215 citations to 10.1109/ACCESS.2019.2921578 (Crossref Cited-By Service)
  1. Ramya Anasseriyil Viswambaran, Gang Chen, Bing Xue, Mohammad Nekooei, 2020 IEEE Congress on Evolutionary Computation (CEC), 2020, 1  crossref
  2. Liyuan Deng, Xiaobo Chang, Peng Wang, “Daily Water Demand Prediction Driven by Multi-source Data”, Procedia Computer Science, 208, 2022, 128  crossref
  3. Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor, “Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting”, Sensors, 21, no. 23, 2021, 8009  crossref
  4. Bo Zhang, Yi Rong, Ruihan Yong, Dongming Qin, Maozhen Li, Guojian Zou, Jianguo Pan, “Deep learning for air pollutant concentration prediction: A review”, Atmospheric Environment, 290, 2022, 119347  crossref
  5. Snehlata Beriwal, A. John, 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2021, 1680  crossref
  6. Lourdes Montalvo, David Fosca, Diego Paredes, Monica Abarca, Carlos Saito, Edwin Villanueva, “An Air Quality Monitoring and Forecasting System for Lima City With Low-Cost Sensors and Artificial Intelligence Models”, Front. Sustain. Cities, 4, 2022, 849762  crossref
  7. Yi Liu, Jiangtian Nie, Xuandi Li, Syed Hassan Ahmed, Wei Yang Bryan Lim, Chunyan Miao, “Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework With UAV Swarms”, IEEE Internet Things J., 8, no. 12, 2021, 9827  crossref
  8. Tao Yang, Xia Yu, Ning Ma, Yuhang Zhao, Hongru Li, “A novel Domain Adaptive Deep Recurrent Network for multivariate time series prediction”, Engineering Applications of Artificial Intelligence, 106, 2021, 104498  crossref
  9. Tao Wang, Sixuan Li, Wenyong Li, Quan Yuan, Jun Chen, Xiang Tang, “A Short-Term Parking Demand Prediction Framework Integrating Overall and Internal Information”, Sustainability, 15, no. 9, 2023, 7096  crossref
  10. Xue Li, Chiaki Ono, Noriko Warita, Tomoka Shoji, Takashi Nakagawa, Hitomi Usukura, Zhiqian Yu, Yuta Takahashi, Kei Ichiji, Norihiro Sugita, Natsuko Kobayashi, Saya Kikuchi, Ryoko Kimura, Yumiko Hamaie, Mizuki Hino, Yasuto Kunii, Keiko Murakami, Mami Ishikuro, Taku Obara, Tomohiro Nakamura, Fuji Nagami, Takako Takai, Soichi Ogishima, Junichi Sugawara, Tetsuro Hoshiai, Masatoshi Saito, Gen Tamiya, Nobuo Fuse, Susumu Fujii, Masaharu Nakayama, Shinichi Kuriyama, Masayuki Yamamoto, Nobuo Yaegashi, Noriyasu Homma, Hiroaki Tomita, “Comprehensive evaluation of machine learning algorithms for predicting sleep–wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate variability”, Front. Psychiatry, 14, 2023, 1104222  crossref
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