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Updated: Oct 5, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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BACS: blockchain and AutoML-based technology for efficient credit scoring classification.

Fan Yang1, Yanan Qiao1, Yong Qi1

  • 1School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi People's Republic of China.

Annals of Operations Research
|January 31, 2022
PubMed
Summary

We developed BACS, a novel blockchain and automated machine learning credit scoring model. This system efficiently and accurately assesses creditworthiness through automated data processing and secure storage.

Keywords:
Automated machine learningBlockchain technologyClassification modelCredit crisisCredit scoringHyperparameter optimisation

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Area of Science:

  • Computer Science
  • Data Science
  • Financial Technology

Background:

  • Credit scoring is crucial but complex, involving time-consuming data mining.
  • The COVID-19 pandemic highlights the urgent need for efficient credit evaluation methods.

Purpose of the Study:

  • To propose BACS, a blockchain and automated machine learning model for efficient and accurate credit scoring.
  • To automate the credit modeling pipeline, ensuring secure and tamper-proof data storage.

Main Methods:

  • Utilizing blockchain for secure, traceable, and tamper-proof credit data storage.
  • Implementing an automated machine learning pipeline with random forest for feature extraction, selection, modeling, and evaluation.

Main Results:

  • BACS successfully automates the credit scoring process.
  • Experimental results confirm the efficiency and accuracy of the BACS model in credit condition assessment.

Conclusions:

  • BACS offers a secure, efficient, and accurate solution for credit scoring.
  • The integration of blockchain and automated machine learning addresses the challenges in traditional credit modeling.