Research on EIoT Reservation Algorithm Based on Deep Learning

Mononteliza, Jennefer

doi:10.47116/apjcri.2020.09.16

Cited by 2 publications

(4 citation statements)

References 6 publications

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“…According to formula (13), formula (14), and formula (15), the optimized distributed system under the full comparison calculation data distribution model is shown in formula (16):…”

Section: ∑ ∑mentioning

confidence: 99%

“…The DDAPSOLB algorithm obtains the best position of the particles that enable the system to achieve load balancing. Therefore, in the population initialization work in the DDAPSOBS algorithm, only N copies of the code are needed, and the code of the particles can be adjusted by satisfying the formula (14) to achieve the diversity of the initial population. The fitness value corresponding to the DDAPSOBS algorithm is the storage space size of the distributed system corresponding to the current code of the particle.…”

Section: Particle Swarm Optimization Realizes Optimal Storage Data Di...mentioning

confidence: 99%

“…When encountering large data sets, this method will become an NP-hard problem. Based on this research, the scholar put forward the idea of using a simulated annealing algorithm to construct a metaheuristic data distribution algorithm, which can effectively reduce the use of storage space in distributed systems [14][15] [16][17] [18].…”

Section: Introductionmentioning

confidence: 99%

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Particle Swarm Optimization Algorithm for Data Distribution Model Design

Taniar¹

2022

JSER

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This paper studies the data distribution problem of full comparative computing, proposes a data distribution model and related algorithms based on Particle Swarm Optimization (PSO) algorithm, and conducts experiments on the algorithm. The experimental results show that the data distribution scheme given by the data distribution model of the particle swarm optimization algorithm can realize the complete localization of the data files required by the task, and can reduce the use of storage space in the distributed system. In terms of load balancing, the task scheduling scheme given by this model can achieve load balancing among various nodes in a distributed system. In terms of computing time, the model can find data distribution schemes and task scheduling schemes at a faster rate and can better complete the data distribution of full comparison calculations in a distributed system. The data distribution model of the particle swarm optimization algorithm effectively solves the data distribution problem of large-scale full-comparison computing and will promote the research progress of bioinformatics, natural language processing, and other fields.

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“…According to formula (13), formula (14), and formula (15), the optimized distributed system under the full comparison calculation data distribution model is shown in formula (16):…”

Section: ∑ ∑mentioning

confidence: 99%

Section: Particle Swarm Optimization Realizes Optimal Storage Data Di...mentioning

confidence: 99%

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Particle Swarm Optimization Algorithm for Data Distribution Model Design

Taniar¹

2022

JSER

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“…Classifying published text is one of the important ways to analyze data, and improving the efficiency and quality of text usage makes it possible to organize or manage text effectively. Text classification refers to the analysis of the content of the text, and it is determined that the text belongs to any of the given categories [7] [8]. In the early days, people relied on manual classification of texts.…”

Section: Introductionmentioning

confidence: 99%

Text Classification of Mixed Model Based on Deep Learning

Lee

2023

Teh. glas. (Online)

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At present, deep learning has been widely used many fields, but the research on text classification is still relatively few. This paper makes full use of the good learning characteristics of deep learning, proposes a hybrid model based on deep learning, and designs a text classifier based on the hybrid model. This hybrid model uses two common deep learning models, sparse automatic encoder and deep confidence network, to mix. The hybrid model is mainly composed of three parts, the first two layers are constructed by sparse automatic encoder, the middle layer is a three-layer depth Convolutional Neural Network (CNN), and finally Softmax regression is used as the classification layer. In order to test the classification performance of the classifier based on deep learning hybrid model, relevant experiments were conducted on English data set 20Newsgroup and Chinese data set Fudan University Chinese Corpus. In the English text classification experiment, the classifier based on deep learning hybrid model is used to classify, and a high classification accuracy rate is obtained. In order to further verify the superiority of its performance, a comparative experiment with naive Bayes classifier, K-Nearest Neighbor (KNN) classifier and Support Vector Machine (SVM) classifier demonstrates that the classification effect of the classifier based on deep learning hybrid model is better than that of naive Bayes classifier, KNN classifier and support vector machine classifier. In the experiment of Chinese text classification, the Chinese corpus of Fudan University is tested, and a good classification effect is obtained. The influence of different parameter settings on the classification accuracy is discussed.

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Research on EIoT Reservation Algorithm Based on Deep Learning

Cited by 2 publications

References 6 publications

Particle Swarm Optimization Algorithm for Data Distribution Model Design

Particle Swarm Optimization Algorithm for Data Distribution Model Design

Text Classification of Mixed Model Based on Deep Learning

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