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Amazon Employees Resources Access Data Extraction via Clonal Selection Algorithm and Logic Mining Approach.

Nur Ezlin Zamri1, Mohd Asyraf Mansor1, Mohd Shareduwan Mohd Kasihmuddin2

  • 1School of Distance Education, Universiti Sains Malaysia, Penang 11800, Malaysia.

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Summary
This summary is machine-generated.

Amazon.com Inc. developed an AI model using Artificial Neural Networks (ANN) and Clonal Selection Algorithm (CSA) to optimize employee resource access. This new system effectively prioritizes applications, improving data extraction accuracy.

Keywords:
Boolean satisfiabilityclonal selection algorithmdata extractionhuman resources managementlogic mining

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Science

Background:

  • Manual systems for granting employees resource access at Amazon.com Inc. require optimization.
  • Existing data mining and machine learning models present limitations in handling complex resource allocation tasks.

Purpose of the Study:

  • To develop an Artificial Intelligence (AI) model to optimize the manual transaction system for granting employees resource access.
  • To enhance the efficiency and accuracy of data extraction for Amazon Employees Resources Access (AERA) data.

Main Methods:

  • A modified Artificial Neural Network (ANN) was constructed, integrating a Discrete Hopfield Neural Network (DHNN) and Clonal Selection Algorithm (CSA).
  • 3-Satisfiability (3-SAT) logic was employed for data representation and optimization tasks within the AI model.
  • A reverse analysis method (SATRA) was integrated for extracting relationships within AERA entries.

Main Results:

  • The proposed AI model demonstrated superior performance in AERA data extraction compared to existing methods.
  • The integration of CSA enhanced the learning phase of DHNN through cloning and hypermutation.
  • The model successfully identified key factors for prioritizing employee resource applications.

Conclusions:

  • The developed AI model offers a viable alternative machine learning approach for industrial data optimization.
  • The hybrid ANN-CSA-3-SAT model effectively addresses challenges in employee resource access management.
  • The study highlights the potential of integrating information theory and logical representations for efficient data extraction.