Journal of Intelligent & Fuzzy Systems - Volume 41, issue 2

Authors: Zheng, Jian | Wang, Jianfeng | Chen, Yanping | Chen, Shuping | Chen, Jingjin | Zhong, Wenlong | Wu, Wenling

Article Type: Research Article

Abstract: Neural networks can approximate data because of owning many compact non-linear layers. In high-dimensional space, due to the curse of dimensionality, data distribution becomes sparse, causing that it is difficulty to provide sufficient information. Hence, the task becomes even harder if neural networks approximate data in high-dimensional space. To address this issue, according to the Lipschitz condition, the two deviations, i.e., the deviation of the neural networks trained using high-dimensional functions, and the deviation of high-dimensional functions approximation data, are derived. This purpose of doing this is to improve the ability of approximation high-dimensional space using neural networks. Experimental results …show that the neural networks trained using high-dimensional functions outperforms that of using data in the capability of approximation data in high-dimensional space. We find that the neural networks trained using high-dimensional functions more suitable for high-dimensional space than that of using data, so that there is no need to retain sufficient data for neural networks training. Our findings suggests that in high-dimensional space, by tuning hidden layers of neural networks, this is hard to have substantial positive effects on improving precision of approximation data. Show more

Keywords: Data sparsity, high-dimensional function, high-dimensional space, neural networks

DOI: 10.3233/JIFS-211417

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3739-3750, 2021

Authors: Wang, Jih-Chang | Chen, Ting-Yu

Article Type: Research Article

Abstract: The theory involving T-spherical fuzziness provides an exceptionally good tool to efficiently manipulate the impreciseness, equivocation, and vagueness inherent in multiple criteria assessment and decision-making processes. By exploiting the notions of score functions and distance measures for complex T-spherical fuzzy information, this paper aims to propound an innovational T-spherical fuzzy ELECTRE (ELimination Et Choice Translating REality) approach to handling intricate and convoluted evaluation problems. Several newly-created score functions are employed from the comparative perspective to constitute a core procedure concerning concordance and discordance determination in the current T-spherical fuzzy ELECTRE method. By the agency of a realistic application, this paper …appraises the usefulness and efficacy of available score functions in the advanced ELECTRE mechanism under T-spherical fuzzy uncertainties. This paper incorporates two forms of Minkowski distance measures into the core procedure; moreover, the effectuality of the advocated measure in differentiating T-spherical fuzzy information is validated. The effectiveness outcomes of the evolved method have been investigated through the medium of an investment decision regarding potential company options for extending the business scope. The real-world application also explores the comparative advantages of distinct score functions in tackling multiple criteria decision-making tasks. Finally, this paper puts forward a conclusion and future research directions. Show more

Keywords: T-spherical fuzziness, multiple criteria assessment, decision-making, score function, T-spherical fuzzy elimination and choice translating reality (ELECTRE)

DOI: 10.3233/JIFS-211431

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3751-3770, 2021

Authors: More, Sujeet | Singla, Jimmy

Article Type: Research Article

Abstract: Deep learning has shown outstanding efficiency in medical image segmentation. Segmentation of knee tissues is an important task for early diagnosis of rheumatoid arthritis (RA) with selecting variant features. Automated segmentation and feature extraction of knee tissues are desirable for faster and reliable analysis of large datasets and further diagnosis. In this paper a novel architecture called as Discrete-MultiResUNet, which is a combination of discrete wavelet transform (DWT) with MultiResUNet architecture is applied for feature extraction and segmentation, respectively. This hybrid architecture captures more prominent features from the knee magnetic resonance image efficiently with segmenting vital knee tissues. The hybrid …model is evaluated on the knee MR dataset demonstrating outperforming performance compared with baseline models. The model achieves excellent segmentation performance accuracy of 96.77% with a dice coefficient of 98%. Show more

Keywords: MultiResUNet, discrete wavelet transform, dice similarity coefficient, rheumatoid arthritis, segmentation

DOI: 10.3233/JIFS-211459

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3771-3781, 2021

Authors: Zhang, Shanshan | Gao, Hui | Wei, Guiwu | Chen, Xudong

Article Type: Research Article

Abstract: The Multi-attribute group decision making (MAGDM) problem is an interesting everyday problem full of complexity and ambiguity. As an extended form of fuzzy sets, intuitionistic fuzzy sets (IFSs) can provide decision-makers (DMs) with a wider range of preferences for MAGDM. The grey relational analysis (GRA) is an effective method for dealing with MAGDM problems. However, in view of the incomplete and asymmetric information and the influence of DMs’ psychological factors on the decision result, we develop a new model that GRA method based on cumulative prospect theory (CPT) under the intuitionistic fuzzy environment. Moreover, the weight of attribute is calculated …by entropy weight, so as to distinguish the importance level of attributes, which greatly improves the credibility of the selected scheme. simultaneously, the proposed method is used to the selection of optimal green suppliers for testifying the availability of this new model and the final comparison between this new method and the existing methods further verify the reliability. In addition, the proposed method provides some references for other selection problems. Show more

Keywords: Multi-attribute group decision making (MAGDM), grey relational analysis (GRA) method, cumulative prospect theory (CPT), intuitionistic fuzzy sets (IFSs)

DOI: 10.3233/JIFS-211461

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3783-3795, 2021

Authors: Chen, Xiaojun | Ding, Ling | Xiang, Yang

Article Type: Research Article

Abstract: Knowledge graph reasoning or completion aims at inferring missing facts based on existing ones in a knowledge graph. In this work, we focus on the problem of open-world knowledge graph reasoning—a task that reasons about entities which are absent from KG at training time (unseen entities). Unfortunately, the performance of most existing reasoning methods on this problem turns out to be unsatisfactory. Recently, some works use graph convolutional networks to obtain the embeddings of unseen entities for prediction tasks. Graph convolutional networks gather information from the entity’s neighborhood, however, they neglect the unequal natures of neighboring nodes. To resolve this …issue, we present an attention-based method named as NAKGR, which leverages neighborhood information to generate entities and relations representations. The proposed model is an encoder-decoder architecture. Specifically, the encoder devises an graph attention mechanism to aggregate neighboring nodes’ information with a weighted combination. The decoder employs an energy function to predict the plausibility for each triplets. Benchmark experiments show that NAKGR achieves significant improvements on the open-world reasoning tasks. In addition, our model also performs well on the closed-world reasoning tasks. Show more

Keywords: Open-world knowledge graph reasoning, neighborhood information, graph attention networks, knowledge representation learning

DOI: 10.3233/JIFS-211889

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3797-3808, 2021

Authors: Xie, Ning | Chen, Dengkai | Fan, Yu | Zhu, Mengya

Article Type: Research Article

Abstract: In the development of product design, one of the elements of market competition for products is to meet the Kansei needs of users. Compared to features, users pay more attention to whether products can match their emotions, which is Kansei needs. The product developers are eager to get the Kansei needs of users more accurately and conveniently. This paper takes the computer cloud platform as the carrier and based on the collaborative filtering algorithm. We used personalized double matrix recommendation algorithm as the core, and the adjectives dimensionality reduction method to filter the image tags to simplify the users’ rating …process and improve the recommendation efficiency. Finally, we construct a Kansei needs acquisition model to quickly and easily obtain the Kansei needs of users. We verify the model using the air purifier as a subject. The results of the case show that the model can find out the user’s Kansei needs more quickly. When the data is more, the prediction will be more accurate and timely. Show more

Keywords: Kansei needs, image tags, double matrix recommendation algorithm, adjectives dimensionality reduction, cloud platform

DOI: 10.3233/JIFS-191241

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3809-3820, 2021

Authors: Li, Dong | Wang, Yuejiao | Li, Muhao | Sun, Xin | Pan, Jingchang | Ma, Jun

Article Type: Research Article

Abstract: In the real world, a large number of social systems can be modeled as signed social networks including both positive and negative relationships. Influence maximization in signed social networks is an interesting and significant research direction, which has gained some attention. All of existing studies mainly focused on positive influence maximization (PIM) problem. The goal of the PIM problem is to select the seed set with maximum positive influence in signed social networks. However, the selected seed set with maximum positive influence may also has a large amount of negative influence, which will cause bad effects in the real applications. …Therefore, maximizing purely positive influence is not the final and best goal in signed social networks. In this paper, we introduce the concept of net positive influence and propose the net positive influence maximization (NPIM) problem for signed social networks, to select the seed set with as much positive influence as possible and as less negative influence as possible. Additionally, we prove that the objective function of NPIM problem under polarity-related independent cascade model is non-monotone and non-submodular, which means the traditional greedy algorithm is not applicable to the NPIM problem. Thus, we propose an improved R-Greedy algorithm to solve the NPIM problem. Extensive experiments on two Epinions and Slashdot datasets indicate the differences between positive influence and net positive influence, and also demonstrate that our proposed solution performs better than the state-of-the-art methods in terms of promoting net positive influence diffusion in less running time. Show more

Keywords: Influence maximization, signed social networks, net positive influence, polarity

DOI: 10.3233/JIFS-191908

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3821-3832, 2021

Authors: Adak, Sudip | Mahapatra, G.S.

Article Type: Research Article

Abstract: This paper develops a fuzzy two-layer supply chain for manufacturer and retailer with defective and non-defective types of products. The manufacturer produces up to a specific time, including faulty and non-defective items, and after the screening, the non-defective item sends to the retailer. The retailer’s strategy is to do the screening of items received from the manufacturer; subsequently, the perfect quality items are used to fulfill the customer’s demand, and the defective items are reworked. The retailer considers that customer demand is time and reliability dependent. The supply chain considers probabilistic deterioration for the manufacturer and retailers along with the …strategies such as production rate, unit production cost, cost of idle time of manufacturer, screening, rework, etc. The optimum average profit of the integrated model is evaluated for both the cases crisp and fuzzy environments. Managerial insights and the effect of changes in the parameters’ values on the optimal inventory policy under fuzziness are presented. Show more

Keywords: Two-layer supply chain, deterioration, imperfect items, trapezoidal fuzzy number, reliability, advertisement

DOI: 10.3233/JIFS-200562

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3833-3847, 2021

Authors: Dai, Ziwei | Zhang, Zhiyong | Chen, Mingzhou

Article Type: Research Article

Abstract: Task scheduling is important in cloud manufacturing because of customers’ increasingly individualized demands. However, when various changes occur, a previous optimal schedule may become non-optimal or even infeasible owing to the uncertainty of the real manufacturing environment where dynamic task arrival over time is a vital source. In this paper, we propose a novel collaborative task scheduling (CTS) model dealing with new task arrival which considers multi-supply chain collaboration. We present an improved multi-population biogeography-based optimization (IMPBBO) algorithm that uses a matrix-based solution representation and integrates the multi-population strategy, local search for the best solution, and the collaboration mechanism, for …determining the optimal schedule. A series of experiments are conducted for verifying the effectiveness of the IMPBBO algorithm for solving the CTS model by comparing it with five other algorithms. The experimental results concerning average best values obtained by the IMPBBO algorithm are better than that obtained by comparison algorithms for 41 out of 45 cases, showing its superior performance. Wilcoxon-test has been employed to strengthen the fact that IMPBBO algorithm performs better than five comparison algorithms. Show more

Keywords: Cloud manufacturing, task scheduling, multi-supply chain collaboration, new task arrival, biogeography-based optimization

DOI: 10.3233/JIFS-201066

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3849-3872, 2021

Authors: Mohammadi Moghadam, Hooman | Foroozan, Hossein | Gheisarnejad, Meysam | Khooban, Mohammad-Hassan

Article Type: Research Article

Abstract: Recently, the Digital Twin (DT) technology, which joints the physical environment and virtual space, has drawn more attention in industry and research academic plans. In general, the virtual model representations of the physical objects are created in the DT manner to simulates the characteristics and behaviors of the real-word system. Applying a supervisory system not only can reduce the failures of components, but also preserve the overall costs associated with the system at a minimum. This paper reviews the DT applications in the power system, while its advantages in wind turbines, solar panels, power electronic converter, and shipboard electrical system …will be briefly discussed. The potential benefits of contemporary technologies to ameliorate the DT in the industry are studied. Besides, it provides a great technique to assess and analyze system performance. As a basis for DT, various new emerging developments as an example of artificial intelligence (AI), big data, the internet of things (IoT), and 5 G are reviewed. Show more

Keywords: Index Terms: Digital Twin (DT), Artificial Intelligence (AI), ship power system, big data, Internet of Things (IoT)

DOI: 10.3233/JIFS-201885

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3873-3893, 2021