Determination of positioning accuracies by using fingerprint localisation and artificial neural networks

Koyuncu, Hakan

doi:10.2298/tsci180912334k

Cited by 5 publications

(5 citation statements)

References 26 publications

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“…In the process of forward transmission of the BP neural network, neurons at the latter layer receive input signals transmitted by neurons at the previous layer, assign weight to these signals, and compare the sum result with the threshold value of current neurons, and then process the result through the activation function to obtain the output of neurons [7]. Common activation functions include Sigmoid activation function, tanh activation function, ReLU activation function, and leaky ReLU activation function.…”

Section: Bp Neural Networkmentioning

confidence: 99%

Big Data’s Analysis and Prediction Method of Art Education Based on the BP Neural Network

Zhu

2022

Security and Communication Networks

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The traditional art education analysis and prediction are mainly based on the evaluation of human supervisors, and the evaluation results given by different evaluators vary greatly. The traditional analysis and prediction is difficult to reflect the objectivity and fairness of art education evaluation. This paper proposes a new idea of using the BP neural network model to analyze and forecast art education to improve the objectivity and impartiality of art education evaluation by big data. The work first constructed the art education evaluation index system covering the content of art education comprehensively and extensively. Then the BP neural network of art education analysis and prediction model was established. According to the content of art education evaluation system, the BP neural network’s input vector contains 30 evaluation indexes that affect art education, and its output vector is the art education evaluation results. The BP neural network with the structure of 30 × 10 × 1 was trained using the collected data. Finally, the work verified the scientificity of the evaluation model. The results of empirical analysis show that the established art education evaluation index system is reasonable and can reflect the artistic level of students, and the model of BP neural network has reliability in the analysis and prediction of art education big data and can objectively evaluate the level of art education.

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Section: Bp Neural Networkmentioning

confidence: 99%

Big Data’s Analysis and Prediction Method of Art Education Based on the BP Neural Network

Zhu

2022

Security and Communication Networks

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“…In the forward transmission process of the BP neural network, the neuron in the latter layer receives the input signals transmitted by the neuron in the previous layer and assigns weights to these signals. The summation result is compared with the threshold value of the current neuron, and then the result is processed by the activation function to obtain the output score [ 47 ]. Due to the large amount of data, we chose the ReLu activation function in order to reduce the dependence between parameters, reduce the overfitting rate, and enhance the robustness of the model.…”

Section: Methodsmentioning

confidence: 99%

The Evaluation of Rural Outdoor Dining Environment from Consumer Perspective

Mian

Wang

Qiu

et al. 2022

IJERPH

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The quality of the environment should be measured by the satisfaction of the public and guided by the issues of public concern. With the development of the internet, social media as the main platform for people to exchange information has become a data source for planning and management analysis. Nowadays, the rural catering industry is becoming increasingly competitive, especially after the pandemic. How to further enhance the competitiveness of the rural catering industry has become a hot topic in the industry. From the perspective of consumers, we explored consumers’ preferences in a rural outdoor dining environment through social media data. The research analyzed the social media data through manual collection and object detection, divided the landscape of the rural outdoor dining environment into eight categories with 35 landscape elements, and then used BP (Back Propagation) neural network nonlinear fitting and least square linear fitting to analyze the 11,410 effective review pictures from eight rural restaurants’ social media comments in Chengdu. We derived the degree of consumer preference for the landscape quality of the rural outdoor dining environment and analyzed the differences in preference among three different groups (regular customers, customers with children, and customers with the elderly). The study found that agricultural resources are an important factor in the competitiveness of rural restaurant environments; that children’s emotions when using activity facilities can positively influence consumers’ dining experiences; that safety and hygiene environment are important factors influencing the decisions of parent–child dining; and that older people are more interested in outdoor nature, etc. The research results provide suggestions and knowledge for rural restaurant managers and designers through human-oriented needs from the perspective of consumers, and clarify the preferences and expectations of different consumer groups for rural restaurant landscapes while achieving the goal of rural landscape protection.

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“…e integration of big data technology with pastoral complexes and landscape design planning is relatively small, but it can process a large amount of data generated in the process of landscape planning and management. e large amount of data generated in landscape planning is of a wide variety and not of the same order of magnitude, which is difficult for the designers of landscape management [27][28][29]. Big data technology can process these data efficiently and accurately, and it can output the information the designer needs according to its data.…”

Section: Review Of Application Of Big Data In Landscape Designmentioning

confidence: 99%

Landscape Planning and Management Methods of Beautiful Rural Pastoral Complexes under the Background of Big Data

Wang

Qin

2022

Mathematical Problems in Engineering

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With the continuous acceleration of the urbanization process, rural areas have gradually put forward construction plans for new rural forms. Pastoral complex is a construction mode of characteristic townships and rural complexes, which has become a new form of social development today. Pastoral complexes provide a new model of new rural construction, and its development is still in the preliminary stage for China. The construction of pastoral complexes involves rural landscape planning, economic management, etc. It is difficult for rural managers to process these complex data characteristics. Big data technology is affecting people’s lives, and the discipline of landscape architecture is also deeply affected by big data. This paper mainly uses data mining technology and neural network method to carry out feature mining and prediction on the landscape planning and management methods of beautiful rural pastoral complexes. In this paper, a data mining algorithm is used to classify the landscape types and locations of rural complex, and the neural network method is used to predict the landscape characteristics. The results show that the methods of data mining and neural network are feasible and reliable both in the proportion of classification and in the prediction of landscape planning. The maximum error of the prediction of landscape design is 2.87%, and the minimum error is only 0.79% using the neural network method, which is an acceptable error range.

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Determination of positioning accuracies by using fingerprint localisation and artificial neural networks

Cited by 5 publications

References 26 publications

Big Data’s Analysis and Prediction Method of Art Education Based on the BP Neural Network

Big Data’s Analysis and Prediction Method of Art Education Based on the BP Neural Network

The Evaluation of Rural Outdoor Dining Environment from Consumer Perspective

Landscape Planning and Management Methods of Beautiful Rural Pastoral Complexes under the Background of Big Data

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