[Retracted] Obesity Mass Monitoring in Medical Big Data Based on High‐Order Simulated Annealing Neural Network Algorithm

Ren, Lijian; Wu, Xiaoyu; Zhao, Kaiqing

doi:10.1155/2021/8336887

Cited by 4 publications

(5 citation statements)

References 40 publications

(46 reference statements)

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“…min (3) We verify the calculation results according to metropolis acceptance criteria and constraints, incorporate the qualified results into the total scheme, and calculate the recognition accuracy and calculation time of the prediction results of the overall scheme (Ren e al. [14]); (4) We determine whether all the data we are going through will stop the calculation, otherwise proceed to step 3; (5) Finally, the minimum value should be taken out. e overall diagram and output of the computation process are shown.…”

Section: E Accuracy Of the Results For Predicting The Level Of Public...mentioning

confidence: 99%

An Annealing Model Analysis and Research on the In of Community Public Sports on Economic Development

Zhen

2022

Computational Intelligence and Neuroscience

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The state vigorously develops the application of community sports and takes it as an important unit of social and economic development. With the development of the economy, residents’ living standards are getting better, which also greatly promotes the development of community public sports. Community public sports have made great progress in terms of scale, hardware, and services, and their impact on society is getting worse. Come bigger. However, there are still some deficiencies in the development of community sports. How to accurately evaluate and predict its facilities, construction, and service levels and put forward corresponding development strategies is an urgent problem to be solved in the field of community public sports. In this paper, an iterative simulated annealing algorithm is proposed to evaluate the level of public sports services and help community public sports institutions identify their own development deficiencies. Through the simulation analysis of the community sports organizations, community members, venue facilities and funds, venue facilities and funds, administrators, and the community sports activities, it is found that the identification accuracy of the evaluation results of the five indicators is above 95%, while the initial threshold of community public sports and social and economic development and the prediction accuracy required by the standard of development status are both 10%. At the same time, the recognition time of the evaluation results in the five indicators is less than 7 seconds. In community public sports development trend prediction and service level judgment, the identification time of the evaluation result is less than 4 seconds, and the overall judgment result is better.

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Section: E Accuracy Of the Results For Predicting The Level Of Public...mentioning

confidence: 99%

An Annealing Model Analysis and Research on the In of Community Public Sports on Economic Development

Zhen

2022

Computational Intelligence and Neuroscience

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“…While the adjusted R-squared value nears unity in this scenario, the RMSE surpassing 0.1 signals inadequate model fitting and hints at overfitting tendencies, leading to local optimization issues, as demonstrated in Figure 8 [31]. Hence, this study employs a simulated annealing algorithm to optimize the model, in line with rigorous research principles [32]. Simultaneously, a statistical analysis was conducted on the brix content of the three points and is presented in Table 1.…”

Section: • Calibration Data Acquisitionmentioning

confidence: 89%

Neural Network-Based Analysis and Its Application to Spectroscopy for Mango

Zhang,

Wang,

Fan

2024

Applied Sciences

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Sugar derived from crops is a crucial organic energy source studied in the Earth sciences, serving as a renewable and clean energy alternative. Biofuels produced from crop sugars are more environmentally friendly than traditional fossil fuel sources and contribute to solar energy storage and conversion within the Earth’s cycle. Using mangoes as a case study, this research employs near-infrared spectral analysis technology to develop an algorithm for a mango brix detection device. The study investigates the relationship between brix and absorbance, as well as changes in brix levels, and their application for on-site mango brix detection. Near-infrared spectral data in the range of 1300 nm to 2300 nm were collected during the mango ripening season in summer and preprocessed using various techniques. A neural network-based least squares modeling approach was utilized to develop a mango sugar content detection model, resulting in a correlation coefficient of 0.9055 and a root-mean-square error of 0.2192. To enhance model accuracy and avoid local optimization issues, this study incorporated the simulated annealing algorithm for model optimization, leading to a correlation coefficient of 0.9854 and a root-mean-square error of 0.0431. The findings demonstrate that the non-destructive testing model of mangoes based on near-infrared spectroscopy effectively detects brix changes and storage potential post-harvest, offering valuable insights for mango quality assessment, optimal picking and selling times, and market selection.

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“…Con el desarrollo de la inteligencia artificial, es posible realizar seguimiento eficaz de las enfermedades crónicas, como la OB; apoyado del Big data, machine learning, y en específico, con las redes neuronales artificiales, se pueden construir modelos de políticas de salud, para prevenir SP y OB, a corto, mediano, y largo plazo (Ren et al, 2021).…”

Section: Discussionunclassified

Quality of life and sociodemographic variables, as explanatory variables of obesity

Leija-Alva,

Díaz-Reséndiz,

Aguilera-Sosa

2024

Rev. estud. psicol.

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Objective: To identify variables related to quality of life and sociodemographics that could explain the percentage of body fat, as well as low-density lipoproteins, very low-density lipoproteins, total cholesterol, and triglycerides, all associated with obesity. Methods: This was a quantitative, non-experimental, convenience, and explanatory study. Participants included 320 adults with different body mass index levels, of both sexes, who were patients at the Higher School of Medicine of the National Polytechnic Institute from 2018 to 2020. We assessed and measured quality of life, lipoproteins, cholesterol, and body fat percentage. Linear regressions, categorical regressions, structural equations, and artificial neural networks were employed. Results: In the artificial neural network, the variables with the highest synaptic weight were marital status, occupation, and age; and in terms of quality of life, cognitive function, medical dependence, and physical performance. In linear models, explanatory factors included concerns, isolation, body perception, attitude towards treatment, leisure time, gender, and marital status. Conclusion: Body perception, age, medical dependence, marital status, and concerns were the input variables that explained the percentage of body fat and blood lipids related to obesity.

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[Retracted] Obesity Mass Monitoring in Medical Big Data Based on High‐Order Simulated Annealing Neural Network Algorithm

Cited by 4 publications

References 40 publications

An Annealing Model Analysis and Research on the In of Community Public Sports on Economic Development

An Annealing Model Analysis and Research on the In of Community Public Sports on Economic Development

Neural Network-Based Analysis and Its Application to Spectroscopy for Mango

Quality of life and sociodemographic variables, as explanatory variables of obesity

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