Journal of Intelligent & Fuzzy Systems - Volume 40, issue 3

Authors: Zhao, Mengwei | Wei, Guiwu | Wei, Cun | Wu, Jiang | Wei, Yu

Article Type: Research Article

Abstract: The urban ecological risk assessment is a new research field, which has been rising and developing with the change of environment management objectives and environment conception. The urban ecological risk assessment could be regarded as a classical multi-attribute group decision making (MAGDM) issue. The interval-valued intuitionistic fuzzy set (IVIFS) can fully describe the uncertain information for the urban ecological risk assessment. Furthermore, the classical TODIM (an acronym in Portuguese for Interactive Multi-Criteria Decision Making) is built on cumulative prospect theory (CPT), which is a selectable method in reflecting the DMs’ psychological behavior. Thus, in this paper, the TODIM method based …on the CPT is proposed for MAGDM issue under IVIFS. At the same time, it is enhancing rationality to get the weight information of attributes by using the interval-valued intuitionistic fuzzy entropy weight method. And focusing on hot issues in contemporary society, this article applies the discussed method to urban ecological risk assessment, and demonstrates urban ecological risk assessment model based on the proposed method. Finally, through comparing the outcome of comparative analysis, we conclude that this improved approach is acceptable. Show more

Keywords: Multi-attribute group decision making (MAGDM), interval-valued intuitionistic fuzzy sets, TODIM, urban ecological risk assessment

DOI: 10.3233/JIFS-200534

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4091-4106, 2021

Authors: Raza, Zahid | Bataineh, Mohammad Saleh | Sukaiti, Mark Essa

Article Type: Research Article

Abstract: Regular plane tessellations can easily be constructed by repeating regular polygons. This design is of extreme importance for direct interconnection networks as it yields high overall performance. The honeycomb and the hexagonal networks are two such popular mesh-derived parallel networks. The first and second Zagreb indices are among the most studied topological indices. We now consider analogous graph invariants, based on the second degrees of vertices, called Zagreb connection indices. The main objective of this paper is to compute these connection indices for the Hex, Hex derived and some honeycomb networks.

Keywords: Honeycomb network, hexagonal network, hex-derived networks, connection number, Zagreb connection indices

DOI: 10.3233/JIFS-200659

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4107-4114, 2021

Authors: Abulaish, Muhammad | Fazil, Mohd

Article Type: Research Article

Abstract: In online social networks (OSNs), socialbots are responsible for various malicious activities, and they are mainly programmed to imitate human-behavior to bypass the existing detection systems. The socialbots are generally successful in their malicious intent due to the existence of OSN users who follow them and thereby increase their reputation in the network. Analysis of the socialbot networks and their users is vital to comprehend the socialbot problem from target users’ perspective. In this paper, we present a machine learning-based approach for characterizing and detecting socialbot targets , i.e., users who are susceptible to be trapped by the socialbots. We …model OSN users based on their identity and behavior information, representing the static and dynamic components of their personality. The proposed approach classifies socialbot targets into three categories viz. active , reactive , and inactive users. We evaluate the proposed approach using three classifiers over a dataset collected from a live socialbot injection experiment conducted on Twitter. We also present a comparative evaluation of the proposed approach with a state-of-the-art method and show that it performs significantly better. On feature ablation analysis , we found that network structure and user intention and personality related dynamic features are most discriminative, whereas static features show the least impact on the classification. Additionally, following rate , multimedia ratio , and follower rate are most relevant to segregate different categories of the socialbot targets . We also perform a detailed topical and behavioral analysis of socialbot targets and found active users to be suspicious. Further, joy and agreeableness are the most dominating personality traits among the three categories of the users. Show more

Keywords: Machine learning, social network analysis, social network security, user profiling, socialbots

DOI: 10.3233/JIFS-200682

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4115-4133, 2021

Authors: Pei, Lidan | Jin, Feifei | Langari, Reza | Garg, Harish

Article Type: Research Article

Abstract: Unlike other linguistic modellings, probabilistic linguistic term sets can express clearly the importance of different linguistic variables. The notion of Probabilistic Linguistic Preference Relations (PLPRs) constitutes an extension of linguistic preference relations, and as such has received increasing attention in recent years. In group decision-making (GDM) problems with PLPRs, the processes of consistency adjustment, consensus-achieving and desirable alternative selection play a key role in deriving the reliable GDM results. Therefore, this paper focuses on the construction of a GDM method for PLPRs with local adjustment strategy. First, we redefine the concepts of multiplicative consistency and consistency index for PLPRs, and …some properties for multiplicative consistent PLPRs are studied. Then, in order to obtain the acceptable multiplicative consistent PLPRs, we propose a convergent consistency adjustment algorithm. Subsequently, a consensus-achieving method with PLPRs is constructed for reaching the consensus goal of experts. In both consistency adjustment process and consensus-achieving method, the local adjustment strategy is utilized to retain the original evaluation information of experts as much as possible. Finally, a GDM method with PLPRs is investigated to determine the reliable ranking order of alternatives. In order to show the advantages of the developed GDM method with PLPRs, an illustration for determining the ranking of fog-haze influence factors is given, which is followed by the comparative analysis to clarify its validity and merits. Show more

Keywords: Group decision making, consistency-improving algorithm, consensus-achieving algorithm, local adjustment strategy, probabilistic linguistic preference relations

DOI: 10.3233/JIFS-200724

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4135-4154, 2021

Authors: Zong, Yi | Xing, Hao

Article Type: Research Article

Abstract: Scientific customer stratification method can help enterprises identify valuable customers, thus effectively improving the operating profit of enterprises. However, current customer stratification methods have not considered the impact of cost to service (CTS) on customer value (such as the RFM model). In this paper, K-mean clustering method is adopted to classify customers into four categories, namely 1) the most valuable customers, 2) valuable customers, 3) general customers and 4) customers with low contribution. By adding a new evaluation dimension of CTS, the original RFM model is improved. In this way, the RFMC model is built and can provide more comprehensive …evaluation on customer value. Finally, the results show that the addition of CTS index significantly changes the clustering results of the original RFM model and the overall consideration of consumption amount and CTS truly reflect the customer value. Thus, the improved RFMC model optimizes the results of customer stratification and it can effectively sort out the valuable customers for enterprises. Enterprises will be more dedicated to serving the valuable customers so as to maximize profits and reduce service costs of customers with lower value to make up for profit losses. Show more

Keywords: Cost-to-serve (CTS), RFM model, RFMC model, customer stratification

DOI: 10.3233/JIFS-200737

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4155-4167, 2021

Authors: Yang, Dongqi | Zhang, Wenyu | Wu, Xin | Ablanedo-Rosas, Jose H. | Yang, Lingxiao | Yu, Wangzhi

Article Type: Research Article

Abstract: With the rapid development of commercial credit mechanisms, credit funds have become fundamental in promoting the development of manufacturing corporations. However, large-scale, imbalanced credit application information poses a challenge to accurate bankruptcy predictions. A novel multi-stage ensemble model with fuzzy clustering and optimized classifier composition is proposed herein by combining the fuzzy clustering-based classifier selection method, the random subspace (RS)-based classifier composition method, and the genetic algorithm (GA)-based classifier compositional optimization method to achieve accuracy in predicting bankruptcy among corporates. To overcome the inherent inflexibility of traditional hard clustering methods, a new fuzzy clustering-based classifier selection method is proposed based …on the mini-batch k-means algorithm to obtain the best performing base classifiers for generating classifier compositions. The RS-based classifier composition method was applied to enhance the robustness of candidate classifier compositions by randomly selecting several subspaces in the original feature space. The GA-based classifier compositional optimization method was applied to optimize the parameters of the promising classifier composition through the iterative mechanism of the GA. Finally, six datasets collected from the real world were tested with four evaluation indicators to assess the performance of the proposed model. The experimental results showed that the proposed model outperformed the benchmark models with higher predictive accuracy and efficiency. Show more

Keywords: Bankruptcy prediction, ensemble learning, fuzzy mini-batch clustering, heterogeneous model construction, genetic algorithm

DOI: 10.3233/JIFS-200741

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4169-4185, 2021

Authors: De (Maity), Ritu Rani | Mudi, Rajani K. | Dey, Chanchal

Article Type: Research Article

Abstract: This paper focuses on the development of a stable Mamdani type-2 fuzzy logic based controller for automatic control of servo systems. The stability analysis of the fuzzy controller has been done by employing the concept of Lyapunov. The Lyapunov approach results in the derivation of an original stability analysis that can be used for designing the rule base of our proposed online gain adaptive Interval Type-2 Fuzzy Proportional Derivative controller (IT2-GFPD) for servo systems with assured stability. In this approach a quadratic positive definite Lyapunov function is used and sufficient stability conditions are satisfied by the adaptive type-2 fuzzy logic …control system. Illustrative simulation studies with linear and nonlinear models as well as experimental results on a real-time servo system validate the stability and robustness of the developed intelligent IT2-GFPD. A comparative performance study of IT2-GFPD with other controllers in presence of noise and disturbance also proves the superiority of the proposed controller. Show more

Keywords: Type-2 fuzzy control, Lyapunov stability, self-tuning, servo position control and real time experimentation

DOI: 10.3233/JIFS-200802

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4187-4205, 2021

Authors: Badr, Majdah M.

Article Type: Research Article

Abstract: Lifetime data collected from reliability tests are among data that often exhibit significant heterogeneity caused by variations in manufacturing which make standard lifetime models inadequate. In this paper we introduce a new lifetime distribution derived from T-X family technique called exponentiated exponential Burr XII (EE-BXII) distribution. We establish various mathematical properties. The maximum likelihood estimates (MLE) for the EE-BXII parameters are derived. We estimate the precision of the maximum likelihood estimators via simulation study. Some numerical illustrations are performed to study the behavior of the obtained estimators. Finally the model is applied to a real dataset. We apply goodness of …fit statistics and graphical tools to examine the adequacy of the EE-BXII distribution. The importance of this research lies in deriving a new distribution under the name EE-BXII, which is considered the best distributions in analyzing data of life times at present if compared to many distributions in analysis real data. Show more

Keywords: EE-BXII distribution, the maximum likelihood method, Monte Carlo simulation, variance covariance matrix

DOI: 10.3233/JIFS-200819

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4207-4221, 2021

Authors: Khan, Muhammad Sajjad Ali | Khan, Amir Sultan | Khan, Israr Ali | Mashwani, Wali Khan | Hussain, Fawad

Article Type: Research Article

Abstract: The aim of this paper is to introduce the notion of linguistic interval-valued q-rung orthopair fuzzy set (LIVq-ROFS) as a generalization of linguistic q-rung orthopair fuzzy set. We develop some basic operations, score and accuracy functions to compare the LIVq-ROF values (LIVq-ROFVs). Based on the proposed operations a series of aggregation techniques to aggregate the LIVq-ROFVs and some of their desirable properties are discussed in detail. Moreover, a TOPSIS method is developed to solve a multi-criteria decision making (MCDM) problem under LIVq-ROFS setting. Furthermore, a MCDM approach is proposed based on the developed operators and TOPSIS method, then a practical …decision making example is given in order to explain the proposed method. To illustrate to effectiveness and application of the proposed method a comparative study is also conducted. Show more

Keywords: Linguistic interval-valued q-rung orthopair fuzzy set (LIVq-ROFS), LIVq-ROF aggregation operators, TOPSIS method, MCDM problem

DOI: 10.3233/JIFS-200845

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4223-4235, 2021

Authors: Muthamil Sudar, K. | Deepalakshmi, P.

Article Type: Research Article

Abstract: Software-defined networking is a new paradigm that overcomes problems associated with traditional network architecture by separating the control logic from data plane devices. It also enhances performance by providing a highly-programmable interface that adapts to dynamic changes in network policies. As software-defined networking controllers are prone to single-point failures, providing security is one of the biggest challenges in this framework. This paper intends to provide an intrusion detection mechanism in both the control plane and data plane to secure the controller and forwarding devices respectively. In the control plane, we imposed a flow-based intrusion detection system that inspects every new …incoming flow towards the controller. In the data plane, we assigned a signature-based intrusion detection system to inspect traffic between Open Flow switches using port mirroring to analyse and detect malicious activity. Our flow-based system works with the help of trained, multi-layer machine learning-based classifier, while our signature-based system works with rule-based classifiers using the Snort intrusion detection system. The ensemble feature selection technique we adopted in the flow-based system helps to identify the prominent features and hasten the classification process. Our proposed work ensures a high level of security in the Software-defined networking environment by working simultaneously in both control plane and data plane. Show more

Keywords: Software-defined networking (SDN), machine learning (ML), intrusion detection system (IDS), feature selection, flow-based IDS

DOI: 10.3233/JIFS-200850

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4237-4256, 2021