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3D LiDAR Vehicle Perception and Classification Using 3D Machine Learning Algorithm
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Deep reinforcement learning-based driving strategy for avoidance of chain collisions and its safety efficiency analysis in autonomous vehicles
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
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
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.]
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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