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  1. 1
    Study and Implementation of Data Mining in Urban Gardening

    Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

    Published 2019
    “…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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  2. 2
    Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde

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

    Published 2022
    “…The method was implemented using Java and the results of the simulation were evaluated using five standard performance metrics: accuracy, AUC, precision, recall, and f-Measure. …”
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  3. 3
    Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization

    Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization by Mohammad Ata, Karimeh Ibrahim

    Published 2019
    “…This study proposes a car parking management system which applies Dijkstra’s algorithm, Ant Colony Optimization (ACO) and Binary Search Tree (BST) in structuring a guidance system for indoor parking. …”
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  4. 4
    Rice Yield prediction - a comparison between Enchanced Back Propagation Learning Algorithms

    Rice Yield prediction - a comparison between Enchanced Back Propagation Learning Algorithms by Puteh, Saad, Nor Khairah, Jamaludin, Nursalasawati, Rusli, Aryati, Bakri, Siti Sakira, Kamarudin

    Published 2009
    “…In this study, we examine the performance of four enhanced BP algorithms to predict rice yield in MAD A plantation area in Kedah, Malaysia. …”
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  5. 5
    Rice yield prediction - a comparison between enhanced back propagation learning algorithms

    Rice yield prediction - a comparison between enhanced back propagation learning algorithms by Saad, Puteh, Jamaludin, Nor Khairah, Rusli, Nursalasawati, Bakri, Aryati, Kamarudin, Siti Sakira

    Published 2004
    “…In this study, we examine the performance of four enhanced BP algorithms to predict rice yield in MADA plantation area in Kedah, Malaysia. …”
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  6. 6
    Towards paddy rice smart farming: a review on big data, machine learning, and rice production tasks

    Towards paddy rice smart farming: a review on big data, machine learning, and rice production tasks by Rayner Alfred, Joe Henry Obit, Christie Chin Pei Yee, Haviluddin Haviluddin, Yuto Lim

    Published 2021
    “…This paper also presents a framework that maps the activities defined in rice smart farming, data used in data modelling and machine learning algorithms used for each activity defined in the production and post-production phases of paddy rice. …”
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  7. 7
    Performances Evaluation and Comparison of Two Algorithms for Fuzzy Logic Rice Cooking System (MATLAB Fuzzy Logic Toolbox and FuzzyTECH)

    Performances Evaluation and Comparison of Two Algorithms for Fuzzy Logic Rice Cooking System (MATLAB Fuzzy Logic Toolbox and FuzzyTECH) by Mohd Aras, Mohd Shahrieel, Abdul Azis, Fadilah, Fara Ashikin , Ali, Syed Mohamad Shazali, Syed Abdul Hamid, Mohd Farriz , Md Basar

    Published 2011
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  8. 8
    Monitoring the drying process of glutinous rice using hyperspectral imaging coupled with multivariate analysis

    Monitoring the drying process of glutinous rice using hyperspectral imaging coupled with multivariate analysis by Jimoh, Kabiru Ayobami, Hashim, Norhashila, Shamsudin, Rosnah, Che Man, Hasfalina, Jahari, Mahirah

    Published 2024
    “…The redundant wavelength was removed and the wavelength features that are strongly associated with the moisture content of glutinous rice were chosen using the competitive adaptive reweighted sampling algorithm (CARS). …”
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  9. 9
    Mathematical modelling of rice mill production by using cubic B-spline and discrete least square method / Siti Noorkhaliesa Che Pa

    Mathematical modelling of rice mill production by using cubic B-spline and discrete least square method / Siti Noorkhaliesa Che Pa by Che Pa, Siti Noorkhaliesa

    Published 2017
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  10. 10
    Performances Evaluation And Comparison Of Two Algorithms For Fuzzy Logic Rice Cooking System (MATLAB Fuzzy Logic Toolbox And Fuzzy Tech)

    Performances Evaluation And Comparison Of Two Algorithms For Fuzzy Logic Rice Cooking System (MATLAB Fuzzy Logic Toolbox And Fuzzy Tech) by Mohd Aras, Mohd Shahrieel, Abdul Azis, Fadilah, Ali, Fara Ashikin, Syed Abdul Hamid , Syed Mohamad Shazali, Md Basar, Mohd Farriz

    Published 2011
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  11. 11
    Ensemble and single algorithm models to handle multicollinearity of UAV vegetation indices for predicting rice biomass

    Ensemble and single algorithm models to handle multicollinearity of UAV vegetation indices for predicting rice biomass by Derraz, Radhwane, Muharam, Farrah Melissa, Nurulhuda, Khairudin, Ahmad Jaafar, Noraini, Keng Yap, Ng

    Published 2023
    “…Nevertheless, VIs are collinear, and their analyses require machine learning algorithms (MLs). The analysis of collinear VIs using base (single) and ensemble MLs is yet to be investigated. …”
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  12. 12
    Applying multi-objective genetic algorithm (MOGA) to optimize the energy inputs and greenhouse gas emissions (GHG) in wetland rice production

    Applying multi-objective genetic algorithm (MOGA) to optimize the energy inputs and greenhouse gas emissions (GHG) in wetland rice production by Elsoragaby, S., Yahya, A., Mahadi, M.R., Nawi, N.M., Mairghany, M., M Elhassan, S.M., Kheiralla, A.F.

    Published 2020
    “…The aim of this study is applying the multi-objective genetic algorithm MOGA to optimize the energy inputs and reduce the greenhouse gas emissions (GHG) for wetland rice production in Malaysia. …”
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  13. 13
    The optimization of solar drying of grain by using a genetic algorithm

    The optimization of solar drying of grain by using a genetic algorithm by Rahman, M.M., Mustayen, A.G.M.B., Mekhilef, Saad, Saidur, R.

    Published 2015
    “…After certain time interval the enzymatic activity and the moisture content have been measured. Genetic Algorithm (GA) has been used for the simulation and the optimization process while the experimental data have been used to fit the thin layer drying model. …”
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  14. 14
    Enhancement of heavy metals sorption via nanocomposites of rice straw and Fe3O4 nanoparticles using artificial neural network (ANN)

    Enhancement of heavy metals sorption via nanocomposites of rice straw and Fe3O4 nanoparticles using artificial neural network (ANN) by Khandanlou, Roshanak, Masoumi, Hamid Reza Fard, Ahmad @ Ayob, Mansor, Shameli, Kamyar, Basri, Mahiran, Kalantari, Katayoon

    Published 2016
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  15. 15
    Enhancement of heavy metals sorption via nanocomposites of rice straw and Fe3O4 nanopeprintss using artificial neural network (ANN)

    Enhancement of heavy metals sorption via nanocomposites of rice straw and Fe3O4 nanopeprintss using artificial neural network (ANN) by Khandanlou, Roshanak, Fard Masoumi, Hamid Reza, Ahmad @ Ayob, Mansor, Shameli, Kamyar, Basri, Mahiran, Kalantari, Katayoon

    Published 2016
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  16. 16
    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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  17. 17
    Rapid and non-destructive monitoring of the drying process of glutinous rice using visible-near infrared hyperspectral imaging

    Rapid and non-destructive monitoring of the drying process of glutinous rice using visible-near infrared hyperspectral imaging by Jimoh, Kabiru Ayobami, Hashim, Norhashila, Shamsudin, Rosnah, Che Man, Hasfalina, Jahari, Mahirah

    Published 2025
    “…The study showed that visible-near infrared hyperspectral imaging coupled with computational intelligence can be used to monitor the quality of glutinous rice during the drying process.…”
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  18. 18
    Prediction of rice biomass using machine learning algorithms

    Prediction of rice biomass using machine learning algorithms by Radhwane, Derraz

    Published 2022
    “…Unmanned aerial vehicles (UAVs) may address these issues. Machine learning algorithms (MLs) can predict rice biomass from UAV-based vegetation indices (VIs). …”
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  19. 19
    A connectionist model to predict rice yield based on disease infection

    A connectionist model to predict rice yield based on disease infection by Kamaruddin, Siti Sakira

    Published 2006
    “…Advance changes in technology, economy and business environment are influencing all sectors including agriculture.Rice as the worlds main dietary food is experiencing a decrease in yield due to the infection of pests and diseases, decreasing level of water sources, the scarcity of suitable land for agriculture and inefficient labour management.Rice Yield losses of approximately 31.5% were attributed to rice plant related diseases.This work describes the development of a connectionist model to predict the rice yield based on the amount of area infected by rice diseases.The Back Propagation learning algorithm were used with 5 input parameters which represents the planting seasons; the plantation district and the 3 main deadly disease recordings from the Muda Agricultural area in Malaysia during various planting seasons from 1995-2001. …”
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  20. 20
    A concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms

    A concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms by Ghazvini, Anahita, Mohd Sharef, Nurfadhlina, Balasundram, Siva Kumar, Lee, Lai Soon

    Published 2024
    “…This paper presents two approaches, namely, (i) single-nutrient concentration prediction and (ii) nutrient composition concentration prediction, which is the result of a predictive digital twin case study that employs six regression algorithms, namely, Elastic Net, Polynomial, Stepwise, Ridge, Lasso, and Linear Regression, to predict rice nutrient content efficiently, particularly considering the coexistence and composition of multiple nutrients. …”
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