Journal of Intelligent & Fuzzy Systems - Volume 39, issue 1

Authors: Kang, Xinhui

Article Type: Research Article

Abstract: The emotional value of products, especially aesthetic qualities are conducive to increasing the market competitiveness along with the improvement of living standards. Therefore, the main purpose of this study is to combine the rough set theory and fuzzy quality function deployment design matrix to construct an innovative model, thus developing an aesthetic product design for customer satisfaction. Taking Blender as an example, the author divides this paper into three phases. Firstly, the author summarizes seven aesthetic qualities through the literature discussions and determines the core aesthetic qualities of Blender by making rough set theory attribute reduction and importance calculation. Secondly, …the results are imported into the fuzzy quality function deployment needs facet, and the correlation matrix is established by consulting the expert’s opinions to obtain the optimal combination of design features. Finally, the combination of the features produces a bio-conceptual shape via a bionics step. The results show that four out of seven aesthetic attributes (ie, concise, original, elegant, and comfortable) are found to be more significant. The optimal combination of product features are integrated with the bio-inspired method to generate three design solutions, in which the styling of butterfly concept effectively enhances the products’ emotional value and customers’ aesthetic satisfaction degree. Show more

Keywords: Product design, customer satisfaction, rough set theory, fuzzy quality function deployment, Blender

DOI: 10.3233/JIFS-192032

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1131-1146, 2020

Authors: Xiao, Yanjun | Zhang, Heng | Zhou, Wei | Wan, Feng | Meng, Zhaozong

Article Type: Research Article

Abstract: The textile industry has a long history and a large market scale around the world. High-speed loom belongs to the high-end production equipment of the textile industry with the characteristics of high precision, high speed and high efficiency. However, due to its expensive cost and complex structure, there might be significant loss once a high-speed loom breaks down. At present, the monitoring and troubleshooting of high-speed loom operation mainly depend on the experience of maintenance people to carry out inspections, which is inefficient, time-consuming, laborious and less efficient. In this paper, a fault diagnosis method for high-speed loom based on …rough set and Bayesian network is investigated. Rough set theory is applied to reduce the attributes of fault causes and results and find the minimum reduction and classification rules. Then, a Bayesian fault diagnosis network model is built, and the probability of each fault cause is calculated to find the maximum probability. Finally, the diagnosis results are obtained. The experimental results have demonstrated the reliability and convenience of the faults diagnosis method for the high-speed loom. Show more

Keywords: High-speed loom, fault diagnosis, rough set theory, Bayesian network

DOI: 10.3233/JIFS-192039

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1147-1161, 2020

Authors: Yan, Yan | Zhang, Jin | Tang, Qiuyu

Article Type: Research Article

Abstract: This paper studies the flight path optimization problem of air cargo companies in aviation line alliance. There are two limitations in this paper. One is to limit of the number and location of airbases and capacity in the air network. The other is to limit of flight time and airspace capacity of full cargo aircraft in actual operation. Considering the influence of alliance on operation, the selection probability of air alliance is introduced. It is assuming that all cargo aircraft is one type, the unit transportation cost of every aviation line is the same as each other, the queuing problem …of aircraft landing is not considered, and the network transportation demand of itself must be completed by an airline. It proposes a directed aircraft fleet routing problem optimization model (SMDDDAAAFRPTW) with multi-airbase stochastic and time constraints to minimize total operating cost and flight distance. Using the multi-objective optimization algorithm NSGA-II by most scholars, and improving the initial solution generation step, introducing Genetic engineering into cross-mutation to solve the optimal number and location of air bases and fleet routing of multiple aircraft. Comparing with the weighted method and ant colony algorithm, it shows that the improved NSGA-II algorithm is effective and has better computational efficiency. The results show that the more segments are selected for outsourcing, the lowest cost of network and the lowest carbon emission. This kind of decision-making behavior is only suitable for the initial operation phase of the enterprise. Show more

Keywords: Aircraft fleet route optimization, multi-airbases stochastic, time and capacity constraints, improved NSGA-II algorithm

DOI: 10.3233/JIFS-192041

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1163-1182, 2020

Authors: Ameen, Mustafa | Alrahmawy, Mohammed | AbouEleneen, Amal | Tolba, Ahmad

Article Type: Research Article

Abstract: Automated visual inspection is becoming an important field of computer vision in many industries. The real-time inspection of flat surface products is a task full of challenges in industrial aspects that requires fast and accurate algorithms for detection and localisation of defects. Structural, statistical and filter-based approaches, such as Gabor Filter Banks, Log-Gabor filter and Wavelets, have high computational complexity. This paper introduces a fast and accurate model for inspection and localization of industrial flat surface products: Neighborhood Preserving Perceptual Fidelity Aware Mean Squared Error (NP-PAMSE). The Extreme Learning Machine (ELM) is used for classification. ELM is found to …be the perfect classifier for detecting defects. The proposed model resulted in defect detection accuracy of 99.86%, with 98.16% sensitivity, and 99.90% specificity. These results show that the proposed model outperforms many existing defect detection approaches. The discriminant power displays the efficiency of ELM in differentiation between normal and abnormal surfaces. Show more

Keywords: Automated visual inspection (AVI), perceptual fidelity aware mean squared error (PAMSE), extreme learning machine (ELM)

DOI: 10.3233/JIFS-192071

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1183-1196, 2020

Authors: Afshari, Robab | Gildeh, Bahram Sadeghpour

Article Type: Research Article

Abstract: The quality of manufactured products plays a very important role in increasing consumer satisfaction. One approach to improving outgoing lot quality is applying screening method. In this paper, a multiple deferred state sampling plan is presented for a unilateral-univariate normal process with imprecise process quality in the presence of the rectifying inspection. To assess the performance of the proposed plan, a mathematical model is derived for calculating the fuzzy average total inspection ( ATI ˜ ) under the operation of the proposed plan. The obtained conclusions indicate that the proposed plan is more economical than the …existing plans in terms of ATI ˜ measure. A numerical example is given to demonstrate how to apply the introduced plan in the real world. Show more

Keywords: Statistical quality control, fuzzy multiple deferred state sampling plan, average total inspection, fuzzy numbers arithmetic

DOI: 10.3233/JIFS-192097

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1197-1211, 2020

Authors: Xie, Mengying | Liu, Xiaolan | Pan, Gan

Article Type: Research Article

Abstract: Multi-view subspace clustering arises in many computer visional tasks such as object recognition and image segmentation. The basic idea is to measure the same instance with multiple views. In this paper, we proposed two centralized joint sparse representation models, namely, Centralized Global Joint Sparse Representation (CGJSR) and Centralized Local Joint Sparse Representation (CLJSR) for multi-view subspace clustering. CGJSR and CLJSR force the concatenated representation matrix of all views and the representation matrix of each view to be sparse respectively. Both CGJSR and CLJSR allow the sparse coefficient matrix to approach a unified latent structure with an acceptable error. Noises and …outliers regularization terms are included in CGJSR and CLJSR to reduce the influence of noises and outliers. Related optimization problems are solved using the alternating direction method of multipliers. Compared with seven state-of-the-art multi-view clustering algorithms, our proposed algorithms can achieve better or comparable results on four real-world datasets. Show more

Keywords: Sparse representation, feature fusion, multi-view subspace clustering

DOI: 10.3233/JIFS-192101

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1213-1226, 2020

Authors: Riaz, Muhammad | Farid, Hafiz Muhammad Athar | Karaaslan, Faruk | Hashmi, Masooma Raza

Article Type: Research Article

Abstract: The q-rung orthopair fuzzy numbers (q-ROFNs) are used to deal with vague and uncertain information and they are superior to the intuitionistic fuzzy numbers (IFNs) and the Pythagorean fuzzy numbers (PFNs). In this paper, we introduce two operators namely q-rung orthopair fuzzy hybrid weighted arithmetic geometric aggregation (q-ROFHWAGA) operator and q-rung orthopair fuzzy hybrid ordered weighted arithmetic geometric aggregation (q-ROFHOWAGA) operator. The suggested operators q-ROFHWAGA and q-ROFHOWAGA are superior to the existing operators defined on q-ROFNs. We present an application of the proposed operator of q-ROFHWAGA to multiple-attribute decision-making (MADM) in computer numerical control (CNC) machine. Furthermore, we present TOPSIS …method based on q-ROFNs for MADM in transport policy problem. Show more

Keywords: q-rung orthopair fuzzy numbers, q-rung orthopair fuzzy hybrid aggregation operators, multi-attribute decision-making, CNC machine

DOI: 10.3233/JIFS-192114

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1227-1241, 2020

Authors: Xie, Ying

Article Type: Research Article

Abstract: As modern industrial processes often have multiple production modes, multimode-process monitoring has become an important issue. In multimode processes, the operating condition may often switch among different modes. As a result, popular process monitoring methods such as principal component analysis (PCA) and partial least squares (PLS) method should not be directly applied because they are based on a fundamental assumption that the process only has one stable operating condition. In this paper, a novel multimode-process data-standardization approach called double-weighted neighborhood standardization (DWNS) is proposed to solve the problem of multimode characteristics. This approach can transform multimode data into approximately single-mode …data, which follow a Gaussian distribution. By analyzing a concrete example, this study indicates that the DWNS strategy is effective for multimode data preprocessing. Moreover, a novel fault detection method called DWNS-PCA is proposed for multimode processes. Finally, a numerical example and the penicillin fermentation process are used to test the validity and effectiveness of the DWNS-PCA. The results demonstrate that the proposed data-standardization method is suitable for multimode data, and the DWNS-PCA process monitoring method is effective for detecting faults in multimode processes. Show more

Keywords: multimode process, double-weighted neighborhood standardization, principal component analysis, fault detection

DOI: 10.3233/JIFS-192158

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1243-1256, 2020

Authors: Mashwani, Wali Khan | Hamdi, Abdelouahed | Asif Jan, Muhammad | Göktaş, Atila | Khan, Fouzia

Article Type: Research Article

Abstract: There are numerous large-scale global optimization problems encountered in real-world applications including engineering, manufacturing, economics, networking fields. Over the last two decades different varieties of swarm intelligence and nature inspired based evolutionary algorithms (EAs) were developed and still. Among them, particles swarm optimization, Firefly algorithm, Ant colony optimization, Bat algorithm are the most popular and recently developed leading swarm intelligence based approaches. They are mainly inspired by the social and cooperative behaviors of swarm likewise herds of animals, flocking of birds, schooling of fish, ant colonies, herds of bisons and packs of wolves working together for their common benefit. Due …to easy implementation and high capability in achieving of absolute optimum, swarm intelligence based algorithms have attained a great deal attention in both academic and industrial applications. This paper proposes a hybrid swarm intelligence (HSI) algorithm that employs the Bat Algorithm (BA) and the Practical Swarm Optimization (PSO) as constituents to perform their search process for dealing with recently designed benchmark functions in the special session of the 2017 IEEE congress of evolutionary computation (CEC’17) [3 ]. The approximate solutions for most of the CEC’17 benchmark functions obtained by the suggested algorithm in its twenty five independent runs of trails are much promising as compared to its competitors. Show more

Keywords: Global optimization, optimization problems, soft computing, evolutionary computing (EC), evolutionary algorithms (EAs), swarm intelligence based approaches and hybrid swarm intelligence algorithm

DOI: 10.3233/JIFS-192162

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1257-1275, 2020

Authors: Ebrahimnejad, Ali | Tabatabaei, Somayeh | Santos-Arteaga, Francisco J.

Article Type: Research Article

Abstract: Shortest path (SP) optimization problems arise in a wide range of applications such as telecommunications and transportation industries. The main purpose of these problems is to find a path between two predetermined nodes within a network as cheaply or quickly as possible. Conventional SP problems generally assume that the arc weights are defined by crisp variables, though imprecise data have been lately incorporated into the analysis. The present study formulates the SP problem in a directed interval-valued triangular fuzzy network. The resulting interval-valued fuzzy SP (IVFSP) problem is converted into a multi objective linear programming (MOLP) problem. Then, a lexicographic …optimization structure is used to obtain the efficient solution of the resulting MOLP problem. The optimization process confirms that the optimum interval-valued fuzzy shortest path weight preserves the form of an interval-valued triangular fuzzy number. The applicability of the proposed approach is illustrated through an example dealing with wireless sensor networks. Show more

Keywords: Shortest path problem, interval-valued triangular fuzzy numbers, lexicographic optimization structure, multi objective linear programming, wireless sensor networks

DOI: 10.3233/JIFS-192176

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1277-1287, 2020

Authors: Xi, Liang | Wang, Ruidong | Zhang, Fengbin | Sun, Yuezhongyi

Article Type: Research Article

Abstract: The clonal selection algorithm(CSA) is a core method in artificial immune system, which is famous for its intelligent evolution in artificial intelligence application. However, There are some shortcomings in the algorithm, such as local optima and low convergence speed, which make its practical effects not ideal. Culture algorithm(CA) is driven by knowledge, which can significantly improve the evolutionary efficiency. Chaos mechanism can make the algorithm have better problem space coverage ability. Therefore, a culture&chaos-inspired CSA(CC-CSA) is proposed in this paper to deal with the problems mentioned before. CC-CSA adopts the double-layer evolutionary framework of CA to extract knowledge and guide …the crossover and chaotic mutation operation to complete the evolution process. The implicit knowledge is used to adaptively control the chaotic mutation scale, guide the individuals to jump out of the local optima, and realize the accurate search in the latter evolution cycle to gradually approach the optimal solution. It can be seen from the mathematical model analysis that CC-CSA can converge to the global optimal solution. Compared with the experimental results of the original CSA and its representative, up-to-date improved methods, CC-CSA has the fastest convergence speed and the best detection performances. It is also proved that CC-CSA can solve the problems of local optima and slow convergence speed by using the knowledge guidance of CA’s double-layer framework and good coverage ability of chaos mechanism to the problem space. Show more

Keywords: Artificial immune system, clonal selection algorithm, culture algorithm, chaos mechanism, abnormal detection

DOI: 10.3233/JIFS-192188

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1289-1301, 2020

Authors: Adeel, Arooj | Akram, Muhammad | Yaqoob, Naveed | Chammam, Wathek

Article Type: Research Article

Abstract: The notion of fuzzy N -soft sets is a hybrid model, which is a more generalized framework than fuzzy soft sets. To investigate the objects of a reference set in medical field, which have uncertainties in data, can be correctly captured by proposed structures of novel decision-making methods, graded TOPSIS and graded ELECTRE-I methods, based on fuzzy N -soft sets (henceforth, (F , N )-soft sets). Both the proposed methods compute the decision-maker estimations in a more flexile and affluent way, as well as improve the reliability of the decisions, that depends on star ratings or grades for the purpose …of the modelization of decision-making problems in medical field. We show the importance and feasibility of proposed methods by applying them on real life example in medical field having ambiguities, that can be accurately occupied by this framework. Finally, we discuss the comparison analysis of both the proposed decision-making methods. Show more

Keywords: N-soft sets, (F, N)-soft sets, graded TOPSIS, graded ELECTRE-I, decision-making

DOI: 10.3233/JIFS-192203

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1303-1318, 2020

Authors: Ul Haq, Amin | Li, Jianping | Memon, Muhammad Hammad | khan, Jalaluddin | Ali, Zafar | Abbas, Syed Zaheer | Nazir, Shah

Article Type: Research Article

Abstract: Accurate and efficient recognition of Parkinson’s disease is one of the prominent issues in the field of healthcare. To address this problem, different methods have been proposed in the literature. However, existing methods are lacking in accurately recognizing the Parkinson’s disease and suffer from efficiency problems. To overcome these problems faced by existing models, this paper presents a machine-learning-based model for Parkinson’s disease recognition. Specifically, a hybrid feature selection algorithm has been designed by integrating the Relief and ant-colony optimization algorithms to select relevant features for training the model. Moreover, the support vector machine has been trained and tested on …the selected features to achieve optimal classification accuracy. Additionally, the K-fold cross-validation technique has been employed for the optimal hyper-parameters value evaluation of the model.The experimental results on a real-world dataset, i.e., Parkinson’s disease dataset is revealed that the proposed system outperforms baseline competitors by accurately recognizing the Parkinson’s disease and achieving 99.50% accuracy on the selected features. Due to high performance is achieved our proposed method, we are highly recommended for the recognition of PD. Show more

Keywords: Relief, ant colony optimization, parkinson’s disease recognition, feature selection algorithm, classification, machine learning

DOI: 10.3233/JIFS-200075

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1319-1339, 2020

Authors: Aygün, Emin | Erdal, Betül

Article Type: Research Article

Abstract: Two important methods are used to transfer algebraic substructures to soft set theory. In the first method, the soft substructure of an algebraic structure is obtained, while in the second method a soft substructure of a soft algebraic structure is obtained. In this paper, we transfer the radical structure of an ideal to a soft set theory in a commutative ring and a semigroup by considering both methods.

Keywords: Radical, nil radical, soft radical, soft ideal

DOI: 10.3233/JIFS-200117

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1341-1346, 2020

Authors: Su, Shuhua | Yang, Shuqun | Li, Qi

Article Type: Research Article

Abstract:  For a fuzzy subset system Z L , the concepts of a Δ Γ L -completion and a Z Γ L -completion of a given fuzzy poset (X , e ) are introduced and their universal properties are investigated. In this paper, we prove that: (1) the Δ Γ L -completion Δ Γ L  (X ) is a join-completion with the universal property; (2) the Z Γ L -completion Z Γ L  (X ) is the smallest Z L -complete fuzzy subposet of Δ Γ L … (X ) in the case that Z L is fuzzy subset-hereditary. The results show that the Dedekind-MacNeille completion is a special case of the Z Γ L -completion. Show more

Keywords: Fuzzy subset system, Fuzzy subset-hereditary, ΔΓL-completion, ΔΓL-continuous mapping, ΔΓL-continuously ⊔-existing, ZΓL-completion

DOI: 10.3233/JIFS-200121

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1347-1359, 2020

Authors: Fan, Liu | Špirková, Jana | Mesiar, Radko | Yager, Ronald R. | Jin, LeSheng

Article Type: Research Article

Abstract: This work firstly proposes some weight adjusting and preference interfering methods to generate more suitable weight vector in two-tier multi-criteria decision making. The proposed models simultaneously consider the original weight information and subjective preferences of decision makers under interval numbers based evaluation environments. A recently proposed weights allocation method based on convex poset is applied to determine the weight vectors from subjective preferences. With well adjusted and melted weight information, some fuzzy comprehensive evaluations are realized by applying Shilkret Integrals with melted preferences. A numerical example with corresponding decision rules for online shop evaluation problem is also presented for practitioners …to refer to. Show more

Keywords: Aggregation operators, evaluation, information fusion, multi-criteria decision making, weights adjustment

DOI: 10.3233/JIFS-200123

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1361-1369, 2020

Authors: Yang, Wei | Jhang, Seong Tae | Shi, Shao Guang | Ma, Zhen Ming

Article Type: Research Article

Abstract: Consistency is related with reasonableness of the priority vector derived from a preference relation. In this paper, it is pointed out by an example that the existing consistency for the intuitionistic multiplicative preference relations (IMPR) is weak that the ranking or the optimal alternative could not always be derived from the given consistent IMPR. We provide a novel consistency for the IMPRs by the score function and accuracy function and characterize it with the S-normalized and A-normalized intuitionistic multiplicative priority vectors (IMPV). Then, we propose methods to check and reach the S-normalization, the acceptable consistency of the IMPR by its …local IMPVs. We also give some examples to show how the proposed methods work and make comparisons with the existing methods to demonstrate the advantages and disadvantages of the proposed methods. Show more

Keywords: Intuitionistic multiplicative preference relation, consistency, intuitionistic multiplicative priority vector

DOI: 10.3233/JIFS-200128

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1371-1380, 2020

Authors: Borzooei, R. A. | Alavi, S. Z. | Kologani, M. Aaly | Ahn, Sun Shin

Article Type: Research Article

Abstract: In this paper, by considering the notion of pseudo-hoops, which introduced by Georgescu [10 ], we presented the concepts of n -fold filters in pseudo-hoop. Concerning ideas, we gave some related results. Also, we extended our definition to n -fold (positive) implicative and n -fold fantastic filters and investigated their properties and the relation among these n -fold filters. In particular, we proved that every n -fold fantastic and positive implicative filter is an n -fold implicative filter. Finally, we studied the quotient of these filters.

Keywords: n-fold pseudo-hoop, n-fold (positive) implicative filter, n-fold fantastic filter

DOI: 10.3233/JIFS-200179

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1381-1390, 2020

Authors: Zhang, Guokai | Ma, Zhengming | Huang, Haidong

Article Type: Research Article

Abstract: In this paper we address the problem of dimensionality reduction of tensor data. There are three contributions in this paper. Local Homeomorphism is the intrinsic mathematical feature of manifolds and the basis of many manifold learning algorithms. However, these algorithms are developed for vector data, not suitable for tensor data. Our first contribution is to derive a tensor version of dimensionality reduction based on local homeomorphism. Tucker decomposition is widely used in dimensionality reduction of tensor data. However, Tucker decomposition without any regularization is actually a traditional subspace learning problem. Our second contribution is to propose a local homeomorphism regularized …Tucker decomposition and applies it to dimensionality reduction of tensor data, called dimensionality reduction of tensor data based on subspace learning and homeomorphism, SLLH for short. As far as dimensionality reduction is concerned, only the core tensor in Tucker decomposition is the target, while the mode product matrices are only by-products. Therefore, many algorithms absorb all these mode product matrices into a big matrix by using the conversion theorem of tensor algebra. However, in Tucker decomposition, each mode product matrix represents dimensionality reduction for a specific dimension of tensor. Our third contribution is to propose an iterative solution method for SLLH, in which each mode product matrix of the current iteration is calculated from other mode product matrices and the core tensor of the previous iteration. The core tensor is evolved iteratively from the iteratively-calculated mode product matrices. The experimental results presented in this paper show that the proposed SLLH outperforms many of the state-of-the-art algorithms. Show more

Keywords: Tensors, dimensionality reduction, subspace learning, local homeomorphism

DOI: 10.3233/JIFS-200182

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1391-1405, 2020