Journal of Intelligent & Fuzzy Systems - Volume 40, issue 1

Authors: Demirkiran, Emin T. | Pak, Muhammet Y. | Cekik, Rasim

Article Type: Research Article

Abstract: Recommender systems have recently become a significant part of e-commerce applications. Through the different types of recommender systems, collaborative filtering is the most popular and successful recommender system for providing recommendations. Recent studies have shown that using multi-criteria ratings helps the system to know the customers better. However, bringing multi aspects to collaborative filtering causes new challenges such as scalability and sparsity. Additionally, revealing the relation between criteria is yet another optimization problem. Hence, increasing the accuracy in prediction is a challenge. In this paper, an aggregation-function based multi-criteria collaborative filtering system using Rough Sets Theory is proposed as a …novel approach. Rough Sets Theory is used to uncover the relationship between the overall criterion and the individual criteria. Experimental results show that the proposed model (RoughMCCF) successfully improves the predictive accuracy without compromising on online performance. Show more

Keywords: Accuracy, multi-criteria collaborative filtering, recommender systems, rough sets theory

DOI: 10.3233/JIFS-201073

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 907-917, 2021

Authors: Karimzadeh Parizi, Morteza | Keynia, Farshid | Khatibi bardsiri, Amid

Article Type: Research Article

Abstract: Success of metaheuristic algorithms depends on the efficient balance between of exploration and exploitation phases. Any optimization algorithm requires a combination of diverse exploration and proper exploitation to avoid local optima. This paper proposes a new improved version of the Woodpecker Mating Algorithm (WMA), based on opposition-based learning, known as the OWMA aiming to develop exploration and exploitation capacities and establish a simultaneous balance between these two phases. This improvement consists of three major mechanisms, the first of which is the new Distance Opposition-based Learning (DOBL) mechanism for improving exploration, diversity, and convergence. The second mechanism is the allocation of …local memory of personal experiences of search agents for developing the exploitation capacity. The third mechanism is the use of a self-regulatory and dynamic method for setting the Hα parameter to improve the Running Away function (RA) performance. The ability of the proposed algorithm to solve 23 benchmark mathematical functions was evaluated and compared to that of a series of the latest and most popular metaheuristic methods reviewed in the research literature. The proposed algorithm is also used as a Multi-Layer Perceptron (MLP) neural network trainer to solve the classification problem on four biomedical datasets and three function approximation datasets. In addition, the OWMA algorithm was evaluated in five optimization problems constrained by the real world. The simulation results proved the superior and promising performance of the proposed algorithm in the majority of evaluations. The results prove the superiority and promising performance of the proposed algorithm in solving very complicated optimization problems. Show more

Keywords: Optimization, metaheuristic, woodpecker mating algorithm, distance opposition-based learning

DOI: 10.3233/JIFS-201075

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 919-946, 2021

Authors: Mostafa, Samih M.

Article Type: Research Article

Abstract: Data preprocessing is a necessary core in data mining. Preprocessing involves handling missing values, outlier and noise removal, data normalization, etc. The problem with existing methods which handle missing values is that they deal with the whole data ignoring the characteristics of the data (e.g., similarities and differences between cases). This paper focuses on handling the missing values using machine learning methods taking into account the characteristics of the data. The proposed preprocessing method clusters the data, then imputes the missing values in each cluster depending on the data belong to this cluster rather than the whole data. The author …performed a comparative study of the proposed method and ten popular imputation methods namely mean, median, mode, KNN, IterativeImputer, IterativeSVD, Softimpute, Mice, Forimp, and Missforest. The experiments were done on four datasets with different number of clusters, sizes, and shapes. The empirical study showed better effectiveness from the point of view of imputation time, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2 score) (i.e., the similarity of the original removed value to the imputed one). Show more

Keywords: Data preprocessing, missing data, imputation, missingness mechanisms

DOI: 10.3233/JIFS-201077

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 947-972, 2021

Authors: El-Sharkasy, M. M.

Article Type: Research Article

Abstract: Topological concepts play an important role in applications and solving real-life problems. Among of these concepts are neighbourhood and minimal structure. In this paper, we introduce a new space-based on a generalized system with a binary relation on a nonempty set by using the concept of a minimal structure, which is called a minimal structure approximation space (briefly, MSAS ), and study some of its properties. Also, we compare the advantages of MSAS with neighbourhood approximation space which are based on the same starting point, and apply the concept of MSAS in some examples of chemistry to extraction …and reduct the information. Finally, we investigate the concepts of the separation axioms on MSAS and study some of its properties in the information system as the process of approximation of information. Show more

Keywords: Rough set, approximation space, minimal structure, topological space, T0, T1 and T2, 54A05, 54C55, 54E05

DOI: 10.3233/JIFS-201090

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 973-982, 2021

Authors: Li, Huan | Tang, Pengyi | Ma, Yuechao

Article Type: Research Article

Abstract: In this paper, a class of observer-based sliding mode controller is designed, and the finite-time H ∞ control problem of uncertain T-S fuzzy systems with time-varying is studied. Firstly, an integral-type sliding surface function with time-delay is devised based on the state estimator, and sufficient criteria of finite-time bounded and finite-time H ∞ bounded can be obtained for the T-S systems. Moreover, the proposed sliding mode control law is integrated to ensure the dynamics of controlled system into the sliding surface in a finite-time interval. Then, according to the linear matrix inequalities (LMIs), the desired gain matrices of …fuzzy sliding mode controller and state estimator are derived. Finally, effectiveness gives some illustrative examples may be used to display the value of the current proposed method as well as a significant improvement. Show more

Keywords: Finite-time H∞ control, T-S fuzzy system, sliding mode, time-varying delay, linear matrix inequalities (LIMs)

DOI: 10.3233/JIFS-201091

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 983-999, 2021

Authors: Chen, Yen-Liang | Chi, Fang-Chi

Article Type: Research Article

Abstract: In the rough set theory proposed by Pawlak, the concept of reduct is very important. The reduct is the minimum attribute set that preserves the partition of the universe. A great deal of research in the past has attempted to reduce the representation of the original table. The advantage of using a reduced representation table is that it can summarize the original table so that it retains the original knowledge without distortion. However, using reduct to summarize tables may encounter the problem of the table still being too large, so users will be overwhelmed by too much information. To solve …this problem, this article considers how to further reduce the size of the table without causing too much distortion to the original knowledge. Therefore, we set an upper limit for information distortion, which represents the maximum degree of information distortion we allow. Under this upper limit of distortion, we seek to find the summary table with the highest compression. This paper proposes two algorithms. The first is to find all summary tables that satisfy the maximum distortion constraint, while the second is to further select the summary table with the greatest degree of compression from these tables. Show more

Keywords: Rough set, reduct, attribute reduction, information system, summarization

DOI: 10.3233/JIFS-201160

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1001-1015, 2021

Authors: Wu, Ziheng | Li, Cong | Zhou, Fang | Liu, Lei

Article Type: Research Article

Abstract: Fuzzy C-means clustering algorithm (FCM) is an effective approach for clustering. However, in most existing FCM type frameworks, only in-cluster compactness is taken into account, whereas the between-cluster separability is overlooked. In this paper, to enhance the clustering, by incorporating the feature weighting and data weighting method, we put forward a new weighted fuzzy C-means clustering approach considering between-cluster separability, in which for achieving good compactness and separability, making the in-cluster distances as small as possible and making the between-cluster distances as large as possible, the in-cluster distances and between-cluster distances are taken into account; To achieve the optimal clustering …result, the iterative formulas of the feature weights, membership degrees, data weights and cluster centers are obtained by maximizing the in-cluster compactness and the between-cluster separability. Experiments on real-world datasets were carried out, the results showed that the new approach could obtain promising performance. Show more

Keywords: Fuzzy C-means, data weighting, feature weighting, between-cluster separability

DOI: 10.3233/JIFS-201178

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1017-1024, 2021

Authors: Mensah, Patrick Kwabena | Weyori, Benjamin Asubam | Ayidzoe, Mighty Abra

Article Type: Research Article

Abstract: Capsule Networks (CapsNets) excel on simple image recognition problems. However, they fail to perform on complex images with high similarity and background objects. This paper proposes Local Binary Pattern (LBP) k-means routing and evaluates its performance on three publicly available plant disease datasets containing images with high similarity and background objects. The proposed routing algorithm adopts the squared Euclidean distance, sigmoid function, and a ‘simple-squash’ in place of dot product, SoftMax normalizer, and the squashing function found respectively in the dynamic routing algorithm. Extensive experiments conducted on the three datasets showed that the proposed model achieves consistent improvement in test …accuracy across the three datasets as well as allowing an increase in the number of routing iterations with no performance degradation. The proposed model outperformed a baseline CapsNet by 8.37% on the tomato dataset with an overall test accuracy of 98.80%, comparable to state-of-the-art models on the same datasets. Show more

Keywords: Capsule network, convolutional neural network, plant disease, classification, activation maps

DOI: 10.3233/JIFS-201226

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1025-1036, 2021

Authors: Wu, Deyin | Li, Yonghong

Article Type: Research Article

Abstract: In this paper, we research a class of axioms in closed G-V fuzzy matroids. The main research method is to transform fuzzy matroids into matroids. First, we study many properties of the basis family of induced matroids, and define a new mapping which can reflect the relationship between bases of induced matroids of a G-V fuzzy matroid. Second, we discuss the new mapping, and reveal the relationship and properties among the fundamental sequence, the induced basis family and the new mapping of a G-V fuzzy matroid. From these relationships and properties, we extract four key attributes: normativity property, inclusion property, …exchange property, and right surjection. Finally, we propose and prove “the induced basis axioms for a closed G-V fuzzy matroid” by these key attributes. With the help of these axioms, a closed G-V fuzzy matroid can be uniquely determined by a finite number sequence, a subset family and a mapping on this subset family when they satisfy above four attributes, and vice versa. Show more

Keywords: Matroids, fuzzy matroids, fundamental sequences, induced matroids, induced basis families, induced basis family mappings

DOI: 10.3233/JIFS-201227

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1037-1049, 2021

Authors: Das, Kousik | Naseem, Usman | Samanta, Sovan | Khan, Shah Khalid | De, Kajal

Article Type: Research Article

Abstract: In the recent phenomenon of social networks, both online and offline, two nodes may be connected, but they may not follow each other. Thus there are two separate links to be given to capture the notion. Directed links are given if the nodes follow each other, and undirected links represent the regular connections (without following). Thus, this network may have both types of relationships/ links simultaneously. This type of network can be represented by mixed graphs. But, uncertainties in following and connectedness exist in complex systems. To capture the uncertainties, fuzzy mixed graphs are introduced in this article. Some operations, …completeness, and regularity and few other properties of fuzzy mixed graphs are explained. Representation of fuzzy mixed graphs as matrix and isomorphism theorems on fuzzy mixed graphs are developed. A network of COVID19 affected areas in India are assumed, and central regions are identified as per the proposed theory. Show more

Keywords: Fuzzy mixed graphs, fuzzy mixed degree, adjacency matrices, isomorphism, COVID19

DOI: 10.3233/JIFS-201249

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1051-1064, 2021