Journal of Intelligent & Fuzzy Systems - Volume 39, issue 4

Authors: Chen, Xuanjun | Metawa, N

Article Type: Research Article

Abstract: Cloud computing technology has the characteristics of low investment costs, strong reliability, flexible expansion, and on-demand services, which can greatly reduce the application threshold of enterprise financial informatization construction, improve the return on investment of informationization, and flexibly adapt to the needs of different stages of business. To solve the problem of enterprise financial management information system based on cloud computing in big data environment. This article proposes the management concept of “business-driven value” as an expense management system. Through the investigation of the company in this article, after 9 years of construction, the number of property in each subsidiary …has dropped from an average of 23 people per company to 3.5 people per subsidiary before. Reduced by about 84.7%. Contracted human capital for the company. Compared with the 23 billion yuan in 2010, after the implementation of financial shared services, after 9 years of development, it has now reached 68.55 billion yuan, nearly three times more than before. Show more

Keywords: Cloud computing, financial management, enterprise informatization, big data

DOI: 10.3233/JIFS-189007

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5223-5232, 2020

Authors: Liu, Ran | Liu, Pingfeng | Zhang, Wang | Metawee, Ahmed K.

Article Type: Research Article

Abstract: The objective of this study is to promote the structural optimization of the banking industry and improve the national economic level. The analysis method based on the co-integration test is adopted to study the relationship between market structure optimization and economic growth in the banking industry. Firstly, the current economic growth condition, development trend, and the development of the banking industry are analyzed. Secondly, the model between the bank market institutions and the economy is constructed, and the data source of the model is analyzed. Thirdly, the stationarity test, co-integration test, and regression analysis of the studied data are carried …out based on the co-integration test. The results show that there is a significant negative correlation between the concentration of banks and the overall economy, and there is a significant negative correlation between the market structure of banks and the downgrading growth of various industries. Also, the variables of social material input level and human capital input have a significant positive correlation with the economy. It is hoped that the results of this study can provide a good guiding significance for China’s economic development. Show more

Keywords: Banking market, economic growth, stationary analysis, co-integration test, regression analysis

DOI: 10.3233/JIFS-189008

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5233-5242, 2020

Authors: Lei, Zhen | Zhu, Liang | Fang, Youliang | Li, Xiaolei | Liu, Beizhan

Article Type: Research Article

Abstract: Pattern recognition technology is applied to bridge health monitoring to solve abnormalities in bridge health monitoring data. Testing is of great significance. For abnormal data detection, this paper proposes a single variable pattern anomaly detection method based on KNN distance and a multivariate time series anomaly detection method based on the covariance matrix and singular value decomposition. This method first performs compression and segmentation on the original data sequence based on important points to obtain multiple time subsequences, then calculates the pattern distance between each time subsequence according to the similarity measure of the time series, and finally selects the …abnormal mode according to the KNN method. In this paper, the reliability of the method is verified through experiments. The experimental results in this paper show that the 5/7/9 / 11-nearest neighbors point to a specific number of nodes. Combined with the original time series diagram corresponding to the time zone view, in this paragraph in the time, the value of the temperature sensor No. 6 stays at 32.5 degrees Celsius for up to one month. The detection algorithm controls the number of MTS subsequences through sliding windows and sliding intervals. The execution time is not large, and the value of K is different. Although the calculated results are different, most of the most obvious abnormal sequences can be detected. The results of this paper provide a certain reference value for the study of abnormal detection of bridge health monitoring data. Show more

Keywords: Artificial intelligence, bridge health monitoring, data anomaly detection, KNN algorithm, multivariate time series

DOI: 10.3233/JIFS-189009

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5243-5252, 2020

Authors: Zhang, Xiaoxian | Zhang, Jianpei | Yang, Jing

Article Type: Research Article

Abstract: The problems caused by network dimension disasters and computational complexity have become an important issue to be solved in the field of social network research. The existing methods for network feature learning are mostly based on static and small-scale assumptions, and there is no modified learning for the unique attributes of social networks. Therefore, existing learning methods cannot adapt to the dynamic and large-scale of current social networks. Even super large scale and other features. This paper mainly studies the feature representation learning of large-scale dynamic social network structure. In this paper, the positive and negative damping sampling of network …nodes in different classes is carried out, and the dynamic feature learning method for newly added nodes is constructed, which makes the model feasible for the extraction of structural features of large-scale social networks in the process of dynamic change. The obtained node feature representation has better dynamic robustness. By selecting the real datasets of three large-scale dynamic social networks and the experiments of dynamic link prediction in social networks, it is found that DNPS has achieved a large performance improvement over the benchmark model in terms of prediction accuracy and time efficiency. When the α value is around 0.7, the model effect is optimal. Show more

Keywords: Feature learning, social network, representation learning, neural network

DOI: 10.3233/JIFS-189010

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5253-5262, 2020

Authors: Shang, Kun

Article Type: Research Article

Abstract: In the process of informatization, there are also some new problems, mainly information can’t be shared and integrated, distributed resources can’t be used effectively, these problems make the industry face new challenges. The goal of this paper is to combine the grid technology and ontology organically, to build a unified information system integration and interoperation platform based on semantics, to realize information sharing and accelerate the pace of informatization. The method is to construct the whole structure of the system according to the actual needs of the system. This paper firstly analyzes the current research status and existing problems of …semantic grid service matching, and proposes a semantic layered matching algorithm based on Massimo Paolucci elastic matching algorithm. To verify the feasibility and effectiveness of the hierarchical matching algorithm based on semantics, a prototype system named SGSM was designed and its functional model, matching process and performance were studied. Experimental results show that for the semantic-based hierarchical matching algorithm proposed in this paper, the threshold value of service semantic correlation degree is 0.84, the threshold value of service basic concept matching degree is 0.89, the threshold value of service comprehensive similarity degree is 0.66, and the threshold value of service quality matching degree is 0.78. Statistics through the experiment, the above three methods of recall, respectively, 33%, 62%, 85%, the precision is respectively: 29%, 57%, 88%, and illustrate the hierarchical matching algorithm based on semantic is feasible in practical application, compared with the traditional service based on keyword matching algorithm and Massimo Paolucci elastic matching algorithm on the recall and precision are improved significantly. Show more

Keywords: Grid environment, semantic research, web services, service discovery

DOI: 10.3233/JIFS-189011

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5263-5272, 2020

Authors: Li, Zhancang

Article Type: Research Article

Abstract: The application of video and image segmentation is carried out from the aspects of improving the accuracy of segmentation and reducing the calculation time, but the segmentation result is affected by the initial curve position, so this paper proposes a new method. As an important part of the Internet, pictures are usually used to help visitors understand. The image contains a lot of deep-level video information, which is an important basis for video content retrieval and data analysis. In this paper, combining the texture and edge features of the image in the process of text location, a multi-scale Gabor filter …bank is proposed to transform the original image, and a priori knowledge of the text region is used to process the non-text object in the transform result. In the part of extracting text from pictures, and improved TF-IDF algorithm, BC-TF-IDF algorithm, is proposed to extract text from pictures. To ensure the integrity of the extracted image, the Sobel algorithm is used to process the image in the edge extraction step. Finally, the above method is applied to the Weibo network, and a system of collecting and recognizing the character content of the Weibo image is set up, which completes the function of collecting and gradually recognizing the Weibo image, and verifies the proposed localization method. Show more

Keywords: Image analysis, text recognition, image separation

DOI: 10.3233/JIFS-189012

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5273-5281, 2020

Authors: Nie, Zhongchun | Tao, Weijun | Huan, Shi

Article Type: Research Article

Abstract: Nowadays, urbanization has become a trend, and the realization of urbanization cannot be separated from the implementation of various projects. In the process of project implementation, the most critical issue is safety, so it is extremely necessary to monitor the project safety. Traditional manual monitoring cannot meet the development of today’s project, and the design of an automatic monitoring system for project safety has become a hot spot. In this paper, based on image processing and monitoring technology, and engineering safety monitoring and control system based on image quality analysis is studied, which can detect the engineering safety in real-time. …Firstly, the image acquisition equipment is used to collect engineering images, and image processing is carried out to improve the image quality. Secondly, the convolutional neural network is used to realize image security analysis and detect the unsafe risk in engineering. Finally, combined with network technology, the automatic monitoring and control system of engineering safety based on image quality analysis is realized. Through simulation analysis, it is found that image processing can effectively remove noise and other interference and improve image quality. And the convolutional neural network can effectively detect the safety problems in the project, which shows that the design and implementation of the project safety monitoring and control system, it can achieve real-time safety monitoring in the implementation of the project, and has a good application effect in the project safety monitoring. Show more

Keywords: Engineering safety, image quality analysis, convolutional neural network, monitoring and control system

DOI: 10.3233/JIFS-189013

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5283-5290, 2020

Authors: Duan, Zhimei | Yuan, Xiaojin | Zhu, Rongfei

Article Type: Research Article

Abstract: Energy is an indispensable material resource for human production and life. It is a powerful engine and an important guarantee for human survival, economic and social sustainable development and world change. The economy is developing rapidly, the demand for energy continues to grow, energy consumption has increased sharply in a short period, and the security of energy supply and demand has also shown a severe trend. Predicting energy demand is especially important. However, due to the many influencing factors and the lack of energy data, the energy demand prediction has great uncertainty in the prediction results. Because of the above …problems, this paper proposes an energy big data demand prediction model based on a fuzzy rough set model. Firstly, according to the data, the factors affecting the energy demand are determined, and the fuzzy C-means clustering algorithm is used to discretize the data according to the characteristics of the fuzzy rough set. Then the decision table is established and the attribute importance is calculated, and then the neighborhood rough set is used for attribute reduction. Then extract the correlation rules to establish a prediction model. Compare the prediction model proposed in this paper with the existing gray prediction method and energy elasticity coefficient method. The results show that this method can more scientifically predict the changes in energy big data demand. Finally, based on the experimental results, the corresponding strategies for optimizing the energy structure are proposed to provide reference for the optimization and development of energy demand. Show more

Keywords: Energy big data, fuzzy rough set, demand prediction, structure optimization

DOI: 10.3233/JIFS-189014

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5291-5300, 2020

Authors: Li, Feng

Article Type: Research Article

Abstract: The diameter and distance parameters of a network play very significant roles in analyzing the efficiency of a communication network, these parameters provide some efficient ways to measure information time delay in communication networks. We use the lexicographic product method to construct a larger network model, which is called the lexicographic product network by some specified small graphs. Network models based on the lexicographic product method contain these small graphs as sub-networks, and many desirable properties of these sub-networks are preserved. By using algebra graph theory, we investigated the diameter parameters of the lexicographic product network, and established an enumeration …formula which only depends on the parameters of sub-networks. By analyzing the diameter formula and comparing it with other network models, it is proved that the lexicographic product network has a smaller time delay. Show more

Keywords: Communication network, transmission delay, lexicographic product, maximum distance, graph

DOI: 10.3233/JIFS-189015

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5301-5309, 2020

Authors: Hu, Zhengquan | Liu, Yu | Niu, Xiaowei | Lei, Guoping

Article Type: Research Article

Abstract: As aerospace technology, computer technology, network communication technology and information technology become more and more perfect, a variety of sensors for measurement and remote sensing are constantly emerging, and the ability to acquire remote sensing data is also continuously enhanced. Synthetic Aperture Radar Interferometry (InSAR) technology greatly expands the function and application field of imaging radar. Differential InSAR (DInSAR) developed based on InSAR technology has the advantages of high precision and all-weather compared with traditional measurement methods. However, DInSAR-based deformation monitoring is susceptible to spatiotemporal coherence, orbital errors, atmospheric delays, and elevation errors. Since phase noise is the main error …of InSAR, to determine the appropriate filtering parameters, an iterative adaptive filtering method for interferogram is proposed. For the limitation of conventional DInSAR, to improve the accuracy of deformation monitoring as much as possible, this paper proposes a deformation modeling based on ridge estimation and regularization as a constraint condition, and introduces a variance component estimation to optimize the deformation results. The simulation experiment of the iterative adaptive filtering method and the deformation modeling proposed in this paper shows that the deformation information extraction method based on differential synthetic aperture radar has high precision and feasibility. Show more

Keywords: InSAR, DInSAR, deformation monitoring, variance component estimation

DOI: 10.3233/JIFS-189016

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5311-5318, 2020