    Classification of cervical cancer using random forest by Bahirah, Mohd Bashah, Ku Muhammad Naim, Ku Khalif, Nor Azuana, Ramli

    Published 2022
    “…In this research, the cervical cancer risk classification model was used by using data mining approach which consider Decision Tree and Random Forest algorithm. …”
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  13. 13
    Finger Motion In Classifying Offline Handwriting Patterns

    Finger Motion In Classifying Offline Handwriting Patterns by Yeoh, Shen Horng

    Published 2017
    “…The preprocessed data is classified using the J48 tree algorithm. …”
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  14. 14
    Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals

    Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals by Too, Jing Wei

    Published 2020
    “…In this regard, this thesis proposes five FS methods for efficient EMG signals classification. The first method is the Binary Tree Growth Algorithm (BTGA), which implements a hyperbolic tangent function to convert the Tree Growth Algorithm into the binary version. …”
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  15. 15
    Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.

    Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining. by Saeed, Walid

    Published 2005
    “…The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
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  16. 16
    Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms

    Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms by Liaghat, Shohreh, Ehsani, Reza, Mansor, Shattri, Mohd Shafri, Helmi Zulhaidi, Meon, Sariah, Sankaran, Sindhuja, Azam, Siti H. M. N.

    Published 2014
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  17. 17
    Data mining techniques for disease risk prediction model: A systematic literature review

    Data mining techniques for disease risk prediction model: A systematic literature review by Ahmad W.M.T.W., Ghani N.L.A., Drus S.M.

    Published 2023
    Conference Paper
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  18. 18
    A Framework For Classification Software Security Using Common Vulnerabilities And Exposures

    A Framework For Classification Software Security Using Common Vulnerabilities And Exposures by Hassan, Nor Hafeizah

    Published 2018
    “…This inclusive of the investigation of vulnerability classification objectives,processes,classifiers and focus domains among prominent framework.Final step is to construct the framework by establishing the formal presentation of the vulnerability classification algo-rithm.The validation process was conducted empirically using statistical method to assess the accuracy and consistency by using the precision and recall rate of the algorithm on five data sets each with 500 samples.The findings show a significant result with precision's error rate or p value is between 0.01 and 0.02 with error rate for recall's error rate is between 0.02 and 0.04.Another validation was conducted to verify the correctness of the classification by using expert opinions,and the results showed that the ambiguity of several cases were subdue. …”
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  19. 19
    Classification Modeling for Malaysian Blooming Flower Images Using Neural Networks

    Classification Modeling for Malaysian Blooming Flower Images Using Neural Networks by Muhammad Ashraq, Salahuddin

    Published 2013
    “…The appearance of the image itself such as variation of lights due to different lighting condition, shadow effect on the object’s surface, size, shape, rotation and position, background clutter, states of blooming or budding may affect the utilized classification techniques. This study aims to develop a classification model for Malaysian blooming flowers using neural network with the back propagation algorithms. …”
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  20. 20
    Application Of Multi-Layer Perceptron Technique To Detect And Locate The Base Of A Young Corn Plant

    Application Of Multi-Layer Perceptron Technique To Detect And Locate The Base Of A Young Corn Plant by Morshidi, Malik Arman

    Published 2007
    “…Another structure of MLP trained using backpropagation algorithm is used to detect and locate the base of the young corn tree using the skeleton of the segmented image. …”
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