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  1. 1
    Development of a fingerprint gender classification algorithm using fingerprint global features

    Development of a fingerprint gender classification algorithm using fingerprint global features by Abdullah, Siti Fairuz, Abdul Rahman, Ahmad Fadzli Nizam, Abal Abas, Zuraida, Mohd Saad, Wira Hidayat

    Published 2016
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  2. 2
    Enhanced ontology-based text classification algorithm for structurally organized documents

    Enhanced ontology-based text classification algorithm for structurally organized documents by Oleiwi, Suha Sahib

    Published 2015
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    Thesis
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  3. 3
    Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly

    Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly by Zulkifly, Ahmad Zuladzlan

    Published 2019
    “…Even a normal people using clustering to grouping their data. …”
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  4. 4
    Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms

    Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms by Teoh, Chin Chuang

    Published 2005
    “…In addition, the best band selected for image classification is not necessarily the best for classification.A Best Band Selection Index (BBSI) algorithm was developed which is capable of selecting the best band combination for image visualization and supervised classification. …”
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  5. 5
    Talent classification using support vector machine technique / Hamidah Jantan, Norazmah Mat Yusof and Mohd Hanapi Abdul Latif

    Talent classification using support vector machine technique / Hamidah Jantan, Norazmah Mat Yusof and Mohd Hanapi Abdul Latif by Jantan, Hamidah, Mat Yusof, Norazmah, Abdul Latif, Mohd Hanapi

    Published 2014
    “…In the experimental phase, we use employee’s performance data from selected organization to develop talent classification model which can be used to handle some tasks in talent management. …”
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    Research Reports
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  6. 6
    Efficient and low complexity modulation classification algorithm for MIMO systems

    Efficient and low complexity modulation classification algorithm for MIMO systems by Bahloul, M.R., Yusoff, M.Z., Saad, M.N.M.

    Published 2015
    “…This study develops a feature-based Automatic Modulation Classification (AMC) algorithm for spatially multiplexed Multiple-Input Multiple-Output (MIMO) systems employing two Higher Order Cumulants (HOCs) of the estimated transmit signal streams as discriminating features and a multiclass Support Vector Machine (SVM) as a classification system. …”
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  7. 7
    SANAsms: Secure short messaging system for secure GSM mobile communication

    SANAsms: Secure short messaging system for secure GSM mobile communication by Anuar, N.B., Azlan, I.M., Wahid, A.W.A., Zakaria, O.

    Published 2008
    “…The system is developed using Java 2 Micro Edition (J2ME) which is written in Java. …”
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  8. 8
    AN INTEGRATED GENERIC TEXT CLASSIFICATION ALGORITHM FOR INDONESIAN AND MALAY NEWS DOCUMENTS

    AN INTEGRATED GENERIC TEXT CLASSIFICATION ALGORITHM FOR INDONESIAN AND MALAY NEWS DOCUMENTS by ,, ZUL INDRA

    Published 2016
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  9. 9
    Classification and visualization on eligibility rate of applicant’s LinkedIn account using Naïve Bayes / Nurul Atirah Ahmad

    Classification and visualization on eligibility rate of applicant’s LinkedIn account using Naïve Bayes / Nurul Atirah Ahmad by Ahmad, Nurul Atirah

    Published 2023
    “…This project implements the Naive Bayes algorithm as the classification algorithm. The collected data from LinkedIn profiles then undergoes data preprocessing. …”
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  10. 10
    A category classification algorithm for Indonesian and Malay news documents

    A category classification algorithm for Indonesian and Malay news documents by Jaafar, J., Indra, Z., Zamin, N.

    Published 2016
    “…Text classification (TC) provides a better way to organize information since it allows better understanding and interpretation of the content. …”
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  11. 11
    Development of Low Cost Heart Rate Monitoring Device and Classification Technique Using Fuzzy Logics Algorithm

    Development of Low Cost Heart Rate Monitoring Device and Classification Technique Using Fuzzy Logics Algorithm by Faili, Zahra

    Published 2016
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  12. 12
    Deep plant: A deep learning approach for plant classification / Lee Sue Han

    Deep plant: A deep learning approach for plant classification / Lee Sue Han by Lee , Sue Han

    Published 2018
    “…Besides using solely a single leaf organ to recognize plant species, numerous studies have employed DL methods to solve multi-organ plant classification problem. …”
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  13. 13
    Intent-IQ: customer’s reviews intent recognition using random forest algorithm

    Intent-IQ: customer’s reviews intent recognition using random forest algorithm by Mazlan, Nur Farahnisrin, Ibrahim Teo, Noor Hasimah

    Published 2025
    “…In order to overcome this problem, a classification model for intent recognition is developed. …”
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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
    “…Electing the best set of features will help to improve the classifier predictions in terms of the normal and abnormal pattern. The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. …”
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  15. 15
    A performance comparison study of pattern recognition systems for volatile organic compounds detection / Emilia Noorsal, Muhammad Khusairi Osman and Norfadzilah Mokhtar

    A performance comparison study of pattern recognition systems for volatile organic compounds detection / Emilia Noorsal, Muhammad Khusairi Osman and Norfadzilah Mokhtar by Noorsal, Emilia, Osman, Muhammad Khusairi, Mokhtar, Norfadzilah

    Published 2007
    “…This project focuses only on the development of an Artificial Neural Network (ANN) for the classification of Volatile Organic Compounds (VOCs). …”
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  16. 16
    Development and usage of self-organising maps in high energy physics analysis with high performance computing / Mohd Adli Md Ali

    Development and usage of self-organising maps in high energy physics analysis with high performance computing / Mohd Adli Md Ali by Mohd Adli , Md Ali

    Published 2017
    “…Thus, development of an SOM algorithm for high energy physics datasets was performed. …”
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  17. 17
    Object detection and classification in marine ecosystem using deep learning neural network / Muhammad Afiq Azman

    Object detection and classification in marine ecosystem using deep learning neural network / Muhammad Afiq Azman by Azman, Muhammad Afiq

    Published 2025
    “…The primary objective is to develop and implement artificial intelligence (AI) and machine learning (ML) algorithms tailored to effectively identify within marine ecosystems. …”
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  18. 18
    Rapid bacterial colony classification using deep learning

    Rapid bacterial colony classification using deep learning by Son Ali, Akbar, Kamarul Hawari, Ghazali, Habsah, Hasan, Zeehaida, Mohamed, Wahyu Sapto, Aji, Yudhana, Anton

    Published 2022
    “…Therefore, medical diagnoses require fast identification and classification of organisms. Artificial Intelligence with deep learning (DL) can now be developed as a rapid bacterial classification. …”
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  19. 19
    Household overspending model amongst B40, M40 and T20 using classification algorithm

    Household overspending model amongst B40, M40 and T20 using classification algorithm by Zulaiha Ali, Othman, Azuraliza, Abu Bakar, Nor Samsiah, Sani, Jamaludin, Sallim

    Published 2020
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  20. 20
    Traffic monitoring system with emergency support using SOM

    Traffic monitoring system with emergency support using SOM by Tan, Hoai Thang

    Published 2020
    “…Several processes will implement such pre-processing, as well as feature extraction, for better classification and localization process. In the classification section, all the processed parameters are input into the classification algorithm to classify the groups to which the input parameters belong. …”
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