Comprehensive Survey of Big Data Mining Approaches in Cloud Systems

Ageed, Zainab Salih; Zeebaree, Subhi R. M.; Sadeeq, Mohammed Mohammed; Kak, Shakir Fattah; Yahia, Hazha Saeed; Mahmood, Mayyadah Ramiz; Ibrahim, Ibrahim Mahmood

doi:10.48161/qaj.v1n2a46

Cited by 98 publications

(44 citation statements)

References 39 publications

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“…Figure 3 shows the structure model of fuzzy neural network. The back-end network is used to generate fuzzy rules, and the front-end network is used to match the generated fuzzy rules with each layer [ 30 ].…”

Section: Construction Of Sports Injury Estimation Model Based On Fuzzy Neural Networkmentioning

confidence: 99%

[Retracted] Analysis of Sports Injury Estimation Model Based on Mutation Fuzzy Neural Network

Wang

Yang

2021

Computational Intelligence and Neuroscience

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In recent years, with the gradual development of sports, the competition between athletes is becoming more and more fierce. The long training time and heavy body load of athletes lead to the increase of the incidence of sports injury, and the evaluation and analysis of athletes’ sports injury need a lot of manpower and material resources. In order to improve the calculation efficiency of sports injury estimation results and save the cost of estimation and analysis, we propose a sports injury estimation model based on the algorithm model of mutation fuzzy neural network. The sports injury model constructed in this paper can not only systematically evaluate and analyze the degree of sports injury of athletes, but also improve the accuracy and efficiency; at the same time, it has universality for the evaluation and analysis of the degree of sports injury. The construction of this model provides the theoretical basis of big data algorithm for the prevention of sports injury and the application of mutation fuzzy neural network in the field of sports.

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Section: Construction Of Sports Injury Estimation Model Based On Fuzzy Neural Networkmentioning

confidence: 99%

[Retracted] Analysis of Sports Injury Estimation Model Based on Mutation Fuzzy Neural Network

Wang

Yang

2021

Computational Intelligence and Neuroscience

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“…Therefore, the models trained on these datasets are not robust enough. At present, deep learning has become an important research direction in the field of artificial intelligence because it can imitate the structure of human brain, extract and abstract information, and solve problems in real life [ 14 – 16 ]. Deep learning is a mathematical model composed of several neurons connected with each other to process complex information, which can reduce human workload and enhance the accuracy of actual results.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Optimization of Personnel Placement Scheme and Big Data Analysis Based on Multilayer Variable Neural Network Algorithm

2021

Computational Intelligence and Neuroscience

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People usually use the method of job analysis to understand the requirements of each job in terms of personnel characteristics, at the same time use the method of psychological measurement to understand the psychological characteristics of each person, and then put the personnel in the appropriate position by matching them with each other. With the development of the information age, massive and complex data are produced. How to accurately extract the effective data needed by the industry from the big data is a very arduous task. In reality, personnel data are influenced by many factors, and the time series formed by it is more accidental and random and often has multilevel and multiscale characteristics. How to use a certain algorithm or data processing technology to effectively dig out the rules contained in the personnel information data and explore the personnel placement scheme has become an important issue. In this paper, a multilayer variable neural network model for complex big data feature learning is established to optimize the staffing scheme. At the same time, the learning model is extended from vector space to tensor space. The parameters of neural network are inversed by high-order backpropagation algorithm facing tensor space. Compared with the traditional multilayer neural network calculation model based on tensor space, the multimodal neural network calculation model can learn the characteristics of complex data quickly and accurately and has obvious advantages.

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“…Feature selection technique is a data preprocessing method for data mining processes, especially in dealing with high dimensional machine learning frameworks. Its goal is to build a simple and easy-to-understand model that can be widely used in statistical analysis, machine learning [33][34][35], and data mining [36][37][38]. The selection of important and suitable identification features can not only simplify the calculation but also allow understand the causal relationship, which is a critical part of machine learning.…”

Section: Feature Selection Methodsmentioning

confidence: 99%

Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry

Chen

Lin

et al. 2021

Mathematics

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In recent years in Taiwan, scholars who study financial bankruptcy have mostly focused on individual listed and over-the-counter (OTC) industries or the entire industry, while few have studied the independent electronics industry. Thus, this study investigated the application of an advanced hybrid Z-score bankruptcy prediction model in selecting financial ratios of listed companies in eight related electronics industries (semiconductor, computer, and peripherals, photoelectric, communication network, electronic components, electronic channel, information service, and other electronics industries) using data from 2000 to 2019. Based on 22 financial ratios of condition attributes and one decision attribute recommended and selected by experts and in the literature, this study used five classifiers for binary logistic regression analysis and in the decision tree. The experimental results show that for the Z-score model, samples analyzed using the five classifiers in five groups (1:1–5:1) of different ratios of companies, the bagging classifier scores are worse (40.82%) than when no feature selection method is used, while the logistic regression classifier and decision tree classifier (J48) result in better scores. However, it is significant that the bagging classifier score improved to over 90% after using the feature selection technique. In conclusion, it was found that the feature selection method can be effectively applied to improve the prediction accuracy, and three financial ratios (the liquidity ratio, debt ratio, and fixed assets turnover ratio) are identified as being the most important determinants affecting the prediction of financial bankruptcy in providing a useful reference for interested parties to evaluate capital allocation to avoid high investment risks.

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Comprehensive Survey of Big Data Mining Approaches in Cloud Systems

Cited by 98 publications

References 39 publications

[Retracted] Analysis of Sports Injury Estimation Model Based on Mutation Fuzzy Neural Network

[Retracted] Analysis of Sports Injury Estimation Model Based on Mutation Fuzzy Neural Network

[Retracted] Optimization of Personnel Placement Scheme and Big Data Analysis Based on Multilayer Variable Neural Network Algorithm

Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry

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