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References
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- Eraqi, H. M., Abouelnaga, Y., Saad, M. H., & Moustafa, M. N. “Driver distraction identification with an ensemble of convolutional neural networksâ€. Journal of Advanced Transportation.2019.
- Shahverdy, M., Fathy, M., Berangi, R., & Sabokrou, M. "Driver behavior detection and classification using deep convolutional neural networks". Expert Systems with Applications. 2020.
- Brombacher, P., Masino, J., Frey, M., & Gauterin, F. "Driving event detection and driving style classification using artificial neural networks". Proceedings of the IEEE International Conference on Industrial Technology. 2017.
- Nuswantoro, F. M., Sudarsono, A., & Santoso, T. B. "Abnormal driving detection based on accelerometer and gyroscope sensor on smartphone using artificial neural network (ann) algorithm". IES 2020 - International Electronics Symposium: The Role of Autonomous and Intelligent Systems for Human Life and Comfort. 2020.
- Matousek, M., El-Zohairy, M., Al-Momani, A., Kargl, F., & Bosch, C. "Detecting anomalous driving behavior using neural networks". IEEE Intelligent Vehicles Symposium, Proceedings. 2019.
- Chen, T., & Guestrin, C. "XGBoost : Reliable Large-scale Tree Boosting System". ArXiv. 2016.
- Shi, X., Wong, Y. D., Li, M. Z. F., Palanisamy, C., & Chai, C. “A feature learning approach based on XGBoost for driving assessment and risk predictionâ€. Accident Analysis and Prevention. 2019.
- Lu, Y., Fu, X., Guo, E., & Tang, F. “XGBoost Algorithm-Based Monitoring Model for Urban Driving Stress: Combining Driving Behaviour, Driving Environment, and Route Familiarityâ€. IEEE Access. 2021.
- Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T. Y. “LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systemsâ€. 2017
- Zeng, H., Yang, C., Zhang, H., Wu, Z., Zhang, J., Dai, G., Babiloni, F., Kong, W., & Chuang, L. “A LightGBM-Based EEG Analysis Method for Driver Mental States Classificationâ€. Computational Intelligence and Neuroscience. 2019.
- Gao, X., Luo, H., Wang, Q., Zhao, F., Ye, L., & Zhang, Y. “A human activity recognition algorithm based on stacking denoising autoencoder and lightGBMâ€. Sensors (Switzerland). 2019.
- Choi, S., & Hur, J. “An ensemble learner-based bagging model using past output data for photovoltaic forecastingâ€. Energies. 2020.
- Demir-Kavuk, O., Kamada, M., Akutsu, T., & Knapp, E. W. “Prediction using step-wise L1, L2 regularization and feature selection for small data sets with large number of featuresâ€. BMC Bioinformatics. 2011.
References
Jusuf, A., Nurprasetio, I. P., & Prihutama, A. “Macro data analysis of traffic accidents in Indonesiaâ€. Journal of Engineering and Technological Sciences. 2017.
Eraqi, H. M., Abouelnaga, Y., Saad, M. H., & Moustafa, M. N. “Driver distraction identification with an ensemble of convolutional neural networksâ€. Journal of Advanced Transportation.2019.
Shahverdy, M., Fathy, M., Berangi, R., & Sabokrou, M. "Driver behavior detection and classification using deep convolutional neural networks". Expert Systems with Applications. 2020.
Brombacher, P., Masino, J., Frey, M., & Gauterin, F. "Driving event detection and driving style classification using artificial neural networks". Proceedings of the IEEE International Conference on Industrial Technology. 2017.
Nuswantoro, F. M., Sudarsono, A., & Santoso, T. B. "Abnormal driving detection based on accelerometer and gyroscope sensor on smartphone using artificial neural network (ann) algorithm". IES 2020 - International Electronics Symposium: The Role of Autonomous and Intelligent Systems for Human Life and Comfort. 2020.
Matousek, M., El-Zohairy, M., Al-Momani, A., Kargl, F., & Bosch, C. "Detecting anomalous driving behavior using neural networks". IEEE Intelligent Vehicles Symposium, Proceedings. 2019.
Chen, T., & Guestrin, C. "XGBoost : Reliable Large-scale Tree Boosting System". ArXiv. 2016.
Shi, X., Wong, Y. D., Li, M. Z. F., Palanisamy, C., & Chai, C. “A feature learning approach based on XGBoost for driving assessment and risk predictionâ€. Accident Analysis and Prevention. 2019.
Lu, Y., Fu, X., Guo, E., & Tang, F. “XGBoost Algorithm-Based Monitoring Model for Urban Driving Stress: Combining Driving Behaviour, Driving Environment, and Route Familiarityâ€. IEEE Access. 2021.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T. Y. “LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systemsâ€. 2017
Zeng, H., Yang, C., Zhang, H., Wu, Z., Zhang, J., Dai, G., Babiloni, F., Kong, W., & Chuang, L. “A LightGBM-Based EEG Analysis Method for Driver Mental States Classificationâ€. Computational Intelligence and Neuroscience. 2019.
Gao, X., Luo, H., Wang, Q., Zhao, F., Ye, L., & Zhang, Y. “A human activity recognition algorithm based on stacking denoising autoencoder and lightGBMâ€. Sensors (Switzerland). 2019.
Choi, S., & Hur, J. “An ensemble learner-based bagging model using past output data for photovoltaic forecastingâ€. Energies. 2020.
Demir-Kavuk, O., Kamada, M., Akutsu, T., & Knapp, E. W. “Prediction using step-wise L1, L2 regularization and feature selection for small data sets with large number of featuresâ€. BMC Bioinformatics. 2011.