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Machine Learning and Cloud-Based Knowledge Graphs to Recognize Suicidal Mental Tendencies.

Vinit Kumar Gunjan1, Y Vijayalata2, Susmitha Valli2

  • 1Department of Computer Science and Engineering, CMR Institute of Technology, Hyderabad, India.

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This summary is machine-generated.

This study introduces a novel method for optimizing cloud manufacturing knowledge services by incorporating user psychological behavior. The developed system effectively identifies psychological issues and suicidal tendencies using advanced natural language processing and machine learning models.

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Area of Science:

  • Cloud Manufacturing
  • Knowledge Services
  • Psychological Behavior Analysis
  • Artificial Intelligence

Background:

  • Selecting appropriate knowledge services in cloud manufacturing is challenging.
  • User psychological behavior significantly influences service selection and effectiveness.
  • Existing methods lack the integration of psychological insights for optimization.

Purpose of the Study:

  • To propose a cloud manufacturing knowledge service optimization decision method based on users' psychological behavior.
  • To develop a system for identifying psychological issues and suicidal tendencies.
  • To enhance the accuracy and efficiency of psychological counseling knowledge acquisition and danger prediction.

Main Methods:

  • Established an optimal evaluation index system for cloud manufacturing knowledge services.
  • Utilized rough set theory for initial weight assignment and adjusted weights based on user multiattribute preference.
  • Developed a knowledge graph from psychological data, employing Han language processing for word segmentation and Chi-square (CHI) feature selection for classification.
  • Implemented a bidirectional long short-term memory (BiLSTM) model for detecting suicidal tendencies and a text classifier for analyzing user utterances.

Main Results:

  • The proposed system demonstrated superior performance in accuracy, recall rate, and F1 value compared to other standard models in detecting psychological issues.
  • The system effectively answers questions related to psychological counseling and identifies users with suicidal tendencies.
  • Knowledge graph construction and CHI feature selection proved effective for problem classification and keyword generation.

Conclusions:

  • The user psychological behavior-based optimization method significantly improves cloud manufacturing knowledge service selection.
  • The developed system offers a reliable tool for counselors to acquire psychological knowledge and identify at-risk individuals.
  • The integration of advanced NLP and machine learning techniques provides a robust solution for psychological issue detection.