Journal of Intelligent & Fuzzy Systems - Volume 41, issue 1

Authors: Ali, Riaz | Abdullah, Saleem | Muhammad, Shakoor | Naeem, Muhammad | Chinram, Ronnason

Article Type: Research Article

Abstract: Due to the indeterminacy and uncertainty of the decision-makers (DM) in the complex decision making problems of daily life, evaluation and aggregation of the information usually becomes a complicated task. In literature many theories and fuzzy sets (FS) are presented for the evaluation of these decision tasks, but most of these theories and fuzzy sets have failed to explain the uncertainty and vagueness in the decision making issues. Therefore, we use complex intuitionistic fuzzy set (CIFS) instead of fuzzy set and intuitionistic fuzzy set (IFS). A new type of aggregation operation is also developed by the use of complex intuitionistic …fuzzy numbers (CIFNs), their accuracy and the score functions are also discussed in detail. Moreover, we utilized the Maclaurin symmetric mean (MSM) operator, which have the ability to capture the relationship among multi-input arguments, as a result, CIF Maclarurin symmetric mean (CIFMSM) operator and CIF dual Maclaurin symmetric mean (CIFDMSM) operator are presented and their characteristics are discussed in detail. On the basis of these operators, a MAGDM method is presented for the solution of group decision making problems. Finally, the validation of the propounded approach is proved by evaluating a numerical example, and by the comparison with the previously researched results. Show more

Keywords: Intuitionistic fuzzy set, complex intuitionistic fuzzy set, multi-attribute group decision making, emergency management program evaluation, Maclaurin symmetric mean operator

DOI: 10.3233/JIFS-202254

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 517-538, 2021

Authors: Deng, Xue | Huang, Cuirong

Article Type: Research Article

Abstract: In the previous uncertain portfolio literature on background risk and mental account, only a general background risk and a few kinds of mental accounts were considered. Based on the above limitations, on the one hand, the multiple background risks are defined by linear weighting of different background asset risks in this paper; on the other hand, the total nine kinds of mental accounts are comprehensively considered. Especially, the risk curve is regarded as the risk measurement of different mental accounts for the first time. Under the framework of uncertainty theory, a novel mean-entropy portfolio model with risk curve and total …mental accounts under multiple background risks is constructed. In addition, transaction fees, chance constraint, upper and lower limits and initial wealth constraints are also considered in our proposed model. In theory, the equivalent forms of the models with different uncertainty distributions (general, normal and zigzag) are presented by three theorems. Simultaneously, the corresponding concrete expressions of risk curves are obtained by another three theorems. In practice, two numerical examples verify the feasibility and effectiveness of our proposed model. Finally, we can obtain the following unique and meaningful findings: (1) investors will underestimate the potential risk if they ignore the existence of multiple background risks; (2) with the increase of the return threshold, the return of the sub-portfolio will inevitably increase, but investors also bear the risk that the risk curve is higher than the confidence curve at this time. Show more

Keywords: Uncertainty theory, mental account, background risk, entropy, risk curve

DOI: 10.3233/JIFS-202256

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 539-561, 2021

Authors: Fu, Yingwen | Lin, Nankai | Lin, Xiaotian | Jiang, Shengyi

Article Type: Research Article

Abstract: Named entity recognition (NER) is fundamental to natural language processing (NLP). Most state-of-the-art researches on NER are based on pre-trained language models (PLMs) or classic neural models. However, these researches are mainly oriented to high-resource languages such as English. While for Indonesian, related resources (both in dataset and technology) are not yet well-developed. Besides, affix is an important word composition for Indonesian language, indicating the essentiality of character and token features for token-wise Indonesian NLP tasks. However, features extracted by currently top-performance models are insufficient. Aiming at Indonesian NER task, in this paper, we build an Indonesian NER dataset (IDNER) …comprising over 50 thousand sentences (over 670 thousand tokens) to alleviate the shortage of labeled resources in Indonesian. Furthermore, we construct a hierarchical structured-attention-based model (HSA) for Indonesian NER to extract sequence features from different perspectives. Specifically, we use an enhanced convolutional structure as well as an enhanced attention structure to extract deeper features from characters and tokens. Experimental results show that HSA establishes competitive performance on IDNER and three benchmark datasets. Show more

Keywords: Indonesian, named entity recognition, named entity corpus, structured-attention, residual gated convolution neural network

DOI: 10.3233/JIFS-202286

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 563-574, 2021

Authors: Zhu, Yan | Fan, Chuanhao | Xiao, Jing | Liu, Shenghua

Article Type: Research Article

Abstract: Quality function deployment (QFD) is a quality management tool that aims to improve customer satisfaction by transforming customer requirements into technical characteristics. It is a crucial procedure to obtain the prioritization of technical characteristics for the products or services in QFD. Traditional QFDs are often implemented by a small number of QFD members. However, with the increasing complexity of product and service design, QFD requires the participation of more QFD members from dispersed departments or institutions. Additionally, the evaluation information given by QFD members may widely differ due to their different knowledge and background. Furthermore, the psychological behaviours of QFD …members also greatly influence the final prioritization of technical characteristics. Hence, this paper proposes a novel QFD framework to prioritize technical characteristics using a consensus-reaching process and prospect theory when large numbers of QFD members are involved. In the large-scale QFD framework, prospect theory is generally utilized to depict the psychological behaviours of QFD members. Then, QFD members are divided into several clusters. Eventually, a consensus-reaching process is established to assist QFD members in reaching a consensus. To verify the practicability of the presented framework, this paper applies it to the evaluation of contingency plan to determine the critical measures. Show more

Keywords: Quality function deployment, consensus-reaching process, prospect theory, large-scale group decision making, contingency plan

DOI: 10.3233/JIFS-202326

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 575-594, 2021

Authors: Jiang, Le | Liu, Hongbin

Article Type: Research Article

Abstract: The use of probabilistic linguistic term sets (PLTSs) means the process of computing with words. The existing methods computing with PLTSs mainly use symbolic model. To provide a semantic model for computing with PLTSs, we propose to represent a PLTS by using an interval type-2 fuzzy set (IT2FS). The key step is to compute the footprint of uncertainty of the IT2FS. To this aim, the upper membership function is computed by aggregating the membership functions of the linguistic terms contained in the PLTS, and the lower membership function is obtained by moving the upper membership function downward with the step …being total entropy of the PLTS. The comparison rules, some operations, and an aggregation operator for PLTSs are introduced. Based on the proposed method of computing with PLTSs, a multi-criteria group decision making model is introduced. The proposed decision making model is then applied in green supplier selection problem to show its feasibility. Show more

Keywords: Green supplier selection, interval type-2 fuzzy set, lower membership function, probabilistic linguistic term set, upper membership function

DOI: 10.3233/JIFS-202386

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 595-612, 2021

Authors: Dong, Yan Yan | Wang, Jun Tao

Article Type: Research Article

Abstract: In this paper, we first point out some mistakes in [12 ]. Especially the Theorem 3.9 [12 ] showed that: Let A be residuated lattice and ∅ ≠ X  ⊆ A , then the least ideal containing X can be expressed as: 〈X 〉 = {a  ∈ A |a  ≤ (·· · ((x 1  ⊕ x 2 ) ⊕ x 3 ) ⊕ ·· ·) ⊕ x n , x i  ∈ X , i  = 1, 2 ·· · , n }. But we present an example to illustrate the ideal generation formula may not hold on residuated lattices. Further we give the correct ideal generation formula on residuated lattices. Moreover, we extend the concepts of annihilators …and α -ideals to MTL-algebras and focus on studying the relations between them. Furthermore, we show that the set I α  (M ) of all α -ideals on a linear MTL-algebra M only contains two trivial α -ideals {0} and M . However, the authors [24 ] studied the structure of I α  (M ) in a linear BL-algebra M , which means some results with respect to I α  (M ) given in [24 ] are trivial. Unlike that, we investigate the lattice structure of I α  (M ) on general MTL -algebras. Show more

Keywords: Residuated lattice, MTL-algebra, Ideal generation formula, Annihilator, α-ideal

DOI: 10.3233/JIFS-202417

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 613-623, 2021

Authors: Silmi Juman, Zeinul Abdeen M. | Masoud, Mahmoud | Elhenawy, Mohammed | Bhuiyan, Hanif | Komol, Md Mostafizur Rahman | Battaïa, Olga

Article Type: Research Article

Abstract: The uncapacitated transportation problem (UTP) deals with minimizing the transportation costs related to the delivery of a homogeneous product from multi-suppliers to multi-consumers. The application of the UTP can be extended to other areas of operations research, including inventory control, personnel assignment, signature matching, product distribution with uncertainty, multi-period production and inventory planning, employment scheduling, and cash management. Such a UTP with interval-defined demands and suppliers capacities (UTPIDS) is investigated in this paper. In UTPIDS, the demands and suppliers capacities may not be known exactly but vary within an interval due to variation in the economic conditions of the global …economy. Following the variation, the minimal total cost of the transportation can also be varied within an interval and thus, the cost bounds can be obtained. Here, although the lower bound solution can be attained methodologically, the correct estimation of the worst case realization (the exact upper bound) on the minimal total transportation cost of the UTPIDS is an NP-hard problem. So, the decision-makers seek for minimizing the transportation costs and they are interested in the estimation of the worst case realization on these minimal costs for better decision making especially, for proper investment and return. In literature very few approaches are available to find this estimation of the worst case realization with some shortcomings. First, we demonstrate that the available heuristic methods fail to obtain the correct estimation of the worst case realization always. In this situation, development of a better heuristic method to find the better near optimal estimation of the worst case realization on the minimal total costs of the UTPIDS is desirable. Then this paper provides a new polynomial time algorithm that runs in O (N2) time (N, higher of the numbers of source and destination nodes) for better estimation. A comparative assessment on solutions of available benchmark instances, some randomly generated numerical example problems and a real-world application shows promising performance of the current technique. So, our new finding would definitely be benefited to practitioners, academics and decision makers who deal with such type of decision making instances. Show more

Keywords: Heuristics, upper bound, least aggregated expense, NP-hard problem

DOI: 10.3233/JIFS-202436

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 625-637, 2021

Authors: Pak, Sunae | Choe, Huichol | Sin, Kinam | Kwon, Sunghyok

Article Type: Research Article

Abstract: In this paper, we investigate the necessary and sufficient conditions for existence of solutions for initial value problem of fuzzy Bagley-Torvik equation and the solution representation by using the multivariate Mittag-Leffler function. First we convert fuzzy initial value problem into the cut problem (system of fractional differential equations with inequality constraints) and obtain existence results for the solution of the cut problem under (1,1)- differentiability. Next we study the conditions for the solutions of the cut problem to constitute the solution of a fuzzy initial value problem and suggest a necessary and sufficient condition for the (1,1)-solution. Also, some examples …are given to verify the effectiveness of our proposed method. The necessary and sufficient condition, solution representation for (1,2)-solution of initial value problem of fuzzy fractional Bagley-Torvik equation are shown in Appendix. Show more

Keywords: Fuzzy Fuzzy Bagley-Torvik equation, generalized Hukuhara differentiability, multivariate Mittag-Leffler function

DOI: 10.3233/JIFS-202453

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 639-654, 2021

Authors: AlAlaween, Wafa’ H. | AlAlawin, Abdallah H. | Al-Durgham, Lamees | Albashabsheh, Nibal T.

Article Type: Research Article

Abstract: A new integrated modelling architecture based on the concept of the fuzzy logic is presented to represent the turning process. Such an architecture consists of two stages. In the first stage, fuzzy logic systems (FLSs) having various topologies are employed to extract rule bases using perhaps limited amount of sparse data. In the second stage, the fuzzy rules extracted are assessed and integrated using the singular value decomposition-QR factorization (SVD-QR) paradigm in order to minimize the computational efforts. Such a step leads to reducing the number of fuzzy rules and results in a reduced FLS model. Such a reduced model …is then employed to represent the turning process and predict both the cutting force and the surface roughness. In addition, it provides a comprehensive understanding of the turning process presented linguistically in the form of If/Then rules. The proposed structure has been validated using a set of laboratory experiments. It has been noticed that it can predict both the cutting force and the surface roughness successfully. In addition, such an integrated architecture outperforms the artificial neural network, the well-known FLS, the radial basis functions and the multilinear regression model, where the overall improvement is of approximately 19%, 13%, 14% and 270%, respectively. Show more

Keywords: Fuzzy logic system, singular value decomposition-QR factorization (SVD-QR) algorithm, turning process

DOI: 10.3233/JIFS-202457

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 655-667, 2021

Authors: Hikal, Noha A. | Shams, Mahmoud Y. | Salem, Hanaa | Eid, Marwa M.

Article Type: Research Article

Abstract: Mobile Ad hock Networks (MANETs) are currently used for developing the privacy and accuracy of modern networks. Furthermore, MANET applications are fit to be data-oriented systems, that introduce a secure and more robust data transmission protocol making it a topmost priority in the design. The lack of infrastructure in the existence of dynamic topology as well as limited resources of MANET is a major challenge facing those interested in the field. Further, the nonexistence of a formerly authorized trust relationship within the connected nodes produces instability of the detection process in MANETs. Basically, by adding adapted LEACH routing protocol to …MANET, enhancement of the preserved nodes vitality will be achieved, moreover, the load balancing with data loss reduction provides MANET ability to tracks along with shortest and limited paths. This paper proposes a newly developed detection scheme for both active and passive black-hole attacks in MANETs. Moreover, the scheme deals with assessing a group of selected features for each node-based AdaBoost-SVM algorithm. These features are collected from cluster members nodes based on Ad hoc On-demand Multipath Distance Vector (OMDV) with LEACH routing protocol clustering approaches. Although SVM is considered a more stable classifier, there are great influences of the AdaBoost weight adaption algorithm to enhance the classification process in terms of strengthening the weights of extracted features. This hybrid algorithm is essential for active black-hole attacks as well as for identifying passive black-hole attacks in MANET. The proposed scheme is tested against the effect of mobility variation to determine the accuracy of the detection process including the routing overhead protocol. The experimental results investigated that the accuracy of detecting both active and passive black-holes attacks in MANET reached 97% with a promising time complexity for different mobility conditions. Moreover, the proposed scheme provides an accurate decision about malicious vs benign node dropping behavior using an adjustable threshold value. Show more

Keywords: Ada-boost, support vector machine, AOMDV, black-hole attack, and MANET

DOI: 10.3233/JIFS-202471

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 669-682, 2021

Authors: Dhamija, Ashutosh | Dubey, R. B.

Article Type: Research Article

Abstract: Face recognition is one of the most challenging and demanding field, since aging affects the shape and structure of the face. Age invariant face recognition is a relatively new area in face recognition studies, which in real-world implementations recently gained considerable interest due to its huge potential and relevance. The Age invariant face recognition, however, is still evolving and evolving, providing substantial potential for further study and progress in accuracy. Major issues with the age invariant face recognition involve major variations in appearance, texture, and facial features and discrepancies in position and illumination. These problems restrict the age invariant face …recognition systems developed and intensify identity recognition tasks. To address this problem, a new technique Quadratic Support Vector Machine- Principal Component Analysis (QSVM-PCA) is introduced. Experimental results suggest that our QSVM-PCA achieved better results especially when the age range is larger than other existing techniques of face-aging dataset of FGNET. The maximum accuracy achieved by demonstrated methodology is 98.87%. Show more

Keywords: Age-invariant face recognition, feature extraction, PCA and QSVM

DOI: 10.3233/JIFS-202485

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 683-697, 2021

Authors: Yang, Yulei | Zhang, Jin | Sun, Wenjie | Pu, Yun

Article Type: Research Article

Abstract: Under the background of the new medical reform, the pharmaceutical industry is in constant transformation and upgrading, and the establishment of a rational and efficient pharmaceutical logistics system is imminent. Carbon emission, cost and time are set as the target to construct the model of location-routing-inventory optimization of highway, rail and air transport hubs with capacity limits. Then the warehouse of pharmaceutical logistics hub is selected, and the distribution path of pharmaceutical logistics and the inventory strategy are planned to realize the scientific decision of the system. The NSGA-III algorithm is used to solve the problem. The diversity of the …population is maintained by the well-distributed reference points, and the optimal solution set of nondominant Pareto is obtained. Spacing, HRS, PR and GD are used to measure the performance of the algorithm. The example analysis shows that the number of Pareto optimal solutions solved by the algorithm is large and evenly distributed, and convergence and operation efficiency of algorithm is good. The sensitivity analysis of three kinds of freight rates shows that the influence of the freight rates on the objective function value should be fully considered when making decisions. The method focuses on the problem of optimizing the layout of multi-modal transport hubs and improves the existing theories of it. Show more

Keywords: Pharmaceutical warehouse, carbon emission, location-routing-inventory problem, NSGA-III

DOI: 10.3233/JIFS-202508

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 699-713, 2021

Authors: Mousa, A.A. | Higazy, M. | Abdel-Khalek, S. | Hussein, Mohamed A. | Farouk, Ahmed

Article Type: Research Article

Abstract: The application and analysis of effective blood supply chain network under natural disaster imposed many critical challenges which addressed through an optimization of multiple objectives functions. In this article, relies on reference point algorithm, a user-preference based enriched swarm optimization algorithm is proposed where, inner reference points were produced depending on the perturbed reference point. For each inner reference point, weakly/ɛ -properly Pareto optimal solution was generated using augmented achievement function. All the generated solutions (points) are presented as potential positions for particles in the particle swarm optimization PSO. The proposed algorithm has been reinforced with a novel chaotic contraction …operator to retain the feasibility of the particles. To prove the validity of our algorithm, the obtained results are compared with true Pareto optimal front and three of the most salient evolutionary algorithms using inverted generational distance metric IGD. In addition it was implement to detect the most cost and time efficient blood supply chain to provide the required blood types demand on the blood transfusion center in emergence situation, where, it is required to solve this real life application with predefined supply time and predefined supply cost, which is considered as reference point to get the nearby Pareto optimal solution. By the experimental outcomes, we proved that the proposed algorithm is capable to find the set of Paetro optimal solutions nearby the predefined reference points. Show more

Keywords: Particle swam optimization, reference point, multi-objective optimization, blood supply chain

DOI: 10.3233/JIFS-202529

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 715-733, 2021

Authors: Ranjith Pillai, R. | Murali, Ganesan

Article Type: Research Article

Abstract: Miniature flexible parallel robots, popularly used for micro positioning application demands the use of non conventional actuators. Shape memory alloys (SMA) are popular smart actuators because of its light weight, integration compatibility, ease of actuation and high power density. Inclusion of shape memory alloy actuators to the parallel robot brings in control challenges due to its nonlinearity, coupling effects and cocontraction of antagonistic pair of actuators in the mechanism in order to achieve bi directional motion. In this paper, a PID like fuzzy controller is designed and applied to a nonlinear SMA spring actuator connected to a symmetric 2 DOF …miniature parallel robot. The fuzzy rules are designed from the general response plot and modified to be applied to a parallel mechanism which involves cocontraction of antagonistic actuators. The paper has also presented the control and electrical circuit design used in the experimental set up. The fuzzy control is implemented in the hardware controller with model based position feedback and tested for the trajectory tracking characteristics of the end effector with disturbances. Experimental results are presented with quantitative analysis to show the effectiveness of the proposed controller in handling nonlinearities and disturbances compared to the conventional PID control and nonlinear Sliding mode control (NSMC). The test results has demonstrated the superior nature of proposed control over other controllers in the trajectory tracking with disturbances and also linearizing the hysteresis of controlled system. Show more

Keywords: SMA actuated parallel robot, PID like fuzzy control, control of cocontraction of actuators, flexible robot, SMA spring control

DOI: 10.3233/JIFS-202572

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 735-755, 2021

Authors: Adu, Kwabena | Yu, Yongbin | Cai, Jingye | Dela Tattrah, Victor | Adu Ansere, James | Tashi, Nyima

Article Type: Research Article

Abstract: The squash function in capsule networks (CapsNets) dynamic routing is less capable of performing discrimination of non-informative capsules which leads to abnormal activation value distribution of capsules. In this paper, we propose vertical squash (VSquash) to improve the original squash by preventing the activation values of capsules in the primary capsule layer to shrink non-informative capsules, promote discriminative capsules and avoid high information sensitivity. Furthermore, a new neural network, (i) skip-connected convolutional capsule (S-CCCapsule), (ii) Integrated skip-connected convolutional capsules (ISCC) and (iii) Ensemble skip-connected convolutional capsules (ESCC) based on CapsNets are presented where the VSquash is applied in the dynamic …routing. In order to achieve uniform distribution of coupling coefficient of probabilities between capsules, we use the Sigmoid function rather than Softmax function. Experiments on Guangzhou Women and Children’s Medical Center (GWCMC), Radiological Society of North America (RSNA) and Mendeley CXR Pneumonia datasets were performed to validate the effectiveness of our proposed methods. We found that our proposed methods produce better accuracy compared to other methods based on model evaluation metrics such as confusion matrix, sensitivity, specificity and Area under the curve (AUC). Our method for pneumonia detection performs better than practicing radiologists. It minimizes human error and reduces diagnosis time. Show more

Keywords: Artificial intelligence, capsule network, convolutional neural network, deep learning, pneumonia, x-ray imaging

DOI: 10.3233/JIFS-202638

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 757-781, 2021

Authors: Huang, Danyang | Zhou, Zhiheng | Deng, Ming | Li, Zhihao

Article Type: Research Article

Abstract: Detecting vehicle at night is critical to both assistant driving systems and autonomous driving systems. In this paper, we propose a deep network scheme assisted by light information with good generalization to detect vehicle at night. Our approach is divided into two branches, the object stream and the pixel stream. The object stream generates a batch of bounding boxes, and the pixel stream utilizes the vehicle light information to calibrate the bounding boxes of the object stream. In the object stream, we propose a new structure, Direction Attention Pooling (DAP), to improve the accuracy of the prior boxes. DAP leads …into attention mechanism. The feature maps obtained from backbone network is divided into two branches. One branch obtains direction perception information through IRNN layer, and the other branch learns attention weights. The weights are multiplied with the direction perception features in an element-wise manner. In the pixel stream, we propose a corner localization algorithm based on Bayes to get more accurate corners with the vehicle light pixels. The locations of the corners are considered as a discrete random variable. When the mask of the object is known, solving the probability distribution of the corner of the object is the next step. The corners with the highest probability is the correct corner. On the nighttime vehicle detection datasets CHUK and SYSU, our method achieves the accuracy of 97.2% and 96.86%, which outperforms other state-of-the-art methods by at least 0.31% and 0.34%. Show more

Keywords: Nighttime vehicle detection, advanced driver-assistance systems, attention mechanism, deep learning

DOI: 10.3233/JIFS-202676

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 783-801, 2021

Authors: Noorullah, R.M. | Mohammed, Moulana

Article Type: Research Article

Abstract: Topic models are widely used in building clusters of documents for more than a decade, yet problems occurring in choosing the optimal number of topics. The main problem is the lack of a stable metric of the quality of topics obtained during the construction of topic models. The authors analyzed from previous works, most of the models used in determining the number of topics are non-parametric and the quality of topics determined by using perplexity and coherence measures and concluded that they are not applicable in solving this problem. In this paper, we used the parametric method, which is an …extension of the traditional topic model with visual access tendency for visualization of the number of topics (clusters) to complement clustering and to choose the optimal number of topics based on results of cluster validity indices. Developed hybrid topic models are demonstrated with different Twitter datasets on various topics in obtaining the optimal number of topics and in measuring the quality of clusters. The experimental results showed that the Visual Non-negative Matrix Factorization (VNMF) topic model performs well in determining the optimal number of topics with interactive visualization and in performance measure of the quality of clusters with validity indices. Show more

Keywords: Interactive visualization, visual non-negative matrix factorization model, an optimal number of topics, cluster validity indices, twitter data clustering

DOI: 10.3233/JIFS-202707

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 803-817, 2021

Authors: Shabir, Muhammad | Mushtaq, Rimsha | Naz, Munazza

Article Type: Research Article

Abstract: In this paper, we focus on two main objectives. Firstly, we define some binary and unary operations on N-soft sets and study their algebraic properties. In unary operations, three different types of complements are studied. We prove De Morgan’s laws concerning top complements and for bottom complements for N-soft sets where N is fixed and provide a counterexample to show that De Morgan’s laws do not hold if we take different N. Then, we study different collections of N-soft sets which become idempotent commutative monoids and consequently show, that, these monoids give rise to hemirings of N-soft sets. Some of …these hemirings are turned out as lattices. Finally, we show that the collection of all N-soft sets with full parameter set E and collection of all N-soft sets with parameter subset A are Stone Algebras. The second objective is to integrate the well-known technique of TOPSIS and N-soft set-based mathematical models from the real world. We discuss a hybrid model of multi-criteria decision-making combining the TOPSIS and N-soft sets and present an algorithm with implementation on the selection of the best model of laptop. Show more

Keywords: N-soft set, algebraic structure, top complement, bottom complement, TOPSIS

DOI: 10.3233/JIFS-202717

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 819-839, 2021

Authors: Cheng, Haodong | Han, Meng | Zhang, Ni | Li, Xiaojuan | Wang, Le

Article Type: Research Article

Abstract: Traditional association rule mining has been widely studied, but this is not applicable to practical applications that must consider factors such as the unit profit of the item and the purchase quantity. High-utility itemset mining (HUIM) aims to find high-utility patterns by considering the number of items purchased and the unit profit. However, most high-utility itemset mining algorithms are designed for static databases. In real-world applications (such as market analysis and business decisions), databases are usually updated by inserting new data dynamically. Some researchers have proposed algorithms for finding high-utility itemsets in dynamically updated databases. Different from the batch processing …algorithms that always process the databases from scratch, the incremental HUIM algorithms update and output high-utility itemsets in an incremental manner, thereby reducing the cost of finding high-utility itemsets. This paper provides the latest research on incremental high-utility itemset mining algorithms, including methods of storing itemsets and utilities based on tree, list, array and hash set storage structures. It also points out several important derivative algorithms and research challenges for incremental high-utility itemset mining. Show more

Keywords: Survey, pattern mining, incremental mining, high-utility patterns, frequent itemsets

DOI: 10.3233/JIFS-202745

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 841-866, 2021

Authors: Yang, Zhan | Li, Chengliang | Zhao, Zhongying | Li, Chao

Article Type: Research Article

Abstract: Aspect-based sentiment classification, a fine-grained sentiment analysis task, aims to predict the sentiment polarity for a specified aspect. However, the existing aspect-based sentiment classification approaches cannot fully model the dependency-relationship between words and are easily disturbed by irrelevant aspects. To address this problem, we propose a novel approach named Dependency-Relationship Embedding and Attention Mechanism-based LSTM. DA-LSTM first merges the word hidden vector output by LSTM with the dependency-relationship embedding to form a combined vector. This vector is then fed into the attention mechanism together with the aspect information which can avoid interference to calculate the final word representation for sentiment …classification. Our extensive experiments on benchmark data sets clearly show the effectiveness of DA-LSTM. Show more

Keywords: Aspect-based sentiment analysis, sentiment classification, dependency-relationship, attention mechanism

DOI: 10.3233/JIFS-202747

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 867-877, 2021

Authors: Sayed, Osama Rashed | Sayed, Nabil Hasan | Chen, Gui-Xiu

Article Type: Research Article

Abstract: In the present paper, a characterization of the intuitionistic fuzzy sets, the interval-valued intuitionistic fuzzy sets and their set-operations are given. By making use of these characterizations, the relationships between the interval-valued intuitionistic fuzzy topology and four fuzzy topologies associated to it are studied. For this reason, some subclasses of the family of interval-valued intuitionistic fuzzy topologies on a set which we call pre-suitable and suitable are introduced. Furthermore, the concepts of homeomorphism functions and compactness in the framework of interval-valued intuitionistic fuzzy topological spaces are introduced and studied.

Keywords: Interval-valued intuitionistic fuzzy set, interval-valued intuitionistic fuzzy topology, fuzzy topology, homeomorphism, compactness

DOI: 10.3233/JIFS-202757

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 879-889, 2021

Authors: Zhang, Qiang | Zhang, Luyu | Sun, Bingzhen

Article Type: Research Article

Abstract: In 2020, the spread of the COVID-19 epidemic has attracted global attention. As a large-scale group that is receiving higher education, college students also show greater mood swings. How to reduce the psychological harm of anxiety to college students is a problem that needs to be solved urgently. Based on this, this paper proposes an evaluation model for the anxiety level of college students in different regions under the influence of COVID-19. First of all, the general influence index of college student’s anxiety level is obtained by correlation analysis. Secondly, the logical OR of the double quantization variable precision fuzzy …set model and the degree fuzzy rough set model is used to establish the evaluation model of the anxiety level of college students under the influence of COVID-19. Finally, used big data, the idea of fuzzy upper and lower approximation, combined with the principle of maximum membership in fuzzy set theory, achieved the quantitative ranking of the anxiety levels of college students in different areas. The research shows that when the accuracy of decision-making is 45%, the anxiety level of the township college students group and the provincial capital or municipality college students group is higher. When the accuracy of decision-making is 65%, the anxiety level of the provincial capital or municipality college students group is higher than others. Show more

Keywords: Fuzzy rough set, two universes, COVID-19, college students anxiety level

DOI: 10.3233/JIFS-202760

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 891-902, 2021

Authors: Aishwarya, N. | BennilaThangammal, C. | Praveena, N.G.

Article Type: Research Article

Abstract: Getting a complete description of scene with all the relevant objects in focus is a hot research area in surveillance, medicine and machine vision applications. In this work, transform based fusion method called as NSCT-FMO, is introduced to integrate the image pairs having different focus features. The NSCT-FMO approach basically contains four steps. Initially, the NSCT is applied on the input images to acquire the approximation and detailed structural information. Then, the approximation sub band coefficients are merged by employing the novel Focus Measure Optimization (FMO) approach. Next, the detailed sub-images are combined using Phase Congruency (PC). Finally, an inverse …NSCT operation is conducted on synthesized sub images to obtain the initial synthesized image. To optimize the initial fused image, an initial decision map is first constructed and morphological post-processing technique is applied to get the final map. With the help of resultant map, the final synthesized output is produced by the selection of focused pixels from input images. Simulation analysis show that the NSCT-FMO approach achieves fair results as compared to traditional MST based methods both in qualitative and quantitative assessments. Show more

Keywords: Image fusion, multi-focus, NSCT, focus measure, decision map

DOI: 10.3233/JIFS-202803

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 903-915, 2021

Authors: Luo, Man

Article Type: Research Article

Abstract: The construction of hydraulic projects inevitably involves land requisition and resettlement, with considerable impact on the society, the environment, and the economy of the project site, and leading to social stability risk events. Therefore, it is necessary to systematically assess social stability risk to put forward corresponding countermeasures. By applying WSR theory (Wuli-Shili-Renli Theory) to the investigation of the case-study of the Jiangxiang Reservoir Project, this paper constructs an evaluation index system for the risk to social stability from land requisition and resettlement, from the three dimensions of “physics”, “matter”, and “human principle”. The GAHP (Group-decision Analytic Hierarchy Process) method …is used to determine the index weights, while the index values of each risk factor are determined by using the interval valued hesitant fuzzy sets (IVHFSs) method. A comprehensive assessment of risks to social stability from land requisition and resettlement in the Jiangxiang Reservoir Project is performed, and coping strategies for major social stability risk factors are proposed. This paper effectively supports the development of assessments of risks to social stability from land requisition and resettlement in other hydraulic projects. Show more

Keywords: Social stability risk assessment, land requisition and resettlement, hydraulic project

DOI: 10.3233/JIFS-202805

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 917-928, 2021

Authors: Mahmood, Nabeel | Qin, Rongjun | Butalia, Tarunjit Singh

Article Type: Research Article

Abstract: A risk assessment model is developed to estimate the potential combined influence of concurrent safety risks facing on-foot construction worker at a certain point in space or instant of time. The model is based on a holistic approach that comprehensively systemizes principal types and subjective values of possible safety risk events. Fuzzy fault tree is built using a deductive approach to identify possible concurrent basic and conditional risk events, not risk symptoms, from the major subgroups of triggering, enabling and environment-related risks. The inclusive risk breakdown structure helps in combating assessment underestimation related to overlooking influential risks. Adequate logic gates …are suggested at tree junctions to overcome assessment overestimation related to accumulating the effect of dependent, redundant, and non-concurrent risks, and ignoring the effectiveness of safety precautions and measures that may reduce or eliminate risks. Operational logic gates are applied to properly combine the residual risk of static (non-moving) events and dynamic (moving) events that can concurrently influence safety. The model is programmed into an interactive interfaced intelligent system to simulate cases of risk assessment input, computations, and output. The system shows the advantages of using the model as a prognostic or diagnostic tool to estimate top risk event. Subjective linguistic risk values can be induced for basic risk events at the bottom of the tree, and conditional risk events controlling residual risk values can be induced at different levels of the tree. Fuzzy logic plays a key role in hosting subjective risk evaluation into computational truth values to generate realistic and meaningful assessment values that are helpful for risk control. Show more

Keywords: On-foot building construction worker, safety risk assessment model, concurrent risk events, static dynamic risks, operational logic gates, fuzzy fault tree analysis, linguistic truth values

DOI: 10.3233/JIFS-202915

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 929-954, 2021

Authors: Riaz, Muhammad | Ali, Nawazish | Davvaz, Bijan | Aslam, Muhammad

Article Type: Research Article

Abstract: The aim of this paper is to introduce the concepts of soft rough q-rung orthopair fuzzy set (SRqROFS) and q-rung orthopair fuzzy soft rough set (qROPFSRS) based on soft rough set and fuzzy soft relation, respectively. We define some fundamental operations on both SRqROFS and qROPFSRS and discuss some key properties of these models by using upper and lower approximation operators. The suggested models are superior than existing soft rough sets, intuitionistic fuzzy soft rough sets and Pythagorean fuzzy soft rough sets. These models are more efficient to deal with vagueness in multi-criteria decision-making (MCDM) problems. We develop Algorithm i …(i  = 1, 2, 3, 4, 5) for the construction of SRqROFS, construction of qROFSRS, selection of a smart phone, ranking of beautiful public parks, and ranking of government challenges, respectively. The notions of upper reduct and lower reduct based on the upper approximations and lower approximations by variation of the decision attributes are also proposed. The applications of the proposed MCDM methods are demonstrated by respective numerical examples. The idea of core is used to find a unanimous optimal decision which is obtained by taking the intersection of all lower reducts and upper reducts. Show more

Keywords: Soft rough q-rung orthopair fuzzy set, q-rung orthopair fuzzy soft rough set, upper reduct, lower reduct, core, multi-criteria decision-making

DOI: 10.3233/JIFS-202916

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 955-973, 2021

Authors: Rath, Adyasha | Patnaik, Srikanta | Panda, Ganapati

Article Type: Research Article

Abstract: The density of population in cities is growing at a faster rate to make the life of people in cities comfortable and save. The city needs to be smart. It can be mainly achieved by intelligent decision making process using computational intelligence based systems. Keeping this in view, many researchers and organizations are working to develop and implement computational intelligence decision support systems. To obtain a comprehensive overview on the current status on SI based smart city community the present investigation has been made. To achieve this objective recently published standard articles on this important sub area have been collected …and reviewed. The summary of the review has been presented in systematic manner to facilitate the researchers who are currently working in the area of smart city community. The important findings of the review have been made and presented. The important performance measures in various aspects of smart city obtained by the computational intelligence methods have been listed. It is expected that the findings and the contribution of the paper will benefit the researchers, the related government and private organizations in terms of furthering their research efforts and producing different smart products pertaining to community development and improvement of comfort level of the dwellers of the smart city. Show more

Keywords: Smart city, computational intelligence, machine learning, intelligent transportation system, smart healthcare, smart environment, smart education, cyber security

DOI: 10.3233/JIFS-202919

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 975-991, 2021

Authors: Priyadharshini, V.M. | Valarmathi, A.

Article Type: Research Article

Abstract: Online social networks (OSNs) are utilized by millions of people from the entire world to communicate with others through Facebook and Twitter. The removal of fake accounts will increase the efficiency of the protection in OSNs. The construction of the OSN model has the nodes and the links to identify the fake profiles on Twitter. This paper proposes a novel technique to detect spam profiles and the proposed classifier is to classify the profile images from the dataset. The malicious profile detection technique is used to identify the fake profiles with the concept of a Twitter crawler that implements the …extraction of data from the profile. The feature set analysis has been implemented with the feature related analysis. The user behavior detection utilizes the adjacent matrix to measure the similarity values within the friend’s profiles. The multi-variant Support Vector Machine classifier is developed for efficient classification with the kernel function. The proposed technique is compared with the well-known techniques of ECRModel, ISMA and DeepLink that the detection rate is 2.5% higher than the related techniques, the computation time is 220 s lesser than the related techniques and the proposed technique has 3.1% higher accuracy. Show more

Keywords: Online social networks, twitter, spam detection, classification, malicious node

DOI: 10.3233/JIFS-202937

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 993-1007, 2021

Authors: Zhang, Huiyuan | Wei, Guiwu | Chen, Xudong

Article Type: Research Article

Abstract: The green supplier selection is one of the popular multiple attribute group decision making (MAGDM) problems. The spherical fuzzy sets (SFSs) can fully express the complexity and fuzziness of evaluation information for green supplier selection. Furthermore, the classic MABAC (multi-attributive border approximation area comparison) method based on the cumulative prospect theory (CPT-MABAC) is designed, which is an optional method in reflecting the psychological perceptions of decision makers (DMs). Therefore, in this article, we propose a spherical fuzzy CPT-MABAC (SF-CPT-MABAC) method for MAGDM issues. Meanwhile, considering the different preferences of DMs to attribute sets, we obtain the objective weights of attributes …through entropy method. Focusing on the current popular problems, this paper applies the proposed method for green supplier selection and proves for green supplier selection based on SF-CPT-MABAC method. Finally, by comparing existing methods, the effectiveness of the proposed method is certified. Show more

Keywords: Multiple attribute group decision making (MAGDM), spherical fuzzy sets (SFSs), MABAC method, cumulative prospect theory (CPT), entropy method, combined weights, green supplier selection

DOI: 10.3233/JIFS-202954

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1009-1019, 2021

Authors: Demirci, Işıl Açık | Gürdal, Mehmet

Article Type: Research Article

Abstract: In this work, we study the lacunary I -statistical convergence concept of complex uncertain triple sequence. Four types of lacunary I -statistically convergent complex uncertain triple sequences are presented, namely lacunary I -statistical convergence in measure, in mean, in distribution and with respect to almost surely, and some basic properties are proved.

Keywords: Triple sequence, statistical convergence, ideal convergence, triple lacunary sequence, complex uncertain variable, uncertainty space

DOI: 10.3233/JIFS-202964

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1021-1029, 2021

Authors: Wu, Qiongling | Lin, Jian | Zhang, Shaohan | Tian, Zhiyong

Article Type: Research Article

Abstract: This paper constructs the continuous-Young optimal weighted arithmetic averaging (C-YOWA) operator and the continuous-Young optimal weighted geometric (C-YOWG) operator based on definite integral and Young inequality. A series of special cases and main properties of the proposed aggregation operators are also investigated. In order to integrate heterogeneous interval data and obtain more accurate prediction results, the heterogeneous interval combination prediction (HICP) model based on C-YOWA operator, C-YOWG operator and Theil coefficient is proposed. The HICP model consider not only the existence of both additive and multiplicative interval information, but also the preference information of experts. Finally, the model is applied …to the empirical analysis of wind energy prediction. The comparison of results shows that the established model can effectively improve the accuracy of prediction. Show more

Keywords: Combination prediction, continuous aggregation operator, interval number, young inequality, Theil coefficient

DOI: 10.3233/JIFS-210004

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1031-1048, 2021

Authors: Dong, Yuanxiang | Deng, Xinglu | Hu, Xinyu | Chen, Weijie

Article Type: Research Article

Abstract: Suppliers can be regarded as unavoidable sources of external risks in modern supply chains, which may cause disruption of supply chains. A resilient supplier usually has a high adaptive ability to reduce the vulnerability against disruptions and recover from disruption to keep continuity in operations. This paper develops an effective multi-attribute group decision-making (MAGDM) framework for resilient supplier selection. Because of the many uncertainties in resilient supplier selection, the dual hesitant fuzzy soft sets (DHFSSs), a very flexible tool to express uncertain and complex information of decision-makers, is utilized to cope with it. In order to obtain the resilient supplier’s …partial order relationship and consider the psychological behavior of decision-makers, this paper proposes the MAGDM framework with DHFSSs based on the TOPSIS method and prospect theory for resilient supplier selection. Furthermore, we consider the consensus level among experts of different backgrounds and experiences and propose a consensus measure method based dual hesitant fuzzy soft numbers (DHFSNs) before selecting a resilient supplier. The expert weights are calculated by the group consensus level between the expert and the group opinions. Meanwhile, we define the entropy of DHFSSs to determine the attribute weights objectively in the decision-making process. Based on this, the proposed method is applied to a practical manufacturing enterprise with an international supply chain for a resilient supplier selection problem. Finally, by performing a sensitivity analysis and a comparative analysis, the results demonstrate the robustness and validity of the proposed method. Show more

Keywords: Resilient supplier selection, group decision making, dual hesitant fuzz soft sets, consensus measure, entropy

DOI: 10.3233/JIFS-210025

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1049-1067, 2021

Authors: Liao, Wei | Wei, Xiaohui | Lai, Jizhou

Article Type: Research Article

Abstract: A novel actor-critic algorithm is introduced and applied to zero-sum differential game. The proposed novel structure consists of two actors and a critic. Different actors represent the control policies of different players, and the critic is used to approximate the state-action utility function. Instead of neural network, the fuzzy inference system is applied as approximators for the actors and critic so that the specific practical meaning can be represented by the linguistic fuzzy rules. Since the goals of the players in the game are completely opposite, the actors for different players are simultaneously updated in opposite directions during the training. …One actor is updated updated toward the direction that can minimize the Q value while the other updated toward the direction that can maximize the Q value. A pursuit-evasion problem with two pursuers and one evader is taken as an example to illustrate the validity of our method. In this problem, the two pursuers the same actor and the symmetry in the problem is used to improve the replay buffer. At the end of this paper, some confrontations between the policies with different training episodes are conducted. Show more

Keywords: Fuzzy inference system, differential game, reinforcement learning, pursuit-evasion problem, deterministic policy gradient

DOI: 10.3233/JIFS-210032

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1069-1082, 2021

Authors: Priyadarshi, Ankur | Saha, Sujan Kumar

Article Type: Research Article

Abstract: In this paper, we present our effort on the development of a Maithili Named Entity Recognition (NER) system. Maithili is one of the official languages of India, with around 50 million native speakers. Although various NER systems have been developed in several Indian languages, we did not find any openly available NER resource or system in Maithili. For the development, we manually annotated a Maithili NER corpus containing around 200K words. We prepared a baseline classifier using Conditional Random Fields (CRF). Then we ran many experiments using various recurrent neural networks (RNN). We collected larger raw corpus to obtain better …word embedding and character embedding. In our experiments, we found, neural models are better than CRF; a CRF layer is effective for the prediction of the final output in the RNN models; character embedding is effective in Maithili language. We also investigated the effectiveness of gazetteer lists in neural models. We prepared a few gazetteer lists from various web resources and used those in the neural models. The incorporation of the gazetteer layer caused performance improvement. The final system achieved an f-measure of 91.6% with 94.9% precision and 88.53% recall. Show more

Keywords: Named entity recognition, Maithili language, corpus annotation, LSTM model, gazetteer lists

DOI: 10.3233/JIFS-210051

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1083-1095, 2021

Authors: Rehman, Hafiz Asadul | Zafar, Kashif | Khan, Ayesha | Imtiaz, Abdullah

Article Type: Research Article

Abstract: Discovering structural, functional and evolutionary information in biological sequences have been considered as a core research area in Bioinformatics. Multiple Sequence Alignment (MSA) tries to align all sequences in a given query set to provide us ease in annotation of new sequences. Traditional methods to find the optimal alignment are computationally expensive in real time. This research presents an enhanced version of Bird Swarm Algorithm (BSA), based on bio inspired optimization. Enhanced Bird Swarm Align Algorithm (EBSAA) is proposed for multiple sequence alignment problem to determine the optimal alignment among different sequences. Twenty-one different datasets have been used in order …to compare performance of EBSAA with Genetic Algorithm (GA) and Particle Swarm Align Algorithm (PSAA). The proposed technique results in better alignment as compared to GA and PSAA in most of the cases. Show more

Keywords: Multiple sequence alignment, Particle swarm optimization, Bioinformatics, Genetic algorithm, swarm intelligence, bird swarm algorithm

DOI: 10.3233/JIFS-210055

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1097-1114, 2021

Authors: Zhou, Ze-Nan | Zhou, Zhiheng | Huang, Junchu

Article Type: Research Article

Abstract: Patch-based deep convolutional neural network (DCNN) has been proved to have advanced performance in no-reference image quality assessment (NR-IQA). However, these methods generally take global quality score as the quality score of each patch mainly since local quality score is not provided. Unfortunately, the perceived quality of image patch is difficult to maintain a high degree of consistency. Thus, the use of the same global quality score in different patches of the same image may hinder training of DCNNs. In this paper, we propose a universal and nearly cost-free model called Gaussian Random Jitter (GRJ). According to the uncertainty of …the perceived quality, GRJ divided the training images into high-confidence distorted images and low-confidence distorted images, and reasonably assigned different local quality scores to each patch through specific gaussian functions with the global quality score as the mean value and the undetermined hyperparameter as the standard deviation. We took one of the most advanced patch-based DCNNs models as backbone and tested the improved performance over three widely used image quality databases. We show that our model can further improve the performance of patch-based models and even help them comparable with those of state-of-the-art NR-IQA algorithms. Show more

Keywords: Patch, gaussian distribution, convolutional neural networks, no-reference image quality assessment

DOI: 10.3233/JIFS-210063

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1115-1124, 2021

Authors: Imran, Muhammad | Ali, Yasir | Malik, Mehar Ali | Hasnat, Kiran

Article Type: Research Article

Abstract: Chromatic spectrum of a colored graph G is a multiset of eigenvalues of colored adjacency matrix of G . The nullity of a disconnected graph is equal to sum of nullities of its components but we show that this result does not hold for colored graphs. In this paper, we investigate the chromatic spectrum of three different classes of 2-regular bipartite colored graphs. In these classes of graphs, it is proved that the nullity of G is not sum of nullities of components of G . We also highlight some important properties and conjectures to extend this problem …to general graphs. Show more

Keywords: Spectrum of graph, nullity of graph, graph coloring

DOI: 10.3233/JIFS-210066

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1125-1133, 2021

Authors: Liu, Haitao | Zhang, Qiang

Article Type: Research Article

Abstract: This paper studies cooperative games in which players have multiple attributes. Such games are applicable to situations in which each player has a finite number of independent additive attributes in cooperative games and the payoffs of coalitions are endogenous functions of these attributes. The additive attributes cooperative game, which is a special case of the multiattribute cooperative game, is studied with respect to the core, the conditions for existence and boundedness and methods of transformation regarding a general cooperative game. A coalitional polynomial form is also proposed to discuss the structure of coalition. Moreover, a Shapley-like solution called the efficient …resource (ER) solution for additive attributes cooperative games is studied via the axiomatical method, and the ER solution of two additive attribute games with equivalent total resources coincides with the Shapley value. Finally, some examples of additive attribute games are given. Show more

Keywords: Multiple attributes, cooperative games, Shapley value, core, solution

DOI: 10.3233/JIFS-210088

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1135-1150, 2021

Authors: Zulqarnain, Rana Muhammad | Xin, Xiao Long | Garg, Harish | Ali, Rifaqat

Article Type: Research Article

Abstract: In this article, we investigate the multi-criteria decision-making complications under Pythagorean fuzzy soft information. The Pythagorean fuzzy soft set (PFSS) is a proper extension of the Pythagorean fuzzy set (PFS) which discusses the parametrization of the attributes of alternatives. It is also a generalization of the intuitionistic fuzzy soft set (IFSS). The PFSS is used to precisely evaluate the deficiencies, anxiety, and hesitation in decision-making (DM). The most essential determination of the current study is to advance some operational laws along with aggregation operators (AOs) within the Pythagorean fuzzy soft environs such as Pythagorean fuzzy soft interaction weighted average (PFSIWA) …and Pythagorean fuzzy soft interaction weighted geometric (PFSIWG) operators with their desirable features. Furthermore, a DM technique has been established based on the developed operators to solve multi-criteria decision-making (MCDM) problems. Moreover, an application of the projected method is presented for the selection of an effective hand sanitizer during the COVID-19 pandemic. A comparative analysis with the merits, effectivity, tractability, along with some available research deduces the effectiveness of this approach. Show more

Keywords: Pythagorean fuzzy sets, Pythagorean fuzzy soft sets, PFSIWA operator, PFSIWG operator, hand sanitizer

DOI: 10.3233/JIFS-210098

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1151-1171, 2021

Authors: Lina, Wang | Zeshui, Xu

Article Type: Research Article

Abstract: Risk management is a significant part of the success of a public-private partnership (PPP) project. There are four phrases for the process of risk management: Constructing a risk management environment, identifying risk factors, evaluating risk factors, and allocating risk factors. After identifying risk factors, it is imperative to analyze and evaluate critical risk factors, which can help participants formulate strategies to allocate risk factors, and thus alleviate the possible adverse results. The objectives of analyzing and evaluating risk factors focus on two aspects: The possibilities of risk occurrence and the degrees of risk loss. On behalf of determining the critical …risk factors effectively, we take the probability degree and linguistic expressions into consideration to manifest experts’ perspectives. We consider critical risk factors in terms of the probabilistic linguistic terms with weakened hedges from the evidential reasoning approach view. The linguistic terms with weakened hedges are applied to express the degree of risk risk loss, and the possibilities of risk occurrence collect from the probabilities of linguistic terms with weakened hedges. First, the commonality function and plausibility function are applied to correct the possibilities of risk occurrence for linguistic terms with weakened hedges. Next, we build a risk evaluation model from experts’ risk propensity and risk perceptions. Moreover, a case study of the risk analyzing and evaluating process of a PPP project is applied to illustrate the availability and effectiveness of the proposed model. We contrast the introduced model with other approaches. Finally, the advantages of this model intend to improve the linguistic terms with weakened hedges for the probabilistic linguistic terms with weakened hedges and evaluate risk factors considering the evidence reasoning approach. Show more

Keywords: Risk evaluation, Probabilistic linguistic terms with weakened hedges, Evidential reasoning theory, Public-private partnership project

DOI: 10.3233/JIFS-210101

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1173-1191, 2021

Authors: Zhao, Yifan | Tian, Shuicheng

Article Type: Research Article

Abstract: Aiming at the problem of large error and long time of early warning response in the traditional system, this paper designs a hazard identification early warning system based on random forest algorithm in underground coal mine. By random classification decision forest created dangerous content in different areas of the downhole information input into the decision tree as a test sample, according to the result of the output of the leaf node determine the risk level of decision trees, and USES the high precision of decision forest classification ability the threat level assessment test sample, radically reducing hazards identification error. Then, …based on the evaluation results, combined with the threshold value of warning criteria to identify the gas exceeding limit area, and determine the fire source warning level, so as to realize the hazard source identification and warning. The simulation results show that the average hazard location identification error of the system is only 4.1%, and the warning response time can be controlled within 9 s. Show more

Keywords: Underground hazard sources, identify early warning, random forest algorithm, decision forest, risk assessment, alarm criteria threshold

DOI: 10.3233/JIFS-210105

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1193-1202, 2021

Authors: Kavitha, N | Ruba Soundar, K | Sathis Kumar, T

Article Type: Research Article

Abstract: In recent years, the Face recognition task has been an active research area in computer vision and biometrics. Many feature extraction and classification algorithms are proposed to perform face recognition. However, the former usually suffer from the wide variations in face images, while the latter usually discard the local facial features, which are proven to be important for face recognition. In this paper, a novel framework based on merging the advantages of the Key points Local Binary/Tetra Pattern (KP-LTrP) and Improved Hough Transform (IHT) with the Improved DragonFly Algorithm-Kernel Ensemble Learning Machine (IDFA-KELM) is proposed to address the face recognition …problem in unconstrained conditions. Initially, the face images are collected from the publicly available dataset. Then noises in the input image are removed by performing preprocessing using Adaptive Kuwahara filter (AKF). After preprocessing, the face from the preprocessed image is detected using the Tree-Structured Part Model (TSPM) structure. Then, features, such as KP-LTrP, and IHT are extracted from the detected face and the extracted feature is reduced using the Information gain based Kernel Principal Component Analysis (IG-KPCA) algorithm. Then, finally, these reduced features are inputted to IDFA-KELM for performing FR. The outcomes of the proposed method are examined and contrasted with the other existing techniques to confirm that the proposed IDFA-KELM detects human faces efficiently from the input images. Show more

Keywords: Face recognition, kernel ensemble learning machine, adaptive kuwahara filter, improved dragonfly algorithm

DOI: 10.3233/JIFS-210130

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1203-1216, 2021

Authors: Li, Lulu

Article Type: Research Article

Abstract: Set-valued data is a significant kind of data, such as data obtained from different search engines, market data, patients’ symptoms and behaviours. An information system (IS) based on incomplete set-valued data is called an incomplete set-valued information system (ISVIS), which generalized model of a single-valued incomplete information system. This paper gives feature selection for an ISVIS by means of uncertainty measurement. Firstly, the similarity degree between two information values on a given feature of an ISVIS is proposed. Then, the tolerance relation on the object set with respect to a given feature subset in an ISVIS is obtained. Next, λ …-reduction in an ISVIS is presented. What’s more, connections between the proposed feature selection and uncertainty measurement are exhibited. Lastly, feature selection algorithms based on λ -discernibility matrix, λ -information granulation, λ -information entropy and λ -significance in an ISVIS are provided. In order to better prove the practical significance of the provided algorithms, a numerical experiment is carried out, and experiment results show the number of features and average size of features by each feature selection algorithm. Show more

Keywords: Rough set theory, ISVIS, feature selection, similarity degree, λ-reduction, λ-discernibility matrix, λ-information granulation, λ-information entropy, λ-significance, algorithm

DOI: 10.3233/JIFS-210135

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1217-1235, 2021

Authors: Wang, Lu

Article Type: Research Article

Abstract: With the prosperity of national economy and the development of highway construction, highway freight transportation plays an increasingly important role in the market economy. Due to its great flexible characteristic, highway freight transportation has been the main mode of transportation in China. On the macro level, there are many factors affecting the development of highway freight transportation especially under the background of the new era. Based on the historical data of the development of highway freight transportation, grey entropy analysis method is applied to analyze the significance of influencing factors for the development of highway freight transportation whose key indicator …is highway freight turnover. Then GM (1, N) model is established to predict the development trend of highway freight turnover and its significant influencing factors. Finally, main problems existing in highway freight transportation and development prospect were discussed and analyzed. The research results show that the three most significant factors affecting the development of road freight turnover in China are the total state revenue, GDP and average distance of highway freight. The established GM (1, N) model can conduct high precision prediction for the development of highway freight transportation. Opportunities and challenges of highway freight transportation industry coexist and its development prospect is promising. It is expected to provide beneficial references for the development strategy and decision-making of highway freight transportation in China. Show more

Keywords: Highway freight transportation, significance analysis, grey entropy analysis method, GM (1, N) prediction model

DOI: 10.3233/JIFS-210141

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1237-1246, 2021

Authors: Tao, Ning | Xiaodong, Duan | Lu, An | Tao, Gou

Article Type: Research Article

Abstract: A disruption management method based on cumulative prospect theory is proposed for the urgent with deteriorating effect arrival in flexible job shop scheduling problem (FJSP). First, the mathematical model of problem is established with minimizing the completion time of urgent order, minimizing the total process time of the system and minimizing the total cost as the target. Then, the cumulative prospect theory equation of the urgent arrival in job shop scheduling process is induced designed. Based on the selected model, an optimized multi-phase quantum particle swarm algorithm (MQPSO) is proposed for selecting processing route. Finally, using Solomon example simulation and …company Z riveting shop example as the study object, the performance of the proposed method is analyzed. It is compared with the current common rescheduling methods, and the results verify that the method proposed in this paper not only meets the goal of the optimized objects, but improves the practical requirements for the stability of production and processing system during urgent arrival. Lastly, the optimized multiphase quantum particle swarm algorithm is used to solve disruption management of urgent arrival problem. Through instance analysis and comparison, the effectiveness and efficiency of urgent arrival disruption management method with deteriorating effect are verified. Show more

Keywords: Flexible job-shop scheduling, deteriorating effect, emergency order insertion, disruption management, multi-phase quantum particle swarm optimization

DOI: 10.3233/JIFS-210166

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1247-1259, 2021

Authors: Bahrami, Vahid | Kalhor, Ahmad | Masouleh, Mehdi Tale

Article Type: Research Article

Abstract: This study intends to investigate the dynamic model estimation and the design of an adaptive neural network based controller for a passive planar robot, performing 2-DoF motion pattern which is in interaction with an actuated cable-driven robot. In fact, the main goal of applying this structure is to use a number of light cables to drive serial robot links and track the desired reference model by the robot’s end-effector. The under study system can be used as a rehabilitation setup which is helpful for those with arm disability. In this way, upon applying sliding mode error dynamics, it is necessary …to determine a vector that contains the matrices related to the robot dynamics. However, finding these matrices requires the use of computational approaches such as Newton-Euler or Lagrange. In addition, since the purpose of this paper is to express comprehensive methods, so with increasing the number of links and degrees of freedom of the robot, finding the dynamics of the robot becomes more difficult. Therefore, the Adaptive Neural Network (ANN) with specific inputs has been used for estimation unknown matrices of the system and the controller design has been performed based on it. So, the main idea in using an adaptive controller is the fact there is no pre-knowledge for the dynamic modeling of the system since the human arm could have different dynamic properties. Hence, the controller is formed by an ANN and robust term. In this way, the adaptation laws of the parameters are extracted by Lyapunov approach, and as a result, as aforementioned, the asymptotic stability of the whole of the system is guaranteed. Simulation results certify the efficiency of the proposed method. Finally, using the Roots Mean Square Error (RMSE) criteria, it has been revealed that, in the presence of bounded disturbance with different amplitude, adding the robust term to the controller leads to improve the tracking error about 34% and 62%, respectively. Show more

Keywords: Dynamic model estimation, adaptive neural network controller, lyapunov approach, passive planar robot, actuated cable-driven robot and rehabilitation setup

DOI: 10.3233/JIFS-210180

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1261-1280, 2021

Authors: Wenbo Huang, First A. | Changyuan Wang, Second B. | Hongbo Jia, Third C.

Article Type: Research Article

Abstract: Traditional intention inference methods rely solely on EEG, eye movement or tactile feedback, and the recognition rate is low. To improve the accuracy of a pilot’s intention recognition, a human-computer interaction intention inference method is proposed in this paper with the fusion of EEG, eye movement and tactile feedback. Firstly, EEG signals are collected near the frontal lobe of the human brain to extract features, which includes eight channels, i.e., AF7, F7, FT7, T7, AF8, F8, FT8, and T8. Secondly, the signal datas are preprocessed by baseline removal, normalization, and least-squares noise reduction. Thirdly, the support vector machine (SVM) is …applied to carry out multiple binary classifications of the eye movement direction. Finally, the 8-direction recognition of the eye movement direction is realized through data fusion. Experimental results have shown that the accuracy of classification with the proposed method can reach 75.77%, 76.7%, 83.38%, 83.64%, 60.49%,60.93%, 66.03% and 64.49%, respectively. Compared with traditional methods, the classification accuracy and the realization process of the proposed algorithm are higher and simpler. The feasibility and effectiveness of EEG signals are further verified to identify eye movement directions for intention recognition. Show more

Keywords: EM, EEG, tactile feedback, wireless sensor network, flying driving, brain electrical signals, data fusion

DOI: 10.3233/JIFS-210191

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1281-1296, 2021

Authors: Narendiranath Babu, T. | Senthilnathan, N. | Pancholi, Shailesh | Nikhil Kumar, S.P. | Rama Prabha, D. | Mohammed, Noor | Wahab, Razia Sultana

Article Type: Research Article

Abstract: This study aims at developing a novel method for condition monitoring technique for detection and classification of developing faults and increase the working life of continuous variable transmission (CVT) using Daubechies Wavelet 06 (DB-06). The vibration data is collected for 4 different types of faults and healthy condition. Using a magnetic accelerometer and signal analyser, vibration data is collected from the system in the time-domain which is then used as input for a MATLAB code producing the plot of the frequency-domain signal. Maximum frequency is determined to diagnose the faults which are induced over three different belts. Collected data for …large scale automotive system (CVT) is used to train the network and then it is tested based on random data points. Faults were classified using ANN with a classification rate of 90.8 %. Show more

Keywords: Continuous variable transmission (CVT), Daubechies wavelet, fault diagnosis, fault classification, artificial neural network

DOI: 10.3233/JIFS-210199

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1297-1307, 2021

Authors: Guo, Wang | Liu, Xingmou | Ma, You | Zhang, Rongjie

Article Type: Research Article

Abstract: The correct identification of gene recombination cold/hot spots is of great significance for studying meiotic recombination and genetic evolution. However, most of the existing recombination spots recognition methods ignore the global sequence information hidden in the DNA sequence, resulting in their low recognition accuracy. A computational predictor called iRSpot-DCC was proposed in this paper to improve the accuracy of cold/hot spots identification. In this approach, we propose a feature extraction method based on dinucleotide correlation coefficients that focus more on extracting potential DNA global sequence information. Then, 234 representative features vectors are filtered by SVM weight calculation. Finally, a convolutional …neural network with better performance than SVM is selected as a classifier. The experimental results of 5-fold cross-validation test on two standard benchmark datasets showed that the prediction accuracy of our recognition method reached 95.11%, and the Mathew correlation coefficient (MCC) reaches 90.04%, outperforming most other methods. Therefore, iRspot-DCC is a high-precision cold/hot spots identification method for gene recombination, which effectively extracts potential global sequence information from DNA sequences. Show more

Keywords: Recombination spots, correlation coefficient, DNA property matrix, support vector machines, convolutional neural network

DOI: 10.3233/JIFS-210213

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1309-1317, 2021

Authors: Shahzadi, Sundas | Rasool, Areen | Sarwar, Musavarah | Akram, Muhammad

Article Type: Research Article

Abstract: Bipolarity plays a key role in different domains such as technology, social networking and biological sciences for illustrating real-world phenomenon using bipolar fuzzy models. In this article, novel concepts of bipolar fuzzy competition hypergraphs are introduced and discuss the application of the proposed model. The main contribution is to illustrate different methods for the construction of bipolar fuzzy competition hypergraphs and their variants. Authors study various new concepts including bipolar fuzzy row hypergraphs, bipolar fuzzy column hypergraphs, bipolar fuzzy k -competition hypergraphs, bipolar fuzzy neighborhood hypergraphs and strong hyperedges. Besides, we develop some relations between bipolar fuzzy k …-competition hypergraphs and bipolar fuzzy neighborhood hypergraphs. Moreover, authors design an algorithm to compute the strength of competition among companies in business market. A comparative analysis of the proposed model is discuss with the existing models such bipolar fuzzy competition graphs and fuzzy competition hypergraphs. Show more

Keywords: Bipolar fuzzy competition hypergraphs, bipolar fuzzy k-competition hypergraphs, bipolar fuzzy neighborhood hypergraphs

DOI: 10.3233/JIFS-210216

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1319-1339, 2021

Authors: Nandal, Amita | Blagojevic, Marija | Milosevic, Danijela | Dhaka, Arvind | Mishra, Lakshmi Narayan

Article Type: Research Article

Abstract: This paper proposes a deep learning framework for Covid-19 detection by using chest X-ray images. The proposed method first enhances the image by using fuzzy logic which improvises the pixel intensity and suppresses background noise. This improvement enhances the X-ray image quality which is generally not performed in conventional methods. The pre-processing image enhancement is achieved by modeling the fuzzy membership function in terms of intensity and noise threshold. After this enhancement we use a block based method which divides the image into smooth and detailed regions which forms a feature set for feature extraction. After feature extraction we insert …a hashing layer after fully connected layer in the neural network. This hash layer is advantageous in terms of improving the overall accuracy by computing the feature distances effectively. We have used a regularization parameter which minimizes the feature distance between similar samples and maximizes the feature distance between dissimilar samples. Finally, classification is done for detection of Covid-19 infection. The simulation results present a comparison of proposed model with existing methods in terms of some well-known performance indices. Various performance metrics have been analysed such as Overall Accuracy, F-measure, specificity, sensitivity and kappa statistics with values 93.53%, 93.23%, 92.74%, 92.02% and 88.70% respectively for 20:80 training to testing sample ratios; 93.84%, 93.53%, 93.04%, 92.33%, and 91.01% respectively for 50:50 training to testing sample ratios; 95.68%, 95.37%, 94.87%, 94.14%, and 90.74% respectively for 80:20 training to testing sample ratios have been obtained using proposed method and it is observed that the results using proposed method are promising as compared to the conventional methods. Show more

Keywords: Covid-19, deep learning, eucledian distance, fuzzy logic, negative likelihood, hashing and machine learning

DOI: 10.3233/JIFS-210222

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1341-1351, 2021

Authors: Yang, Lehua | Li, Dongmei | Tan, Ruipu

Article Type: Research Article

Abstract: Solving the shortest path problem is very difficult in situations such as emergency rescue after a typhoon: road-damage caused by a typhoon causes the weight of the rescue path to be uncertain and impossible to represent using single, precise numbers. In such uncertain environments, neutrosophic numbers can express the edge distance more effectively: membership in a neutrosophic set has different degrees of truth, indeterminacy, and falsity. This paper proposes a shortest path solution method for interval-valued neutrosophic graphs using the particle swarm optimization algorithm. Furthermore, by comparing the proposed algorithm with the Dijkstra, Bellman, and ant colony algorithms, potential shortcomings …and advantages of the proposed method are deeply explored, and its effectiveness is verified. Sensitivity analysis performed using a 2020 typhoon as a case study is presented, as well as an investigation on the efficiency of the algorithm under different parameter settings to determine the most reasonable settings. Particle swarm optimization is a promising method for dealing with neutrosophic graphs and thus with uncertain real-world situations. Show more

Keywords: Interval-valued neutrosophic numbers, neutrosophic graph, particle swarm optimization algorithm, shortest path problem

DOI: 10.3233/JIFS-210233

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1353-1373, 2021

Authors: Yu, Xiaobing | Liu, Zhenjie | Wu, XueJing | Wang, Xuming

Article Type: Research Article

Abstract: Differential evolution (DE) is one of the most effective ways to solve global optimization problems. However, considering the traditional DE has lower search efficiency and easily traps into local optimum, a novel DE variant named hybrid DE and simulated annealing (SA) algorithm for global optimization (HDESA) is proposed in this paper. This algorithm introduces the concept of “ranking” into the mutation operation of DE and adds the idea of SA to the selection operation. The former is to improve the exploitation ability and increase the search efficiency, and the latter is to enhance the exploration ability and prevent the algorithm …from trapping into the local optimal state. Therefore, a better balance can be achieved. The experimental results and analysis have shown its better or at least equivalent performance on the exploitation and exploration capability for a set of 24 benchmark functions. It is simple but efficient. Show more

Keywords: Differential evolution, simulated annealing, ranking, mutation operator, selection operator

DOI: 10.3233/JIFS-210239

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1375-1391, 2021

Authors: Al-shami, Tareq M. | Alshammari, Ibtesam | El-Shafei, Mohammed E.

Article Type: Research Article

Abstract: In 1982, Pawlak proposed the concept of rough sets as a novel mathematical tool to address the issues of vagueness and uncertain knowledge. Topological concepts and results are close to the concepts and results in rough set theory; therefore, some researchers have investigated topological aspects and their applications in rough set theory. In this discussion, we study further properties of N j -neighborhoods; especially, those are related to a topological space. Then, we define new kinds of approximation spaces and establish main properties. Finally, we make some comparisons of the approximations and accuracy measures introduced herein and their counterparts …induced from interior and closure topological operators and E -neighborhoods. Show more

Keywords: Nj-neighborhoods, Ej-neighborhoods, j-neighborhood space, lower and upper approximations, accuracy measure, topological space

DOI: 10.3233/JIFS-210272

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1393-1406, 2021

Authors: Nguyen, Huyen Trang | Chu, Ta-Chung

Article Type: Research Article

Abstract: Understanding employees’ perceptions in team collaboration may help managers select and develop effective teamwork and efficient job completion. Numerous criteria, including qualitative and quantitative, and their importance weights must be considered in evaluating individual diversity perception; therefore, evaluating individual diversity perception is a fuzzy multiple criteria decision-making (MCDM) problem. The purpose of this paper is to use a fuzzy MCDM method to evaluate the personal perception of working in a diverse workgroup. A ranking method using the mean of relative values is proposed to rank the final fuzzy values to complete the model. Formulas of the ranking procedure are derived …to help execute the decision-making procedure and a numerical comparison is conducted to demonstrate the advantage of the proposed ranking method. In addition, a survey about personal diversity perception and willingness to work verifies the feasibility and validity of the proposed mean of relative values based fuzzy MCDM method. The results indicate that decision-makers prefer to work in a different countries-same working field group. More experienced decision-makers, unlike students, prefer to work in the same working sector group. Show more

Keywords: Individual diversity perception, fuzzy MCDM, ranking method, mean of relative values

DOI: 10.3233/JIFS-210291

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1407-1428, 2021

Authors: He, Yan | Wei, Guiwu | Chen, Xudong | Wei, Yu

Article Type: Research Article

Abstract: The financial products selection in the financial services sector is a traditional multi-attribute group decision making (MAGDM) problem. Probabilistic uncertain linguistic sets (PULTSs) could be used to evaluate the financial products with uncertain linguistic terms and corresponding weights (probabilistic). The bidirectional projection (BP) method could take the bidirectional projection values into account. In this paper, we develop an integration model of information entropy and BP method under PULTSs. First of all, utilizing information entropy derives the priority weights of attributes. Next, utilizing the BP method of the PULTSs to obtain the final ranking of the alternatives. To depict the BP …method, the formative vectors of two alternatives are defined, and a weighted vector model and inner product are improved under the PULTSs. In addition, through giving the case of financial products selection and some existing MAGDM methods for comparative analysis, it is proved that the method is practical and effective. The proposed approach also contributes to the effective selection of appropriate options in other decision-making matters. Show more

Keywords: Multi-attribute group decision making (MAGDM), probabilistic uncertain linguistic sets (PULTSs), bidirectional projection (BP) method, information entropy, financial products selection

DOI: 10.3233/JIFS-210313

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1429-1443, 2021

Authors: Botsa, Devaki Rani | Peddi, Phani Bushan Rao | Boddu, Vikas

Article Type: Research Article

Abstract: This paper proposes a new method to rank the parametric form of fuzzy numbers based on defuzzification. The defuzzification process use centroids, value, ambiguity and decision levels on fuzzy number developed from the parametric form of a generalized fuzzy number. The proposed method avoids reducing function to remove lower alpha levels and can overcome the shortcomings in some of the existing fuzzy ranking methods. The proposed method can effectively rank symmetric fuzzy numbers with the same core and different heights, fuzzy numbers with the same support and different cores, crisp numbers, crisp numbers having the same support and different heights, …and fuzzy numbers having compensation of areas. A demonstration of the proposed method through examples and a comparative study with other methods in the literature shows that the proposed method gives effective results. Show more

Keywords: Fuzzy numbers, ranking, value, ambiguity, centroids, decision level

DOI: 10.3233/JIFS-210327

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1445-1459, 2021

Authors: Wang, Lei | Peng, Xindong

Article Type: Research Article

Abstract: It is prominent important for managers to assess the personal risk of mental patients. The evaluation process refers to numerous indexes, and the evaluation values are general portrayed by uncertainty information. In order to conveniently model the complicated uncertainty information in realistic decision making, interval-valued complex Pythagorean fuzzy set is proposed. Firstly, with the aid of Einstein t-norm and t-conorm, four fundamental operations for interval-valued complex Pythagorean fuzzy number (IVCPFN) are constructed along with some operational properties. Subsequently, according to these proposed operations, the weighted average and weighted geometric forms of aggregation operators are initiated for fusing IVCPFNs, and their …anticipated properties are also examined. In addition, a multiple attribute decision making issue is examined under the framework of IVCPFNs when employing the novel suggested operators. Ultimately, an example regarding the assessment on personal risk of mental patients is provided to reveal the practicability of the designed approach, and the attractiveness of our results is further found through comparing with other extant approaches.The main novelty of the coined approach is that it not only can preserve the original assessment information adequately by utilizing the IVCPFNs, but also can aggregate IVCPFNs effectively. Show more

Keywords: Multiple attribute decision making, Einstein operation, interval-valued complex Pythagorean fuzzy number, aggregation operators, personal risk

DOI: 10.3233/JIFS-210352

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1461-1486, 2021

Authors: Tang, Bin | Guo, Shiwei | Yeboah, Mathias | Wang, Zhenhua | Cheng, Song

Article Type: Research Article

Abstract: After sudden outbreak of COVID-19 pandemic, the university campuses were closed and millions of university teachers and students had to shift teaching and learning activities from the classrooms to online courses in China. The COVID-19 pandemic undoubtedly brought significant negative effects to university education activities. How does COVID-19 influenced teaching quality and the degree of influences have been studied by many researches. However, the online course quality which is influences by COVID-19 pandemic was commonly evaluated qualitatively rather than quantitatively. In order to obtain quantitative evaluation results of online course quality during the pandemic period, the integrated FCE-AHP evaluation was …applied. Based on real case of online courses, the influence factors of online course quality were divided into four first-level indicators and further subdivided into 14 second level indicators. The weight vectors of evaluation indicators were determined based on experts’ comments from the Teaching Affairs Committee and the fuzzy evaluation memberships were calculated based on questionnaire results of 2021 students. The evaluation results revealed that the integral performance of online courses is acceptable and the performances of students and hardware are relative weaker. Finally, some improvement measures were conducted to deal with difficulties encountered in online courses during COVID-19 pandemic period. Show more

Keywords: Fuzzy comprehensive evaluation, analytic hierarchy process, COVID-19, online courses, quantitative evaluation

DOI: 10.3233/JIFS-210362

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1487-1498, 2021

Authors: Wang, Qian

Article Type: Research Article

Abstract: With the rapid development of China’s economic globalization in the new era, the demand for English majors is obviously on the rise, which puts forward new and higher requirements for application-oriented undergraduate colleges to train compound English majors. However, from the perspective of teaching quality evaluation of English majors in application-oriented undergraduate colleges, the results are not optimistic. Therefore, it is an important task for higher education research in China to explore the problems existing in the process of teaching quality evaluation for English majors in application-oriented undergraduate colleges and how to better train qualified and versatile talents for English …majors to adapt to the economic and social development in the new era. The teaching quality evaluation of college English is frequently viewed as a multi-attribute group decision-making (MAGDM). Thus, a novel MAGDM method is used to tackle it. Depending on the conventional CODAS method and interval-valued intuitionistic fuzzy sets (IVIFSs), this paper designs a novel distance based IVIF-CODAS method to assess the teaching quality evaluation of college English. First of all, a related literature review is conducted. What’s more, some necessary theories related to IVIFSs are briefly reviewed. In addition, since subjective randomness frequently exists in determining criteria weights, the weights of criteria is decided objectively by utilizing CRITIC method. Afterwards, relying on novel distance measures between IVIFSs, the conventional CODAS method is extended to the IVIFSs to calculate assessment score of every alternative. Therefore, all alternatives can be ranked and the one with the best teaching quality. Eventually, an application about teaching quality evaluation of college English and some comparative methods have been employed to show the superiority of the developed method. The results illustrate that the defined framework is very useful for assessing the teaching quality of college English. Show more

Keywords: MAGDM issues, interval-valued intuitionistic fuzzy sets (IVIFSs), CODAS method, CRITIC method, teaching quality, college English

DOI: 10.3233/JIFS-210366

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1499-1508, 2021

Authors: Yue, Xiaofeng | Ma, Guoyuan | Liu, Fuqiuxuan | Gao, Xueliang

Article Type: Research Article

Abstract: Due to the complexity and variety of textures on Strip steel, it is very difficult to detect defects on rigid surfaces. This paper proposes a metal surface defect classification method based on an improved bat algorithm to optimize BP neural network. First, this paper uses the Local Binary Pattern(LBP) algorithm to extract features from six types of defect images including inclusion, patches, crazing, pitted, rolled-in, and scratches, and build a feature sample library with the extracted feature values. Then, the WG-BA-BP network is used to classify the defect images with different characteristics. The weighted experience factor added by the network …can control the flight speed of the bat according to the number of iterations and the change of the fitness function. And the gamma distribution is added in the process of calculating loudness, which enhances the local searchability. The BP network optimized by this method has higher accuracy. Finally, to verify the effectiveness of the method, this article introduces the five evaluation indicators of accuracy, precision, sensitivity, specificity, and F1 value under the multi-class model. To prove that this algorithm is more feasible and effective compared with other swarm intelligence algorithms. The best prediction performance of WG-BA-BP is 0.010905, and the accuracy rate can reach 0.9737. Show more

Keywords: Image classification, BP neural network, Bat Algorithm, weighted experience factor, Gamma distribution

DOI: 10.3233/JIFS-210374

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1509-1521, 2021

Authors: Aslam, Muhammad | Albassam, Mohammed

Article Type: Research Article

Abstract: In this paper, tests of skewness and kurtosis are introduced under neutrosophic statistics. The necessary measures and neutrosophic forms of these estimators are introduced. The application of the proposed tests is given using the data associated with heart diseases. From the real example analysis, the proposed tests are quite flexible and informative than the existing tests under classical statistics. In addition, it is concluded from the analysis that the proposed tests give information about the measure of indeterminacy in the presence of uncertainty.

Keywords: Skewness, kurtosis, normality, Neutrosophy, heart disease

DOI: 10.3233/JIFS-210375

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1523-1529, 2021

Authors: Li, Baolin | Yang, Lihua

Article Type: Research Article

Abstract: Picture fuzzy set (PFS) and linguistic term set (LTS) are two significant notions in multi-criteria decision-making (MCDM). In practice, decision-makers sometimes need utilize the multiple probable membership degrees for an uncertain linguistic term to express evaluation information. Motivated by these, to better convey the vagueness and uncertainty of cognitive information, multi-valued picture fuzzy uncertain linguistic set combining picture hesitant fuzzy set with uncertain linguistic term set is proposed. We firstly define the concepts of multi-valued picture fuzzy uncertain linguistic set and multi-valued picture fuzzy uncertain linguistic number. Hamacher operations are more general and flexible in information fusion, thus, Hamacher operations …and comparison method are developed at the same time. Improved generalized Heronian Mean operator can simultaneously reflect correlations between values and prevent the redundant calculation. Then, two operators of improved generalized weighted Heronian mean and improved generalized geometric weighted Heronian mean in view of Hamacher operations are proposed. Meanwhile, some distinguished properties and instances of two operators are explored as well. Moreover, a novel MCDM approach applying the developed operators is constructed. Ultimately, an illustrative example on vendor selection is performed, and sensitivity analysis and comparison analysis are provided to verify the powerfulness of the proposed method. Show more

Keywords: Hamacher, improved generalized heronian operator, multi-criteria decision-making, multi-valued picture fuzzy uncertain linguistic set

DOI: 10.3233/JIFS-210404

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1531-1552, 2021

Authors: Akram, Muhammad | Siddique, Saba | Ahmad, Uzma

Article Type: Research Article

Abstract: The main objective of this research article is to classify different types of m -polar fuzzy edges in an m -polar fuzzy graph by using the strength of connectedness between pairs of vertices. The identification of types of m -polar fuzzy edges, including α -strong m -polar fuzzy edges, β -strong m -polar fuzzy edges and δ -weak m -polar fuzzy edges proved to be very useful to completely determine the basic structure of m -polar fuzzy graph. We analyze types of m -polar fuzzy edges in strongest m -polar fuzzy path and m -polar fuzzy cycle. Further, we define …various terms, including m -polar fuzzy cut-vertex, m -polar fuzzy bridge, strength reducing set of vertices and strength reducing set of edges. We highlight the difference between edge disjoint m -polar fuzzy path and internally disjoint m -polar fuzzy path from one vertex to another vertex in an m -polar fuzzy graph. We define strong size of an m -polar fuzzy graph. We then present the most celebrated result due to Karl Menger for m -polar fuzzy graphs and illustrate the vertex version of Menger’s theorem to find out the strongest m -polar fuzzy paths between affected and non-affected cities of a country due to an earthquake. Moreover, we discuss an application of types of m -polar fuzzy edges to determine traffic-accidental zones in a road network. Finally, a comparative analysis of our research work with existing techniques is presented to prove its applicability and effectiveness. Show more

Keywords: α-strong m-polar fuzzy edges, β-strong m-polar fuzzy edges, Menger’s theorem for m-polar fuzzy graphs, Traffic-accidental zones in a road network, Flowchart

DOI: 10.3233/JIFS-210411

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1553-1574, 2021

Authors: Vaiyapuri, Thavavel | Alaskar, Haya | Sbai, Zohra | Devi, Shri

Article Type: Research Article

Abstract: Medical images that are acquired with reduced radiation exposure or following the administration of imaging agents with a low dose, are often known to experience problems by the noise stemming from acquisition hardware as well as psychological sources. This noise can adversely affect the quality of diagnosis, but also prevent practitioners from computing quantitative functional information. With a view to overcoming these challenges, the current paper puts forward optimization of multi-objective for denoising medical images within the wavelet domain. This proposed technique entails the use of genetic algorithm (GA) to get the threshold optimized within the denoising framework of wavelets. …Two purposes are associated with this technique: First, its ability to adapt with different noise types of noise in the image without requiring prior information about the imaging process per se. In addition, it balances relevant diagnostic details’ preservation against the reduction of noise by considering the optimization of the error factor of Liu and SNR as the foundation of objective function. According to the implementation of this method on magnetic resonance (MR) and ultrasound (US) images of the brain, a better performance has been observed as compared to the existing wavelet-based denoising methods with regard to quantitative and qualitative metrics. Show more

Keywords: Medical image denoising, rician noise, speckle noise, wavelet thresholding, threshold optimization, optimization techniques, multi-objective optimization

DOI: 10.3233/JIFS-210429

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1575-1588, 2021

Authors: Yin, Fangchen | Ji, Qinzhi | Jin, Chengwei | Wang, Jing

Article Type: Research Article

Abstract: Milling force prediction is one of the most important ways to improve the quality of products and stability in robot stone machining. In this paper, support vector machines (SVMs) are introduced to model the milling force of white marble, and the model parameters in the SVMs are optimized by the improved quantum-behaved particle swarm optimization (IQPSO) algorithm. A set of online inspection data from stone-machining robotic manipulators is adopted to train and test the model. The overall performance of the model is evaluated based on the decision coefficient (R2), mean absolute percentage error (MAPE) and root mean square error (RMSE), …and the results obtained by IQPSO-SVM are superior to those of the PSO-SVM model. On this basis, the relationship between the milling force of white marble and various machining parameters is explored to obtain optimal machining parameters. The proposed model provides a tool for the adjustment of machining parameters to ensure stable machining quality. This approach is a new method and concept for milling force control and optimization research in the robotic stone milling process. Show more

Keywords: Robot stone machining, quantum-behaved particle swarm algorithm, regression of support vector machines, milling force of white marble, machining parameters

DOI: 10.3233/JIFS-210430

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1589-1609, 2021

Authors: Dang, Xingyue | Liao, Shan | Cheng, Pengsen | Liu, Jiayong

Article Type: Research Article

Abstract: Recently, deep learning methods have been applied to deal with the opinion target extraction (OTE) task with fruitful achievements. On the other hand, since the features captured by the embedding layer can make a multiple-perspective analysis from a sentence, an embedding layer that can grasp the high-level semantics of the sentences is of essence for processing the OTE task and can improve the performance of model into a more efficient manner. However, most of the existing studies focused on the network structure rather than the significant embedded layer, which may be the fundamental reason for the problem of relatively poor …performance in this field, not mention the Chinese extraction model. To compensate these shortcomings, this paper proposes a model using multiple effective features and Bidirectional Encoder Representations from Transformers (BERT) on the architecture of Bidirectional Long Short-Term Memory (BiLSTM) and Conditional Random Field (CRF) for Chinese opinion target extraction task, namely MF-COTE, which can construct features from different perspectives to capture the context and local features of the sentences. Besides, to handle the difficult case of multiple nouns in one sentence, we innovatively propose noting words feature to regulate the model emphasize on the noun near the transition or contrast word, thus leading a better opinion target location. Moreover, to demonstrate the superiorities of the proposed model, extensive comparison experiments are systematically conducted compared with other existing state-of-the-art methods, with the F1-score of 90.76%, 92.10%, 89.63% on the Baidu, the Dianping, and the Mafengwo dataset, respectively. Show more

Keywords: Chinese opinion target extraction, multiple features, noting words, BERT, Long short-term memory

DOI: 10.3233/JIFS-210440

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1611-1626, 2021

Authors: Garg, Harish | Ali, Zeeshan | Yang, Zaoli | Mahmood, Tahir | Aljahdali, Sultan

Article Type: Research Article

Abstract: The paper aims to present a concept of a Complex interval-valued q-rung orthopair uncertain linguistic set (CIVQROULS) and investigated their properties. In the presented set, the membership grades are considered in terms of the interval numbers under the complex domain while the linguistic features are added to address the uncertainties in the data. To further discuss more, we have presented the operation laws and score function for CIVQROULS. In addition to them, we present some averaging and geometric operators to aggregate the different pairs of the CIVQROULS. Some fundamental properties of the proposed operators are stated. Afterward, an algorithm for …solving the decision-making problems is addressed based on the proposed operator using the CIVQROULS features. The applicability of the algorithm is demonstrated through a case study related to brain tumors and their effectiveness is compared with the existing studies. Show more

Keywords: Aggregation operators, classifications of brain tumors, complex interval valued; q-rung orthopair uncertain linguistic sets

DOI: 10.3233/JIFS-210442

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1627-1656, 2021

Authors: Rodriguez, Luis | Castillo, Oscar | Garcia, Mario | Soria, Jose

Article Type: Research Article

Abstract: The main goal of this paper is to outline a new optimization algorithm based on String Theory, which is a relative new area of physics. The String Theory Algorithm (STA) is a nature-inspired meta-heuristic, which is based on studies about a theory stating that all the elemental particles that exist in the universe are strings, and the vibrations of these strings create all particles existing today. The newly proposed algorithm uses equations based on the laws of physics that are stated in String Theory. The main contribution in this proposed method is the new techniques that are devised in order …to generate potential solutions in optimization problems, and we are presenting a detailed explanation and the equations involved in the new algorithm in order to solve optimization problems. In this case, we evaluate this new proposed meta-heuristic with three cases. The first case is of 13 traditional benchmark mathematical functions and a comparison with three different meta-heuristics is presented. The three algorithms are: Flower Pollination Algorithm (FPA), Firefly Algorithm (FA) and Grey Wolf Optimizer (GWO). The second case is the optimization of benchmark functions of the CEC 2015 Competition and we are also presenting a statistical comparison of these results with respect to FA and GWO. In addition, we are presenting a third case, which is the optimization of a fuzzy inference system (FIS), specifically finding the optimal design of a fuzzy controller, where the main goal is to optimize the membership functions of the FIS. It is important to mention that we used these study cases in order to analyze the proposed meta-heuristic with: basic problems, complex problems and control problems. Finally, we present the performance, results and conclusions of the new proposed meta-heuristic. Show more

Keywords: New algorithm, stochastic process, performance, string theory, metaheuristics, control problem

DOI: 10.3233/JIFS-210459

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1657-1675, 2021

Authors: Saeed, Muhammad | Ahsan, Muhammad | Ur Rahman, Atiqe | Saeed, Muhammad Haris | Mehmood, Asad

Article Type: Research Article

Abstract: Brain tumors are one of the leading causes of death around the globe. More than 10 million people fall prey to it every year. This paper aims to characterize the discussions related to the diagnosis of tumors with their related problems. After examining the side effects of tumors, it encases similar indications, and it is hard to distinguish the precise type of tumors with their seriousness. Since in practical assessment, the indeterminacy and falsity parts are frequently dismissed, and because of this issue, it is hard to notice the precision in the patient’s progress history and cannot foresee the period …of treatment. The Neutrosophic Hypersoft set (NHS) and the NHS mapping with its inverse mapping has been design to overcome this issue since it can deal with the parametric values of such disease in more detail considering the sub-parametric values; and their order and arrangement. These ideas are capable and essential to analyze the issue properly by interfacing it with scientific modeling. This investigation builds up a connection between symptoms and medicines, which diminishes the difficulty of the narrative. A table depending on a fuzzy interval between [0, 1] for the sorts of tumors is constructed. The calculation depends on NHS mapping to adequately recognize the disease and choose the best medication for each patient’s relating sickness. Finally, the generalized NHS mapping is presented, which will encourage a specialist to extricate the patient’s progress history and to foresee the time of treatment till the infection is relieved. Show more

Keywords: Tumor, neutrosophic hypersoft, mapping, inverse mapping

DOI: 10.3233/JIFS-210482

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1677-1699, 2021

Authors: Gou, Zhinan | Li, Yan

Article Type: Research Article

Abstract: With the development of the web 2.0 communities, information retrieval has been widely applied based on the collaborative tagging system. However, a user issues a query that is often a brief query with only one or two keywords, which leads to a series of problems like inaccurate query words, information overload and information disorientation. The query expansion addresses this issue by reformulating each search query with additional words. By analyzing the limitation of existing query expansion methods in folksonomy, this paper proposes a novel query expansion method, based on user profile and topic model, for search in folksonomy. In detail, …topic model is constructed by variational antoencoder with Word2Vec firstly. Then, query expansion is conducted by user profile and topic model. Finally, the proposed method is evaluated by a real dataset. Evaluation results show that the proposed method outperforms the baseline methods. Show more

Keywords: Query expansion, user profile, topic model, Word2Vec

DOI: 10.3233/JIFS-210508

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1701-1711, 2021

Authors: Zeng, Detian | Shi, Jingjia | Zhan, Jun | Liu, Shu

Article Type: Research Article

Abstract: To use the electromagnetic chuck to precisely absorb industrial parts in manufacturing, this paper presents a hybrid algorithm for grasping pose optimization, especially for the part with a large surface area and irregular shape. The hybrid algorithm is based on the Gaussian distribution sampling and the hybrid particle swarm optimization (PSO). The Gaussian distribution sampling based on the geometric center point is used to initialize the population, and the dynamic Alpha-stable mutation enhances the global optimization capability of the hybrid algorithm. Compared with other algorithms, the experimental results show that ours achieves the best results on the dataset presented in …this work. Moreover, the time cost of the hybrid algorithm is near a fifth of the conventional PSO in the discovery of optimal grasping pose. In summary, the proposed algorithm satisfies the real-time requirements in industrial production and still has the highest success rate, which has been deployed on the actual production line of SANY Group. Show more

Keywords: Particle swarm optimization, Gaussian distribution, alpha-stable distribution, grasping pose

DOI: 10.3233/JIFS-210520

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1713-1726, 2021

Authors: Li, Yingxin | Li, Shihua | Peng, Shuangyun | Zhao, Shoulu | Yang, Wenxian | Qiu, Lidan

Article Type: Research Article

Abstract: Changes in plateau body lake water are an important indicator of global ecosystem changes, and a timely and accurate grasp of this change information can provide a scientific reference for the formulation of relevant policies. The traditional fuzzy C-means clustering (FCM) algorithm takes into account the ambiguity of the classification of the ground object pixels but does not consider the rich spectral information of the neighboring pixels and is very sensitive to the background noise” of the remote sensing image, resulting in low water extraction accuracy. Aiming to compensate for the shortcomings of the traditional FCM algorithm, this paper proposes …an improved FCM algorithm. This algorithm replaces the Euclidean distance of the traditional FCM algorithm with a combination of the Mahalanobis distance and spectral angle matching (SAM) to fully take into account the spectral information of neighboring pixels and improve the clustering accuracy. The study selected Sentinel-2 images of the Fuxian Lake and Xingyun Lake basins during normal, wet, and dry periods as the data source. Under the same conditions, the clustering accuracy was compared with the traditional FCM algorithm, improved FCM algorithm, K-means clustering method and iterative self-organizing data analysis (ISODATA) clustering method. The experimental results show that the improved FCM algorithm has a higher water extraction accuracy than the traditional FCM algorithm, K-means clustering method and ISODATA clustering method. The kappa coefficient and overall accuracy (OA) of the improved FCM algorithm can be increased by 5.56%–9.45% and 2.66%–5.32%, respectively, and the omission error and commission error can be reduced by 1.72%–4.55% and 12.14%–22.10%, respectively. When the improved FCM algorithm is used, the extraction accuracy is higher for plateau deep lakes than for plateau shallow lakes, and the extraction effect for lakes with poor water environments is more significant than that of other methods. The improved FCM algorithm better maintains the integrity of the water boundary and overcomes the influence of a certain number of mountain shadows and urban building pixels on the clustering results. Show more

Keywords: Remote sensing, fuzzy clustering, FCM algorithm, mahalanobis distance, spectral angle matching

DOI: 10.3233/JIFS-210526

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1727-1740, 2021

Authors: Nan, TaiBen | Zhang, Haidong | He, Yanping

Article Type: Research Article

Abstract: The overwhelming majority of existing decision-making methods combined with the Pythagorean fuzzy set (PFS) are based on aggregation operators, and their logical foundation is imperfect. Therefore, we attempt to establish two decision-making methods based on the Pythagorean fuzzy multiple I method. This paper is devoted to the discussion of the full implication multiple I method based on the PFS. We first propose the concepts of Pythagorean t-norm, Pythagorean t-conorm, residual Pythagorean fuzzy implication operator (RPFIO), Pythagorean fuzzy biresiduum, and the degree of similarity between PFSs based on the Pythagorean fuzzy biresiduum. In addition, the full implication multiple I method for …Pythagorean fuzzy modus ponens (PFMP) is established, and the reversibility and continuity properties of the full implication multiple I method of PFMP are analyzed. Finally, a practical problem is discussed to demonstrate the effectiveness of the Pythagorean fuzzy full implication multiple I method in a decision-making problem. The advantages of the new method over existing methods are also explained. Overall, the proposed methods are based on logical reasoning, so they can more accurately and completely express decision information. Show more

Keywords: Full implication multiple I method, PFS, RPFIO, decision-making problem

DOI: 10.3233/JIFS-210527

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1741-1755, 2021

Authors: Karbasaki, M. Miri | Balooch Shahryari, M. R. | Sedaghatfar, O.

Article Type: Research Article

Abstract: This article identifies and presents the generalized difference (g-difference) of fuzzy numbers, Fréchet and Gâteaux generalized differentiability (g-differentiability) for fuzzy multi-dimensional mapping which consists of a new concept, fuzzy g-(continuous linear) function; Moreover, the relationship between Fréchet and Gâteaux g-differentiability is studied and shown. The concepts of directional and partial g-differentiability are further framed and the relationship of which will the aforementioned concepts are also explored. Furthermore, characterization is pointed out for Fréchet and Gâteaux g-differentiability; based on level-set and through differentiability of endpoints real-valued functions a characterization is also offered and explored for directional and partial g-differentiability. The sufficient …condition for Fréchet and Gâteaux g-differentiability, directional and partial g-differentiability based on level-set and through employing level-wise gH-differentiability (LgH-differentiability) is expressed. Finally, to illustrate the ability and reliability of the aforementioned concepts we have solved some application examples. Show more

Keywords: Fuzzy multi-dimensional mappings, g-(linear continuous) function, g-differentiability, Fréchet g-derivative, Gâteaux g-derivative, Directional g-derivative, Partial g-derivative

DOI: 10.3233/JIFS-210530

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1757-1775, 2021

Authors: Kalli, SivaNagiReddy | Suresh, T. | Prasanth, A. | Muthumanickam, T. | Mohanram, K.

Article Type: Research Article

Abstract: Automatic moving object detection has gained increased research interest due to its widespread applications like security provision, traffic monitoring, and various types of anomalies detection, etc. In the video surveillance system, the video is processed for the detection of motion objects in a step-by-step process. However, the detection has become complex and less effective due to various complex constraints. To obtain an effective performance in the detection of motion objects, this research work focuses to develop an automatic motion object detection system based on the statistical properties of video and supervised learning. In this paper, a novel Background Modeling mechanism …is proposed with the help of a Biased Illumination Field Fuzzy C-means algorithm to detect the moving objects more accurately. Here, the non-stationary pixels are separated from stationary pixels through the Background Subtraction. Afterward, the Biased Illumination Field Fuzzy C-means approach has accomplished to improve the segmentation accuracy through clustering under noise and varying illumination conditions. The performance of the proposed algorithm compared with conventional methods in terms of accuracy, precision, recall, and F- measure. Show more

Keywords: Background modeling, fuzzy c-means, motion object detection, video surveillance system

DOI: 10.3233/JIFS-210563

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1777-1789, 2021

Authors: Cheng, Fangmin | Yu, Suihuai | Qin, Shengfeng | Chu, Jianjie | Chen, Jian

Article Type: Research Article

Abstract: Evaluating the quality of the user experience (UX) of existing products is important for new product development. Conventional UX evaluation methods, such as questionnaire, have the disadvantages of the great subjective influence of investigators and limited number of participants. Meanwhile, online product reviews on e-commerce platforms express user evaluations of product UX. Because the reviews objectively reflect the user opinions and contain a large amount of data, they have potential as an information source for UX evaluation. In this context, this study explores how to evaluate product UX through using online product reviews. A pilot study is conducted to define …the key elements of a review. Then, a systematic method of product UX evaluation based on reviews is proposed. The method includes three parts: extraction of key elements, integration of key elements, and quantitative evaluation based on rough number. The effectiveness of the proposed method is demonstrated by a case study using reviews of a wireless vacuum cleaner. Based on the proposed method, designers can objectively evaluate the UX quality of existing products and obtain detailed suggestions for product improvement. Show more

Keywords: User experience (UX) evaluation, Online product reviews, Opinion mining, UX aspect, Product design

DOI: 10.3233/JIFS-210564

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1791-1805, 2021

Authors: Gogo, Kevin Otieno | Nderu, Lawrence | Mutua, Makau

Article Type: Research Article

Abstract: Fuzzy logic is a branch of artificial intelligence that has been used extensively in developing Fuzzy systems and models. These systems usually offer artificial intelligence based on the predictive mathematical models used; in this case linear regression mathematical model. Interval type 2 Gaussian fuzzy logic is a fuzzy logic that utilizes Gaussian upper membership function and the lower membership function, with a footprint of uncertainty in between the Gaussian membership functions. The artificial intelligence solutions predicted by these interval type 2 fuzzy systems depends on the training and the resultant linear regression mathematical model developed, which usually extract their training …data from the expert knowledge stored in their knowledge bases. The variances in the expert knowledge stored in these knowledge-bases usually affect the overall accuracy of the linear regression predictive models of these systems, due to the variances in the training data. This research therefore establishes the extent that these variances in knowledge bases affect the predictive accuracy of these models, with a case study on knowledge bases used to predict learners’ knowledge level abilities. The calculated linear regression predictive models show that for every variance in the knowledge base, there occurs a change in linear regression predictive model with an intercept value factor commensurate to the variances and their respective weights in the knowledge bases. Show more

Keywords: Interval type 2 gaussian fuzzy logic, linear regression predictive models, intelligent system models, knowledge-bases

DOI: 10.3233/JIFS-210568

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1807-1820, 2021

Authors: Deng, Min-Hui | Zhou, Xiao-Yu | Wang, Jian-Qiang | Li, Jun-Bo | Cheng, Peng-Fei

Article Type: Research Article

Abstract: The development of new energy industry is a pressing issue due to the deterioration of the environment. The selection of new energy projects is a critical problem for decision makers. Incomplete and uncertain information appears in the process of new energy project selection. Compared with other linguistic expressions, probabilistic linguistic term set (PLTS) simultaneously reflects all possible linguistic terms and their corresponding weights, which conforms to the cognitive habits of people. Thus, a multi-criteria decision-making framework under PLTS environment is constructed for energy project selection. Firstly, a normalised projection model of PLTS, which considers the distance and the angle between …two objects, is proposed to overcome the limitations of distance measurement. Secondly, a comprehensive weight-determination method combining the maximum deviation and expert scoring methods is developed to calculate the weight vector of the criteria. Furthermore, a projection-based VIKOR (Višekriterijumska optimizacija i kompromisno rešenje) method is established to select new energy projects, which can reflect the preferences of decision makers for group utility and individual regret. Finally, a numerical study on new energy project selection is performed to determine the validity and applicability of this method. Sensitive and comparative analyses are also conducted to reflect the rationality and feasibility of the method. Show more

Keywords: Multi-criteria decision-making, probabilistic linguistic term set, projection measurement, VIKOR method, new energy project selection

DOI: 10.3233/JIFS-210573

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1821-1836, 2021

Authors: Zhu, Siyu | He, Chongnan | Song, Mingjuan | Li, Linna

Article Type: Research Article

Abstract: In response to the frequent counterfeiting of Wuchang rice in the market, an effective method to identify brand rice is proposed. Taking the near-infrared spectroscopy data of a total of 373 grains of rice from the four origins (Wuchang, Shangzhi, Yanshou, and Fangzheng) as the observations, kernel principal component analysis(KPCA) was employed to reduce the dimensionality, and Fisher discriminant analysis(FDA) and k-nearest neighbor algorithm (KNN) were used to identify brand rice respectively. The effects of the two recognition methods are very good, and that of KNN is relatively better. Howerver the shortcomings of KNN are obvious. For instance, it has …only one test dimension and its test of samples is not delicate enough. In order to further improve the recognition accuracy, fuzzy k-nearest neighbor set is defined and fuzzy probability theory is employed to get a new recognition method –Two-Parameter KNN discrimination method. Compared with KNN algorithm, this method increases the examination dimension. It not only examines the proportion of the number of samples in each pattern class in the k-nearest neighbor set, but also examines the degree of similarity between the center of each pattern class and the sample to be identified. Therefore, the recognition process is more delicate and the recognition accuracy is higher. In the identification of brand rice, the discriminant accuracy of Two-Parameter KNN algorithm is significantly higher than that of FDA and that of KNN algorithm. Show more

Keywords: Brand rice, fuzzy probability, kernel principal component analysis, two-parameter k-nearest neighbor algorithm

DOI: 10.3233/JIFS-210584

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1837-1843, 2021

Authors: Neffati, Syrine | Ben Abdellafou, Khaoula | Aljuhani, Ahamed | Taouali, Okba

Article Type: Research Article

Abstract: The development of Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) systems in the past decade has led to a remarkable advance in biomedical applications and devices. Particularly, CAM and CAD systems are employed in medical engineering, robotic surgery, clinical medicine, dentistry and other biomedical areas. Hence, the accuracy and precision of the CAD and CAM systems are extremely important for proper treatment. This work suggests a new CAD system for brain image classification by analyzing Magnetic Resonance Images (MRIs) of the brain. Firstly, we use the proposed Downsized Rank Kernel Partial Least Squares (DR-KPLS) as a feature extraction technique. Then, …we perform the classification using Support Vector Machines (SVM) and we validate with a k-fold cross validation approach. Further, we utilize the Tabu search metaheuristic approach in order to determine the optimal parameter of the kernel function. The proposed algorithm is entitled DR-KPLS+SVM. The algorithm is tested on the OASIS MRI database. The proposed kernel-based classifier is found to be better performant than the existing methods. Show more

Keywords: Dimensionality reduction, CAD system, Optimization, KLPS, classification

DOI: 10.3233/JIFS-210595

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1845-1854, 2021

Authors: Juneja, Poonam | Garg, Rachana | Kumar, Parmod

Article Type: Research Article

Abstract: The paper presents a novel method for processing uncertain data of Phasor measurement unit (PMU) modules first time in the literature using Fuzzy Reasoning Petri net (FPN). It addresses several key issues such as exploitation of Petri net representation from operating state of PMU to its failure state whereas Fuzzy logic is used to deal with the uncertain data of PMU modules. Sprouting tree, an information flow path, of PMU failure is drawn due to various components and estimation accuracy can be enhanced by integration of more truthiness input data. Fault tree diagram, Fuzzy Petri net model (FPN), production rule …sets for PMU are developed and finally degree of truthiness of proposition is computed from sprouting tree. Fuzzy logic reasoning is used for routing the sprouting tree whereas Petri net is employed for dynamics of states due to failure of modules of PMU. The fusion of two technologies is made for the dynamic response, processing and reasoning to sprouting tree information flow from operating state to unavailability of PMU. The research work is useful to pinpoint the weakness in design of modules of PMU and to assess its reliability. Show more

Keywords: Fuzzy logic system, Petri net, Phasor measurement unit, reliability, sprouting tree

DOI: 10.3233/JIFS-210602

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1855-1867, 2021

Authors: Wang, Shengwei | Li, Ping | Ji, Hao | Zhan, Yulin | Li, Honghong

Article Type: Research Article

Abstract: Intelligent algorithms using deep learning can help learn feature data with nonlinearity and uncertainty, such as time-series particle concentration data. This paper proposes an improved particle swarm optimization (IPSO) algorithm using nonlinear decreasing weights to optimize the hyperparameters, such as the number of hidden layer neurons, learning rate, and maximum number of iterations of the long short-term memory (LSTM) neural network, to predict the time series for air particulate concentration and capture its data dependence. The IPSO algorithm uses nonlinear decreasing weights to make the inertia weights nonlinearly decreasing during the iteration process to improve the convergence speed and capability …of finding the global optimization of the PSO. This study addresses the limitations of the traditional method and exhibits accurate predictions. The results of the improved algorithm reveal that the root means square, mean absolute percentage error, and mean absolute error of the IPSO-LSTM model predicted changes in six particle concentrations, which decreased by 1.59% to 5.35%, 0.25% to 3.82%, 7.82% to 13.65%, 0.7% to 3.62%, 0.01% to 3.55%, and 1.06% to 17.21%, respectively, compared with the LSTM and PSO-LSTM models. The IPSO-LSTM prediction model has higher accuracy than the other models, and its accurate prediction model is suitable for regional air quality management and effective control of the adverse effects of air pollution. Show more

Keywords: Particle concentration, particle swarm optimization, long short-term memory network, nonlinear decreasing weight, air pollution

DOI: 10.3233/JIFS-210603

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1869-1885, 2021

Authors: Liu, Fuwei | Wang, Yansen

Article Type: Research Article

Abstract: The freezing pipe fracture can cause freezing wall to thaw and even lead to major accidents such as mine flooding easily, which seriously threatens the safety in construction. Therefore, scientific and effective comprehensive risk assessment for freezing pipe fracture is of great significance. In this work, a risk assessment method is put forward based on improved AHP-Cloud model with 19 evaluation indicators. First, the multi-dimension evaluation index system and evaluation model are established, on the basis of in-depth analysis of the risk factors that may lead to accidents. Second, synthesizing the normalization process and the improved analytic hierarchy process (AHP), …the evaluation grade cloud and comprehensive evaluation cloud of freezing pipe fracture can be acquired by using the forward cloud generator. Finally, According to the max-subjection principle and the comprehensive evaluation method, we obtain the risk level of freezing pipe fracture. The model is applied to Yangcun Coal Mine. It has been verified that the risk assessment problem of freezing pipe fracture in freezing sinking can be successfully solved by the model we proposed. Above all, the study offers a new research idea for the risk management of freezing pipe fracture in freeze sinking. Show more

Keywords: Freezing pipe fracture, risk assessment, improved AHP-Cloud model, fuzzy factors, freeze sinking

DOI: 10.3233/JIFS-210608

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1887-1900, 2021

Authors: Lian, Jie

Article Type: Research Article

Abstract: In order to improve the distribution efficiency of cold chain logistics and reduce the distribution cost, an optimization model of cross-docking scheduling of cold chain logistics based on fuzzy time window is constructed. According to the complexity of cold chain logistics network, a multi-objective optimization model of cross-docking scheduling of cold chain logistics vehicle routing with fuzzy time window is established. In order to ensure the lowest total cost of cold chain logistics distribution and improve the overall customer satisfaction with service time, the Drosophila optimization algorithm is used to solve the model to obtain the optimal vehicle routing of …cross-docking scheduling optimization of cold chain logistics. The simulation test results show that: after the application of the model, the cold chain logistics distribution time is significantly shortened, the distribution cost is significantly reduced, the damage cost is reduced, the carbon emission of vehicles is reduced, and the economic and low-carbon benefits are significantly improved, which can be used as an effective tool to solve the cross-docking scheduling optimization problem of cold chain logistics. Show more

Keywords: Fuzzy time window, cold chain, logistics, cross-docking, scheduling optimization model, Drosophila optimization algorithm

DOI: 10.3233/JIFS-210611

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1901-1915, 2021

Authors: Wang, Xiaoyan | Sun, Jianbin | Zhao, Qingsong | You, Yaqian | Jiang, Jiang

Article Type: Research Article

Abstract: It is difficult for many classic classification methods to consider expert experience and classify small-sample datasets well. The evidential reasoning rule (ER rule) classifier can solve these problems. The ER rule has strong processing and comprehensive analysis abilities for diversified mixed information and can solve problems with expert experience effectively. Moreover, the initial parameters of the classifier constructed based on the ER rule can be set according to empirical knowledge instead of being trained by a large number of samples, which can help the classifier classify small-sample datasets well. However, the initial parameters of the ER rule classifier need to …be optimized, and choosing the best optimization algorithm is still a challenge. Considering these problems, the ER rule classifier with an optimization operator recommendation is proposed in this paper. First, the initial ER rule classifier is constructed based on training samples and expert experience. Second, the adjustable parameters are optimized, in which the optimization operator recommendation strategy is applied to select the best algorithm by partial samples, and then experiments with full samples are carried out. Finally, a case study on a turbofan engine degradation simulation dataset is carried out, and the results indicate that the ER rule classifier has a higher classification accuracy than other classic classifiers, which demonstrates the capability and effectiveness of the proposed ER rule classifier with an optimization operator recommendation. Show more

Keywords: Evidential reasoning rule (ER rule), optimization operator recommendation, classification, turbofan engine degradation status

DOI: 10.3233/JIFS-210629

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1917-1929, 2021

Authors: Congdong, Li | Weiming, Yang | Yinyun, Yu | Bingjun, Li

Article Type: Research Article

Abstract: In the process of product development, the identification and evaluation of important nodes is of great significance for the effective control of complex product engineering change. In order to identify and evaluate important nodes accurately, this paper proposes a method to evaluate the importance of complex product nodes. Firstly, an engineering change expression method based on multi-stage complex network is proposed. Then, the evaluation index system of important nodes of complex products is constructed. A three parameter grey relational model based on subjective and objective weights is proposed to identify and evaluate the important nodes of complex products. Finally, an …example of a large permanent magnet synchronous centrifugal compressor is analyzed. The example shows that the top nodes are node 4, 1, 7, 9 and 24. Compared with other experiments, the proposed method can effectively and reasonably evaluate the node importance of complex products. Show more

Keywords: Complex product, node importance evaluation, three-parameter interval grey number, grey relational model

DOI: 10.3233/JIFS-210635

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1931-1948, 2021

Authors: Sirbiladze, Gia | Matsaberidze, Bidzina | Ghvaberidze, Bezhan | Midodashvili, Bidzina | Mikadze, David

Article Type: Research Article

Abstract: The attributes influencing the decision-making process in planning transportation of goods from selected facilities locations in disaster zones are considered. Experts evaluate each candidate for humanitarian aid distribution centers (HADCs) (service centers) against each uncertainty factor in q-rung orthopair fuzzy sets (q-ROFS). For representation of experts’ knowledge in the input data for planning emergency service facilities locations a q-rung orthopair fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach is developed. Based on the offered fuzzy TOPSIS aggregation a new innovative objective function is introduced which maximizes a candidate HADC’s selection index and reduces HADCs opening risks …in disaster zones. The HADCs location and goods transportation problem is reduced to the bi-criteria problem of partitioning the set of customers by the set of service centers: 1) Minimization of opened HADCs and goods transportation total costs; 2) Maximization of HADCs selection index. Partitioning type transportation constraints are also constructed. Our approach for solving the constructed bi-criteria partitioning problem consists of two phases. In the first phase, based on the covering’s matrix, we generate a new matrix with columns allowing to find all possible partitioning of the demand points with the opened HADCs. In the second phase, using the generated matrix and our exact algorithm we find the partitioning –allocations of the HADCs to the centers corresponded to the Pareto-optimal solutions. The constructed model is illustrated with a numerical example. Show more

Keywords: q-rung orthopair fuzzy sets, TOPSIS, fuzzy multi-objective facility location-transportation problem, partitioning problem, Pareto-optimal solution

DOI: 10.3233/JIFS-210636

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1949-1962, 2021

Authors: Tang, Xing | Yu, Suihuai | Chu, Jianjie | Fan, Hao

Article Type: Research Article

Abstract: When the proximity sensor of a smartphone is impaired, it would easily lead to screen mistouch during conversation, which will significantly affect the user experience. However, there are relatively few studies that have been focused on the quality of user experience following sensor impairment. The purpose of this study was to compare and evaluate different machine learning models in forecasting the user’s posture during a phone call, thereby providing a compensation approach for detecting proximity to the human ear during a phone call following sensor damage. The built-in accelerometer sensors of smartphones were employed to collect posture data while users …were employing their smartphones. Three main postures (holding, moving and answering) were identified; the posture data were obtained through training and prediction using five machine learning models. The results showed that the model that utilized triaxial data had better prediction accuracy than the model that used single-axis data. Furthermore, models with time-domain features had a higher accuracy rate. Among the five models, neural networks had the best prediction accuracy (0.982). The proposed approach could be of immense benefit to the users following proximity sensor damage, and would be advantageous in the design of the smartphone, particularly in the early stages of the design process. Show more

Keywords: Accelerometer sensor, damage, posture, proximity sensor, smartphone

DOI: 10.3233/JIFS-210646

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1963-1974, 2021

Authors: Akram, Muhammad | Ullah, Inayat | Allahviranloo, Tofigh | Edalatpanah, S.A.

Article Type: Research Article

Abstract: A Pythagorean fuzzy set is a powerful model for depicting fuzziness and uncertainty. This model is more flexible and practical as compared to an intuitionistic fuzzy model. This research article presents a new model called LR -type fully Pythagorean fuzzy linear programming problem. We consider the notions of LR -type Pythagorean fuzzy number, ranking for LR -type Pythagorean fuzzy numbers and arithmetic operations for unrestricted LR -type Pythagorean fuzzy numbers. We propose a method to solve LR -type fully Pythagorean fuzzy linear programming problems with equality constraints. We describe our proposed method with numerical examples including diet problem.

Keywords: Pythagorean fuzzy linear programming problem, ranking function, LR-type Pythagorean fuzzy numbers

DOI: 10.3233/JIFS-210655

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1975-1992, 2021

Authors: Van Nguyen, Kiet | Duy Nguyen, Nhat | Do, Phong Nguyen-Thuan | Gia-Tuan Nguyen, Anh | Nguyen, Ngan Luu-Thuy

Article Type: Research Article

Abstract: Machine Reading Comprehension has attracted significant interest in research on natural language understanding, and large-scale datasets and neural network-based methods have been developed for this task. However, most developments of resources and methods in machine reading comprehension have been investigated using two resource-rich languages, English and Chinese. This article proposes a system called ViReader for open-domain machine reading comprehension in Vietnamese by using Wikipedia as the textual knowledge source, where the answer to any particular question is a textual span derived directly from texts on Vietnamese Wikipedia. Our system combines a sentence retriever component, based on techniques of information retrieval …to extract the relevant sentences, with a transfer learning-based answer extractor trained to predict answers based on Wikipedia texts. Experiments on multiple datasets for machine reading comprehension in Vietnamese and other languages demonstrate that (1) our ViReader system is highly competitive with prevalent machine learning-based systems, and (2) multi-task learning by using a combination consisting of the sentence retriever and answer extractor is an end-to-end reading comprehension system. The sentence retriever component of our proposed system retrieves the sentences that are most likely to provide the answer response to the given question. The transfer learning-based answer extractor then reads the document from which the sentences have been retrieved, predicts the answer, and returns it to the user. The ViReader system achieves new state-of-the-art performances, with values of 70.83 % EM (exact match) and 89.54 % F1, outperforming the BERT-based system by 11.55% and 9.54% , respectively. It also obtains state-of-the-art performance on UIT-ViNewsQA (another Vietnamese dataset consisting of online health-domain news) and BiPaR (a bilingual dataset on English and Chinese novel texts). Compared with the BERT-based system, our system achieves significant improvements (in terms of F1) with 7.65% for English and 6.13% for Chinese on the BiPaR dataset. Furthermore, we build a ViReader application programming interface that programmers can employ in Artificial Intelligence applications. Show more

Keywords: Machine reading comprehension, question answering, transfer learning, sentence transformer

DOI: 10.3233/JIFS-210683

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1993-2011, 2021

Authors: Kumar, Mukul | Katyal, Nipun | Ruban, Nersisson | Lyakso, Elena | Mary Mekala, A. | Joseph Raj, Alex Noel | Maarc Richard, G.

Article Type: Research Article

Abstract: Over the years the need for differentiating various emotions from oral communication plays an important role in emotion based studies. There have been different algorithms to classify the kinds of emotion. Although there is no measure of fidelity of the emotion under consideration, which is primarily due to the reason that most of the readily available datasets that are annotated are produced by actors and not generated in real-world scenarios. Therefore, the predicted emotion lacks an important aspect called authenticity, which is whether an emotion is actual or stimulated. In this research work, we have developed a transfer learning and …style transfer based hybrid convolutional neural network algorithm to classify the emotion as well as the fidelity of the emotion. The model is trained on features extracted from a dataset that contains stimulated as well as actual utterances. We have compared the developed algorithm with conventional machine learning and deep learning techniques by few metrics like accuracy, Precision, Recall and F1 score. The developed model performs much better than the conventional machine learning and deep learning models. The research aims to dive deeper into human emotion and make a model that understands it like humans do with precision, recall, F1 score values of 0.994, 0.996, 0.995 for speech authenticity and 0.992, 0.989, 0.99 for speech emotion classification respectively. Show more

Keywords: Deep learning, speech fidelity classification, linear prediction cepstral coefficients (LPCC), mel frequency cepstral coefficients (MFCC), speech emotion recognition

DOI: 10.3233/JIFS-210711

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2013-2024, 2021

Authors: Gul, Rizwan | Shabir, Muhammad

Article Type: Research Article

Abstract: Pawlak’s rough set theory based on single granulation has been extended to multi-granulation rough set structure in recent years. Multi-granulation rough set theory has become a flouring research direction in rough set theory. In this paper, we propose the notion of (α , β )-multi-granulation bipolar fuzzified rough set ((α , β )-MGBFRSs). For this purpose, a collection of bipolar fuzzy tolerance relations has been used. In the framework of multi-granulation, we proposed two types of (α , β )-multi-granulation bipolar fuzzified rough sets model. One is called the optimistic (α , β )-multi-granulation bipolar fuzzified rough sets ((α , …β ) o -MGBFRSs) and the other is called the pessimistic (α , β )-multi-granulation bipolar fuzzified rough sets ((α , β ) p -MGBFRSs). Subsequently, a number of important structural properties and results of proposed models are investigated in detail. The relationships among the (α , β )-MGBFRSs, (α , β ) o -MGBFRSs and (α , β ) p -MGBFRSs are also established. In order to illustrate our proposed models, some examples are considered, which are helpful for applying this theory in practical issues. Moreover, several important measures associated with (α , β )-multi-granulation bipolar fuzzified rough set like the measure of accuracy , the measure of precision , and accuracy of approximation are presented. Finally, we construct a new approach to multi-criteria group decision-making method based on (α , β )-MGBFRSs, and the validity of this technique is illustrated by a practical application. Compared with the existing results, we also expound its advantages. Show more

Keywords: Rough set, multi-granulation rough approximations, bipolar fuzzy tolerance relation, (α, β)-bipolar fuzzified rough set, (α, β)-multi-granulation bipolar fuzzified rough sets, decision making method

DOI: 10.3233/JIFS-210717

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2025-2060, 2021

Authors: Sakthidasan Sankaran, K. | Gao, Xiao-Zhi

Article Type: Research Article

Abstract: Nowadays, numerous algorithms on power allocation have been proposed for maximizing the EE (Energy efficiency) and SE (Spectral efficiency) in the Distributed Antenna System (DAS). Moreover, the conservative techniques employed for power allocation seem to be problematic, due to their high computational complexity. The main objective of this paper focuses on optimizing the power allocation in order to enhance the EE and SE along with the improved antenna capacity using an effective optimization approach with the clustering model. To obtain the optimized power allocation and antenna capacity, Multi-scale resource Grasshopper Optimization Algorithm (Multi-scale resource GOA) scheme is proposed and employed. …Furthermore, clustering is developed based on the Discriminative cluster-based Expectation maximization (DC-EM) clustering algorithms, which also helps to reduce the interference rate and computational complexity. The performance analysis is made under various scenarios and circumstances. The proposed system (DAS with GOA-EM) is assessed and compared with the existing approaches in terms of both the EE and SE, which demonstrates that its superiority. Show more

Keywords: Distributed Antenna system, power allocation, energy efficiency, spectral efficiency, optimization algorithms

DOI: 10.3233/JIFS-210727

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2061-2072, 2021

Authors: Islam, Sk Rabiul | Pal, Madhumangal

Article Type: Research Article

Abstract: Topological indices have an important role in molecular chemistry, network theory, spectral graph theory and several physical worlds. Most of the topological indices are defined in a crisp graph. As fuzzy graphs are more generalization of crisp graphs, those indices have more application in fuzzy graphs also. In this article, we introduced the fuzzy hyper-Wiener index (FHWI) and studied this index for various fuzzy graphs like path, cycle, star, etc and provided some interesting bounds of FHWI for that fuzzy graph. A lower bound of FHWI is established for n -vertex connected fuzzy graph depending on strength of a strong …edges. A relation between FHWI of a tree and its maximum spanning tree is established and this index is calculated for the saturated cycle. Also, at the end of the article, an application in the share market of this index is presented. Show more

Keywords: Fuzzy graph, wiener index, hyper-wiener index, fuzzy hyper-wiener index

DOI: 10.3233/JIFS-210736

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2073-2083, 2021

Authors: Qureshi, Shahana Gajala | Shandilya, Shishir Kumar

Article Type: Research Article

Abstract: WSN (Wireless Sensor Network) is a network of devices which can transfer the data collected from an examined field via wireless links. Thus secure data transmission is required for accurate transfer of data from source to destination as data passes through various intermediate nodes. The study intends to perform shortest, secure path routing on the basis of trust through novel Hybridized Crow Whale Optimization (H-CWO) and QoS based bipartite Coverage Routing (QOS-CR) as well as to analyze the system’s performance. Nodes are randomly deployed in the network area. Initially, a trust metric formation is implemented via novel H-CWO and the …authenticated nodes are selected. Then through the secure routing protocol, Cluster head (CH) is selected to perform clustering. Neighbourhood hop prediction is executed to determine the shortest path routing and secure data transfer is performed through novel QOS-CR. The proposed system is analyzed by comparing it with various existing methods in terms of delay, throughput, energy and alive nodes. The results attained from comparative analysis revealed the efficiency of the proposed system. The proposed novel H-CWO and QOS-CR exhibited minimum delay, high throughput, energy and maximum alive nodes thereby ensuring safe transmission of data from source node to destination node. Show more

Keywords: Wireless sensor networks, trust metric, secured routing, hybrid crow whale optimization and QOS based bipartite coverage routing

DOI: 10.3233/JIFS-210766

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2085-2099, 2021

Authors: Cao, Mengxue | Lu, Laijun | Zhong, Yu

Article Type: Research Article

Abstract: How to more effectively perform anomaly detection of combination information has always been an important issue for the scholars in various fields. In order to identify and extract the geochemical anomaly information related to polymetallic mineralization in the Hunjiang area, this article uses the hybrid method that combines multivariate canonical harmonic trend analysis (MCHTA), singularity analysis with radius-areal metal amount and improved adaptive fuzzy self-organizing map (IAFSOM). First, multiple sets of combination feature information with multi-dimensional variables will be obtained through the MCHTA method, which information is considered as the initial information for the subsequent analysis. Next, the singularity analysis …method is used to process the combination concentration value to calculate the singularity indexes. Finally, the singularity indexes are classified by the IAFSOM method, and nine groups of sample data are obtained. The analysis results found that the samples information in fourth group covered most of the low α -values. The main conclusions in this study are as follows: (1) The MCHTA method can effectively detect the combination information related to geochemical anomaly; (2) The application of singularity analysis method with radius-areal metal amount can reveal the significant characteristics of mineralization combination elements; (3) IAFSOM can be used as an effective tool for the classification and identification of geochemical anomaly with combination information; (4) the hybrid method that combines MCHTA method, singularity analysis and IAFSOM model has a good indication significance in the prospecting of geochemical anomalies, and could provide a good method for geochemical prospecting. Show more

Keywords: Key words: Multivariate canonical harmonic trend analysis (MCHTA), singularity analysis with radius-areal metal amount, improved adaptive fuzzy self-organizing mapping (IAFSOM), iron polymetallic mineralization, Hunjiang district

DOI: 10.3233/JIFS-210786

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2101-2110, 2021

Authors: Song, Juan | Ni, Zhiwei | Jin, Feifei | Wu, Wenying | Li, Ping

Article Type: Research Article

Abstract: Probabilistic dual hesitant fuzzy sets (PDHFSs) have good flexibility and integrity in expressing fuzzy and uncertain information. However, some crucial problems related to PDHFSs remain unsolved, such as how to define probabilistic dual hesitant fuzzy preference relations (PDHFPRs) and solve group decision-making (GDM) problems with PDHFPRs. This paper establishes the concept of PDHFPRs and investigates consensus-based GDM methods with PDHFPRs. First, a new distance measure is proposed to quantify the difference between two PDHFPRs, which does not increase the virtual elements of membership and non-membership degrees, and can contain all distance combination of membership and non-membership elements. Therefore, the distance …calculation results are not affected by the subjectivity of decision-makers (DMs). Second, the consensus measures for PDHFPRs are proposed, which are effective tool to measure the consensus level among DMs. Moreover, two consensus-based GDM methods are proposed, which can improve the group consensus level for PDHFPRs by changing the PDHFPR with the worst consensus level or modifying the weights of DMs. Finally, the proposed methods are applied to the location selection of large-scale industrial solid waste treatment facilities. The comparison with existing methods illustrates the validity and feasibility of the proposed methods. Show more

Keywords: Group decision-making, probabilistic dual hesitant fuzzy preference relations, distance measure, consensus

DOI: 10.3233/JIFS-210796

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2111-2128, 2021

Authors: Zhang, Xiangxiang | Chang, Liu | Luo, Jingwen | Wu, Jia

Article Type: Research Article

Abstract: With the rise of the Internet of Things, the opportunistic network of portable smart devices has become a new hot spot in academic research in recent years. The mobility of nodes in opportunistic networks makes the communication links between nodes unstable, so data forwarding is an important research content in opportunistic networks. However, the traditional opportunistic network algorithm only considers the transmission of information and does not consider the social relationship between people, resulting in a low transmission rate and high network overhead. Therefore, this paper proposes an efficient data transmission model based on community clustering. According to the user’s …social relationship and the release location of the points of interest, the nodes with a high degree of interest relevance are divided into the same community. Weaken the concept of a central point in the community, and users can share information to solve the problem of excessive load on some nodes in the network and sizeable end-to-end delay. Show more

Keywords: Opportunistic social networks, community clustering, interest point, community reconstruction, data transmission, IoT system

DOI: 10.3233/JIFS-210807

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2129-2144, 2021

Authors: Zhao, Peng | Han, Baoming | Li, Dewei | Li, Yawei

Article Type: Research Article

Abstract: As a key operation for the daily maintenance of electric multiple units (EMU), the first-level maintenance operation directly affects the utilization efficiency of the EMU. The fixed operation sequence of EMU trains, the limitation of the track capacity and inconsistent arrival time of EMU trains give rise to such problems as extended waiting time, idle tracks and waste of maintenance capacity. To solve these problems and optimize the assignment of EMU-to-track, we propose a flexible job-shop sequence scheduling (Flexible-JSS) mode for the first-level maintenance of EMU trains, and a flexible sequence and tracks sharing (FSTS) model for the first-level maintenance …at electric multiple units depot (EMUD) has also been proposed in this paper. The FSTS model is designed to shorten the latest completion time after taking into account the constraints such as the train length, track capacity, the operation sequence of all EMU trains, the operation process of a single EMU train, and the train-set scheduling plan. A modified genetic algorithm is used to solve the model. The feasibility and effectiveness of the model and algorithm are verified by a real case, and the comparison with the other two fixed job-shop sequence scheduling (Fixed-JSS) modes proves that the Flexible-JSS mode can improve the efficiency and ability of the first-level maintenance at EMUD impressively. Show more

Keywords: First-level maintenance operation, flexible job-shop sequence scheduling mode, flexible sequence and tracks sharing (FSTS) model, modified genetic algorithm, the latest completion time

DOI: 10.3233/JIFS-210823

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2145-2160, 2021