PREDICTIVE MICROBIAL COMMUNIT MODELING USING A COMBINATION OF PHYLOGENY, GENOTYPING AND MACHINE LEARING ALGORITHMS

• 1. 一种基于多样性指数与时空关系的幂法则标度模型预测微生物群落结构变化的方法 [P] . 中国专利： CN110021350B . 2021.05.14
• 2. 一种基于多样性指数和区域范围关系的幂法则标度模型预测微生物群落结构的方法 [P] . 中国专利： CN110021337B . 2021.06.25
• 3. PREDICTIVE MICROBIAL COMMUNITY MODELING USING A COMBINATION OF PHYLOGENY, GENOTYPING AND MACHINE LEARNING ALGORITHMS [P] . 美国专利： US2016292353A1 . 2016-10-06

  机译： 应用系统学，基因分型和机器学习算法的组合预测微生物群落模型

• 4. TRAINING TREE-BASED MACHINE-LEARNING MODELING ALGORITHMS FOR PREDICTING OUTPUTS AND GENERATING EXPLANATORY DATA [P] . US2021174264A1 . 2021-06-10

  机译： 培训基于树的机器学习建模算法，用于预测输出和产生解释性数据

• 5. Training tree-based machine-learning modeling algorithms for predicting outputs and generating explanatory data [P] . US10963817B2 . 2021-03-30

  机译： 培训基于树的机器学习建模算法，用于预测输出和产生解释性数据

• 1. Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms [J] . Chengyu Xie, Hoang Nguyen, Xuan-Nam Bui, 地学前缘(英文版) . 2021,第003期
• 2. Gully erosion spatial modelling:Role of machine learning algorithms in selection of the best controlling factors and modelling process [J] . Hamid Reza Pourghasemi, Nitheshnirmal Sadhasivam, Narges Kariminejad, 地学前缘(英文版) . 2020,第006期
• 3. GIS-based landslide susceptibility modeling:A comparison between fuzzy multi-criteria and machine learning algorithms [J] . Sk Ajim Ali, Mohammad Ali Ghorbani, Farhana Parvin, 地学前缘(英文版) . 2021,第002期
• 4. Modelling the performance of EPB shield tunnelling using machine and deep learning algorithms [J] . Song-Shun Lin, Shui-Long Shen, Ning Zhang, 地学前缘(英文版) . 2021,第005期
• 5. Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya, India [J] . Kanu Mandal, Sunil Saha, Sujit Mandal 地学前缘(英文版) . 2021,第005期
• 6. Data Mining and Machine Learning Algorithms Using IL28B Genotype and Biochemical Markers Best Predicted Advanced Liver Fibrosis in Chronic Hepatitis C [J] . Hend Ibrahim Shousha, Abubakr Hussein Awad, Dalia Abdelhamid Omran, Japanese journal of infectious diseases . 2018,第1期
• 7. Predicting postmortem interval based on microbial community sequences and machine learning algorithms [J] . Environmental microbiology . 2020,第6期
• 8. Predictive analysis of microbial water quality using machine-learning algorithms [J] . Mohammed, Hadi, Longva, Environmental Research, Engineering and Management . 2018,第1期
• 9. Electricity Price Prediction by Enhanced Combination of Autoregression Moving Average and Kernal Extreme Learing Machine [C] . Sahibzada Muhammad Shuja, Nadeem Javaid, Sajjad Khan, International Conference on Advanced Information Networking and Applications . 2019
• 10. Predicting Credit Default Risk via Statistical Model and Machine Learning Algorithms [D] . Choubey, Anand Mohan. 2018
• 11. Interpretability Versus Accuracy: A Comparison of Machine Learning Models Built Using Different Algorithms Performance Measures and Features to Predict [O] . Daniel L. Weller, Tanzy M. T. Love, Martin Wiedmann 2021
• 12. Algorithm for Implementation of Optimal Vector Combinations in Model Predictive Current Control of Six-Phase Induction Machines [O] . Carlos Romero, Larizza Delorme, Osvaldo Gonzalez, 2021

• 1. 基于组合机器学习算法的软件缺陷预测模型 [C] . FU Yi-Qi ,傅艺绮 ,DONG Wei . 第十四届全国软件与应用学术会议 . 2015
• 2. 一类混合预测模型的研究及其应用——ARIMAX和GRU组合对北京PM2.5含量的预测 [A] . 王俊娜 . 2020