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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Catak, Ferhat Ozgur | Mustacoglu, Ahmet Fatih
Article Type: Research Article
Abstract: Today, many companies are faced with the huge network traffics mainly consisting of the various type of network attacks due to the increased usage of the botnet, fuzzier, shellcode or network related vulnerabilities. These types of attacks are having a negative impact on the organization because they block the day-to-day operations. By using the classification models, the attacks could be identified and separated earlier. The Distributed Denial of Service Attacks (DDoS) primarily focus on preventing or reducing the availability of a service to innocent users. In this research, we focused primarily on the classification of network traffics based on the …deep learning methods and technologies for network flow models. In order to increase the classification performance of a model that is based on the deep neural networks has been used. The model used in this research for the classification of network traffics evaluated and the related metrics showing the classification performance have been depicted in the figures and tables. As the results indicate, the proposed model can perform well enough for detecting DDoS attacks through deep learning technologies. Show more
Keywords: cyber security, ddos, deep learning, autoencoder
DOI: 10.3233/JIFS-190159
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 3969-3979, 2019
Authors: Leninfred, A. | Dhanya, D. | Kavitha, S. | Ashwini, M.
Article Type: Research Article
Abstract: Cloud computing is used for processing resources that are conveyed as an administration over a network and a prototype to enable beneficial on-interest network access to a general loch of configurable reckoning resources which are rapidly provisioned and discharged. While adopting cloud computing, major challenges like resource provisioning, resource allocation and security are arising. Only prevailing resource provisioning algorithm are depending upon single tier application utilizing meta-heuristic methodology. Here, we presented a multi-tier application for provisioning dynamic resources utilizing meta-heuristic methodology like Ant Colony Optimization algorithm (ACO), Simulated Annealing (SA) algorithm and hybrid algorithm which fuses ACO and SA and …also an improved cost based scheduling is used to schedule jobs within the cloud with reduced cost. Implementation outcomes displays the efficiency of provisioning resources using ACO-SA algorithm in multitier application of hybrid cloud is greater than other resource provisioning algorithms in cloud computing. Show more
Keywords: Hybrid cloud, cloud computing, resource provisioning, meta-heuristic technique, hybrid ACO-SA, improved cost based scheduling
DOI: 10.3233/JIFS-190160
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 3981-3990, 2019
Authors: Liu, Xiaoyong | Yun, Zhonghua | Yang, Hang | Zhang, Qiang
Article Type: Research Article
Abstract: In this paper, a novel method for fault detection based on an adaptive interval regression model characterized by the upper regression model (URM) and lower regression model (LRM) has been proposed. Applying the proposed method, a confidence band for the measured data, derived in the normal operating conditions of a system, is constructed.The method combines the superiorities of model sparse representation and computational efficiency of linear programming support vector regression (LP-SVR) with some ideas from L 1 -norm on approximation errors. First, the upper and lower L 1 -norms with respect to upper bound approximation error are considered, and the …both norms subject to respective constraints are integrated into LP-SVR to form new upper and lower optimization problems, respectively. Following that, optimization problem corresponding to URM and LRM are solved by linear programming and interval regression model is thus constructed to judge whether the fault occurs or not. The proposed method returns an interval output as opposed to a point output. Finally, the efficacy of this method is demonstrated by applying it on the benchmark Tennessee Eastman problem, and has been compared with conventional techniques such as principal component analysis (PCA), dynamic-PCA (DPCA) and One-Class Support Vector Machine(1-class SVM). It is shown that the proposed method is superior to those approaches in terms of performance measure of detection latency. Show more
Keywords: Fault detection, LP-SVR, L1-Norm minimization, linear programming, interval regression model
DOI: 10.3233/JIFS-190176
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 3991-4001, 2019
Authors: Too, Edna C. | Li, Yujian | Kwao, Pius | Njuki, Sam | Mosomi, Mugendi E. | Kibet, Julius
Article Type: Research Article
Abstract: Deep learning is a field of Artificial Intelligence that has recently drawn a lot of attention with the desire to build up a quick, automatic and accurate system for image identification and classification. Deep learning serves as a fundamental part of modern computer vision solutions. However, as the architectures become deep and powerful new challenges in the process of training emerge. This includes the computational cost associated with training deep and large networks. In this work, the focus is on pruning and evaluation of state-of-the-art deep convolutional neural network for image-based plant disease and plants species classification. Pruning filters allow …the reduction of parameters by removing unimportant filters and its feature maps. In this paper, the performance of pruned networks is evaluated across three datasets. It is observed that pruned DenseNet with Self-Normalization Neural Network (SNN) approach learns 2x faster compared to the initial DenseNet architecture. Additionally, pruning filters allow the reduction of the number of parameters and FLOPs by approximately 14% and 25% respectively. The aim is to create a fast and efficient model for the purpose of identification of plant diseases. Fast methods are desired for early identifications of diseases before damages occur. The proposed method achieves a satisfactory accuracy performance on PlantVillage, LeafSnap and Swedish-leaf dataset using held-out dataset. Our best pruned model gives an accuracy of 99.24%, 86.64%, and 97.5% on PlantVillage, LeafSnap, and Swedish-leaf datasets respectively. Show more
Keywords: Deep learning, convolutional neural network, pruning, image-based disease classification
DOI: 10.3233/JIFS-190184
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4003-4019, 2019
Authors: Yang, Jie | Yu, Shujuan | Zhang, Yun
Article Type: Research Article
Abstract: The increase of depth is essential for the success of Deep Neural Networks while also leads to the difficulty of training. In light of this, the authors propose a novel multi-layer LSTM model called Highway-DC via introducing Highway Networks (Highway) to Densely Connected Bi-LSTM (DC-Bi-LSTM) which representation of each layer concatenates the output of itself and all preceding layers. Highway is applied to control the volume of input or output of each layer in DC-Bi-LSTM to the next. However, results reveal that Highway-DC shows no improvement over DC-Bi-LSTM, thus an extended version of Highway named Highway II is proposed via …eliminating the multiplicative connections between transform gate and the output in Highway thus preserve the learning of each layer. And the Highway II-based model is named Highway II-DC. Evaluated on 7 benchmark datasets of text classification with compare to DC-Bi-LSTM and other state-of-the-art approaches, results indicate that Highway II-DC shows promising performance for achieving state-of-the-art on 3 datasets and surpassing DC-Bi-LSTM on 6 datasets with faster speed to converge. Besides, it can still enjoy the gain of increased layers with depth up to 30, while DC-Bi-LSTM gets saturated early at a depth of 15. Show more
Keywords: Deep neural networks, Bi-LSTM, text classification, highway
DOI: 10.3233/JIFS-190191
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4021-4032, 2019
Authors: Rubio, José de Jesús | Cruz, David Ricardo | Elias, Israel | Ochoa, Genaro | Balcazar, Ricardo | Aguilar, Arturo
Article Type: Research Article
Abstract: Recently, the Adaptive-Network-Based Fuzzy Inference System (ANFIS) is applied in many areas of knowledge, and there are multiple optimization algorithms for its learning. This work shows the design of a novel optimization algorithm for an ANFIS system that learns and classifies the behavior of brain signals between normal and abnormal. For this goal, different types of optimization algorithms for the learning of an ANFIS system are evaluated, such as the backpropagation, the mini-lots, and the Adam algorithm (adaptive moment estimation). As a result, utilizing the ANFIS with Adam and mini-lots provides the most accurate, fastest, and with least computational …costs results. Show more
Keywords: Adam algorithm, ANFIS system, mini-lots, classification of brain signals
DOI: 10.3233/JIFS-190207
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4033-4041, 2019
Authors: Xia, Yaowei | Qin, Jiejie
Article Type: Research Article
Abstract: In this paper, a new optimization methodology to assess the designs of the various renewable generation systems of electrical energy is used. This methodology utilizes Whale Optimization Algorithm (WOA) to minimize the cost of the electrical energy generated. The methodology permits to examine and to combine different sources of energy as to touch base at an optimal configuration of the hybrid system. This system is capable of providing energy to the predefined site in an achievable way as indicated by certain specialized and financial criteria. The system incorporates wind generation, photovoltaic generation and batteries for energy storage. The recreation results …have been acquired with the help of MATLAB programming. Moreover, the outcomes of the proposed methodology have been compared with Particle Swarm Optimization (PSO) Algorithm for validation. The recreation results demonstrated the predominance of the proposed methodology. Show more
Keywords: Hybrid renewable energy system, whale optimization algorithm, optimization, photovoltaic, wind, off-grid
DOI: 10.3233/JIFS-190213
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4043-4053, 2019
Authors: Mantas, C.J.
Article Type: Research Article
Abstract: First-order recurrent neural networks can be trained to recognize strings of a regular language. Finite state automata can be extracted from these neural networks. Normally, a search process in the output domain of the neurons is necessary for carrying out this extraction procedure. On the other hand, studies about fuzzy rules extraction from feedforward multilayered neural networks can be considered to define new techniques that transform first-order recurrent neural networks into finite state automata. With these new techniques, a fuzzy description of the action of each neuron can be obtained. From these descriptions, the transition function of the automaton can …be directly found and, in this way, the search process is not necessary. A technique with this approach is presented in this paper. Besides, the used method to extract fuzzy rules from a neuron has the advantage that the inputs of the fuzzy system coincide with the inputs of the neuron. Thus, the fuzzy system is more intuitive. Once the transition function is obtained, the automaton structure can be found with the analysis of the transitions for every state and input from the initial state. Finally, several examples are presented to illustrate the method. Show more
Keywords: First-order recurrent neural networks, regular grammars, fuzzy rules, finite state automata
DOI: 10.3233/JIFS-190215
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4055-4070, 2019
Authors: Athira, T.M. | John, Sunil Jacob | Garg, Harish
Article Type: Research Article
Abstract: A Pythagorean fuzzy soft set is a parameterized family of Pythagorean fuzzy sets and a generalization of intuitionistic fuzzy soft sets. In this paper, the notions of entropy and distance measures are defined for the Pythagorean fuzzy soft sets (PFSSs). Since, the already existing techniques for finding entropy and distance measures are not working for PFSSs, it is necessary to introduce these techniques in the contest of PFSSs. This work proposes a characterization of the Pythagorean fuzzy soft entropy. Also, the expressions for the standard distance measures like Hamming distance and Euclidean distance are obtained. Further, the applications of PFSSs …in decision making problem and pattern recognition problem are discussed. Finally, comparative studies with other existing equations are also carried out. Show more
Keywords: Pythagorean fuzzy soft sets, fuzzy soft sets, entropy, distance measure, decision making problem, pattern recognition problem
DOI: 10.3233/JIFS-190217
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4071-4084, 2019
Authors: Sree Priya, S. | Sivarani, T.S.
Article Type: Research Article
Abstract: This paper proposes an optimal control of Induction Motor (IM) drives using a new optimization technique. The optimization technique is the joined execution of both the Improved Moth flame Optimization (IMFO) algorithm and Radial Basis Function Neural Network (RBFNN). The main objective of the proposed strategy is to enhance the control performance of the IM while reducing the Total Harmonic Distortion (THD), eliminating the oscillation period of the stator current, torque, and speed. Here, the IMFO technique is optimized the gain parameters of the PI controller based on the IM speed variation and generates the reference quadrature axis current. By …using the RBFNN, the reference three-phase current for accurate control pulses of the voltage source inverter (VSI) is predicted. The RBFNN is trained by the input motor actual quadrature axis current and the reference quadrature axis current with the corresponding target reference three-phase current. Furthermore, the proposed method control signals are connected with random pulse width modulation (RPWM) scheme and appropriate pulses are generated and applied to the inverter. With the proposed strategy, the control pulses of VSI are optimized and the proposed system offers a reliable solution. The proposed methodology is implemented in MATLAB/Simulink working platform. The performance of the IM drive is assessed by utilizing the comparative analysis with the existing techniques. The result obtained using the proposed optimization strategy showed that; it can provide the optimal control of IM drive. Also, the proposed strategy is effective in minimize the acoustic noise, torque ripple, eliminate the oscillation period with less computation, and reduces the complexity of the algorithm. Show more
Keywords: Induction motor (IM), IMFO, RBFNN, total harmonic distortion (THD), random pulse width modulation (RPWM)
DOI: 10.3233/JIFS-190244
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4085-4102, 2019
Authors: Wang, Xiao | Ning, Yufu
Article Type: Research Article
Abstract: Uncertain delay differential equations are a type of differential equations driven by a Liu process. So far, it has been proved that uncertain delay differential equation has a unique solution in the finite domain, in which its coefficients satisfy the global Lipschitz continuity. This paper continues to focus on the existence and uniqueness of the solutions of uncertain delay differential equations on the infinite domain. Meanwhile, a new existence and uniqueness theorem for uncertain delay differential equations under one-sided local Lipschitz condition and linear growth condition is deduced.
Keywords: Uncertain delay differential equation, existence and uniqueness, one-sided local Lipschitz condition, linear growth condition, infinite domain
DOI: 10.3233/JIFS-190264
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4103-4111, 2019
Authors: Wu, Hsien-Chung
Article Type: Research Article
Abstract: The intersection and union of non-normal fuzzy sets using general aggregation functions are studied in this paper. The conventional intersection and union of fuzzy sets are based on the membership functions using the max and min functions, or t-norm and s-norm in general in which the fuzzy sets may assume to be normal in order to follow the boundary conditions that include 0 and 1 in the unit interval [0, 1]. When the fuzzy sets are taken to be non-normal, the α -level sets may be empty for some α ∈ [0, 1]. In this paper, we shall consider the aggregation …functions, instead of t-norm and s-norm, satisfying the concepts of compatibility that are based on the α -level sets of intersection and union. In this case, the emptiness of α -level sets should be avoided, which is the main issue of this paper. Show more
Keywords: Aggregation functions; Interval range; Normal fuzzy sets; Set intersection; Set union.
DOI: 10.3233/JIFS-190270
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4113-4132, 2019
Authors: Zhao, Meng | Gao, Mei | Li, Zichao
Article Type: Research Article
Abstract: Social network (SN) provides a new perspective for large-scale multi-attribute group decision making (LMAGDM), and the scale and complexity of group compositions have received considerable attention. In recent study, the SN is constructed artificially and subjectively by using the number of communication or by giving the trust value directly. This paper constructs a directed and weighted SN by integrating collaboration network and reference network of decision makers (DMs) objectively. The spin-glass of community detection method is used to identify the subgroups and the weight of DMs in subgroups and then obtain the weight of subgroup pair. The uncertain linguistic weighted …average operator is used to represent each subgroup’s assessment. The closeness between two subgroups is defined to measure consensus level. A targeted local feedback mechanism with three identification rules and a recommendation rule is designed to guide the consensus reaching process (CRP) more precisely and effectively. An illustrative example proves the feasibility and validity of the proposed consensus method, and the comparative analysis highlights the advantages and characteristics of this model. Show more
Keywords: Large-scale multi-attribute group decision making, collaboration-reference network, social network, weight of DMs and subgroup pair, consensus model, spin-glass community detection
DOI: 10.3233/JIFS-190276
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4133-4156, 2019
Authors: Fan, Chao-Zan | Shi, Fu-Gui
Article Type: Research Article
Abstract: In this paper, we give a new characterization of M -fuzzifying span mappings defined by M -fuzzifying rank functions. Then we research the relations between M -fuzzifying span mappings and other M -fuzzifying mappings on M -fuzzifying matroid, and characterize the M -fuzzifying coindependent, nonspan and hyperplane mappings. Based on these characterizations, we put forward one class of special fuzzifying matroids.
Keywords: M-fuzzifying matroids, M-fuzzifying rank function, M-fuzzifying span mapping, fuzzifying dual matroids
DOI: 10.3233/JIFS-190280
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4157-4169, 2019
Authors: Wang, Ying | Sun, Bingzhen | Hu, Xiaoyuan
Article Type: Research Article
Abstract: This paper proposes a new approach to uncertainty multiple attribute group decision problem with linguistic preference relation based on multigranulation probabilistic rough set and the Multimoora method. According to the classical Pawlak rough set and the neighborhood rough set, we present a multigranulation probabilistic fuzzy rough set based on neighborhood relation with linguistic preference information. We investigate the rough approximation of a crisp decision-making object and a fuzzy decision-making object under the framework of multigranulation rough set theory with linguistic preference inforamtion, respectively. That is, a multigranulation probabilistic rough set model and a multigranulation probabilistic fuzzy rough set model based …on δ -neighborhood relation are established, respectively. Meanwhile, the proposed multigranulation probabilistic fuzzy rough set model is compared with the existing model and illustrate the superiority of the new model established. Furthermore, by combining the multigranulation probabilistic fuzzy rough set and the Multimoora method, a new method for multiple attribute group decision making with linguistic preference information is proposed. The decision-making procedure and the algorithm of the proposed method are also given. Finally, the effectiveness and validity of the proposed method is verified by investigating a multiple attribute group decision problem with the selection of suppliers in the context of e-commerce. Show more
Keywords: Neighborhood relationship, multigranulation probability rough set, multiple attribute group decision-making, Multimoora, linguistic preference information
DOI: 10.3233/JIFS-190290
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4171-4194, 2019
Authors: Muhiuddin, G. | Mahboob, Ahsan | Mohammad Khan, Noor
Article Type: Research Article
Abstract: The concept of an (∈ , ∈ ∨ (k * , q k ))-fuzzy semiprime subset in an ordered semigroup is introduced, and investigate the properties of (∈ , ∈ ∨ (k * , q k ))-fuzzy generalized bi-ideals by concerning the (∈ , ∈ ∨ (k * , q k ))-fuzzy semiprime subsets. Moreover, some properties of (∈ , ∈ ∨ (k * , q k ))-fuzzy semiprime subsets are defined in terms of its (k * , k )-lower part. Finally, completely regular ordered semigroups are characterized in terms of its (∈ , ∈ ∨ (k * , q k ))-fuzzy semiprime generalized bi-ideals and their (k * …, k )-lower parts. Show more
Keywords: Ordered semigroups, fuzzy subsets, (∈ , ∈ ∨ (k*, qk))-fuzzy semiprime subsets
DOI: 10.3233/JIFS-190293
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4195-4204, 2019
Authors: Che, Gaofeng | Liu, Lijun | Yu, Zhen
Article Type: Research Article
Abstract: In this paper, an iterative adaptive dynamic programming algorithm is proposed to deal with the optimal trajectory-tracking control problems for autonomous underwater vehicle. Two iteration procedures are used in the method, which are the i -iteration and the j -iteration.The i -iteration aims to obtain iterative trajectory-tracking control laws and the j -iteration aims to obtain iterative value functions in the i -iteration. The optimal tracking control problem is converted into an optimal regulation problem by system transformation. Then the optimal regulation problem is solved by the policy iteration adaptive dynamic programming algorithm. The policy iteration algorithm is the interacting …policy and value iteration algorithms. The neural networks are used to realize the proposed algorithm and the convergence and optimality properties of the proposed algorithm are analysed. Finally, simulation example is given to show the performance of the iterative adaptive dynamic programming algorithm. Show more
Keywords: adaptive dynamic programming, autonomous underwater vehicle, discrete-time, neural network, trajectory-tracking control
DOI: 10.3233/JIFS-190294
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4205-4215, 2019
Authors: Shoaib, Abdullah | Shahzad, Aqeel
Article Type: Research Article
Abstract: In this paper, we have achieved fixed point results for pair of fuzzy mappings satisfying Ciric type contraction on a sequence contained in an open ball in ordered left (right) K -sequentially complete dislocated quasi metric space. An example and an application are presented to demonstrate the novelty of the results. Our results generalize and extend some recent results in literature.
Keywords: Fixed point, left (right) K-sequentially complete dislocated quasi metric space, open ball, fuzzy mapping, generalized contraction, 46S40, 47H10, 54H25
DOI: 10.3233/JIFS-190325
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4217-4229, 2019
Authors: Tao, Zhifu | Liu, Xi | Zhou, Ligang | Chen, Huayou
Article Type: Research Article
Abstract: Z-numbers contain both of the cognitive information and the reliability of information. Since there are many types of cognitive information, by considering the reliability of these information, decision making with hybrid Z-information would be a practical issue, which has merely been considered. The aim of this paper is to introduce a multi-attribute decision making (MADM) with hybrid Z-information based on ranking aggregation method. By using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, single rankings of alternatives under attributes are firstly produced. Then, final ranking(s) of alternatives with the lowest disagreement among single rankings could …be derived according to ranking aggregation. As a result, a MADM with hybrid Z-information is transformed to be a ranking aggregation problem. The advantage of the developed method is that no algebra structures of hybrid Z-information is needed, so complex calculating process is avoided. In this paper, the mathematical structure of the case that MADM with hybrid Z-numbers is clear. Besides, the combination of ranking aggregation method and studies on Z-numbers could simplify the processing of hybrid Z-numbers. Finally, an example of supplier selection in green supply chain environment is introduced to illustrate the feasibility and validity of the developed model. Show more
Keywords: Multi-attribute decision making, hybrid Z-information, rank aggregation, supplier selection, order relation
DOI: 10.3233/JIFS-190344
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4231-4239, 2019
Authors: Bush, Idoko John | Abiyev, Rahib | Arslan, Murat
Article Type: Research Article
Abstract: In this study, we propose a vision-based mouse controller capable of controlling objects from a distant location via hand gestures. The proposed hybrid model constitutes hand detection, prediction of hand states and direction and finally, with the aid of deep learning algorithm, we systematically control hand gestures to reposition objects on computer screen. This hybrid system is explicitly designed to control mouse on computer screen during formal presentation. Random movement of hand from up to down and right to left move the mouse pointer and sends signal to the system utilizing states of the hand. Here, close hand places the …mouse button on active mode while open hand releases the button. The proposed hybrid model is made up of two modules: Single Shot Multi Box Detection (SSD) structure utilized to detect hand while Convolutional Neural Network (CNN) is utilized for prediction. For comparative purposes, we performed similar experiment where SSD is used for hand detection while Radial Basis Function Network (RBFN) is used for hand states prediction. In the comparative results of hand states prediction, SSD+CNN greatly outperformed SSD+RBFN. The proposed hybrid model is vision-based hence, it does not require additional hardware to perform its task. Overall performance of the framework depicts that the system is accurate and robust. Show more
Keywords: Hand gesture, convolutional neural network, radial basis function network, computer vision, deep learning
DOI: 10.3233/JIFS-190353
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4241-4252, 2019
Authors: Du, Yuxiang | Sheng, Qian | Fu, Xiaodong | Tang, Hua | Zhang, Zhenping | Zhao, Xin
Article Type: Research Article
Abstract: The stability of a colluvial slope, which is different from a rock or soil slope, is determined by the properties of both the bedrock and the colluvium. Coupled with artificial excavation and environmental effects, the stability factors of such slopes are complicated. To rapidly and effectively evaluate the risk of a colluvial cutting slope, a risk evaluation system for this type of slope is established herein. First, an evaluation index system is established, and reasonable risk evaluation indices are selected. Second, the fuzzy analytic hierarchy process (FAHP) is applied, a fuzzy pairwise comparison matrix, that must satisfy a consistency test, …is constructed, and the weight of each index is determined. Third, the risk evaluation grades are divided into 4 risk grades, and the risk evaluation criteria for each basic index are determined. Finally, the three-level fuzzy comprehensive evaluation (FCE) method is applied, the membership function for each index is constructed, the membership degree is calculated, and the risk grade of the colluvial cutting slope is determined. This risk evaluation system is used to evaluate the risks of 148 colluvial cutting slopes along the Xiaomengyang-Mohan highway in Yunnan, China. The results show that there are 24 slopes of low risk (grade I), 85 of medium risk (grade II), 22 of high risk (grade III), and 17 of very high risk (grade IV). The evaluation results obtained are in good agreement with the actual slope instability states: failure occurred in 15 out of 85 slopes of risk grade II, 13 out of 22 slopes of risk grade III, and 16 out of 17 slopes of risk grade IV. This application demonstrates that the proposed risk evaluation system for colluvial cutting slopes is universal, and stable and that the calculation results are objective. Show more
Keywords: Colluvial cutting slope, risk evaluation system, fuzzy analytic hierarchy process, fuzzy comprehensive evaluation
DOI: 10.3233/JIFS-190367
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4253-4271, 2019
Authors: Rehmani, Sameeha | Sunitha, M.S.
Article Type: Research Article
Abstract: In this paper, the edge version of the geodesic number of a fuzzy graph is introduced and the properties satisfied are identified. A comparison between the vertex and edge version of the geodesic number of fuzzy graphs is obtained. The edge geodesic number of fuzzy trees, complete fuzzy graphs, complete bipartite fuzzy graphs and of fuzzy cycles are identified. A necessary and sufficient condition for the existence of an edge geodesic cover in a fuzzy graph is obtained. An application of edge geodesic sets in transportation systems in optimizing the number of traffic inspectors patrolling an urban road network is demonstrated. …The fuzziness in the problem helps to identify routes receiving less priority among passengers, elimination of which minimizes the loss suffered by various transport corporations due to lack of collection. Show more
Keywords: Edge geodesic cover, edge geodesic basis, edge geodesic number AMS Mathematics Subject Classification (2010): 05C72, 05C12, 05C38, 05C40, 90C35
DOI: 10.3233/JIFS-190383
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4273-4286, 2019
Authors: Yogashanthi, T. | Mohanaselvi, S. | Ganesan, K.
Article Type: Research Article
Abstract: In this paper a new centroid based ranking grade for generalized intuitionistic fuzzy numbers is proposed. The centroid point of membership function and non membership function of generalized intuitionistic fuzzy numbers in term of its parametric form is used for grading. The parametric representation of generalized intuitionistic fuzzy numbers involves left fuzziness index, right fuzziness index and modal value of membership and non membership functions. To reveal the performance of the proposed ranking grade, a comparison study has been made over the existing methods. Furthermore the proposed ranking method has been used for estimating the minimum total elapsed time to …a flow shop scheduling problem involving generalized intuitionistic fuzzy number. An improved result for flow shop scheduling problem has been attained using the proposed ranking grade and has been illustrated through an example. Show more
Keywords: Fuzzy set, generalized intuitionistic fuzzy numbers, centroid point, ranking grade, flow shop scheduling problem
DOI: 10.3233/JIFS-190395
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4287-4297, 2019
Authors: Yadav, Nidhika | Chatterjee, Niladri
Article Type: Research Article
Abstract: Rough Sets provide a mathematical tool to handle decision making under uncertainty. One major domain that can be characterized with inherent ambiguity is natural language texts which often leads to uncertainty in understanding the intent and relative importance of a sentence with respect to its context in the whole text. As a consequence, the process of sentence selection for generation of extractive summary can logically be considered as a process of decision making under uncertainty. In this paper we use rough set based techniques to deal with this uncertainty. This paper’s contribution is two-fold. Firstly, this paper proposes a novel …Rough Set based uncertainty measure called span and define special Rough subsets of universe called spanning sets . Span is Rough Set based measure for salience of a subset of universe and spanning set is the subset that maximizes the span. This corresponds to the key elements representing a problem and can be used to solve various real-life applications. Secondly, the concepts are applied to determine extracts of text documents. The idea behind the present work is to determine the most suitable subset(s) of the universe of sentences under consideration. An optimization problem is formulated to generate the extract for the text under consideration using the proposed uncertainty measure of span and is solved using Particle Swarm Optimization. The experimental results on DUC2001, DUC2002 single document data sets and Enron Email datasets establish the effectiveness of the proposed technique. There has been substantial work on Rough Sets though considering a stochastic Rough-subset of the universe and determining its aptness as a representative of the universe is still unexplored. The proposed technique is a novel effort to fill this gap. Show more
Keywords: Rough set, extractive text summarization, span, spanning set, particle swarm optimization, ROUGE, extraction, lexical chains, DUC2001, DUC2002, LSA, graph, random indexing, GLOVE
DOI: 10.3233/JIFS-190402
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4299-4309, 2019
Authors: Li, Jing | Zhang, Yulin
Article Type: Research Article
Abstract: Interval fuzzy preference relations (IFPRs) have been widely adopted in describing vagueness and uncertainty in real-life decision problems. Different methods have been applied in aggregating decision makers’ (DMs’) IFPRs. Nevertheless, the objective weights of DMs are often neglected in the group decision literature. Besides, the commonly methods used in aggregating decision makers’ (DMs’) IFPRs may make the final result too average. This paper investigates the plant growth simulation algorithm (PGSA) to aggregate interval fuzzy preference relations (IFPRs) and then derives the objective weights of decision makers (DMs) based on the deviation measure method. Next, the weighted aggregation IFPR is obtained …by PGSA and the alternatives are ranked based on the continuous ordered weighted averaging (COWA) operator. The new aggregation method creatively converts the elements of IFPRs into two-dimensional coordinates and the ideal IFPR can be aggregated by PGSA based on the minimum Euclidean distance model. Then the weight of each DM can be derived according to the Euclidean distance between the individual IFPR and ideal IFPR based on the deviation measure method. Finally, a weighted aggregated IFPR can be obtained by PGSA and the ranking of alternatives is obtained by the COWA operator. Numerical examples are given to verify the efficiency and superiority of the method. Show more
Keywords: Weights of decision makers (DMs), interval fuzzy preference relations (IFPRs), deviation measure method, the minimum Euclidean distance model, plant growth simulation algorithm (PGSA)
DOI: 10.3233/JIFS-190410
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4311-4323, 2019
Authors: Li, Lei-Jun | Li, Mei-Zheng | Mi, Ju-Sheng | Xie, Bin
Article Type: Research Article
Abstract: Attribute reduction is one of the crucial issues in Formal Concept Analysis. Discernibility matrix plays an important role in attribute reduction, and has been achieved many successful applications in different concept lattice models. Nevertheless, it requires the construction of the concept lattice before the discernibility matrices are computed when applying traditional approaches, which is both time and space consuming. Furthermore, in some discernibility matrices, the comparisons between every two concepts result in a high computation complexity. To address these problems, granular concepts, i.e., the object concepts and the attribute concepts, are considered in this paper, and a simple discernibility matrix …named Object-Attribute discernibility matrix is proposed. It averts the construction of the whole concept lattice and the comparisons between every two concepts. Consequently, the time complexity is greatly reduced, and a lot of storage space can also be saved. Theoretical analysis and experimental results show the efficiency of Object-Attribute discernibility matrix. Show more
Keywords: Formal concept analysis, concept lattice, attribute reduction, discernibility matrix, granular concept
DOI: 10.3233/JIFS-190436
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4325-4337, 2019
Authors: Wu, Ziheng | Wang, Bing
Article Type: Research Article
Abstract: Fuzzy c-means algorithm (Fcm) frequently applid in machine learning has been proven an effective clustering approach. However, the traditional Fcm cannot distinguish the importance of the different data objects and the discriminative ability of the different features in the clustering process. In this paper, we propose a new kind of Fcm clustering framework: DwfwFcm.Considering the different data weights and feature weights, an adaptive data weights vector and an adaptive feature weights matrix are introduced into the conventional Fcm and a new objective function is constructed. By the proposed objective function, the corresponding scientific updating iterative rules of the membership matrix, …the weights of the different feature, the weights of the different data object and the cluster centers can be derived theoretically.Experimental results have demonstrated that the algorithm proposed in this paper can deliver consistently promising results and improve the clustering performance greatly. Show more
Keywords: Fuzzy c-means algorithm, machine learning, clustering, adaptive data weights vector, adaptive feature weights vector
DOI: 10.3233/JIFS-190440
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4339-4347, 2019
Authors: Yang, Qiang | Li, Yan-Lai | Chin, Kwai-Sang
Article Type: Research Article
Abstract: Quality function deployment (QFD) is an effective tool for the design and improvement of products/services. The prioritization of customer requirements (CRs), as an essential component of the house of quality, is fundamental and strategic in the whole process of QFD product planning. This study proposes a novel ordinal scale values based group decision-making (GDM) approach first and it is subsequently used to prioritize CRs in QFD product planning. The proposed approach is composed of three stages, that is, constructing the integrated preference vector, defining the extraction sequence and constructing the comprehensive preference vector. The proposed GDM approach is good for …utilizing the ordinal scale values provided by respondents due to limited experience and knowledge. An illustrative example is presented to verify the applicability and efficiency of the proposed approach. Further, to demonstrate the superiority of the proposed approach, comparisons are made between the proposed approach and two other similar methods. Practical results demonstrated that the proposed approach can be effective when the importance of customers and the preference evaluations of CRs are given by an ordinal scale. Show more
Keywords: Quality function deployment (QFD), customer requirement (CR), group decision-making (GDM), ordinal scale, preference ordering
DOI: 10.3233/JIFS-190444
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4349-4367, 2019
Authors: Ali, Abbas | Rehman, Noor | Jang, Sun Young | Park, Choonkil
Article Type: Research Article
Abstract: The notions of fuzzy upward β -covering, the fuzzy upward β -neighborhood, upward β -neighborhood and fuzzy complement β -neighborhood are introduced and several related properties are studied. Furthermore, multigranulation optimistic/pessimistic fuzzy rough sets based on fuzzy upward β -covering are initiated and their fundamental properties are investigated. We also find the upward β -neighborhood in the fuzzy upward covering approximation space and present the optimistic/pessimistic multigranulation rough sets to further enrich the presented notions. The medicine selection via fuzzy upward β -covering rough sets in medical diagnosis is another main contribution of the present work. It is also explored …that which medicine can be prescribed for which particular symtom(s) and which disease. Show more
Keywords: Fuzzy preference relation, fuzzy rough set, β-covering rough sets
DOI: 10.3233/JIFS-190447
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4369-4390, 2019
Authors: Fan, Jianping | Liu, Jie | Wu, Meiqin
Article Type: Research Article
Abstract: Cross-efficiency assessment method is a useful tool for assessing the relative performance of decision-making units (DMUs). It is generally assumed that decision makers (DMs) are completely rational in the cross-efficiency model, and DMs’ risk attitude has not been considered important in the evaluation process. When the self-evaluation score of the DMU is optimal, the input and output weights are non-unique, resulting in non-unique cross-efficiency score, which affects the ranking result. The relative importance of DMUs is ignored when aggregating cross-efficiency scores. in view of the above problems, a cross-efficiency method based on prospect theory is proposed to capture the bounded …rational psychological behavior of risk DMs. This method considers all multiple optimal solutions and constructs interval cross-efficiency. The credibility of each cross-efficiency score is obtained based on the D-S evidence theory, and the weight of each DMU is obtained by using the Dempster rule. DMUs are ranked by calculating the prospect value. The validity and feasibility of the proposed method and how does the risk preference of DMs characterized by parameters α , β , λ affect the evaluation results are verified by an illustrative example. Show more
Keywords: Data envelopment analysis, cross-efficiency, interval number, dempster-shafer theory, prospect theory
DOI: 10.3233/JIFS-190450
Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 4391-4404, 2019
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