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  1. 1
    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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  2. 2
    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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  3. 3
    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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  4. 4
    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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  5. 5
    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 developed multi-objective genetic algorithm (MOGA) model, showed an excess of energy inputs used by the farmers more than the required energy by 37.8 and 40 for the transplanting and broadcast seeding methods. …”
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  6. 6
    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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  7. 7
    Prediction of international rice production using long short- term memory and machine learning models

    Prediction of international rice production using long short- term memory and machine learning models by Arya, Suraj, Anju, ., Nor Azuana, Ramli

    Published 2025
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  8. 8
    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
    “…Different preprocessing methods and effective wavelength selection techniques were used to eliminate the noise and redundant wavelength in the reflectance spectra, and predictive models were developed for the glutinous rice quality. …”
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  9. 9
    Estimation of microclimate parameters on infestation rate of yellow stem borer (Scirpophaga incertulas) on MR297 rice variety

    Estimation of microclimate parameters on infestation rate of yellow stem borer (Scirpophaga incertulas) on MR297 rice variety by Idris, Dauda

    Published 2023
    “…However, information on the estimation of microclimate parameters in Malaysia on the infestation rate of yellow stem borer on MR297 rice variety is still not studied. The use of Artificial Neuron Network (ANN) and Multi-Linear Regression (MLR) in forecasting pest infestation and development has been used. …”
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  10. 10
    Design and development of detection scheme using multiline image scanning for aerial mapping of a simulated rice paddy field and implemented on unmanned aerial vehicle / Mohamad Farid Misnan

    Design and development of detection scheme using multiline image scanning for aerial mapping of a simulated rice paddy field and implemented on unmanned aerial vehicle / Mohamad Fa... by Misnan, Mohamad Farid

    Published 2018
    “…A conventional analog camera as front end sensor was used in this work as it could provide vast raw image data that can be utilized to identify the captured image using the designed algorithm. …”
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  11. 11
    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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  12. 12
    Development of submergence tolerant rice variety through marker-assisted backcross breeding between MR219 and Swarna-Sub1

    Development of submergence tolerant rice variety through marker-assisted backcross breeding between MR219 and Swarna-Sub1 by Ahmed, Fahim

    Published 2015
    “…Marker-assisted selection (MAS) is an effective approach than the conventional breeding for rice varietal development. In this study, a popular high yielding but susceptible to submergence, MR219 rice variety was crossed with submergence tolerant variety, Swarna-Sub1 for development of variety tolerant to submergence through MAS. …”
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  13. 13
    Design and development of detection scheme using multiline image scanning for aerial mapping of a simulated rice paddy field and implemented on unmanned aerial vehicle / Mohamad Farid Misnan

    Design and development of detection scheme using multiline image scanning for aerial mapping of a simulated rice paddy field and implemented on unmanned aerial vehicle / Mohamad Fa... by Misnan, Mohamad Farid

    Published 2018
    “…A conventional analog camera as front end sensor was used in this work as it could provide vast raw image data that can be utilized to identify the captured image using the designed algorithm. …”
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    Book Section
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  14. 14
    Genome-wide computational identification of biologically significant cis-regulatory elements and associated transcription factors from rice

    Genome-wide computational identification of biologically significant cis-regulatory elements and associated transcription factors from rice by Ho, Chai Ling, Geisler, Matt

    Published 2019
    “…In this study, we tested the applicability of two algorithms developed for other model systems for the identification of biologically significant CREs of co-expressed genes from rice. …”
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  15. 15
    Neural Network Modeling And Optimization For Enzymatic Hydrolysis Of Xylose From Rice Straw

    Neural Network Modeling And Optimization For Enzymatic Hydrolysis Of Xylose From Rice Straw by Norhalim, Nur’atiqah

    Published 2015
    “…In this thesis, enzymatic hydrolysis was utilized in the production of xylose from rice straw. The process model was developed by the modeling techniques using feed-forward artificial neural network (FANN) and optimized using both particle swarm optimization (PSO) and genetic algorithm (GA). …”
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  16. 16
    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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  17. 17
    Hyperspectral imaging for detection of macronutrients retained in glutinous rice under different drying conditions

    Hyperspectral imaging for detection of macronutrients retained in glutinous rice under different drying conditions by Jimoh, Kabiru Ayobami, Hashim, Norhashila, Shamsudin, Rosnah, Che Man, Hasfalina, Jahari, Mahirah, Megat Ahmad Azman, Puteri Nurain, Onwude, Daniel I.

    Published 2025
    “…Therefore, the developed SGID-VISSA-PLSR can be used to build a smart and reliable spectral system for detecting the macronutrients in GR grains.…”
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  18. 18
    Intelligent inventory forecasting system / Fadzlinor Mustapa

    Intelligent inventory forecasting system / Fadzlinor Mustapa by Mustapa, Fadzlinor

    Published 2006
    “…Lastly, this project must achieve an objective of developing a prototype of intelligent forecasting system that can make a prediction of the rice's stock level in the inventory. …”
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  19. 19
    Optimization of energy inputs and greenhouse gas emissions of wetland rice cultivation in Malaysia

    Optimization of energy inputs and greenhouse gas emissions of wetland rice cultivation in Malaysia by Elsoragaby, Suha Gaafar Babekir

    Published 2019
    “…Later, the energy inputs and GHG emissions were optimized using the multi objective genetic algorithm (MOGA) analysis techniques. …”
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  20. 20
    Comparative modelling of strength properties of hydrated-lime activated rice husk-ash (HARHA) modified soft soil for pavement construction purposes by artificial neural network (ANN) and fuzzy logic (FL)

    Comparative modelling of strength properties of hydrated-lime activated rice husk-ash (HARHA) modified soft soil for pavement construction purposes by artificial neural network (AN... by Onyelowe, K. C., Alaneme, G. U., Onyia, M. E., Bui Van, D., Dimonyeka, M. U., Nnadi, E., Ogbonna, C., Odum, L. O., Aju, D. E., Abel, C., Udousoro, I. M., Onukwugha, E.

    Published 2021
    “…For the ANN model, feed forward back propagation and Levernberg Marquardt training algorithm were utilized for the model development with the optimized network architecture of 8-6-3 derived based on MSE performance criteria; while for the fuzzy logic model, the mamdani FIS with both triangular and trapezoidal membership function with both models formulated, simulated and computed using MATLAB toolbox. …”
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