Journal of Intelligent & Fuzzy Systems - Volume 41, issue 6

Authors: Lou, Ping | Xu, Kun | Jiang, Xuemei | Xiao, Zheng | Yan, Junwei

Article Type: Research Article

Abstract: Path planning in an unknown environment is a basic task for mobile robots to complete tasks. As a typical deep reinforcement learning, deep Q-network (DQN) algorithm has gained wide popularity in path planning tasks due to its self-learning and adaptability to complex environment. However, most of path planning algorithms based on DQN spend plenty of time for model training and the learned model policy depends only on the information observed by sensors. It will cause poor generalization capability for the new task and time waste for model retraining. Therefore, a new deep reinforcement learning method combining DQN with prior knowledge …is proposed to reduce training time and enhance generalization capability. In this method, a fuzzy logic controller is designed to avoid the obstacles and help the robot avoid blind exploration for reducing the training time. A target-driven approach is used to address the lack of generalization, in which the learned policy depends on the fusion of observed information and target information. Extensive experiments show that the proposed algorithm converges faster than DQN algorithm in path planning tasks and the target can be reached without retraining when the path planning task changes. Show more

Keywords: Path planning, prior knowledge, deep reinforcement learning, deep Q-network, fuzzy logic controller

DOI: 10.3233/JIFS-192171

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 5773-5789, 2021

Authors: Niu, Guo | Ma, Zhengming | Chen, Haoqing | Su, Xue

Article Type: Research Article

Abstract: Manifold learning plays an important role in nonlinear dimensionality reduction. But many manifold learning algorithms cannot offer an explicit expression for dealing with the problem of out-of-sample (or new data). In recent, many improved algorithms introduce a fixed function to the object function of manifold learning for learning this expression. In manifold learning, the relationship between the high-dimensional data and its low-dimensional representation is a local homeomorphic mapping. Therefore, these improved algorithms actually change or damage the intrinsic structure of manifold learning, as well as not manifold learning. In this paper, a novel manifold learning based on polynomial approximation (PAML) …is proposed, which learns the polynomial approximation of manifold learning by using the dimensionality reduction results of manifold learning and the original high-dimensional data. In particular, we establish a polynomial representation of high-dimensional data with Kronecker product, and learns an optimal transformation matrix with this polynomial representation. This matrix gives an explicit and optimal nonlinear mapping between the high-dimensional data and its low-dimensional representation, and can be directly used for solving the problem of new data. Compare with using the fixed linear or nonlinear relationship instead of the manifold relationship, our proposed method actually learns the polynomial optimal approximation of manifold learning, without changing the object function of manifold learning (i.e., keeping the intrinsic structure of manifold learning). We implement experiments over eight data sets with the advanced algorithms published in recent years to demonstrate the benefits of our algorithm. Show more

Keywords: Out-of-sample, new data, polynomial, dimensionality reduction, manifold learning

DOI: 10.3233/JIFS-200202

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 5791-5806, 2021

Authors: Chen, Deguang | Ma, Ziping | Wei, Lin | Zhu, Yanbin | Ma, Jinlin | Gong, Yuanwen | Zhou, Jie

Article Type: Research Article

Abstract: Text-based reading comprehension models have great research significance and market value and are one of the main directions of natural language processing. Reading comprehension models of single-span answers have recently attracted more attention and achieved significant results. In contrast, multi-span answer models for reading comprehension have been less investigated and their performances need improvement. To address this issue, in this paper, we propose a text-based multi-span network for reading comprehension, ALBERT_SBoundary, and build a multi-span answer corpus, MultiSpan_NMU. We also conduct extensive experiments on the public multi-span corpus, MultiSpan_DROP, and our multi-span answer corpus, MultiSpan_NMU, and compare the proposed method …with the state-of-the-art. The experimental results show that our proposed method achieves F1 scores of 84.10 and 92.88 on MultiSpan_DROP and MultiSpan_NMU datasets, respectively, while it also has fewer parameters and a shorter training time. Show more

Keywords: Multi-span answer, ALBERT model, reading comprehension, minimum order matching prediction algorithm

DOI: 10.3233/JIFS-200581

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 5807-5819, 2021

Authors: Yan, Chun | Liu, Jiahui | Liu, Wei | Liu, Xinhong

Article Type: Research Article

Abstract: With the development of automobile insurance industry, how to identify automobile insurance fraud from massive data becomes particularly important. The purpose of this paper is to improve automobile insurance fraud management and explore the application of data mining technology in automobile insurance fraud identification. To this aim, an Apriori algorithm based on simulated annealing genetic fuzzy C-means (SAGFCM-Apriori) have been proposed. The SAGFCM-Apriori algorithm combines fuzzy theory with association rule mining, expanding the application scope of the Apriori algorithm. Considering that the clustering center of the traditional fuzzy C-means (FCM) algorithm is easy to fall into local optimal, the simulated …annealing genetic (SAG) algorithm is used to optimize it. The SAG algorithm optimized FCM (SAGFCM) is used to generate fuzzy membership degrees and introduces fuzzy data into the Apriori algorithm. The Apriori algorithm is improved by reducing the rule mining time when acquiring rules. The results of empirical studies on several data sets demonstrate that the optimization of FCM by SAG can effectively avoid the local optimal problem, improve the accuracy of clustering, and enable SAGFCM-Apriori to obtain better fuzzy data during data preprocessing. Moreover, the proposed algorithm can reduce the mining time of association rules and improve mining efficiency. Finally, the SAGFCM-Apriori algorithm is applied to the scene of automobile insurance fraud identification, and the automobile insurance fraud data is mined to obtain fuzzy association rules that can identify fraud claims. Show more

Keywords: Fuzzy association rules, fuzzy clustering, data mining, automobile insurance fraud

DOI: 10.3233/JIFS-201301

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 5821-5834, 2021

Authors: Zhou, Xinyu | Ma, Xiaoqi

Article Type: Research Article

Abstract: Conflict is an inevitable social phenomenon and the analysis of it can effectively resolve disputes, improve the position of decision makers (DMs), forecast compromise solutions as well. At present, the Graph Model for Conflict Resolution (GMCR), a completely non-quantitative decision support system (DSS) based on DM’s ordinal preference information, is constructed to combat conflict analysis complicated by multiple participant or multiple criteria or both. The purpose of the study is to introduce an overview of GMCR in conflict analyzing through bibliometrics. In order to achieve this goal, a systematic review of articles in leading journals of Web of Science Core …Collection (WoSCC) during 1987–2019 is posed, referring to the distribution of countries, institutions, authors, subjects and journals, research topics and hotspots exploration as well as frontiers prediction, by utilizing VOSviewer and CiteSpace. The contributions of this study are not only providing a handy method to grasp generalized scientific research situation, but also demonstrating status quo and emerging trends of GMCR for researchers and everyone who interested in. Show more

Keywords: Graph model for conflict resolution (GMCR), conflict analysis, decision support system (DSS), bibliometrics, visualization

DOI: 10.3233/JIFS-201320

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 5835-5846, 2021

Authors: Xu, Changlin | Shen, Juhong

Article Type: Research Article

Abstract:  Higher-order fuzzy decision-making methods have become powerful tools to support decision-makers in solving their problems effectively by reflecting uncertainty in calculations better than crisp sets in the last 3 decades. Fermatean fuzzy set proposed by Senapati and Yager, which can easily process uncertain information in decision making, pattern recognition, medical diagnosis et al., is extension of intuitionistic fuzzy set and Pythagorean fuzzy set by relaxing the restraint conditions of the support for degrees and support against degrees. In this paper, we focus on the similarity measures of Fermatean fuzzy sets. The definitions of the Fermatean fuzzy sets similarity measures and …its weighted similarity measures on discrete and continuous universes are given in turn. Then, the basic properties of the presented similarity measures are discussed. Afterward, a decision-making process under the Fermatean fuzzy environment based on TOPSIS method is established, and a new method based on the proposed Fermatean fuzzy sets similarity measures is designed to solve the problems of medical diagnosis. Ultimately, an interpretative multi-criteria decision making example and two medical diagnosis examples are provided to demonstrate the viability and effectiveness of the proposed method. Through comparing the different methods in the multi-criteria decision making and the medical diagnosis application, it is found that the new method is as efficient as the other methods. These results illustrate that the proposed method is practical in dealing with the decision making problems and medical diagnosis problems. Show more

Keywords: Similarity measure, Fermatean fuzzy set, Multi-criteria decision making, TOPSIS method, Medical diagnosis

DOI: 10.3233/JIFS-201557

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 5847-5863, 2021

Authors: Zhou, Jun | Zhang, Daixin | Zhou, Liuling | Liang, Guangchuan | Zhou, Xuan | Li, Zelong

Article Type: Research Article

Abstract: Oil&gas gathering pipeline network structure is a significant part of oil&gas field construction, and the rational construction of pipeline network is directly related to the efficiency and benefits of oil&gas field production. Therefore, optimizing the gathering and transportation system of oil and gas fields is the key to reducing development costs. The star-tree type pipe network is widely used in the gathering and transportation system. In order to optimize the star-tree pipe networks (STPNs) that has restrictions on the processing capacity and gathering radius of the station as a whole, this paper establishes four models of pipe network layout with …specific constraints. They are Mixed-Integer Linear Programming Models with a large number of discrete variables. We take two virtual fields as examples, use CPLEX solver to solve the above four models as a whole, to obtain the optimal scheme, and also figure out the investment of the pipeline network. We further optimize the hierarchical optimization of the pipeline network with special constraints, then compare and analyze results obtained by the overall optimization. Finally, models are applied to an actual oil field and an actual gas field as examples to optimize the layout, which verifies the validity and feasibility of the models. Show more

Keywords: Star-tree pipeline network, layout, mixed-Integer linear programming, optimization

DOI: 10.3233/JIFS-201694

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 5865-5886, 2021

Authors: Abdelhamid, Abdelaziz A. | Alotaibi, Sultan R.

Article Type: Research Article

Abstract: Internet of things (IoT) plays significant role in the fourth industrial revolution and attracts an increasing interest due to the rapid development of smart devices. IoT comprises factors of twofold. Firstly, a set of things (i.e., appliances, devices, vehicles, etc.) connected together via network. Secondly, human-device interaction to communicate with these things. Speech is the most natural methodology of interaction that can enrich user experience. In this paper, we propose a novel and effective approach for building customized voice interaction for controlling smart devices in IoT environments (i.e., Smart home). The proposed approach is based on extracting customized tiny decoding …graph from a large graph constructed using weighted finite sates transducers. Experimental results showed that tiny decoding graphs are very efficient in terms of computational resources and recognition accuracy in clean and noisy conditions. To emphasize the effectiveness of the proposed approach, the standard Resources Management (RM1) dataset was employed and promising results were achieved when compared with four competitive approaches. Show more

Keywords: Intelligent systems, internet-of-things, voice interaction

DOI: 10.3233/JIFS-201781

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 5887-5902, 2021

Authors: Zhang, Yan-Lan | Li, Chang-Qing

Article Type: Research Article

Abstract: The rough set theory and the evidence theory are two important methods used to deal with uncertainty. The relationships between the rough set theory and the evidence theory have been discussed. In covering rough set theory, several pairs of covering approximation operators are characterized by belief and plausibility functions. The purpose of this paper is to review and examine interpretations of belief functions in covering approximation operators. Firstly, properties of the belief structures induced by two pairs of covering approximation operators are presented. Then, for a belief structure with the properties, there exists a probability space with a covering such …that the belief and plausibility functions defined by the given belief structure are, respectively, the belief and plausibility functions induced by one of the two pairs of covering approximation operators. Moreover, two necessary and sufficient conditions for a belief structure to be the belief structure induced by one of the two pairs of covering approximation operators are presented. Show more

Keywords: Belief and plausibility functions, Covering approximation operators, Covering rough set, Evidence theory

DOI: 10.3233/JIFS-201887

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 5903-5913, 2021

Authors: Rafi, Muhammad | Wahab, Mohammad Taha | Khan, Muhammad Bilal | Raza, Hani

Article Type: Research Article

Abstract: Automatic Teller Machine (ATM) are still largely used to dispense cash to the customers. ATM cash replenishment is a process of refilling ATM machine with a specific amount of cash. Due to vacillating users demands and seasonal patterns, it is a very challenging problem for the financial institutions to keep the optimal amount of cash for each ATM. In this paper, we present a time series model based on Auto Regressive Integrated Moving Average (ARIMA) technique called Time Series ARIMA Model for ATM (TASM4ATM). This study used ATM back-end refilling historical data from 6 different financial organizations in Pakistan. There …are 2040 distinct ATMs and 18 month of replenishment data from these ATMs are used to train the proposed model. The model is compared with the state-of- the-art models like Recurrent Neural Network (RNN) and Amazon’s DeepAR model. Two approaches are used for forecasting (i) Single ATM and (ii) clusters of ATMs (In which ATMs are clustered with similar cash-demands). The Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the models. The suggested model produces far better forecasting as compared to the models in comparison and produced an average of 7.86/7.99 values for MAPE/SMAPE errors on individual ATMs and average of 6.57/6.64 values for MAPE/SMAPE errors on clusters of ATMs. Show more

Keywords: Time series analysis, amazon deep ar, recurrent neural network, ATM cash prediction

DOI: 10.3233/JIFS-201953

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 5915-5927, 2021