scholarly journals Competitiveness Evaluation of Tourist Attractions Based on Artificial Neural Network

2020 ◽  
Vol 34 (5) ◽  
pp. 623-630
Author(s):  
Bingna Lou ◽  
Nan Chen ◽  
Lan Ma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Evaluation Model ◽  
Rapid Development ◽  
Core Layer ◽  
Evaluation Index System ◽  
Tourist Attractions ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Fierce Competition

With the rapid development of tourism, tourists have become more aware of tourism level and quality. This triggers fierce competition between tourist attractions. To promote the core competitiveness of tourist attractions, this paper proposes a new evaluation model for the competitiveness of tourist attractions based on artificial neural network. First, a four-layer evaluation index system (EIS) was constructed for the competitiveness of tourist attractions, including detail elements, basic layer, core layer, and characterization layer. Next, all the evaluation indices were optimized through clustering by improved k-modes algorithm. Finally, a backpropagation neural network (BPNN) was established to evaluate the competitiveness of tourist attractions. Experimental results confirm the effectiveness of the proposed method. The research findings provide a reference for the application of artificial neural network (ANN) in other prediction fields.

scholarly journals Evaluation of the Motivation Status of Enterprises and Higher Vocational Schools Participating in Modern Apprenticeship and Its Key Influencing Factors Based on Artificial Neural Network

2021 ◽  
Vol 16 (08) ◽  
pp. 188
Author(s):  
Shu Ji ◽  
Jun Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Influencing Factors ◽  
Evaluation Model ◽  
Intelligent Manufacturing ◽  
Evaluation Index System ◽  
Vocational Schools ◽  
Participation Motivation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

During the reform of talent training mode, higher vocational schools must promote and apply modern apprenticeship to meet the needs of intelligent manufacturing. However, most enterprises and schools differ greatly in the participation enthusiasm and implementation motivation for modern apprenticeship. To enhance the participation motivation, it is critical to correctly evaluate the motivation status of enterprises and schools participating in modern apprenticeship, and analyze its key influencing factors. For this reason, this paper employs the Artificial Neural Network (ANN) to evaluate such motivation status. Firstly, a Modern Apprenticeship Motivation Status (MAMS) evaluation model was established, along with its evaluation index system (EIS). Then, differences in the motivation status were compared from seven aspects. After that, an improved backpropagation (BP) neural network was built to construct and optimize the MAMS prediction model. Finally, the constructed model was proved valid through experiments.

scholarly journals Tropospheric Ozone Formation Estimation in Urban City, Bangi, Using Artificial Neural Network (ANN)

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Fatin Aqilah Binti Abdul Aziz ◽  
Norliza Abd. Rahman ◽  
Jarinah Mohd Ali
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ozone Concentration ◽  
Tropospheric Ozone ◽  
Air Pollutants ◽  
Rapid Development ◽  
Ground Level ◽  
Urban City ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Due to the rapid development of economy and society around the world, the most urban city is experiencing tropospheric ozone or commonly known as ground-level air pollutants. The concentration of air pollutants must be identified as an early precaution step by the local environmental or health agencies. This work aims to apply the artificial neural network (ANN) in estimating the ozone concentration forecast in Bangi. It consists of input variables such as temperature, relative humidity, concentration of nitrogen dioxide, time, UVA and UVB rays obtained from routine monitoring, and data recorded. Ten hidden layer is utilized to obtain the optimized ozone concentration, which is the output layer of the ANN framework. The finding showed that the meteorology condition and emission patterns play an important part in influencing the ozone concentration. However, a single network is sufficient enough to estimate the concentration despite any circumstances. Thus, it can be concluded that ANN is able to give reliable and satisfactory estimations of ozone concentration for the following day.

scholarly journals An Evaluation Strategy for Commercial Precision Marketing Based on Artificial Neural Network

2020 ◽  
Vol 34 (5) ◽  
pp. 637-644
Author(s):  
Jianfeng Cheng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Principal Component ◽  
Principal Evaluation ◽  
Evaluation Index System ◽  
Benefit Sharing ◽  
Optimized Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Smart Mobile

With the proliferation of the Internet and smart mobile terminals, great progress has been made in the precision placement and benefit-sharing mechanism of commercial advertisements. Meanwhile, media marketing has become increasingly in-depth and precise. So far, mature theories have been proposed on consumer value and precision marketing. But further research is needed to mine the value from the big data on commercial precision marketing. To improve the accuracy of commercial precision marketing, this paper presents an evaluation index system (EIS) for commercial precision marketing based on improved attention-interest-desire-memory-action (ADIMA) model, and determines the principal evaluation indices through principal component analysis (PCA). Next, an artificial neural network (ANN) was established to evaluate commercial precision marketing, and optimized through k-means clustering (KMC). Finally, the optimized model was realized on MATLAB. The proposed EIS and ANN were proved scientific and effectiveness through simulations. The research results provide a reference for the application of the ANN in other fields of marketing.

The Construction of Index System and Comprehensive Evaluation Model Based on Improved Genetic Algorithm and Fuzzy Neural Network

2010 ◽  
Vol 20-23 ◽  
pp. 1229-1235 ◽  
Author(s):  
Yuan Yuan Zhang ◽  
Shi Song Yang ◽  
Peng Dong
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Index System ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Back Propagation ◽  
Improved Genetic Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Artificial neural network(ANN) and genetic algorithm (GA) have both prevalent uses in large area. Along with the development of technology a method based on the combination of Artificial neural network (ANN) and genetic algorithm (GA) aroused. In such a case, the paper uses the combination of Artificial neural network(ANN) and genetic algorithm (GA) to solve the problems of costructing index system and comprehensive evaluation. Firstly establishing feedforward neural network model and make sure about the input and output variables. Secondly improved genetic algorithm is used to solve the problem of network weight and threshold value which is constitute by three steps real codes, random selection and Genetic Manipulation of Chromosome. Moreover as it know to all, error back propagation(BP) algorithm is effective in local searching so adding error back propagation(BP) algorithm to genetic algorithm is a good way to get the satisfying result. Thirdly the paper gets the output of index effectiveness. Thirdly according to the entropy theory that the summation of effective value which could be involved in the index system should be larger than a certain critical value, the paper screened out the final index. Fourthly it uses the fuzzy neural network method to establishing the comprehensive evaluation model. Finally take the evaluation for teaching quality for example to authenticate the feasibility of the method.

scholarly journals Internship Effect Prediction for Physical Education Majors Based on Artificial Neural Network

2021 ◽  
Vol 16 (24) ◽  
pp. 165-176
Author(s):  
Bo Yang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Physical Education ◽  
Prediction Model ◽  
Evaluation Index System ◽  
Job Competencies ◽  
Physical Education Majors ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Professional internship offers college students a golden chance to apply their theoretical knowledge to practice. Through internship, physical education (PE) majors can match the professional knowledge and skills learned at school with the competencies required by actual jobs. The relevant studies at home and abroad mainly attempt to improve the internship effect. This paper explores the influence of the diversity of job competencies on the internship effect of PE majors, and establishes a prediction model based on artificial neural network (ANN). Firstly, an evaluation index system (EIS) was constructed for the internship quality of PE majors, and a table was prepared for four types of internship jobs for PE majors, as well as their core competences. Then, the sample data for quality evaluation of PE majors’ internship were preprocessed and subjected to feature extraction, in the light of their sequential property. After that, a prediction model was proposed for the internship quality of PE majors, along with its optimization algorithm. The proposed model was proved effective through experiments.

scholarly journals An Evaluation Model of Subgrade Stability Based on Artificial Neural Network

2020 ◽  
Vol 10 (5) ◽  
pp. 679-688
Author(s):  
Youkun Cheng ◽  
Zhenwu Shi ◽  
Fajin Zu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Influencing Factors ◽  
Evaluation Model ◽  
Mapping Function ◽  
Traffic Load ◽  
Fuzzy Neural ◽  
Artificial Neural ◽  
The Law ◽  
Artificial Neural Network Ann

In special areas, the highways may suffer from such diseases as deformation, cracking, subsidence, and potholes, making highway maintenance a complex and difficult task. To obtain the law-term deformation law of the subgrade and accurately evaluate the subgrade and pavement stability, this paper establishes a subgrade stability evaluation model based on artificial neural network (ANN). Firstly, the law of unstable subgrade deformation in highways of special areas was derived by analyzing the influencing factors on subgrade stability, namely, temperature field, moisture field, and traffic load. Next, the correlations between input and output characteristic quantities were extracted, and used to construct the nonredundant mapping function between influencing factors of subgrade unstable deformation and the levels of subgrade stability. Finally, a fuzzy neural network (FNN) was constructed based on Takagi-Sugeno model, realizing the evaluation of subgrade stability. The proposed model was proved effective and accurate through experiments.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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