RETRACTED ARTICLE: Optimization model design of cross-border e-commerce transportation path under the background of prevention and control of COVID-19 pneumonia

Abudureheman, Abuduaini; Nilupaer, Aishanjiang

doi:10.1007/s00500-021-05685-6

Cited by 31 publications

(10 citation statements)

References 13 publications

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“…is will help merchants make decisions, achieve overall optimization, and increase the sales of products for a period of time in the future, and timely replenish products for sales. Regarding the possible decline in the future sales of commodities, timely promotion and price reduction are used to reduce commodity inventory and minimize the loss of e-commerce merchants [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Research on the Construction of Crossborder e‐Commerce Logistics Service System Based on Machine Learning Algorithms

Jin-bo

2022

Discrete Dynamics in Nature and Society

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Based on machine learning algorithms, this paper designs a crossborder e-commerce logistics service system recommendation algorithm. First, we introduce the meaning of query recommendation, analyze the mechanism of e-commerce platform shopping search, redesign the query recommendation process on this basis, establish a Markov decision process model for the problem, and solve the optimal recommendation strategy through deep machine learning algorithms. Second, we design a simple calculation example, use Python programming through a simulated shopping environment, give the solution process of the optimal recommendation strategy in the whole process, and prove the feasibility of the algorithm. The sentiment synthesis word vector is used as the input data structure of the text, the convolutional neural network model and the recurrent neural network model in machine learning are independently designed and constructed, and a shunt is proposed. The rule (shunt) realizes the operation of judging the data and inputting the two machine learning networks. The shunt fully realizes the combination of the advantages of the local feature characterization of the convolutional neural network and the timing characteristics of the recurrent neural network and achieves a more efficient and accurate electrical system. Finally, through simulation experiments, a series of data processing work such as data outlier cleaning, sliding window construction features of data variables, and training set and test set division are designed to convert regression prediction problems into classification problems to predict commodity demand. At the same time, it also compared the effect of the time series model, random forest model, GBDT, single Xgboost model, and the model used in this topic and analyzed the reasons for this difference and the application of each model.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Research on the Construction of Crossborder e‐Commerce Logistics Service System Based on Machine Learning Algorithms

Jin-bo

2022

Discrete Dynamics in Nature and Society

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“…Deep Learning (DL) models have been widely utilized in various challenging image processing and classification tasks [22][23][24], including the COVID19 positive cases' early detection and diagnosis [25]. Deep-COVID [26] was almost the pioneer in COVID19 detection using DL models.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Evolving deep convolutional neutral network by hybrid sine–cosine and extreme learning machine for real-time COVID19 diagnosis from X-ray images

Khishe²,

Mohammadi

et al. 2021

Soft Comput

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The COVID19 pandemic globally and significantly has affected the life and health of many communities. The early detection of infected patients is effective in fighting COVID19. Using radiology (X-Ray) images is, perhaps, the fastest way to diagnose the patients. Thereby, deep Convolutional Neural Networks (CNNs) can be considered as applicable tools to diagnose COVID19 positive cases. Due to the complicated architecture of a deep CNN, its real-time training and testing become a challenging problem. This paper proposes using the Extreme Learning Machine (ELM) instead of the last fully connected layer to address this deficiency. However, the parameters’ stochastic tuning of ELM’s supervised section causes the final model unreliability. Therefore, to cope with this problem and maintain network reliability, the sine–cosine algorithm was utilized to tune the ELM’s parameters. The designed network is then benchmarked on the COVID-Xray-5k dataset, and the results are verified by a comparative study with canonical deep CNN, ELM optimized by cuckoo search, ELM optimized by genetic algorithm, and ELM optimized by whale optimization algorithm. The proposed approach outperforms comparative benchmarks with a final accuracy of 98.83% on the COVID-Xray-5k dataset, leading to a relative error reduction of 2.33% compared to a canonical deep CNN. Even more critical, the designed network’s training time is only 0.9421 ms and the overall detection test time for 3100 images is 2.721 s.

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“…Besides, Covid-19 further increased E-Commerce due to lockdown and an increased dependency on online platforms 2017). According to (Valarezo et al, 2018), online merchants, especially in the United States, China, Russia, and the United Kingdom, experienced record growth in E-Commerce during and after the Covid-19 outbreak (Abudureheman & Nilupaer, 2021). As a result, we consider E-Commerce as a part of our economic crisis management system.…”

Section: Introductionmentioning

confidence: 99%

Rise of Cross-Border E-Commerce: A Systematic Literature Review

Derindağ

2022

JATTS

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E-Commerce is trending almost everywhere globally. However, the new trends in E-Commerce further transformed it into Cross-Border trade, leading to extensive benefits at every level of social and commercial life. Considering the rise of Cross-Border online shopping, this study has extensively highlighted how the Cross-Border E-Commerce phenomenon is reflected in the literature. The researcher applied a systematic review approach and selected n= 45 research papers published between 2010 to 2022. Analysis indicated that Cross-Border E-Commerce had taken the globe by storm due to its rapid growth and unprecedented COVID-19 emergence. With the introduction of the ICT and Internet, it has become increasingly prevalent and simple to become a global seller and reap the benefits of international trade by the means of E-Commerce. Besides, the rise of E-Commerce drives new economic growth and triggers innovation among retailers by encouraging them to differentiate their products and provide an engaging user experience. On the other hand, digital payment patterns such as cryptocurrencies further add to the adoption, adding improved payment trends in Cross-Border E-Commerce. Thus, it is concluded that advanced technology, strategic policies, increased demand, and all others played a potential role in the success of Cross-Border E-Commerce today. However, traders face many challenges that need to be resolved to attain greater opportunities. Finally, the researcher emphasized the study's limitations and recommended future studies.

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RETRACTED ARTICLE: Optimization model design of cross-border e-commerce transportation path under the background of prevention and control of COVID-19 pneumonia

Cited by 31 publications

References 13 publications

[Retracted] Research on the Construction of Crossborder e‐Commerce Logistics Service System Based on Machine Learning Algorithms

[Retracted] Research on the Construction of Crossborder e‐Commerce Logistics Service System Based on Machine Learning Algorithms

RETRACTED ARTICLE: Evolving deep convolutional neutral network by hybrid sine–cosine and extreme learning machine for real-time COVID19 diagnosis from X-ray images

Rise of Cross-Border E-Commerce: A Systematic Literature Review

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