Abstract
The development of the intelligent system for labor conflict management was carried out with the aim of improving the identification, prevention, and resolution of conflicts in organizational environments through the use of advanced artificial intelligence tools and social network analysis. The system was designed to detect conflict patterns, generate automated solutions, and dynamically adjust its responses using machine learning techniques, natural language processing, and graph theory analysis. The methodology included a detailed analysis of the organizational structure, the application of a survey to assess perceptions and workplace dynamics, and the implementation of a continuous feedback system to enhance the system’s performance. Network analysis enabled the identification of key employees with high social influence and the detection of critical points within the organizational structure, while sentiment analysis and automated responses strengthened the system’s ability to resolve conflicts efficiently. The results showed a significant improvement in conflict detection accuracy (from 60% to 90%), a 30% reduction in the number of reported conflicts, and an increase in employee satisfaction (from 60% to 88%). The improvement in the work environment and the system’s ability to adapt to different organizational settings confirmed the effectiveness and scalability of the implemented solution.
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