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  1. 1
    Optimal placement, sizing and power factor of distributed generation: A comprehensive study spanning from the planning stage to the operation stage

    Optimal placement, sizing and power factor of distributed generation: A comprehensive study spanning from the planning stage to the operation stage by Huy P.D., Ramachandaramurthy V.K., Yong J.Y., Tan K.M., Ekanayake J.B.

    Published 2023
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  2. 2
    Performance evaluation and benchmarking of an extended computational model of ant colony system for DNA sequence design

    Performance evaluation and benchmarking of an extended computational model of ant colony system for DNA sequence design by Zuwairie, Ibrahim, Mohd Falfazli, Mat Jusof, Mohd Zaidi, Mohd Tumari

    Published 2014
    “…Ant colony system (ACS) algorithm is one of the biologically inspired algorithms that have been introduced to effectively solve a variety of combinatorial optimisation problems. …”
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  3. 3
    A competitive co-evolutionary approach for the nurse scheduling problem

    A competitive co-evolutionary approach for the nurse scheduling problem by Mohamad Nazri, Maizatul Farhana, Mohd Yusoh, Zeratul Izzah, Basiron, Halizah, Daud, Azlina

    Published 2026
    “…The competitive approach further exhibits smoother convergence behaviour across generations, indicating stronger optimisation dynamics and improved robustness. These findings demonstrate that competitive co-evolution provides an effective and practical alternative to static fitness-based evolutionary methods for nurse scheduling, with broader applicability to healthcare scheduling and constraint optimisation problems.…”
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  4. 4
    An extended ID3 decision tree algorithm for spatial data

    An extended ID3 decision tree algorithm for spatial data by Sitanggang, Imas Sukaesih, Yaakob, Razali, Mustapha, Norwati, Nuruddin, Ahmad Ainuddin

    Published 2011
    “…Empirical result demonstrates that the proposed algorithm can be used to join two spatial objects in constructing spatial decision trees on small spatial dataset. …”
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  5. 5
    A hyper-heuristic based strategy for image segmentation using multilevel thresholding

    A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, Fakhrud Din, Shah Khalid, Kamal Zuhairi Zamli, Aftab Alam

    Published 2025
    “…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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  6. 6
    Classification model for hotspot occurrences using spatial decision tree algorithm

    Classification model for hotspot occurrences using spatial decision tree algorithm by Sitanggang, Imas Sukaesih, Yaakob, Razali, Mustapha, Norwati, Nuruddin, Ahmad Ainuddin

    Published 2013
    “…Empirical result demonstrates that the proposed algorithm can be used to join two spatial objects in constructing a spatial decision tree from a spatial dataset. …”
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  7. 7
    Extended spatial decision tree algorithm for classifying hotspot occurrence

    Extended spatial decision tree algorithm for classifying hotspot occurrence by Sitanggang, Imas Sukaesih

    Published 2013
    “…The proposed algorithm uses spatial information gain to choose the best splitting layer from a set of explanatory layers. …”
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  8. 8
    A hyper-heuristic based strategy for image segmentation using multilevel thresholding

    A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, ., Fakhrud, Din, Shah, Khalid, Kamal Z., Zamli, Alam, Aftab

    Published 2025
    “…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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  9. 9
    Clustering Spatial Data Using a Kernel-Based Algorithm

    Clustering Spatial Data Using a Kernel-Based Algorithm by Awan, A. Majid, Md. Sap, Mohd. Noor

    Published 2005
    “…The proposed algorithm can effectively handle noise, outliers and auto-correlation in the spatial data. …”
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  10. 10
    Stock market turning points rule-based prediction / Lersak Photong … [et al.]

    Stock market turning points rule-based prediction / Lersak Photong … [et al.] by Photong, Lersak, Sukprasert, Anupong, Boonlua, Sutana, Ampant, Pravi

    Published 2021
    “…Finally, rule-based optimisation techniques such as Particle Swarm Optimization (PSO), Differential Evolution (DE) and Grey Wolf Optimizer (GWO) were used to minimise the amount of time employed in the stock market turning points prediction. …”
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  11. 11
    Spatial Clustering Algorithm for Time Series Rainfall Data Using X-Means Data Splitting

    Spatial Clustering Algorithm for Time Series Rainfall Data Using X-Means Data Splitting by Ali, Noor Rasidah, Ku Mahamud, Ku Ruhana

    Published 2017
    “…Therefore, a clustering algorithm by introducing data transformation using X-means data splitting is proposed to investigate the spatial homogeneity of time series rainfall data. …”
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  12. 12
    Improving pipelined time stepping algorithm for distributed memory multicomputers

    Improving pipelined time stepping algorithm for distributed memory multicomputers by Ng, Kok Fu, Norhashidah Hj. Mohd. Ali

    Published 2010
    “…Time stepping algorithm with spatial parallelisation is commonly used to solve time dependent partial differential equations. …”
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  13. 13
    Modeling forest fires risk using spatial decision tree

    Modeling forest fires risk using spatial decision tree by Yaakob, Razali, Mustapha, Norwati, Nuruddin, Ahmad Ainuddin, Sitanggang, Imas Sukaesih

    Published 2011
    “…This paper presents our initial work in developing a spatial decision tree using the spatial ID3 algorithm and Spatial Join Index applied in the SCART (Spatial Classification and Regression Trees) algorithm. …”
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  14. 14
    SA-EVPS ALGORITHM FOR DISCRETE SIZE OPTIMIZATION OF THE 582-BAR SPATIAL TRUSS STRUCTURE

    SA-EVPS ALGORITHM FOR DISCRETE SIZE OPTIMIZATION OF THE 582-BAR SPATIAL TRUSS STRUCTURE by Paknahad, M., Hosseini, P., Hakim, S.J.S

    Published 2023
    “…As a well-known and large-scale structure, the 582-bar spatial truss structure was analyzed using the finite element method, and optimization processes were implemented using MATLAB. …”
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  15. 15
    Hybrid Spatial-Artificial Intelligence Approach for Renewable Energy Sources Sites Identification and Integration in Sarawak State

    Hybrid Spatial-Artificial Intelligence Approach for Renewable Energy Sources Sites Identification and Integration in Sarawak State by Far Chen, Jong

    Published 2022
    “…It is followed by identifying RES sites using spatial data and Multi-Criteria Decision Making-Analytical Hierarchy Process (MCDM-AHP) algorithm. …”
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  16. 16
    A decision tree based on spatial relationships for predicting hotspots in peatlands

    A decision tree based on spatial relationships for predicting hotspots in peatlands by Sitanggang, Imas Sukaesih, Yaakob, Razali, Mustapha, Norwati, Nuruddin, Ahmad Ainuddin

    Published 2014
    “…The spatial tree has produces higher accuracy than the non-spatial trees that were created using the ID3 and C4.5 algorithm. …”
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  17. 17
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. …”
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  18. 18
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. …”
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  19. 19
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. …”
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  20. 20
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. …”
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