Human-Computer Interaction Data Scheduling Algorithm Based on Artificial Intelligence

• Published in: 2024 4th International Conference on Neural Networks, Information and Communication (NNICE) pp. 1599 - 1603
• Main Authors: An, Ning, Ibrahim, Huda, Ahmad, Mazida
• Format: Conference Proceeding
• Language: English
• Published: IEEE 19.01.2024
• Subjects: Artificial Intelligence; Data Scheduling; Heuristic algorithms; Human computer interaction; Machine learning algorithms; Optimal scheduling; Prediction algorithms; Scheduling; Scheduling Algorithm; Scheduling algorithms
• DOI: 10.1109/NNICE61279.2024.10499143

Traditional scheduling algorithms have certain limitations when faced with complex tasks and dynamically changing resource environments, making it difficult to achieve efficient task allocation and resource scheduling. This paper proposes a human-computer interaction data scheduling algorithm based on artificial intelligence, and elaborates its research methods. The algorithm uses machine learning and intelligent optimization technology to learn and optimize resource allocation strategies through the analysis of historical data and real-time data, improving resource utilization and scheduling accuracy. The algorithm also has the characteristics of adaptive adjustment and decision-making optimization, and can dynamically adjust according to task characteristics and resource status to find the optimal scheduling strategy. Through the comparison and experimental evaluation of the human-computer interaction data scheduling algorithm based on artificial intelligence and the traditional scheduling algorithm, this paper obtains the research results, and the mean square error of the algorithm in this paper is kept between 0.06-0.08. In terms of resource utilization, the algorithm in this paper shows obvious advantages, which can accurately predict the resource requirements of tasks and avoid resource waste and idleness. In terms of scheduling accuracy, this algorithm can better match tasks and resources and improve task execution efficiency and response time. In terms of mean square error, the algorithm in this paper reduces prediction errors and improves system stability and performance by establishing task requirements and resource allocation models.