E-Commerce Workshop Scheduling Based on Deep Learning and Genetic Algorithm

Wu, Peijian; Yang, Dar-Li

doi:10.2507/ijsimm20-1-co4

Cited by 14 publications

(9 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GRU: GRU is a very effective variant of the LSTM network, which is simpler and more effective than the structure of the LSTM network, so it is also a very streamlined network at present [ 70 ]. Three gate functions are introduced in LSTM: input gate, forget gate, and output gate to control the input value, memory value, and output value [ 71 ]. Moreover, there are only two gates in the GRU model: update gate and reset gate.…”

Section: Introductionmentioning

confidence: 99%

Bus Single-Trip Time Prediction Based on Ensemble Learning

Huang

Lei

Song

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The prediction of bus single-trip time is essential for passenger travel decision-making and bus scheduling. Since many factors could influence bus operations, the accurate prediction of the bus single-trip time faces a great challenge. Moreover, bus single-trip time has obvious nonlinear and seasonal characteristics. Hence, in order to improve the accuracy of bus single-trip time prediction, five prediction algorithms including LSTM (Long Short-term Memory), LR (Linear Regression), KNN (K-Nearest Neighbor), XGBoost (Extreme Gradient Boosting), and GRU (Gate Recurrent Unit) are used and examined as the base models, and three ensemble models are further constructed by using various ensemble methods including Random Forest (bagging), AdaBoost (boosting), and Linear Regression (stacking). A data-driven bus single-trip time prediction framework is then proposed, which consists of three phases including traffic data analysis, feature extraction, and ensemble model prediction. Finally, the data features and the proposed ensembled models are analyzed using real-world datasets that are collected from the Beijing Transportation Operations Coordination Center (TOCC). Through comparing the predicting results, the following conclusions are drawn: (1) the accuracy of predicting by using the three ensemble models constructed is better than the corresponding prediction results by using the five sub-models; (2) the Random Forest ensemble model constructed based on the bagging method has the best prediction accuracy among the three ensemble models; and (3) in terms of the five sub-models, the prediction accuracy of LR is better than that of the other four models.

show abstract

Section: Introductionmentioning

confidence: 99%

Bus Single-Trip Time Prediction Based on Ensemble Learning

Huang

Lei

Song

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…We propose GA-RF model to solve this problem, and use genetic algorithm to optimize the decision tree in random forest to improve the accuracy of comprehensive classification. Genetic algorithm has outstanding global search ability, so it can be perfectly combined with Random Forest to solve classification problems (Wu and Yang, 2021;Huang et al, 2021). The method we put forward is to use genetic algorithm to optimize the search of decision trees in random forest, search for the best combination of decision trees and improve the performance of the model.…”

Section: Predictive Analysis Of the High-quality Basic Assetsmentioning

confidence: 99%

A novel approach to screening patents for securitization: a machine learning-based predictive analysis of high-quality basic asset

Liu,

Shi,

Xie

et al. 2023

View full text Add to dashboard Cite

PurposeThis paper aims to provide a complete analysis framework and prediction method for the construction of the patent securitization (PS) basic asset pool.Design/methodology/approachThis paper proposes an integrated classification method based on genetic algorithm and random forest algorithm. First, comprehensively consider the patent value evaluation model and SME credit evaluation model, determine 17 indicators to measure the patent value and SME credit; Secondly, establish the classification label of high-quality basic assets; Then, genetic algorithm and random forest model are used to predict and screen high-quality basic assets; Finally, the performance of the model is evaluated.FindingsThe machine learning model proposed in this study is mainly used to solve the screening problem of high-quality patents that constitute the underlying asset pool of PS. The empirical research shows that the integrated classification method based on genetic algorithm and random forest has good performance and prediction accuracy, and is superior to the single method that constitutes it.Originality/valueThe main contributions of the article are twofold: firstly, the machine learning model proposed in this article determines the standards for high-quality basic assets; Secondly, this article addresses the screening issue of basic assets in PS.

show abstract

“…Molina et al [ 16 ] showed better performance in stock market price prediction by combining the LSTM model and empirical mode decomposition. In the field of e-commerce, LSTM models can effectively predict the future behavior of customers [ 17 ]. Yan et al [ 18 ] improved the genetic algorithm by using the long short-term memory network LSTM and constructed the fitness function in a new way.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Prediction of Internet Financial Market Based on Deep Learning

Zhang

Jia

Chen

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

P2P lending is an important part of Internet finance, which is popular among users because of its efficiency, low cost, wide range, and ease of operation. The problem of predicting loan defaults is affected by many factors, such as the linear and nonlinear nature of the data itself and time dependence and multiple external factors, which have not been well captured in the previous work. In this paper, we propose a multiattention mechanism to capture the different effects of various time slices and various external factors on the results, introduce ARIMA and LSTM to capture the linear and nonlinear characteristics of the lending data respectively, and establish a Time Series Multiattention Prediction Model (MAT-ALSTM) based on LSTM and ARIMA. This paper uses the Lending Club dataset from the United States to prove that our model is superior to ANN, SVM, LSTM, GRU, and ARIMA models in the prediction effect of MAE, RMSE, and DA.

show abstract

E-Commerce Workshop Scheduling Based on Deep Learning and Genetic Algorithm

Cited by 14 publications

References 21 publications

Bus Single-Trip Time Prediction Based on Ensemble Learning

Bus Single-Trip Time Prediction Based on Ensemble Learning

A novel approach to screening patents for securitization: a machine learning-based predictive analysis of high-quality basic asset

Dynamic Prediction of Internet Financial Market Based on Deep Learning

Contact Info

Product

Resources

About