Search Results - (( structures perceptions learning algorithm ) OR ( java application stemming algorithm ))*

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    Deep reinforcement learning-based driving strategy for avoidance of chain collisions and its safety efficiency analysis in autonomous vehicles by Abu Jafar, Md Muzahid, Syafiq Fauzi, Kamarulzaman, Rahman, Md. Arafatur, Alenezi, Ali H.

    Published 2022
    “…To address this, A reward function is being developed to deal with the challenge of multiple vehicle collision avoidance. A perception network structure based on formation and on actor-critic methodologies is employed to enhance the decision-making process. …”
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    Deep reinforcement learning based driving strategy for avoidance of chain collisions and its safety efficiency analysis in autonomous vehicles by Abu Jafar, Md Muzahid, Syafiq Fauzi, Kamarulzaman, Rahman, Md. Arafatur, Alenezi, Ali H.

    Published 2022
    “…To address this, A reward function is being developed to deal with the challenge of multiple vehicle collision avoidance. A perception network structure based on formation and on actor-critic methodologies is employed to enhance the decision-making process. …”
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    Grammar-based prosody modification for explicit control Malay language storytelling speech synthesis / Muhammad Izzad Ramli by Ramli, Muhammad Izzad

    Published 2018
    “…Based on the prosody analysis, a grammar-based prosody modification rules are proposed by integrating grammatical structure. Consequently, new rules and algorithm that is MustFront rule, limitation rule, and two-steps pitch contour algorithm are introduced to increase the synthesized speech quality. …”
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    Application of artificial neural network in discriminating the agarwood oil quality using significant chemical compounds / Mohd Hezri Fazalul Rahiman … [et al.] by Rahiman, Mohd Hezri Fazalul, Ismail, Nurlaila, Taib, Mohd Nasir, Mohd Ali, Nor Azah, Tajuddin, Saiful Nizam

    Published 2014
    “…Data Processing - ANN Application ( Data pre-processing using Z-score, ANN design structure/architecture - parameter optimisation, training and testing the algorithm) Result & Discussion: ANN parameter optimisation - final error for learning rate, momentum rate and hidden layer size ANN final design parameter - Nodes in input layer: 7, Nodes in hidden layer size: 2, Output layer size: 1, learning rate: 0.9, Momentum rate: 0.7, Error goal: 0.01, Epochs: 100 ANN prediction: high accuracy for training and testing prediction (refer to the figure in poster) Patent & List of contributions: 1. …”
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