Search Results - (( evolution identification method algorithm ) OR ( spatial visualization learning algorithm ))

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  1. 1

    Voltage stability margin identification using evolution programming learning algorithm / Zamzuhairi Darus by Darus, Zamzuhairi

    Published 2003
    “…This project proposed on an investigation on the voltage stability margin identification using evolution programming learning algorithm. …”
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    Thesis
  2. 2

    Lightweight spatial attentive network for vehicular visual odometry estimation in urban environments by Gadipudi, N., Elamvazuthi, I., Lu, C.-K., Paramasivam, S., Su, S.

    Published 2022
    “…Traditional visual odometry algorithms require the careful fabrication of state-of-the-art building blocks based on geometry. …”
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    Article
  3. 3

    Lightweight spatial attentive network for vehicular visual odometry estimation in urban environments by Gadipudi, N., Elamvazuthi, I., Lu, C.-K., Paramasivam, S., Su, S.

    Published 2022
    “…Traditional visual odometry algorithms require the careful fabrication of state-of-the-art building blocks based on geometry. …”
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    Article
  4. 4

    Hybrid DE and PEM algorithm for identification of small scale Autonomous helicopter model by Legowo, Ari

    Published 2012
    “…The proposed hybrid identification method explored the evolutionary search optimization strength of DE for parameter initialization for PEM identification algorithm…”
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    Monograph
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    Autotuning PID Controllers for Quadplane Hybrid UAV using Differential Evolution Algorithm by Pairan, Mohammad Fahmi, Shamsudin, Syariful Syafiq

    Published 2024
    “…The paper presents a method for autotuning attitude PID for a quadplane UAV using differential evolution (DE), X-Plane simulation, and neural network (NN)-based system identification. …”
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    Article
  7. 7

    Autotuning PID Controllers for Quadplane Hybrid UAV using Differential Evolution Algorithm by Pairan, Mohammad Fahmi, Shamsudin, Syariful Syafiq, Yaakub, Mohd Fauzi

    Published 2024
    “…The paper presents a method for autotuning attitude PID for a quadplane UAV using differential evolution (DE), X-Plane simulation, and neural network (NN)-based system identification. …”
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    Article
  8. 8

    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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    Thesis
  9. 9

    The comparison of interactive 3D visualization between static and animated approaches for learning binary tree topic / Mohd Zulhisam Yaakub by Yaakub, Mohd Zulhisam

    Published 2016
    “…This shows that both 3D visualization methods implemented in this study can increase the student learning achievements and spatial abilities. …”
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    Thesis
  10. 10

    A hybrid spiking neural network model for multivariate data classification and visualization. by Ming, Leong Yii, Teh, Chee Siong, Chen, Chwen Jen

    Published 2011
    “…SOM is one of the most prominent unsupervised learning algorithms. Recently, many extensions for SOM have been proposed for temporal processing. …”
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    Proceeding
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    A review of machine learning in hyperspectral imaging for food safety by Mainak Das, Yeo, Wan Sieng, Agus Saptoro

    Published 2025
    “…To address these limitations, advances in non-destructive monitoring techniques with the implementation of machine learning (ML) algorithms can be alternative solutions. …”
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    Article
  12. 12

    Kernerlized Correlation Filters Parameters Optimization For Enhanced Visual Tracking by Ong, Chor Keat

    Published 2017
    “…A lot of researches have been conducted and many types of state-of-the-art methods and modifications such as sparse representation, online similarity learning, self-expressive, spatial kernel phase correlation filter and others are proposed in order to increase the robustness of the tracking. …”
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    Monograph
  13. 13

    Vibration-based structural damage detection and system identification using wavelet multiresolution analysis / Seyed Alireza Ravanfar by Seyed Alireza, Ravanfar

    Published 2017
    “…In addition, the structural parameters of a system can be accurately estimated through the proposed system identification methods for both cases of linear and nonlinear conditions. …”
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    Thesis
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    Coherent crowd analysis with visual attributes / Nurul Japar by Nurul , Japar

    Published 2022
    “…Therefore, contextual information from visual attributes is essential in learning semantic relations among individuals. …”
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    Thesis
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    Clustering Based on Customers’ Behaviour in Accepting Personal Loan using Unsupervised Machine Learning by Lim, Wai Ping, Goh, Ching Pang

    Published 2023
    “…Focusing on clustering algorithms, the study employs popular methods like K-Means Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Agglomerative Hierarchical Clustering, and Mean Shift Clustering to understand customer characteristics and behaviors. …”
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    Article
  17. 17

    Predicting saliency existence using reduced salient features based on compactness and boundary cues by Nadzri, Nur Zulaikhah

    Published 2020
    “…The selected salient features were trained, tested and compared on 3 learning algorithms which included generalised linear regression, Naïve Bayes, and Support Vector Machine. …”
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    Thesis
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    Real-Time Flood Inundation Map Generation Using Decision Tree Machine Learning Method: Case Study of Kelantan River Basins by Sidek L.M., Basri H., Marufuzzaman M., Deros A.M., Osman S., Hassan F.A.

    Published 2024
    “…To forecast unexpected flood occurrences, faster flood prediction necessitates computational prediction models such as Machine Learning (ML) algorithms, which are extensively utilized around the world. …”
    Book chapter
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