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  1. 1

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

    Published 2015
    “…Recently, various techniques based on different algorithms have been developed. However, the classification accuracy and computational cost are not satisfied. …”
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    Thesis
  2. 2

    Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde by Ogunfolajin Maruff , Tunde

    Published 2022
    “…This work employed the use of machine learning approach. Four conventional classification algorithms: naïve bayes (NB), support vector machines (SVM), nearest neighbor (k-NN), and decision trees (J48) classifiers are implemented in identifying and categorizing tweet data of three political figures in Malaysia: Dato Seri Anwar, Dato Hadi Awang, and Lim Guang Eng, as either positive, negative, or neutral perceptions. …”
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    Thesis
  3. 3

    Face classification for three major ethnic of Orang Asli using Back Propagation Neural Network / Nor Intan Shafini Nasaruddin by Nasaruddin, Nor Intan Shafini

    Published 2012
    “…The image classification prototype is developed by using JAVA programming language which is based on supervised learning algorithm, Backpropagation Neural Network. …”
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  4. 4

    Development Of Machine Learning User Interface For Pump Diagnostics by Lee, Zhao Yang

    Published 2022
    “…The result from the SVM algorithms will be used as database for the machine learning in Microsoft Azure. …”
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    Monograph
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    Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

    Published 2016
    “…The results reveal that the proposed hybrid algorithm is capable of achieving classification accuracy values of (95.82 % and 97.68 %), detection rates values of (95.8 % and 99.3 %) and false alarm rates values of (0.083 % and 0.045 %) on both KDD CUP 99 and NSL KDD. …”
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    Thesis
  7. 7

    Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions by Saraswati, Galuh Wilujeng

    Published 2017
    “…The fuzzy inference model will be used to capture both fasting and non-fasting membership functions before feeding the results for classification to the neural network model. …”
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    Functional link neural network with modified bee-firefly learning algorithm for classification task by Mohmad Hassim, Yana Mazwin

    Published 2016
    “…The aim is to introduce an improved learning algorithm that can provide a better solution for training the FLNN network for the task of classification…”
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    Thesis
  10. 10

    Autism Spectrum Disorder Classification Using Deep Learning by Abdulrazak Yahya, Saleh, Lim Huey, Chern

    Published 2021
    “…The goal of this paper is to evaluate the deep learning algorithm for people placed in the Autism Spectrum Disorder (ASD) classification. …”
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    Article
  11. 11

    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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    Thesis
  12. 12

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

    Published 2017
    “…The problems in applying unsupervised learning/clustering is that this method requires teacher during the classification process and it has to learn independently which may lead to poor classification. …”
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    Thesis
  13. 13

    Waste management using machine learning and deep learning algorithms by Sami, Khan Nasik, Amin, Zian Md Afique, Hassan, Raini

    Published 2020
    “…So, we are proposing an automated waste classification problem utilizing Machine Learning and Deep Learning algorithms. …”
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    Article
  14. 14

    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
    “…Hence, this situation is believed in yielding of decreasing the classification accuracy. In this article, we present the exploration on the combination of the clustering based algorithm with an ensemble classification learning. …”
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    Article
  15. 15

    Evaluation of the Transfer Learning Models in Wafer Defects Classification by Jessnor Arif, Mat Jizat, Anwar, P. P. Abdul Majeed, Ahmad Fakhri, Ab. Nasir, Zahari, Taha, Yuen, Edmund, Lim, Shi Xuen

    Published 2022
    “…Transfer Learning is one of the common methods. Various algorithms under Transfer Learning had been developed for different applications. …”
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    Conference or Workshop Item
  16. 16

    Phylogenetic tree classification system using machine learning algorithm by Tan, Jia Kae

    Published 2015
    “…This study adopted supervised machine learning algorithm which is the Support Vector Machine (SVM) for classification. …”
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    Final Year Project Report / IMRAD
  17. 17

    An improved algorithm for iris classification by using support vector machine and binary random machine learning by Kamarulzalis, Ahmad Haadzal

    Published 2018
    “…In machine learning, there are three type of learning branch that can used in classification procedures for data mining. …”
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    Thesis
  18. 18

    Application of deep learning algorithms in lung sound classification: A systematic review since 2015 by Sundaraj, Kenneth, Neili, Zakaria

    Published 2025
    “…The article’s thorough analysis revealed that deep learning algorithms have outperformed traditional machine learning techniques in lung sound classification.…”
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    Article
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    Using KNN algorithm for classification of textual documents by Moldagulova A., Sulaiman R.B.

    Published 2023
    “…Classification (of information); Information retrieval systems; Learning systems; Nearest neighbor search; Tellurium compounds; Text processing; Automated classification; Exponential growth; K nearest neighbor (KNN); k-NN algorithm; Text classification; Textual documents; Two sources; Learning algorithms…”
    Conference Paper