26 citations to 10.1007/978-3-319-52920-2_7 (Crossref Cited-By Service)
  1. Dapeng Dong, Zili Li, “Smartphone Sensing of Road Surface Condition and Defect Detection”, Sensors, 21, no. 16, 2021, 5433  crossref
  2. Adrian Ostermann, Yann Fabel, Kim Ouan, Hyein Koo, “Forecasting Charging Point Occupancy Using Supervised Learning Algorithms”, Energies, 15, no. 9, 2022, 3409  crossref
  3. Xiao Ling, Yueqin Zhu, Dongping Ming, Yangyang Chen, Liang Zhang, Tongyao Du, “Feature Engineering of Geohazard Susceptibility Analysis Based on the Random Forest Algorithm: Taking Tianshui City, Gansu Province, as an Example”, Remote Sensing, 14, no. 22, 2022, 5658  crossref
  4. Boyarkin Denis, Krupenev Dmitriy, Iakubovskiy Dmitriy, Sidorov Denis, 2017 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON), 2017, 201  crossref
  5. Yuan Zhong, Hongyu Yang, Yanci Zhang, Ping Li, Cheng Ren, “Long short-term memory self-adapting online random forests for evolving data stream regression”, Neurocomputing, 457, 2021, 265  crossref
  6. Jordi Pascual-Fontanilles, Aida Valls, Antonio Moreno, Pedro Romero-Aroca, “Continuous Dynamic Update of Fuzzy Random Forests”, Int J Comput Intell Syst, 15, no. 1, 2022, 74  crossref
  7. N. Tomin, A. Zhukov, V. Kurbatsky, D. Sidorov, M. Negnevitsky, 2017 IEEE Manchester PowerTech, 2017, 1  crossref
  8. Qinkai Han, Sai Ma, Tianyang Wang, Fulei Chu, “Kernel density estimation model for wind speed probability distribution with applicability to wind energy assessment in China”, Renewable and Sustainable Energy Reviews, 115, 2019, 109387  crossref
  9. António Couto, Paula Costa, Teresa Simões, “Identification of Extreme Wind Events Using a Weather Type Classification”, Energies, 14, no. 13, 2021, 3944  crossref
  10. Muzaffer Can Iban, Erman Şentürk, “Machine learning regression models for prediction of multiple ionospheric parameters”, Advances in Space Research, 69, no. 3, 2022, 1319  crossref
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