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Construction of a multimodal poetry translation corpus based on adaboost model

APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年第1期9卷

作者： Li, Shan Southwest Jiaotong Univ Sch Foreign Languages Chengdu 610031 Sichuan Peoples R China Chengdu Univ Technol Coll Foreign Languages & Cultures Chengdu 610059 Sichuan Peoples R China

This paper aims to construct a multimodal poetry translation corpus for easy retrieval by poetry translation researchers and enthusiasts. In this paper, we combine the adaboost model and ELM network and propose the ELM-adaboost method to put the existing poetry translation corpus into the ELM network for learning, to obtain several weak predictors, and then use adaboost for classification iteration, and update the weights according to the prediction sequence weights, and then obtain strong predictors, and finally construct a multimodal poetry translation corpus. The search of this corpus shows that, in terms of the ideographic performance of the translations, the ancient poems perform the best, followed by the five-line stanzas, with mean evaluation scores of 83.2 and 80.9, respectively. The seven-line stanzas have the best phonetic performance, with an average rating of 73.2. The rhetoric of five-verse poems was the best, with an average rating of 63.5 marks. The overall translation effect is relatively poor because the meaning is often difficult to account for, or there is a cultural gap in the translation of poems. The multimodal translation corpus based on the adaboost model is a powerful tool for poetry translation research, which provides strong data support for Chinese poetry translation research and is of great significance for Chinese poetry culture.

关键词： adaboost model Predictor Poetry translation Corpus

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Application of Hyperspectral Technology Combined With Bat Algorithm-adaboost model in Field Soil Nutrient Prediction

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IEEE ACCESS 2022年 10卷 100286-100299页

作者： Wang, Huan Zhang, Lixin Zhao, Jiawei Hu, Xue Ma, Xiao Shihezi Univ Coll Mech & Elect Engn Shihezi 832000 Peoples R China Ind Technol Res Inst XPCC Shihezi 832000 Peoples R China

This paper proposes a hyperspectral soil nutrient estimation method based on the bat algorithm (BA)-adaboost model. The spectral reflectance, the first derivative of the reflectance, and the reciprocal logarithm of the reflectance are analyzed based on the 800 field soil samples and their hyperspectral data collected. The first derivative of the reciprocal logarithm of the reflectance and the sensitive band was extracted using the correlation coefficient method, and the correlation of the content of soil organic matter, phosphorus, and potassium was solved. The BA is used to optimize the two core parameters of the adaboost model (i.e., the maximum number of iterations (n) and the weight reduction coefficient (v) of the weak learner), the classification and regression trees(CART) decision tree is selected as the weak regression learner of the model, and the coefficient of determination is used as parameter optimization. Based on the objective function value, a BA-adaboost model was constructed to estimate soil organic matter and phosphorus and potassium contents. The results show that the BA-adaboost combined model can better search for globally optimal parameters. The adaboost model optimized by BA significantly improved accuracy and reliability. Among the three elements, soil organic matter estimation accuracy is the highest, and the coefficient of determination and the root mean square error are 0.867 and 0.151g . kg(-1), respectively. Compared with the model before optimization, the model accuracy and reliability improved by 29.0% and 24.1%, respectively. The results indicate that hyperspectral technology combined with the BA-adaboost model has certain application prospects in field soil nutrient estimation.

关键词： Hyperspectral soil nutrient content machine learning bat algorithm adaboost model

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An integrated approach based on Gaussian noises-based data augmentation method and adaboost model to predict faecal coliforms in rivers with small dataset

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JOURNAL OF HYDROLOGY 2021年 599卷 126510-126510页

作者： El Bilali, Ali Taleb, Abdeslam Bahlaoui, Moulay Abdellah Brouziyne, Youssef Hassan II Univ Casablanca Fac Sci & Tech Mohammedia Casablanca Morocco Mohammed VI Polytech Univ UM6P Int Water Res Inst Ben Guerir Morocco

Machine Learning (ML) techniques can be valuable for modelling the faecal contamination in the rivers to overcome the limitations of the process-based models. However, this approach requires large sufficient data for training and validation processes to avoid the over-fitting problem. This study attempts to overcome the small dataset limitation by relying on the data augmentation techniques. To that end, Adaptive boosting (adaboost) models were trained and integrated into the data augmentation method to generate 600 virtual samples based on 40 original datasets. The results revealed that the proposed method significantly improved the accuracy (RMSE = 0.716ln(Colony Forming Unit (CFU)/100 ml)) and generalization ability of the adaboost model for predicting the faecal coliform in the rivers compared to the baseline model developed only with a small dataset (RMSE = 2.348ln(CFU/100 ml)). However, the study showed that generating and using too many virtual data could deteriorate the generalization ability of the ML model and the optimal virtual datasets are about (337-415) virtual samples. Globally, the results of this study provide new insights to improve the prediction accuracy of the health risk related to the faecal coliforms in raw water used for drinking purposes under a small dataset. The developed method can broaden the application of ML to water resources and environmental sciences when it is impossible to get a large dataset required by ML models.

关键词： Faecal coliform Rivers adaboost model Data augmentation Prediction accuracy Generalization ability

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Non-isothermal spreading and solidification of a molten droplet on solid substrates: An experimental and machine learning-based study

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JOURNAL OF PLASTIC FILM & SHEETING 2025年

作者： Hemmati, Parnian Subramanian, Santoshi Yan, Peiwen Kavehpour, Pirouz Univ Calif Los Angeles Mech & Aerosp Engn Dept 7400 Boelter Hall Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Bioengn Los Angeles CA USA Univ Calif Los Angeles David Geffen Sch Med Dept Neurosurg LosAngeles Los Angeles CA USA

Understanding the dynamics of non-isothermal droplet spreading is critical for various industrial applications, including turbine blade cooling, biodiagnostics, and aerospace manufacturing. This study investigates the behavior of molten droplets impacting cold solid substrates, where simultaneous spreading and solidification occur due to heat exchange. Traditional analytical and numerical methods often fall short in accurately predicting key outcomes such as arrested diameter ( D * ) and contact angle ( theta * ), largely due to oversimplified assumptions that neglect complex dynamics like viscous dissipation and solidification effects. Here, for the first time, we employ machine learning (ML) models to develop a predictive framework for nonisothermal droplet spreading. A comprehensive dataset was generated from 100 controlled experiments, capturing the effects of varying temperature gradients and flow rates on droplet behavior. Ensemble ML models, including Extra Trees Regressor, Random Forest, Gradient Boosting, and adaboost, were trained and evaluated using input features such as temperature, flow rate, and material properties. The adaboost model demonstrated superior performance with R 2 = 0.885 and the lowest error metrics, effectively capturing the complex, nonlinear relationships in the dataset. A comparison with traditional analytical models highlights the ML approach's enhanced predictive accuracy, providing a novel, data-driven tool for applications in surface coating, soldering, and microfabrication.

关键词： Droplet spreading machine learning decision tree models adaboost model contact line dynamics solidification\melting

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Improved pore structure prediction based on a stacking machine learning model for low-permeability reservoir in Tazhong area, Tarim Basin

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2024年 241卷

作者： Wang, Xidong FengTian Yuan, Xinyi Xinjiang Univ Sch Geol & Min Engn Urumqi 830047 Xinjiang Peoples R China Xinjiang Key Lab Geodynam Proc & Metallogen Progno Urumqi Peoples R China

Stacking is an ensemble machine learning method designed to improve overall performance by combining the predictions of multiple base learners. The core idea of Stacking is to use the output of different base learners as input and make final predictions through a meta-learner, allowing the model to benefit from the strengths of various base learners and adapt better to complex data patterns. Predicting pore structures in low permeability is a sophisticated nonlinear modeling issue affected by multiple factors, including sedimentary and diagenetic effects on the structure. An individual model to predict pore structure is not satisfactory. In this paper, we applied a fusion of random forest and adaboost models as base learners, which emerges as the cornerstone of this research, showcasing remarkable versatility and integration of diverse learner strengths. The Silurian system in Tazhong area features a vast expanse of lithological reservoirs with low abundance and high heterogeneity. Capillary pressure plays a crucial role in the distribution of these less permeable reservoirs by pore structure type influence. Aiming to predict the pore structure for the low-permeability reservoir in the field, which is characterized by low abundance and high heterogeneity, the study strategically integrates geological theory into specialized databases and harnesses machine learning methods. The resulting machine learning modeling dataset leverages geological factors and logging response characteristics to strengthen the ensemble machine learning stacking model. This sophisticated model not only achieves exceptional accuracy but also undergoes rigorous validation by analyzing predicted pore structure types alongside production data. The model's average accuracy was 79.4%, which practically hit the test set's accuracy threshold, meaning that the model was in an optimal state. In essence, the proficiency of the stacking model in predicting pore structure types serves as a transformative

关键词： Pore structure Stacking model Specialized dataset adaboost model

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Directed Machine Learning Approaches for Determining Alzheimer Disease 3

Directed Machine Learning Approaches for Determining Alzheim...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Vashist, Ansh Jain, Tarun Sharma, Vijay Shankar Manipal University Jaipur Department of Computer Science and Engineering India Manipal University Jaipur Department of Iot & Intelligent Systems India

ISBN: (纸本)9798350319125

Alzheimer's is a neurogenic disease which progress into neurological disorder that primarily affects cognitive function and memory. It's a Neurodegenerative (ND) disease, characterized by the gradual deterioration of cognitive function, memory, thinking, and behaviour. There are two most common diseases among neurodegenerative diseases: (a) Alzheimer's Disease and (b) Parkinson's Disease. We used 12 classifiers on the given dataset on UC Irvine Machine Learning Repository. The machine learning algorithms were engaged to identify Alzheimer Disease. Our research results showed that the XGB model is the one that shows the best accuracy, of 100%, of all the 12 classifiers. © 2023 IEEE.

关键词： adaboost model Alzheimer's ANN model Binary Decision Tree model Classification Complement Naive Baye model Disease Detection Ensemble Voting model Gaussian Naive Baye model Logistic Regression Machine Learning Multinomial Naive Baye model Random Forest SGD model XG Boost model

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Machine Learning improvements to the accuracy of predicting Specific Language Impairment

Machine Learning improvements to the accuracy of predicting ...

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International Conference on Image Processing, Computer Vision and Machine Learning (ICICML)

作者： Huang, George Cheng, Andrew Gao, Yujie Wellington Coll Int Shanghai Shanghai 200126 Peoples R China Basis Int Sch Shenzhen Shenzhen Guangdong Peoples R China Pierce Coll Puyallup WA 98498 USA

ISBN: (纸本)9781665464680

Specific Language Impairment (SLI) has been a main obstacle of children's growth in the long-run. To diagnose this by applying Machine Learning (ML), we argue that it is crucial to understand which models should use to generate an accurate result. The key to this is the identification of positive between a large number of children, which relates to the big data that the machine has to read. In ML, the accuracy can be affected by the accidental delete of important data, failure of interpreting positive due to the random selection, imbalanced data and incorrect expression of specific features like the probability set. These problems might affect the overall accuracy in machine. Based on these issues, we gave a series of models that might be capable of solving it and train them in ML, then we can determine which model gives the best accuracy. We then consider that the big data might affect our results on accuracy, and therefore we introduced PCA. Generally speaking, we theoretically present the effectiveness of implementing different algorithms and models, which leads to the conclusion that the usefulness of application in models to the problem of diagnosing SLI.

关键词： Specific Language Impairment Imbalanced data AUC (F1 and Recall) Scores Logistic regression model adaboost model Random Forest model BPNN model Principal Component Analysis

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Evaluation and Prediction of Blast Furnace Status Based on Big Data Platform of Ironmaking and Data Mining

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ISIJ INTERNATIONAL 2021年第1期61卷 108-118页

作者： Li, Hongyang Bu, Xiangping Liu, Xiaojie Li, Xin Li, Hongwei Liu, Fulong Lyu, Qing North China Univ Sci & Technol Coll Met & Energy Tangshan 063009 Peoples R China Hangzhou Pailie Technol Co Ltd Hangzhou 310000 Peoples R China HBIS Grp Technol Res Inst Shijiazhuang 050023 Hebei Peoples R China

The applications of big data in the steel industry are widely developed. Ironmaking is a multi-sectoral joint-operation production process that generates massive data constantly. It is required to build the big data platform to efficiently organize and fully utilize the production data of the ironmaking. In this work, we build a comprehensive status evaluation and prediction system for the blast furnace (BF) to achieve the goal of high production, low consumption, high quality and long life of the BF The evaluation system is based on the big data platform and equipped with the factor analysis method, which can define and extract the hidden common factors in the production index of the BF by considering 19 state parameters and can calculate the comprehensive BF status index as well. The prediction system employs the adaboost model which can accurately predict the BF status index 3 hours in advance. Evaluation results show that the proposed BF status index is highly consistent with the actual status of the BF in the selected time period. The coincidence degree between BF status index in different time periods and the actual situation is also verified by factor analysis. Although the evaluation and prediction system demonstrates high accuracy in current production environment, it may still need calibrate and update regularly due to the changing of the BF production in the long run. The online comprehensive evaluation and prediction system for BF can effectively assist operators to optimize the BF operation and maintain the stabilization of BF.

关键词： big data platform of ironmaking factor analysis adaboost model BF comprehensive status

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An adaboost Based Link Planning Scheme in Space-Air-Ground Integrated Networks

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MOBILE NETWORKS & APPLICATIONS 2021年第2期26卷 669-680页

作者： Wang, Feng Jiang, Dingde Qi, Sheng Qiao, Chen Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu 611731 Peoples R China

The Space-air-ground integrated network (SAGIN) has been a valuable architecture for communication support due to its characteristics of wide coverage and low transmission delay. Both low earth orbit (LEO) satellites and UAVs can serve as relay nodes to provide reliable communication services for ground devices. However, the design of data transmission links (DTL) in SAGIN is not easy, considering different accessing layers and resource usage of network segments. Moreover, network topology, available resources, and relative motion need to be analyzed comprehensively for the deployment of DTLs. To address these problems in a heterogeneous SAGIN network, the software defined networking (SDN) architecture is utilized to realize global knowledge acquisition. Then a traffic prediction method based on autoregressive moving average (ARMA) model is utilized to forecast the resource usage of SAGIN segments. After the analysis of link performance, the adaboost algorithm is used to classify network nodes according to their data transmission capacity for DTL deployment. Simulation results show that the proposed adaboost-based link planning scheme is feasible and effective.

关键词： Space-air-ground integrated network Software defined networking Traffic prediction adaboost model Data transmission link

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A Tree-Based Machine Learning Method for Pipeline Leakage Detection

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WATER 2022年第18期14卷 2833-2833页

作者： Shen, Yongxin Cheng, Weiping Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310058 Peoples R China

Leak detection techniques based on Machine Learning (ML) models can assist or even replace manual work in leak detection operations in water distribution systems (WDSs). However, studies on leakage detection based on on-site leak signals are limited compared to studies on lab-scale leak detection. The on-site leak signals have stronger interference and randomness, while leak signals in the laboratory are relatively simpler. To better assist on-site leak detection operations, the present paper develops and compares three ML-based models. For this purpose, many on-site tests were carried out, and tens of thousands of sets of on-site leak detection signals were collected. More than 6000 sets of these signals were marked and the signal features were extracted and analyzed from a statistical point of view. It was found that features such as the main frequency, the spectral roll-off rate, the spectral flatness, and one-dimensional (1-D) Mel Frequency Cepstrum Coefficient (MFCC) could well distinguish the leakage signals from non-leakage signals. After training the decision tree model, the performances of the random forest and adaboost models were thoroughly compared. It was found that the false positive rates of the three models were 9.80%, 8.27% and 7.35%, all lower than 10%. In particular, the adaboost model had the lowest false positive rate of 7.35%. The recall rate of the random forest and adaboost models were 100% and 99.52%.

关键词： water distribution system leak detection machine learning adaboost model random forest model

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