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  1. 1
    Common spatial pattern with feature scaling (FSc-CSP) for motor imagery classification

    Common spatial pattern with feature scaling (FSc-CSP) for motor imagery classification by Prathama, Y.B.H., Shapiai, M.I., Aris, S.A.M., Ibrahim, Z., Jaafar, J., Fauzi, H.

    Published 2017
    “…A spatial filtering algorithm called Common Spatial Pattern (CSP) was developed and known to have excellent performance, especially in motor imagery for BCI application. …”
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  2. 2
    A cognitive mapping approach in real-time haptic rendering interaction for improved spatial learning ability among autistic people / Kesavan Krishnan

    A cognitive mapping approach in real-time haptic rendering interaction for improved spatial learning ability among autistic people / Kesavan Krishnan by Kesavan , Krishnan

    Published 2024
    “…Nevertheless, the use of haptic technology in terms of spatial learning is still not fully utilized, and this weakens autistic people in the process of learning about their surroundings. …”
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  3. 3
    Maximizing deep learning-based energy efficiency in 5G downlink MIMO-NOMA systems by using MLP-CNN.

    Maximizing deep learning-based energy efficiency in 5G downlink MIMO-NOMA systems by using MLP-CNN. by Audah, Kamil, Hussein, Walaa, Noordin, Nor Kamariah, Sali, Aduwati, A.Rasid, Mohd Fadlee

    Published 2024
    “…The framework optimizes the data rate and energy efficiency by addressing the power allocation problems. It can be utilized with multiple convolutional and hidden layers, trained using specific algorithms to solve power allocation problems. …”
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  4. 4
    Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning

    Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning by Safa, Soodabeh

    Published 2016
    “…Moreover, instead of concatenating feature vectors together and send to classifier, sparse coding and dictionary learning methods are used and instead of considering all features as one view (visual feature), K-SVD algorithm that is one of the famous algorithms for sparse representation is optimized and developed to multi-view model.The experimental results prove that the proposed methods has improved accuracy by 53.77% compared to concatenating features and classic K-SVD dictionary learning model as well.…”
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  5. 5
    A Novel Path Prediction Strategy for Tracking Intelligent Travelers

    A Novel Path Prediction Strategy for Tracking Intelligent Travelers by Motlagh, Omid Reza Esmaeili

    Published 2009
    “…The FCM nodes are a novel selection of kinematical factors. Genetic algorithm (GA) is then used to train the FCM to be able to replicate the decisional behaviors of the intelligent traveler. …”
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  6. 6
    Topological mapping and qualitative localization based on K-adjacent union clustering algorithm

    Topological mapping and qualitative localization based on K-adjacent union clustering algorithm by Karasfi, Babak

    Published 2012
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  7. 7
    Restoration of blurred images using geometric and chebichef moments / Ahlad Kumar

    Restoration of blurred images using geometric and chebichef moments / Ahlad Kumar by Ahlad, Kumar

    Published 2016
    “…In the second method, Gaussian blur estimation problem is modelled as regression problem and is solved using Weighted Geometric moments (WGM) and extreme learning machine (ELM). …”
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  8. 8
    Robust Data Fusion Techniques Integrated Machine Learning Models For Estimating Reference Evapotranspiration

    Robust Data Fusion Techniques Integrated Machine Learning Models For Estimating Reference Evapotranspiration by Chia, Min Yan

    Published 2022
    “…As for the NNE, a novel meta-learner based on the stochastic-enabled extreme learning machine integrated with whale optimisation algorithm (WOA-ELM) was developed and used in such an application for the first time. …”
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  9. 9
    Orientation and scale based weights initialization scheme for deep convolutional neural networks

    Orientation and scale based weights initialization scheme for deep convolutional neural networks by Azizi Abdullah, Wong, En Ting

    Published 2020
    “…The filter weights are then automatically learned by the neural network throughout the back- propagation training algorithm. However, most initialization schemes used in the deep convolutional neural networks are mainly to deal with vanishing gradient problems. …”
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  10. 10
    Content-based indexing of low resolution documents

    Content-based indexing of low resolution documents by Md Nor, Danial

    Published 2016
    “…K-means clustering are used for visual features like colour since their spatial distribution give a good image’s global information. …”
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  11. 11
    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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  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
    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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  14. 14
    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
    “…In addition, criteria evaluation and weighting method are most applied to evaluate the small problem containing few criteria. 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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    Conference or Workshop Item
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  15. 15
    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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  16. 16
    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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  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
    Genetic-local hybrid optimizer for solving advance layout problem

    Genetic-local hybrid optimizer for solving advance layout problem by Taha, Imad, Ibrahim Habra, Hind Saleem

    Published 2006
    “…Advance Layout problem(ALP) is used to search for an optimal layout of machines. …”
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