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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
    “…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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  2. 2
    Runtime reduction in optimal multi-query sampling-based motion planning

    Runtime reduction in optimal multi-query sampling-based motion planning by Khaksar W., Sahari K.S.B.M., Ismail F.B., Yousefi M., Ali M.A.

    Published 2023
    Conference Paper
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  3. 3
    A hybrid sampling-based path planning algorithm for mobile robot navigation in unknown environments

    A hybrid sampling-based path planning algorithm for mobile robot navigation in unknown environments by Khaksar, Weria

    Published 2013
    “…Sampling-based motion planning is a class of randomized path planning algorithms with proven completeness. …”
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  4. 4
    A multi-objective parametric algorithm for sensor-based navigation in uncharted terrains

    A multi-objective parametric algorithm for sensor-based navigation in uncharted terrains by Khaksar W., Sahari K.S.M.

    Published 2023
    “…Sensor-based motion planning is one the most challenging tasks in robotics where various approaches and algorithms have been proposed to achieve different planning goals. …”
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  5. 5
    Web-based expert system for material selection of natural fiber- reinforced polymer composites

    Web-based expert system for material selection of natural fiber- reinforced polymer composites by Ahmed Ali, Basheer Ahmed

    Published 2015
    “…Finally, the developed expert system was deployed over the internet with central interactive interface from the server as a web-based application. As Java is platform independent and easy to be deployed in web based application and accessible through the World Wide Web (www), this expert system can be one stop application for materials selection.…”
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  6. 6
    Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition

    Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition by Ali Yahya, Anwar

    Published 2007
    “…In the selection phase, a new variable length genetic algorithm is applied to select the lexical cues. …”
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  7. 7
    A bacteria foraging algorithm for solving integrated multi-period cell formation and subcontracting production planning in a dynamic cellular manufacturing system

    A bacteria foraging algorithm for solving integrated multi-period cell formation and subcontracting production planning in a dynamic cellular manufacturing system by Tang, Sai Hong, Nouri, Hossein, Motlagh, Omid Reza Esmaeili

    Published 2011
    “…In addition, a newly-developed BFA-based optimisation algorithm has been compared with the branch and bound algorithm. …”
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  8. 8
    Optimization of Upstream Offshore Oilfield Production Planning under Uncertainty and Downstream Crude Oil Scheduling at Refinery Front-End

    Optimization of Upstream Offshore Oilfield Production Planning under Uncertainty and Downstream Crude Oil Scheduling at Refinery Front-End by Tan , Yin Keong

    Published 2009
    “…The solution obtained from the LB-MILP model, i.e., the decision variables (binary variables), was used to obtain a feasible solution for model UB-NLP. …”
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  9. 9
    Optimization of Upstream Offshore Oilfield Production Planning under Uncertainty and Downstream Crude Oil Scheduling at Refinery Front-End

    Optimization of Upstream Offshore Oilfield Production Planning under Uncertainty and Downstream Crude Oil Scheduling at Refinery Front-End by Tan Yin Keong, Tan Yin

    Published 2012
    “…The solution obtained from the LB–MILP model, i.e., the decision variables (binary variables), was used to obtain a feasible solution for model UB–NLP. …”
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  10. 10
    A Fuzzy Logic Positional-Based Controller For Sensorbased Robotic Motion Planning

    A Fuzzy Logic Positional-Based Controller For Sensorbased Robotic Motion Planning by Khaksar W., Yousefi M., Saharia K.S.M., Ismail F.B.

    Published 2023
    “…Despite the large volume of research in this field, the computational complexity of the motion planning and the increasing potential application domains require more accurate and efficient motion planning algorithms. …”
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  11. 11
    Harmony Search algorithm-based gasoline consumption modeling for Indonesia

    Harmony Search algorithm-based gasoline consumption modeling for Indonesia by Anggarani, Riesta, Watada, Junzo

    Published 2012
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    Working Paper
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  12. 12
    A Constraint Programming-based Genetic Algorithm (CPGA) for Capacity Output Optimization

    A Constraint Programming-based Genetic Algorithm (CPGA) for Capacity Output Optimization by Ean, Kate Nee Goh, Jeng, Feng Chin, Wei, Ping Loh, Chea, Ling Tan

    Published 2014
    “…Purpose: The manuscript presents an investigation into a constraint programming-based genetic algorithm for capacity output optimization in a back-end semiconductor manufacturing company. …”
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  13. 13
    Optimal planning of photovoltaic distributed generation considering uncertainties using monte carlo pdf embedded MVMO-SH

    Optimal planning of photovoltaic distributed generation considering uncertainties using monte carlo pdf embedded MVMO-SH by Norhafidzah, Mohd Saad

    Published 2021
    “…A hybrid population – based stochastic optimization method named MVMO-SH algorithm is proposed to optimize PVDG locations and sizes in the grid system network. …”
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  14. 14
    Automated time series forecasting

    Automated time series forecasting by Ismail, Suzilah, Zakaria, Rohaiza, Tuan Muda, Tuan Zalizam

    Published 2011
    “…While quantitative technique is based on statistical concepts and requires large amount of data in order to formulate the mathematical models.This technique can be classified into projective and causal technique.The projective technique (or univariate modelling) just involve one variable while the causal technique (or econometric modelling) suitable for multi-variables.Since forecasting involves uncertainty, several methods need to be executed on one set of time series data in order to produce accurate forecast.Hence, usually in practice forecaster need to use several softwares to obtain the forecast values.If this practice can be transformed into algorithm (well-defined rules for solving a problem) and then the algorithm can be transformed into a computer program, less time will be needed to compute the forecast values where in business world time is money.In this study, we focused on algorithm development for univariate forecasting techniques only and will expand towards econometric modelling in the future.Two set of simulated data (yearly and non-yearly) and several univariate forecasting techniques (i.e. …”
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    Monograph
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  15. 15
    Optimum Feeder Routing and Distribution Substation Placement and Sizing using PSO and MST

    Optimum Feeder Routing and Distribution Substation Placement and Sizing using PSO and MST by Hasan, Ihsan Jabbar, Gan, Chin Kim, Shamshiri, Meysam, Ab Ghani, Mohd Ruddin, Omar, Rosli

    Published 2014
    “…A long term distribution network planning consists of several complexity aspects due to the multiple decision variables in objective functions. …”
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  16. 16
    One day ahead daily peak hour load forecasting by using invasive weed optimization learning algorithm based Artificial Neural Network

    One day ahead daily peak hour load forecasting by using invasive weed optimization learning algorithm based Artificial Neural Network by Rahim, Muhammad Fitri

    Published 2012
    “…By using 'seen' and 'unseen' of electrical energy demand data were used to test the performance of the proposed algorithm. Based on result obtained, it shows that IWO learning algorithm is capable to produce accurate prediction load demand. …”
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  17. 17
    Process Planning Optimization In Reconfigurable Manufacturing Systems

    Process Planning Optimization In Reconfigurable Manufacturing Systems by Musharavati, Farayi

    Published 2008
    “…The five (5) AADTs include; a variant of the simulated annealing algorithm that implements heuristic knowledge at critical decision points, two (2) cooperative search schemes based on a “loose hybridization” of the Boltzmann Machine algorithm with (i) simulated annealing, and (ii) genetic algorithm search techniques, and two (2) modified genetic algorithms. …”
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  18. 18
    Solving the optimal reactive power dispatch problem based on moth-flame optimizer for power system operation and planning

    Solving the optimal reactive power dispatch problem based on moth-flame optimizer for power system operation and planning by Zuriani, Mustaffa, Mohd Herwan, Sulaiman, Muhammad Ikram, Mohd Rashid, Rebecca Ng, Shin Mei, Nidzamuddeen, Ishak, Hamdan, Daniyal

    Published 2018
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  19. 19
    Hybrid evolutionarybarnacles mating optimisation-artificial neural network based technique for solving economic power dispatch planning and operation / Nor Laili Ismail

    Hybrid evolutionarybarnacles mating optimisation-artificial neural network based technique for solving economic power dispatch planning and operation / Nor Laili Ismail by Ismail, Nor Laili

    Published 2024
    “…Thus, special efforts need to be conducted to forecast several possible scenarios under the variations of the input variables in ED studies. This thesis presents the Hybrid Evolutionary-Barnacles Mating Optimisation-Artificial Neural Network Based Technique for Solving Economic Power Dispatch Planning and Operation. …”
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  20. 20
    Capacity Planning For Mixed-Load Tester Under Demand And Testing Time Uncertainty

    Capacity Planning For Mixed-Load Tester Under Demand And Testing Time Uncertainty by Asih, Hayati Mukti

    Published 2018
    “…Capacity planning is an important decision in production planning as it determines the capacity to install in order to satisfy customer demands and also to allocate products to those capacities.This research is based on mixed-load machine problem which is categorized by multiple products that can be processed simultaneously with different processing time.The problem is further complicated with high product varieties and high demand variabilities.This research was conducted based on a case company from a multinational manufacturing company in Malaysia that produces hard disk drives.The study focused on the automated testing process characterized by long lead time and high product variability.Each testing machine with 2880 slots is a mixed load tester with the ability to load and test multiple product families simultaneously.In addition,the uncertain demand and testing time makes the problem more challenging. …”
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