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  1. 1
    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm

    Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm by Norulazmi, Kasim

    Published 2008
    “…The prototype system, known as Java Plagiarism Detection System (JPDS) implements the Greedy-String-Tiling algorithm to detect similarities among tokens in a Java source code files. …”
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  2. 2
    An Educational Tool Aimed at Learning Metaheuristics

    An Educational Tool Aimed at Learning Metaheuristics by Kader, Md. Abdul, Jamaluddin, Jamal A., Kamal Z., Zamli

    Published 2020
    “…Implemented with Java, this tool provides a friendly GUI for setting the parameters and display the result from where the learner can see how the selected algorithm converges for a particular problem solution. …”
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  3. 3
    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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  4. 4
    A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment

    A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment by Ruzita, Ahmad

    Published 2013
    “…The methodology used consists of theoretical study, design of formal framework on intelligent software quality, identification of Feature Ranking Technique (FRT), construction and evaluation of FRA algorithm. The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. …”
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  5. 5
    Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly

    Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly by Zulkifly, Ahmad Zuladzlan

    Published 2019
    “…This project will use fuzzy k-means clustering algorithm to cluster the data because it is easy to implement and have many advantages. …”
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  6. 6
    Features selection for intrusion detection system using hybridize PSO-SVM

    Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

    Published 2016
    “…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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  7. 7
    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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  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
    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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  10. 10
    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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  11. 11
    Talkout : Protecting mental health application with a lightweight message encryption

    Talkout : Protecting mental health application with a lightweight message encryption by Gavin Teo Juen

    Published 2022
    “…The investigation of lightweight message encryption algorithms is conducted with systematic quantitative literature and experiment implementation in Java and Android running environment. …”
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  12. 12
    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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  13. 13
    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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  14. 14
    Mathematical simulation for 3-dimensional temperature visualization on open source-based grid computing platform

    Mathematical simulation for 3-dimensional temperature visualization on open source-based grid computing platform by Alias, Norma, Satam, Noriza, Abd. Ghaffar, Zarith Safiza, Darwis, Roziha, Hamzah, Norhafiza, Islam, Md. Rajibul

    Published 2009
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  15. 15
    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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  16. 16
    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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  17. 17
    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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  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
    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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  20. 20
    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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