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  1. 1
    Image clustering comparison of two color segmentation techniques

    Image clustering comparison of two color segmentation techniques by Subramaniam, Kavitha Pichaiyan

    Published 2010
    “…Finally, the algorithm found, which would solve the image segmentation problem.…”
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    Thesis
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  2. 2
    Automatic Number Plate Recognition on android platform: With some Java code excerpts

    Automatic Number Plate Recognition on android platform: With some Java code excerpts by ., Abdul Mutholib, Gunawan, Teddy Surya, Kartiwi, Mira

    Published 2016
    “…On the other hand, the traditional algorithm using template matching only obtained 83.65% recognition rate with 0.97 second processing time. …”
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    Book
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  3. 3
    Development of seven segment display recognition using TensorFlow on Raspberry Pi

    Development of seven segment display recognition using TensorFlow on Raspberry Pi by Gunawan, Teddy Surya, Mohammad Reduwan, Haque, Yaacob, Mashkuri, Mohamed Azmin, Nor Fadhillah, Ismail, Nanang, Ismail, Md Yusof, Kartiwi, Mira, A. Rahman, Azhar

    Published 2022
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    Proceeding Paper
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  4. 4
    Ant system-based feature set partitioning algorithm for classifier ensemble construction

    Ant system-based feature set partitioning algorithm for classifier ensemble construction by Abdullah, , Ku-Mahamud, Ku Ruhana

    Published 2016
    “…In this study, Ant system-based feature set partitioning algorithm for classifier ensemble construction is proposed.The Ant System Algorithm is used to form an optimal feature set partition of the original training set which represents the number of classifiers.Experiments were carried out to construct several homogeneous classifier ensembles using nearest mean classifier, naive Bayes classifier, k-nearest neighbor and linear discriminant analysis as base classifier and majority voting technique as combiner. …”
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  5. 5
    An ensemble learning method for spam email detection system based on metaheuristic algorithms

    An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

    Published 2015
    “…In the second phase, a classifier ensemble learning model is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on Binary Quantum Gravitational Search Algorithm (QBGSA), (ii) To mine data streams using various data chunks and overcome a failure of single classifiers based on SVM, MLP and K-NN algorithms. …”
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  6. 6
    Real-time Traffic Classification Algorithm Based on Hybrid of Signature Statistical and Port to Identify Internet Applications

    Real-time Traffic Classification Algorithm Based on Hybrid of Signature Statistical and Port to Identify Internet Applications by Hamza Awad, Hamza Ibrahim, Sulaiman, Mohd Nor, Izzeldin, I. Mohd, Mohamed Saad, Mahoub, Haitham, A. Jamil

    Published 2014
    “…The SSPC algorithm was used to classifying four types of Internet applications in two stages, initially offline and later online. …”
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  7. 7
    Classification of tropical rainforest using different classification algorithm based on remote sensing imagery: A study of Gunung Basor

    Classification of tropical rainforest using different classification algorithm based on remote sensing imagery: A study of Gunung Basor by Intan Noradybah Md Rodi

    Published 2019
    “…Three different classification algorithm, minimum distance classifier, Mahalanobis distance classifier and maximum likelihood algorithm was applied to classify the forest area in Gunung Basor. …”
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    Undergraduate Final Project Report
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  8. 8
    Comparison of Naïve bayes classifier with back propagation neural network classifier based on f - folds feature extraction algorithm for ball bearing fault diagnostic system

    Comparison of Naïve bayes classifier with back propagation neural network classifier based on f - folds feature extraction algorithm for ball bearing fault diagnostic system by Osman Mohamed Addin, Addin, Salit, Mohd Sapuan, Othman, Mohamed, Ahmed Ali, Basheer Ahmed

    Published 2011
    “…This paper is intended to compare the Naïve bayes classifier for ball bearing fault diagnostic system with the back propagation neural network based on the f-folds feature extraction algorithm. …”
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  9. 9
    Ant system-based feature set partitioning algorithm for K-NN and LDA ensembles construction

    Ant system-based feature set partitioning algorithm for K-NN and LDA ensembles construction by Abdullah, , Ku-Mahamud, Ku Ruhana

    Published 2015
    “…Combination of several classifiers has been very useful in improving the prediction accuracy and in most situations multiple classifiers perform better than single classifier.However not all combining approaches are successful at producing multiple classifiers with good classification accuracy because there is no standard resolution in constructing diverse and accurate classifier ensemble.This paper proposes ant system-based feature set partitioning algorithm in constructing k-nearest neighbor (k-NN) and linear discriminant analysis (LDA) ensembles. …”
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  10. 10
    Statistical Based Real-Time Selective Herbicide Weed Classifier

    Statistical Based Real-Time Selective Herbicide Weed Classifier by Ahmed, Irshad, Abdul Muhamin , Naeem, Muhammad, Islam, Azween, Abdullah

    Published 2007
    “…This paper deals with the development of an algorithm for real time specific weed recognition system based on Sample Variance of an image that is used for the weed classification and comparison of its result with the algorithm based on population variance. …”
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  11. 11
    Feature Selection and Classifier Parameter Estimation for Egg Signal Peak Detection using Gravitational Search Algorithm

    Feature Selection and Classifier Parameter Estimation for Egg Signal Peak Detection using Gravitational Search Algorithm by Zuwairie, Ibrahim, Mohd Zaidi, Mohd Tumari, Asrul, Adam, Norrima, Mokhtar, Marizan, Mubin, Mohd Ibrahim, Shapiai

    Published 2014
    “…Moreover, a rule-based classifier is employed to distinguish a peak point based on the selected features. …”
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  12. 12
    Realization Of The 1D Local Binary Pattern (LBP) Algorithm In Raspberry Pi For Iris Classification Using K-NN Classifier

    Realization Of The 1D Local Binary Pattern (LBP) Algorithm In Raspberry Pi For Iris Classification Using K-NN Classifier by Siow, Shien Loong

    Published 2018
    “…Secondly, the K-NN classifier is used to classify the 1D-LBP based features from the first stage. …”
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    Monograph
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  13. 13
    Non-invasive pathological voice classifications using linear and non-linear classifiers

    Non-invasive pathological voice classifications using linear and non-linear classifiers by Hariharan, Muthusamy

    Published 2010
    “…Linear classifier namely LDA based classifier and non-linear classifiers such as k-NN classifier, MLP network, PNN, and GRNN are suggested to discriminate pathological voices from normal voices. …”
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  14. 14
    Aplikasi rangkaian neural dalam pengesanan simpang bagi penterjemah lakaran pintar

    Aplikasi rangkaian neural dalam pengesanan simpang bagi penterjemah lakaran pintar by Subri, Syarul Haniz

    Published 2006
    “…Back propagation neural network algorithm was used to develop and produce the classifier for corner detector algorithm. …”
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  15. 15
    Intrusion Detection Systems, Issues, Challenges, and Needs

    Intrusion Detection Systems, Issues, Challenges, and Needs by Aljanabi, Mohammad, Mohd Arfian, Ismail, Ali, Ahmed Hussein

    Published 2021
    “…Such parameters are C in SVM and gamma which effect the performance of SVM if not tuned well. Optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) algorithm , ant colony algorithm, and many other algorithms are used along with classifiers to improve the work of these classifiers in detecting intrusion and to increase the performance of these classifiers. …”
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  16. 16
    A recommender system approach for classifying user navigation patterns using longest common subsequence algorithm.

    A recommender system approach for classifying user navigation patterns using longest common subsequence algorithm. by Jalali, Mehrdad, Mustapha, Norwati, Sulaiman, Md. Nasir, Mamat, Ali

    Published 2009
    “…The approach is based on the using longest common subsequence algorithm to classify current user activities to predict user next movement. …”
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  17. 17
    The Bacterial Foraging Optimisation Algorithm using Prototype Selection and Prototype Generation for Data Classification

    The Bacterial Foraging Optimisation Algorithm using Prototype Selection and Prototype Generation for Data Classification by Faizol, Bin Mohd Suria

    Published 2020
    “…Technically, BFOA has been applied as supplementary algorithm for optimizing weight, parameters for other classifier algorithms and selecting optimised features for other classifiers. …”
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  18. 18
    FLUORESCENCE INTENSITY POSITIVITY CLASSIFICATION OF HEP-2 CELLS IMAGES USING FUZZY LOGIC

    FLUORESCENCE INTENSITY POSITIVITY CLASSIFICATION OF HEP-2 CELLS IMAGES USING FUZZY LOGIC by ABANG SAZALI, DAYANG FARZANAI

    Published 2013
    “…The purpose of this study is to classify the fluorescence intensity by using fuzzy logic algorithm to determine the positivity of the Hep2-cell serum samples. …”
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    Final Year Project
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  19. 19
    Feature extraction and selection algorithm based on self adaptive ant colony system for sky image classification

    Feature extraction and selection algorithm based on self adaptive ant colony system for sky image classification by Petwan, Montha

    Published 2023
    “…The performance of FESSIC was evaluated against ten benchmark image classification algorithms and six classifiers on four ground-based sky image datasets. …”
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  20. 20
    Static code analysis of permission-based features for android malware classification using apriori algorithm with particle swarm optimization

    Static code analysis of permission-based features for android malware classification using apriori algorithm with particle swarm optimization by Adebayo, Olawale Surajudeen, Abdul Aziz, Normaziah

    Published 2015
    “…In this method, permission-based features were extracted from Android applications byte-code through static code analysis, selected and were used to train supervised classifiers. …”
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