Search Results - (( based application drops algorithm ) OR ( its application warping algorithms ))*

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    NN with DTW-FF Coefficients and Pitch Feature for Speaker Recognition by Sudirman, Rubita, Salleh, Sh-Hussain, Salleh, Shaharuddin

    Published 2006
    “…The idea behind this method is due to neural network (NN) limitation where a fixed amount of input nodes is needed for every input class especially in the application of multiple inputs. The new feature processing used the modified version of traditional DTW called as DTW-FF algorithm to fix the input size so that the source and template frames have equal number of frames. …”
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    Neighbour-based on-demand routing algorithms for mobile ad hoc networks by Ejmaa, Ali Mohamed E.

    Published 2017
    “…The dropping decision of the redundant RREQ in the NCPR algorithm completely relies on preset variables, such variables require to be set by the system administrator based on the scenario. …”
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    Thesis
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    Rain streak removal using emboss and spatial-temporal depth filtering technique in video keyframes by Shariah, Sawsan Kamel

    Published 2012
    “…The filter algorithm is based on an enhanced harmonic mean filter algorithm. …”
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    Thesis
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    On determination of input parameters of the mass transfer process by fuzzy approach. by Maan, Normah, Talib, Jamalludin, Arshad, Khairil Annuar, Ahmad, Tahir

    Published 2005
    “…This is an extension of the work done on the mass transfer process of a single drop in single stage RDC column. The algorithm is based on fuzzy approach and the assumptions made in mass transfer process as adopted in previous work are also being used. …”
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    Immune-based technique for undergraduate programmes recommendation / Muhammad Azrill Mohd Zamri by Mohd Zamri, Muhammad Azrill

    Published 2017
    “…The right determination of an undergraduate programme selection by applicants is not an easy task. Some applicants who enrolled for unsuitable programme ended up failed to progress or dropped out from the programme. …”
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    Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm by Reza M.S., Hannan M.A., Mansor M., Ker P.J., Rahman S.A., Jang G., Mahlia T.M.I.

    Published 2025
    “…In addition, to validate the prediction performance of the proposed LSA + LSTM model, extensive comparisons are performed with other popular optimization-based deep learning methods including artificial bee colony (ABC) based LSTM (ABC + LSTM), gravitational search algorithm (GSA) based LSTM (GSA + LSTM), and particle swarm optimization (PSO) based LSTM (PSO + LSTM) model using different error matrices. …”
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