Research on Smart Growth of Sustainable Cities Based on Information Entropy and Super-Efficiency DEA Model

Lv, Ximing; Zhang, Shunkai; Li, Ang; Li, Jianbao

doi:10.4236/jamp.2017.55103

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…Information entropy refers to the degree of uncertainty of an event. It is not a measure of the amount of information, but a measure of the uncertainty of the event itself [17]. e more effective information is added to the information entropy, the lower the uncertainty of an event and the higher the prediction accuracy, and vice versa, the lower the prediction accuracy of an event.…”

Section: Information Entropymentioning

confidence: 99%

Model and Simulation of Engineering Safety Risk Control Based on Artificial Intelligence Algorithm

Liu

Zhao

Tan

et al. 2022

International Transactions on Electrical Energy Systems

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The implementation of engineering projects has a profound impact on the national economy and people’s livelihood. The current engineering projects are in full swing with the vigorous development of the economy. However, due to the high complexity of the engineering project environment, a large number of participants, the high technical standards, and the high labor intensity, it is easy to induce engineering safety risk accidents. Therefore, the demand for research on safety risks in engineering construction is becoming more and more urgent. However, the traditional construction safety risk management lacks systematicness and standardization, and it has limitations in engineering safety risk early warning and control. In order to solve this problem, this paper applies artificial intelligence algorithms to engineering construction safety risk management and establishes an engineering safety risk early warning control model. The Bayesian formula, information entropy theory, and other algorithms provide the theoretical basis and feasibility analysis for the model. Four engineering safety risk factors, including human factors, physical factors, management factors, and environmental factors, are analyzed through simulation experiments. The results show that the probability of injury to construction personnel has been reduced by 51.3%, the qualified rate of production materials in construction projects has increased by 6.5%, the risk factors of management have been reduced by 7%, and the environmental risk factors have been reduced by 7.7%; the final risk early warning control effect has increased by 7.45%.

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Section: Information Entropymentioning

confidence: 99%

Model and Simulation of Engineering Safety Risk Control Based on Artificial Intelligence Algorithm

Liu

Zhao

Tan

et al. 2022

International Transactions on Electrical Energy Systems

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“…Given the planned objectives of long-term sustainable and stable development, many cities are implementing initial plans to move towards smart growth [4][5][6] . Given the planned objectives of long-term sustainable and stable development, many cities are implementing initial plans to move towards smart growth.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation System of Urban Smart Growth in Wuhou District of Chengdu, China

Wang¹,

Niu²,

Zhang³

et al. 2017

Res. Rev. J Mat. Sci

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The new and diversified urban smart growth model, has been gradually developed and accepted by people around the world. First of all, according to the scientific, systematic and comparable design principles, the author establishes the urban growth intelligibility evaluation system composed of four subsystems, such as urban growth intensity, urban land growth efficiency, urban livable level and urban growth structure. Then, we use the improved entropy value method to carry out dimensionless processing on each index, and then determine the weight of each index. Finally, the evaluation system is used to quantitatively evaluate and analyze the degree of smart growth in Wuhou District of Chengdu, and then putting forward the development plan to guide its growth towards smart.

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“…Mobile Information Systems 2.4.2. DEA Model Construction [28][29][30][31]. First is the C 2 R model.…”

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confidence: 99%

Innovative Application of Big Data Combined with Machine Learning in Education and Training Product Marketing

Dong-mei

2022

Mobile Information Systems

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Education and training (EAT) enterprise C is taken as an example to enable EAT enterprises to choose appropriate marketing strategies according to their own characteristics to improve their competitiveness. The marketing of its EAT products is taken as the research object to analyze its marketing philosophy and marketing behavior. Initially, the marketing environment of EAT products is analyzed from macro aspects such as political environment, economic environment, and internal environment of enterprises and micro aspects such as peer competition, alternative competition, and bargaining power of buyers. Then, the main problems existing in the marketing of EAT products of enterprise C are analyzed through a questionnaire survey. Afterward, the model analyzes the effectiveness of marketing revenue and output of enterprise C. The result shows that 48% of the teachers are dissatisfied with the EAT products of enterprise C, 59% of the students dislike the training courses of enterprise C, and only 11% of the students express their willingness to continue studying the training courses of enterprise C. To solve the above problems, enterprise C can increase investment in EAT products or develop new EAT products to optimize the product marketing strategy. Additionally, it can develop agent channels rationally and adopt multichannel integration to improve the efficiency of channel strategy. Meanwhile, the new media platform can broaden marketing channels to push targeted content to users according to big data analysis results, thereby improving user experience. The marketing model provided here has referencing value as well as important theoretical and practical significance for the development of EAT enterprises.

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Research on Smart Growth of Sustainable Cities Based on Information Entropy and Super-Efficiency DEA Model

Cited by 6 publications

References 18 publications

Model and Simulation of Engineering Safety Risk Control Based on Artificial Intelligence Algorithm

Model and Simulation of Engineering Safety Risk Control Based on Artificial Intelligence Algorithm

An Evaluation System of Urban Smart Growth in Wuhou District of Chengdu, China

Innovative Application of Big Data Combined with Machine Learning in Education and Training Product Marketing

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