Neural Network Model for Automated Task Assignment

In this paper, we present a neural network model for optimizing the allocation of project tasks among performers based on their skills, experience, and other relevant characteristics. The proposed approach relies on deep learning, employing a method of vector representation of skills and tasks within a unified semantic space. Experimental results demonstrate a 27% improvement in the quality of performer selection compared to traditional methods, as well as a 68% reduction in the time required for task allocation.

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Publication Details

Journal
International journal of mathematics and computer science
Published
2025-01-01
DOI
https://doi.org/10.69793/ijmcs/02.2025/khairullaev
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
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article

Neural Network Model for Automated Task Assignment

U. B. Khairullaev, K. S. Sagidullaev, B. M. Kholboev
International journal of mathematics and computer science
Neural Networks and Applications
article

Neural Network Model for Automated Task Assignment

U. B. Khairullaev, K. S. Sagidullaev, B. M. Kholboev
article en

Abstract

In this paper, we present a neural network model for optimizing the allocation of project tasks among performers based on their skills, experience, and other relevant characteristics. The proposed approach relies on deep learning, employing a method of vector representation of skills and tasks within a unified semantic space. Experimental results demonstrate a 27% improvement in the quality of performer selection compared to traditional methods, as well as a 68% reduction in the time required for task allocation.

International journal of mathematics and computer science
Openalex Percentile: Top 97%
Neural Networks and Applications
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Neural Network Model for Automated Task Assignment — U. B. Khairullaev, K. S. Sagidullaev, et al. · International journal of mathematics and computer science (2025) | TGRS Research Map | TGRS