Journal of Intelligent & Fuzzy Systems - Volume 40, issue 3

Authors: Wang, Zicheng | Chen, Huayou | Zhu, Jiaming | Ding, Zhenni

Article Type: Research Article

Abstract: Faced with the rapid update of nonlinear and irregular big data from the environmental monitoring system, both the public and managers urgently need reliable methods to predict possible air pollutions in the future. Therefore, a multi-scale deep learning (MDL) and optimal combination ensemble (OCE) approach for hourly air quality index (AQI) forecasting is proposed in this paper, named MDL-OCE model. Before normal modeling, all original data are preprocessed through missing data filling and outlier testing to ensure smooth computation. Due to the complexity of such big data, slope-based ensemble empirical mode decomposition (EEMD) is adopted to decompose the time series …of AQI and meteorological conditions into a finite number of simple intrinsic mode function (IMF) components and one residue component. Then, to unify the number of components of different variables, the fine-to-coarse (FC) technique is used to reconstruct all components into high frequency component (HF), low frequency component (LF), and trend component (TC). For purpose of extracting the underlying relationship between AQI and meteorological conditions, the three components are respectively trained and predicted by different deep learning architectures (stacked sparse autoencoder (SSAE)) with a multilayer perceptron (MLP). The corresponding forecasting results of three components are merged by OCE method to better achieve the ultimate AQI forecasting outputs. The empirical results clearly demonstrate that our proposed MDL-OCE model outperforms other advanced benchmark models in terms of forecasting performances in all cases. Show more

Keywords: AQI forecasting, multi-scale deep learning, optimal combination ensemble, meteorological conditions, big data

DOI: 10.3233/JIFS-202481

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5483-5500, 2021

Authors: Sharma, Shalini | Kumar, Naresh | Kaswan, Kuldeep Singh

Article Type: Research Article

Abstract: Big data requires new technologies and tools to process, analyze and interpret the vast amount of high-speed heterogeneous information. A simple mistake in processing software, error in data, and malfunctioning in hardware results in inaccurate analysis, compromised results, and inadequate performance. Thus, measures concerning reliability play an important role in determining the quality of Big data. Literature related to Big data software reliability was critically examined in this paper to investigate: the type of mathematical model developed, the influence of external factors, the type of data sets used, and methods employed to evaluate model parameters while determining the system reliability …or component reliability of the software. Since the environmental conditions and input variables differ for each model due to varied platforms it is difficult to analyze which method gives the better prediction using the same set of data. Thus, paper summarizes some of the Big data techniques and common reliability models and compared them based on interdependencies, estimation function, parameter evaluation method, mean value function, etc. Visualization is also included in the study to represent the Big data reliability distribution, classification, analysis, and technical comparison. This study helps in choosing and developing an appropriate model for the reliability prediction of Big data software. Show more

Keywords: Reliability models, Big data, stochastic equation, hazard rate, jump diffusion

DOI: 10.3233/JIFS-202503

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5501-5516, 2021

Authors: Kişi, Ömer

Article Type: Research Article

Abstract: We investigate the concepts of pointwise and uniform I θ -convergence and type of convergence lying between mentioned convergence methods, that is, equi-ideally lacunary convergence of sequences of fuzzy valued functions and acquire several results. We give the lacunary ideal form of Egorov’s theorem for sequences of fuzzy valued measurable functions defined on a finite measure space ( X , M , μ ) . We also introduce the concept of I θ -convergence in measure for sequences of fuzzy valued functions and proved some …significant results. Show more

Keywords: Pointwise convergence, uniformly convergence, ideal convergence, lacunary convergence, fuzzy-valued function

DOI: 10.3233/JIFS-202624

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5517-5526, 2021

Authors: Zhao, Baohua | Sung, Tien-Wen | Zhang, Xin

Article Type: Research Article

Abstract: The artificial bee colony (ABC) algorithm is one of the classical bioinspired swarm-based intelligence algorithms that has strong search ability, because of its special search mechanism, but its development ability is slightly insufficient and its convergence speed is slow. In view of its weak development ability and slow convergence speed, this paper proposes the QABC algorithm in which a new search equation is based on the idea of quasi-affine transformation, which greatly improves the cooperative ability between particles and enhances its exploitability. During the process of location updating, the convergence speed is accelerated by updating multiple dimensions instead of one …dimension. Finally, in the overall search framework, a collaborative search matrix is introduced to update the position of particles. The collaborative search matrix is transformed from the lower triangular matrix, which not only ensures the randomness of the search, but also ensures its balance and integrity. To evaluate the performance of the QABC algorithm, CEC2013 test set and CEC2014 test set are used in the experiment. After comparing with the conventional ABC algorithm and some famous ABC variants, QABC algorithm is proved to be superior in efficiency, development ability, and robustness. Show more

Keywords: Artificial bee colony algorithm, bioinspired swarm intelligence, optimization, quasi-affine transformation, collaborative search matrix

DOI: 10.3233/JIFS-202712

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5527-5544, 2021

Authors: Zulqarnain, Rana Muhammad | Xin, Xiao Long | Garg, Harish | Khan, Waseem Asghar

Article Type: Research Article

Abstract: The Pythagorean fuzzy soft sets (PFSS) is a parametrized family and one of the appropriate extensions of the Pythagorean fuzzy sets (PFS). It’s also a generalization of intuitionistic fuzzy soft sets, used to accurately assess deficiencies, uncertainties, and anxiety in evaluation. The most important advantage of PFSS over existing sets is that the PFS family is considered a parametric tool. The PFSS can accommodate more uncertainty comparative to the intuitionistic fuzzy soft sets, this is the most important strategy to explain fuzzy information in the decision-making process. The main objective of the present research is to progress some operational laws …along with their corresponding aggregation operators in a Pythagorean fuzzy soft environment. In this article, we introduce Pythagorean fuzzy soft weighted averaging (PFSWA) and Pythagorean fuzzy soft weighted geometric (PFSWG) operators and discuss their desirable characteristics. Also, develop a decision-making technique based on the proposed operators. Through the developed methodology, a technique for solving decision-making concerns is planned. Moreover, an application of the projected methods is presented for green supplier selection in green supply chain management (GSCM). A comparative analysis with the advantages, effectiveness, flexibility, and numerous existing studies demonstrates the effectiveness of this method. Show more

Keywords: Pythagorean fuzzy sets, Pythagorean fuzzy soft sets, PFSWA operator, PFSWG operator, GSCM

DOI: 10.3233/JIFS-202781

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5545-5563, 2021

Authors: Sfiris, D.S.

Article Type: Research Article

Abstract: This paper deals with improving the approximation capability of fuzzy systems. Fuzzy negations produced via conical sections are a promising methodology towards better fuzzy implications in fuzzy rules. The linguistic variables and the fuzzy rules are induced automatically following a fuzzy equivalence relation. The uncertainty of linear or nonlinear systems is thus dealt with. In this study, the clustering is optimized without human intervention, but also the best inference mechanism for a particular dataset is prescribed. It has been found that clustering based on fuzzy equivalence relation and fuzzy inference via conical sections leads to remarkably accurate approximations. A fuzzy …rule based system with fewer control parameters is proposed. An application on telecom data shows the use of the methodology, its applicability to a real problem and its performance compared to other alternatives in terms of quality. Show more

Keywords: Fuzzy inference, fuzzy negation, rule based systems, fuzzy clustering, fuzzy equivalence relation

DOI: 10.3233/JIFS-192029

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5565-5581, 2021

Authors: Jia, Heming | Peng, Xiaoxu

Article Type: Research Article

Abstract: With the advent of the information age, people have higher requirements for basic algorithms. Meta-heuristic algorithms have received wide attention as a high-level strategy to study and generate fully optimized solutions to data-driven optimization problems. Using the advantage of equilibrium optimizer (EO) with better balance mode, combined with the strategy of memetic algorithm, different proportion of temperature is introduced in different stages. That is, EO and thermal exchange optimization (TEO) are fused to obtain a new highly balanced optimizer (HEO). While keeping the guiding strategy and memory mode unchanged of EO, the accuracy of optimization is greatly improved. 14 well-known …benchmark functions and 7 selective algorithms were used for HEO evaluation comparison experiments. On the basis of the fitness function curve, the optimal solution and other experimental data are tested statistically. The experimental results show that the improved algorithm has high accuracy and stability, but at the cost of running a little more time. Application testing of complex engineering problems is also one of the main purposes of algorithm design. In this paper, three typical engineering design problems (three truss, welded beam and rolling bearing design) are tested and the experimental results show that this algorithm has certain competitiveness and superiority in classical engineering design. Show more

Keywords: Equilibrium optimizer, thermal exchange optimization, memetic algorithm, benchmark functions, engineering design problems

DOI: 10.3233/JIFS-200101

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5583-5594, 2021

Authors: Ni, Na | Zhu, Yuanguo

Article Type: Research Article

Abstract: Bacteria foraging optimization (BFO) algorithm is easy to fall into the local optimal solution and slow in convergence. In this paper, we have come up with a self-adaptive bacterial foraging algorithm based on estimation of distribution to overcome the mentioned shortages. First, in the chemotactic operator, the swimming step size of bacterium is adaptively adjusted by its fitness value and bacteria move in a random direction. Second, the bacteria obtain the probability of replication based on the fitness value. We choose half of the population for replication by the roulette wheel method. Finally, the possibility of elimination-dispersal is adjusted by …the fitness value. Selected bacteria are dispersed to the new locations produced by BOX-Muller formula. Compared with some relative heuristic algorithms on finding the optimal value of ten benchmark functions, the proposed algorithm shows higher convergence speed and accuracy. Show more

Keywords: Bacteria foraging optimization algorithm, self-adaptive, estimation of distribution, benchmark function

DOI: 10.3233/JIFS-200439

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5595-5607, 2021

Authors: Huang, Hui-Yu | Lin, Chih-Hung

Article Type: Research Article

Abstract: Inpainting is a technique to enhance digital videos. Based on the spatiotemporal domain, we herein propose a video inpainting method to repair the removal objects in the videos. The method consists of an adaptive foreground model, the motion rate estimation of objects, and a repairing scheme. Initially, the adaptive foreground model based on the background subtraction method is developed. The model is used to estimate the motion rate for each moving object in the frame. According to the estimated motion rate, the model specifies an adaptive interval between the forwarding reference frame and backward reference frame to obtain the useful …information and to repair the removal objects. The remaining un-repaired areas are filled using an exemplar-based inpainting technique with color variance. The results show that the proposed method can produce visually pleasing results. Additionally, it reduces the inpainting time and provides efficient computing. Show more

Keywords: Video inpainting, image inpainting, exemplar-based inpainting, spatiotemporal domain

DOI: 10.3233/JIFS-200542

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5609-5622, 2021

Authors: Zhang, Yu | Yu, Zhengtao | Mao, Cunli | Huang, Yuxin | Gao, Shengxiang

Article Type: Research Article

Abstract: Correlation analysis of law-related news is a task to of dividing news into law-related or law-unrelated news, which is the basis of public opinion analysis. Public opinion news consists of the title and the body. The title describes the theme of the news, and the body describes the content of the news. They are equally important and interdependent in the analysis of lawrelated news. Therefore, we make full use of the dependence between the title and the body and propose a learning method that combines the bidirectional attention flow of the title and the body. This method encodes the title …and the body respectively by using a bidirectional gated recurrent unit (BiGRU) to obtain the word-level feature matrix of the title and the word-level feature matrix of the body. Then it further extracts the law relevant key features from the body feature matrix, to obtain the word-level feature representation of the body. Finally, we combine the word-level feature representation of the title and the body to build bidirectional attention flow. In this way, the information of the two is fully integrated and interacted to improve the accuracy of the legal correlation analysis of news. To verify the validity of the method in this paper, we conducted experiments on the analysis of law-related news. The results show that our method has achieved good results. Compared with the baseline method, the F1 values of our method is increased by 2.2%, which strongly proves that the interaction between title and body has a good supporting effect on news text classification. Show more

Keywords: Law-related news, public opinion analysis, title combined body, bidirectional attention flow

DOI: 10.3233/JIFS-201162

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 5623-5635, 2021