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  1. 1
    A performance analysis of association rule mining algorithms

    A performance analysis of association rule mining algorithms by Fageeri, S.O., Ahmad, R., Alhussian, H.

    Published 2016
    “…The results of the CPU profiler of Java VisualVM showed that Binary-Based algorithm performs better than Eclat algorithm in-terms of both execution times and memory usage. …”
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  2. 2
    Development of classification algorithms of human gait

    Development of classification algorithms of human gait by Koh, Chee Hong

    Published 2022
    “…In addtion, after compared with other state-of-the-arts of gait classification algorithms, our proposed classification algorithm produced comparable results with other state-of-arts using a smaller dataset with fewer training features.…”
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  3. 3
    A novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network

    A novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network by Salari, Nader, Shohaimi, Shamarina, Najafi, Farid, Nallappan, Meenakshii, Karishnarajah, Isthrinayagy

    Published 2014
    “…Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. …”
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  4. 4
    Correlation Feature Selection Weighting Algorithms for Better Support Vector Classification: An Empirical Study

    Correlation Feature Selection Weighting Algorithms for Better Support Vector Classification: An Empirical Study by Sim, Doreen Ying Ying, Teh, Chee Siong, Ahmad Izuanuddin, Ismail

    Published 2020
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  5. 5
    Performance evaluation of real-time multiprocessor scheduling algorithms

    Performance evaluation of real-time multiprocessor scheduling algorithms by Alhussian, H., Zakaria, N., Abdulkadir, S.J., Fageeri, S.O.

    Published 2016
    “…We hyave used the CPU profiler of Oracle JavaTM VisualVM to monitor the execution of LRE-TL as well as USG algorithms. …”
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  6. 6
    EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter

    EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter by Woon, W.C., Yahya, N., Badruddin, N.

    Published 2019
    “…Hence, this work aims to develop an algorithm using statistical-CSP feature for eye state classification from EEG signal. …”
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  7. 7
    EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter

    EEG Eye State Identification based on Statistical Feature and Common Spatial Pattern Filter by Woon, W.C., Yahya, N., Badruddin, N.

    Published 2019
    “…Hence, this work aims to develop an algorithm using statistical-CSP feature for eye state classification from EEG signal. …”
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  8. 8
    An adaptive ant colony optimization algorithm for rule-based classification

    An adaptive ant colony optimization algorithm for rule-based classification by Al-Behadili, Hayder Naser Khraibet

    Published 2020
    “…Various classification algorithms have been developed to produce classification models with high accuracy. …”
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  9. 9
    EEG EYE STATE IDENTIFICATION BASED ON STATISTICAL FEATURES AND COMMON SPATIAL PATTERN

    EEG EYE STATE IDENTIFICATION BASED ON STATISTICAL FEATURES AND COMMON SPATIAL PATTERN by WANG, CHIA WOON

    Published 2019
    “…Hence, the proposed work aimed to analyse the EEG eye state signal as well as develop an algorithm using statistical-CSP features on the eye state identification.…”
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  10. 10
    Algorithms of Classification of Mass Problems of Production Subject Domains

    Algorithms of Classification of Mass Problems of Production Subject Domains by Malakhov, Eugene, Shchelkonogov, Denys, Mezhuyev, Vitaliy

    Published 2019
    “…This paper develops the algorithms for classification of mass problems of production subject domains. …”
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  11. 11
    Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy

    Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy by Ganesh , Krishnasamy

    Published 2019
    “…The proposed algorithm is compared with the state-of-the-art feature selection algorithms using three different datasets. …”
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  12. 12
    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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  13. 13
    An efficient algorithm for cardiac arrhythmia classification using ensemble of depthwise Separable convolutional neural networks

    An efficient algorithm for cardiac arrhythmia classification using ensemble of depthwise Separable convolutional neural networks by Ihsanto, Eko, Ramli, Kalamullah, Sudiana, Dodi, Gunawan, Teddy Surya

    Published 2020
    “…Many algorithms have been developed for automated electrocardiogram (ECG) classification. …”
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  14. 14
    Visdom: Smart guide robot for visually impaired people

    Visdom: Smart guide robot for visually impaired people by Lee, Zhen Ting

    Published 2025
    “…An Android mobile application, developed using Java and the java.net.Socket library, provides an intuitive and accessible user interface for seamless interaction with the robot. …”
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  15. 15
    Automatic Classification of Cervix Type

    Automatic Classification of Cervix Type by Muktiar Singh, Jasdeep Singh

    Published 2019
    “…Due to that, in this study, few algorithms were developed by using image classification methods to correctly classify the cervix types based on cervical images by using segmentation and classification method. …”
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  16. 16
    Analysing the performance of classification algorithms on diseases datasets

    Analysing the performance of classification algorithms on diseases datasets by Lydia E.L., Sharmil N., Shankar K., Maseleno A.

    Published 2023
    “…The proposed classification algorithms measure the diseases using the disease datasets which estimates the accurate prediction. …”
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  17. 17
    Optimized tree-classification algorithm for classification of protein sequences

    Optimized tree-classification algorithm for classification of protein sequences by Iqbal, M.J., Faye, I., Said, A.M., Belhaouari Samir, B.

    Published 2016
    “…In this work, we have proposed an optimized tree-classification technique which uses cluster k nearest neighbor classification algorithm to classify protein sequences into superfamilies. …”
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  18. 18
    Optimized tree-classification algorithm for classification of protein sequences

    Optimized tree-classification algorithm for classification of protein sequences by Iqbal, M.J., Faye, I., Said, A.M., Belhaouari Samir, B.

    Published 2016
    “…In this work, we have proposed an optimized tree-classification technique which uses cluster k nearest neighbor classification algorithm to classify protein sequences into superfamilies. …”
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  19. 19
    Data-driven state of charge estimation of lithium-ion batteries: Algorithms, implementation factors, limitations and future trends

    Data-driven state of charge estimation of lithium-ion batteries: Algorithms, implementation factors, limitations and future trends by Hossain Lipu M.S., Hannan M.A., Hussain A., Ayob A., Saad M.H.M., Karim T.F., How D.N.T.

    Published 2023
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  20. 20
    Logistic regression methods for classification of imbalanced data sets

    Logistic regression methods for classification of imbalanced data sets by Santi Puteri Rahayu, -

    Published 2012
    “…This thesis aims to develop the simple and effective imbalanced classification algorithms by previously improving the algorithms performance of general classifiers i.e. …”
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