Journal of Intelligent & Fuzzy Systems - Volume 40, issue 5

Authors: Luo, Sheng

Article Type: Research Article

Abstract: An information system as a database that represents relationships between objects and attributes is an important mathematical model in the field of artificial intelligence. Hybrid data means boolean, categorical, real-valued, set-valued data and missing data in this paper. A hybrid information system is an information system where its attribute is hybrid data. This paper proposes a three-way decision method based on hybrid data. First, the distance between two objects based on the conditional attribute set in a given hybrid information system is developed and Gaussian kernel based on this distance is acquired. Then, the fuzzy T cos -equivalence relation, …induced by this information system, is obtained by using Gaussian kernel. Next, the decision-theoretic rough set model in this hybrid information system is presented. Moreover, a three-way decision method is given by means of this decision-theoretic rough set model and inclusion degree between two fuzzy sets. Finally, an example is employed to illustrate the feasibility of the proposed method, which may provide an effective method for hybrid data analysis in real applications. Show more

Keywords: Three-way decision, hybrid data, decision-theoretic rough set, gaussian kernel, feasibility

DOI: 10.3233/JIFS-182764

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8639-8650, 2021

Authors: Shobha Rani, N. | Yadhu, C. R. | Karthik, U.

Article Type: Research Article

Abstract: Assessing the age of an individual via bones serves as a technique in determination of individual skills. In this work, the assessment of chronological age for varying age groups of individuals is carried out using left hand wrist radiographs. The datasets employed for experimentation are preprocessed and extracted using an automated segmentation technique using bit plane level data of radiograph images. The flow of proposed work is comprised of three stages, in stage 1 preprocessing is carried out, classification of preprocessed radiographs are classified into male and female samples using convolution kernels based deep neural net. Further, distance features are …extracted from the origin of carpal bones to tip of extracted phalangeal regions in the classified outcomes from stage 2 using imtool image analyzer. Finally, classification of distance features is performed using Support Vector Machines with Gaussian Kernel (SVM-GK) to label the radiographs into ages from 1 to 17. The experimentation is performed on the datasets of Pediatric Bone Age challenge of Radiological Society of North America (RSNA) of about 12000 images of 1–17 year age groups. The convergence between actual and clinically validated chronological age is also tested with Gaussian process regression model (GPRM) along with SVM. A very minimal loss of about 4.7% is occurred during classification using deep neural network. The classification accuracy is found to be 76.8% and 88.1% and 0.75 and 1.41 RMSE with respect to GPRM and SVM-GK. Show more

Keywords: Bone age assessment, deep neural net, GPRM, SVM-GK, medical image processing, classification

DOI: 10.3233/JIFS-190779

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8651-8663, 2021

Authors: Fezai, Radhia | Mansouri, Majdi | Abodayeh, Kamaleldin | Nounou, Hazem | Nounou, Mohamed | Puig, Vicenç | Bouzrara, Kais

Article Type: Research Article

Abstract: This paper aims at improving the operation of the water distribution networks (WDN) by developing a leak monitoring framework. To do that, an online statistical hypothesis test based on leak detection is proposed. The developed technique, the so-called exponentially weighted online reduced kernel generalized likelihood ratio test (EW-ORKGLRT), is addressed so that the modeling phase is performed using the reduced kernel principal component analysis (KPCA) model, which is capable of dealing with the higher computational cost. Then the computed model is fed to EW-ORKGLRT chart for leak detection purposes. The proposed approach extends the ORKGLRT method to the one that …uses exponential weights for the residuals in the moving window. It might be able to further enhance leak detection performance by detecting small and moderate leaks. The developed method’s main advantages are first dealing with the higher required computational time for detecting leaks and then updating the KPCA model according to the dynamic change of the process. The developed method’s performance is evaluated and compared to the conventional techniques using simulated WDN data. The selected performance criteria are the excellent detection rate, false alarm rate, and CPU time. Show more

Keywords: Leak detection, water distribution networks, kernel principal component analysis, online reduced kernel generalized likelihood ratio test, exponentially weighted moving average

DOI: 10.3233/JIFS-191524

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8665-8681, 2021

Authors: Arivudainambi, D. | Pavithra, R.

Article Type: Research Article

Abstract: Wireless Sensor Network (WSN) has emerged recently due to its advancements and applications in various scientific and industrial fields. WSN consists a set of low cost and readily deployable sensors to monitor targets and recognise the physical phenomena. The principal challenge in WSN is to deploy these sensor nodes in optimal positions to achieve efficient network. Such network should satisfy the quality of service requirements in order to achieve high performance levels. Hence, this paper focuses on target Q-coverage problem where each target requires different number of sensors to monitor them. A Sequential Vertex Coloring based Sensor Placement (SVC-SP) algorithm …is proposed to determine the number of sensors required and its optimal spot to satisfy the coverage quality requirement. The SVC-SP algorithm determines sensor requirement by partitioning the target set into independent subsets depending on the target’s position and the sensor’s sensing range. Each independent set consists set of targets that are nearer in the network such that a common sensor is sufficient to monitor them. The cardinality of such independent subsets provides the sensor requirement for target coverage. The optimal spot for each target is determined by the mean positioning of the targets in each independent set. This process is repeated until the q-requirement for each target is satisfied. Further, to improve the optimal spot for sensors, the random based SVC-SP algorithm, cuckoo search based SVC-SP algorithm and the genetic algorithm based SVC-SP algorithm are utilized. The simulation results show that genetic algorithm based SVC-SP algorithm performs better than other existing algorithms. Show more

Keywords: Optimal sensor placement, target coverage, Q-coverage, vertex coloring, sequential vertex coloring

DOI: 10.3233/JIFS-191795

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8683-8695, 2021

Authors: Miao, Ya Lin | Cheng, Wen Fang | Ji, Yi Chun | Zhang, Shun | Kong, Yan Long

Article Type: Research Article

Abstract: Aiming at the problem that the Aspect-based sentiment analysis in Chinese has low recognition rate due to many steps, this paper proposes an improved BiLSTM-CRF model based on combine the Chinese character vector and Chinese words position feature, which can extract attribute words and sentiment words jointly simultaneously, while extracting Polarity judges of sentiment words. Experiments show that the improved model improves the precision rate by 9.2%  13.32%, recall rate improves 0.48%  21.29%, F-measure improves 7.33%  15.74% compared with Conditional Random Fields (CRF) model and Long Short Term Memory (LSTM) model on the self-built 6357 mobile reviews dataset. The experimental …results show that the model improves the accuracy of Aspect-based sentiment analysis and can effectively obtain the information required by users need in evaluation texts. Show more

Keywords: Aspect-based sentiment analysis in Chinese, BiLSTM-CRF model, attribute words, emotional words, mobile reviews

DOI: 10.3233/JIFS-192078

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8697-8707, 2021

Authors: Barma, Partha Sarathi | Dutta, Joydeep | Mukherjee, Anupam | Kar, Samarjit

Article Type: Research Article

Abstract: This study designs a new variant of the capacitated vehicle routing problem (CVRP) under a fuzzy environment. In CVRP, several vehicles start their journey from a central depot to provide services to different cities and finally return to the depot. This paper introduces an additional time beyond the service time at each city to fulfill the pre-ordered demands. The need for this excess service time is to provide the services to new customers who are not enlisted at the start of the process. It is a market enhancement step. The proposed model’s main objective is to find the maximum time-dependent …profit by using the optimum number of vehicles in an appropriate route and spending optimum excess service time in each city. The model considers travel time and travel cost as fuzzy numbers. An expected value model (EVM) is formulated using the credibility approach on fuzzy variables. A hybrid meta-heuristic method combining a genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) is designed to solve the proposed model. The proposed model is explained with the help of some numerical examples. Sensitivity analyses based on different independent parameters of the algorithms are also conducted. Show more

Keywords: Capacitated vehicle routing problem, profit maximization, fuzzy credibility theory, hybrid algorithm, genetic algorithm, bacteria foraging optimization algorithm

DOI: 10.3233/JIFS-192134

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8709-8725, 2021

Authors: Singh, Rajvir | Rama Krishna, C. | Sharma, Rajnish | Vig, Renu

Article Type: Research Article

Abstract: Dynamic and frequent re-clustering of nodes along with data aggregation is used to achieve energy-efficient operation in wireless sensor networks. But dynamic cluster formation supports data aggregation only when clusters can be formed using any set of nodes that lie in close proximity to each other. Frequent re-clustering makes network management difficult and adversely affects the use of energy efficient TDMA-based scheduling for data collection within the clusters. To circumvent these issues, a centralized Fixed-Cluster Architecture (FCA) has been proposed in this paper. The proposed scheme leads to a simplified network implementation for smart spaces where it makes more sense …to aggregate data that belongs to a cluster of sensors located within the confines of a designated area. A comparative study is done with dynamic clusters formed with a distributive Low Energy Adaptive Clustering Hierarchy (LEACH) and a centralized Harmonic Search Algorithm (HSA). Using uniform cluster size for FCA, the results show that it utilizes the available energy efficiently by providing stability period values that are 56% and 41% more as compared to LEACH and HSA respectively. Show more

Keywords: Wireless sensor network, clustered architecture

DOI: 10.3233/JIFS-192177

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8727-8740, 2021

Authors: Li, Dong | Liu, Shulin | Gao, Furong | Sun, Xin

Article Type: Research Article

Abstract: Classification methods play an important role in many fields. However, they cannot effectively classify the samples from sample spaces that are varying with time, for they lack continual learning ability. A continual learning classification method for time-varying data space based on artificial immune system, CLCMTVD, is proposed. It is inspired by the intelligent mechanism that memory cells of the biological immune system can recognize and eliminate previous invaders when they attack again very fast and more efficiently, and these memory cells can evolve with the evolution of previous invaders. Memory cells were continuously updated by learning testing data during the …testing stage, thus realize the self-improvement of classification performance. CLCMTVD changes a linearly inseparable spatial problem into many classification problems of several different times, and it degenerates into a common supervised learning classification method when all data independent of time. To assess the performance and possible advantages of CLCMTVD, the experiments on well-known datasets from UCI repository, synthetic data and XJTU-SY rolling element bearing accelerated life test datasets were performed. Results show that CLCMTVD has better classification performance for time-invariant data, and outperforms the other methods for time-varying data space. Show more

Keywords: Artificial immune system, classification, continual learning, machine learning, time-varying data

DOI: 10.3233/JIFS-200044

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8741-8754, 2021

Authors: Oguz, Gulay | Davvaz, Bijan

Article Type: Research Article

Abstract: Molodtsov proposed the theory of soft sets which can be considered as a recent mathematical tool to deal with uncertainties. The main purpose of this paper is to give the definition of soft topological hypergrupoid by examining the concept of hypergrupoid which is one of the hyperystructures with soft set theory from the topological point of view. Also, the relation between soft topological hypergroupoids and soft hypergroupoids is examined and some theoretical results are obtained. By introducing the concept of soft good topological homomorphism, the category of soft topological hypergrupoids is constructed. At last, the definition of soft topological subhypergrupoid …is presented and some related properties are studied. Show more

Keywords: Soft set, topological hypergroupoid, soft hypergrupoid, soft topological hypergroupoid

DOI: 10.3233/JIFS-200242

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8755-8764, 2021

Authors: Zhao, Hu | Sayed, O.R. | El-Sanousy, E. | Ragheb Sayed, Y.H. | Chen, Gui-Xiu

Article Type: Research Article

Abstract: Different from the separation axioms in the framework of (L , M )-fuzzy convex spaces defined by Liang et al.(2019). In this paper, we give some new investigations on separation axioms in (L , M )-fuzzy convex structures by L -fuzzy hull operators and r -L -fuzzy biconvex. We introduce the concepts of r -LFS i spaces where i  = {0, 1, 2, 3, 4}, and obtain various properties. In particular, we discuss the invariance of these separation properties under subspace and product.

Keywords: r-LFS0 space, r-LFS1 space, r-LFS2 space, r-LFS3 space, r-LFS4 space

DOI: 10.3233/JIFS-200340

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8765-8773, 2021

Authors: Li, Zhaowen | Liao, Shimin | Qu, Liangdong | Song, Yan

Article Type: Research Article

Abstract: Attribute selection in an information system (IS) is an important issue when dealing with a large amount of data. An IS with incomplete interval-value data is called an incomplete interval-valued information system (IIVIS). This paper proposes attribute selection approaches for an IIVIS. Firstly, the similarity degree between two information values of a given attribute in an IIVIS is proposed. Then, the tolerance relation on the object set with respect to a given attribute subset is obtained. Next, θ -reduction in an IIVIS is studied. What is more, connections between the proposed reduction and information entropy are revealed. Lastly, three reduction …algorithms base on θ -discernibility matrix, θ -information entropy and θ -significance in an IIVIS are given. Show more

Keywords: Rough set theory, IIVIS, similarity degree, θ-reduction, θ-discernibility matrix, θ-information entropy, θ-significance, algorithm

DOI: 10.3233/JIFS-200394

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8775-8792, 2021

Authors: Li, Dong | Sun, Xin | Gao, Furong | Liu, Shulin

Article Type: Research Article

Abstract: Compared with the traditional negative selection algorithms produce detectors randomly in whole state space, the boundary-fixed negative selection algorithm (FB-NSA) non-randomly produces a layer of detectors closely surrounding the self space. However, the false alarm rate of FB-NSA is higher than many anomaly detection methods. Its detection rate is very low when normal data close to the boundary of state space. This paper proposed an improved FB-NSA (IFB-NSA) to solve these problems. IFB-NSA enlarges the state space and adds auxiliary detectors in appropriate places to improve the detection rate, and uses variable-sized training samples to reduce the false alarm rate. …We present experiments on synthetic datasets and the UCI Iris dataset to demonstrate the effectiveness of this approach. The results show that IFB-NSA outperforms FB-NSA and the other anomaly detection methods in most of the cases. Show more

Keywords: Negative selection algorithm, anomaly detection, artificial immune algorithms, machine learning

DOI: 10.3233/JIFS-200405

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8793-8806, 2021

Authors: Fan, Yun | Fang, Zhigeng | Liu, Sifeng | Liu, Jun

Article Type: Research Article

Abstract: The construction of more nursing homes has become one of the most needed pension services in China, and the issue of site selection is one of the most important steps in their construction. The problem of site selection for nursing homes is a complex system engineering problem that involves not only economic interests but also social interests. Due to the limitations of human thinking in the evaluation process, the evaluation value of a nursing home site might be an interval grey number. Moreover, the evaluation indicator system for nursing home locations is a two-layer system that has been neglected in …the literature. Therefore, the fuzzy analytical hierarchy process is extended to a new grey approach, i.e., the grey analytic hierarchy process, which can solve the evaluation problems for a two-layer indicator system under an interval grey environment. By constructing a three-point interval grey number, grey evaluation criteria are given to obtain a judgment matrix for interval grey numbers. Definitions of the initial weights, nongreyness weights and integrated weights are proposed to find the best evaluation object. Finally, the effectiveness of the method proposed by this paper is verified by comparative analyses of other grey methods. Show more

Keywords: Nursing home site, site selection, grey analytic hierarchy process, fuzzy analytic hierarchy process, interval grey number

DOI: 10.3233/JIFS-200480

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8807-8818, 2021

Authors: Meniz, Busra | Bas, Sema Akin | Ozkok, Beyza Ahlatcioglu | Tiryaki, Fatma

Article Type: Research Article

Abstract: Decision making (DM) is an important process encountered in every moment of life. Since it is difficult to interpret life depending on a single criterion, Multi-Criteria Decision Making (MCDM) enables to make decisions easier by creating appropriate choice in situations of uncertainty, complexity, and conflicting objectives. Therefore, we have studied the Analytic Hierarchy Process (AHP) which is one of the MCDM methods based on binary comparison logic. When uncertainties concerning the nature of life are considered, the solution procedure of AHP has been addressed by using Interval Type-2 Fuzzy Numbers (IT2FN)s to obtain more realistic results. The usability of AHP …with IT2FN is increased by amplifying hierarchy with sub-levels. Since sub-criterion may also need to be evaluated on sub-criteria in some cases of real multi-criteria problems, it is explicitly essential that each of sub-sub-criterion is included in the hierarchy at the own level in the real sense. In this paper, a new multilevel type-2 fuzzy AHP method is expanded by adding sub-criteria to the Interval Type-2 Fuzzy AHP (IT2FAHP) method developed by Kahraman et al. [C. Kahraman, B. Öztayşi, İ. Sarı and B. Turanoğlu, Fuzzy analytic hierarchy process with interval type-2 fuzzy sets, Knowledge-Based Systems 59 (2014), 48–57.]. Thanks to the extended method, another aim is to ensure that even complex situations that have multiple levels can be solved simply. Also, the proposed method is illustrated with a portfolio selection problem. Thus, the AHP method with type-2 fuzzy sets is carried out to the portfolio selection problem, which is in the scope of finance theory, for the first time in the literature. Show more

Keywords: Interval type-2 fuzzy numbers, multilevel AHP, multi-criteria decision making, portfolio selection

DOI: 10.3233/JIFS-200512

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8819-8829, 2021

Authors: Liu, Wanzheng | Xu, Ying | Shao, Meng | Yue, Guodong | An, Dong

Article Type: Research Article

Abstract: In this paper, a Stewart’s positive solution optimization model is proposed, for obtaining the complex solution to a Stewart’s forward kinematics problem, considering the existence of multiple solutions. The model converts the positive kinematics problem into an optimization problem, in which the value of the objective function is used to represent the precision of Stewart’s positive solution. A self-aggregating moth–flame optimization algorithm (SMFO) is used to improve the accuracy of Stewart’s forward kinematics solution. Two features were added to the conventional MFO algorithm to obtain a more stable balance between global and local explorations. First, Gaussian distribution was used for …the flame population to select suitable individuals for Levy Flight operation, increase the diversity of the population, and enhance the algorithm’s ability to jump out of a local optimum. Second, in the middle and late iterations, the positions of the flames were periodically adjusted using the light intensity-attraction characteristic (LIAC) to strengthen the connection between individual flames and enhance the local exploration ability of the algorithm. The proposed SMFO algorithm is compared with three classic meta-heuristic algorithms for eight benchmark functions. Experimental results indicate that the SMFO algorithm is significantly better than the other three algorithms in terms of solution quality and convergence rate. To verify the effectiveness of the SMFO algorithm in solving the Stewart positive kinematics optimization model, values of eight sets of conventional position and posture parameters as well as limiting position and posture parameters were randomly obtained, and values of 16 sets of position and posture parameters were obtained using four algorithms. The results indicate that the SMFO algorithm can improve the accuracy of the forward kinematics solution to 4.05E-09 mm. Show more

Keywords: Stewart platform, positive solution optimization model, light intensity-attraction characteristics, levy flight

DOI: 10.3233/JIFS-200656

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8831-8846, 2021

Authors: Elakkiya, R.

Article Type: Research Article

Abstract: Epilepsy is found to be the fourth most common chronic neurological disorder that tends to abnormal and unpredictable brain activity and seizure states. According to statistics, 70% of the epilepsy patients can be cured if identified and treated with anti-epileptic drugs or shock stimulations. Only about 7% to 8% need to be operated. Electroencephalogram (EEG) is a cheap and effective way to record the prolonged activities of the brain through electrical impulses between neural cells. Seizure is difficult to detect in neonates as the signal involves a lot of disturbances and the existing high accuracy system for adults can’t be …used for neonates. In an attempt to build an impregnable system to detect seizure in early stages, EEG signals of neonates procured from Neonatal Intensive Care Unit (NICU) at the Helsinki University Hospital. These signals were processed and fed into three different robust algorithms –Support Vector Machine (SVM), Artificial Neural Network (ANN) and 1-Dimensional Convolutional Neural Network (1D-CNN). The experimental results were compared and the proposed CNN model with 95.99% accuracy outperforms all the state-of-art models for automated Epileptic Seizure prediction in Neonates. Deep CNN has been a powerful tool in extracting robust features from EEG signals. This generalized system can be used by medical experts for detecting Seizure in neonates with better accuracy and reliability. Show more

Keywords: Neonatal Epileptic Seizure, neurological disorder, neonates, EEG, ANN, 1D-CNN, deep learning, Helsinki dataset, computer vision

DOI: 10.3233/JIFS-200800

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8847-8855, 2021

Authors: Li, Xi | Suo, Chunfeng | Li, Yongming

Article Type: Research Article

Abstract: An essential topic of interval-valued intuitionistic fuzzy sets(IVIFSs) is distance measures. In this paper, we introduce a new kind of distance measures on IVIFSs. The novelty of our method lies in that we consider the width of intervals so that the uncertainty of outputs is strongly associated with the uncertainty of inputs. In addition, better than the distance measures given by predecessors, we define a new quaternary function on IVIFSs to construct the above-mentioned distance measures, which called interval-valued intuitionistic fuzzy dissimilarity function. Two specific methods for building the quaternary functions are proposed. Moreover, we also analyzed the degradation of …the distance measures in this paper, and show that our measures can perfectly cover the measures on a simpler set. Finally, we provide illustrative examples in pattern recognition and medical diagnosis problems to confirm the effectiveness and advantages of the proposed distance measures. Show more

Keywords: Interval-valued intuitionistic fuzzy set, interval-valued distance measure, interval-valued intuitionistic fuzzy dissimilarity function, pattern recognition, medical diagnosis

DOI: 10.3233/JIFS-200889

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8857-8869, 2021

Authors: Wei, Guangcun | Rong, Wansheng | Liang, Yongquan | Xiao, Xinguang | Liu, Xiang

Article Type: Research Article

Abstract: Aiming at the problem that the traditional OCR processing method ignores the inherent connection between the text detection task and the text recognition task, This paper propose a novel end-to-end text spotting framework. The framework includes three parts: shared convolutional feature network, text detector and text recognizer. By sharing convolutional feature network, the text detection network and the text recognition network can be jointly optimized at the same time. On the one hand, it can reduce the computational burden; on the other hand, it can effectively use the inherent connection between text detection and text recognition. This model add the …TCM (Text Context Module) on the basis of Mask RCNN, which can effectively solve the negative sample problem in text detection tasks. This paper propose a text recognition model based on the SAM-BiLSTM (spatial attention mechanism with BiLSTM), which can more effectively extract the semantic information between characters. This model significantly surpasses state-of-the-art methods on a number of text detection and text spotting benchmarks, including ICDAR 2015, Total-Text. Show more

Keywords: Scene text spotting, End-to-end, Joint optimization, TCM, SAM-BiLSTM

DOI: 10.3233/JIFS-200903

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8871-8881, 2021

Authors: Guo, Feiyan | Tang, Bing | Zhang, Jiaming

Article Type: Research Article

Abstract: The rapid development of the Internet of Things and 5G networks have generated a large amount of data. By offloading computing tasks from mobile devices to edge servers with sufficient computing resources, network congestion and data transmission delays can be effectively reduced. The placement of edge server is the core of task offloading and is a multi-objective optimization problem with multiple resource constraints. Efficient placement approach can effectively meet the needs of mobile users to access services with low latency and high bandwidth. To this end, an optimization model of edge server placement has been established in this paper through …minimizing both communication delay and load difference as the optimization goal. Then, an E dge S erver placement based on meta-H euristic alG orithM (ESH-GM) has been proposed to achieve multi-objective optimization. Firstly, the K-means algorithm is combined with the ant colony algorithm, and the pheromone feedback mechanism is introduced into the placement of edge servers by emulating the mechanism of ant colony sharing pheromone in the foraging process, and the ant colony algorithm is improved by setting the taboo table to improve the convergence speed of the algorithm. Then, the improved heuristic algorithm is used to solve the optimal placement of edge servers. Experimental results using Shanghai Telecom’s real datasets show that the proposed ESH-GM achieves an optimal balance between low latency and load balancing, while guaranteeing quality of service, which outperforms several existing representative approaches. Show more

Keywords: Mobile edge computing, server placement, heuristic algorithm, performance optimization

DOI: 10.3233/JIFS-200933

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8883-8897, 2021

Authors: Kang, Keming | Tian, Shengwei | Yu, Long

Article Type: Research Article

Abstract: For deep learning’s insufficient learning ability of a small amount of data in the Chinese named entity recognition based on deep learning, this paper proposes a named entity recognition of local adverse drug reactions based on Adversarial Transfer Learning, and constructs a neural network model ASAIBC consisting of Adversarial Transfer Learning, Self-Attention, independently recurrent neural network (IndRNN), Bi-directional long short-term memory (BiLSTM) and conditional random field (CRF). However, of the task of Chinese named entity recognition (NER), there are only few open labeled data sets. Therefore, this article introduces Adversarial Transfer Learning network to fully utilize the boundary of Chinese …word segmentation tasks (CWS) and NER tasks for information sharing. Plus, the specific information in the CWS is also filtered. Combing with Self-Attention mechanism and IndRNN, this feature’s expression ability is enhanced, thus allowing the model to concern the important information of different entities from different levels. Along with better capture of the dependence relations of long sentences, the recognition ability of the model is further strengthened. As all the results gained from WeiBoNER and MSRA data sets by ASAIBC model are better than traditional algorithms, this paper conducts an experiment on the data set of Xinjiang local named entity recognition of adverse drug reactions (XJADRNER) based on manual labeling, with the accuracy, precision, recall and F-Score value being 98.97%, 91.01%, 90.21% and 90.57% respectively. These experimental results have shown that ASAIBC model can significantly improve the NER performance of local adverse drug reactions in Xinjiang. Show more

Keywords: Transfer learning, self-Attention mechanism, IndRNN, named entity recognition of adverse drug reactions, deep learning

DOI: 10.3233/JIFS-201017

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8899-8914, 2021

Authors: Zhu, Wenhua | Peng, Hu | Leng, Chaohui | Deng, Changshou | Wu, Zhijian

Article Type: Research Article

Abstract: Breast cancer is a severe disease for women health, however, with expensive diagnostic cost or obsolete medical technique, many patients are hard to obtain prompt medical treatment. Thus, efficient detection result of breast cancer while lower medical cost may be a promising way to protect women health. Breast cancer detection using all features will take a lot of time and computational resources. Thus, in this paper, we proposed a novel framework with surrogate-assisted firefly algorithm (FA) for breast cancer detection (SFA-BCD). As an advanced evolutionary algorithm (EA), FA is adopted to make feature selection, and the machine learning as classifier …identify the breast cancer. Moreover, the surrogate model is utilized to decrease computation cost and expensive computation, which is the approximation function built by offline data to the real object function. The comprehensive experiments have been conducted under several breast cancer dataset derived from UCI. Experimental results verified that the proposed framework with surrogate-assisted FA significantly reduced the computation cost. Show more

Keywords: Breast cancer detection, firefly algorithm, machine learning, surrogate model, feature selection

DOI: 10.3233/JIFS-201124

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8915-8926, 2021

Authors: Iranmanesh, Seyed Mehdi | Nasrabadi, Nasser M.

Article Type: Research Article

Abstract: In this paper, we present a simple approach to train Generative Adversarial Networks (GANs) in order to avoid a mode collapse issue. Implicit models such as GANs tend to generate better samples compared to explicit models that are trained on tractable data likelihood. However, GANs overlook the explicit data density characteristics which leads to undesirable quantitative evaluations and mode collapse. To bridge this gap, we propose a hybrid generative adversarial network (HGAN) for which we can enforce data density estimation via an autoregressive model and support both adversarial and likelihood framework in a joint training manner which diversify the …estimated density in order to cover different modes. We propose to use an adversarial network to transfer knowledge from an autoregressive model (teacher) to the generator (student) of a GAN model. A novel deep architecture within the GAN formulation is developed to adversarially distill the autoregressive model information in addition to simple GAN training approach. We conduct extensive experiments on real-world datasets (i.e., MNIST, CIFAR-10, STL-10) to demonstrate the effectiveness of the proposed HGAN under qualitative and quantitative evaluations. The experimental results show the superiority and competitiveness of our method compared to the baselines. Show more

Keywords: Generative adversarial network, adversarial training, mode collapse, network distillation, autoregressive model

DOI: 10.3233/JIFS-201202

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8927-8938, 2021

Authors: Liu, Peide | Hendalianpour, Ayad | Hamzehlou, Mohammad

Article Type: Research Article

Abstract: The present study investigates a two-echelon supply chain including a usual retailer and two competing manufacturers. The objective function of our model is the maximization of the whole profit of the supply chain, which consists of the stochastic demand, shortage cost, and holding costs. This paper aims to analyze a single period with two products to define the optimum retail prices and wholesales under different game theory approaches (e.g., Bertrand, cooperation, and Stackelberg competitions) based on Double Interval Grey Numbers (DIGN). The other aim of this paper is to specify the price using the manufacturers and the common retailer and …considering the stochastic different channel power structures and demand function. In this paper, it is considered that different power structures of channel members may affect the optimal pricing decisions. In this paper, two pricing policies of manufacturers, eight pricing models and various structures of distribution channel members are utilized. In these pricing models, the impacts of retail substitutability are evaluated on the decisions of the chain members and the equilibrium profits. In this paper, the products are substitutable and the demand is stochastic. In this model, the demand is not certain then, we may have shortages or unsold products. Finally, sensitivity analysis is provided for illustrating the theoretical outcomes established in each case. Show more

Keywords: Pricing, stochastic demand, supply chain, game theory, double interval grey numbers

DOI: 10.3233/JIFS-201206

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8939-8961, 2021

Authors: Rababa, Salahaldeen | Al-Badarneh, Amer

Article Type: Research Article

Abstract: Large-scale datasets collected from heterogeneous sources often require a join operation to extract valuable information. MapReduce is an efficient programming model for processing large-scale data. However, it has some limitations in processing heterogeneous datasets. This is because of the large amount of redundant intermediate records that are transferred through the network. Several filtering techniques have been developed to improve the join performance, but they require multiple MapReduce jobs to process the input datasets. To address this issue, the adaptive filter-based join algorithms are presented in this paper. Specifically, three join algorithms are introduced to perform the processes of filters creation …and redundant records elimination within a single MapReduce job. A cost analysis of the introduced join algorithms shows that the I/O cost is reduced compared to the state-of-the-art filter-based join algorithms. The performance of the join algorithms was evaluated in terms of the total execution time and the total amount of I/O data transferred. The experimental results show that the adaptive Bloom join, semi-adaptive intersection Bloom join, and adaptive intersection Bloom join decrease the total execution time by 30%, 25%, and 35%, respectively; and reduce the total amount of I/O data transferred by 18%, 25%, and 50%, respectively. Show more

Keywords: Join algorithms, big data management, query optimization, MapReduce

DOI: 10.3233/JIFS-201220

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8963-8980, 2021

Authors: Lu, Ting | Xiang, Yan | Liang, Junge | Zhang, Li | Zhang, Mingfang

Article Type: Research Article

Abstract: The grand challenge of cross-domain sentiment analysis is that classifiers trained in a specific domain are very sensitive to the discrepancy between domains. A sentiment classifier trained in the source domain usually have a poor performance in the target domain. One of the main strategies to solve this problem is the pivot-based strategy, which regards the feature representation as an important component. However, part-of-speech information was not considered to guide the learning of feature representation and feature mapping in previous pivot-based models. Therefore, we present a fused part-of-speech vectors and attention-based model (FAM) . In our model, we fuse part-of-speech …vectors and feature word embeddings as the representation of features, giving deep semantics to mapping features. And we adopt Multi-Head attention mechanism to train the cross-domain sentiment classifier to obtain the connection between different features. The results of 12 groups comparative experiments on the Amazon dataset demonstrate that our model outperforms all baseline models in this paper. Show more

Keywords: Part-of-speech vectors, Multi-Head attention mechanism, cross-domain sentiment analysis

DOI: 10.3233/JIFS-201295

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8981-8989, 2021

Authors: Jamil, Faisal | Kim, DoHyeun

Article Type: Research Article

Abstract: In recent few years, the widespread applications of indoor navigation have compelled the research community to propose novel solutions for detecting objects position in the Indoor environment. Various approaches have been proposed and implemented concerning the indoor positioning systems. This study propose an fuzzy inference based Kalman filter to improve the position estimation in indoor navigation. The presented system is based on FIS based Kalman filter aiming at predicting the actual sensor readings from the available noisy sensor measurements. The proposed approach has two main components, i.e., multi sensor fusion algorithm for positioning estimation and FIS based Kalman filter algorithm. …The position estimation module is used to determine the object location in an indoor environment in an accurate way. Similarly, the FIS based Kalman filter is used to control and tune the Kalman filter by considering the previous output as a feedback. The Kalman filter predicts the actual sensor readings from the available noisy readings. To evaluate the proposed approach, the next-generation inertial measurement unit is used to acquire a three-axis gyroscope and accelerometer sensory data. Lastly, the proposed approach’s performance has been investigated considering the MAD, RMSE, and MSE metrics. The obtained results illustrate that the FIS based Kalman filter improve the prediction accuracy against the traditional Kalman filter approach. Show more

Keywords: ANN, FIS based Kalman Filter, navigation system, inertial measurement unit, indoor navigation, sensors fusion

DOI: 10.3233/JIFS-201352

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8991-9005, 2021

Authors: Subudhi, Jyotirmayee | Indumathi, P.

Article Type: Research Article

Abstract: Non-Orthogonal Multiple Access (NOMA) provides a positive solution for multiple access issues and meets the criteria of fifth-generation (5G) networks by improving service quality that includes vast convergence and energy efficiency. The problem is formulated for maximizing the sum rate of MIMO-NOMA by assigning power to multiple layers of users. In order to overcome these problems, two distinct evolutionary algorithms are applied. In particular, the recently implemented Salp Swarm Algorithm (SSA) and the prominent Optimization of Particle Swarm (PSO) are utilized in this process. The MIMO-NOMA model optimizes the power allocation by layered transmission using the proposed Joint User Clustering …and Salp Particle Swarm Optimization (PPSO) power allocation algorithm. Also, the closed-form expression is extracted from the current Channel State Information (CSI) on the transmitter side for the achievable sum rate. The efficiency of the proposed optimal power allocation algorithm is evaluated by the spectral efficiency, achievable rate, and energy efficiency of 120.8134bits/s/Hz, 98Mbps, and 22.35bits/Joule/Hz respectively. Numerical results have shown that the proposed PSO algorithm has improved performance than the state of art techniques in optimization. The outcomes on the numeric values indicate that the proposed PSO algorithm is capable of accurately improving the initial random solutions and converging to the optimum. Show more

Keywords: Energy efficiency, MIMO-NOMA, Non-orthogonal multiple access, PSO optimization, power allocation, layered transmission, user clustering

DOI: 10.3233/JIFS-201412

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9007-9019, 2021

Authors: He, Peng | Wang, Xue-ping

Article Type: Research Article

Abstract: This paper first describes a characterization of a lattice L which can be represented as the collection of all up-sets of a poset. It then obtains a representation of a complete distributive lattice L 0 which can be embedded into the lattice L such that all infima, suprema, the top and bottom elements are preserved under the embedding by defining a monotonic operator on a poset. This paper finally studies the algebraic characterization of a finite distributive.

Keywords: 03E72, 06D05, L-fuzzy set, cut set, complete distributive lattice, embedding, monotonic operator

DOI: 10.3233/JIFS-201430

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9021-9030, 2021

Authors: Xiao, Hui-Min | Wang, Mei-Qi | Cao, Yan-Li | Guo, Yu-Jie

Article Type: Research Article

Abstract: In this paper, to improve the situation of singleness of selecting results in hesitant fuzzy set decision-making and expand the range of choices for decision makers, we construct a hesitant fuzzy set clustering algorithm combined with fuzzy matroid operation. The algorithm synthesizes the r-cut set, fuzzy shrinking matroids in the fuzzy matroids and the operational properties of the fuzzy derived matroids, the r value also is used to connect the two types of fuzzy matroids to form a clustering algorithm. Finally, we apply the algorithm to the hesitant fuzzy set decision-making of job seekers choosing recruitment websites, each recruitment website …as an optional scheme is divided into three categories of excellent to inferior schemes to provide job seekers with ideas and methods for favorably selecting recruitment websites. Show more

Keywords: Hesitant fuzzy set decision-making, fuzzy matroid, contraction matroid, derived matroid, clustering algorithm

DOI: 10.3233/JIFS-201476

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9031-9039, 2021

Authors: Sahoo, Arun Kumar | Panigrahi, Tapas Kumar | Dhiman, Gaurav | Singh, Krishna Kant | Singh, Akansha

Article Type: Research Article

Abstract: In this paper, an enhanced version of the emperor penguin optimization algorithm is proposed for solving dynamic economic dispatch (DED) problem incorporating renewable energy sources and microgrid. Dynamic economic load dispatch optimally shares the power on an hourly basis for a day among the committed generating units to satisfy the feasible load demand. Emission of pollutants from the combustion fossil fuel and gradual depletion of fossil fuel encourages the usage of renewable energy sources. Implementation of renewable energy sources with the reinforcement of green energy transforms the fossil fuel-based plant into a hybrid generating plant. The increase in power production …with the increase in electricity demand implicates challenges for economical operation. The proposed algorithm is applied to the DED problem for fossil fuel based and renewable energy system to find economic schedule of generated power among the committed generating units. The proposed optimization algorithm is inspired by the huddling behavior of the emperor penguin. The exploration strategy is enhanced by adapting oppositional based learning. Chaotic mapping is used to maintain a proper balance between exploration and exploitation in the entire search space, which minimizes the cost of generation in the power system. Show more

Keywords: Dynamic economic dispatch (DED), emperor penguin optimization (EPO), chaotic oppositional learning-based emperor penguin optimization (COLEPO), constraints, wind energy, micro grid

DOI: 10.3233/JIFS-201483

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9041-9058, 2021

Authors: Priambodo, Bagus | Ahmad, Azlina | Kadir, Rabiah Abdul

Article Type: Research Article

Abstract: Traffic congestion on a road results in a ripple effect to other neighbouring roads. Previous research revealed existence of spatial correlation on neighbouring roads. Similar traffic patterns with regards to day and time can be seen amongst roads in a neighbouring area. Presently, nonlinear models of neural network are applied on historical data to predict traffic congestion. Even though neural network has successfully modelled complex relationships, more time is needed to train the network. A non-parametric approach, the k-nearest neighbour (K-NN) is another method for forecasting traffic condition which can capture the nonlinear characteristics of traffic flow. An earlier study …has been done to predict traffic flow using K-NN based on connected roads (both downstream and upstream). However, impact of road congestion is not only to connected roads, but also to roads surrounding it. Surrounding roads that are impacted by road congestion are those having ‘high relationship’ with neighbouring roads. Thus, this study aims to predict traffic state using K-NN by determining high relationship roads within neighbouring roads. We determine the highest relationship neighbouring roads by clustering the surrounding roads by combining grey level co-occurrence matrix (GLCM) with k-means. Our experiments showed that prediction of traffic state using K-NN based on high relationship roads using both GLCM and k-means produced better accuracy than using k-means only. Show more

Keywords: Classification algorithm, clustering algorithm, machine learning algorithm, nearest neighbour search, intelligent transportation system

DOI: 10.3233/JIFS-201493

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9059-9072, 2021

Authors: Zhang, Mo | Zhang, Qinghua | Gao, Man

Article Type: Research Article

Abstract: As a new extended model of fuzzy sets, hesitant fuzzy set theory is a useful tool to process uncertain information in decision making problems. The traditional hesitant fuzzy multi-attribute decision making (MADM) can only choose an optimal strategy, which is not suitable for all of the complex scenarios. Typically, in practical application, decision making problems may be more complicated involving three options of acceptance, non-commitment and rejection decisions. Three-way decisions, which divide universe into three disjoint regions by a pair of thresholds, are more efficient to deal with these problems. Therefore, how to utilize three-way decision theory to process hesitant …fuzzy information is an essential issue to be studied. In this paper, from the perspective of hesitant fuzzy distance, a hesitant fuzzy three-way decision model is proposed. First, because hesitant fuzzy element (HFE) is a set of several possible membership degrees, it cannot be compared with thresholds directly. Hence, this paper converts it into the comparison between the distance and the thresholds. Then, to calculate thresholds more reasonably, shadowed set theory is introduced to avoid the subjectivity of threshold acquisition. Furthermore, sequential strategy is adopted to solve the multi-attribute decision making problems. Finally, an example of medical diagnosis and simulation experiments are given to prove the accuracy and efficiency of the proposed hesitant fuzzy three-way decision model. Show more

Keywords: Hesitant fuzzy sets, three-way decisions, shadowed sets, sequential strategy

DOI: 10.3233/JIFS-201524

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9073-9084, 2021

Authors: Liu, Peide | Pan, Qian | Xu, Hongxue

Article Type: Research Article

Abstract: The normal intuitionistic fuzzy number (NIFN), which membership function and non-membership function are expressed by normal fuzzy numbers (NFNs), can better describe the normal distribution phenomenon in the real world, but it cannot deal with the situation where the sum of membership function and non-membership function is greater than 1. In order to make up for this defect, based on the idea of q-rung orthopair fuzzy numbers (q-ROFNs), we put forward the concept of normal q-rung orthopair fuzzy numbers (q-RONFNs), and its remarkable characteristic is that the sum of the qth power of membership function and the qth …power of non-membership function is less than or equal to 1, so it can increase the width of expressing uncertain information for decision makers (DMs). In this paper, firstly, we give the basic definition and operational laws of q-RONFNs, propose two related operators to aggregate evaluation information from DMs, and develop an extended indifference threshold-based attribute ratio analysis (ITARA) method to calculate attribute weights. Then considering the multi-attributive border approximation area comparison (MABAC) method has strong stability, we combine MABAC with q-RONFNs, put forward the q-RONFNs-MABAC method, and give the concrete decision steps. Finally, we apply the q-RONFNs-MABAC method to solve two examples, and prove the effectiveness and practicability of our proposed method through comparative analysis. Show more

Keywords: Normal q-rung orthopair fuzzy numbers, multi-attributive border approximation area comparison, the q-RONFNs-MABAC method, indifference threshold-based attribute ratio analysis

DOI: 10.3233/JIFS-201526

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9085-9111, 2021

Authors: Xu, Tingting | Zhang, Hui | Li, Boquan

Article Type: Research Article

Abstract: In this paper, the concept of 2-tuple probability weight is presented, and on this basis, the technique for order preference by similarity to ideal solution (TOPSIS) method in Pythagorean fuzzy environment is given. First, the definition of 2-tuple probability weight is put forward, and two examples are provided to illustrate that 2-tuple probability weight can effectively prevent the loss of information. Second, the notion of real-value 2-tuple is defined for any two real numbers, and some basic operations, operation properties, and sorting functions are introduced. Finally, a 2-tuple probability weight Euclidean distance is provided, a new Pythagorean fuzzy TOPSIS method …is further proposed, and the flexibility and effectiveness of the proposed methods are illustrated by an example and two comparative analyses. Show more

Keywords: Pythagorean fuzzy set, 2-tuple probability weight, real-value 2-tuple, TOPSIS method

DOI: 10.3233/JIFS-201533

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9113-9126, 2021

Authors: Yan, Zheping | Zhang, Jinzhong | Zeng, Jia | Tang, Jialing

Article Type: Research Article

Abstract: In this paper, a water wave optimization (WWO) algorithm is proposed to solve the autonomous underwater vehicle (AUV) path planning problem to obtain an optimal or near-optimal path in the marine environment. Path planning is a prerequisite for the realization of submarine reconnaissance, surveillance, combat and other underwater tasks. The WWO algorithm based on shallow wave theory is a novel evolutionary algorithm that mimics wave motions containing propagation, refraction and breaking to obtain the global optimization solution. The WWO algorithm not only avoids jumps out of the local optimum and premature convergence but also has a faster convergence speed and …higher calculation accuracy. To verify the effectiveness and feasibility, the WWO algorithm is applied to solve the randomly generated threat areas and generated fixed threat areas. Compared with other algorithms, the WWO algorithm can effectively balance exploration and exploitation to avoid threat areas and reach the intended target with minimum fuel costs. The experimental results demonstrate that the WWO algorithm has better optimization performance and is robust. Show more

Keywords: Water wave optimization (WWO), autonomous underwater vehicle (AUV), path planning, randomly generated threat areas, generated fixed threat areas

DOI: 10.3233/JIFS-201544

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9127-9141, 2021

Authors: Shakeri Aski, Baharak | Toroghi Haghighat, Abolfazl | Mohsenzadeh, Mehran

Article Type: Research Article

Abstract: Using Web services to assess data in a distributed configuration, apart from different hardware and software platforms for employing standard criteria, is practical because of development in the Internet and network infrastructure. Distributed applications can transfer data using web services. Trust is the main criterion to select the appropriate web service. Neuro-fuzzy systems including clustering are applied to assess the trust of single web services. This paper considers nine criteria including quality of service, subjective perspectives, user preference, credibility of raters, objective perspectives, dynamic computing, bootstrapping, independency and security. To obtain a neuro-fuzzy system with high prediction accuracy, the paper …considers eight neuro-fuzzy membership functions (i.e., trapmf, gbellmf, trimf, gaussmf, dsigmf, psigmf, gauss2mf, pimf) using the k-means clustering. Also, to increase the speed and reduce the fuzzy rules, a three-level neuro-fuzzy system (13 neuro-fuzzy) is investigated. The main target of this paper is evaluating the trust of single web services using the nine aforementioned criteria, as web services selection is a main issue which is still absorbing researchers to conduct research works on this field and analyze it. Ultimately, the results show reasonable root mean square error (RMSE) amount, precision value, recall value, and F-score value. In comparison to previous research works, this study obtained the lower amounts of errors and presents the more accurate trust of single web services. Show more

Keywords: Web service, internet service, trust, neuro-fuzzy system, k-means

DOI: 10.3233/JIFS-201560

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9143-9157, 2021

Authors: Ye, Jun | Du, Shigui | Yong, Rui | Zhang, Fangwei

Article Type: Research Article

Abstract: In indeterminate and inconsistent setting, existing simplified neutrosophic indeterminate set (SNIS) can be depicted by the neutrosophic number (NN) functions of the truth, falsity and indeterminacy. Then, the three NN functions in SNIS lack their refined expressions and then the simplified neutrosophic indeterminate decision making (DM) method cannot carry out the multicriteria DM problems with both criteria and sub-criteria in the setting of SNISs. To overcome the flaws, this study first proposes a new notion of a refined simplified neutrosophic indeterminate set (RSNIS), which is described by the refined truth, falsity and indeterminate NN information regarding both elements and sub-elements …in a universe set, as the extension of SNIS. Next, we propose the arccosine and arctangent similarity measures of RSNISs and their multicriteria DM method with various indeterminate risk ranges so as to carry out multicriteria DM problems with weight values of both criteria and sub-criteria in RSNIS setting. Lastly, the proposed DM method is applied to a multicriteria DM example of slope design schemes for an open pit mine to illustrate its application in the indeterminate DM problem with RSNISs. The decision results and comparative analysis indicate the rationality and efficiency of the proposed DM method with different indeterminate risk ranges. Show more

Keywords: Refined simplified neutrosophic indeterminate set, arccosine similarity measure, arctangent similarity measure, multicriteria decision making, Slope design scheme

DOI: 10.3233/JIFS-201571

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9159-9171, 2021

Authors: Gao, Yuxuan | Liang, Haiming | Sun, Bingzhen

Article Type: Research Article

Abstract: With the rapid development of e-commerce, whether network intelligent recommendation can attract customers has become a measure of customer retention on online shopping platforms. In the literature about network intelligent recommendation, there are few studies that consider the difference preference of customers in different time periods. This paper proposes the dynamic network intelligent hybrid recommendation algorithm distinguishing time periods (DIHR), it is a integrated novel model combined with the DEMATEL and TOPSIS method to solved the problem of network intelligent recommendation considering time periods. The proposed method makes use of the DEMATEL method for evaluating the preference relationship of customers …for indexes of merchandises, and adopt the TOPSIS method combined with intuitionistic fuzzy number (IFN) for assessing and ranking the merchandises according to the indexes. We specifically introduce the calculation steps of the proposed method, and then calculate its application in the online shopping platform. Show more

Keywords: DIHR, recommendation algorithm, network intelligent recommendation system, online shopping platform

DOI: 10.3233/JIFS-201579

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9173-9185, 2021

Authors: Jothikumar, C. | Venkataraman, Revathi | Sai Raj, T. | Selvin Paul Peter, J. | Nagamalleswari, T.Y.J.

Article Type: Research Article

Abstract: Wireless sensor network is a wide network that works as a cutting edge model in industrial applications. The sensor application is mostly used for high security systems that provide safety support to the environment. The sensor system senses the physical phenomenon, processes the input signal and communicates with the base station through its neighbors. Energy is the most important criterion to support a live network for long hours. In the proposed system, the EUCOR (Efficient Unequal Clustering and Optimized Routing) protocol uses the objective function to identify the efficient cluster head with variable cluster size. The computation of the objective …function deals with the ant colony approach for minimum energy consumption and the varying size of the cluster in each cycle is calculated based on the competition radius. The system prolongs the lifespan of the nodes by minimizing the utilization of energy in the transmission of packets in the networks when compared with the existing system. Show more

Keywords: Energy optimization, routing, cluster head, wireless sensor network

DOI: 10.3233/JIFS-201607

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9187-9195, 2021

Authors: Rajeswari, A.R. | Kulothungan, K. | Ganapathy, Sannasi | Kannan, Arputharaj

Article Type: Research Article

Abstract: WSN plays a major role in the design of IoT system. In today’s internet era IoT integrates the digital devices, sensing equipment and computing devices for data sensing, gathering and communicate the data to the Base station via the optimal path. WSN, owing to the characteristics such as energy constrained and untrustworthy environment makes them to face many challenges which may affect the performance and QoS of the network. Thus, in WSN based IoT both security and energy efficiency are considered as herculean design challenges and requires important concern for the enhancement of network life time. Hence, to address these …problems in this paper a novel secure energy aware cluster based routing algorithm named Trusted Energy Efficient Fuzzy logic based clustering Algorithm (TEEFCA) has been proposed. This algorithm consists of two major objectives. Firstly, the trustworthy nodes are identified, which may act as candidate nodes for cluster based routing. Secondly, the fuzzy inference system is employed under the two circumstances namely selection of optimal Cluster Leader (CL) and cluster formation process by considering the following three parameters such as (i) node’s Residual Energy level (ii) Cluster Density (iii) Distance Node BS. From, the experiment outcomes implemented using MATLAB it have been proved that TEEFCA shows significant improvement in terms of power conservation, network stability and lifetime when compared to the existing cluster aware routing approaches. Show more

Keywords: Internet of Things (IoT), WSN, energy, trust, clustering and routing

DOI: 10.3233/JIFS-201633

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9197-9211, 2021

Authors: Gu, Tianlong | Liang, Haohong | Bin, Chenzhong | Chang, Liang

Article Type: Research Article

Abstract: How to accurately model user preferences based on historical user behaviour and auxiliary information is of great importance in personalized recommendation tasks. Among all types of auxiliary information, knowledge graphs (KGs) are an emerging type of auxiliary information with nodes and edges that contain rich structural information and semantic information. Many studies prove that incorporating KG into personalized recommendation tasks can effectively improve the performance, rationality and interpretability of recommendations. However, existing methods either explore the independent meta-paths for user-item pairs in KGs or use a graph convolution network on all KGs to obtain embeddings for users and items separately. …Although both types of methods have respective effects, the former cannot fully capture the structural information of user-item pairs in KGs, while the latter ignores the mutual effect between the target user and item during the embedding learning process. To alleviate the shortcomings of these methods, we design a graph convolution-based recommendation model called Combining User-end and Item-end Knowledge Graph Learning (CUIKG) , which aims to capture the relevance between users’ personalized preferences and items by jointly mining the associated attribute information in their respective KG. Specifically, we describe user embedding from a user KG and then introduce user embedding, which contains the user profile into the item KG, to describe item embedding with the method of Graph Convolution Network. Finally, we predict user preference probability for a given item via multilayer perception. CUIKG describes the connection between user-end KG and item-end KG, and mines the structural and semantic information present in KG. Experimental results with two real-world datasets demonstrate the superiority of the proposed method over existing methods. Show more

Keywords: Personalized recommendation, property knowledge graph, graph convolution network

DOI: 10.3233/JIFS-201635

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9213-9225, 2021

Authors: Liu, Shulin

Article Type: Research Article

Abstract: Under the background of the national fitness craze, the demand space for social sports professionals is constantly expanding. However, according to the author’s investigation, the overall situation shows that the number of high-quality social sports professionals in Chinese colleges and universities is relatively small. Among them, the unsound teaching quality evaluation system of social sports major is one of the important reasons affecting the cultivation of high-quality talents, so it is imperative to construct a sound teaching quality evaluation system of social sports major. At the same time, the perfect social physical education teaching quality evaluation system is an important …basis for teachers’ teaching job evaluation and strengthening teachers’ management. And it is frequently considered as a multi-attribute group decision-making (MAGDM) issue. Thus, a novel MAGDM method is needed to tackle it. Depending on the conventional TOPSIS method and intuitionistic fuzzy sets (IFSs), this essay designs a novel intuitive distance based IF-TOPSIS method for teaching quality evaluation of physical education. First of all, a related literature review is conducted. What’s more, some necessary theories related to IFSs are briefly reviewed. In addition, since subjective randomness frequently exists in determining criteria weights, the weights of criteria are decided objectively by utilizing CRITIC method. Afterwards, relying on novel distance measures between IFNs, the conventional TOPSIS method is extended to the intuitionistic fuzzy environment to calculate assessment score of each alternative. Eventually, an application about teaching quality evaluation of physical education and some comparative analysis have been given. The results think that the designed method is useful for teaching quality evaluation of physical education. Show more

Keywords: Multi-attribute group decision-making (MAGDM), intuitionistic fuzzy sets (IFSs), TOPSIS method, CRITIC method, teaching quality evaluation, physical education

DOI: 10.3233/JIFS-201672

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9227-9236, 2021

Authors: Liu, Peide | Khan, Qaisar | Mahmood, Tahir | Khan, Rashid Ali | Khan, Hidayat Ullah

Article Type: Research Article

Abstract: Pythagorean fuzzy set (PyFS) is an extension of various fuzzy concepts, such as fuzzy set (FS), intuitionistic FS, and it is enhanced mathematical gizmo to pact with uncertain and vague information. In this article, some drawbacks in the Dombi operational rules for Pythagorean fuzzy numbers (PyFNs) are examined and some improved Dombi operational laws for PyFNs are developed. We also find out that the value aggregated using the existing Dombi aggregation operators (DAOs) is not a PyFN. Furthermore, we developed two new aggregations, improved existing aggregation operators (AOs) for aggregating Pythagorean fuzzy information (PyFI) and are applied to multiple-attribute decision …making (MADM). To acquire full advantage of power average (PA) operators proposed by Yager, the Pythagorean fuzzy Dombi power average (PyFDPA) operator, the Pythagorean fuzzy Dombi weighted power average (PyFDWPA) operator, Pythagorean fuzzy Dombi power geometric (PyFDPG) operator, Pythagorean fuzzy Dombi weighted geometric (PyFDPWG) operator, improved the existing AOs and their desirable properties are discussed. The foremost qualities of these developed Dombi power aggregation operators is that they purge the cause of discomfited data and are more supple due to general parameter. Additionally, based on these Dombi power AOs, a novel MADM approach is instituted. Finally, a numerical example is given to show the realism and efficacy of the proposed approach and judgment with the existing approaches is also specified. Show more

Keywords: Pythagorean fuzzy set, PA operator, Dombi t-norm and Dombi t-conorm, MADM

DOI: 10.3233/JIFS-201723

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9237-9257, 2021

Authors: Liu, Man | Zhang, Hongjun | Hao, Wenning | Qi, Xiuli | Cheng, Kai | Jin, Dawei | Feng, Xinliang

Article Type: Research Article

Abstract: It is a challenge for existing artificial intelligence algorithms to deal with incomplete information of computer tactical wargames in military research, and one effective method is to take advantage of game replays based on data mining or supervised learning. However, the open source datasets of wargame replays are extremely rare, which obstruct the development of research on computer wargames. In this paper, a data set of wargame replays is opened for predicting algorithm on the condition of incomplete information, to be specific, we propose the dataset processing method for deep learning and an network model for enemy locations predicting. We …first introduce the criteria and methods of data preprocessing, parsing and feature extraction, then the training set and test set for deep learning are predefined. Furthermore, we have designed a newly specific network model for enemy locations predicting, including multi-head input, multi-head output, CNN and GRU layers to deal with the multi-agent and long-term memory problems. The experimental results demonstrate that our method achieves good performance of 84.9% on top-50 accuracy. Finally, we open source the data set and methods on https://github.com/daman043/AAGWS-Wargame-master. Show more

Keywords: Incomplete information, dataset, tactical wargame, locations prediction, deep learning, prediction model

DOI: 10.3233/JIFS-201726

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9259-9275, 2021

Authors: Jia, Heming | Lang, Chunbo

Article Type: Research Article

Abstract: Salp swarm algorithm (SSA) is a meta-heuristic algorithm proposed in recent years, which shows certain advantages in solving some optimization tasks. However, with the increasing difficulty of solving the problem (e.g. multi-modal, high-dimensional), the convergence accuracy and stability of SSA algorithm decrease. In order to overcome the drawbacks, salp swarm algorithm with crossover scheme and Lévy flight (SSACL) is proposed. The crossover scheme and Lévy flight strategy are used to improve the movement patterns of salp leader and followers, respectively. Experiments have been conducted on various test functions, including unimodal, multimodal, and composite functions. The experimental results indicate that the …proposed SSACL algorithm outperforms other advanced algorithms in terms of precision, stability, and efficiency. Furthermore, the Wilcoxon’s rank sum test illustrates the advantages of proposed method in a statistical and meaningful way. Show more

Keywords: Salp swarm algorithm, crossover scheme, Lévy flight, functions optimization

DOI: 10.3233/JIFS-201737

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9277-9288, 2021

Authors: Huang, Jinfang | Jin, Xin | Lee, Shin-Jye | Huang, Shanshan | Jiang, Qian

Article Type: Research Article

Abstract: Since the intuitionistic fuzzy set (IFS) was proposed by Atanassov, many explorations of this particular fuzzy set were conducted. One of the most important areas is the study of similarity and distance between IFSs, which can measure the degree of deviation of objects with uncertain and vague features, and this technique has great value and potential to solve the fuzzy and uncertain problems in the real world. Based on our previous similarity/distance measure model D JJ  (α , β ), a new method is proposed for improving the performance of similarity/distance measure model of IFSs, which is derived from …the sum of the areas of two triangles constructed by the transformed isosceles triangles of two IFSs. A great effort is made to prove the validity of the proposed method by mathematical derivation. In order to further demonstrate the performance of the proposed method, we apply this method to solve some practical problems such as pattern recognition, medical diagnosis, and cluster analysis. In addition, we also list a series of the existing methods which are used to compare with the proposed method to prove the effectiveness and superiority. The experimental results confirm that the performance of the proposed method exceeds most of the existing methods. Show more

Keywords: Intuitionistic fuzzy set, similarity/distance measure, transformed isosceles triangle fuzzy number, decision-making, cluster analysis

DOI: 10.3233/JIFS-201763

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9289-9309, 2021

Authors: Kalsum, Tehmina | Mehmood, Zahid | Kulsoom, Farzana | Chaudhry, Hassan Nazeer | Khan, Amjad Rehman | Rashid, Muhammad | Saba, Tanzila

Article Type: Research Article

Abstract: Facial emotion recognition system (FERS) recognize the person’s emotions based on various image processing stages including feature extraction as one of the major processing steps. In this study, we presented a hybrid approach for recognizing facial expressions by performing the feature level fusion of a local and a global feature descriptor that is classified by a support vector machine (SVM) classifier. Histogram of oriented gradients (HoG) is selected for the extraction of global facial features and local intensity order pattern (LIOP) to extract the local features. As HoG is a shape-based descriptor, with the help of edge information, it can …extract the deformations caused in facial muscles due to changing emotions. On the contrary, LIOP works based on the information of pixels intensity order and is invariant to change in image viewpoint, illumination conditions, JPEG compression, and image blurring as well. Thus both the descriptors proved useful to recognize the emotions effectively in the images captured in both constrained and realistic scenarios. The performance of the proposed model is evaluated based on the lab-constrained datasets including CK+, TFEID, JAFFE as well as on realistic datasets including SFEW, RaF, and FER-2013 dataset. The optimal recognition accuracy of 99.8%, 98.2%, 93.5%, 78.1%, 63.0%, 56.0% achieved respectively for CK+, JAFFE, TFEID, RaF, FER-2013 and SFEW datasets respectively. Show more

Keywords: Facial emotion recognition, histogram-of-oriented-gradients, local intensity order pattern, support vector machine, texture features

DOI: 10.3233/JIFS-201799

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9311-9331, 2021

Authors: Keikha, Abazar

Article Type: Research Article

Abstract: Uncertainty has long been explored as an objective and inalienable reality, and then modeled via different theories such as probability theory, fuzzy sets (FSs) theory, vague sets, etc. Hesitant fuzzy sets (HFSs) as a generalization of FSs, because of their flexibility and capability, extended and applied in many practical problems very soon. However, the above theories cannot meet all the scientific needs of researchers. For example, in some decision-making problems we encounter predetermined definite data, which have inductive uncertainties. In other words, the numbers themselves are crisp in nature, but are associated with varying degrees of satisfaction or fairness from …the perspective of each decision-maker/judge. To this end, in this article, hesitant fuzzy numbers as a generalization of hesitant fuzzy sets will be introduced. Some concepts such as the operation laws, the arithmetic operations, the score function, the variance of hesitant fuzzy numbers, and a way to compare hesitant fuzzy numbers will be proposed. Mean-based aggregation operators of hesitant fuzzy numbers, i.e. hesitant fuzzy weighted arithmetic averaging (HWAA), hesitant fuzzy weighted geometric averaging (HWGA), hesitant fuzzy ordered weighted arithmetic averaging (HOWAA), and hesitant fuzzy ordered weighted geometric averaging (HOWGA) operators have been discussed in this paper, too. These new concepts will be used to model, and solve an uncertain multi-attribute group decision making (MAGDM) problem. The proposed method will be illustrated by a numerical example and the validity of the obtained solution will be checked by test criteria. Show more

Keywords: Hesitant fuzzy numbers, hesitant fuzzy sets, self-assessment, hesitant fuzzy averaging, hesitant fuzzy weighted averaging

DOI: 10.3233/JIFS-201808

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9333-9344, 2021

Authors: Liu, Haiqing | Li, Daoxing | Li, Yuancheng

Article Type: Research Article

Abstract: Reading digits from natural images is a challenging computer vision task central to a variety of emerging applications. However, the increased scalability and complexity of datasets or complex applications bring about inevitable label noise. Because the label noise in the scene digit recognition dataset is sequence-like, most existing methods cannot deal with label noise in scene digit recognition. We propose a novel sequence class-label noise filter called Confident Sequence Learning. Confident Sequence Learning consists of two critical parts: the sequence-like confidence segmentation algorithm and the Confident Learning method. The sequence-like confidence segmentation algorithms slice the sequence-like labels and the sequence-like …predicted probabilities, reorganize them in the form of the independent stochastic process and the white noise process. The Confident Learning method estimates the joint distribution between observed labels and latent labels using the segmented labels and probabilities. The TRDG dataset and SVHN dataset experiments showed that the confident sequence learning could find label errors with high accuracy and significantly improve the VGG-Attn and the TPS-ResNet-Attn model’s performance in the presence of synthetic sequence class-label noise. Show more

Keywords: Scene digit recognition, label noise, confident learning

DOI: 10.3233/JIFS-201825

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9345-9359, 2021

Authors: Iqbal, Naeem | Ahmad, Rashid | Jamil, Faisal | Kim, Do-Hyeun

Article Type: Research Article

Abstract: Quality prediction plays an essential role in the business outcome of the product. Due to the business interest of the concept, it has extensively been studied in the last few years. Advancement in machine learning (ML) techniques and with the advent of robust and sophisticated ML algorithms, it is required to analyze the factors influencing the success of the movies. This paper presents a hybrid features prediction model based on pre-released and social media data features using multiple ML techniques to predict the quality of the pre-released movies for effective business resource planning. This study aims to integrate pre-released and …social media data features to form a hybrid features-based movie quality prediction (MQP) model. The proposed model comprises of two different experimental models; (i) predict movies quality using the original set of features and (ii) develop a subset of features based on principle component analysis technique to predict movies success class. This work employ and implement different ML-based classification models, such as Decision Tree (DT), Support Vector Machines with the linear and quadratic kernel (L-SVM and Q-SVM), Logistic Regression (LR), Bagged Tree (BT) and Boosted Tree (BOT), to predict the quality of the movies. Different performance measures are utilized to evaluate the performance of the proposed ML-based classification models, such as Accuracy (AC), Precision (PR), Recall (RE), and F-Measure (FM). The experimental results reveal that BT and BOT classifiers performed accurately and produced high accuracy compared to other classifiers, such as DT, LR, LSVM, and Q-SVM. The BT and BOT classifiers achieved an accuracy of 90.1% and 89.7%, which shows an efficiency of the proposed MQP model compared to other state-of-art- techniques. The proposed work is also compared with existing prediction models, and experimental results indicate that the proposed MQP model performed slightly better compared to other models. The experimental results will help the movies industry to formulate business resources effectively, such as investment, number of screens, and release date planning, etc. Show more

Keywords: Movie quality prediction, machine learning, data mining, business intelligence, predictive analytics

DOI: 10.3233/JIFS-201844

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9361-9382, 2021

Authors: Chen, Huiping

Article Type: Research Article

Abstract:  This paper aims to put forward a hesitant fuzzy multi-attribute group decision making (MAGDM) method based on the weighted power aggregation operators in social network. From the point of view of social network analysis, decision makers (DMs) are interconnected in the process of MAGDM. Furthermore, the dimension of the obtained hesitant fuzzy element (HFE) by original power operators will be greater with the increasing number of attributes and alternatives and DMs, which will lead to the problem of “intermediate expression swell". This paper combines the order operation laws with the power operators to redefine two novel hesitant fuzzy power aggregation …operators to simplify the involved calculation and explore new operators’ properties. Meanwhile, when two given elements have different number of values, we use the strength of social ties and social influence to develop an algorithm for extending the HFEs objectively. On the other hand, the PageRank algorithm and the deviation method are used to determine DMs’ combined weights. The feasibility of the proposed hesitant fuzzy MAGDM method based on social network is illustrated by the application to the actual issue of decision making and the comparative analysis with the existing method. Show more

Keywords: Group decision making, hesitant fuzzy set, social network, power operator, PageRank algorithm

DOI: 10.3233/JIFS-201859

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9383-9401, 2021

Authors: Jia, Zhifu | Liu, Xinsheng | Zhang, Yu

Article Type: Research Article

Abstract: Uncertain pantograph differential equation (UPDE for short) is a special unbounded uncertain delay differential equation. Stability in measure, stability almost surely and stability in p -th moment for uncertain pantograph differential equation have been investigated, which are not applicable for all situations, for the sake of completeness, this paper mainly gives the concept of stability in distribution, and proves the sufficient condition for uncertain pantograph differential equation being stable in distribution. In addition, the relationships among stability almost surely, stability in measure, stability in p -th moment, and stability in distribution for the uncertain pantograph differential equation are also discussed.

Keywords: uncertainty theory, uncertain pantograph differential equation, stability in distribution, the relationships among stabilities

DOI: 10.3233/JIFS-201864

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9403-9411, 2021

Authors: Bhullar, Amrit Kaur | Kaur, Ranjit | Sondhi, Swati

Article Type: Research Article

Abstract: Today optimization algorithms are widely used in every application to increase quality, quantity and efficiency of making products as well as to minimize the production cost. Most of the techniques applied on different applications try to satisfy more than one parameter of interest in the design problem. In doing so, an objective function based on weighted aggregation has been designed to fulfill multi-objective optimization (MOO). A lot of computational time and energy is wasted in tuning the value of weighting factor in terms of number of trials each having hundreds of iterations to achieve the optimum solution. To reduce such …tedious practice of adjustment of weighting factor with multiple iterations, Fuzzy technique is proposed for auto-tuning of weighting factor in this paper that will benefit the researchers who are working upon optimization of their designed objectives using artificial intelligence techniques. This paper proposes MOO settlement method that does not require complex mathematical equations in order to simplify the weight finding problem of weighted aggregation objective function (WAOF). The results have been compared in terms of time and space efficiency to show the importance of Fuzzy-WAOF (F-WAOF). Further the results taken on Automatic Voltage Regulator (AVR) system for set point tracking, load disturbance, controller effort and modelling errors, prove the superior performance of the proposed method as compared to state of the art techniques. Show more

Keywords: Fuzzy technique, optimization techniques, weighted aggregation objective function (WAOF), weighting factor, automatic voltage regulator (AVR) system

DOI: 10.3233/JIFS-201911

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9413-9436, 2021

Authors: Durairaj, M. | Asha, J. Hirudhaya Mary

Article Type: Research Article

Abstract: Biometric features are used to verify the people identity in the living places like smart apartments. To increase the chance of classification and recognition rate, the recognizing procedure contains various steps such as detection of silhouette from the gait profile, silhouette segmentation, reading features from the silhouette, classification of features and finally recognition of person using its probability value. Person recognition accuracy will be oscillated and declined due to blockage, radiance and posture variance problems. In the proposed work, the gait profile will be formed by capturing the gait of a targeted person in stipulated time to reach the destination. …From the profile the silhouettes are detected using frame difference and segmented from the background using immediate thresholding and features are extracted from the silhouette using gray-level covariance matrix and optimized feature set is formed using PSO. These optimized features are fused, trained and classified using nearest neighbor support vectors. The fuzzy probability method is used for recognizing the person based on the probability value of the authentic and imposter scores. The relationship between the CMS, TPR, TNR and F-rate are calculated for 1 : 1 matcher from the gallery set. The performance of the classifiers are found to be perfect by plotting the DET graph and ROC curve. The proposed fuzzy probability theory is mingled with GLCMPSO and NSFV method for human recognition purpose. The performance of the proposed is proved to be acceptable for recognition with the optimal parameters (Entropy, SSIM, PSNR, CQM) calculation From the work, it is clear that, the rank probability is proportional to the match score value of the silhouette stored in the gallery. Show more

Keywords: Gait cycle, silhouette image, feature detection, feature extraction, feature classification, person recognition using fuzzy probability

DOI: 10.3233/JIFS-201913

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9437-9452, 2021

Authors: Kumar, Rajeev | Singh, Laxman | Tiwari, Rajdev

Article Type: Research Article

Abstract: Path planning for robots plays a vital role to seek the most feasible path due to power requirement, environmental factors and other limitations. The path planning for the autonomous robots is tedious task as the robot needs to locate a suitable path to move between the source and destination points with multifaceted nature. In this paper, we introduced a new technique named modified grey wolf optimization (MGWO) algorithm to solve the path planning problem for multi-robots. MGWO is modified version of conventional grey wolf optimization (GWO) that belongs to the category of metaheuristic algorithms. This has gained wide popularity for …an optimization of different parameters in the discrete search space to solve various problems. The prime goal of the proposed methodology is to determine the optimal path while maintaining a sufficient distance from other objects and moving robots. In MGWO method, omega wolves are treated equally as those of delta wolves in exploration process that helps in escalating the convergence speed and minimizing the execution time. The simulation results show that MGWO gives satisfactory performance than other state of art methods for path planning of multiple mobile robots. The performance of the proposed method is compared with the standard evolutionary algorithms viz., Particle Swarm Optimization (PSO), Intelligent BAT Algorithm (IBA), Grey Wolf Optimization (GWO), and Variable Weight Grey Wolf Optimization (VW-GWO) and yielded better results than all of these. Show more

Keywords: Meta-heuristic, particle swarm optimization, intelligent BAT algorithm, grey wolf optimization (GWO), modified grey wolf optimization (MGWO), variable weight grey wolf optimization (VW-GWO)

DOI: 10.3233/JIFS-201926

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9453-9470, 2021

Authors: Jin, Yilun | Liu, Yanan | Zhang, Wenyu | Zhang, Shuai | Lou, Yu

Article Type: Research Article

Abstract: With the advancement of machine learning, credit scoring can be performed better. As one of the widely recognized machine learning methods, ensemble learning has demonstrated significant improvements in the predictive accuracy over individual machine learning models for credit scoring. This study proposes a novel multi-stage ensemble model with multiple K-means-based selective undersampling for credit scoring. First, a new multiple K-means-based undersampling method is proposed to deal with the imbalanced data. Then, a new selective sampling mechanism is proposed to select the better-performing base classifiers adaptively. Finally, a new feature-enhanced stacking method is proposed to construct an effective ensemble model by …composing the shortlisted base classifiers. In the experiments, four datasets with four evaluation indicators are used to evaluate the performance of the proposed model, and the experimental results prove the superiority of the proposed model over other benchmark models. Show more

Keywords: Credit scoring, ensemble model, imbalanced learning, K-means, stacking

DOI: 10.3233/JIFS-201954

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9471-9484, 2021

Authors: Tang, Han | Li, Wenfei

Article Type: Research Article

Abstract: Interest rate, stock and option are all important parts of finance. This paper applies uncertain differential equation to the study of the evolution of interest rate and stock price separately. Based on actual observations, we estimate the parameters in uncertain differential equation with the method of moments. Using the introduced interest rate and stock models, we price European options and compare the results with actual observations. Finally, a paradox of the stochastic financial model is stated.

Keywords: Uncertain differential equation, geometric Liu process, uncertain exponential Ornstein-Uhlenbeck process, parameter estimation, European option pricing

DOI: 10.3233/JIFS-201955

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9485-9492, 2021

Authors: Nadeem, Asim | Kashif, Agha | Zafar, Sohail | Zahid, Zohaib

Article Type: Research Article

Abstract: The partition dimension is a variant of metric dimension in graphs. It has arising applications in the fields of network designing, robot navigation, pattern recognition and image processing. Let G  (V  (G ) , E  (G )) be a connected graph and Γ  = {P 1 , P 2 , …, P m } be an ordered m -partition of V  (G ). The partition representation of vertex v with respect to Γ is an m -vector r  (v |Γ ) = (d  (v , P 1 ) , d  (v , P 2 ) , …, d  (v , P m …)), where d  (v , P ) = min  {d  (v , x ) |x  ∈ P } is the distance between v and P . If the m -vectors r  (v |Γ ) differ in at least 2 positions for all v  ∈ V  (G ), then the m -partition is called a 2-partition generator of G . A 2-partition generator of G with minimum cardinality is called a 2-partition basis of G and its cardinality is known as the 2-partition dimension of G . Circulant graphs outperform other network topologies due to their low message delay, high connectivity and survivability, therefore are widely used in telecommunication networks, computer networks, parallel processing systems and social networks. In this paper, we computed partition dimension of circulant graphs C n  (1, 2) for n  ≡ 2 (mod 4), n  ≥ 18 and hence corrected the result given by Salman et al. [Acta Math. Sin. Engl. Ser. 2012, 28, 1851-1864]. We further computed the 2-partition dimension of C n  (1, 2) for n  ≥ 6. Show more

Keywords: Network topology design, Circulant graphs, partition dimension, k-partition dimension

DOI: 10.3233/JIFS-201982

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9493-9503, 2021

Authors: Wahid, Khola | Das, Angsuman | Rani, Anam | Amanat, Saira | Imran, Muhammad | Ali, Usman

Article Type: Research Article

Abstract: There are several approaches to lower the complexity of huge networks. One of the key notions is that of twin nodes, exhibiting the same connection pattern to the rest of the network. We extend this idea by defining a twin preserving spanning subgraph (TPS-subgraph) of a simple graph as a tool to compute certain graph related invariants which are preserved by the subgraph. We discuss how these subgraphs preserve some distance based parameters of the simple graph. We introduce a sub-skeleton graph on a vector space and examine its basic properties. The sub-skeleton graph is a TPS-subgraph of the non-zero …component graph defined over a vector space. We prove that some parameters like the metric-dimension are preserved by the sub-skeleton graph. Show more

Keywords: Basis, graph, independent set, maximal clique, metric-dimension, twins, 05C12, 05C35

DOI: 10.3233/JIFS-201989

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9505-9513, 2021

Authors: Zeng, Qingtian | Zhao, Xishi | Hu, Xiaohui | Duan, Hua | Zhao, Zhongying | Li, Chao

Article Type: Research Article

Abstract: Word embeddings have been successfully applied in many natural language processing tasks due to its their effectiveness. However, the state-of-the-art algorithms for learning word representations from large amounts of text documents ignore emotional information, which is a significant research problem that must be addressed. To solve the above problem, we propose an emotional word embedding (EWE ) model for sentiment analysis in this paper. This method first applies pre-trained word vectors to represent document features using two different linear weighting methods. Then, the resulting document vectors are input to a classification model and used to train a text sentiment classifier, …which is based on a neural network. In this way, the emotional polarity of the text is propagated into the word vectors. The experimental results on three kinds of real-world data sets demonstrate that the proposed EWE model achieves superior performances on text sentiment prediction, text similarity calculation, and word emotional expression tasks compared to other state-of-the-art models. Show more

Keywords: Sentiment analysis, word embedding, classification, representation learning

DOI: 10.3233/JIFS-201993

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9515-9527, 2021

Authors: Emeç, Şeyma | Akkaya, Gökay

Article Type: Research Article

Abstract: Energy consumption increases due to technological developments, urbanization, industrialization and population. The fact that the constantly increasing energy demand is not exactly known is an important issue for countries. In addition, due to changing climate conditions, the amount of emission emitted and energy produced from energy sources are also not quite known. Therefore, determining the energy demand, protecting the environment, and minimizing the energy cost by using resources effectively has become one of the most important problems of countries. In this context, the present study developed a fuzzy optimal renewable energy model (F-OREM) to solve the energy problem involving fuzzy …parameters. Fuzzy linear programming (FLP) models provide the best decision by producing faster and more flexible solutions compared to classical linear programming (CLP) models in situations where there are uncertainties and a lack of information. The purpose of the developed model was to minimize the cost of generating electrical energy from different energy sources in an uncertain environment under potential, demand, emission and efficiency constraints. The developed F-OREM was operated using CPLEX decoder in the GAMS 24.2.3 package program and using the particle swarm optimization (PSO) for ∝ different values between 0-1. The results showed that the results of the metaheuristic method and the results of the GAMS package program were the same, and the results were consistent. According to the results obtained, the emission level at which the objective function was minimum (when ∝=1) was at the lowest level. In this case, the total emitted amount was 1,06125E+14 g-CO2/kWh. In this context, the developed model can be applied using metaheuristic or heuristic methods for larger test cases with thousands of variables. This study contributed to the practicality of FLP by offering decision-makers a wider solution area than the CLP approach. Show more

Keywords: Energy economics, energy policy, fuzzy programming, mathematical model, optimization

DOI: 10.3233/JIFS-201994

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9529-9542, 2021

Authors: Liu, Peide | Wang, Dongyang | Zhang, Hui | Yan, Liang | Li, Ying | Rong, Lili

Article Type: Research Article

Abstract: T-spherical fuzzy numbers (FNs), which add an abstinence degree based on membership and non-membership degrees, can express neutral information conveniently and have a considerable large range of information expression. The normal FNs (NFNs) are very available to characterize normal distribution phenomenon widely existing in social life. In this paper, we first define the normal T-SFNs (NT-SFNs) which can combine the advantages of T-SFNs and NFNs. Then, we define their operational laws, score value, and accuracy value. By considering the interrelationship among multi-input parameters, we propose the Maclaurin symmetric mean operator with NT-SFNs (NT-SFMSM) and its weighted form (NT-SFWMSM). Furthermore, we …study some characteristics and special cases of them. Based on the NT-SFWMSM operator, we put forward a novel multi-attribute decision-making (MADM) approach. Finally, some numerical examples are conducted to prove that the proposed approach is valid and superior to some other existing methods. Show more

Keywords: MADM, normal T-spherical fuzzy numbers, normal distribution

DOI: 10.3233/JIFS-202000

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9543-9565, 2021

Authors: Tak, Nihat | Egrioglu, Erol | Bas, Eren | Yolcu, Ufuk

Article Type: Research Article

Abstract: Intuitionistic meta fuzzy forecast combination functions are introduced in the paper. There are two challenges in the forecast combination literature, determining the optimum weights and the methods to combine. Although there are a few studies on determining the methods, there are numerous studies on determining the optimum weights of the forecasting methods. In this sense, the questions like “What methods should we choose in the combination?” and “What combination function or the weights should we choose for the methods” are handled in the proposed method. Thus, the first two contributions that the paper aims to propose are to obtain the …optimum weights and the proper forecasting methods in combination functions by employing meta fuzzy functions (MFFs). MFFs are recently introduced for aggregating different methods on a specific topic. Although meta-analysis aims to combine the findings of different primary studies, MFFs aim to aggregate different methods based on their performances on a specific topic. Thus, forecasting is selected as the specific topic to propose a novel forecast combination approach inspired by MFFs in this study. Another contribution of the paper is to improve the performance of MFFs by employing intuitionistic fuzzy c-means. 14 meteorological datasets are used to evaluate the performance of the proposed method. Results showed that the proposed method can be a handy tool for dealing with forecasting problems. The outstanding performance of the proposed method is verified in terms of RMSE and MAPE. Show more

Keywords: Forecast combination, meta-analysis, intuitionistic fuzzy c-means, meta fuzzy functions, meteorology

DOI: 10.3233/JIFS-202021

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9567-9581, 2021

Authors: Li, Chenliang | Yu, Xiaobing

Article Type: Research Article

Abstract: Communities are the fundamental units of society, and community-based disaster management is the foundation of societal disaster management systems. It is important to implement disaster prevention and mobilize all residents in the community to participate in preparedness activities. However, people’s attitudes and understanding of these issues are often ambiguous because meteorological disaster prevention and mitigation (MDPM) is complex. A hybrid model based on probabilistic term sets (PLTSs) and PROMETHEE method is put forward to solve this problem. To solve the problem from the view of big data, the experimental data are from Baidu’s disaster prevention and mitigation questionnaires. The data …of these questionnaires are aggregated through PLTSs. Then, the PROMETHEE method is used to learn about the public’s understanding of community meteorological disaster prevention and mitigation (CMDPM) information and their willingness to participate in activities. The results indicate that communities in East, Northwest, Southwest, and North China have a higher willingness to join volunteer services. The proposed model makes it more convenient for decision-makers (DMs) to describe problems by PLTSs and is more appropriate for individuals’ understanding and communication. Show more

Keywords: Meteorological disaster prevention and mitigation, PROMETHEE method, community-based disaster management

DOI: 10.3233/JIFS-202026

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9583-9595, 2021

Authors: Jiang, Kui | Shang, Yujuan | Wang, Lei | Zhang, Zheqing | Zhou, Siwei | Dong, Jiancheng | Wu, Huiqun

Article Type: Research Article

Abstract: This study aims to propose a framework for developing a sharable predictive model of diabetic nephropathy (DN) to improve the clinical efficiency of automatic DN detection in data intensive clinical scenario. Different classifiers have been developed for early detection, while the heterogeneity of data makes meaningful use of such developed models difficult. Decision tree (DT) and random forest (RF) were adopted as training classifiers in de-identified electronic medical record dataset from 6,745 patients with diabetes. After model construction, the obtained classification rules from classifier were coded in a standard PMML file. A total of 39 clinical features from 2159 labeled …patients were included as risk factors in DN prediction after data preprocessing. The mean testing accuracy of the DT classifier was 0.8, which was consistent to that of the RF classifier (0.823). The DT classifier was choose to recode as a set of operable rules in PMML file that could be transferred and shared, which indicates the proposed framework of constructing a sharable prediction model via PMML is feasible and will promote the interoperability of trained classifiers among different institutions, thus achieving meaningful use of clinical decision making. This study will be applied to multiple sites to further verify feasibility. Show more

Keywords: Meaningful use, prediction model, diabetic nephropathy, real world data

DOI: 10.3233/JIFS-202030

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9597-9608, 2021

Authors: Chen, Xiangtang | Sun, Bingzhen | Zhang, Xinrui | Qi, Chang | Chu, Xiaoli | Wang, Ting | Huang, Yantai

Article Type: Research Article

Abstract: Linguistic variable is an effective method of representation the preferences of a decision-maker for inaccuracy available information in decision making under uncertainty. This article investigates a multiple attribute ranking decision making problem with linguistic preference by using linguistic value soft rough set. Firstly, we present the definition of linguistic value fuzzy set by introducing the concept of linguistic variable into the original Zadeh’s fuzzy set. We then define the concept of linguistic value soft set and the pseudo linguistic value soft set over the alternative set and parameter set of discourse. Moreover, we investigate the basic operators and the mathematical …properties of the linguistic value soft set. Subsequently, we establish the rough approximation of an uncertainty concept with linguistic value over the object set and parameter set, i.e., the linguistic value soft rough set model. Meanwhile, we discuss several deformations of the linguistic value soft rough lower and upper approximations as well as some fundamental properties of the linguistic value soft approximation operators. With reference on the exploring of the fundamental of linguistic value soft rough set, we construct a new method for handling with the multiple attribute ranking decision making problems with linguistic information by combining the proposed soft rough set and the VIKOR method. Then, we give the detailed decision procedure and steps for the established decision approach. At last, an extensive numerical example is further conducted to illustrate the process of the decision making principle and the results are satisfactory. The main contribution of this paper is twofold. One is to provide a new model of granular computing by infusion the soft set and rough set theory with linguistic valued information. Another is to try making a new way to handle multiple attribute decision making problems based on linguistic value soft rough set and the VIKOR method. Show more

Keywords: Rough set, Soft set, Linguistic variable, Linguistic value soft approximation space, VIKOR method

DOI: 10.3233/JIFS-202085

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9609-9626, 2021

Authors: Ansar, Wazib | Goswami, Saptarsi | Chakrabarti, Amlan | Chakraborty, Basabi

Article Type: Research Article

Abstract: Aspect-Based Sentiment Analysis (ABSA) has become a trending research domain due to its ability to transform lives as well as the technical challenges involved in it. In this paper, a unique set of rules has been formulated to extract aspect-opinion phrases. It helps to reduce the average sentence length by 84% and the complexity of the text by 50%. A modified rank-based version of Term-Frequency - Inverse-Document-Frequency (TF-IDF) has been proposed to identify significant aspects. An innovative word representation technique has been applied for aspect categorization which identifies both local as well as global context of a word. For sentiment …classification, pre-trained Bidirectional Encoder Representations from Transformers (BERT) has been applied as it helps to capture long-term dependencies and reduce the overhead of training the model from scratch. However, BERT has drawbacks like quadratic drop in efficiency with an increase in sequence length which is limited to 512 tokens. The proposed methodology mitigates these drawbacks of a typical BERT classifier accompanied by a rise in efficiency along with an improvement of 8% in its accuracy. Furthermore, it yields enhanced performance and efficiency compared to other state-of-the-art methods. The assertions have been established through extensive analysis upon movie reviews and Sentihood data-sets. Show more

Keywords: Aspect-based sentiment analysis, aspect extraction, BERT, TF-IDF, word embedding

DOI: 10.3233/JIFS-202140

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9627-9644, 2021

Authors: Pirozmand, Poria | Kalantari, Kimia Rezaei | Ebrahimnejad, Ali | Motameni, Homayun

Article Type: Research Article

Abstract: Many methods have been presented in recent years for identifying the quality of agricultural products using machine vision that due to the huge amount of redundant information and noisy data of images of products, the retrieval accuracy and speed of such methods were not much acceptable. All of them try to provide approaches to extract efficient features and determine optimal methods to measure similarity between images. One of the basic problems of these methods is determination of desirable features of the user as well as using an appropriate similarity measure. This study tries to recognize the importance of each feature …according to user’s opinion in every feedback stage through using weighted feature vector, rough theory and fuzzy logic for identifying important features and finding a higher accuracy in retrieval result. The proposed method is compared with fuzzy color histogram, combined approach and fuzzy neighborhood entropy characterized by color location. The simulation results indicate that the proposed method has higher applicability in image marketing compared to the existing methods. Show more

Keywords: Quality evaluation, machine vision, rough theory, fuzzy logic, image processing

DOI: 10.3233/JIFS-202147

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9645-9654, 2021

Authors: Zhang, Pengdan | Liu, Qing | Kang, Bingyi

Article Type: Research Article

Abstract: Multi-attribute decision-making (MADM) is an important part of modern decision-making science. Fuzzy Analytic Hierarchy Process (Fuzzy AHP) is a popular model to deal with the issue of MADM for its flexible and effective advantages. However, The traditional Fuzzy AHP with some limitations does not consider the preference (attitude) of decision makers (DMs). In addition, some ideas of combining Ordered Weighted Average (OWA) and Fuzzy AHP don’t investigated the MADM well. Some programs are only applicable to a few examples, and more general cases do not result in effective decision making. Considering these shortcomings, an OWA-Fuzzy AHP decision model using OWA …weights and Fuzzy AHP is proposed in this paper. Our contribution is that the proposed method can handle situations where the degree of fuzzy synthesis is not intersected. Moreover, the loss of information can be reduced in the process of applying the proposed method, so that the decision result is more reasonable than the previous methods. Several examples and comparative experimental simulation are given to illustrate the effectiveness and superiority of the proposed model. Show more

Keywords: Fuzzy analytic hierarchy process(Fuzzy AHP), ordered weighted average (OWA), analytic hierarchy process (AHP), uncertain preferences, multi-attribute decision-making (MADM)

DOI: 10.3233/JIFS-202168

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9655-9668, 2021

Authors: Shi, Honghua | Ni, Yaodong

Article Type: Research Article

Abstract: Today’s supply chains have a greater likelihood of disruption risks than ever before. Sometimes, a lengthy recovery period is needed for supply chains to return to regular operation after being disrupted. During the recovery time window, how to increase the performance of supply chains is not sufficiently studied. Furthermore, the works considering parameter uncertainty arising from the lack of historical data are also limited. To address these problems, we formulate the recovery scheduling of supply chains under major disruption as mixed-integer linear programming models. In the presented models, outsourcing strategy and capacity expansion strategy are introduced to increase the service …level of the supply chain after the disruption. The effects of disruption risks on supply chain performance are quantified using uncertainty theory in the absence of historical data. A set of computational examples illustrate that cost may increase markedly when more facilities are disrupted simultaneously. Thus, decision-makers have to pay close attention to supply chain disruption management and plan for disruption in advance. Moreover, the results suggest that outsourcing strategy is more useful to reduce cost when a higher service level is required. Show more

Keywords: Supply chain, facility disruptions, recovery strategies, uncertainty

DOI: 10.3233/JIFS-202176

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9669-9686, 2021

Authors: Bao, Jun

Article Type: Research Article

Abstract: The dual generalized Bonferroni mean (DGBM) operator is a meaningful decision-making tool which can consider the relationship between any numbers of being fused arguments and has been applied to many MAGDM domains in past few years. The intuitionistic fuzzy sets (IFSs), which is characterized by the functions of membership degree and non-membership degree, has been investigated by numerous scholars. In this manuscript, combine the DGBM operator and IFSs, the major contribution and objective of the work is to develop two new aggregation operators: the dual generalized intuitionistic fuzzy BM (DGIFBM) operator and the dual generalized intuitionistic fuzzy weighted BM (DGIFWBM) …operator. The last, we give an application example for evaluating the green technological innovation ability of the enterprises and some comparative analysis to testify the effective and scientific of our developed methods. Show more

Keywords: Multiple attribute group decision making (MAGDM), intuitionistic fuzzy sets (IFSs), DGBM operator, DGIFBM operator, DGIFWBM operator, green technological innovation ability of the enterprises

DOI: 10.3233/JIFS-202194

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9687-9707, 2021

Authors: Xu, Di | Wang, Zhili

Article Type: Research Article

Abstract: This paper proposes a better semi-supervised semantic segmentation network using an improved generative adversarial network. It is important for the discriminator on the pixel level to know whether it correctly distinguishes the predicted probability map. However, currently there is no correlation between the actual credibility and the confidence map generated by the pixel-level discriminator. We study this problem and a new network is proposed, which includes one generator and two discriminators. One of the discriminators can output more reliable confidence maps on the pixel level and the other is trained to generate the probability on the image level, which is …used as the dynamic threshold in the semi-supervised module instead of being set manually. In addition, the trusted region shared by the two discriminators is used to provide the semi-supervised reference. Through experiments on the PASCAL VOC 2012 and Cityscapes datasets, the proposed network brings better gains, proving the effectiveness of the network. Show more

Keywords: Semi-supervise semantic segmentation, generative adversarial network, confidence map

DOI: 10.3233/JIFS-202220

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9709-9719, 2021

Authors: Ayyub, Kashif | Iqbal, Saqib | Nisar, Muhammad Wasif | Ahmad, Saima Gulzar | Munir, Ehsan Ullah

Article Type: Research Article

Abstract:  Sentiment analysis is the field that analyzes sentiments, and opinions of people about entities such as products, businesses, and events. As opinions influence the people’s behaviors, it has numerous applications in real life such as marketing, politics, social media etc. Stance detection is the sub-field of sentiment analysis. The stance classification aims to automatically identify from the source text, whether the source is in favor, neutral, or opposed to the target. This research study proposed a framework to explore the performance of the conventional (NB, DT, SVM), ensemble learning (RF, AdaBoost) and deep learning-based (DBN, CNN-LSTM, and RNN) machine learning …techniques. The proposed method is feature centric and extracted the (sentiment, content, tweet specific and part-of-speech ) features from both datasets of SemEval2016 and SemEval2017. The proposed study has also explored the role of deep features such as GloVe and Word2Vec for stance classification which has not received attention yet for stance detection. Some base line features such as Bag of words, N-gram, TF-IDF are also extracted from both datasets to compare the proposed features along with deep features. The proposed features are ranked using feature ranking methods such as (information gain, gain ration and relief-f). Further, the results are evaluated using standard performance evaluation measures for stance classification with existing studies. The calculated results show that the proposed feature sets including sentiment, (part-of-speech, content , and tweet specific) are helpful for stance classification when applied with SVM and GloVe a deep feature has given the best results when applied with deep learning method RNN. Show more

Keywords: Stance classification, deep learning, deep features, sentiment analysis, content based

DOI: 10.3233/JIFS-202269

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9721-9740, 2021

Authors: Xu, Junxiang | Guo, Jingni | Sun, Yongdong | Tang, Qiuyu | Zhang, Jin

Article Type: Research Article

Abstract: We not only firstly applied the theory of hub-and-spoke network to the field of integrated transportation network planning, but also combined our proposed method with Sichuan-Tibet railway, one of super large projects in China, to discuss the optimization and the layout of hub-and-spoke integrated transportation network along the Sichuan-Tibet railway after it is put into operation in the future and put forward some directional policy recommendations. In our study, we have made clear the topological structure of the multi hub and single allocation hybrid hub-and-spoke integrated transportation network in the passenger transportation corridors, established the integer programming model aiming at …the minimum generalized travel cost in the network, and we designed the simulated annealing algorithm to solve this problem. In the empirical study, we find that if 5 nodes are selected as hub nodes in hub-and-spoke integrated transportation network, the generalized cost of network travel will be minimized and these specific location of 5 hub nodes can be determined by the selecting principle of hub nodes location, which we proposed in our study. The simulated annealing algorithm can help us to find the connection relationship between nodes. Then we can achieve three types of hub-and-spoke integrated transportation network layout patterns with railway, highway and aviation as the hub nodes. Though further comparative analysis, we find that it is more feasible to choose the integrated transportation network with railway nodes as the hub in transportation organization. Based on this understanding, we put forward policy recommendations on transportation organization to support high-quality planning and operation of integrated transportation network to Sichuan Tibet region in China in the future. Show more

Keywords: Sichuan-Tibet railway, comprehensive transportation network, multiple hubs and single allocation, simulated annealing algorithm

DOI: 10.3233/JIFS-202276

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9741-9763, 2021

Authors: Liu, Fang | Liu, Yi | Abdullah, Saleem

Article Type: Research Article

Abstract: Based on decision theory rough sets (DTRSs), three-way decisions (TWDs) provide a risk decision method for solving multi-attribute decision making (MADM) problems. The loss function matrix of DTRS is the basis of this method. In order to better solve the uncertainty and ambiguity of the decision problem, we introduce the q-rung orthopair fuzzy numbers (q-ROFNs) into the loss function. Firstly, we introduce concepts of q-rung orthopair fuzzy β -covering (q-ROF β -covering) and q-rung orthopair fuzzy β -neighborhood (q-ROF β -neighborhood). We combine covering-based q-rung orthopair fuzzy rough set (Cq-ROFRS) with the loss function matrix of DTRS in the q-rung …orthopair fuzzy environment. Secondly, we propose a new model of q-ROF β -covering DTRSs (q-ROFCDTRSs) and elaborate its relevant properties. Then, by using membership and non-membership degrees of q-ROFNs, five methods for solving expected losses based on q-ROFNs are given and corresponding TWDs are also derived. On this basis, we present an algorithm based on q-ROFCDTRSs for MADM. Then, the feasibility of these five methods in solving the MADM problems is verified by an example. Finally, the sensitivity of each parameter and the stability and effectiveness of these five methods are compared and analyzed. Show more

Keywords: Covering-based q-rung orthopair fuzzy rough sets, q-ROF β-covering decision-theoretic rough sets, q-ROF β-neighborhood, MADM, DTRSs

DOI: 10.3233/JIFS-202291

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9765-9785, 2021

Authors: YE, Lv | Yang, Yue | Zeng, Jian-Xu

Article Type: Research Article

Abstract: The existing recommender system provides personalized recommendation service for users in online shopping, entertainment, and other activities. In order to improve the probability of users accepting the system’s recommendation service, compared with the traditional recommender system, the interpretable recommender system will give the recommendation reasons and results at the same time. In this paper, an interpretable recommendation model based on XGBoost tree is proposed to obtain comprehensible and effective cross features from side information. The results are input into the embedded model based on attention mechanism to capture the invisible interaction among user IDs, item IDs and cross features. The …captured interactions are used to predict the match score between the user and the recommended item. Cross-feature attention score is used to generate different recommendation reasons for different user-items.Experimental results show that the proposed algorithm can guarantee the quality of recommendation. The transparency and readability of the recommendation process has been improved by providing reference reasons. This method can help users better understand the recommendation behavior of the system and has certain enlightenment to help the recommender system become more personalized and intelligent. Show more

Keywords: Intelligent recommendation, interpretability, XGBoost, attention mechanism, cross feature

DOI: 10.3233/JIFS-202308

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9787-9798, 2021

Authors: Ahmed, Dliouah | Dai, Binxiang

Article Type: Research Article

Abstract: In this paper, we give a new notion of the picture m-polar fuzzy sets (Pm-PFSs) (i.e, combination between the picture fuzzy sets (PFSs) and the m-polar fuzzy sets (m-PFSs)) and study several of the structure operations including subset, equal, union, intersection, and complement. After that, the basic definitions, theorems, and examples on Pm-PFSs are explained. Also, the certain distance between two Pm-PFSs and a novel similarity measure for Pm-PFSs based on distances are defined. MCDM is animated for Pm-PFS data that take into account the distances for the best alternative (solution) by proposed an application of similarity measure for Pm-PFSs …in decision-making. Finally, we construct a new methodology to extend the TOPSIS to Pm-PFS in which capable of different objects recognizing belonging to the same family and illustrate its applicability via a numerical example. Show more

Keywords: Picture m-polar fuzzy set, distance measure, similarity measure, decision-making, multi-expert TOPSIS technique

DOI: 10.3233/JIFS-202309

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9799-9814, 2021

Authors: Akram, Muhammad | Shahzadi, Gulfam | Butt, Muhammad Arif | Karaaslan, Faruk

Article Type: Research Article

Abstract: Soft set (S f S ) theory is a basic tool to handle vague information with parameterized study during the process as compared to fuzzy as well as q -rung orthopair fuzzy theory. This research article is devoted to establish some general aggregation operators (AOs), based on Yager’s norm operations, to cumulate the q -rung orthopair fuzzy soft data in decision making environments. In this article, the valuable properties of q -rung orthopair fuzzy soft set (q  - ROFS f S ) are merged with the Yager operator to propose four new operators, namely, q -rung orthopair fuzzy soft …Yager weighted average (q  - ROFS f YWA ), q -rung orthopair fuzzy soft Yager ordered weighted average (q  - ROFS f YOWA ), q -rung orthopair fuzzy soft Yager weighted geometric (q  - ROFS f YWG ) and q -rung orthopair fuzzy soft Yager ordered weighted geometric (q  - ROFS f YOWG ) operators. The dominant properties of proposed operators are elaborated. To emphasize the importance of proposed operators, a multi-attribute group decision making (MAGDM) strategy is presented along with an application in medical diagnosis. The comparative study shows superiorities of the proposed operators and limitations of the existing operators. The comparison with Pythagorean fuzzy TOPSIS (PF-TOSIS) method shows that PF-TOPSIS method cannot deal with data involving parametric study but developed operators have the ability to deal with decision making problems using parameterized information. Show more

Keywords: q-rung orthopair fuzzy soft numbers, Yager operators, aggregation operators, TOPSIS method

DOI: 10.3233/JIFS-202336

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9815-9830, 2021

Authors: Zhao, Fuqiang | Zhu, Zhengyu | Han, Ping

Article Type: Research Article

Abstract: To measure semantic similarity between words, a novel model DFRVec that encodes multiple semantic information of a word in WordNet into a vector space is presented in this paper. Firstly, three different sub-models are proposed: 1) DefVec: encoding the definitions of a word in WordNet; 2) FormVec: encoding the part-of-speech (POS) of a word in WordNet; 3) RelVec: encoding the relations of a word in WordNet. Then by combining the three sub-models with an existing word embedding, the new model for generating the vector of a word is proposed. Finally, based on DFRVec and the path information in WordNet, a …new method DFRVec+Path to measure semantic similarity between words is presented. The experiments on ten benchmark datasets show that DFRVec+Path can outperform many existing methods on semantic similarity measurement. Show more

Keywords: Semantic similarity, WordNet, word embedding, POS, synset

DOI: 10.3233/JIFS-202337

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9831-9842, 2021

Authors: Bai, Luyi | Li, Nan | Liu, Lishuang | Hao, Xuesong

Article Type: Research Article

Abstract: With the rapid development of the environmental, meteorological and marine data management, fuzzy spatiotemporal data has received considerable attention. Even though some achievements in querying aspect have been made, there are still some unsolved problems. Semantic and structural heterogeneity may exist among different data sources, which will lead to incomplete results. In addition, there are ambiguous query intentions and conditions when the user queries the data. This paper proposes a fuzzy spatiotemporal data semantic model. Based on this model, the RDF local semantic models are converted into a RDF global semantic model after mapping relational data and XML data to …RDF local semantic models. The existing methods mainly convert relational data to RDF Schema directly. But our approach converts relational data to XML Schema and then converts it to RDF, which utilizes the semi-structured feature of XML schema to solve the structural heterogeneity between different data sources. The integration process enables us to perform global queries against different data sources. In the proposed query algorithms, the query conditions inputted are converted into exact queries before the results are returned. Finally, this paper has carried out extensive experiments, calculated the recall , precision and F-Score of the experimental results, and compared with other state-of-the-art query methods. It shows the importance of the data integration method and the effectiveness of the query method proposed in this paper. Show more

Keywords: Data integration, fuzzy query, fuzzy spatiotemporal data, RDF semantic model

DOI: 10.3233/JIFS-202357

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9843-9854, 2021

Authors: Guo, Shunsheng | Gao, Yuji | Guo, Jun | Yang, Zhijie | Du, Baigang | Li, Yibing

Article Type: Research Article

Abstract: With the aggravation of market competition, strategic supplier is becoming more and more critical for the success of manufacturing enterprises. Suppler selection, being the critical and foremost activity must ensure that selected suppliers are capable of supporting the long-term development of organizations. Hence, strategic supplier selection must be restructures considering the long-term relationships and prospects for sustainable cooperation. This paper proposes a novel multi-stage multi-attribute group decision making method under an interval-valued q-rung orthopair fuzzy linguistic set (IVq-ROFLS) environment considering the decision makers’ (DMs) psychological state in the group decision-making process. First, the initial comprehensive fuzzy evaluations of DMs are …represented as IVq-ROFLS. Subsequently, two new operators are proposed for aggregating different stages and DMs’ preferences respectively by extending generalized weighted averaging (GWA) to IVq-ROFLS context. Later, a new hamming distance based linear programming method based on entropy measure and score function is introduced to evaluate the unknown criteria weights. Additionally, the Euclidean distance is employed to compute the gain and loss matrix, and objects are prioritized by extending the popular Prospect theory (PT) method to the IVq-ROFLS context. Finally, the practical use of the proposed decision framework is validated by using a strategic supplier selection problem, as well as the effectiveness and applicability of the framework are discussed by using comparative analysis with other methods. Show more

Keywords: Strategic supplier selection, multi-stage multi-attribute group decision making, interval-valued q-rung orthopair fuzzy linguistic set, hamming distance based linear programming, prospect theory

DOI: 10.3233/JIFS-202415

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9855-9871, 2021

Authors: Ejegwa, Paul Augustine | Wen, Shiping | Feng, Yuming | Zhang, Wei | Chen, Jia

Article Type: Research Article

Abstract: Pythagorean fuzzy set is a reliable technique for soft computing because of its ability to curb indeterminate data when compare to intuitionistic fuzzy set. Among the several measuring tools in Pythagorean fuzzy environment, correlation coefficient is very vital since it has the capacity to measure interdependency and interrelationship between any two arbitrary Pythagorean fuzzy sets (PFSs). In Pythagorean fuzzy correlation coefficient, some techniques of calculating correlation coefficient of PFSs (CCPFSs) via statistical perspective have been proposed, however, with some limitations namely; (i) failure to incorporate all parameters of PFSs which lead to information loss, (ii) imprecise results, and (iii) less …performance indexes. Sequel, this paper introduces some new statistical techniques of computing CCPFSs by using Pythagorean fuzzy variance and covariance which resolve the limitations with better performance indexes. The new techniques incorporate the three parameters of PFSs and defined within the range [-1, 1] to show the power of correlation between the PFSs and to indicate whether the PFSs under consideration are negatively or positively related. The validity of the new statistical techniques of computing CCPFSs is tested by considering some numerical examples, wherein the new techniques show superior performance indexes in contrast to the similar existing ones. To demonstrate the applicability of the new statistical techniques of computing CCPFSs, some multi-criteria decision-making problems (MCDM) involving medical diagnosis and pattern recognition problems are determined via the new techniques. Show more

Keywords: Intuitionistic fuzzy set, Pythagorean fuzzy set, medical diagnosis, pattern recognition, medical diagnosis, correlation coefficient measure

DOI: 10.3233/JIFS-202469

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9873-9886, 2021

Authors: Wang, Jian | Zhu, Yuanguo

Article Type: Research Article

Abstract: Uncertain delay differential equation is a class of functional differential equations driven by Liu process. It is an important model to describe the evolution process of uncertain dynamical system. In this paper, on the one hand, the analytic expression of a class of linear uncertain delay differential equations are investigated. On the other hand, the new sufficient conditions for uncertain delay differential equations being stable in measure and in mean are presented by using retarded-type Gronwall inequality. Several examples show that our stability conditions are superior to the existing results.

Keywords: Uncertainty theory, uncertain delay differential equation, analytic solution, stability

DOI: 10.3233/JIFS-202507

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9887-9897, 2021

Authors: Darabi, M. | Allahviranloo, T.

Article Type: Research Article

Abstract: According to a huge interest in implementation of the fuzzy Volterra integral equations, especially the second kind, researchers have been investigating to solve such equations using numerical methods since analytical ones might not be accessible usually. In this research paper, we introduce a new approach based on Fibonacci polynomials collocation method to numerically solve them. Several properties of such polynomials were considered to implement in the collocation method due to approximate the solution of the second kind of fuzzy Volterra integral equations. We approved the existence, uniqueness of the solution, convergence and the error analysis of the proposed method in …detail. In order to show the authenticity and applicability of the proposed method, we employed several illustrative examples. The numerical results show that the convergence and precision of the recent method were in a good settlement with the exact solution. Also, the calculations of the suggested method are simple and low computational complexity in respect to other methods as an advantage feature of the presented approach. Show more

Keywords: Fuzzy Volterra integral equation, Fibonacci polynomial, collocation method

DOI: 10.3233/JIFS-202523

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9899-9914, 2021

Authors: Liu, Peide | Wang, Xiyu | Teng, Fei

Article Type: Research Article

Abstract: In today’s education industry, online teaching is increasingly becoming an important teaching way, and it is necessary to evaluate the quality of online teaching so as to improve the overall level of the education industry. The online teaching quality evaluation is a typical multi-attribute group decision-making (MAGDM) problem, and its evaluation index can be expressed by linguistic term sets (LTSs) by decision makers (DMs). Especially, multi-granularity probabilistic linguistic term sets (MGPLTSs) produced from many DMs are more suitable to express complex fuzzy evaluation information, and they can not only provide different linguistic term set for different DMs the give their …preferences, but also reflect the importance of each linguistic term. Based on the advantages of MGPLTSs, in this paper, we propose a transformation function of MGPLTSs based on proportional 2-tuple fuzzy linguistic representation model. On this basis, the operational laws and comparison rules of MGPLTSs are given. Then, we develop a new Choquet integral operator for MGPLTSs, which considers the relationship among attributes and does not need to consider the process of normalizing the probabilistic linguistic term sets (PLTSs), and can effectively avoid the loss of evaluation information. At the same time, the properties of the proposed operator are also proved. Furthermore, we propose a new MAGDM method based on the new operator, and analyze the effectiveness of the proposed method by online teaching quality evaluation. Finally, by comparing with some existing methods, the advantages of the proposed method are shown. Show more

Keywords: Multiple-attribute group decision-making, online teaching quality evaluation, multi-granularity probabilistic linguistic term sets, Choquet integral

DOI: 10.3233/JIFS-202543

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9915-9935, 2021

Authors: Mama, Rachid | Machkour, Mustapha

Article Type: Research Article

Abstract:  Nowadays several works have been proposed that allow users to perform fuzzy queries on relational databases. But most of these systems based on an additional software layer to translate a fuzzy query and a supplementary layer of a classic database management system (DBMS) to evaluate fuzzy predicates, which induces an important overhead. They are not also easy to implement by a non-expert user. Here we have proposed a simple and intelligent approach to extend the SQL language to allow us to write flexible conditions in our queries without the need for translation. The main idea is to use a view …to manipulate the satisfaction degrees related to user-defined fuzzy predicates, instead of calculating them at runtime employing user functions embedded in the query. Consequently, the response time of executing a fuzzy query statement will be reduced. This approach allows us to easily integrate most fuzzy request characters such as fuzzy modifiers, fuzzy quantifiers, fuzzy joins, etc. Moreover, we present a user-friendly interface to make it easy to use fuzzy linguistic values in all clauses of a select statement. The main contribution of this paper is to accelerate the execution of fuzzy query statements. Show more

Keywords: Fuzzy query, fuzzy logic, fuzzy SQL, relational database, user interface

DOI: 10.3233/JIFS-202551

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9937-9948, 2021

Authors: Zhang, Wei Min | Zhang, Long | Zhang, Zheyu | Sun, Mingjun

Article Type: Research Article

Abstract: With the many varieties of AI hardware prevailing on the market, it is often hard to decide which one is the most suitable to use but not only with the best performance. As there is an industry-wide trend demand for deep learning deployment, the inference benchmark for the effectiveness of DNN processor becomes important and is of great help to select and optimize AI hardware. To systematically benchmark deep learning deployment platforms, and give more objective and useful metrics comparison. In this paper, an end to end benchmark evaluation system was brought up called IBD, it combined 4 steps include …three components with 6 metrics. The performance comparison results are obtained from the chipsets from Qualcomm, HiSilicon, and NVIDIA, which can provide hardware acceleration for AI inference. To comprehensively reflect the current status of the DNN processor deploying performance, we chose six devices from three kinds of deployment scenarios which are cloud, desktop and mobile, ten models from three different kinds of applications with diverse characteristics are selected, and all these models are trained from three major training frameworks. Several important observations were made by using our methodologies. Experimental results showed that workload diversity should focus on the difference came from training frameworks, inference frameworks with specific processors, input size and precision (floating and quantized). Show more

Keywords: AI, deep neural network processor, benchmark, end to end, inference

DOI: 10.3233/JIFS-202552

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9949-9961, 2021

Authors: Ding, Xiangwen | Wang, Shengsheng

Article Type: Research Article

Abstract: Melanoma is a very serious disease. The segmentation of skin lesions is a critical step for diagnosing melanoma. However, skin lesions possess the characteristics of large size variations, irregular shapes, blurring borders, and complex background information, thus making the segmentation of skin lesions remain a challenging problem. Though deep learning models usually achieve good segmentation performance for skin lesion segmentation, they have a large number of parameters and FLOPs, which limits their application scenarios. These models also do not make good use of low-level feature maps, which are essential for predicting detailed information. The Proposed EUnet-DGF uses MBconv to implement …its lightweight encoder and maintains a strong encoding ability. Moreover, the depth-aware gated fusion block designed by us can fuse feature maps of different depths and help predict pixels on small patterns. The experiments conducted on the ISIC 2017 dataset and PH2 dataset show the superiority of our model. In particular, EUnet-DGF only accounts for 19% and 6.8% of the original Unet in terms of the number of parameters and FLOPs. It possesses a great application potential in practical computer-aided diagnosis systems. Show more

Keywords: Skin lesion segmentation, dermoscopic images, deep learning, Unet, gated fusion

DOI: 10.3233/JIFS-202566

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9963-9975, 2021

Authors: Saleem, Naeem | Işık, Hüseyin | Furqan, Salman | Park, Choonkil

Article Type: Research Article

Abstract: In this paper, we introduce the concept of fuzzy double controlled metric space that can be regarded as the generalization of fuzzy b -metric space, extended fuzzy b -metric space and controlled fuzzy metric space. We use two non-comparable functions α and β in the triangular inequality as: M q ( x , z , t α ( x , y ) + s β ( y , z ) ) ≥ M q ( x , y , t ) ∗ M q ( y , z , s ) . …We prove Banach contraction principle in fuzzy double controlled metric space and generalize the Banach contraction principle in aforementioned spaces. We give some examples to support our main results. An application to existence and uniqueness of solution for an integral equation is also presented in this work. Show more

Keywords: Extended fuzzy b-metric space, controlled fuzzy metric space, fixed point

DOI: 10.3233/JIFS-202594

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9977-9985, 2021

Authors: Wang, Junbin | Qin, Zhongfeng

Article Type: Research Article

Abstract: The hub maximal covering location problem aims to find the best locations for hubs so as to maximize the total flows covered by predetermined number of hubs. Generally, this problem is defined in the framework of binary coverage. However, there are many real-life cases in which the binary coverage assumption may yield unexpected decisions. Thus, the partial coverage is considered by stipulating that the coverage of an origin-destination pair is determined by a non-increasing decay function. Moreover, as this problem contains strategic decisions in long range, the precise information about the parameters such as travel times may not be obtained …in advance. Therefore, we present uncertain hub maximal covering location models with partial coverage in which the travel times are depicted as uncertain variables. Specifically, the partial coverage parameter is introduced in uncertain environment and the expected value of partial coverage parameter is further derived and simplified with specific decay functions. Expected value model and chance constrained programming model are respectively proposed and transformed to their deterministic equivalent forms. Finally, a greedy variable neighborhood search heuristic is presented and the efficiency of the proposed models is evaluated through computational experiments. Show more

Keywords: Hub maximal covering location problem, partial coverage, decay function, uncertain variable

DOI: 10.3233/JIFS-202635

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9987-10002, 2021

Authors: Gan, Zibang | Zeng, Biqing | Cheng, Lianglun | Liu, Shuai | Yang, Heng | Xu, Mayi | Ding, Meirong

Article Type: Research Article

Abstract: In multi-turn dialogue generation, dialogue contexts have been shown to have an important influence on the reasoning of the next round of dialogue. A multi-turn dialogue between two people should be able to give a reasonable response according to the relevant context. However, the widely used hierarchical recurrent encoder-decoder model and the latest model that detecting the relevant contexts with self-attention are facing the same problem. Their given response doesn’t match the identity of the current speaker, which we call it role ambiguity. In this paper, we propose a new model, named RoRePo, to tackle this problem by detecting the …role information and relative position information. Firstly, as a part of the decoder input, we add a role embedding to identity different speakers. Secondly, we incorporate self-attention mechanism with relative position representation to dialogue context understanding. Besides, the design of our model architecture considers the influence of latent variables in generating more diverse responses. Experimental results of our evaluations on the DailyDialog and DSTC7_AVSD datasets show that our proposed model advances in multi-turn dialogue generation. Show more

Keywords: Dialogue system, natural language generation, multi-turn dialogue, deep learning

DOI: 10.3233/JIFS-202641

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 10003-10015, 2021

Authors: Akram, Muhammad | Shumaiza,

Article Type: Research Article

Abstract:  The q -rung picture fuzzy sets serve the fuzzy set theory as a competent, broader and accomplished extension of q -rung orthopair fuzzy sets and picture fuzzy sets which exhibit excellent performance in modeling the obscure data beyond the limits of existing approaches owing to the parameter q and three real valued membership functions. The accomplished strategy of VIKOR method is established on the major concepts of regret measure and group utility measure to specify the compromise solution. Further, TOPSIS method is another well established multi-criteria decision-making strategy that finds out the best solution with reference to the distances …from ideal solutions. In this research study, we propose the innovative and modified versions of VIKOR and TOPSIS techniques using the numerous advantages of q -rung picture fuzzy information for obtaining the compromise results and rankings of alternatives in decision-making problems with the help of two different point-scales of linguistic variables. The procedure for the entropy weighting information is adopted to compute the normal weights of attributes. The q -rung picture fuzzy VIKOR (q -RPF VIKOR) method utilizes ascending order to rank the alternatives on the basis of maximum group utility and minimum individual regret of opponent. Moreover, a compromise solution is established by scrutinizing the acceptable advantage and the stability of decision. In the case of TOPSIS technique, the distances of alternatives to ideal solutions are determined by employing the Euclidean distance between q -rung picture fuzzy numbers. The TOPSIS method provides the ranking of alternatives by considering the descending order of closeness coefficients. For explanation, the presented methodologies are practiced to select the right housing society and the suitable industrial robot. The comparative results of the proposed techniques with four existing approaches are also presented to validate their accuracy and effectiveness. Show more

Keywords: q-Rung picture fuzzy numbers, VIKOR, TOPSIS, entropy weight information, decision-making

DOI: 10.3233/JIFS-202646

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 10017-10042, 2021

Authors: Shi, Xiaoping | Zou, Shiqi | Song, Shenmin | Guo, Rui

Article Type: Research Article

Abstract:  The asset-based weapon target assignment (ABWTA) problem is one of the important branches of the weapon target assignment (WTA) problem. Due to the current large-scale battlefield environment, the ABWTA problem is a multi-objective optimization problem (MOP) with strong constraints, large-scale and sparse properties. The novel model of the ABWTA problem with the operation error parameter is established. An evolutionary algorithm for large-scale sparse problems (SparseEA) is introduced as the main framework for solving large-scale sparse ABWTA problem. The proposed framework (SparseEA-ABWTA) mainly addresses the issue that problem-specific initialization method and genetic operators with a reward strategy can generate solutions efficiently …considering the sparsity of variables and an improved non-dominated solution selection method is presented to handle the constraints. Under the premise of constructing large-scale cases by the specific case generator, two numerical experiments on four outstanding multi-objective evolutionary algorithms (MOEAs) show Runtime of SparseEA-ABWTA is faster nearly 50% than others under the same convergence and the gap between MOEAs improved by the mechanism of SparseEA-ABWTA and SparseEA-ABWTA is reduced to nearly 20% in the convergence and distribution. Show more

Keywords: Weapon target assignment, multi-objective optimization, evolutionary algorithm, reward strategy, non-dominated solution selection

DOI: 10.3233/JIFS-202679

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 10043-10061, 2021

Authors: Wang, Jing | Yang, Yichuan

Article Type: Research Article

Abstract: We introduce rough approximations into basic algebras. After investigating elementary properties of the upper (lower) approximations in basic algebras and discussing the convexity of these two approximations in linearly ordered basic algebras, we generalize related results for MV-algebras, lattice ordered effect algebras, and orthomodular lattices to basic algebras. We also study the relationship between upper (lower) rough ideals of basic algebras and upper (lower) approximations of their homomorphic images.

Keywords: Basic algebras, rough approximations, rough ideals, homomorphic images, 03G25, 06B10

DOI: 10.3233/JIFS-202699

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 10063-10071, 2021

Authors: Pang, Zhicheng | Li, Hong | Wang, Chiyu | Shi, Jiawen | Zhou, Jiale

Article Type: Research Article

Abstract: In practice, the class imbalance is prevalent in sentiment classification tasks, which is harmful to classifiers. Recently, over-sampling strategies based on data augmentation techniques have caught the eyes of researchers. They generate new samples by rewriting the original samples. Nevertheless, the samples to be rewritten are usually selected randomly, which means that useless samples may be selected, thus adding this type of samples. Based on this observation, we propose a novel balancing strategy for text sentiment classification. Our approach takes word replacement as foundation and can be divided into two stages, which not only can balance the class distribution of …training set, but also can modify noisy data. In the first stage, we perform word replacement on specific samples instead of random samples to obtain new samples. According to the noise detection, the second stage revises the sentiment of noisy samples. Toward this aim, we propose an improved term weighting called TF-IGM-CW for imbalanced text datasets, which contributes to extracting the target rewritten samples and feature words. We conduct experiments on four public sentiment datasets. Results suggest that our method outperforms several other resampling methods and can be integrated with various classification algorithms easily. Show more

Keywords: Imbalanced text sentiment classification, resampling, noise modification, data augmentation, word replacement

DOI: 10.3233/JIFS-202716

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 10073-10086, 2021

Authors: Jiang, Bichuan | Shu, Lan

Article Type: Research Article

Abstract: In this paper, we study the evolutionary game dynamics of the death-birth process with interval payoffs on graphs. First of all, we derive the interval replication dynamic equation. Secondly, we derive the fixation probability of the B-C prisoner’s dilemma game based on the death-birth process under the condition of weak selection, analyze the condition of the strategy fixed in the population, that is the condition of strategy A being dominant is analyzed. So we can judge whether natural selection is beneficial to strategy A in the game process through this condition. Finally, the feasibility of this method is …verified by several examples. Show more

Keywords: Interval-valued functions, death-birth process, fixation probability, evolutionary dynamics

DOI: 10.3233/JIFS-202774

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 10087-10098, 2021

Authors: Lai, Xiaocong | Li, Hua | Pan, Ying

Article Type: Research Article

Abstract: With the increasing attention to the environment and air quality, PM2.5 has been paid more and more attention. It is expected to excavate useful information in meteorological data to predict air pollution, however, the air quality is greatly affected by meteorological factors, and how to establish an effective air quality prediction model has always been a problem that people urgently need to solve. This paper proposed a combined model based on feature selection and Support Vector Machine (SVM) for PM2.5 prediction. Firstly, aiming at the influence of meteorological factors on PM2.5, a feature selection method based on linear causality is …proposed to find out the causality between features and select the features with strong causality, so as to remove the redundant features in air pollution data and reduce the workload of data analysis. Then, a method based on SVM is proposed to analyze and solve the nonlinear problems in the data, for reducing the prediction error, a method of particle swarm optimization is also used to optimize SVM parameters. Finally, the above methods are combined into a prediction model, which is suitable for the current air pollution control. 12 representative data sets on the UCI (University of California, Irvine) website are used to verify the combined model, and the experimental results show that the model is feasible and effective. Show more

Keywords: Feature selection, linear regression, support vector machine, combined forecasting model, PM2.5 prediction

DOI: 10.3233/JIFS-202812

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 10099-10113, 2021

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

Article Type: Research Article

Abstract: With the development of society, people’s living standard is constantly improving. Meanwhile, people need various food to satisfy their needs in daily life. Under this situation, more and more food enterprises are appearing in the market. However, some issues about food safety come out. Because of the huge number of food company, managers are difficult in achieving profitability. Therefore, some of the managers try to use some unhealthy materials to produce food in the society. So, it is important for people to distinguish healthy and unhealthy food enterprises in their daily life. In order to help government discern and control …the quality of healthy food enterprises in the market, we need to propose an effective evaluation system in estimating food enterprises. In this paper, we introduce a method of evaluating the quality degree of food enterprises which can help us to distinguish enterprises effectively. As we all know, the method of TODIM is widely used in multiple attribute decision making (MADM). In this article, we describe the extended TODIM which based on the cumulative prospect theory (CPT) with picture fuzzy numbers (PF-CPT-TODIM) and use it to evaluate food companies. What’s more, we use entropy method to decide the weights of various attributes. Finally, we select optimal enterprise by using the PF-CPT-TODIM method. Furthermore, we use the comparison of the results of classical PF-TODIM method and PFWA operators to test the availability of PF-CPT-TODIM. It not only can enrich decision-making methods but also make up for the traditional PF-TODIM method in considering the psychological aspects of decision makers. Show more

Keywords: Multiple attribute group decision making (MAGDM), CPT-TODIM, picture fuzzy sets (PFSs), food enterprise, quality credit evaluation

DOI: 10.3233/JIFS-202839

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 10115-10128, 2021

Authors: Zuo, Jiankai | Zhang, Yaying

Article Type: Research Article

Abstract: In the field of intelligent robot engineering, whether it is humanoid, bionic or vehicle robots, the driving forms of standing, moving and walking, and the consciousness discrimination of the environment in which they are located have always been the focus and difficulty of research. Based on such problems, Naive Bayes Classifier (NBC), Support Vector Machine(SVM), k-Nearest-Neighbor (KNN), Decision Tree (DT), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) were introduced to conduct experiments. The six individual classifiers have an obvious effect on a particular type of ground, but the overall performance is poor. Therefore, the paper proposes a “Novel Hybrid …Evolutionary Learning” method (NHEL) which combines every single classifier by means of weighted voting and adopts an improved genetic algorithm (GA) to obtain the optimal weight. According to the fitness function and evolution times, this paper designs the adaptively changing crossover and mutation rate and applies the conjugate gradient (CG) to enhance GA. By making full use of the global search capabilities of GA and the fast local search ability of CG, the convergence speed is accelerated and the search precision is upgraded. The experimental results show that the performance of the proposed model is significantly better than individual machine learning and ensemble classifiers. Show more

Keywords: Hybrid classification model, improved GA, machine learning, ground recognition

DOI: 10.3233/JIFS-202940

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 10129-10143, 2021

Authors: Khoshaim, Ahmad Bakr | Qiyas, Muhammad | Abdullah, Saleem | Naeem, Muhammad | Muneeza,

Article Type: Research Article

Abstract: This article is an advanced approach to picture fuzzy set through the application of cubic set theory. For instance, we establish the idea of the picture cubic fuzzy sets (PCFSs) theory and define several operations for PCFS. Also, presented some weighted aggregation operators under picture cubic fuzzy information, so called picture cubic fuzzy weighted averaging (PCFWA) operator, picture cubic fuzzy order weighted averaging (PCFOWA) operator, picture cubic fuzzy weighted geometric (PCFWG) operator, and picture cubic fuzzy order weighted geometric (PCFOWG) operator. Further, we study their fundamental properties and showed the relationship among these aggregation operators. In order to determine the …feasibility and practicality of the mentioned new technique, we developed multi-attribute group decision -making algorithm with picture cubic fuzzy environment. Further, the developed method applied to supply chain management and for implementation, consider numerical application of supply chain management. Compared the developed approach with other preexisting aggregation operators, and we concluded that the defined technique is better, reliable and effective. Show more

Keywords: Picture cubic fuzzy sets, picture cubic fuzzy average aggregation operators, picture cubic fuzzy geometric aggregation operators, multi-attribute decision-making

DOI: 10.3233/JIFS-200194

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 10145-10162, 2021