Lin Ye

Water Science and Technology · with Ren · 2022

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Lin Ye trusts

Their strongest outbound trust, strongest first, inferred from their citations.

  1. J. Urgen Schmidhuber (in Artificial Intelligence)
  2. Kyoung-jae Kim (in Artificial Intelligence)
  3. Chaopeng Shen (in Environmental Engineering)

Papers from Lin Ye's perspective

Ordered by how strongly the researchers Lin Ye trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Deep learning in neural networks: An overview
  2. A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (Water Resources Research)
  3. Materials discovery and design using machine learning
  4. Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing
  5. Comparison between fuzzy logic and water quality index methods: A case of water quality assessment in Ikare community, Southwestern Nigeria (Environmental Challenges)
  6. Water quality prediction using machine learning methods
  7. Groundwater quality forecasting using machine learning algorithms for irrigation purposes
  8. A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources
  9. Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data. (Water Research)
  10. Machine Learning and Deep Learning Applications-A Vision (Global Transitions Proceedings)

Their papers