Journal of Intelligent & Fuzzy Systems - Volume 38, issue 2

Authors: Siguang, Dai | Guihua, Nie | Pingfeng, Liu

Article Type: Research Article

Abstract: An optimization algorithm is designed for the solution of the two-way principal-agent model with lower and upper bounds. This work analyzed the two-way principal-agent relationship between the two players who both have dual status: the principal status and the agent status, then proposed the expected utility function for the virtual principal, and a two-way principal-agent model with lower and upper bounds which embodies two players and two-side constraints was established too, then the upper and lower bounds of the parameters are determined by the fixed point theorem, finally Pivoting algorithm and sequence of quadratic programming method were used to solve …those models. The example indicated that it is necessary to balance the investment and return values of the alliance members to maximize the utility of the alliance and achieve real incentive for them by determining the appropriate reserved utility value. Show more

Keywords: Lower and upper bounds, two-way principal-agent model, virtual principal, pivoting algorithm, sequential quadratic programming

DOI: 10.3233/JIFS-179513

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 2, pp. 1499-1508, 2020

Authors: Hui, Liu | Xiaojun, Zhang

Article Type: Research Article

Abstract: In view of identification of strong mine quake and rock burst, the mine geology, structural mechanics, mining production data and acoustic emission monitoring process data are infused to build two fuzzy process neural network models based on fuzzy set theory. The model integrates fuzzy logic inference mechanism with process neural network process signal analysis and learning capacity. It presents domain knowledge based on fuzzy set and membership function, and adaptively establishes computational logic and fuzzy decision rules based on process signal distribution features, which can effectively infuse multi-source information and prior knowledge, demonstrating good ability to comprehensively analyze various quantitative …and qualitative mixed information and identify microquake features, as well as small sample modeling capacity. It has good adaptability for predictive analysis of strong mine quake and rock burst with uncertainty. Show more

Keywords: Coal rock mass impact, fuzzy, neural network

DOI: 10.3233/JIFS-179514

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 2, pp. 1509-1518, 2020

Authors: Wang, Chang | Sun, Qinyu | Li, Zhen | Zhang, Hongjia | Fu, Rui

Article Type: Research Article

Abstract: The frequent false alarms in Forward Collision Warning systems not only disturb the normal operation of drivers, but also reduce the user acceptance of the warning systems. However, drivers with disparate driving characteristics possess different safety cognition of car-following braking behavior; systems with stationary warning thresholds inevitably lead to higher false positive and false negative rates for aggressive and conservative drivers, respectively. In this study, we proposed an adaptive algorithm that learns the characteristics of individual drivers during car-following braking processes, and determined the optimal threshold online to adapt to different drivers. Signal detection theory was employed and the results …of the accuracy, false negative rate, and false positive rate were used to capture drivers’ characteristics of car-following braking behavior. The optimal warning thresholds were continuously selected online during the learning stage based on changes in the drivers’ characteristics. The developed algorithm by conducting actual vehicle tests with two participants were evaluated. The offline statistical analysis results of the participants’ car-following braking characteristics were compared with the online results of the warning threshold adjustments from the adaptive algorithm. The comparison results indicated that the adaptive algorithm could effectively capture the drivers’ car-following braking characteristics and determine an appropriate warning threshold. Show more

Keywords: Car-following braking, forward collision warning system, self-learning, signal detection theory

DOI: 10.3233/JIFS-179515

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 2, pp. 1519-1530, 2020

Authors: Xu, Moli | Xiong, Deping | Yang, Mengyuan

Article Type: Research Article

Abstract: In view of the current demand for risk identification and classification prevention of bank outlets caused by the difficulty in identifying operational efficiency and wind control capability, a risk data measurement and warning classification model based on information entropy and BP neural network is proposed. The model establishes two-level risk data measurement elements from three dimensions. Based on the data set itself, the information entropy is used to determine the weights of the two-level risk elements, and then calculates the risk quantities recorded under the first-level risk measurement elements in the data set. The BP neural network is used to …output the risk data classification results without presupposing the weights of the measurement. The proposed model obtains smaller reductions and higher classification accuracies with relatively low computational cost. Experiments show that the model can measure and classify risk data with very low mis-judgment rate and small mis-judgment bias. Show more

Keywords: Information Entropy, BP neural network, risk classification

DOI: 10.3233/JIFS-179516

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 2, pp. 1531-1538, 2020

Authors: Cai, Fangbo | He, Jingsha | Ali Zardari, Zulfiqar | Han, Song

Article Type: Research Article

Abstract: Access control is an important mechanism to protect sensitive information and relational system resources. The traditional access control model (TACM), such as DAC, MAC, RBAC, etc., is no longer suitable for open network due to the lack of dynamic permission management. The increasing network nodes make the information storage and resource access becoming distributed. The traditional access control model has the characteristics of low adaptive ability and single deployment and application mode due to the centralized management mode. Therefore, this access control environment inevitably puts access control pressure on access control authorization. In order to overcome the shortcomings of traditional …access control model, a new access control model named DMPAC (Distributed management of permission for access control model) is proposed in the paper. The authorization mechanism of the model has a distributed and dynamic management access permission, and all nodes covered by the model have the opportunity to participate in the execution of access and control. The model DMPAC provides the benefits of traditional access control models in terms of secure access and dynamic management. We also describe the framework and execution process of the model and the application of DMPAC in access control. At last, we will present some experimental results to show that while maintaining the effectiveness of distributed access control through the management of access permissions, DMPAC can achieve the performance of traditional access control models. Show more

Keywords: Network security, access control, distributed model

DOI: 10.3233/JIFS-179517

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 2, pp. 1539-1548, 2020

Authors: Fang, Weijian | Tan, Xiaoling | Wilbur, Dominic

Article Type: Research Article

Abstract: With the increasingly serious network security situation, intrusion detection technology has become an important means to ensure network security. Therefore, it has become a consensus to introduce the theory and method of machine learning into intrusion detection, and has made good progress in this research field in recent years. In this paper, a machine learning intrusion detection system is proposed. The system uses the intrusion detection of Elman neural network and the intrusion detection of robust SVM neighbour classification to solve the above problems. Elman neural network intrusion detection uses clustering algorithm to cluster the text of the network packet, …which overcomes the defect of missing the text information of the network packet. At the same time, the ability to detect abnormal behaviour between network packet sequences is improved. At the same time, robust SVM neighbour classification intrusion detection can achieve the feature space weighting of the optimal classification face host system log, eliminate the negative impact of noise data, reduce the false alarm rate of intrusion detection, and improve the detection accuracy. Under the requirement of false alarm rate of 0, the intrusion detection based on robust SVM neighbour classification can achieve 87.3% detection rate; when the false alarm rate is 2.8%, the detection rate is 100%. Show more

Keywords: Information security, machine learning method, intrusion detection technology, Elman neural network, robust SVM

DOI: 10.3233/JIFS-179518

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 2, pp. 1549-1558, 2020

Authors: Tan, Huobin | Tian, Yongfeng | Wang, Linfeng | Lin, Guangyan

Article Type: Research Article

Abstract: The name disambiguation task is designed to solve the name ambiguity problem of documents of multiple persons who have the same name with one another. The task aims to partition all the publications belonging to multiple person with the same name and realize that each decomposed partition is composed of publications of a unique person. Many works on name disambiguation task have a common feature that clustering method is usually used in the last step. The paper presents a complementary study to these works from another point of view. Based on the idea that documents with strong association relationships are …likely to belong to the same author, this paper proposes a method of discovering meta clusters by graph partition with a heuristic rule to improve these clustering-based works. Specially, different from these works, this work uses clustering ensemble method instead of clustering method in the last step. Experimental results on a real-life dataset show that the improved method has satisfactory performance compared with the clustering-based baseline method. Show more

Keywords: Name disambiguation, meta clusters, clustering ensemble, graph partition

DOI: 10.3233/JIFS-179519

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 2, pp. 1559-1568, 2020

Authors: Fan, Linyuan

Article Type: Research Article

Abstract: Locally linear embedding (LLE) is a classical nonlinear dimensionality reduction algorithm, and it has been widely used in image feature selection. LLE reduces the dimensions of a data set only by exploring the geometric structure, which is calculated by Euclidean distance and makes the embedding result be sensitive to noise. Moreover, the choice of the number of nearest neighbors is fixed for all data points and only given by human experience. In order to overcome these problems, a geometric parameter adaptive LLE (PALLE) algorithm is proposed in this paper. This algorithm jointly uses Geodesic distance and Cosine similarity to replace …Euclidean distance, and then the number of neighbors is adaptable selected by weak-σ rule. Extensive experimental results over various real-life data sets have demonstrated the superiority of the proposed algorithm in terms of image feature dimensionality reduction compared with classical LLE and other well-known algorithms. Show more

Keywords: Image feature, dimensionality reduction, LLE, parameter adaptive

DOI: 10.3233/JIFS-179520

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 2, pp. 1569-1577, 2020

Authors: Tang, Ziyan

Article Type: Research Article

Abstract: This paper uses the improved AHP comprehensive evaluation method to evaluate the purchase intention of Chinese retailer private brands. This method can reduce the subjectivity of the weight selection and improve the accuracy of weights. The paper also uses the degree of membership to grade the evaluation levels and conducts the backward evaluation to the different levels of indicators one by one, thus obtaining the relative development level of each indicator. The evaluation results show that consumers have higher acceptance of retailer private brands and they have strong purchase intentions.

Keywords: Fuzzy comprehensive evaluation, intention of retailer private brands, improved AHP method

DOI: 10.3233/JIFS-179521

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 2, pp. 1579-1584, 2020

Authors: Zhou, Qingyuan | Zhang, Zongming | Wang, Yuancong

Article Type: Research Article

Abstract: With the rapid popularization and rapid development of the Internet in the world, e-commerce has gradually become the mainstream trade mode. E-commerce has its own unique trading mode and brand-new global business opportunities. More and more people conduct online transactions through the Internet. According to the characteristics of data in B2C e-commerce system, data mining management module is designed in B2C e-commerce management system. Data mining technology is used to preprocess data, data mining and mining results. It is implemented using J2EE’s B/S architecture. With the continuous growth of B2C e-commerce scale, logistics bottlenecks have become increasingly prominent, and e-commerce …distribution model based on cloud logistics integrates IT information technology with traditional logistics information systems. Integrate logistics service demand and logistics distribution capabilities, and provide corresponding cloud logistics information and management platform system. It will help solve the problem of logistics and distribution of B2C e-commerce in China and promote the healthy and rapid development of e-commerce economy. Show more

Keywords: Data Cube, B2C E-Commerce, Intelligent Logistics

DOI: 10.3233/JIFS-179522

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 2, pp. 1585-1592, 2020