Hybrid data mining and data-driven algorithms for a green logistics transportation network in the post-COVID era: A case study in the USA

Abbasi, Sina; Mousavi, Seyedeh Saeideh; Farbod, Ebrahim; Yousefi Sorkhi, Mohammad; Parvin, Mohammad

doi:10.1016/j.sasc.2024.200156

Systems and Soft Computing

2024

DOI: 10.1016/j.sasc.2024.200156

|View full text |Cite

Hybrid data mining and data-driven algorithms for a green logistics transportation network in the post-COVID era: A case study in the USA

Sina Abbasi,

Seyedeh Saeideh Mousavi,

Ebrahim Farbod

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Cross-functional group decision making with heterogeneous cooperation for digital transformation in supply chain resilience

Tang,

Liao

2024

Applied Soft Computing

View full text Add to dashboard Cite

Cross-functional group decision making with heterogeneous cooperation for digital transformation in supply chain resilience

Tang,

Liao

2024

Applied Soft Computing

View full text Add to dashboard Cite

Sustainable optimisation of SMEs closed-loop supply chain for lithium battery manufacturing

Wang,

Liu,

Huang

et al. 2024

International Journal of Systems Science: Operations & Logi

View full text Add to dashboard Cite

Decomposition and Symmetric Kernel Deep Neural Network Fuzzy Support Vector Machine

El Moutaouakil,

Roudani,

Ouhmid

et al. 2024

Symmetry

View full text Add to dashboard Cite

Algorithms involving kernel functions, such as support vector machine (SVM), have attracted huge attention within the artificial learning communities. The performance of these algorithms is greatly influenced by outliers and the choice of kernel functions. This paper introduces a new version of SVM named Deep Decomposition Neural Network Fuzzy SVM (DDNN-FSVM). To this end, we consider an auto-encoder (AE) deep neural network with three layers: input, hidden, and output. Unusually, the AE’s hidden layer comprises a number of neurons greater than the dimension of the input samples, which guarantees linear data separation. The encoder operator is then introduced into the FSVM’s dual to map the training samples to high-dimension spaces. To learn the support vectors and autoencoder parameters, we introduce the loss function and regularization terms in the FSVM dual. To learn from large-scale data, we decompose the resulting model into three small-dimensional submodels using Lagrangian decomposition. To solve the resulting problems, we use SMO, ISDA, and SCG for optimization problems involving large-scale data. We demonstrate that the optimal values of the three submodels solved in parallel provide a good lower bound for the optimal value of the initial model. In addition, thanks to its use of fuzzy weights, DDNN-FSVM is resistant to outliers. Moreover, DDNN-FSVM simultaneously learns the appropriate kernel function and separation path. We tested DDNN-FSVM on several well-known digital and image datasets and compared it to well-known classifiers on the basis of accuracy, precision, f-measure, g-means, and recall. On average, DDNN-FSVM improved on the performance of the classic FSVM across all datasets and outperformed several well-known classifiers.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Hybrid data mining and data-driven algorithms for a green logistics transportation network in the post-COVID era: A case study in the USA

Cited by 3 publications

References 54 publications

Cross-functional group decision making with heterogeneous cooperation for digital transformation in supply chain resilience

Cross-functional group decision making with heterogeneous cooperation for digital transformation in supply chain resilience

Sustainable optimisation of SMEs closed-loop supply chain for lithium battery manufacturing

Decomposition and Symmetric Kernel Deep Neural Network Fuzzy Support Vector Machine

Contact Info

Product

Resources

About