Open Access
SHS Web Conf.
Volume 139, 2022
The 4th ETLTC International Conference on ICT Integration in Technical Education (ETLTC2022)
Article Number 03002
Number of page(s) 7
Section Topics in Computer Science
Published online 13 May 2022
  1. P. Nduka, “The Causes of Potholes On Roads, Their Effects And Control Methods,” Engineering All: Engineering (Science & Technology), Technical Posts (Diy), And Business Investments In Nigeria, Aug. 20, 2021. (accessed Aug. 27, 2021). [Google Scholar]
  2. H.-W. Wang, C.-H. Chen, D.-Y. Cheng, C.-H. Lin, and C.-C. Lo, “A Real-Time Pothole Detection Approach for Intelligent Transportation System,” Mathematical Problems in Engineering, vol. 2015, pp. 1–7, 2015, doi: 10.1155/2015/869627. [Google Scholar]
  3. S. Yamuna, R. Pavithra, and M. Ranjitha, “Automatic Pothole Detection System using Laser,” International Journal for Research in Applied Science and Engineering Technology, vol. V, no. III, pp. 1226–1231, Mar. 2017, doi: 10.22214/ijraset.2017.3226. [CrossRef] [Google Scholar]
  4. C. C. Aggarwal, Neural networks and deep learning: a textbook. New York: Springer, 2019. [Google Scholar]
  5. Jo, Y., Ryu, S.-K., & Kim, Y.-R. (2016). Pothole Detection Based on the Features of Intensity and Motion. Transportation Research Record, 2595(1), 18–28. [CrossRef] [Google Scholar]
  6. S. Silvister, D. Komandur, S. Kokate, and A. Joshi, “Deep Learning Approach to Detect Potholes in Real-Time using Smartphone,” IEEE Pune Section International Conference (PuneCon), 2019. [Google Scholar]
  7. 1Alfandino Rasyid et al., “Pothole Visual Detection using Machine Learning Method integrated with Internet of Thing Video Streaming Platform *,” 2019 International Electronics Symposium (IES), 2019. [Google Scholar]
  8. R. Sadiq, M. Rodriguez, and H. Mian, “Empirical Models to Predict Disinfection By-Products (DBPs) in Drinking Water: An Updated Review,”, vol. 2, pp. 324–338, Jan. 2019, doi: 10.1016/B978-0-12-409548-9.11193-5. [Google Scholar]
  9. S. Abbasi, M. Hajabdollahi, N. Karimi, and S. Samavi, “Modeling Teacher-Student Techniques in Deep Neural Networks for Knowledge Distillation,” Jul. 2019. Accessed: Oct. 27, 2021. [Online]. [Google Scholar]
  10. C. Xi, X. Zhiqiang, and C. Yuyang, “Introduction to Model Compression Knowledge Distillation,” 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP), Apr. 2021, doi:10.1109/icsp51882.2021.9408881. [Google Scholar]
  11. C.-W. Kuan, W.-H. Chen, and Y.-C. Lin, “Pothole Detection and Avoidance via Deep Learning on Edge Devices,” Jun. 2021, Accessed: Nov. 03, 2021. [Online]. [Google Scholar]
  12. N. Camilleri and T. Gatt, “Detecting road potholes using computer vision techniques,” May 2020, Accessed: Nov. 03, 2021. [Online]. [Google Scholar]
  13. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015, doi: 10.1038/nature14539. [CrossRef] [Google Scholar]
  14. Y. Cheng, D. Wang, P. Zhou, and T. Zhang, “Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges,” IEEE Signal Processing Magazine, vol. 35, no. 1, pp. 126–136, Jan. 2018, doi: 10.1109/msp.2017.2765695. [CrossRef] [Google Scholar]
  15. M.-C. Wu and C.-T. Chiu, “Multi-teacher knowledge distillation for compressed video action recognition based on deep learning,” Journal of Systems Architecture, vol. 103, p. 101695, Feb. 2020, doi: 10.1016/j.sysarc.2019.101695. [CrossRef] [Google Scholar]
  16. H. Ni, J. Shen, and C. Yuan, “Enhanced Knowledge Distillation for Face Recognition,” 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), Dec. 2019, doi: 10.1109/ispa-bdcloud-sustaincomsocialcom48970.2019.00207. [Google Scholar]
  17. J. Yim, D. Joo, J. Bae, and J. Kim, “A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning,” 2017, Accessed: Nov. 04, 2021. [Online]. [Google Scholar]
  18. Z. Allen-Zhu, M. Research, and R. Li, “Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning,” 2020. Accessed: Nov. 04, 2021. [Online]. [Google Scholar]
  19. G. Hinton, O. Vinyals, and J. Dean, “Distilling the Knowledge in a Neural Network,” 2015. Accessed: Nov. 15, 2021. [Online]. [Google Scholar]
  20. V. Gutierraz Cruz, “Global number of connected IoT devices 2015-2025,” Statista, Mar. 2021. [Google Scholar]
  21. E. Onyinyechi, M. Hamada, S. Yusuf, and M. Hassan, “The Role of Linear Discriminant Analysis for Accurate Prediction of Breast Cancer,” Dec. 2021, doi: 10.1109/MCSoC51149.2021.00057. [Google Scholar]
  22. M. Hamada, H. Kakudi, J. Tanimu, P. Robert, and M. Hassan, “Evaluation of Recursive Feature Elimination and LASSO Regularization-based optimized feature selection approaches for cervical cancer prediction,” presented at the 14th IEEE International Symposium on Multi core many core System on chips, Singapore, 2021, doi: 10.1109/MCSoC51149.2021.00056. [Google Scholar]
  23. A. Musa, M. Hamada, F. Aliyu, and M. Hassan, “An Intelligent Plant Dissease Detection System for Smart Hydroponic using Convolutional Neural Network,” presented at the 14th IEEE International Symposium on Multi core many core System on chips, Singapore, 2021, doi:10.1109/MCSoC51149.2021.00058. [Google Scholar]

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