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1
Framework for pedestrian walking behaviour recognition to minimize road accident
Published 2021“…The results indicate the following: (1) From 262 samples, 66.80% and 48.10% of respondents use mobile phones for calling and chatting, respectively. (2) 263 samples of participants are obtained and analysed, and 90 features are extracted from each sample. (3) 100% classification accuracy are obtained for each class (normal walking, calling, chatting, and running) using the grid optimiser method in machine learning. (4) The precision of classification using Euclidean algorithm for normal walking and calling is 70%. …”
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2
Framework for pedestrian walking behaviour recognition to minimize road accident
Published 2021“…The results indicate the following: (1) From 262 samples, 66.80% and 48.10% of respondents use mobile phones for calling and chatting, respectively. (2) 263 samples of participants are obtained and analysed, and 90 features are extracted from each sample. (3) 100% classification accuracy are obtained for each class (normal walking, calling, chatting, and running) using the grid optimiser method in machine learning. (4) The precision of classification using Euclidean algorithm for normal walking and calling is 70%. …”
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3
Drowsiness detection and alert system using face recognition with Raspberry Pi / Nur Syazwina Mohd Fadzalisham, Nor Adora Endut and Muhammad Nizamuddin Rosli
Published 2023“…Recently, drowsiness detection has garnered significant attention due to its crucial implications in various industries, such as transportation, healthcare, and workplace safety. …”
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4
Drowsiness detection and alert system using face recognition with Raspberry Pi / Nur Syazwina Mohd Fadzalisham, Nor Adora Endut and Muhammad Nizamuddin Rosli
Published 2023“…Recently, drowsiness detection has garnered significant attention due to its crucial implications in various industries, such as transportation, healthcare, and workplace safety. …”
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