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  1. 1
    Genetic ensemble biased ARTMAP method of ECG-Based emotion classification

    Genetic ensemble biased ARTMAP method of ECG-Based emotion classification by Loo, C.K., Liew, W.S., Sayeed, M.S.

    Published 2012
    “…The optimal combination of λ and training sequence can be computed efficiently using a genetic permutation algorithm. …”
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  2. 2
    Detection of Workers’ Behaviour in the Manufacturing Plant using Deep Learning

    Detection of Workers’ Behaviour in the Manufacturing Plant using Deep Learning by Goh, Ching Pang

    Published 2023
    “…Utilizing machine learning algorithms, our system learns and detects intricate activities from worker behavior sequences, offering a sophisticated analysis of worker efficiency. …”
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  3. 3
    NETASA: neural network based prediction of solvent accessibility

    NETASA: neural network based prediction of solvent accessibility by Ahmad, Shandar, Gromiha, M. Michael

    Published 2002
    “…Our prediction method achieved the accuracy level upto 90% for training and 88% for test data sets. …”
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  4. 4
    A study on component-based technology for development of complex bioinformatics software

    A study on component-based technology for development of complex bioinformatics software by Ali Shah, Zuraini, Deris, Safaai, Othman, Muhamad Razib, Zakaria, Zalmiyah, Saad, Puteh, Hassan, Rohayanti, Muda, Mohd. Hilmi, Kasim, Shahreen, Roslan, Rosfuzah

    Published 2004
    “…The second layer uses discriminative SVM algorithm with a state-of-the-art string kernel based on PSI-BLAST profiles that is used to leverage the unlabeled data. …”
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  5. 5
    Data Mining for Building Neural Protein Sequence Classification Systems with Improved Performance

    Data Mining for Building Neural Protein Sequence Classification Systems with Improved Performance by Wang, Dianhui, Lee, Nung Kion, Dillon, Tharam S.

    Published 2003
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  6. 6
    A new LoRa based positioning algorithm utilizing sequence based deep learning technique

    A new LoRa based positioning algorithm utilizing sequence based deep learning technique by Suseenthiran, Kavetha

    Published 2023
    “…Then, a new positioning algorithm is developed which incorporates distance estimation, Kalman Filter and trilateration technique according to the classification with different sequence length data and the positioning error is being analysed. …”
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  7. 7
    Parallel implementation of genomic sequences classification using modified gabor wavelet transform on multicore systems

    Parallel implementation of genomic sequences classification using modified gabor wavelet transform on multicore systems by Gunawan, Teddy Surya, Ambikairajah, Eliathamby

    Published 2012
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  8. 8
    Protein Sequences Classification Using Modular RBF Neural Networks

    Protein Sequences Classification Using Modular RBF Neural Networks by Wang, Dianhui, Lee, Nung Kion, Dillon, Tharam S., Hoogenraad, Nicholas J.

    Published 2002
    “…Therefore, there is a need to build an improved classification system for effectively identifying protein sequences. …”
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  9. 9
    Development Of Vehicle Tracking And Counting System From Traffic Surveillance Video

    Development Of Vehicle Tracking And Counting System From Traffic Surveillance Video by Kueh , Chiung Lin

    Published 2015
    “…The existing Vehicle Detection and Classification System does not have tracking and counting module implemented. …”
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  10. 10
    Extraction and Optimization of Fuzzy Protein Sequences Classification Rules Using GRBF Neural Networks

    Extraction and Optimization of Fuzzy Protein Sequences Classification Rules Using GRBF Neural Networks by Wang, Dianhui, Lee, Nung Kion, Dillon, Tharam S.

    Published 2003
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  11. 11
    Classification of eukaryotic splice-junction genetic sequences using averaged one-dependence estimators with subsumption resolution

    Classification of eukaryotic splice-junction genetic sequences using averaged one-dependence estimators with subsumption resolution by Htike@Muhammad Yusof, Zaw Zaw, Win, Shoon Lei

    Published 2013
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  12. 12
    Development of Phasor Measurement Unit Based Fault Detection and Faulty Line Classification in Electrical Power System

    Development of Phasor Measurement Unit Based Fault Detection and Faulty Line Classification in Electrical Power System by Muhammad Qasim, Khan

    Published 2019
    “…Secondly, the study proposes an index for faulty node classification (FNC) based on the positive sequence voltage (PSV) measurements of the pre-fault and post-fault conditions. …”
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  13. 13
    Parallel implementation of genomic sequences classification using modified Gabor wavelet transform on multicore systems

    Parallel implementation of genomic sequences classification using modified Gabor wavelet transform on multicore systems by Gunawan, Teddy Surya, Ambikairajah, Eliathamby

    Published 2012
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  14. 14
    An accurate algorithm of PMU-based wide area measurements for fault detection using positive-sequence voltage and unwrapped dynamic angles

    An accurate algorithm of PMU-based wide area measurements for fault detection using positive-sequence voltage and unwrapped dynamic angles by Muhammad Qasim, Khan, Musse Mohamud, Ahmed, Ahmed Mohamed, Ahmed Haidar

    Published 2022
    “…The algorithm includes an index for faulty bus classification based on the positive-sequence voltage measurements of the pre-fault and post-fault conditions, where the bus with a maximum differential percentage is identified as a faulted bus. …”
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  15. 15
    Deep learning object detector using a combination of Convolutional Neural Network (CNN) architecture (MiniVGGNet) and classic object detection algorithm

    Deep learning object detector using a combination of Convolutional Neural Network (CNN) architecture (MiniVGGNet) and classic object detection algorithm by Ismail, Asmida, Ahmad, Siti Anom, Che Soh, Azura, Hassan, Mohd Khair, Harith, Hazreen Haizi

    Published 2020
    “…The system consists of two main processes, which are classification and detection. …”
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  16. 16
    Improved GART neural network model for pattern classification and rule extraction with application to power systems

    Improved GART neural network model for pattern classification and rule extraction with application to power systems by Yap K.S., Lim C.P., Au M.T.

    Published 2023
    “…To assess the effectiveness of IGART and to compare its performances with those from other methods, three datasets that are related to power systems are employed. The experimental results demonstrate the usefulness of IGART with the rule extraction capability in undertaking classification problems in power systems engineering. � 2006 IEEE.…”
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  17. 17
    Moment-based extraction on handwritten digits

    Moment-based extraction on handwritten digits by Taliba, Jumail, Shamsuddin, Siti Mariyam, Tan, Shuen Chuan

    Published 2005
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  18. 18
    Evaluation and optimization of frequent association rule based classification

    Evaluation and optimization of frequent association rule based classification by Izwan Nizal Mohd Shaharanee, Jastini Jamil

    Published 2014
    “…In this paper, a systematic way to evaluate the association rules discovered from frequent itemset mining algorithms, combining common data mining and statistical interestingness measures, and outline an appropriated sequence of usage is presented. …”
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  19. 19
    VLSI floor planning optimization using genetic algorithm and cross entropy method / Angeline Teoh Szu Fern

    VLSI floor planning optimization using genetic algorithm and cross entropy method / Angeline Teoh Szu Fern by Angeline Teoh, Szu Fern

    Published 2012
    “…These two models are based on topological placement method. DM is optimized using genetic algorithm (GA). …”
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  20. 20
    Data-driven continuous-time Hammerstein modeling with missing data using improved Archimedes optimization algorithm

    Data-driven continuous-time Hammerstein modeling with missing data using improved Archimedes optimization algorithm by Islam, Muhammad Shafiqul, Mohd Ashraf, Ahmad

    Published 2024
    “…This research introduces the improved Archimedes optimization algorithm (IAOA) for data-driven modeling of continuous-time Hammerstein models with missing data. …”
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