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  1. 1
    Comparison of conjugate gradient methods for developing the multiple linear regression model for rice production in Malaysia / Nur Atikah Mustapha

    Comparison of conjugate gradient methods for developing the multiple linear regression model for rice production in Malaysia / Nur Atikah Mustapha by Mustapha, Nur Atikah

    Published 2018
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  2. 2
    The implications for ahybrid detection technique against malicious sqlattacks on web applications

    The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

    Published 2025
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  3. 3
    Machine learning techniques for reference evapotranspiration and rice irrigation requirements prediction: a case study of Kerian irrigation scheme, Malaysia

    Machine learning techniques for reference evapotranspiration and rice irrigation requirements prediction: a case study of Kerian irrigation scheme, Malaysia by Mohd Nasir, Muhammad Adib, Harun, Sobri, Zainuddin, Zaitul Marlizawati, Kamal, Md Rowshon, Che Rose, Farid Zamani

    Published 2025
    “…Two machine learning algorithms, named Support Vector Regression (SVR) and Random Forest (RF), were applied to predict ETo and rice irrigation requirements using only climatic data (rainfall, temperature, relative humidity, and wind speed). …”
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  4. 4
    Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN)

    Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN) by Khan T., Manan T.S.B., Isa M.H., Ghanim A.A.J., Beddu S., Jusoh H., Iqbal M.S., Ayele G.T., Jami M.S.

    Published 2023
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  5. 5
    Cr(VI) adsorption from aqueous solution by an agricultural waste based carbon

    Cr(VI) adsorption from aqueous solution by an agricultural waste based carbon by Khan, T., Isa, M.H., Ul Mustafa, M.R., Yeek-Chia, H., Baloo, L., Binti Abd Manan, T.S., Saeed, M.O.

    Published 2016
    “…The study examined the adsorption of hexavalent chromium Cr(vi) from aqueous solution by acidically prepared rice husk carbon (APRHC). …”
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  6. 6
    Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method

    Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

    Published 2024
    thesis::doctoral thesis
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  7. 7
    Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN)

    Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN) by Khan, T., Manan, T.S.B., Isa, M.H., Ghanim, A.A.J., Beddu, S., Jusoh, H., Iqbal, M.S., Ayele, G.T., Jami, M.S.

    Published 2020
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  8. 8
    Modeling of cu(ii) adsorption from an aqueous solution using an artificial neural network (ann)

    Modeling of cu(ii) adsorption from an aqueous solution using an artificial neural network (ann) by Khan, Taimur, Abd Manan, Teh Sabariah, Hasnain Isa, Mohamed, A. J. Ghanim, Abdulnoor, Beddu, Salmia, Jusoh, Hisyam, Iqbal, Muhammad Shahid, Ayele, Gebiaw T, Jami, Mohammed Saedi

    Published 2020
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  9. 9
    A modified linear programming model for cost minimisation of Pangasius hypophthalmus feed formulation / Zuraida Alwadood, Anis Syahirah Rosli and Norlenda Mohd Noor

    A modified linear programming model for cost minimisation of Pangasius hypophthalmus feed formulation / Zuraida Alwadood, Anis Syahirah Rosli and Norlenda Mohd Noor by Alwadood, Zuraida, Rosli, Anis Syahirah, Mohd Noor, Norlenda

    Published 2022
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  10. 10
    Aerial imagery paddy seedlings inspection using deep learning

    Aerial imagery paddy seedlings inspection using deep learning by Anuar, Mohamed Marzhar, Abdul Halin, Alfian, Perumal, Thinagaran, Kalantar, Bahareh

    Published 2022
    “…Experimental results showed that our proposed methods were capable of detecting the defective paddy rice seedlings with the highest precision and an F1-Score of 0.83 and 0.77, respectively, using a one-stage pretrained object detector called EfficientDet-D1 EficientNet.…”
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