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  1. 1
    A conceptual framework for a lightweight AI system for skin disease risk prediction using epidemiological data in rural Bangladesh

    A conceptual framework for a lightweight AI system for skin disease risk prediction using epidemiological data in rural Bangladesh by Islam, Mohammad Raihanul, Abdul Kadir, Andi Fitriah, Ismail, Syazwan Aizat

    Published 2026
    “…The findings further support the practical feasibility of deploying the proposed model in resourcelimited rural clinics to aid early risk identification and more efficient allocation of healthcare resources. …”
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  2. 2
    Malware Detection using Deep Learning (DL)

    Malware Detection using Deep Learning (DL) by Chowdhury Sajadul Islam, Madihah Mohd Saudi, Nur Hafiza Zakaria, Muji Setiyo

    Published 2024
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  3. 3
    Design of intelligent Qira’at identification algorithm

    Design of intelligent Qira’at identification algorithm by Kamarudin, Noraziahtulhidayu

    Published 2017
    “…To evaluate the algorithm, 350 samples for 10 types of Qira’at recitation are in used, and for justifying the best pattern classification, few algorithms are tested in the early preliminary evaluation with K-Nearest Neighbour, GMM and PPCA. …”
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  4. 4
    Machine-learning approach using thermal and synthetic aperture radar data for classification of oil palm trees with basal stem rot disease

    Machine-learning approach using thermal and synthetic aperture radar data for classification of oil palm trees with basal stem rot disease by Che Hashim, Izrahayu

    Published 2021
    “…The main benefit of this study is the development of an appropriate model for early identification and severity classification of BSR disease in oil palms via remote sensing and data mining approaches rapidly and cost-effectively.…”
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  5. 5
    Identification of microcalcification in mammographic images / Noor Azliza Abdul Shauti

    Identification of microcalcification in mammographic images / Noor Azliza Abdul Shauti by Abdul Shauti, Noor Azliza

    Published 2008
    “…By using this identification, it can simulate in flexible images post processing and analysis in easy image management using Java aplication. …”
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  6. 6
    Early Detection Of ADHD Among Children Using Machine Learning

    Early Detection Of ADHD Among Children Using Machine Learning by Nur Atiqah, Kamal

    Published 2023
    “…Early identification using fMRI and machine learning holds great potential for improving the lives of children with ADHD through timely interventions and targeted support.…”
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  7. 7
    Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models

    Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models by Quadros, Jaimon Dennis, Khan, Sher Afghan, Aabid, Abdul, Baig, Muneer

    Published 2023
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  8. 8
    Comparative study on leaf disease identification using Yolo v4 and Yolo v7 algorithm

    Comparative study on leaf disease identification using Yolo v4 and Yolo v7 algorithm by Wang, Xinming, Tang, Sai Hong, Mohd Ariffin, Mohd Khairol Anuar, Ismail, Mohd Idris Shah

    Published 2023
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  9. 9
    Modeling of cardiovascular diseases (CVDs) and development of predictive heart risk score

    Modeling of cardiovascular diseases (CVDs) and development of predictive heart risk score by Mirza Rizwan, Sajid

    Published 2021
    “…Secondly, the ML models offer better and consistent risk prediction models as compared to LRA-based model. …”
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  10. 10
    Methods for identification of the opportunistic gut mycobiome from colorectal adenocarcinoma biopsy tissues

    Methods for identification of the opportunistic gut mycobiome from colorectal adenocarcinoma biopsy tissues by Aisyah, Yunus, Norfilza, Mohd Mokhtar, Raja Affendi, Raja Ali *, Siti Maryam, Ahmad Kendong, Hajar, Fauzan Ahmad

    Published 2024
    “…•Detailed method to identify the gut mycobiome in colorectal cancer patients using ITS-specific amplicon sequencing. •Application of machine learning algorithms to the identification of potential mycobiome biomarkers for non-invasive colorectal cancer screening. …”
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  11. 11
    Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms

    Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms by Raheem, Ajiboye Adeleke, Hauwau, Isah-Kebbe, O., Oladele Tinuke

    Published 2014
    “…This study explores the performance accuracies of partitioning-based algorithms and probabilistic model-based algorithm. …”
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  12. 12
    Development of MyCGPA for early predicting students’ academic performance

    Development of MyCGPA for early predicting students’ academic performance by Abdul Mutalib, Muhammad Yazid, Zainol, Zuraini, Nohuddin, Puteri Nor Ellyza, Abdul Rauf, Ummul Fahri

    Published 2025
    “…One of the primary concerns in higher education is the early identification of underperforming students. …”
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  13. 13
    Dynamic modelling of a flexible beam structure using feedforward neural networks for active vibration control

    Dynamic modelling of a flexible beam structure using feedforward neural networks for active vibration control by Tuan Abdul Rahman, Tuan Ahmad Zahidi, As'arry, Azizan, Abdul Jalil, Nawal Aswan, Raja Ahmad, Raja Mohd Kamil

    Published 2019
    “…Correlation tests were used to validate the obtained model. Based on the proposed method, a small mean squared error value has been achieved in the validation phase. …”
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  14. 14
    Optimization of assembly line balancing with energy efficiency by using tiki-taka algorithm

    Optimization of assembly line balancing with energy efficiency by using tiki-taka algorithm by Ariff Nijay, Ramli

    Published 2023
    “…Lastly, a study of the industrial case was performed as a validation of the developed model and algorithm. An automotive company is selected, and the collected actual data is used for validation purposes. …”
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  15. 15
    Investigation of An Early Prediction System of Cardiac Arrest Using Machine Learning Techniques

    Investigation of An Early Prediction System of Cardiac Arrest Using Machine Learning Techniques by Muhammad Afnan, Mohammad Nasir

    Published 2022
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  16. 16
    Model structure selection for a discrete-time non-linear system using genetic algorithm

    Model structure selection for a discrete-time non-linear system using genetic algorithm by Ahmad, Robiah, Jamaluddin , Hishamuddin, Hussain, Mohd. Azlan

    Published 2004
    “…The adequacy of the developed models is tested using one-step-ahead prediction and correlation-based model validation tests. …”
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  17. 17
    Analysis of Toothbrush Rig Parameter Estimation Using Different Model Orders in Real Coded Genetic Algorithm (RCGA)

    Analysis of Toothbrush Rig Parameter Estimation Using Different Model Orders in Real Coded Genetic Algorithm (RCGA) by Ainul, H. M. Y., Salleh, S. M., Halib, N., Taib, H., Fathi, M. S.

    Published 2024
    “…The validation test-through correlation analysis was used to validate the model. …”
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  18. 18
    AI-Enabled Deep Learning Model for COVID-19 Identification Leveraging Internet of Things

    AI-Enabled Deep Learning Model for COVID-19 Identification Leveraging Internet of Things by Mohd Arfian, Ismail, Siti Nur Fathin Najwa, Mustaffa, Abed, Munther H.

    Published 2023
    “…A chest X-ray dataset is used to compare the deep ensemble model against six transfer learning algorithms. …”
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  19. 19
    Model structure selection for a discrete-time non-linear system using a genetic algorithm

    Model structure selection for a discrete-time non-linear system using a genetic algorithm by Ahmad, R., Jamaluddin, H., Hussain, M. A.

    Published 2004
    “…The adequacy of the developed models is tested using one-step-ahead prediction and correlation-based model validation tests. …”
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  20. 20
    Model structure selection for a discrete-time non-linear system using a genetic algorithm

    Model structure selection for a discrete-time non-linear system using a genetic algorithm by Ahmad, R., Jamaluddin, H., Hussain, M. A.

    Published 2004
    “…The adequacy of the developed models is tested using one-step-ahead prediction and correlation-based model validation tests. …”
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