Showing 121 - 140 results of 142 for search '((elms OR esa) OR ((delsa OR (((abdell OR abel) OR abel) OR aballa)) OR (ella OR bell)))', query time: 0.19s Refine Results
  1. 121

    Empirical comparison of bagging-based ensemble classifiers by Suganthan, P. N., Ye, Ren

    Published 2014
    “…This paper compares empirically four bagging-based ensemble classifiers, namely the ensemble adaptive neuro-fuzzy inference system (ANFIS), the ensemble support vector machine (SVM), the ensemble extreme learning machine (ELM) and the random forest. The comparison of these four ensemble classifiers is novel because it has not been reported in the existing literature. …”
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    Conference Paper
  2. 122
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  5. 125

    A review of reporting quality in case-control studies of pancreatic cancer by MacCarthy, A, MacCarthy, A, Dhiman, P, Dhiman, P, Kirtley, S, Kirtley, S, Logullo, P, Logullo, P, Copsey, B, Copsey, B, Collins, G, Collins, G

    Published 2020
    “…Each study was scored by both a researcher and a statistician using a reporting adherence form based on the STROBE checklist for case-control studies. -- (1) von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies. …”
    Dataset
  6. 126

    Boundary-aware feature propagation for scene segmentation by Ding, Henghui, Jiang, Xudong, Liu, Ai Qun, Thalmann, Nadia Magnenat, Wang, Gang

    Published 2020
    “…The proposed BFP is capable of splitting the feature propagation into a set of semantic groups via building strong connections among the same segment region but weak connections between different segment regions. Without bells and whistles, our approach achieves new state-of-the-art segmentation performance on three challenging semantic segmentation datasets, i.e., PASCAL-Context, CamVid, and Cityscapes.…”
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    Conference Paper
  7. 127

    Toward achieving robust low-level and high-level scene parsing by Shuai, Bing, Ding, Henghui, Liu, Ting, Wang, Gang, Jiang, Xudong

    Published 2020
    “…Extensive experimental studies justify each contribution separately. Without bells and whistles, EFCN achieves state-of-the-arts on segmentation datasets of ADE20K, Pascal Context, SUN-RGBD and Pascal VOC 2012.…”
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    Journal Article
  8. 128

    Real-time facial expression recognition system by Xu, Meng Yao

    Published 2015
    “…To implement the face expression recognition, histogram-of-oriented-gradient (HOG) based object detector, active shape model (ASM) and extreme learning machines (ELM) algorithms are used in this project. The real-time recognition system could locate face regions, extract facial features and recognise basic human expressions that could be considered as an important study or exploration for the future driving safety enhancement.…”
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    Final Year Project (FYP)
  9. 129

    Biosignals-based driver’s awareness/emotion monitoring for future car design by Yao, Xibing

    Published 2016
    “…The aim of this project is to design experiments to obtain and compare the test accuracy of ELM in simulated driving and real driving. As well as to test the performance of artifact removal based on ICA in simulated driving and real driving. …”
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    Final Year Project (FYP)
  10. 130
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    Raindrop removal from single image by Song, Rongzihan

    Published 2019
    “…For faster processing of images for surveillance, an Extreme Learning Machining(ELM) method is also used. It can classify these surveillance images into two parts: degraded images and non-degraded images. …”
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    Thesis
  12. 132

    Effect of contextual information in human action recognition in videos by Koh, Hong Wei

    Published 2016
    “…The first phase of the project is to extract features from the videos using log covariance matrix with Support Vector Machine (SVM) and Extreme Learning Machine (ELM) as the classifiers to discriminate the actions. …”
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    Final Year Project (FYP)
  13. 133

    Ensemble neural networks with input optimization for flood forecasting by Mohd Khairudin, Nazli, Mustapha, Norwati, Mohd Aris, Teh Noranis, Zolkepli, Maslina

    Published 2024
    “…This paper proposed an ensemble of neural networks for long-term flood forecasting that combine the output of backpropagation neural network (BPNN) and extreme learning machine (ELM). The proposed ensemble neural networks model has been applied towards the rainfall data from eight rainfall stations of Kelantan River Basin to forecast the water level of Kuala Krai. …”
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    Article
  14. 134

    Discrete wavelet transform coefficients for emotion recognition from EEG signals by Ser, Wee, Huang, Guang-Bin, Yohanes, Rendi E. J.

    Published 2013
    “…Two classifiers were used: Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Experimental results confirmed that the proposed DWT coefficients method showed improvement of performance compared to previous methods.…”
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    Conference Paper
  15. 135
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    A novel low-velocity impact region identification method for cantilever beams using a support vector machine by Wang, Fengde, Kang, Yongtian, Xiao, Wensheng, Li, Changjiang, Liu, Qi

    Published 2023
    “…Through the comparative study, it is found that the recognition rate of SVM is higher than that of the probabilistic neural network (PNN) and extreme learning machine (ELM) for low-velocity impact area recognition of cantilever beams. …”
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    Journal Article
  17. 137

    Mujeres a la fuga: narrativa del viaje como vehículo de resistencia para las mujeres en tránsito por México = Women on the run: narrative of the journey as a vehicle of resistance... by López, María E., Díaz de León, Alejandra, Castro Sam, Ana Sabina

    Published 2024
    “…Las mujeres, muchas de ellas al cargo de sus hijos, surgen en el relato como sujetos interesantes atravesados por múltiples y cambiantes realidades, con autonomía, agencia y subjetividad política. …”
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    Article
  18. 138

    Universal machine learning classifier using extreme learning machines by Chen, Jinnan

    Published 2019
    “…By using different scales of datasets, we could see the better performance of the proposed classifier and compared with the results of other popular algorithms, so the advantage of the ELM-based universal classifier could be demonstrated. …”
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    Final Year Project (FYP)
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