<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The arrival of the big data era makes the amount of data explosive growth, which puts forward new challenges and demands for computer network technology, and the integration of big data and network technology has become an important trend. This paper uses the optimization strategy and the elimination mechanism of the genetic algorithm to optimize the inertia weight and particle position speed updating mechanism of the particle swarm algorithm and combines the searching method of the Tennessee whisker algorithm with the sharing mechanism of the particle swarm algorithm to achieve the optimal data searching ability. Finally, the improved artificial intelligence algorithm and MapReduce are combined to improve the performance of the computer neural network algorithm in big data processing. The average data redundancy rate of this paper’s algorithm for big data processing is only 1.18%, and the resource integration checking rate always exceeds 85%, according to simulation experiments. In addition, the algorithm also shows good performance in practical applications, and it can achieve accurate classification of big data labels in big data label classification tasks while maintaining a low energy overhead. Meanwhile, it can accurately recognize electronic medical record data in large medical databases. Big data processing can benefit greatly from the proposed neural network algorithm in this paper.</p>
</abstract>

The arrival of the big data era makes the amount of data explosive growth, which puts forward new challenges and demands for computer network technology, and the integration of big data and network technology has become an important trend. This paper uses the optimization strategy and the elimination mechanism of the genetic algorithm to optimize the inertia weight and particle position speed updating mechanism of the particle swarm algorithm and combines the searching method of the Tennessee whisker algorithm with the sharing mechanism of the particle swarm algorithm to achieve the optimal data searching ability. Finally, the improved artificial intelligence algorithm and MapReduce are combined to improve the performance of the computer neural network algorithm in big data processing. The average data redundancy rate of this paper’s algorithm for big data processing is only 1.18%, and the resource integration checking rate always exceeds 85%, according to simulation experiments. In addition, the algorithm also shows good performance in practical applications, and it can achieve accurate classification of big data labels in big data label classification tasks while maintaining a low energy overhead. Meanwhile, it can accurately recognize electronic medical record data in large medical databases. Big data processing can benefit greatly from the proposed neural network algorithm in this paper.

Application of Artificial Intelligence in Computer Neural Network Algorithm Technology in the Age of Big Data

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The arrival of the big data era makes the amount of data explosive growth, which puts forward new challenges and demands for computer network technology, and...

Application of Artificial Intelligence in Computer Neural Network Algorithm Technology in the Age of Big Data

Pubblicato online: 05 ago 2024

Pagine: -

Ricevuto: 17 apr 2024

Accettato: 02 lug 2024

DOI: https://doi.org/10.2478/amns-2024-2164

Parole chiave
Neural network algorithm, Particle swarm algorithm, Tenno whisker algorithm, MapReduce, Big data processing

© 2024 Sheng Zhou, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Application of Artificial Intelligence in Computer Neural Network Algorithm Technology in the Age of Big Data

Pubblicato online: 05 ago 2024

Pagine: -

Ricevuto: 17 apr 2024

Accettato: 02 lug 2024

DOI: https://doi.org/10.2478/amns-2024-2164

Parole chiaveNeural network algorithm, Particle swarm algorithm, Tenno whisker algorithm, MapReduce, Big data processing

© 2024 Sheng Zhou, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Neural network algorithm, Particle swarm algorithm, Tenno whisker algorithm, MapReduce, Big data processing