相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。
Article
Engineering, Industrial
Kangqian Zheng et al.
Summary: This study proposes a reliable framework for manufacturing data sharing using blockchain technology in cross-enterprise collaborative manufacturing supply chains (CMSC). By establishing a blockchain network system and smart contract mechanisms, the credibility and standardization of manufacturing data are ensured, leading to improved operational efficiency for core enterprises and the creation of a trustworthy social community.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Economics
Wen Zhang et al.
Summary: This study proposes a novel approach called DeepRisk to improve the credit risk prediction of SMEs in supply chain finance (SCF) by fusing enterprise demographic data and financing behavioral data. The experiments on a real SCF dataset demonstrate that the DeepRisk approach outperforms the baseline methods in terms of precision, recall, F1-score, AUC, and economic loss. The fusion of the two different sources of data is found to be crucial for credit risk prediction in SCF.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2022)
Article
Computer Science, Information Systems
Yang Liu et al.
Summary: In this article, the authors propose a privacy-preserving machine learning model called Federated Forest to address the challenges of data islands and data privacy & security. They also develop a secure cross-regional machine learning system based on this model, allowing joint training over different regions' clients without exchanging raw data. A novel prediction algorithm is introduced to reduce communication overhead. Experimental results demonstrate the accuracy and efficiency of the proposed model. Overall, the model is practical, scalable, and extensible for real-life tasks.
IEEE TRANSACTIONS ON BIG DATA
(2022)
Article
Business
Arijit Bhattacharya et al.
Summary: This article focuses on the collaboration and risks between Greensill and Credit Suisse in the supply chain finance sector. While Greensill engaged in high-risk business practices, the senior bank officials of Credit Suisse turned a blind eye to misconduct, leading to a ruptured relationship and significant losses for the bank.
GLOBAL BUSINESS REVIEW
(2022)
Article
Computer Science, Artificial Intelligence
Chen Zhang et al.
Summary: Federated learning is a setup where multiple clients collaborate to solve machine learning problems under the coordination of a central aggregator. It reduces systematic privacy risks and costs through local computing and model transmission. This method ensures data privacy for each device and improves learning efficiency and security.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Rebeen Ali Hamad et al.
Summary: Systems of sensor human activity recognition are increasingly popular, but face challenges due to the complexity of human behaviors. Recurrent neural networks show promising results in sequential learning, but are hindered by slow training and memory consumption. One-dimensional convolutional neural networks offer effective parallel processing of input temporal sequential batches.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Biochemistry & Molecular Biology
Ittai Dayan et al.
Summary: Federated learning, a method for training artificial intelligence algorithms while protecting data privacy, was used to predict future oxygen requirements of symptomatic patients with COVID-19 using data from 20 different institutes globally. The study showed improved predictive accuracy and generalizability, setting the stage for wider applications in healthcare.
Article
Engineering, Electrical & Electronic
Yi Wang et al.
Summary: Smart meters provide detailed electricity consumption data for utilities to identify characteristics of consumers and offer services. A distributed identification method based on federated learning is proposed to address data privacy concerns, showing comparable performance with centralized models on datasets.
IEEE TRANSACTIONS ON SMART GRID
(2021)
Article
Computer Science, Information Systems
Nader Bouacida et al.
Summary: With the increase in regulations protecting users' privacy-sensitive data, access to such data has become more restricted. Federated Learning (FL) allows multiple clients to collaboratively learn a machine learning model without sharing data, but it also introduces potential security implications that may hinder its adoption. It is important to raise awareness of the vulnerabilities in FL systems and address the unique challenges to ensure more robust FL in the future.
Article
Engineering, Industrial
Alexandra Brintrup et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2020)
Article
Engineering, Electrical & Electronic
Tian Li et al.
IEEE SIGNAL PROCESSING MAGAZINE
(2020)
Article
Engineering, Industrial
You Zhu et al.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2019)
Article
Computer Science, Information Systems
Theodora S. Brisimi et al.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2018)
Article
Engineering, Industrial
Nina Yan et al.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2016)
Article
Business, Finance
Lang Zhang et al.
FINANCIAL INNOVATION
(2015)
Article
Operations Research & Management Science
Nina Yan et al.
Article
Computer Science, Artificial Intelligence
Loris Nanni et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2009)
Article
Business, Finance
M Crouhy et al.
JOURNAL OF BANKING & FINANCE
(2000)
Article
Computer Science, Interdisciplinary Applications
D West
COMPUTERS & OPERATIONS RESEARCH
(2000)
