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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

Abstract: Especially in recent years, translation quality has been greatly improved with the emergence of neuralmachine 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    

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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

Abstract: The string-to-tree model is one of the most successful syntax-based statistical machine translation (paper, we propose two methods to use semantic roles to improve the performance of the string-to-tree translationWe then perform string-to-tree machine translation using the newly generated trees.Our methods enable the system to train and choose better translation rules using semantic informationOur experiments showed significant improvements over the state-of-the-art string-to-tree translation

Keywords： Machine translation     Semantic role     Syntax tree     String-to-tree    

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5′-tiRNA-Gln inhibits hepatocellular carcinoma progression by repressing translation through the interaction

Frontiers of Medicine 2023, Volume 17, Issue 3,   Pages 476-492 doi: 10.1007/s11684-022-0966-6

Abstract: binding eukaryotic initiation factor 4A-I (EIF4A1), which unwinds complex RNA secondary structures during translationinitiation, causing the partial inhibition of translation.intramolecular G-quadruplex structure is crucial for 5′-tiRNA-Gln to strongly bind EIF4A1 and repress translationmRNA binding through the intramolecular G-quadruplex structure, and this process partially inhibits translation

Keywords： EIF4A1     G-quadruplex     hepatocellular carcinoma     tRNA-derived small RNA     translation initiation    

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Estimation of optimum design of structural systems via machine learning

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 6,   Pages 1441-1452 doi: 10.1007/s11709-021-0774-0

Abstract: The other used an estimation application that was done via artificial neural networks (ANN) to find out

Keywords： optimization     metaheuristic algorithms     harmony search     structural designs     machine learning     artificialneural networks    

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Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion

Frontiers in Energy doi: 10.1007/s11708-023-0891-7

Abstract: of AI methods in lithium-ion battery health management and in particular analyses the application of machinebranches of AI, to lithium-ion battery state of health (SOH), focusing on the advantages and strengths of neural

Keywords： machine learning     lithium-ion battery     state of health     neural network     artificial intelligence    

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Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques

Amit SHIULY; Debabrata DUTTA; Achintya MONDAL

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 3,   Pages 347-358 doi: 10.1007/s11709-022-0819-z

Abstract: In the present study, a new image-based machine learning method is used to predict concrete compressiveThese include support-vector machine model and various deep convolutional neural network models, namelyThe images and corresponding compressive strength were then used to train machine learning models toOverall, the present findings validated the use of machine learning models as an efficient means of estimating

Keywords： support vector machine     deep convolutional neural network     microscope     digital image     curing period    

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Machine vision-based automatic fruit quality detection and grading

Frontiers of Agricultural Science and Engineering doi: 10.15302/J-FASE-2023532

Abstract:

● A machine vision-based prototype system was developed for fruit grading

Keywords： Computer and machine vision     convolution neural network     deep learning     defective fruit detection     fruit    

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Damage assessment and diagnosis of hydraulic concrete structures using optimization-based machine learning

Frontiers of Structural and Civil Engineering   Pages 1281-1294 doi: 10.1007/s11709-023-0975-9

Abstract: assessment method for concrete structures is established using an artificial bee colony backpropagation neural

Keywords： hydraulic structure     curvature mode     damage detection     artifical neural network     artificial bee colony    

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Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 2,   Pages 284-305 doi: 10.1007/s11709-022-0901-6

Abstract: In the machine learning (ML) approach to estimating the CS of FRSCC, the current research gaps includeArtificial neural network, random forest, and categorical gradient boosting (CatBoost) models were optimized

Keywords： compressive strength     self-compacting concrete     artificial neural network     decision tree     CatBoost    

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Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural networkand support vector machine

Ali Reza GHANIZADEH, Hakime ABBASLOU, Amir Tavana AMLASHI, Pourya ALIDOUST

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 1,   Pages 215-239 doi: 10.1007/s11709-018-0489-z

Abstract: This paper aims to explore two machine learning algorithms including artificial neural network (ANN)and support vector machine (SVM) to predict the compressive strength of bentonite/sepiolite plastic concretes

Keywords： bentonite/sepiolite plastic concrete     compressive strength     artificial neural network     support vector machine    

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Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 4,   Pages 523-535 doi: 10.1007/s11705-021-2083-5

Abstract: descriptors based on understanding dissolution behavior to establish two solubility prediction models by machineconsidered in the prediction models, which were constructed by random decision forests and artificial neural

Keywords： solubility prediction     machine learning     artificial neural network     random decision forests    

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Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2,   Pages 520-536 doi: 10.1007/s11709-021-0689-9

Abstract: Two artificial-intelligence-based models including artificial neural networks and support vector machinessupport vector machines in predicting the strength of the investigated soils compared with artificial neuralThe type of kernel function used in support vector machine models contributed positively to the performance

Keywords： unconfined compressive strength     artificial neural network     support vector machine     predictive models     regression    

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A comprehensive review and analysis of solar forecasting techniques

Pardeep SINGLA, Manoj DUHAN, Sumit SAROHA

Frontiers in Energy 2022, Volume 16, Issue 2,   Pages 187-223 doi: 10.1007/s11708-021-0722-7

Abstract: In the last two decades, renewable energy has been paid immeasurable attention to toward the attainment of electricity requirements for domestic, industrial, and agriculture sectors. Solar forecasting plays a vital role in smooth operation, scheduling, and balancing of electricity production by standalone PV plants as well as grid interconnected solar PV plants. Numerous models and techniques have been developed in short, mid and long-term solar forecasting. This paper analyzes some of the potential solar forecasting models based on various methodologies discussed in literature, by mainly focusing on investigating the influence of meteorological variables, time horizon, climatic zone, pre-processing techniques, air pollution, and sample size on the complexity and accuracy of the model. To make the paper reader-friendly, it presents all-important parameters and findings of the models revealed from different studies in a tabular mode having the year of publication, time resolution, input parameters, forecasted parameters, error metrics, and performance. The literature studied showed that ANN-based models outperform the others due to their nonlinear complex problem-solving capabilities. Their accuracy can be further improved by hybridization of the two models or by performing pre-processing on the input data. Besides, it also discusses the diverse key constituents that affect the accuracy of a model. It has been observed that the proper selection of training and testing period along with the correlated dependent variables also enhances the accuracy of the model.

Keywords： forecasting techniques     hybrid models     neural network     solar forecasting     error metric     support vector machine    

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Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges Review

Xinbo Qi, Guofeng Chen, Yong Li, Xuan Cheng, Changpeng Li

Engineering 2019, Volume 5, Issue 4,   Pages 721-729 doi: 10.1016/j.eng.2019.04.012

Abstract: Today, the machine learning (ML) method has been demonstrated to be a valid way to perform complex patternAmong ML algorithms, the neural network (NN) is the most widely used model due to the large dataset that

Keywords： Additive manufacturing     3D printing     Neural network     Machine learning     Algorithm    

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Presentation of machine learning methods to determine the most important factors affecting road traffic

Hamid MIRZAHOSSEIN; Milad SASHURPOUR; Seyed Mohsen HOSSEINIAN; Vahid Najafi Moghaddam GILANI

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 5,   Pages 657-666 doi: 10.1007/s11709-022-0827-z

Abstract: For more accurate prediction, Multi-Layer Perceptron (MLP) and Radius Basis Function (RBF) neural networks

Keywords： safety     rural accidents     multiple logistic regression     artificial neural networks    

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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

Estimation of optimum design of structural systems via machine learning

Journal Article

Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion

Journal Article

Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques

Amit SHIULY; Debabrata DUTTA; Achintya MONDAL

Journal Article

Machine vision-based automatic fruit quality detection and grading

Journal Article

Damage assessment and diagnosis of hydraulic concrete structures using optimization-based machine learning

Journal Article

Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting

Journal Article

Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural networkand support vector machine

Ali Reza GHANIZADEH, Hakime ABBASLOU, Amir Tavana AMLASHI, Pourya ALIDOUST

Journal Article

Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

Journal Article

Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

Journal Article

A comprehensive review and analysis of solar forecasting techniques

Pardeep SINGLA, Manoj DUHAN, Sumit SAROHA

Journal Article

Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges

Xinbo Qi, Guofeng Chen, Yong Li, Xuan Cheng, Changpeng Li

Journal Article

Presentation of machine learning methods to determine the most important factors affecting road traffic

Hamid MIRZAHOSSEIN; Milad SASHURPOUR; Seyed Mohsen HOSSEINIAN; Vahid Najafi Moghaddam GILANI

Journal Article