- Wei Jiang, Yuxia Fu, Fabing Lin, Jing Liu, Choujun Zhan, 1449, Neural Computing for Advanced Applications, 2021, 757
- Yu-Shun Mao, Shie-Jue Lee, Chih-Hung Wu, Chun-Liang Hou, Chen-Sen Ouyang, Chih-Feng Liu, “A hybrid deep learning network for forecasting air pollutant concentrations”, Appl Intell, 53, no. 10, 2023, 12792
- Jiayu Yang, Linchang Shi, Jaeyoung Lee, Ingon Ryu, “Spatiotemporal prediction of particulate matter concentration based on traffic and meteorological data”, Transportation Research Part D: Transport and Environment, 127, 2024, 104070
- Aysenur Gilik, Arif Selcuk Ogrenci, Atilla Ozmen, “Air quality prediction using CNN+LSTM-based hybrid deep learning architecture”, Environ Sci Pollut Res, 29, no. 8, 2022, 11920
- Ditsuhi Iskandaryan, Francisco Ramos, Sergio Trilles, “Features Exploration from Datasets Vision in Air Quality Prediction Domain”, Atmosphere, 12, no. 3, 2021, 312
- Srinivas Soumitri Miriyala, Ravikiran Inapakurthi, Kishalay Mitra, Statistical Modeling in Machine Learning, 2023, 307
- Afrah Naeem, Nadeem Javaid, Zeeshan Aslam, Muhammad Imran Nadeem, Kanwal Ahmed, Yazeed Yasin Ghadi, Tahani Jaser Alahmadi, Nivin A. Ghamry, Sayed M. Eldin, “A novel data balancing approach and a deep fractal network with light gradient boosting approach for theft detection in smart grids”, Heliyon, 9, no. 9, 2023, e18928
- Jun Yang, Jingbin Qu, Qiang Mi, Qing Li, “A CNN-LSTM Model for Tailings Dam Risk Prediction”, IEEE Access, 8, 2020, 206491
- Afrah Naeem, Zeeshan Aslam, Tamara Al Shloul, Aqdas Naz, Muhammad Imran Nadeem, Mosleh Hmoud Al-Adhaileh, Yazeed Yasin Ghadi, Heba G. Mohamed, “A Novel Combined DenseNet and Gated Recurrent Unit Approach to Detect Energy Thefts in Smart Grids”, IEEE Access, 11, 2023, 59496
- Ibrahim Demir, Zhongrun Xiang, Bekir Demiray, Muhammed Sit, “WaterBench-Iowa: a large-scale benchmark dataset for data-driven streamflow forecasting”, Earth Syst. Sci. Data, 14, no. 12, 2022, 5605