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

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