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  1. 1
    Direct approach for mining association rules from structured XML data

    Direct approach for mining association rules from structured XML data by Abazeed, Ashraf Riad

    Published 2012
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  2. 2
    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection

    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection by Saputra , Dhany, Rambli, Dayang R.A., Foong, Oi Mean

    Published 2008
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  3. 3
    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
    “…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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  4. 4
    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
    “…The quality of data collected from sensors greatly influences the performance of the modelling processes using ML algorithms. …”
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  5. 5
    Mining Sequential Patterns Using I-PrefixSpan

    Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

    Published 2007
    “…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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  6. 6
    A web-based implementation of k-means algorithms

    A web-based implementation of k-means algorithms by Lee, Quan

    Published 2022
    “…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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  7. 7
    Mining Sequential Patterns using I-PrefixSpan

    Mining Sequential Patterns using I-PrefixSpan by Saputra , Dhany, Dayang R.A. Rambli, Foong, Oi Mean

    Published 2008
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  8. 8
    Smart Agriculture Economics and Engineering: Unveiling the Innovation Behind AI-Enhanced Rice Farming

    Smart Agriculture Economics and Engineering: Unveiling the Innovation Behind AI-Enhanced Rice Farming by Zun Liang, Chuan, Tham, Ren Sheng, Tan, Chek Cheng, Abraham Lim, Bing Sern, David Lau, King Luen, Chong, Yeh Sai

    Published 2024
    “…To address these challenges, an innovative Artificial Intelligence-based (AI-based) predictive algorithm has been proposed, leveraging the Cross Industry Standard Process for Data Mining (CRISP-DM) data science framework. …”
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  9. 9
    Smart agriculture economics and engineering: Unveiling the innovation behind ai-enhanced rice farming

    Smart agriculture economics and engineering: Unveiling the innovation behind ai-enhanced rice farming by Chuan, Zun Liang, Tham, Ren Sheng, Tan, Chek Cheng, Abraham Lim, Bing Sern, Chong, Yeh Sai

    Published 2025
    “…This article introduced innovative Artificial Intelligence-based (AI-based) predictive algorithms for short-term rice production, utilizing the Cross Industry Standard Process for Data Mining (CRISP-DM) data science framework. …”
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  10. 10
    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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  11. 11
    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
    “…The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.…”
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  12. 12
    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
    “…All implemented techniques were processed simultaneously on embedded controller of the customized UAV to get real-time processed image and mapping data. …”
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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
    “…All implemented techniques were processed simultaneously on embedded controller of the customized UAV to get real-time processed image and mapping data. …”
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  14. 14
    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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  15. 15
    Producer gas composition prediction using artificial neural network algorithm / Mohd Mahadzir Mohammud ... [et al.]

    Producer gas composition prediction using artificial neural network algorithm / Mohd Mahadzir Mohammud ... [et al.] by Mohammud, Mohd Mahadzir, Mohamad Bakre, Muhammad Syaham, Mohd Fohimi, Nor Azirah, Rabilah, Rosniza, Ahmad, Muhammad Iqbal

    Published 2023
    “…The goals are to predict the output producer gas using an algorithm and to compare the trained prediction result with actual experiment data for rice husk gasification. …”
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  16. 16
    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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  17. 17
    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
    “…Rice is the second highestyielding grain and a staple diet in most countries. …”
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  18. 18
    Non-linear modelling and control of a conveyor-belt grain dryer utilizing neuro-fuzzy systems

    Non-linear modelling and control of a conveyor-belt grain dryer utilizing neuro-fuzzy systems by Lutfy, Omar Farouq, Mohd Noor, Samsul Bahari, Marhaban, Mohammad Hamiruce, Abbas, Kassim Ali

    Published 2011
    “…Utilizing this dryer, a real-time experiment was conducted to dry paddy (rough rice) grains. Then, the input–output data collected from this experiment were presented to an ANFIS network to develop a control-oriented dryer model. …”
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  19. 19
    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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  20. 20
    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
    “…Langmuir and Freundlich isotherm equations were fitted to the equilibrium adsorption data; the former isotherm yielded a better fit. The thermodynamic results indicate that the process of Cr(vi) adsorption by APRHC was endothermic in nature. …”
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