Journal of Intelligent & Fuzzy Systems - Volume 37, issue 5

Authors: Yue, Wu

Article Type: Research Article

Abstract: With the rapid expansion of chain network, enterprises meet the consumption demand scattered around in a large range. In this paper, SOM neural network algorithm is introduced for empirical test. Design includes the structure of the fuzzy neural network identification and parameter identification, structural identification include input space division and the number of fuzzy rules to determine. Through summarizing and analyzing the characteristics of chain retail enterprises, this paper proposes to build a hierarchical and differentiated incentive mechanism by cultivating retail culture. The result shows that the knowledge staff is been higher the education level, the work creativity is stronger, …cooperates the demand to the team members. In the era of the knowledge economy, knowledge has replaced capital as the core source of the enterprise core competence. The performance evaluation of knowledge workers is complex and the performance of the general staff is often easier to get a more objective evaluation. In conclusion, performance characteristics of knowledge workers should include general knowledge staff quality, knowledge staff performance behavior and performance results three aspects of characteristics. Show more

Keywords: SOM neural network, fuzzy model, chain enterprises, performance analysis

DOI: 10.3233/JIFS-179210

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6287-6300, 2019

Authors: Li, Bin | Wei, Xing | Li, Chao | Ding, Shuai

Article Type: Research Article

Abstract: Due to the lack of uniform standards for pathological cell detection, it is difficult to identify. In order to improve the accuracy of pathological cell identification, this study combines the actual situation of cell detection based on traditional particle algorithm to construct a C-V model based on level set algorithm and curve evolution theory, which realizes the effective separation of different substances inside the cell. At the same time, in order to effectively extract the characteristics of cell images, this paper uses the global research method to extract the features of the research object and adopts the improved gray level …co-occurrence matrix to extract the texture features, thus effectively improving the feature extraction quality. In addition, in order to study the accuracy of the algorithm model identification in this study, this paper designs a comparative experiment for performance analysis. The research shows that the proposed algorithm model has good performance, can achieve accurate recognition and feature extraction of pathological cells, has certain practical effects, and can provide theoretical reference for subsequent related research. Show more

Keywords: Particle algorithm, neural network, cell detection, model

DOI: 10.3233/JIFS-179211

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6301-6313, 2019

Authors: Gao, Shuyan | Xu, Jiaqi | Lu, Weiheng

Article Type: Research Article

Abstract: Traditional nuclear magnetic resonance technology has grayscale inhomogeneity in brain tumor detection, which directly affects the formulation of follow-up treatment plans. In order to improve the detection effect of nuclear magnetic resonance on brain tumors, this study uses a convolutional neural network as the basis algorithm to construct an algorithm model suitable for multimodal MRI image recognition. At the same time, combined with the actual case, this paper uses the model to segment and identify brain tumors, and this paper combines the principle of machine learning and collects data for data training to construct a multi-channel deep deconvolution network model. …In addition, in order to explore the effectiveness of the algorithm in this study, the performance analysis was carried out by comparative experiment method, and the multi-faceted performance of the model was studied, and the corresponding test result images were obtained. Through experimental comparison, it can be seen that the algorithm model constructed in this study has certain validity, can be applied to practice, and can provide theoretical reference for subsequent related research. Show more

Keywords: Nuclear magnetic resonance, brain tumor, diagnosis, segmentation, convolutional neural network

DOI: 10.3233/JIFS-179212

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6315-6324, 2019

Authors: Lijun, Cheng | Yubo, Zhang

Article Type: Research Article

Abstract: The Internet of Things (IOT) is the main technical support of smart agriculture. The sensor equipment of the Internet of Things (IOT) in agriculture is developing in the direction of low cost, self-adaptation, high reliability and low power consumption. In the future, the sensor network will gradually have the characteristics of distributed, multi-protocol compatibility, self-organization and high throughput. In this paper, the authors analyze the intelligent agricultural system and control mode based on fuzzy control and sensor network. Intelligent agriculture is based on the most efficient use of various agricultural resources to minimize agricultural energy consumption and costs. It is …supported by Internet of Things technologies such as comprehensive perception, reliable transmission and intelligent processing. Using ROF technology, the WiFi signal is pulled far, and the wireless coverage is expanded greatly. At the same time, through the combination of wireless sensor technology such as ZigBee, the transmission and centralized control of sensing signals are realized, and the monitoring system of intelligent agricultural greenhouse is established. The simulation results show that the system can effectively improve the level of intelligence and information of agricultural greenhouse management, and greatly improve crop production efficiency. Show more

Keywords: Intelligent agriculture, wireless technology, sensor networks, fuzzy control

DOI: 10.3233/JIFS-179213

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6325-6336, 2019

Authors: Li, Wenxian

Article Type: Research Article

Abstract: Reasonable fire risk assessment system can demonstrate the occurrence of fire and ensure the safe evacuation of fire. The selected indicators of the evaluation system play a fundamental role in the establishment of the system. In the evaluation model, the general problem is transformed into a specific mathematical model by using the method of fuzzy information processing, which makes the evaluation result more direct and measurable. This paper uses a measure of feature attributes to measure the contribution of clusters, that is, the method of calculating the weight of features. When the value of the equilibrium discriminant function reaches the …minimum value, the clustering result under the optimal condition can be obtained. Then, the author analyzes the fire risk assessment and factor analysis of buildings based on multi-target decision and fuzzy mathematical model. The simulation results show that the improved fuzzy model proposed in this paper makes the calculation results more accurate. The fire risk analysis and control system based on the theory of fuzzy information processing can be widely used in various high-rise buildings to ensure safety. Show more

Keywords: Multi-objective decision, high-rise building, fire risk, fuzzy mathematics

DOI: 10.3233/JIFS-179214

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6337-6348, 2019

Authors: Wenwen, Liang

Article Type: Research Article

Abstract:  In order to realize the intelligent evaluation of effective teaching quality and make up for the lack of research in this aspect, in the research, BP neural network is used as the basis for model construction analysis. Political education in colleges and universities is an important course, and its teaching quality evaluation is particularly important. Through comparative analysis, LMBP is selected as the learning algorithm, and the neural network evaluation model mechanism of college classroom teaching quality evaluation system is determined through theory and practical methods, and the simulation model is simulated by MATLAB as a simulation tool. At the …same time, this paper uses the experimental method to carry out simulation training experiments in the MATLAB neural network toolbox, select the training algorithm for comparative analysis, and display the results in the form of statistical graphs. In addition, this paper sets the convergence speed and error curve as evaluation indicators, determines the appropriate training algorithm, and verifies the validity of the model. The research indicates that the BP teaching quality evaluation model based on BP neural network is a reasonable and feasible evaluation model and can provide theoretical reference for subsequent related research. Show more

Keywords: BP neural network, teaching quality, model, training function, simulation analysis

DOI: 10.3233/JIFS-179215

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6349-6361, 2019

Authors: Peng, Qin

Article Type: Research Article

Abstract:  With the full spread of various IT application systems, a large number of business data are stored in the business systems of enterprises. In this paper, the author analyzes the aviation industry management mode based on big data analysis. In this paper, the author analyses aviation industry management model and exchange rate index analysis based on error correction model and fuzzy mathematics. The BP algorithm uses the error of the output layer to estimate the error of the direct predecessor layer of the output layer, and then gradually estimates the error forward, and thus, the error of all layers is …obtained. The weights and thresholds of the layers are adjusted according to the error so that the modified network output can approach the expected value. The aviation industry data include both the financial and internal data of airlines, and the external data such as flight information and user data. From the ETL process, building an enterprise data warehouse is an important strategy for the development of the aviation industry. It has a positive effect on the application of automatic data mining and business intelligence in the aviation industry. On this basis, we put forward relevant suggestions for aviation industry management. Show more

Keywords: Big data, aviation industry, operation management, error correction model, fuzzy mathematics

DOI: 10.3233/JIFS-179216

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6363-6375, 2019

Authors: Zhen, Zhen | Yanqing, Yao

Article Type: Research Article

Abstract: Technological innovation in manufacturing industry is a kind of R&D activity that produces new technologies, including input and output of technological innovation. In this paper, the authors analyze the lean production and technological innovation in manufacturing industry based on SVM algorithms and data mining technology. Data mining can discover novel, effective, potential and ultimately understandable data patterns from a deeper level, and encode the data to predict the development trend of enterprises. The machine learning support vector machine method is used to analyze and model the collected data. At the same time, we constructed a decision tree using random forest, …and explained the significance of the training algorithm through the visualization results. The simulation results show that learning growth dimension and market dimension have the greatest impact on business model innovation. In the context of TEC, business model innovation must pay attention to market grasp and customer demand oriented, so as to improve the competitiveness of manufacturing enterprises. Show more

Keywords: SVM Algorithms, data mining, manufacturing enterprises, science and technology level

DOI: 10.3233/JIFS-179217

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6377-6388, 2019

Authors: Yangjun, Ren | Chuanxu, Wang | Lang, Xu | Chao, Yu | Suyong, Zhang

Article Type: Research Article

Abstract: Using the panel data for China’s 30 provinces from 2008 to 2016, this paper analyzes the impact of producer services agglomeration on green economic efficiency at its spillover effects, through spatial autocorrelation test and the establishment of spatial econometric models. It comes to the results as follows: First, China’s regional green economic efficiency is significant positive spatial dependence. Second, the producer services specialized agglomeration not only inhibits the green economic efficiency of one region but also has significantly negative spatial spillover effects on adjacent areas, while the producer services diversified agglomeration only enhance the green economic efficiency in the region. …Third, the impact of the agglomeration mode selection of producer services industry on green economic efficiency in the eastern region is basically consistent with the empirical analysis at the national level, while the green economic efficiency improvement in the central region only benefits from producer services specialized agglomeration, and the green economic efficiency in the western region is not significantly affected by the producer services agglomeration mode selection. Show more

Keywords: Production services agglomeration, green economic efficiency, spatial Durbin model, spillover effects

DOI: 10.3233/JIFS-179218

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6389-6402, 2019

Authors: Qiang, Qunli

Article Type: Research Article

Abstract: Currently, there is a certain fluctuation in the real estate industry, so it is particularly important to analyze the solvency of real estate enterprises. In order to find a reliable model suitable for studying the difference in house prices, this study collects the research data through data collection, and uses the K-means clustering method to construct the corresponding model as a basic research in combination with the machine learning research method. At the same time, this paper compares the analysis effects of several common machine learning models and finds the advantages and disadvantages of these methods through mathematical statistics. In …addition, combined with practice, this paper constructs a nonlinear generalized additive model, and based on machine learning technology, validates the validity of the model based on data analysis, the collected predictors. In view of the improvement of the solvency of real estate enterprises, diversified operation of real estate enterprises can maintain reasonable cash flow and make up for the defect of poor liquidity of real estate. Furthermore, this paper uses the stability method to find the optimal model. In addition, the generalized additive model effectively reveals the complex nonlinear relationship between continuous predictors and house prices. Through research, it can be seen that the nonlinear generalized additive model based on machine learning can play an important role in real estate industry forecasting and has certain theoretical reference significance for subsequent related research. Show more

Keywords: Real estate, generalized additive model, machine learning, K-means Algorithm

DOI: 10.3233/JIFS-179219

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6403-6414, 2019