Journal of Intelligent & Fuzzy Systems - Volume 40, issue 5

Authors: Priambodo, Bagus | Ahmad, Azlina | Kadir, Rabiah Abdul

Article Type: Research Article

Abstract: Traffic congestion on a road results in a ripple effect to other neighbouring roads. Previous research revealed existence of spatial correlation on neighbouring roads. Similar traffic patterns with regards to day and time can be seen amongst roads in a neighbouring area. Presently, nonlinear models of neural network are applied on historical data to predict traffic congestion. Even though neural network has successfully modelled complex relationships, more time is needed to train the network. A non-parametric approach, the k-nearest neighbour (K-NN) is another method for forecasting traffic condition which can capture the nonlinear characteristics of traffic flow. An earlier study …has been done to predict traffic flow using K-NN based on connected roads (both downstream and upstream). However, impact of road congestion is not only to connected roads, but also to roads surrounding it. Surrounding roads that are impacted by road congestion are those having ‘high relationship’ with neighbouring roads. Thus, this study aims to predict traffic state using K-NN by determining high relationship roads within neighbouring roads. We determine the highest relationship neighbouring roads by clustering the surrounding roads by combining grey level co-occurrence matrix (GLCM) with k-means. Our experiments showed that prediction of traffic state using K-NN based on high relationship roads using both GLCM and k-means produced better accuracy than using k-means only. Show more

Keywords: Classification algorithm, clustering algorithm, machine learning algorithm, nearest neighbour search, intelligent transportation system

DOI: 10.3233/JIFS-201493

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9059-9072, 2021

Authors: Zhang, Mo | Zhang, Qinghua | Gao, Man

Article Type: Research Article

Abstract: As a new extended model of fuzzy sets, hesitant fuzzy set theory is a useful tool to process uncertain information in decision making problems. The traditional hesitant fuzzy multi-attribute decision making (MADM) can only choose an optimal strategy, which is not suitable for all of the complex scenarios. Typically, in practical application, decision making problems may be more complicated involving three options of acceptance, non-commitment and rejection decisions. Three-way decisions, which divide universe into three disjoint regions by a pair of thresholds, are more efficient to deal with these problems. Therefore, how to utilize three-way decision theory to process hesitant …fuzzy information is an essential issue to be studied. In this paper, from the perspective of hesitant fuzzy distance, a hesitant fuzzy three-way decision model is proposed. First, because hesitant fuzzy element (HFE) is a set of several possible membership degrees, it cannot be compared with thresholds directly. Hence, this paper converts it into the comparison between the distance and the thresholds. Then, to calculate thresholds more reasonably, shadowed set theory is introduced to avoid the subjectivity of threshold acquisition. Furthermore, sequential strategy is adopted to solve the multi-attribute decision making problems. Finally, an example of medical diagnosis and simulation experiments are given to prove the accuracy and efficiency of the proposed hesitant fuzzy three-way decision model. Show more

Keywords: Hesitant fuzzy sets, three-way decisions, shadowed sets, sequential strategy

DOI: 10.3233/JIFS-201524

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9073-9084, 2021

Authors: Liu, Peide | Pan, Qian | Xu, Hongxue

Article Type: Research Article

Abstract: The normal intuitionistic fuzzy number (NIFN), which membership function and non-membership function are expressed by normal fuzzy numbers (NFNs), can better describe the normal distribution phenomenon in the real world, but it cannot deal with the situation where the sum of membership function and non-membership function is greater than 1. In order to make up for this defect, based on the idea of q-rung orthopair fuzzy numbers (q-ROFNs), we put forward the concept of normal q-rung orthopair fuzzy numbers (q-RONFNs), and its remarkable characteristic is that the sum of the qth power of membership function and the qth …power of non-membership function is less than or equal to 1, so it can increase the width of expressing uncertain information for decision makers (DMs). In this paper, firstly, we give the basic definition and operational laws of q-RONFNs, propose two related operators to aggregate evaluation information from DMs, and develop an extended indifference threshold-based attribute ratio analysis (ITARA) method to calculate attribute weights. Then considering the multi-attributive border approximation area comparison (MABAC) method has strong stability, we combine MABAC with q-RONFNs, put forward the q-RONFNs-MABAC method, and give the concrete decision steps. Finally, we apply the q-RONFNs-MABAC method to solve two examples, and prove the effectiveness and practicability of our proposed method through comparative analysis. Show more

Keywords: Normal q-rung orthopair fuzzy numbers, multi-attributive border approximation area comparison, the q-RONFNs-MABAC method, indifference threshold-based attribute ratio analysis

DOI: 10.3233/JIFS-201526

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9085-9111, 2021

Authors: Xu, Tingting | Zhang, Hui | Li, Boquan

Article Type: Research Article

Abstract: In this paper, the concept of 2-tuple probability weight is presented, and on this basis, the technique for order preference by similarity to ideal solution (TOPSIS) method in Pythagorean fuzzy environment is given. First, the definition of 2-tuple probability weight is put forward, and two examples are provided to illustrate that 2-tuple probability weight can effectively prevent the loss of information. Second, the notion of real-value 2-tuple is defined for any two real numbers, and some basic operations, operation properties, and sorting functions are introduced. Finally, a 2-tuple probability weight Euclidean distance is provided, a new Pythagorean fuzzy TOPSIS method …is further proposed, and the flexibility and effectiveness of the proposed methods are illustrated by an example and two comparative analyses. Show more

Keywords: Pythagorean fuzzy set, 2-tuple probability weight, real-value 2-tuple, TOPSIS method

DOI: 10.3233/JIFS-201533

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9113-9126, 2021

Authors: Yan, Zheping | Zhang, Jinzhong | Zeng, Jia | Tang, Jialing

Article Type: Research Article

Abstract: In this paper, a water wave optimization (WWO) algorithm is proposed to solve the autonomous underwater vehicle (AUV) path planning problem to obtain an optimal or near-optimal path in the marine environment. Path planning is a prerequisite for the realization of submarine reconnaissance, surveillance, combat and other underwater tasks. The WWO algorithm based on shallow wave theory is a novel evolutionary algorithm that mimics wave motions containing propagation, refraction and breaking to obtain the global optimization solution. The WWO algorithm not only avoids jumps out of the local optimum and premature convergence but also has a faster convergence speed and …higher calculation accuracy. To verify the effectiveness and feasibility, the WWO algorithm is applied to solve the randomly generated threat areas and generated fixed threat areas. Compared with other algorithms, the WWO algorithm can effectively balance exploration and exploitation to avoid threat areas and reach the intended target with minimum fuel costs. The experimental results demonstrate that the WWO algorithm has better optimization performance and is robust. Show more

Keywords: Water wave optimization (WWO), autonomous underwater vehicle (AUV), path planning, randomly generated threat areas, generated fixed threat areas

DOI: 10.3233/JIFS-201544

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9127-9141, 2021

Authors: Shakeri Aski, Baharak | Toroghi Haghighat, Abolfazl | Mohsenzadeh, Mehran

Article Type: Research Article

Abstract: Using Web services to assess data in a distributed configuration, apart from different hardware and software platforms for employing standard criteria, is practical because of development in the Internet and network infrastructure. Distributed applications can transfer data using web services. Trust is the main criterion to select the appropriate web service. Neuro-fuzzy systems including clustering are applied to assess the trust of single web services. This paper considers nine criteria including quality of service, subjective perspectives, user preference, credibility of raters, objective perspectives, dynamic computing, bootstrapping, independency and security. To obtain a neuro-fuzzy system with high prediction accuracy, the paper …considers eight neuro-fuzzy membership functions (i.e., trapmf, gbellmf, trimf, gaussmf, dsigmf, psigmf, gauss2mf, pimf) using the k-means clustering. Also, to increase the speed and reduce the fuzzy rules, a three-level neuro-fuzzy system (13 neuro-fuzzy) is investigated. The main target of this paper is evaluating the trust of single web services using the nine aforementioned criteria, as web services selection is a main issue which is still absorbing researchers to conduct research works on this field and analyze it. Ultimately, the results show reasonable root mean square error (RMSE) amount, precision value, recall value, and F-score value. In comparison to previous research works, this study obtained the lower amounts of errors and presents the more accurate trust of single web services. Show more

Keywords: Web service, internet service, trust, neuro-fuzzy system, k-means

DOI: 10.3233/JIFS-201560

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9143-9157, 2021

Authors: Ye, Jun | Du, Shigui | Yong, Rui | Zhang, Fangwei

Article Type: Research Article

Abstract: In indeterminate and inconsistent setting, existing simplified neutrosophic indeterminate set (SNIS) can be depicted by the neutrosophic number (NN) functions of the truth, falsity and indeterminacy. Then, the three NN functions in SNIS lack their refined expressions and then the simplified neutrosophic indeterminate decision making (DM) method cannot carry out the multicriteria DM problems with both criteria and sub-criteria in the setting of SNISs. To overcome the flaws, this study first proposes a new notion of a refined simplified neutrosophic indeterminate set (RSNIS), which is described by the refined truth, falsity and indeterminate NN information regarding both elements and sub-elements …in a universe set, as the extension of SNIS. Next, we propose the arccosine and arctangent similarity measures of RSNISs and their multicriteria DM method with various indeterminate risk ranges so as to carry out multicriteria DM problems with weight values of both criteria and sub-criteria in RSNIS setting. Lastly, the proposed DM method is applied to a multicriteria DM example of slope design schemes for an open pit mine to illustrate its application in the indeterminate DM problem with RSNISs. The decision results and comparative analysis indicate the rationality and efficiency of the proposed DM method with different indeterminate risk ranges. Show more

Keywords: Refined simplified neutrosophic indeterminate set, arccosine similarity measure, arctangent similarity measure, multicriteria decision making, Slope design scheme

DOI: 10.3233/JIFS-201571

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9159-9171, 2021

Authors: Gao, Yuxuan | Liang, Haiming | Sun, Bingzhen

Article Type: Research Article

Abstract: With the rapid development of e-commerce, whether network intelligent recommendation can attract customers has become a measure of customer retention on online shopping platforms. In the literature about network intelligent recommendation, there are few studies that consider the difference preference of customers in different time periods. This paper proposes the dynamic network intelligent hybrid recommendation algorithm distinguishing time periods (DIHR), it is a integrated novel model combined with the DEMATEL and TOPSIS method to solved the problem of network intelligent recommendation considering time periods. The proposed method makes use of the DEMATEL method for evaluating the preference relationship of customers …for indexes of merchandises, and adopt the TOPSIS method combined with intuitionistic fuzzy number (IFN) for assessing and ranking the merchandises according to the indexes. We specifically introduce the calculation steps of the proposed method, and then calculate its application in the online shopping platform. Show more

Keywords: DIHR, recommendation algorithm, network intelligent recommendation system, online shopping platform

DOI: 10.3233/JIFS-201579

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9173-9185, 2021

Authors: Jothikumar, C. | Venkataraman, Revathi | Sai Raj, T. | Selvin Paul Peter, J. | Nagamalleswari, T.Y.J.

Article Type: Research Article

Abstract: Wireless sensor network is a wide network that works as a cutting edge model in industrial applications. The sensor application is mostly used for high security systems that provide safety support to the environment. The sensor system senses the physical phenomenon, processes the input signal and communicates with the base station through its neighbors. Energy is the most important criterion to support a live network for long hours. In the proposed system, the EUCOR (Efficient Unequal Clustering and Optimized Routing) protocol uses the objective function to identify the efficient cluster head with variable cluster size. The computation of the objective …function deals with the ant colony approach for minimum energy consumption and the varying size of the cluster in each cycle is calculated based on the competition radius. The system prolongs the lifespan of the nodes by minimizing the utilization of energy in the transmission of packets in the networks when compared with the existing system. Show more

Keywords: Energy optimization, routing, cluster head, wireless sensor network

DOI: 10.3233/JIFS-201607

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9187-9195, 2021

Authors: Rajeswari, A.R. | Kulothungan, K. | Ganapathy, Sannasi | Kannan, Arputharaj

Article Type: Research Article

Abstract: WSN plays a major role in the design of IoT system. In today’s internet era IoT integrates the digital devices, sensing equipment and computing devices for data sensing, gathering and communicate the data to the Base station via the optimal path. WSN, owing to the characteristics such as energy constrained and untrustworthy environment makes them to face many challenges which may affect the performance and QoS of the network. Thus, in WSN based IoT both security and energy efficiency are considered as herculean design challenges and requires important concern for the enhancement of network life time. Hence, to address these …problems in this paper a novel secure energy aware cluster based routing algorithm named Trusted Energy Efficient Fuzzy logic based clustering Algorithm (TEEFCA) has been proposed. This algorithm consists of two major objectives. Firstly, the trustworthy nodes are identified, which may act as candidate nodes for cluster based routing. Secondly, the fuzzy inference system is employed under the two circumstances namely selection of optimal Cluster Leader (CL) and cluster formation process by considering the following three parameters such as (i) node’s Residual Energy level (ii) Cluster Density (iii) Distance Node BS. From, the experiment outcomes implemented using MATLAB it have been proved that TEEFCA shows significant improvement in terms of power conservation, network stability and lifetime when compared to the existing cluster aware routing approaches. Show more

Keywords: Internet of Things (IoT), WSN, energy, trust, clustering and routing

DOI: 10.3233/JIFS-201633

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9197-9211, 2021