Journal of Intelligent & Fuzzy Systems - Volume 42, issue 3

Authors: Suthanthiradevi, P. | Karthika, S.

Article Type: Research Article

Abstract: Social networks have become a popular communication tool for information sharing. Twitter offers access to data and provides a significant opportunity to analyze data. During pandemics, Twitter becomes a big source for the dispersal of unverified information. In social media, it is difficult to find the sources of rumors. To tackle this problem the authors have developed a hybrid rumor centrality algorithm for rumor source detection in social networks. The authors propose an S-RSI algorithm for identifying a single rumor centre and an M-RSI algorithm for identifying the propagations of multiple rumor centres in the thread of conversation. The proposed …rumor centrality algorithm efficiently predicts the rumor disseminating possibilities in a conversation tree with the aid of graph theoretical approach. The authors have evaluated the performance of the algorithms on the PHEME dataset containing seven real-time event conversational trees based on the tweet messages. The results show that the proposed is best suitable in finding the rumor source centre with a high probability in social media during a crisis. Show more

Keywords: Social network, general tree, general graph, rumor source identifier, rumor centrality

DOI: 10.3233/JIFS-210540

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1421-1431, 2022

Authors: Rashid, Ismat | Nazeer, Irfan | Rashid, Tabasam

Article Type: Research Article

Abstract: Connectivity parameters have a crucial role in the study of different networks in the physical world. The notion of connectivity plays a key role in both theory and application of different graphs. In this article, a prime idea of connectivity concepts in intuitionistic fuzzy incidence graphs (IFIGs) with various examples is examined. IFIGs are essential in interconnection networks with influenced flows. Therefore, it is of paramount significance to inspect their connectivity characteristics. IFIGs is an extended structure of fuzzy incidence graphs (FIGs). Depending on the strength of a pair, this paper classifies three different types of pairs such as an …α  - strong , β  - strong , and δ -pair. The benefit of this kind of stratification is that it helps to comprehend the fundamental structure of an IFIG thoroughly. The existence of a strong intuitionistic fuzzy incidence path among vertex, edge, and pair of an IFIG is established. Intuitionistic fuzzy incidence cut pairs (IFICPs) and intuitionistic fuzzy incidence trees (IFIT) are characterized using the idea of strong pairs (SPs). Complete IFIG is defined, and various other structural properties of IFIGs are also investigated. The proof that complete IFIG does not contain any δ -pair is also provided. A real-life application of these concepts related to the network of different computers is also provided. Show more

Keywords: Fuzzy sets, incidence graphs, fuzzy incidence graphs, intuitionistic fuzzy graph

DOI: 10.3233/JIFS-210590

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1433-1443, 2022

Authors: Zhou, Lexin | Yang, Wenzhong | Wang, Ting | Wu, Yongzhi

Article Type: Research Article

Abstract: Aspect-based sentiment analysis (ABSA) contains three subtasks, namely aspect term extraction, opinion term extraction and aspect-level sentiment classification. In order to make full use of the relationship between the three subtasks, some recent studies have successfully tried to use a unified framework to solve the problem of aspect-based sentiment analysis. However, these studies have not yet integrated domain knowledge into the model. Inspired by the post-training task, we propose a joint model (RACL-BERT-PT). This model combines the pre-training model BERT-PT with domain knowledge and the unified joint training framework RACL. The experimental results show that our model has achieved better …results than previous experiments on three public data. Show more

Keywords: Aspect-based sentiment analysis, post-training, domain knowledge

DOI: 10.3233/JIFS-210632

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1445-1454, 2022

Authors: Guo, Hai | Song, Yifan | Tang, Haoran | Zhao, Jingying

Article Type: Research Article

Abstract: In recent years, lakes pollution has become increasingly serious, so water quality monitoring is becoming increasingly important. The concentration of total organic carbon (TOC) in lakes is an important indicator for monitoring the emission of organic pollutants. Therefore, it is of great significance to determine the TOC concentration in lakes. In this paper, the water quality dataset of the middle and lower reaches of the Yangtze River is obtained, and then the temperature, transparency, pH value, dissolved oxygen, conductivity, chlorophyll and ammonia nitrogen content are taken as the impact factors, and the stacking of different epochs’ deep neural networks (SDE-DNN) …model is constructed to predict the TOC concentration in water. Five deep neural networks and linear regression are integrated into a strong prediction model by the stacking ensemble method. The experimental results show the prediction performance, the Nash-Sutcliffe efficiency coefficient (NSE) is 0.5312, the mean absolute error (MAE) is 0.2108 mg/L, the symmetric mean absolute percentage error (SMAPE) is 43.92%, and the root mean squared error (RMSE) is 0.3064 mg/L. The model has good prediction performance for the TOC concentration in water. Compared with the common machine learning models, traditional ensemble learning models and existing TOC prediction methods, the prediction error of this model is lower, and it is more suitable for predicting the TOC concentration. The model can use a wireless sensor network to obtain water quality data, thus predicting the TOC concentration of lakes in real time, reducing the cost of manual testing, and improving the detection efficiency. Show more

Keywords: Water quality monitoring, total organic carbon (TOC) concentration, ensemble learning, deep learning

DOI: 10.3233/JIFS-210708

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1455-1482, 2022

Authors: Mehmood, Arif | Al Ghour, Samer | Abdullah, Saleem | Park, Choonkil | Rye Lee, Jung

Article Type: Research Article

Abstract: This paper concerns the study of the notion of vague soft β -open set and vague soft separation axioms in vague soft topological spaces. By using such notions and that of the vague soft pints, we study the separation axioms β i  (with  i  = 0, 1, 2, 3, 4) in vague soft topological spaces. We give some peculiar examples about them and we prove some relationships between them. The relationship of β i  (with  i  = , 1, 2, 3, 4) spaces with the closer of vague soft β -open set by means of soft points, vague soft countable …spaces and their relationship with β i  (with  i  = , 1, 2) spaces by means of soft points are addressed. In continuation, vague soft topological, vague soft inverse topological spaces properties, Bolzano Weirstrass Property(BVP) and its topological characteristics, compact spaces and sequentially compact spaces and their relationship with separation axioms by means soft points are addressed in vague soft topological spaces. Show more

Keywords: Vague soft set (VSS), vague soft point (VSP), vague soft β-open set and vague soft β-separation axioms.

DOI: 10.3233/JIFS-210828

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1483-1499, 2022

Authors: Li, Maodong | Xu, Guanghui | Fu, Yuanwang | Zhang, Tingwei | Du, Li

Article Type: Research Article

Abstract: In this paper, a whale optimization algorithm based on adaptive inertia weight and variable spiral position updating strategy is proposed. The improved algorithm is used to solve the problem that the whale optimization algorithm is more dependent on the randomness of the parameters, so that the algorithm’s convergence accuracy and convergence speed are insufficient. The adaptive inertia weight, which varies with the fitness of individual whales, is used to balance the algorithm’s global search ability and local exploitation ability. The variable spiral position update strategy based on the collaborative convergence mechanism is used to dynamically adjust the search range and …search accuracy of the algorithm. The effective combination of the two can make the improved whale optimization algorithm converge to the optimal solution faster. It had been used 18 international standard test functions, including unimodal function, multimodal function, and fixed-dimensional function to test the improved whale optimization algorithm in this paper. The test results show that the improved algorithm has faster convergence speed and higher algorithm accuracy than the original algorithm and several classic algorithms. The algorithm can quickly converge to near the optimal value in the early stage, and then effectively jump out of the local optimal through adaptive adjustment, and has a certain ability to solve large-scale optimization problems. Show more

Keywords: Whale optimization algorithm, adaptive inertia weight, convergence speed, variable spiral position update, cooperative convergence mechanism

DOI: 10.3233/JIFS-210842

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1501-1517, 2022

Authors: Xu, Xinliang | Yan, Fu

Article Type: Research Article

Abstract: Autonomous groups of particles swarm optimization (AGPSO), inspired by individual diversity in biological swarms such as insects or birds, is a modified particle swarm optimization (PSO) variant. The AGPSO method is simple to understand and easy to implement on a computer. It has achieved an impressive performance on high-dimensional optimization tasks. However, AGPSO also struggles with premature convergence, low solution accuracy and easily falls into local optimum solutions. To overcome these drawbacks, random-walk autonomous group particle swarm optimization (RW-AGPSO) is proposed. In the RW-AGPSO algorithm, Levy flights and dynamically changing weight strategies are introduced to balance exploration and exploitation. The …search accuracy and optimization performance of the RW-AGPSO algorithm are verified on 23 well-known benchmark test functions. The experimental results reveal that, for almost all low- and high-dimensional unimodal and multimodal functions, the RW-AGPSO technique has superior optimization performance when compared with three AGPSO variants, four PSO approaches and other recently proposed algorithms. In addition, the performance of the RW-AGPSO has also been tested on the CEC’14 test suite and three real-world engineering problems. The results show that the RW-AGPSO is effective for solving high complexity problems. Show more

Keywords: Autonomous groups of particle swarm optimization, particle swarm optimization, levy flights, dynamically changing weight, function optimization

DOI: 10.3233/JIFS-210867

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1519-1545, 2022

Authors: Chen, Kuen-Suan | Yu, Chun-Min

Article Type: Research Article

Abstract: Industry 4.0 has fostered innovation in industries around the world. Manufacturing industries in particular are advancing towards smart manufacturing by integrating and applying relevant technologies. The output value of machine tools in Taiwan is among the top of the world and the central region is a key area for this industry chain, which supplies manufacturers in Taiwan and their international downstream customers. To support innovation in this industry, the current study used the Six Sigma quality indices for smaller-the-better, larger-the-better, and nominal-the-best quality characteristics to construct a fuzzy decision-making model. Based on this model, we propose a process quality fuzzy …analysis chart (PQFAC) for process quality improvement. Our use of fuzzy decision values to replace lower confidence limits decreases the probability of misjudgment made by sampling errors. The proposed fuzzy model also offers a more accurate assessment of process improvement requirements. We provide a real-world example to demonstrate the applicability of the proposed approach. Machine tool manufacturers can apply the platform and proposed model to evaluate their process capabilities for the vital parts suppliers and downstream customers, determine optimal machine parameter settings for processes with inadequate accuracy or precision, establish more suitable machine repair and maintenance systems, and combine the improvement experiences of customers to create an improvement knowledge base. This will enhance product value and industry competitiveness for the entire machine tool industry chain. Show more

Keywords: Fuzzy decision-making model, six sigma quality index, quality characteristic, production data, process quality fuzzy analysis chart

DOI: 10.3233/JIFS-210868

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1547-1558, 2022

Authors: Zhang, Jun | Qin, Yanping | Zhang, Xinyu | Che, Gen | Sun, Xuan | Duo, Huaqiong

Article Type: Research Article

Abstract: Non-equidistant GM(1,1) (abbreviated as NEGM) model is widely used in building settlement prediction because of its high accuracy and outstanding adaptability. To improve the building settlement prediction accuracy of the NEGM model, the fractional-order non-equidistant GM(1,1) model (abbreviated as FNEGM) is established in this study. In the modeling process of the FNEGM model, the fractional-order accumulated generating sequence is extended based on the first-order accumulated generating sequence, and the optimal parameters that increase the prediction precision of the model are obtained by using the whale optimization algorithm. The FNEGM model and the other two grey prediction models are applied to …three cases, and five prediction performance indexes are used to evaluate the prediction precision of the three models. The results show that the FNEGM model is more suitable for predicting the settlement of buildings than the other two grey prediction models. Show more

Keywords: Non-equidistant GM(1, 1) model, fractional-order accumulation, grey prediction model

DOI: 10.3233/JIFS-210936

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1559-1573, 2022

Authors: Xu, Guoteng | Lu, Tingjie | Chen, Xia | Liu, Yiman

Article Type: Research Article

Abstract: The paper constructs a research model mainly based on the Deng’s correlation analysis model on the convergence level measurement, the GM (1,1) coordinated development prediction model and PLS-Structural Equation Model (PLS-SEM) analysis model on the influencing factors. The data about China’s digital economy and real economy from 2005 to 2019 (totaled 2,250) is adopted to conduct an empirical analysis of the convergence level from 2005 to 2019 and predict the development trend from 2020 to 2029. The paper could further analyze the influencing factors of convergence, in an attempt to put forward relevant development suggestions. We hope the study could …provide an objective reference and theoretical basis for improving the convergence level in China in some extent. Show more

Keywords: Grey model, PLS-structural equation model, convergence of digital economy and real economy, influencing factors

DOI: 10.3233/JIFS-210981

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1575-1605, 2022

Authors: Xu, Binbin | Chen, Chang | Tang, Jinrui | Tang, Ruoli

Article Type: Research Article

Abstract: Due to the increasingly demand of wireless broadband applications in modern society, the device-to-device (D2D) communication technique plays an important role for improving communication spectrum efficiency and quality of service (QoS). This study focuses on the optimal allocation of link resource in D2D communication systems using intelligent approaches, in order to obtain optimal energy efficiency of D2D-pair users (DP) and also ensure communication QoS. To be specific, the optimal resource allocation (ORA) model for ensuring the cooperation between DP and cellular users (CU) is established, and a novel coding strategy of ORA model is also proposed. Then, for efficiently optimizing …the ORA model, a novel swarm-intelligence-based algorithm called the dynamic topology coevolving differential evolution (DTC-DE) is developed, and the efficiency of DTC-DE is also tested by a comprehensive set of benchmark functions. Finally, the DTC-DE algorithm is employed for optimizing the proposed ORA model, and some state-of-the-art algorithms are also employed for comparison. Result of case study shows that the DTC-DE outperforms its competitors significantly, and the optimal resource allocation can be obtained by DTC-DE with robust performance. Show more

Keywords: Device-to-device communication, intelligent communication system, communication resource allocation, differential evolution, swarm intelligence

DOI: 10.3233/JIFS-211008

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1607-1621, 2022

Authors: Tong, Huagang | Zhu, Jianjun | Yi, Yang

Article Type: Research Article

Abstract: Sharing economy is significant for economic development, stable matching plays an essential role in sharing economy, but the large-scale sharing platform increases the difficulties of stable matching. We proposed a two-sided gaming model based on probabilistic linguistic term sets to address the problem. Firstly, in previous studies, the mutual assessment is used to obtain the preferences of individuals in large-scale matching, but the procedure is time-consuming. We use probabilistic linguistic term sets to present the preferences based on the historical data instead of time-consuming assessment. Then, to generate the satisfaction based on the preference, we regard the similarity between the …expected preferences and actual preferences as the satisfaction. Considering the distribution features of probabilistic linguistic term sets, we design a shape-distance-based method to measure the similarity. After that, the previous studies aimed to maximize the total satisfaction in matching, but the individuals’ requirements are neglected, resulting in a weak matching result. We establish the two-sided gaming matching model from the perspectives of individuals based on the game theory. Meanwhile, we also study the competition from other platforms. Meanwhile, considering the importance of the high total satisfaction, we balance the total satisfaction and the personal requirements in the matching model. We also prove the solution of the matching model is the equilibrium solution. Finally, to verify the study, we use the experiment to illustrate the advantages of our study. Show more

Keywords: Sharing economy, two-sided gaming matching, shape-distance-based similarity, probabilistic linguistic term sets, efficient sharing

DOI: 10.3233/JIFS-211042

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1623-1641, 2022

Authors: Li, Wenwen | Yin, Shiqun | Pu, Ting

Article Type: Research Article

Abstract:  The purpose of aspect-based sentiment analysis is to predict the sentiment polarity of different aspects in a text. In previous work, while attention has been paid to the use of Graph Convolutional Networks (GCN) to encode syntactic dependencies in order to exploit syntactic information, previous models have tended to confuse opinion words from different aspects due to the complexity of language and the diversity of aspects. On the other hand, the effect of word lexicality on aspects’ sentiment polarity judgments has not been considered in previous studies. In this paper, we propose lexical attention and aspect-oriented GCN to solve the …above problems. First, we construct an aspect-oriented dependency-parsed tree by analyzing and pruning the dependency-parsed tree of the sentence, then use the lexical attention mechanism to focus on the features of the lexical properties that play a key role in determining the sentiment polarity, and finally extract the aspect-oriented lexical weighted features by a GCN.Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our approach. Show more

Keywords: Sentiment analysis, GCN, lexical attention, dependency parsing

DOI: 10.3233/JIFS-211045

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1643-1654, 2022

Authors: Yang, Ziyu | Zhang, Liyuan | Li, Tao

Article Type: Research Article

Abstract: Interval-valued Pythagorean fuzzy preference relation (IVPFPR) plays an important role in representing the complex and uncertain information. The application of IVPFPRs gives better solutions in group decision making (GDM). In this paper, we investigate a new method to solve GDM problems with IVPFPRs. Firstly, novel multiplicative consistency and consensus measures are proposed. Subsequently, the procedure for improving consistency and consensus levels are put forward to ensure that every individual IVPFPR is of acceptable multiplicative consistency and consensus simultaneously. In the context of minimizing the deviations between the individual and collective IVPFPRs, the objective experts’ weights are decided according to the …optimization model and the aggregated IVPFPR is derived. Afterwards, a programming model is built to derive the normalized Pythagorean fuzzy priority weights, then the priority weights of alternatives are identified as well. An algorithm for GDM method with IVPFPRs is completed. Finally, an example is cited and comparative analyses with previous approaches are conducted to illustrate the applicability and effectiveness of the proposed method. Show more

Keywords: Group decision making, interval-valued pythagorean fuzzy preference relation, multiplicative consistency, consensus

DOI: 10.3233/JIFS-211131

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1655-1677, 2022

Authors: Wang, Jianfeng | Wang, Ruomei | Liu, Shaohui

Article Type: Research Article

Abstract: Session-based recommendation is an overwhelming task owing to the inherent ambiguity in anonymous behaviors. Graph convolutional neural networks are receiving wide attention for session-based recommendation research for the sake of their ability to capture the complex transitions of interactions between sessions. Recent research on session-based recommendations mainly focuses on sequential patterns by utilizing graph neural networks. However, it is undeniable that proposed methods are still difficult to capture higher-order interactions between contextual interactions in the same session and has room for improvement. To solve it, we propose a new method based on graph attention mechanism and target oriented items to …effectively propagate information, HOGAN for brevity. Higher-order graph attention networks are used to select the importance of different neighborhoods in the graph that consists of a sequence of user actions for recommendation applications. The complementarity between high-order networks is adopted to aggregate and propagate useful signals from the long distant neighbors to solve the long-range dependency capturing problem. Experimental results consistently display that HOGAN has a significantly improvement to 71.53% on precision for the Yoochoose1_64 dataset and enhances the property of the session-based recommendation task. Show more

Keywords: Long-range dependency, higher-order network, context-aware, intelligent recommendation

DOI: 10.3233/JIFS-211155

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1679-1691, 2022

Authors: Yang, Bo | Xu, Kaiyong | Wang, Hengjun | Zhang, Hengwei

Article Type: Research Article

Abstract: Deep neural networks (DNNs) are vulnerable to adversarial examples, which are crafted by adding small, human-imperceptible perturbations to the original images, but make the model output inaccurate predictions. Before DNNs are deployed, adversarial attacks can thus be an important method to evaluate and select robust models in safety-critical applications. However, under the challenging black-box setting, the attack success rate, i.e., the transferability of adversarial examples, still needs to be improved. Based on image augmentation methods, this paper found that random transformation of image brightness can eliminate overfitting in the generation of adversarial examples and improve their transferability. In light of …this phenomenon, this paper proposes an adversarial example generation method, which can be integrated with Fast Gradient Sign Method (FGSM)-related methods to build a more robust gradient-based attack and to generate adversarial examples with better transferability. Extensive experiments on the ImageNet dataset have demonstrated the effectiveness of the aforementioned method. Whether on normally or adversarially trained networks, our method has a higher success rate for black-box attacks than other attack methods based on data augmentation. It is hoped that this method can help evaluate and improve the robustness of models. Show more

Keywords: Adversarial examples, black-box attacks, deep neural networks (DNNs)

DOI: 10.3233/JIFS-211157

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1693-1704, 2022

Authors: Jiang, Nan-Yun | Yan, Hong-Sen

Article Type: Research Article

Abstract: For the fixed-position assembly workshop, the integrated optimization problem of production planning and scheduling in the uncertain re-entrance environment is studied. Based on the situation of aircraft assembly workshops, the characteristics of fixed-position assembly workshop with uncertain re-entrance are abstracted. As the re-entrance repetition obeys some type of probability distribution, the expected value is used to describe the repetition, and a bi-level stochastic expected value programming model of integrated production planning and scheduling is constructed. Recursive expressions for start time and completion time of assembly classes and teams are confirmed. And the relation between the decision variable in the lower-level …model of scheduling and the overtime and earliness of assembly classes and teams in the upper-level model of production planning is identified. Addressing the characteristics of bi-level programming model, an alternate iteration method based on Improved Genetic Algorithm (AI-IGA) is proposed to solve the models. Elite Genetic Algorithm (EGA) is introduced for the upper-level model of production planning, and Genetic Simulated Annealing Algorithm based on Stochastic Simulation Technique (SS-GSAA) is developed for the lower-level model of scheduling. Results from our experiments demonstrate that the proposed method is feasible for production planning and optimization of the fixed-position assembly workshop with uncertain re-entrance. And algorithm comparison verifies the effectiveness of the proposed algorithm. Show more

Keywords: Uncertain re-entrance, fixed-position assembly workshop, integrated optimization of production planning and scheduling, improved genetic algorithm

DOI: 10.3233/JIFS-211159

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1705-1722, 2022

Authors: Jiang, Zhiwei | Wei, Guiwu | Chen, Xudong

Article Type: Research Article

Abstract: For the long-term development of shopping mall, the managers of shopping mall tend to build a new store to expand the enterprise’s market share in a new city. After holding a preliminary survey of the city, managers have initially identified five sites for construction. In order to select an optimal site, managers invite four experts who come from university, marking statistics, corporate executives and accounting to score sites. And they choose the best site on the basis of scores. The trait of EDAS method is to select an optimal alternative by using the distance of each alternative from the first-rank …value. In this manuscript, we build the picture fuzzy EDAS method based on the cumulative prospect theory (PF-CPT-EDAS) for multiple attribute group decision-making (MAGDM) and it can help managers to choose an optimal alternative effectively. During the procedure of PF-CPT-EDAS means, we take advantage of the entropy means to calculate the original weights of all attributes. Ultimately, we testify the effectiveness of the novel model by comparing the overcome of PF-CPT-EDAS means with the results of PF-EDAS approach and other methods. Show more

Keywords: Multiple attribute group decision-making (MAGDM), picture fuzzy sets (PFSs), EDAS method, cumulative prospect theory (CPT), site selection

DOI: 10.3233/JIFS-211171

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1723-1735, 2022

Authors: Fan, Jianping | Zhai, Shanshan | Wu, Meiqin

Article Type: Research Article

Abstract: Neutrosophic cubic set (NCS) can process complex information by combining interval neutrosophic set and single-valued neutrosophic set. It can simultaneously describe the uncertain and certain part of information. Prospect theory (PT) is based on bounded rationality and can reflect decision maker’s different risk attitudes to gains and losses. Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) method can measure and rank the alternatives according to compromise solution. Considering the bounded rationality of decision makers and compromise solution of alternatives, this paper combines the PT with MARCOS method to neutrosophic cubic environment to solve multi-attribute decision-making problem. First, the …theoretical basis of NCS is introduced. Second, the PT and MARCOS method are combined. To reflect subjective views of decision makers and the objectivity of decision-making information, this paper uses geometric average method to combine subjective weights (calculated by the best-worst method) and objective weights (calculated ed by the entropy method). Then, the PT-MARCOS method is applied to a decision-making problem. Further, a sensitivity analysis is conducted to study the influence of different attenuation factor values and different expectation coefficient on the ranking; and through comparative analysis to illustrate the superiority of the PT-MARCOS method. Finally is the conclusion. Show more

Keywords: Multi-attribute decision-making, neutrosophic cubic set, prospect theory, measurement of alternatives and ranking according to compromise solution, best-worst method

DOI: 10.3233/JIFS-211189

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1737-1748, 2022

Authors: Kumar, Satish | Gupta, Sunanda | Arora, Sakshi

Article Type: Research Article

Abstract: Network Intrusion detection systems (NIDS) detect malicious and intrusive information in computer networks. Presently, commercial NIDS is based on machine learning approaches that have complex algorithms and increase intrusion detection efficiency and efficacy. These machine learning-based NIDS use high dimensional network traffic data from which intrusive information is to be detected. This high-dimensional network traffic data in NIDS needs to be preprocessed and normalized to make it suitable for machine learning tools. A machine learning approach with appropriate normalization and prepossessing increases NIDS performance. This paper presents an empirical study on various normalization methods implemented on a benchmark network traffic …dataset, KDD Cup’99, that has been used to evaluate the NIDS model. The present study shows decimal normalization has a better prediction performance than non-normalized traffic data categorized into ‘normal’ or ‘intrusive’ classes. Show more

Keywords: Intrusion detection system, machine learning, normalization, classification, KDD cup’99 dataset

DOI: 10.3233/JIFS-211191

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1749-1766, 2022

Authors: Wang, Weibing | Wang, Shenquan | Zhao, Shuanfeng | Lu, Zhengxiong | He, Haitao

Article Type: Research Article

Abstract: The complexity of the coalface environment determines the non-linear and fuzzy characteristics of the drum adjustment height. To overcome this challenge, this study proposes an adaptive fuzzy reasoning Petri net (AFRPN) model based on fuzzy reasoning and fuzzy Petri net (FPN) and then applies it to the intelligent adjustment height of the shearer drum. This study constructs adaptive and reasoning algorithms. The former was used to optimize the AFRPN parameters, and the latter made the AFRPN model run. AFRPN could represent rules that had non-linear and attribute mapping relationships and could adjust the parameters adaptively to improve the accuracy of …the output. Subsequently, the drum adjustment height model was established and compared to three models neural network (NN), classification and regression tree(CART) and gradient boosting decision tree (GBDT). The experimental results showed that this method is superior to other drum adjustment height methods and that AFRPN can achieve intelligent adjustment of the shearer drum height by constructing fuzzy inference rules. Show more

Keywords: Drum intelligent adjustment, fuzzy reasoning, adaptive, Petri net

DOI: 10.3233/JIFS-211193

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1767-1781, 2022

Authors: Hu, Yuanjiao | Sun, Zhaoyun | Li, Wei | Pei, Lili

Article Type: Research Article

Abstract: The rational distribution of public bicycle rental fleets is crucial for improving the efficiency of public bicycle programs. The accurate prediction of the demand for public bicycles is critical to improve bicycle utilization. To overcome the shortcomings of traditional algorithms such as low prediction accuracy and poor stability, using the 2011–2012 hourly bicycle rental data provided by the Washington City Bicycle Rental System, this study aims to develop an optimized and innovative public bicycle demand forecasting model based on grid search and eXtreme Gradient Boosting (XGBoost) algorithm. First, the feature ranking method based on machine learning models is used to …analyze feature importance on the original data. In addition, a public bicycle demand forecast model is established based on important factors affecting bicycle utilization. Finally, to predict bicycle demand accurately, this study optimizes the model parameters through a grid search (GS) algorithm and builds a new prediction model based on the optimal parameters. The results show that the optimized XGBoost model based on the grid search algorithm can predict the bicycle demand more accurately than other models. The optimized model has an R-Squared of 0.947, and a root mean squared logarithmic error of 0.495. The results can be used for the effective management and reasonable dispatch of public bicycles. Show more

Keywords: Bicycle demand forecast, feature importance, grid search algorithm, optimal parameters, eXtreme Gradient Boosting

DOI: 10.3233/JIFS-211202

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1783-1801, 2022

Authors: Zhang, Tao | Yu, Long | Tian, Shengwei

Article Type: Research Article

Abstract: In this paper, we presents an apporch for real-world human face close-up images cartoonization. We use generative adversarial network combined with an attention mechanism to convert real-world face pictures and cartoon-style images as unpaired data sets. At present, the image-to-image translation model has been able to successfully transfer style and content. However, some problems still exist in the task of cartoonizing human faces:Hunman face has many details, and the content of the image is easy to lose details after the image is translated. the quality of the image generated by the model is defective. The model in this paper uses …the generative adversarial network combined with the attention mechanism, and proposes a new generative adversarial network combined with the attention mechanism to deal with these problems. The channel attention mechanism is embedded between the upper and lower sampling layers of the generator network, to avoid increasing the complexity of the model while conveying the complete details of the underlying information. After comparing the experimental results of FID, PSNR, MSE three indicators and the size of the model parameters, the new model network proposed in this paper avoids the complexity of the model while achieving a good balance in the conversion task of style and content. Show more

Keywords: Generative adversarial networks, attention mechanism, style transfer, image cartoonization

DOI: 10.3233/JIFS-211210

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1803-1811, 2022

Authors: Zhai, Longzhen | Feng, Shaohong

Article Type: Research Article

Abstract: In order to solve the problem of finding the best evacuation route quickly and effectively, in the event of an accident, a novel evacuation route planning method is proposed based on Genetic Algorithm and Simulated Annealing algorithm in this paper. On the one hand, the simulated annealing algorithm is introduced and a simulated annealing genetic algorithm is proposed, which can effectively avoid the problem of the search process falling into the local optimal solution. On the other hand, an adaptive genetic operator is designed to achieve the purpose of maintaining population diversity. The adaptive genetic operator includes an adaptive crossover …probability operator and an adaptive mutation probability operator. Finally, the path planning simulation verification is carried out for the genetic algorithm and the improved genetic algorithm. The simulation results show that the improved method has greatly improved the path planning distance and time compared with the traditional genetic algorithm. Show more

Keywords: Genetic Algorithm (GA), stimulated annealing (SA), adaptive Algorithm, evacuation

DOI: 10.3233/JIFS-211214

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1813-1823, 2022

Authors: Akbar, Sumaiya Begum | Thanupillai, Kalaiselvi | Govindarajan, Valarmathi

Article Type: Research Article

Abstract: Bitcoin is an innovative decentralized digital currency without intermediaries. Bitcoin price prediction is a demanding need in the present situation. This paper makes an investigation on the Bitcoin price forecast with a Bi-directional Gated Recurrent Unit (GRU) time series method, combined with opinion mining based on Twitter and Reddit feeds. An hourly basis sentimental analysis through the implementation of Natural Language Processing presents a positive impact of sentimental analysis on the Bitcoin price prediction. For prediction, RNN, long-short memory, GRU has been utilized. Unidirectional and Bi-directional versions of all three networks with and without sentimental analysis were implemented for comparison. …Of all the techniques implemented Bi-directional GRU along with sentimental analysis gives a minimum RMSE and Minimum absolute percentage error of 1108.33 and 7.384%. Thus, the framework including Bi-Directional GRU along with Sentimental Analysis provides better results than the State-of-art methods. Show more

Keywords: Bitcoin, neural network, mining, GRU, RMSE, MAPE

DOI: 10.3233/JIFS-211217

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1825-1833, 2022

Authors: Tufail, Faiza | Shabir, Muhammad

Article Type: Research Article

Abstract: Bipolarity indicates the positive and negative aspects of a particular problem. The concept behind the bipolarity is that a huge range of human decision analysis is involved in bipolar subjective thoughts. The VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje) which means multicriteria optimization and compromise solution, has already become a quite popular multi-criteria decision making tool for its computational simplicity and solution accuracy. In this article, we propose a hybrid model for multi-criteria decision-making (MCDM) based on bipolar fuzzy soft β -covering based bipolar fuzzy rough sets using VIKOR technique. It consists of a suitable redesign of the VIKOR approach so …that it can use information with bipolar configurations. This method focuses on selecting and ranking from a set of feasible alternatives, and determines compromise solution for a problem with conflicting criteria to help the decision maker in reaching a final course of action. It determines the compromise ranking list based on the particular measure of closeness to the ideal solution. For illustration, the proposed technique is applied to a decision-making problems, namely, the selection of site for renewable energy project (solar power plant). A comparison of this method with another aggregation operator method and with the existing decision making algorithm Fuzzy VIKOR is also presented. Show more

Keywords: Bipolar fuzzy soft β-neighborhood, bipolar fuzzy soft complementry β-neighborhood, bipolar fuzzy soft β-covering, bipolar fuzzy soft β-covering based bipolar fuzzy rough set, decision-making application

DOI: 10.3233/JIFS-211223

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1835-1857, 2022

Authors: Zhao, Shuai | You, Fucheng | Chang, Wen | Zhang, Tianyu | Hu, Man

Article Type: Research Article

Abstract: The BERT pre-trained language model has achieved good results in various subtasks of natural language processing, but its performance in generating Chinese summaries is not ideal. The most intuitive reason is that the BERT model is based on character-level composition, while the Chinese language is mostly in the form of phrases. Directly fine-tuning the BERT model cannot achieve the expected effect. This paper proposes a novel summary generation model with BERT augmented by the pooling layer. In our model, we perform an average pooling operation on token embedding to improve the model’s ability to capture phrase-level semantic information. We use …LCSTS and NLPCC2017 to verify our proposed method. Experimental data shows that the average pooling model’s introduction can effectively improve the generated summary quality. Furthermore, different data needs to be set with varying pooling kernel sizes to achieve the best results through comparative analysis. In addition, our proposed method has strong generalizability. It can be applied not only to the task of generating summaries, but also to other natural language processing tasks. Show more

Keywords: Summary generation, fine-tuning bert, average pooling, transformer

DOI: 10.3233/JIFS-211229

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1859-1868, 2022

Authors: Selvaraj, Poovarasan | Chandra, E.

Article Type: Research Article

Abstract: The most challenging process in recent Speech Enhancement (SE) systems is to exclude the non-stationary noises and additive white Gaussian noise in real-time applications. Several SE techniques suggested were not successful in real-time scenarios to eliminate noises in the speech signals due to the high utilization of resources. So, a Sliding Window Empirical Mode Decomposition including a Variant of Variational Model Decomposition and Hurst (SWEMD-VVMDH) technique was developed for minimizing the difficulty in real-time applications. But this is the statistical framework that takes a long time for computations. Hence in this article, this SWEMD-VVMDH technique is extended using Deep Neural …Network (DNN) that learns the decomposed speech signals via SWEMD-VVMDH efficiently to achieve SE. At first, the noisy speech signals are decomposed into Intrinsic Mode Functions (IMFs) by the SWEMD Hurst (SWEMDH) technique. Then, the Time-Delay Estimation (TDE)-based VVMD was performed on the IMFs to elect the most relevant IMFs according to the Hurst exponent and lessen the low- as well as high-frequency noise elements in the speech signal. For each signal frame, the target features are chosen and fed to the DNN that learns these features to estimate the Ideal Ratio Mask (IRM) in a supervised manner. The abilities of DNN are enhanced for the categories of background noise, and the Signal-to-Noise Ratio (SNR) of the speech signals. Also, the noise category dimension and the SNR dimension are chosen for training and testing manifold DNNs since these are dimensions often taken into account for the SE systems. Further, the IRM in each frequency channel for all noisy signal samples is concatenated to reconstruct the noiseless speech signal. At last, the experimental outcomes exhibit considerable improvement in SE under different categories of noises. Show more

Keywords: Speech enhancement, SWEMD-VVMDH, DNN, ideal ratio mask, speech quality, speech intelligibility, generalizability

DOI: 10.3233/JIFS-211236

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1869-1883, 2022

Authors: Zhang, Yanteng | Teng, Qizhi | Qing, Linbo | Liu, Yan | He, Xiaohai

Article Type: Research Article

Abstract: Alzheimer’s disease (AD) is a degenerative brain disease and the most common cause of dementia. In recent years, with the widespread application of artificial intelligence in the medical field, various deep learning-based methods have been applied for AD detection using sMRI images. Many of these networks achieved AD vs HC (Healthy Control) classification accuracy of up to 90%but with a large number of computational parameters and floating point operations (FLOPs). In this paper, we adopt a novel ghost module, which uses a series of cheap operations of linear transformation to generate more feature maps, embedded into our designed ResNet architecture …for task of AD vs HC classification. According to experiments on the OASIS dataset, our lightweight network achieves an optimistic accuracy of 97.92%and its total parameters are dozens of times smaller than state-of-the-art deep learning networks. Our proposed AD classification network achieves better performance while the computational cost is reduced significantly. Show more

Keywords: Deep learning, ghost module, residual network, AD classification

DOI: 10.3233/JIFS-211247

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1885-1893, 2022

Authors: Jyoshna, Girika | Zia Ur Rahman, Md.

Article Type: Research Article

Abstract: Removing of noise component is an important task in all practical applications like hearing aids, speech therapy etc. In speech communication applications the speech components are contaminated with various types of noises. Separation of speech and noise component is a key issue in hearing aids, speech therapy applications. This paper demonstrates a hybrid version of singular spectrum analysis (SSA) and independent component analysis (ICA) based adaptive noise canceller (ANC) to separate noise and speech components. As ICA is not suitable for single channel sources, SSA is used to map signal channel data to multivariant data. Therefore, SSA based ICA decomposition …is used to generate reference for noise cancellation process. Variable Step based adaptive learning algorithm is used to separate noise contaminations from speech signals. To reduce computational complexity of system, sign regressor operation is applied to the data vector of the proposed adaptive learning methodology. Performance measures such as Signal to noise ratio improvement, excess mean square error and misadjustment are calculated for various considered ANCs, their values for crane noise are 29.6633 dB, – 27.4854 dB and 0.2058 respectively. Among the various adaptive learning algorithms, sign regressor based step variable method performs better than the other algorithms. Hence this learning methodology is well suited for hearing aids and speech therapy applications due to its robustness, less computational complexity and filtering ability. Show more

Keywords: Adaptive learning, computational complexity, reference generation, speech enhancement, independent component analysis

DOI: 10.3233/JIFS-211249

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1895-1906, 2022

Authors: Hao, Dong | Zhang, Runtong | Bai, Kaiyuan

Article Type: Research Article

Abstract: Online health communities (OHCs) have emerged as a significant platform for people communicating health information and self-healthcare management. In recent, the researches focusing on its performance measurement and the service quality evaluation have drawn intensive attention. Although some qualitative methods have made evaluation and analyses for the OHCs performance, the studies based on fuzzy multi-attribute decision making theory are rarely developed in the service quality evaluation of OHCs. In view of the complexity and uncertainty of evaluation mission, this paper develops an integrated evaluation approach of the OHC service quality based on the q-rung orthopair fuzzy linguistic aggregation operators. Firstly, …we propose the cross-entropy of q-rung orthopair fuzzy numbers, which is applied in solving the optimal weight of indicators by a linear programming model. Next, the q-rung orthopair fuzzy linguistic power average (q-ROFLPA) and q-rung orthopair fuzzy linguistic partitioned dual Maclaurin symmetric mean (q-ROFLPDMSM) operators are developed for aggregating the assessment information and ranking the OHCs. Based on the proposed aggregation operators, the evaluation indicator system and an evaluation framework are constructed to accomplish the service quality evaluation of OHCs. Finally, a practical evaluation case of OHCs is provided to demonstrate the reliability and advantages of the proposed approach. Show more

Keywords: Online health communities, q-rung orthopair fuzzy linguistic sets, partitioned dual Maclaurin symmetric mean, multi-attribute decision making, service quality evaluation

DOI: 10.3233/JIFS-211257

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1907-1924, 2022

Authors: An, Qingxian | Zhang, Ruiyi | Shen, Yongchang

Article Type: Research Article

Abstract: Data envelopment analysis (DEA) is widely used to evaluate the performance of a group of homogeneous decision making units (DMUs). Considering the uncertainty, interval DEA has been introduced to fit into more situations. In this paper, an interval efficiency method based on slacks-based measure is proposed to solve the uncertain problems in DEA. Firstly, the maximum and minimum efficiency values of the evaluated DMU are calculated by the furthest and closest distance from the evaluated DMU to the projection points on the Pareto-efficient frontier, respectively. Then, the AHP method is used for the full ranking of DMUs. The paper uses …the pairwise comparison relationship between each pair of DMUs to construct the interval multiplicative preference relations (IMPRs) matrix. If the matrix does not meet the consistency condition, a method to obtain consistency IMPRs is introduced. According to the consistency judgment matrix, the full ranking of DMUs can be obtained. Finally, we apply our method to the performance evaluation of 12 tourist hotels in Taipei in 2019. Show more

Keywords: Performance measurement, data envelopment analysis, interval efficiency, interval multiplicative preference relations, full ranking

DOI: 10.3233/JIFS-211292

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1925-1936, 2022

Authors: Seethappan, K. | Premalatha, K.

Article Type: Research Article

Abstract: Although there have been various researches in the detection of different figurative language, there is no single work in the automatic classification of euphemisms. Our primary work is to present a system for the automatic classification of euphemistic phrases in a document. In this research, a large dataset consisting of 100,000 sentences is collected from different resources for identifying euphemism or non-euphemism utterances. In this work, several approaches are focused to improve the euphemism classification: 1. A Combination of lexical n-gram features 2.Three Feature-weighting schemes 3.Deep learning classification algorithms. In this paper, four machine learning (J48, Random Forest, Multinomial Naïve …Bayes, and SVM) and three deep learning algorithms (Multilayer Perceptron, Convolutional Neural Network, and Long Short-Term Memory) are investigated with various combinations of features and feature weighting schemes to classify the sentences. According to our experiments, Convolutional Neural Network (CNN) achieves precision 95.43%, recall 95.06%, F-Score 95.25%, accuracy 95.26%, and Kappa 0.905 by using a combination of unigram and bigram features with TF-IDF feature weighting scheme in the classification of euphemism. These results of experiments show CNN with a strong combination of unigram and bigram features set with TF-IDF feature weighting scheme outperforms another six classification algorithms in detecting the euphemisms in our dataset. Show more

Keywords: Euphemism, TF-IDF, n-gram, Support Vector Machine, CNN

DOI: 10.3233/JIFS-211295

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1937-1948, 2022

Authors: Jan, Atif | Khan, Gul Muhammad

Article Type: Research Article

Abstract:  Identification/recognition of assault, fighting, shooting, and vandalism from video sequence using deep 2D and 3D convolutional neural networks (CNNs) is explored in this paper. Recent wave of extensive unrestricted urbanization has not only uplifted the standard of living, but has also threatened the safety of a common man leading to an extraordinary rise in crime rate. Although Closed-circuit television (CCTV) footage provides a monitoring framework, yet, it’s useless without an auto volume crime detection system. The system proposed in this work is an effort to eradicate volume crimes through accurate detection in real-time. Firstly, a fine-grained annotated dataset including instance …and activity information has been developed for real-world volume crimes. Secondly, a comparison between 3D CNN and 2D CNN network has been presented to identify the malicious event from the video sequence. This is carried out to explore the significance of spatial and temporal information present in the video for event recognition. It has been observed that 2D CNN even with lesser parameters achieved a promising classification accuracy of 91.2%and Area under the curve (AUC) of 95.2%on four classes. The system also reduces false alarm rate in comparison to state-of-the-art approaches. Show more

Keywords: Convolutional neural network, spatio-temporal features, malicious activity detection, deep learning

DOI: 10.3233/JIFS-211338

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1949-1961, 2022

Authors: Shi, Maolin | Wang, Zihao | Xu, Lizhang

Article Type: Research Article

Abstract: Data clustering based on regression relationship is able to improve the validity and reliability of the engineering data mining results. Surrogate models are widely used to evaluate the regression relationship in the process of data clustering, but there is no single surrogate model that always performs the best for all the regression relationships. To solve this issue, a fuzzy clustering algorithm based on hybrid surrogate model is proposed in this work. The proposed algorithm is based on the framework of fuzzy c -means algorithm, in which the differences between the clusters are evaluated by the regression relationship instead of Euclidean …distance. Several surrogate models are simultaneously utilized to evaluate the regression relationship through a weighting scheme. The clustering objective function is designed based on the prediction errors of multiple surrogate models, and an alternating optimization method is proposed to minimize it to obtain the memberships of data and the weights of surrogate models. The synthetic datasets are used to test single surrogate model-based fuzzy clustering algorithms to choose the surrogate models used in the proposed algorithm. It is found that support vector regression-based and response surface-based fuzzy clustering algorithms show competitive clustering performance, so support vector regression and response surface are used to construct the hybrid surrogate model in the proposed algorithm. The experimental results of synthetic datasets and engineering datasets show that the proposed algorithm can provide more competitive clustering performance compared with single surrogate model-based fuzzy clustering algorithms for the datasets with regression relationships. Show more

Keywords: Data clustering, fuzzy clustering, regression relationship, hybrid surrogate model, engineering data

DOI: 10.3233/JIFS-211340

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1963-1976, 2022

Authors: Wang, Heng | Ye, Xiang | Li, Yong

Article Type: Research Article

Abstract: Model pruning aims to reduce the parameter amount of deep neural networks while retaining the performance. Existing strategies often treat all layers equally and all layers simply share the same pruning rate. However, it is observed from our experiments that the redundancy degree differs from layer to layer. Based on this observation, this work proposes a pruning strategy depending on the layer-wise redundancy degree. Firstly, we define the redundancy degree for each layer by the norm and similarity redundancy of filters. Then a novel layer-wise strategy, Redundancy-dependent Filter Pruning (RedFiP), is proposed which prunes different proportion of filters at different …layers according to the defined redundancy degree. Since the redundancy analysis and experimental results of RedFiP show that deeper layers need fewer filters, a phase-wise strategy, Phased Filter Pruning (PFP), is proposed that divides the layers into three phases and layers in each phase share the same pruning rate. The phase-wise PFP allows the layer-wise RedFiP to be easily implemented in existing structures of deep neural networks. Experimental results show that when total parameters are pruned by 40%, RedFiP outperforms the state-of-the-art strategy FPGM-Mixed by 1.83% on CIFAR-100, and even slightly outperforms the non-pruned model by 0.11% on CIFAR-10. On ImageNet-1k, RedFiP (30%) and PFP (30%) outperform FPGM-Mixed (30%) by 1.3% and 0.8% with ResNet-18. Show more

Keywords: Filter pruning, redundancy, phase, importance

DOI: 10.3233/JIFS-211346

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1977-1990, 2022

Authors: Chen, Yong | Zhang, Tianbao | Wang, Ruojun | Cai, Lei

Article Type: Research Article

Abstract: The failure of complex engineering systems is easy to lead to disastrous consequences. To prevent the failure, it is necessary to model complex engineering systems using probabilistic techniques with limited data which is a major feature of complex engineering systems. It is a good choice to perform such modeling using Bayesian network because of its advantages in probabilistic modeling. However, few Bayesian network structural learning algorithms are designed for complex engineering systems with limited data. Therefore, an algorithm for learning the Bayesian network structure of them should be developed. Based on the process of self-purification of water, a complex engineering …system is segmented into three components according to the degree of difficulty in solving them. And then a Bayesian network learning algorithm with three components (TC), including PC algorithm, MIK algorithm which is originated by the paper through combining Mutual Information and K2 algorithm, and the Hill-Climbing method, is developed, i.e. TC algorithm. To verify its effectiveness, TC algorithm, K2 algorithm, and Max-Min Hill-Climbing are respectively used to learn Alarm network with different sizes of samples. The results imply that TC algorithm has the best performance. Finally, TC algorithm is applied to study tank spill accidents with 220 samples. Show more

Keywords: Bayesian network structural learning, algorithm, complex engineering systems, failure probability

DOI: 10.3233/JIFS-211354

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1991-2004, 2022

Authors: Bai, Shenshen | Li, Longjie | Chen, Xiaoyun

Article Type: Research Article

Abstract: The Dempster-Shafer evidence theory has been extensively used in various applications of information fusion owing to its capability in dealing with uncertain modeling and reasoning. However, when meeting highly conflicting evidence, the classical Dempster’s combination rule may give counter-intuitive results. To address this issue, we propose a new method in this work to fuse conflicting evidence. Firstly, a new evidence distance metric, named Belief Mover’s Distance, which is inspired by the Earth Mover’s Distance, is defined to measure the difference between two pieces of evidence. Subsequently, the credibility weight and distance weight of each piece of evidence are computed according …to the Belief Mover’s Distance. Then, the final weight of each piece of evidence is generated by unifying these two weights. Finally, the classical Dempster’s rule is employed to fuse the weighted average evidence. Several examples and applications are presented to analyze the performance of the proposed method. Experimental results manifest that the proposed method is remarkably effective in comparison with other methods. Show more

Keywords: Evidence theory, conflicting evidence, combination rule, evidence distance, Belief Mover’s Distance

DOI: 10.3233/JIFS-211397

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2005-2021, 2022

Authors: Li, Fang | Zhang, Lihua | Wang, Xiao | Liu, Shihu

Article Type: Research Article

Abstract: In the existing high-order fuzzy logical relationship (FLR) based forecasting model, each FLR is used to describe the association between multiple premise observations and a consequent observation. Therefore, these FLRs concentrate on the one-step-ahead forecasting. In real applications, there exist another kind of association: the association between multiple premise observations and multiple consequent observations. For such association, the existing FLRs can’t express and ignored. To depict it, the high-order multi-point association FLR is raised in this study. The antecedent and consequent of a high-order multi-point association FLR are consisted of multiple observations. Thus, the proposed FLR reflects the influence of …multiple premise observations on the multiple consequent observations, and can be applied for multi-step-ahead forecasting with no cumulative errors. On the basis of high-order multi-point association FLR, the high-order multi-point trend association FLR is constructed, it describes the trend association in time series. By using these two new kinds of FLRs, a fuzzy time series based multi-step-ahead forecasting model is established. In this model, the multi-point (trend) association FLRs effective in capturing the associations of time series and improving forecasting accuracy. The benefits of the proposed FLRs and the superior performance of the established forecasting model are demonstrated through the experimental analysis. Show more

Keywords: Fuzzy time series, high-order multi-point association fuzzy logical relationship, high-order multi-point trend association fuzzy logical relationship, multi-step-ahead forecasting

DOI: 10.3233/JIFS-211405

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2023-2039, 2022

Authors: Zhang, Zhaojun | Lu, Rui | Zhao, Minglong | Luan, Shengyang | Bu, Ming

Article Type: Research Article

Abstract: The research of path planning method based on genetic algorithm (GA) for the mobile robot has received much attention in recent years. GA, as one evolutionary computation model, mimics the process of natural evolution and genetics. The quality of the initial population plays an essential role in improving the performance of GA. However, when GA based on a random initialization method is applied to path planning problems, it will lead to the emergence of infeasible solutions and reduce the performance of the algorithm. A novel GA with a hybrid initialization method, termed NGA, is proposed to solve this problem in …this paper. In the initial population, NGA first randomly selects three free grids as intermediate nodes. Then, a part of the population uses a random initialization method to obtain the complete path. The other part of the population obtains the complete path using a greedy-related method. Finally, according to the actual situation, the redundant nodes or duplicate paths in the path are deleted to avoid the redundant paths. In addition, the deletion operation and the reverse operation are also introduced to the NGA iteration process to prevent the algorithm from falling into the local optimum. Simulation experiments are carried out with other algorithms to verify the effectiveness of the NGA. Simulation results show that NGA is superior to other algorithms in convergence accuracy, optimization ability, and success rate. Besides, NGA can generate the optimal feasible paths in complex environments. Show more

Keywords: Path planning, mobile robot, genetic algorithm, initial population

DOI: 10.3233/JIFS-211423

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2041-2056, 2022

Authors: Huang, Yuchong | Xu, Ning | Wang, Nan | Li, Jie

Article Type: Research Article

Abstract: Through innovatively introducing the receding horizon into probabilistic model checking, an online strategy synthesis method for multi-robot systems from local automatons is proposed to complete complex tasks that are assigned to each robot. Firstly, each robot is modeled as a Markov decision process which models both probabilistic and nondeterministic behavior. Secondly, the task specification of each robot is expressed as a linear temporal logic formula. For some tasks that robots cannot complete by themselves, the collaboration requirements take the form of atomic proposition into the LTL specifications. And the LTL specifications are transformed to deterministic rabin automatons over which a …task progression metric is defined to determine the local goal states in the finite-horizon product systems. Thirdly, two horizons are set to determine the running steps in automatons and MDPs. By dynamically building local finite-horizon product systems, the collaboration strategies are synthesized iteratively for each robot to satisfy the task specifications with maximum probability. Finally, through simulation experiments in an indoor environment, the results show that the method can synthesize correct strategies online for multi-robot systems which has no restriction on the LTL operators and reduce the computational burden brought by the automaton-based approach. Show more

Keywords: Receding horizon, linear temporal logic, Markov decision process, probabilistic model checking, multi-robot collaboration

DOI: 10.3233/JIFS-211427

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2057-2069, 2022

Authors: She, Chunyan | Zeng, Shaohua

Article Type: Research Article

Abstract: Outlier detection is a hot issue in data mining, which has plenty of real-world applications. LOF (Local Outlier Factor) can capture the abnormal degree of objects in the dataset with different density levels, and many extended algorithms have been proposed in recent years. However, the LOF needs to search the nearest neighborhood of each object on the whole dataset, which greatly increases the time cost. Most of these extended algorithms only consider the distance between an object and its neighborhood, but ignore the local distribution of an object within its neighborhood, resulting in a high false-positive rate. To improve the …running speed, a rough clustering based on triple fusion is proposed, which divides a dataset into several subsets and outlier detection is performed only on each subset. Then, considering the local distribution of an object within its neighborhood, a new local outlier factor is constructed to estimate the abnormal degree of each object. Finally, the experimental results indicate that the proposed algorithm has better performance and lower running time than the others. Show more

Keywords: Outlier detection, local outlier factor, rough Clustering

DOI: 10.3233/JIFS-211433

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2071-2082, 2022

Authors: Wang, Yun | Jin, Xin | Yang, Jie | Jiang, Qian | Tang, Yue | Wang, Puming | Lee, Shin-Jye

Article Type: Research Article

Abstract: Multi-focus image fusion is a technique that integrates the focused areas in a pair or set of source images with the same scene into a fully focused image. Inspired by transfer learning, this paper proposes a novel color multi-focus image fusion method based on deep learning. First, color multi-focus source images are fed into VGG-19 network, and the parameters of convolutional layer of the VGG-19 network are then migrated to a neural network containing multilayer convolutional layers and multilayer skip-connection structures for feature extraction. Second, the initial decision maps are generated using the reconstructed feature maps of a deconvolution module. …Third, the initial decision maps are refined and processed to obtain the second decision maps, and then the source images are fused to obtain the initial fused images based on the second decision maps. Finally, the final fused image is produced by comparing the Q ABF metrics of the initial fused images. The experimental results show that the proposed method can effectively improve the segmentation performance of the focused and unfocused areas in the source images, and the generated fused images are superior in both subjective and objective metrics compared with most contrast methods. Show more

Keywords: Deep learning, feature extraction, multi-focus images fusion, neural networks, transfer learning

DOI: 10.3233/JIFS-211434

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2083-2102, 2022

Authors: Özdemir, Özgür | Akın, Emre Salih | Velioğlu, Rıza | Dalyan, Tuğba

Article Type: Research Article

Abstract: Machine translation (MT) is an important challenge in the fields of Computational Linguistics. In this study, we conducted neural machine translation (NMT) experiments on two different architectures. First, Sequence to Sequence (Seq2Seq) architecture along with a variation that utilizes attention mechanism is performed on translation task. Second, an architecture that is fully based on the self-attention mechanism, namely Transformer, is employed to perform a comprehensive comparison. Besides, the contribution of employing Byte Pair Encoding (BPE) and Gumbel Softmax distributions are examined for both architectures. The experiments are conducted on two different datasets: TED Talks that is one of the popular …benchmark datasets for NMT especially among morphologically rich languages like Turkish and WMT18 News dataset that is provided by The Third Conference on Machine Translation (WMT) for shared tasks on various aspects of machine translation. The evaluation of Turkish-to-English translations’ results demonstrate that the Transformer model with combination of BPE and Gumbel Softmax achieved 22.4 BLEU score on TED Talks and 38.7 BLUE score on WMT18 News dataset. The empirical results support that using Gumbel Softmax distribution improves the quality of translations for both architectures. Show more

Keywords: Neural machine translation, Gumbel Softmax, sequence to sequence, transformer

DOI: 10.3233/JIFS-211453

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2103-2113, 2022

Authors: Tian, Yun Bo | Ma, Zhen Ming

Article Type: Research Article

Abstract: Both Heronian mean (HM) operators and Bonferroni mean (BM) operators can capture the interrelationship between input arguments and have been a hot research topic as a useful aggregation technique in fuzzy and intuitionistic fuzzy environments. In this paper, associated with the common characters of these operators we propose the covering-based compound mean operators in fuzzy environments to capture various interrelationships between input arguments, some desirable properties and special cases of the proposed mean operators are provided. Then, conditions under which these covering-based compound mean operators can be directly used to aggregate the membership degrees and nonmembership degrees of intuitionistic fuzzy …information, are provided. In particular, novel intuitionistic fuzzy HM operators and intuitionistic fuzzy BM operators are directly derived from the classical ones. We list the detailed steps of multiple attribute decision making with the developed aggregation operators, and give a comparison of the new extensions of BM operators by this paper with the corresponding existing ones to prove the rationality and effectiveness of the proposed method. Show more

Keywords: Heronian mean operator, Bonferroni mean operator, Covering-based compound mean operator, Intuitionistic fuzzy sets, Multiple attribute decision making

DOI: 10.3233/JIFS-211457

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2115-2126, 2022

Authors: He, Fang | Zhang, Wenyu | Yan, Zhijia

Article Type: Research Article

Abstract: Credit scoring has become increasingly important for financial institutions. With the advancement of artificial intelligence, machine learning methods, especially ensemble learning methods, have become increasingly popular for credit scoring. However, the problems of imbalanced data distribution and underutilized feature information have not been well addressed sufficiently. To make the credit scoring model more adaptable to imbalanced datasets, the original model-based synthetic sampling method is extended herein to balance the datasets by generating appropriate minority samples to alleviate class overlap. To enable the credit scoring model to extract inherent correlations from features, a new bagging-based feature transformation method is proposed, which …transforms features using a tree-based algorithm and selects features using the chi-square statistic. Furthermore, a two-layer ensemble method that combines the advantages of dynamic ensemble selection and stacking is proposed to improve the classification performance of the proposed multi-stage ensemble model. Finally, four standardized datasets are used to evaluate the performance of the proposed ensemble model using six evaluation metrics. The experimental results confirm that the proposed ensemble model is effective in improving classification performance and is superior to other benchmark models. Show more

Keywords: Ensemble learning, credit scoring, synthetic sampling, feature transformation

DOI: 10.3233/JIFS-211467

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2127-2142, 2022

Authors: Shao, Dangguo | Li, Chengyao | Huang, Chusheng | An, Qing | Xiang, Yan | Guo, Junjun | He, Jianfeng

Article Type: Research Article

Abstract: Aiming at the low effectiveness of short texts feature extraction, this paper proposes a short texts classification model based on the improved Wasserstein-Latent Dirichlet Allocation (W-LDA), which is a neural network topic model based on the Wasserstein Auto-Encoder (WAE) framework. The improvements of W-LDA are as follows: Firstly, the Bag of Words (BOW) input in the W-LDA is preprocessed by Term Frequency–Inverse Document Frequency (TF-IDF); Subsequently, the prior distribution of potential topics in W-LDA is replaced from the Dirichlet distribution to the Gaussian mixture distribution, which is based on the Variational Bayesian inference; And then the sparsemax function layer is …introduced after the hidden layer inferred by the encoder network to generate a sparse document-topic distribution with better topic relevance, the improved W-LDA is named the Sparse Wasserstein-Variational Bayesian Gaussian mixture model (SW-VBGMM); Finally, the document-topic distribution generated by SW-VBGMM is input to BiGRU (Bidirectional Gating Recurrent Unit) for the deep feature extraction and the short texts classification. Experiments on three Chinese short texts datasets and one English dataset represent that our model is better than some common topic models and neural network models in the four evaluation indexes (accuracy, precision, recall, F1 value) of text classification. Show more

Keywords: Short texts classification, neural network topic model, Variational Bayesian Gaussian mixture model (VBGMM), sparsemax, BiGRU (Bidirectional Gating Recurrent Unit)

DOI: 10.3233/JIFS-211471

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2143-2155, 2022

Authors: Yang, Yaxu | Guo, Zixue | He, Zefang

Article Type: Research Article

Abstract: The occurrence of public health emergency will cause huge economic losses and casualties, which posed a huge threat to the economic and social development. In response to the emergency, a large amount of emergency relief supplies will be transported to the affected areas. Faced with this public health emergency of international concern, the concept of emergency logistics capacity and the evaluation model based on probabilistic linguistic term sets are proposed. In this paper, the emergency logistics capability evaluation is transformed into user demand evaluation, and the importance of each index of emergency logistics capability is determined by using Quality Function …Deployment (QFD) and prospect theory. Under the probabilistic language information environment, a multi-attribute decision making method is established by using TODIM method. Finally, an example is given to verify the feasibility of the proposed method. Show more

Keywords: Emergency logistics capacity, probabilistic linguistic term sets, quality function deployment (QFD)

DOI: 10.3233/JIFS-211495

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2157-2168, 2022

Authors: Zheng, Wei | Du, Qing | Fan, Yongjian | Tan, Lijuan | Xia, Chuanlin | Yang, Fengyu

Article Type: Research Article

Abstract: Personalized exercise recommendation is an important research project in the field of online learning, which can explore students’ strengths and weaknesses and tailor exercises for them. However, programming exercises differs from other disciplines or types of exercises due to the comprehensive of the exercises and the specificity of program debugging. In order to assist students in learning programming, this paper proposes a programming exercise recommendation algorithm based on knowledge structure tree (KSTER). Firstly, the algorithm provides a calculation method for quantifying students’ cognitive level to obtain their knowledge needs through individual learning-related data. Secondly, a knowledge structure tree is constructed …based on the association relationship of knowledge points, and a learning objective prediction method is proposed by combining the knowledge needs and the knowledge structure tree to represent and update the learning objective. Finally, KSTER imports a matching operator that calculates cognitive level and exercise difficulty based on learning objectives, and makes top-η recommendation for exercises. Experiments show that the proposed algorithm significantly outperforms the other algorithms in both precision and recall. The comparison experiments with real-world data demonstrate that KSTER effectively improves students’ learning efficiency. Show more

Keywords: Personalized recommendation, learning objectives, knowledge structure tree, online learning

DOI: 10.3233/JIFS-211499

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2169-2180, 2022

Authors: Dai, Tianhong | Cong, Shijie | Huang, Jianping | Zhang, Yanwen | Huang, Xinwang | Xie, Qiancheng | Sun, Chunxue | Li, Kexin

Article Type: Research Article

Abstract: In agricultural production, weed removal is an important part of crop cultivation, but inevitably, other plants compete with crops for nutrients. Only by identifying and removing weeds can the quality of the harvest be guaranteed. Therefore, the distinction between weeds and crops is particularly important. Recently, deep learning technology has also been applied to the field of botany, and achieved good results. Convolutional neural networks are widely used in deep learning because of their excellent classification effects. The purpose of this article is to find a new method of plant seedling classification. This method includes two stages: image segmentation and …image classification. The first stage is to use the improved U-Net to segment the dataset, and the second stage is to use six classification networks to classify the seedlings of the segmented dataset. The dataset used for the experiment contained 12 different types of plants, namely, 3 crops and 9 weeds. The model was evaluated by the multi-class statistical analysis of accuracy, recall, precision, and F1-score. The results show that the two-stage classification method combining the improved U-Net segmentation network and the classification network was more conducive to the classification of plant seedlings, and the classification accuracy reaches 97.7%. Show more

Keywords: Deep learning, plant seedlings classification, machine learning, U-Net

DOI: 10.3233/JIFS-211507

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2181-2191, 2022