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Progress in Machine Translation Review
Haifeng Wang,Hua Wu,Zhongjun He,Liang Huang,Kenneth Ward Church
Engineering 2022, Volume 18, Issue 11, Pages 143-153 doi: 10.1016/j.eng.2021.03.023
After more than 70 years of evolution, great achievements have been made in machine translation.machine translation (NMT).In this article, we first review the history of machine translation from rule-based machine translationto example-based machine translation and statistical machine translation.We then describe various products and applications of machine translation.
Keywords: Machine translation Neural machine translation Simultaneous translation
Incorporating target language semantic roles into a string-to-tree translation model Article
Chao SU, Yu-hang GUO, He-yan HUANG, Shu-min SHI, Chong FENG
Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 10, Pages 1534-1542 doi: 10.1631/FITEE.1601349
Keywords: Machine translation Semantic role Syntax tree String-to-tree
Frontiers of Medicine 2023, Volume 17, Issue 3, Pages 476-492 doi: 10.1007/s11684-022-0966-6
Keywords: EIF4A1 G-quadruplex hepatocellular carcinoma tRNA-derived small RNA translation initiation
Challenges of human–machine collaboration in risky decision-making
Frontiers of Engineering Management 2022, Volume 9, Issue 1, Pages 89-103 doi: 10.1007/s42524-021-0182-0
Keywords: human–machine collaboration risky decision-making human–machine team and interaction task allocation human–machine relationship
Spatial prediction of soil contamination based on machine learning: a review
Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 8, doi: 10.1007/s11783-023-1693-1
● A review of machine learning (ML) for spatial prediction of soil
Keywords: Soil contamination Machine learning Prediction Spatial distribution
Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning
Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 10, doi: 10.1007/s11783-023-1721-1
● A method based on ATR-FTIR and ML was developed to predict CHNS contents in waste.
Keywords: Elemental composition Infrared spectroscopy Machine learning Moisture interference Solid waste Spectral
State-of-the-art applications of machine learning in the life cycle of solid waste management
Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 4, doi: 10.1007/s11783-023-1644-x
● State-of-the-art applications of machine learning (ML) in solid waste
Keywords: Machine learning (ML) Solid waste (SW) Bibliometrics SW management Energy utilization Life cycle
Luosi WEI, Zongxia JIAO
Frontiers of Mechanical Engineering 2009, Volume 4, Issue 2, Pages 184-191 doi: 10.1007/s11465-009-0034-9
Keywords: machine vision visual location solder paste printing VisionPro
Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method
Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 11, doi: 10.1007/s11783-023-1738-5
● A novel integrated machine learning method to analyze O3
Keywords: Ozone Integrated method Machine learning
Evaluation and prediction of slope stability using machine learning approaches
Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 4, Pages 821-833 doi: 10.1007/s11709-021-0742-8
Keywords: slope stability factor of safety regression machine learning repeated cross-validation
Liquefaction prediction using support vector machine model based on cone penetration data
Pijush SAMUI
Frontiers of Structural and Civil Engineering 2013, Volume 7, Issue 1, Pages 72-82 doi: 10.1007/s11709-013-0185-y
Keywords: earthquake cone penetration test liquefaction support vector machine (SVM) prediction
Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2, Pages 183-197 doi: 10.1007/s11705-021-2073-7
Keywords: machine learning flowsheet simulations constraints exploration
Big data and machine learning: A roadmap towards smart plants
Frontiers of Engineering Management Pages 623-639 doi: 10.1007/s42524-022-0218-0
Keywords: big data machine learning artificial intelligence smart sensor cyber–physical system Industry 4.0
Frontiers of Environmental Science & Engineering 2022, Volume 16, Issue 3, doi: 10.1007/s11783-021-1472-9
• A spectral machine learning approach is proposed for predicting mixed
Keywords: Antibiotic contamination Spectral detection Machine learning
Coupling evaluation for material removal and thermal control on precision milling machine tools
Frontiers of Mechanical Engineering 2022, Volume 17, Issue 1, Pages 12-12 doi: 10.1007/s11465-021-0668-9
Keywords: machine tools cutting energy efficiency thermal stability machining accuracy coupling evaluation
Title Author Date Type Operation
Progress in Machine Translation
Haifeng Wang,Hua Wu,Zhongjun He,Liang Huang,Kenneth Ward Church
Journal Article
Incorporating target language semantic roles into a string-to-tree translation model
Chao SU, Yu-hang GUO, He-yan HUANG, Shu-min SHI, Chong FENG
Journal Article
5′-tiRNA-Gln inhibits hepatocellular carcinoma progression by repressing translation through the interaction
Journal Article
Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning
Journal Article
State-of-the-art applications of machine learning in the life cycle of solid waste management
Journal Article
Research and application of visual location technology for solder paste printing based on machine vision
Luosi WEI, Zongxia JIAO
Journal Article
Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method
Journal Article
Liquefaction prediction using support vector machine model based on cone penetration data
Pijush SAMUI
Journal Article
Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet
Journal Article
A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning
Journal Article