Journal of Intelligent & Fuzzy Systems - Volume 40, issue 6

Authors: Jiang, Chunmao | Guo, Doudou | Sun, Lijuan

Article Type: Research Article

Abstract: The basic idea of the three-way decisions (3WD) is ‘thinking in threes.’ The TAO (trisecting-acting-outcome) model of 3WD includes three components, trisect a whole into three reasonable regions, devise a corresponding strategy on the trisection, and measure the effectiveness of the outcome. By reviewing existing studies, we found that only a few papers touch upon the third component, i.e., measure the effect. This paper’s principal aim is to present an effectiveness measure framework consisting of three parts: a specific TAO model - Change-based TAO model, interval sets, and utility functions with unique characteristics. Specifically, the change-based TAO model provides a …method to measure effectiveness based on the difference before and after applying a strategy or an action. First, we use interval sets to represent these changes when a strategy or an action is applied. These changes correspond to three different intervals. Second, we use the utility measurement method to figure out three change intervals. Namely, different utility measures correspond to the different intervals, concave utility metric, direct utility metric, and convex utility metric, respectively. Third, it aggregates the toll utility through the joint of the three utilities mentioned above. The weights among these three are adjusted by a dual expected utility function that conveys the decision-makers’ preferences. We give an example and experiment highlighting the validity and practicability of the utility measure method in the change-based TAO model of three-way decisions. Show more

Keywords: Three-way decisions, change-based TAO model, interval set, utility measurement, dual expected utility

DOI: 10.3233/JIFS-202207

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11071-11084, 2021

Authors: Qian, Tiancheng | Mei, Xue | Xu, Pengxiang | Ge, Kangqi | Qiu, Zhelei

Article Type: Research Article

Abstract: Recently many methods use encoder-decoder framework for video captioning, aiming to translate short videos into natural language. These methods usually use equal interval frame sampling. However, lacking a good efficiency in sampling, it has a high temporal and spatial redundancy, resulting in unnecessary computation cost. In addition, the existing approaches simply splice different visual features on the fully connection layer. Therefore, features cannot be effectively utilized. In order to solve the defects, we proposed filtration network (FN) to select key frames, which is trained by deep reinforcement learning algorithm actor-double-critic. According to behavior psychology, the core idea of actor-double-critic is …that the behavior of agent is determined by both the external environment and the internal personality. It avoids the phenomenon of unclear reward and sparse feedback in training because it gives steady feedback after each action. The key frames are sent to combine codec network (CCN) to generate sentences. The operation of feature combination in CCN make fusion of visual features by complex number representation to make good semantic modeling. Experiments and comparisons with other methods on two datasets (MSVD/MSR-VTT) show that our approach achieves better performance in terms of four metrics, BLEU-4, METEOR, ROUGE-L and CIDEr. Show more

Keywords: Video captioning, deep reinforcement learning, frame sampling, feature fusion, sparse reward, actor-critic

DOI: 10.3233/JIFS-202249

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11085-11097, 2021

Authors: Chen, Yang | Yang, Jiaxiu

Article Type: Research Article

Abstract: In recent years, interval type-2 fuzzy logic systems (IT2 FLSs) have become a hot topic for the capability of coping with uncertainties. Compared with the centroid type-reduction (TR), investigating the center-of-sets (COS) TR of IT2 FLSs is more favorable for applying IT2 FLSs. Actually, it is still an open question for comparing Karnik-Mendel (KM) types of algorithms and other types of alternative algorithms for COS TR. This paper gives the block of fuzzy reasoning, COS TR, and defuzzification of IT2 FLSs based on Nagar-Bardini (NB), Nie-Tan (NT) and Begian-Melek-Mendel (BMM) noniterative algorithms. Six simulation experiments are used to show the …performances of three types of noniterative algorithms. The proposed noniterative algorithms can obtain much higher computational efficiencies compared with the KM algorithms, which give the potential value for designing T2 FLSs. Show more

Keywords: Interval type-2 fuzzy logic systems, center-of-sets type-reduction, Nagar-Bardini algorithms, computational efficiency, Nie-Tan algorithms

DOI: 10.3233/JIFS-202264

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11099-11106, 2021

Authors: Li, Mengmeng | Zhang, Chiping | Chen, Minghao | Xu, Weihua

Article Type: Research Article

Abstract: Multi-granulation decision-theoretic rough sets uses the granular structures induced by multiple binary relations to approximate the target concept, which can get a more accurate description of the approximate space. However, Multi-granulation decision-theoretic rough sets is very time-consuming to calculate the approximate value of the target set. Local rough sets not only inherits the advantages of classical rough set in dealing with imprecise, fuzzy and uncertain data, but also breaks through the limitation that classical rough set needs a lot of labeled data. In this paper, in order to make full use of the advantage of computational efficiency of local rough …sets and the ability of more accurate approximation space description of multi-granulation decision-theoretic rough sets, we propose to combine the local rough sets and the multigranulation decision-theoretic rough sets in the covering approximation space to obtain the local multigranulation covering decision-theoretic rough sets model. This provides an effective tool for discovering knowledge and making decisions in relation to large data sets. We first propose four types of local multigranulation covering decision-theoretic rough sets models in covering approximation space, where a target concept is approximated by employing the maximal or minimal descriptors of objects. Moreover, some important properties and decision rules are studied. Meanwhile, we explore the reduction among the four types of models. Furthermore, we discuss the relationships of the proposed models and other representative models. Finally, illustrative case of medical diagnosis is given to explain and evaluate the advantage of local multigranulation covering decision-theoretic rough sets model. Show more

Keywords: Covering rough sets, local rough sets, local covering rough sets, multigranulation rough sets

DOI: 10.3233/JIFS-202274

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11107-11130, 2021

Authors: Wei, Shanting | Zhang, Zhuo | Chen, Xintong

Article Type: Research Article

Abstract: Network-based incubation has undergone rapid developments and the incubation mechanism has begun to change recently. To incentive the start-ups on the basis of ensuring its own interests, the incubator needs to design a feasible contract. According to network theory, a single network cannot adequately describe the heterogeneous alliances of incubated start-ups in the business incubator. Therefore, by constructing super-network structure of incubated start-ups, this paper designs two types of linear incentive contracts and uses numerical simulation to further discuss the model. The results indicate that the business incubator should design the contract according to the different capability levels and risk …preference degree of start-ups: linear screening contract (LSC) is more effective to motivate the incubated start-ups to improve the capability, while the incentive effect will be weakened by the increasing proportion of high-capability start-ups; for high risk-preference start-ups, linear pooling contract (LPC) is superior than LSC. The results can serve as a theoretical direction for the business incubator to effectively distinguish different capability levels of start-ups and make better decision on contract design to motivate start-ups on the basis of ensuring the maximization of its own utility. Show more

Keywords: Business incubator, start-up, super-network, dynamic capability, cooperative network, knowledge network

DOI: 10.3233/JIFS-202279

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11131-11144, 2021

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

Article Type: Research Article

Abstract: Many corporations purchase components from suppliers, which can reduce operating costs and enable firms to focus their resources on core advantages. Studies have indicated that process quality and manufacturing time performance are two crucial indicators for supplier selection. We used the process quality index and a manufacturing time performance index to create a dual dimensional fuzzy supplier selection model. First, the upper confidence limits of these two indices were derived, and a fuzzy membership function based on these limits was constructed. Based on the fuzzy test rules for process quality and manufacturing time performance, we divided the fuzzy supplier selection …matrix into nine evaluation zones. Using the upper confidence limits of these two indices, we created evaluation coordinates and assigned weights based on the location of the coordinates. Then, the total of all the weights was employed to form a supplier selection index for which a higher value means a higher ranking. The use of confidence limits decreased the chance of misjudgment resulting from sampling errors while the fuzzy test rules increased the applicability of the model. Consequently, the proposed model can be used to select suppliers efficiently so as to form partnerships in which corporations and suppliers can grow together. Show more

Keywords: Fuzzy supplier selection model, fuzzy membership function, manufacturing time performance, process quality, upper confidence limit

DOI: 10.3233/JIFS-202349

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11145-11158, 2021

Authors: Lei, Shi

Article Type: Research Article

Abstract: An improved Web community discovery algorithm is proposed in this paper based on the attraction between Web pages to effectively reduce the complexity of Web community discovery. The proposed algorithm treats each Web page in the Web pages collection as an individual with attraction based on the theory of universal gravitation, elaborates the discovery and evolution process of Web community from a Web page in the Web pages collection, defines the priority rules of Web community size and Web page similarity, and gives the calculation formula of the change in Web page similarity. Finally, an experimental platform is built to …analyze the specific discovery process of the Web community in detail, and the changes in cumulative distribution of Web page similarity are discussed. The results show that the change in the similarity of a new page satisfies the power-law distribution, and the similarity of a new page is proportional to the size of Web community that the new page chooses to join. Show more

Keywords: Web community, web page, attraction, evolution process, web page similarity

DOI: 10.3233/JIFS-202366

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11159-11169, 2021

Authors: He, Weiming | Wu, You | Xiao, Jing | Cao, Yang

Article Type: Research Article

Abstract: Feature pyramids are commonly applied to solve the scale variation problem for object detection. One of the most representative works of feature pyramid is Feature Pyramid Network (FPN), which is simple and efficient. However, the fully power of multi-scale features might not be completely exploited in FPN due to its design defects. In this paper, we first analyze the structure problems of FPN which prevent the multi-scale feature from being fully exploited, then propose a new feature pyramid structure named Mixed Group FPN (MGFPN) , to mitigate these design defects of FPN. Concretely, MGFPN strengthens the feature utilization by two …modules named Mixed Group Convolution(MGConv) and Contextual Attention(CA) . MGConv reduces the spatial information loss of FPN in feature generation stage. And CA narrows the semantic gaps between features of different receptive field before lateral summation. By replacing FPN with MGFPN in FCOS, our method can improve the performance of detectors in many major backbones by 0.7 to 1.2 Average Precision(AP) on MS-COCO benchmark without adding too much parameters and it is easy to be extended to other FPN-based models. The proposed MGFPN can serve as a simple and strong alternative for many other FPN based models. Show more

Keywords: Object Detection, Feature Pyramids, FPN, Mixed Group Convolution, Contextual Attention

DOI: 10.3233/JIFS-202372

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11171-11181, 2021

Authors: Touqeer, Muhammad | Umer, Rimsha | Ali, Muhammad Irfan

Article Type: Research Article

Abstract: Pythagorean fuzzy sets and interval-valued Pythagorean fuzzy sets are more proficient in handling uncertain and imprecise information than intuitionistic fuzzy sets and fuzzy sets. In this article, we put forward a chance-constraint programming method to solve linear programming network problems with interval-valued Pythagorean fuzzy constraints. This practice is developed using score function and upper and lower membership functions of interval-valued Pythagorean fuzzy numbers. The feasibility of the anticipated approach is illustrated by solving an airway network application and shown to be used to solve different types of network problems with objective function having interval-valued Pythagorean fuzzy numbers by employing it …on shortest path problem and minimum spanning tree problem. Furthermore, a comparative examination was performed to validate the effectiveness and usefulness of the projected methodology. Show more

Keywords: Interval-valued pythagorean fuzzy number (IVPFN), interval-valued trapezoidal pythagorean number (IVTrPFN), linear programming problem (LPP), chance-constraint programming (CCP)

DOI: 10.3233/JIFS-202383

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11183-11199, 2021

Authors: Zeng, Shaohua | Wu, Yalan | Wang, Shuai | He, Ping

Article Type: Research Article

Abstract: The segmentation and extraction of the purple soil region from purple soil color image can effectively avoid the influence of background on recognition of soil types. A scale weighted fuzzy c-means clustering algorithm(SWFCM) is proposed for effective segmentation of purple soil color image. The main work is to establish the maximum difference optimization model with the mean of Gaussian distance between each pixel and each peak of the image histogram, and optimize the clustering number and the initial clustering centers. Then, the compactness of each class is defined to weight the Euclidean distance between the pixel and the clustering center …and improve the optimization model of FCM for raising its clustering performance. Aiming at the problem of removing scattered small soil blocks in the background and filling holes in the purple soil region, the algorithm of extracting the boundary of the purple soil region and the algorithm of filling the purple soil region are proposed. Finally, the normal and robust experiments are carried out on the normal sample set and robust sample set. And the performances of relative algorithms are compared, which involves the previously released FCM algorithms and some methods for the segmentation of purple soil color image and our proposed algorithm. Experimental results show that performance of SWFCM is better and it can provide a high reference for adaptive segmentation of purple soil color images. Especially for robust experiment images, its average segmentation accuracy is improved by 6 . 64% ∼ 8 . 25 % compared with other purple soil segmentation algorithms. Show more

Keywords: color image segmentation, purple soil, fuzzy c-means clustering(FCM)

DOI: 10.3233/JIFS-202401

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11201-11215, 2021

Authors: Ghosh, Arijit | Dey, Munmun | Mondal, Sankar Prasad | Shaikh, Azharuddin | Sarkar, Anirban | Chatterjee, Banashree

Article Type: Research Article

Abstract: E-Rickshaw is an E-vehicle that has three wheels, a rechargeable battery driven electric motor as engine. E-rickshaw has become very popular due to low operating cost, low maintenance cost, eco-friendliness and ease of driving. It is perfect for small distance transport. As a last mile connector, it has transformed the public transport system in India. The low cost electric vehicle carries enough people to make a decent income and hence has become a source of livelihood for many. For considering the issues in this paper, detailed attributes of E-rickshaw are studied and Analytical Hierarchy Process (AHP) has been applied to …calculate criteria weights for the sorted attributes. Subsequently, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), a Multi Criteria Decision Making (MCDM) technique has been applied for the selection of best E-Rickshaw. In this paper, sensitivity analysis and comparative analysis have been conducted for further insight. Show more

Keywords: AHP method, E-Rickshaws selection, sensitivity analysis, TOPSIS method

DOI: 10.3233/JIFS-202406

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11217-11230, 2021

Authors: Mi, Xiangjun | Tian, Ye | Kang, Bingyi

Article Type: Research Article

Abstract: Describing and processing complex as well as ambiguous and uncertain information has always been an inescapable and challenging topic in multi-attribute decision analysis (MADA) problems. As an extension of Dempster-Shafer (D-S) evidence theory, D numbers breaks through the constraints of the constraint framework and is a new way of expressing uncertainty. The soft likelihood function based on POWA operator is one of the most useful tools recently developed for dealing with uncertain information, since it provides a more excellent performance for the aggregation of multiple compatible evidence. Recently, a new MADA model based on D numbers has been proposed, called …DMADA. In this paper, inspired by the above mentioned theories, based on soft likelihood functions, POWA aggregation and D numbers we design a novel model to improve the performance of representing and processing uncertain information in MADA problems as an improvement of the DMADA approach. In contrast, our advantages include mainly the following. Firstly, the proposed method considers the reliability characteristics of each initial D number information. Secondly, the proposed method empowers decision makers with the possibility to express their perceptions through attitudinal features. In addition, an interesting finding is that the preference parameter in the proposed method can clearly distinguish the variability between candidates by adjusting the space values between adjacent alternatives, making the decision results clearer. Finally, the effectiveness and superiority of this model are proved through analysis and testing. Show more

Keywords: Multi-attribute decision analysis (MADA), D numbers, ordered weighted averaging (OWA), power OWA (POWA), soft likelihood function (SLF), reliability

DOI: 10.3233/JIFS-202413

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11231-11255, 2021

Authors: Pei, Huili | Li, Hongliang | Liu, Yankui

Article Type: Research Article

Abstract: In practical decision-making problems, decision makers are often affected by uncertain parameters because the exact distributions of uncertain parameters are usually difficult to determine. In order to deal with this issue, the major contribution in this paper is to propose a new type of type-2 fuzzy variable called level interval type-2 fuzzy variable from the perspective of level-sets, which is a useful tool in modeling distribution uncertainty. With our level interval type-2 fuzzy variable, we give a method for constructing a parametric level interval (PLI) type-2 fuzzy variable from a nominal possibility distribution by introducing the horizontal perturbation parameters. The …proposed horizontal perturbation around the nominal distribution is different from the vertical perturbation discussed in the literature. In order to facilitate the modeling in practical decision-making problems, for a level interval type-2 fuzzy variable, we define its selection variable whose distribution can be determined via its level-sets. The numerical characteristics like expected value and second order moments are important indices in practical optimization and decision-making problems. With this consideration, we establish the analytical expressions about the expected values and second order moments of the selection variables of PLI type-2 trapezoidal, normal and log-normal fuzzy variables. Furthermore, in order to derive the analytical expressions about the numerical characteristics of the selection variable for the sums of the common PLI type-2 fuzzy variables, we discuss the arithmetic about the sums of common PLI type-2 fuzzy variables. Finally, we apply the proposed optimization method to a pricing decision problem to demonstrate the efficiency of our new method. The computational results show that even a small perturbation of the nominal possibility distribution can affect the quality of solutions. Show more

Keywords: Level interval type-2 fuzzy variable, Selection variable, Second order moments, Pricing decision

DOI: 10.3233/JIFS-202421

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11257-11272, 2021

Authors: Wang, Huiru | Zhou, Zhijian

Article Type: Research Article

Abstract: Multi-view learning utilizes information from multiple representations to advance the performance of categorization. Most of the multi-view learning algorithms based on support vector machines seek the separating hyperplanes in different feature spaces, which may be unreasonable in practical application. Besides, most of them are designed to balanced data, which may lead to poor performance. In this work, a novel multi-view learning algorithm based on maximum margin of twin spheres support vector machine (MvMMTSSVM) is introduced. The proposed method follows both maximum margin principle and consensus principle. By following the maximum margin principle, it constructs two homocentric spheres and tries to …maximize the margin between the two spheres for each view separately. To realize the consensus principle, the consistency constraints of two views are introduced in the constraint conditions. Therefore, it not only deals with multi-view class-imbalanced data effectively, but also has fast calculation efficiency. To verify the validity and rationlity of our MvMMTSSVM, we do the experiments on 24 binary datasets. Furthermore, we use Friedman test to verify the effectiveness of MvMMTSSVM. Show more

Keywords: Multi-view learning, twin spheres, SVM, maximum margin, consensus principle

DOI: 10.3233/JIFS-202427

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11273-11286, 2021

Authors: Liu, Peide | Mahmood, Tahir | Ali, Zeeshan

Article Type: Research Article

Abstract: Complex q-rung orthopair fuzzy set (CQROFS) is a proficient technique to describe awkward and complicated information by the truth and falsity grades with a condition that the sum of the q-powers of the real part and imaginary part is in unit interval. Further, Schweizer–Sklar (SS) operations are more flexible to aggregate the information, and the Muirhead mean (MM) operator can examine the interrelationships among the attributes, and it is more proficient and more generalized than many aggregation operators to cope with awkward and inconsistence information in realistic decision issues. The objectives of this manuscript are to explore the SS operators …based on CQROFS and to study their score function, accuracy function, and their relationships. Further, based on these operators, some MM operators based on PFS, called complex q-rung orthopair fuzzy MM (CQROFMM) operator, complex q-rung orthopair fuzzy weighted MM (CQROFWMM) operator, and their special cases are presented. Additionally, the multi-criteria decision making (MCDM) approach is developed by using the explored operators based on CQROFS. Finally, the advantages and comparative analysis are also discussed. Show more

Keywords: Complex q-rung orthopair fuzzy sets, Schweizer-Sklar Muirhead means operators, multi-criteria decision making

DOI: 10.3233/JIFS-202440

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11287-11309, 2021

Authors: Xuan, Cho Do | Duong, Duc | Dau, Hoang Xuan

Article Type: Research Article

Abstract: Advanced Persistent Threat (APT) is a dangerous network attack method that is widely used by attackers nowadays. During the APT attack process, attackers often use advanced techniques and tools, thus, causing many difficulties for information security systems. In fact, to detect the APT attacks, intrusion detection systems cannot rely on one technique or method but often combine multiple techniques and methods. In addition, the approach for APT attack detection using behavior analysis and evaluation techniques is facing many difficulties due to the lack of characteristic data of attack campaigns. For the above reasons, in this paper, we propose a method …for APT attack detection based on a multi-layer analysis. The multi-layer analysis technique in our proposal computes and analyzes various events in Network Traffic to detect and synthesize abnormal signs and behaviors in order to make conclusions about the existence of APT in the system. Specifically, in our proposal, we will use serial 3 main layers for the APT attack detection process including i) Detecting APT attacks based on analyzing abnormal connection; ii) Detecting APT attacks based on analyzing and evaluating Suricata log; iii) Detecting APT attacks based on analyzing behavior profiles that are compiled from layers (i) and (ii). To achieve these goals, the multi-layer analysis technique for APT attack detection will perform 2 main tasks: i) Analyzing and evaluating components of Network Traffic based on abnormal signs and behaviors. ii) building and classifying behavior profile based on each component of network traffic. In the experimental section, we will compare and evaluate the effectiveness of the APT attack detection process of each layer in the multi-layer analysis model using machine learning. Experimental results have shown that the APT attack detection method based on analyzing behavior profile has yielded better results than individual detection methods on all metrics. The research results shown in the paper not only demonstrate the effectiveness of the multilayer analysis model for APT attack detection but also provide a novel approach for detecting several other cyber-attack techniques. Show more

Keywords: Advanced persistent threat, APT attack detection, network traffic, multi-layer detection, abnormal behavior, machine learning

DOI: 10.3233/JIFS-202465

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11311-11329, 2021

Authors: Revathi, T. | Rajalaxmi, T.M. | Sundara Rajan, R. | Freire, Wilhelm Passarella

Article Type: Research Article

Abstract: Salient object detection plays a vital role in image processing applications like image retrieval, security and surveillance in authentic-time. In recent times, advances in deep neural network gained more attention in the automatic learning system for various computer vision applications. In order to decrement the detection error for efficacious object detection, we proposed a detection classifier to detect the features of the object utilizing a deep neural network called convolutional neural network (CNN) and discrete quaternion Fourier transform (DQFT). Prior to CNN, the image is pre-processed by DQFT in order to handle all the three colors holistically to evade loss …of image information, which in-turn increase the effective use of object detection. The features of the image are learned by training model of CNN, where the CNN process is done in the Fourier domain to quicken the method in productive computational time, and the image is converted to spatial domain before processing the fully connected layer. The proposed model is implemented in the HDA and INRIA benchmark datasets. The outcome shows that convolution in the quaternion Fourier domain expedite the process of evaluation with amended detection rate. The comparative study is done with CNN, discrete Fourier transforms CNN, RNN and masked RNN. Show more

Keywords: Convolutional neural networks, quaternion complex variable, object detection, image enhancement, discrete quaternion Fourier transform

DOI: 10.3233/JIFS-202502

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11331-11340, 2021

Authors: Beseiso, Majdi | Kumar, Gulshan

Article Type: Research Article

Abstract: This paper presents a fuzzy computational approach for selecting project portfolio by combining fuzzy logic, Quality Function Deployment (QFD) and Genetic algorithm (GA) approaches with the consideration of prioritized selection criteria as per objectives of the organization to make decisions effectively with incomplete and ambiguous information to help in portfolio selection. This approach addresses the issues of the uncertainty of experts in selecting projects, prioritizing criteria before initiating project selection process and evaluating the number of interdependent projects for their maximal values. It completes the task in three stages. Firstly, it involves interaction with experts to extract fuzzy input about …the benefits of organization and selection criteria for selecting a project portfolio. The second stage requires the application of fuzzy QFD to prioritize criteria before deciding the project portfolio. In this stage, the paper contributes a method for using fuzzy values in a distinct way for obtaining priorities of selection criteria. The final stage evaluates the candidate projects concurrently based on top priority selection criteria by considering interrelation among projects by proposing a distinct fitness function of GA. The validity of the proposed approach is demonstrated by an example that considers three experts, three objectives of the organization and four selection criteria. Show more

Keywords: Fuzzy quality function development (FQFD), genetic algorithm, project portfolio management, project portfolio selection, quality function development (QFD)

DOI: 10.3233/JIFS-202506

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11341-11354, 2021

Authors: Khan, Vakeel A. | Tuba, Umme | Ashadul Rahaman, SK. | Ahmad, Ayaz

Article Type: Research Article

Abstract: In 1990, Diamond [16 ] primarily established the base of fuzzy star–shaped sets, an extension of fuzzy sets and numerous of its properties. In this paper, we aim to generalize the convergence induced by an ideal defined on natural numbers ℕ , introduce new sequence spaces of fuzzy star–shaped numbers in ℝ n and examine various algebraic and topological properties of the new corresponding spaces as well. In support of our results, we provide several examples of these new resulting sequences.

Keywords: Fuzzy star–shaped numbers, Lp–metric, I–convergence, solidity and convergence free

DOI: 10.3233/JIFS-202534

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11355-11362, 2021

Authors: Aydoğmuş, Hacer Yumurtacı | Kamber, Eren | Kahraman, Cengiz

Article Type: Research Article

Abstract: The purpose of this study is to develop an extension of CODAS method using picture fuzzy sets. In this respect, a new methodology is introduced to figure out how picture fuzzy numbers can be applied to CODAS method. COmbinative Distance-based Assessment (CODAS) is a new MCDM method proposed by Ghorabaee et al. Picture fuzzy sets (PFSs) are a new extension of ordinary fuzzy sets for representing human’s judgments having possibility more than two answers such as yes, no, refusal and neutral. Compared with other studies, the proposed method integrates multi-criteria decision analysis with picture fuzzy uncertainty based on Euclidean and …Taxicab distances and negative ideal solution. ERP system selection problem is handled as the application area of the developed method, picture fuzzy CODAS. Results indicate that the new proposed method finds meaningful rankings through picture fuzzy sets. Comparative analyzes show that the presented method gives successful and robust results for the solutions of MCDM problems under fuzziness. Show more

Keywords: Fuzzy, picture fuzzy sets, CODAS method, ERP selection

DOI: 10.3233/JIFS-202564

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11363-11373, 2021

Authors: Chou, ShuoYan | Duong, Truong Thi Thuy | Thao, Nguyen Xuan

Article Type: Research Article

Abstract: Energy plays a central part in economic development, yet alongside fossil fuels bring vast environmental impact. In recent years, renewable energy has gradually become a viable source for clean energy to alleviate and decouple with a negative connotation. Different types of renewable energy are not without trade-offs beyond costs and performance. Multiple-criteria decision-making (MCDM) has become one of the most prominent tools in making decisions with multiple conflicting criteria existing in many complex real-world problems. Information obtained for decision making may be ambiguous or uncertain. Neutrosophic is an extension of fuzzy set types with three membership functions: truth membership function, …falsity membership function and indeterminacy membership function. It is a useful tool when dealing with uncertainty issues. Entropy measures the uncertainty of information under neutrosophic circumstances which can be used to identify the weights of criteria in MCDM model. Meanwhile, the dissimilarity measure is useful in dealing with the ranking of alternatives in term of distance. This article proposes to build a new entropy and dissimilarity measure as well as to construct a novel MCDM model based on them to improve the inclusiveness of the perspectives for decision making. In this paper, we also give out a case study of using this model through the process of a renewable energy selection scenario in Taiwan performed and assessed. Show more

Keywords: Dissimilarity measure, renewable energy, interval neutrosophic set

DOI: 10.3233/JIFS-202571

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11375-11392, 2021

Authors: Zhang, Chengling | Li, Jinjin | Lin, Yidong

Article Type: Research Article

Abstract: Three-way concept analysis is a mathematical model of the combination of formal concept analysis and three-way decision, and knowledge discovery plays a significant impact on formal fuzzy contexts since such datasets are frequently encountered in real life. In this paper, a novel type of one-sided fuzzy three-way concept lattices is presented in a given formal fuzzy context with its complement, in which a ternary classification is available. In such case, we comprehensively explore the connections between the proposed models and classical fuzzy concept lattices among elements, sets, and orders. Furthermore, approaches to granular matrix-based reductions are investigated, by which granular …consistent sets, and granular reducts via discernibility Boolean matrices are tectonically put forward. At last, the demonstrated results are performed by several experiments which enrich the research of three-way concept analysis. Show more

Keywords: Formal fuzzy contexts, granular reduction, one-sided fuzzy concept lattices, three-way decision

DOI: 10.3233/JIFS-202573

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11393-11410, 2021

Authors: Malini, A. | Priyadharshini, P. | Sabeena, S.

Article Type: Research Article

Abstract: To develop a surveillance and detection system for automating the process of road maintenance work which is being carried out by surveying and inspection of roads manually in the current situation. The need of the system lies in the fact that traditional methods are time-consuming, tiresome and require huge workforce. This paper proposes an automation system using Unmanned Aerial Vehicle which monitors and detects the pavement defects like cracks and potholes by processing real-time video footage of Indian highways. The collected data is processed and stored as images in a road defects database which serves as input for the system. …The behavior of Region Proposal Network (RPN) is made smooth by varying the number of region proposals utilized in the model. A regularization technique called dropout is used to achieve higher performance in the proposed Faster Region based Convolutional Neural Networks object detection model. The detections are made with 62.3% mean Average Precision @ Intersection over Union (IoU)> = 0.5 for the generation of 300 region proposals which is a good score for object detections. The comparisons between proposed and existing systems shows that the proposed Faster RCNN with modified VGG-16 performs well than the existing variants. Show more

Keywords: pothole, region proposal network, mean average precision, unmanned aerial vehicle, dropout

DOI: 10.3233/JIFS-202596

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11411-11422, 2021

Authors: Liu, Hui | He, Boxia | He, Yong | Tao, Xiaotian

Article Type: Research Article

Abstract: The existing seal ring surface defect detection methods for aerospace applications have the problems of low detection efficiency, strong specificity, large fine-grained classification errors, and unstable detection results. Considering these problems, a fine-grained seal ring surface defect detection algorithm for aerospace applications is proposed. Based on analysis of the stacking process of standard convolution, heat maps of original pixels in the receptive field participating in the convolution operation are quantified and generated. According to the generated heat map, the feature extraction optimization method of convolution combinations with different dilation rates is proposed, and an efficient convolution feature extraction network containing …three kinds of dilated convolutions is designed. Combined with the O-ring surface defect features, a multiscale defect detection network is designed. Before the head of multiscale classification and position regression, feature fusion tree modules are added to ensure the reuse and compression of the responsive features of different receptive fields on the same scale feature maps. Experimental results show that on the O-rings-3000 testing dataset, the mean condition accuracy of the proposed algorithm reaches 95.10% for 5 types of surface defects of aerospace O-rings. Compared with RefineDet, the mean condition accuracy of the proposed algorithm is only reduced by 1.79%, while the parameters and FLOPs are reduced by 35.29% and 64.90%, respectively. Moreover, the proposed algorithm has good adaptability to image blur and light changes caused by the cutting of imaging hardware, thus saving the cost. Show more

Keywords: Deep learning, feature extraction network, lightweight algorithm, multiscale classification, surface defect detection, O-rings

DOI: 10.3233/JIFS-202614

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11423-11440, 2021

Authors: Naderi, Katayoun | Ahari, Roya M. | Jouzdani, Javid | Amindoust, Atefeh

Article Type: Research Article

Abstract: Fierce competition in the global markets forced companies to improve the design and management of supply chains, because companies are always looking for more profit and higher customer satisfaction. The emergence of the green supply chain is one of the most important developments of the last decade. It provides an opportunity for companies to adjust their supply chains according to environmental goals and sustainability. The integrated production-inventory-routing is a new field that aims to optimize these three decision-making levels. It can be described as follow: a factory produces one or more products, and sells them to several customers (by direct …delivery or a specific customer chain). The current study aims to model a production-inventory-routing system using a system dynamics approach to design a green supply chain under uncertain conditions. For this purpose, first, the association between selected variables was determined. Then, the proposed model was validated. Finally, to identify variables with the highest influence, four scenarios were developed. The results indicated that minimum total transportation cost, the total warehouse capacity of the supply chain, and the maximum production rate are the most influential strategies to achieve ideal condition. Show more

Keywords: System dynamics, integrated production-inventory-routing problem, green supply chain, uncertainty

DOI: 10.3233/JIFS-202622

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11441-11454, 2021

Authors: Yao, Wei | Chen, Ye

Article Type: Research Article

Abstract: The arity of convex spaces is a numerical feature which shows the ability of finite subsets spanning to the whole space via the hull operators. This paper gives it a formal and strict definition by introducing the truncation of convex spaces. The relations that between the arity of quotient spaces and the original spaces, that between the arity of subspaces and superspaces, that between the arity of product spaces and factors spaces, and that between the arity of disjoint sums and term spaces, are systematically studied. A mistake of a formula in [M. Van De Vel, Theory of Convex Structures, …North-Holland, Amsterdam, 1993] is corrected. It is shown that a convex space is Alexandrov iff its arity is 1. The convex structures with arity ≤n are equivalent to structured sets with n -restricted hull operators. Show more

Keywords: convex space, arity, product space, disjoint sum, n-restricted hull operator

DOI: 10.3233/JIFS-202643

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11455-11462, 2021

Authors: Saravana Kumar, P. | Vasuki, S.

Article Type: Research Article

Abstract: In chromosome analysis, centromere is an essential component. By analyzing centromere, genetic disorder can be identified easily. In this paper, automatic classification and centromere detection in human chromosome image using Band Distance Feature is proposed. Initially the microscopic image of G-band chromosome is preprocessed in order to remove the blobs. Then, the image is segmented using labelling algorithm and the endpoints are calculated. Now, the overlapping chromosomes are removed when the number of end points is greater than two. The non-overlapped chromosomes are straightened using Reversible Projection algorithm. From the straightened chromosome band distance feature is calculated. The extracted features …are given to the ANN classifier to identify the class of chromosome and to calculate the centromere. From the experimental results, it is observed that the proposed method is superior to the traditional method. Show more

Keywords: Artificial neural networks, chromosome classification, labeling algorithm, straightening algorithm

DOI: 10.3233/JIFS-202682

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11463-11474, 2021

Authors: Hadi, Sarem H. | Madhi, Zainab S. | Park, Choonkil

Article Type: Research Article

Abstract: The purpose of this study is to introduce a new concept of the modular space, which is C Ω -modular space, and then some of the convex properties are discussed. We also study finding fixed-point in C Ω -modular space.

Keywords: CΩ-modular space, 𝒢-convergence, 𝒢-Cauchy sequence, fixed-point in CΩ-modular space

DOI: 10.3233/JIFS-202698

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11475-11478, 2021

Authors: Naeem, Muhammad | Khan, Muhammad Ali | Abdullah, Saleem | Qiyas, Muhammad | Khan, Saifullah

Article Type: Research Article

Abstract: Probabilistic hesitant fuzzy Set (PHFs) is the most powerful and comprehensive idea to support more complexity than developed fuzzy set (FS) frameworks. In this paper a novel and improved TOPSIS-based method for multi-criteria group decision making (MCGDM) is explained through the probabilistic hesitant fuzzy environment, in which the weights of both experts and criteria are completely unknown. Firstly, we discuss the concept of PHFs, score functions and the basic operating laws of PHFs. In fact, to compute the unknown weight information, the generalized distance measure for PHFs was defined based on the Probabilistic hesitant fuzzy entropy measure. Second, MCGDM will …be presented with the PHF information-based decision-making process. Show more

Keywords: Probabilistic hesitant fuzzy Set, extended TOPSIS method, application in decision making

DOI: 10.3233/JIFS-202700

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11479-11490, 2021

Authors: Jia, Xiang | Wang, Xinfan | Zhu, Yuanfang | Zhou, Lang | Zhou, Huan

Article Type: Research Article

Abstract: This study proposes a two-sided matching decision-making (TSMDM) approach by combining the regret theory under the intuitionistic fuzzy environment. At first, according to the Hamming distance of intuitionistic fuzzy sets and regret theory, superior and inferior flows are defined to describe the comparative preference of subjects. Hereafter, the satisfaction degrees are obtained by integrating the superior and inferior flows of the subjects. The comprehensive satisfaction degrees are calculated by aggregating the satisfaction degrees, based on which, a multi-objective TSMDM model is built. Furthermore, the multi-objective TSMDM model is converted to a single-objective model, the optimal solution of the latter is …derived. Finally, an illustrative example and several analyses are provided to verify the feasibility and the effectiveness of the proposed approach. Show more

Keywords: Multi-criteria decision-making, intuitionistic fuzzy sets, regret theory, two-sided matching, optimal model

DOI: 10.3233/JIFS-202720

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11491-11508, 2021

Authors: Fang, HaiFeng | Cao, Jin | Cai, LiHua | Zhou, Ta | Wang, MingQiang

Article Type: Research Article

Abstract: Both classification rate and accuracy are crucial for the recyclable PET bottles, and the existing combination methods of SVM all simply use SVM as the unit classifier, ignoring the improvement of SVM’s classification performance in the training process of deep learning. A linear multi hierarchical deep structure based on Support Vector Machine (SVM) is proposed to cover this problem. A novel definition of the input matrix in each layer enhances the optimization of Lagrange multipliers in Sequential Minimal Optimization (SMO) algorithm, thus the datapoint in maximum interval of SVM hyperplane could be recognized, improving the classification performance of SVM classifier …in this layer. The loss function defined in this paper could control the depth of Linear Multi Hierarchical SVM (LMHSVM), the generalization parameters are added in the loss function and the input matrix to enhance the generalization performance of LMHSVM. The process of creating Bottle dataset by Histogram of Oriented Gradient (HOG) and Principal Component Analysis (PCA) is introduced meanwhile, reducing the data size of bottles. Experiments are conducted on LMHSVM and multiple typical classification algorithms with Bottle dataset and UCI datasets, the results indicated that LMHSVM has excellent classification performances than FNN classifier, LIBSVM (Gaussian) and GFS-AdaBoost-C in KEEL. Show more

Keywords: Recycling plastic bottles, deep learning structure, SVM, Linear multi hierarchical, extract dataset

DOI: 10.3233/JIFS-202729

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11509-11522, 2021

Authors: Fan, Jianping | Yan, Feng | Wu, Meiqin

Article Type: Research Article

Abstract: In this article, the gained and lost dominance score (GLDS) method is extended into the 2-tuple linguistic neutrosophic environment, which also combined the power aggregation operator with the evaluation information to deal with the multi-attribute group decision-making problem. Since the power aggregation operator can eliminate the effects of extreme evaluating data from some experts with prejudice, this paper further proposes the 2-tuple linguistic neutrosophic numbers power-weighted average operator and 2-tuple linguistic neutrosophic numbers power-weighted geometric operator to aggregate the decision makers’ evaluation. Moreover, a model based on the score function and distance measure of 2-tuple linguistic neutrosophic numbers (2TLNNs) is …developed to get the criteria weights. Combing the GLDS method with 2-tuple linguistic neutrosophic numbers and developing a 2TLNN-GLDS method for multiple attribute group decision making, it can express complex fuzzy information more conveniently in a qualitative environment and also consider the dominance relations between alternatives which can get more effective results in real decision-making problems. Finally, an applicable example of selecting the optimal low-carbon logistics park site is given. The comparing results show that the proposed method outperforms the other existing methods, as it can get more reasonable results than others and it is more convenient and effective to express uncertain information in solving realistic decision-making problems. Show more

Keywords: Multiple attribute group decision making, 2-tuple linguistic neutrosophic numbers, power average operator, power geometric operator, the gained and lost dominance score method

DOI: 10.3233/JIFS-202748

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11523-11538, 2021

Authors: Zhang, Yongjie | Cao, Kang | Liang, Ke | Zeng, Yongqi

Article Type: Research Article

Abstract: Commonality, a typical commercial feature of serialized civil aircraft study and development, refers to a series of methods of reusing and sharing assets, which were developed based on broad similarity. The common design of serialized civil aircraft is capable of maximally saving R&D, production, operation, and disposal. To maximize the total benefits of manufacturers and operators, the common design of serialized civil aircrafts primarily exploits the commercial experience of serialized products in other fields (e.g., automobiles and mobile phones), whereas a scientific index system and quantitative evaluation model has not been formed. Accordingly, this study proposes a new civil aircraft …commonality index evaluation model in accordance with fuzzy set theory and methods. The model follows two branches, i.e., attribute commonality and structural commonality, to develop a multi-level civil aircraft commonality index system. The proposed model can split the commonality into six commonality sub-intervals and build the corresponding standard fuzzy set with the characteristic attribute parameters of the civil aircraft as the elements. Next, based on considerable civil aircraft sample data, a fuzzy test is designed to yield the membership function of the fuzzy set. Thus, a model of evaluating civil aircraft commonality is constructed, taking the characteristic parameters of the civil aircraft to be evaluated as input, and selecting the degree of commonality of each level as output. Lastly, this study employs the evaluation model to evaluate the commonality of Boeing 757-200 with other civil aircrafts. Furthermore, the evaluated results well explain the actual situation, which verifies the effectiveness and practicability of the proposed model. Show more

Keywords: Cost benefit analysis, serialized civil aircraft, commonality index, fuzzy set, membership function

DOI: 10.3233/JIFS-202749

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11539-11558, 2021

Authors: Li, Chunhua | Xu, Baogen | Huang, Huawei

Article Type: Research Article

Abstract: In this paper, the notion of a fuzzy *–ideal of a semigroup is introduced by exploiting generalized Green’s relations L * and R * , and some characterizations of fuzzy *–ideals on an arbitrary semigroup are obtained. Our main purpose is to establish the relationship between fuzzy *–ideals and abundance for an arbitrary semigroup. As an application of our results, we also give some new necessary and sufficient conditions for an arbitrary semigroup to be regular and inverse, respectively.

Keywords: fuzzy*–ideals, abundant semigroups, abundance, 20M20

DOI: 10.3233/JIFS-202759

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11559-11566, 2021

Authors: Wu, Yaoqiang

Article Type: Research Article

Abstract: In this paper, we introduce the concept of weak partial-quasi k-metrics, which generalizes both k-metric and weak metric. Also, we present some examples to support our results. Furthermore, we obtain some fixed point theorems in weak partial-quasi k-metric spaces.

Keywords: weak metric, k-metric, partial-quasi k-metric, weak partial-quasi k-metric, fixed point theorem

DOI: 10.3233/JIFS-202768

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11567-11575, 2021

Authors: Luo, D. | Zhang, G.Z.

Article Type: Research Article

Abstract: The purpose of this paper is to solve the prediction problem of nonlinear sequences with multiperiodic features, and a multiperiod grey prediction model based on grey theory and Fourier series is established. For nonlinear sequences with both trend and periodic features, the empirical mode decomposition method is used to decompose the sequences into several periodic terms and a trend term; then, a grey model is used to fit the trend term, and the Fourier series method is used to fit the periodic terms. Finally, the optimization parameters of the model are solved with the objective of obtaining a minimum mean …square error. The novel model is applied to research on the loss rate of agricultural droughts in Henan Province. The average absolute error and root mean square error of the empirical analysis are 0.3960 and 0.5086, respectively. The predicted results show that the novel model can effectively fit the loss rate sequence. Compared with other models, the novel model has higher prediction accuracy and is suitable for the prediction of multiperiod sequences. Show more

Keywords: Nonlinear sequences, multiperiod, grey model, empirical mode decomposition, Fourier series

DOI: 10.3233/JIFS-202775

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11577-11586, 2021

Authors: Yang, Xu | Guo, Yu | Liu, Qiang | Zhang, Deming

Article Type: Research Article

Abstract: Effective enterprise quality immune response can grasp the “pathogenesis”, “clinical manifestations” and “treatment” of enterprise quality dissident factors, which is the goal pursued continuously by enterprise quality management. Drawing on the theory of enterprise immunity, construct the evaluation index system of enterprise quality immune response effect, and use the evaluation model of interval binary semantic grey target decision based on two-dimensional association sampling (EMIBSGTD-TAS) to evaluate the quality immune response effect of the selected target company. The boundary and internal distribution situation weaken the influence of the extreme value of the index on the decision result, and introduce the interval …binary semantic set value statistical method to determine the index weight, reduce the information loss and fuzzy error. It can be seen from the evaluation results that the model has practicability and feasibility, and provides a new idea for the evaluation of the effect of enterprise quality immune response. Show more

Keywords: Quality Immune Response, Two-dimensional Association Sampling (TAS), Interval Binary Semantics (IBS), Gray Target Decision (GTD)

DOI: 10.3233/JIFS-202794

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11587-11606, 2021

Authors: Ya-Na, Wang | Guo-Hua, Zhou

Article Type: Research Article

Abstract: The aim of this paper is to investigate a profit-maximization firm how to determine the composition and prices of multiple bundles. Bundles are sets of components that must meet some technical constraints; furthermore, customers differ in their quality valuations and choose the bundle that maximizes their utility. A mixed integer non-linear program is proposed to solve this problem. First, a two-step approach is employed to obtain the firm’s optimal decision. The result indicates that when the firm faces deterministic demand, the optimal set of bundles it offers is independent of the distribution of customer valuations and does not contain any …dominated bundle. In addition, dominated components cannot be used to construct the optimal bundles. Second, the impact of demand uncertainty on the firm’s performance is explored. The results suggest that disregarding the demand risk may result in broader assortment and suboptimal prices. Finally, numerical experiments and sensitive analysis are conducted to provide managerial insights for the pricing and composition of multiple bundles. Show more

Keywords: Vertical differentiation, uncertain demand, pricing, composition of bundle, consumer choice model

DOI: 10.3233/JIFS-202799

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11607-11623, 2021

Authors: Norouzi, Ashraf | hajiagha, Hossein Razavi

Article Type: Research Article

Abstract: Multi criteria decision-making problems are usually encounter implicit, vague and uncertain data. Interval type-2 fuzzy sets (IT2FS) are widely used to develop various MCDM techniques especially for cases with uncertain linguistic approximation. However, there are few researches that extend IT2FS-based MCDM techniques into qualitative and group decision-making environment. The present study aims to adopt a combination of hesitant and interval type-2 fuzzy sets to develop an extension of Best-Worst method (BWM). The proposed approach provides a flexible and convenient way to depict the experts’ hesitant opinions especially in group decision-making context through a straightforward procedure. The proposed approach is called …IT2HF-BWM. Some numerical case studies from literature have been used to provide illustrations about the feasibility and effectiveness of our proposed approach. Besides, a comparative analysis with an interval type-2 fuzzy AHP is carried out to evaluate the results of our proposed approach. In each case, the consistency ratio was calculated to determine the reliability of results. The findings imply that the proposed approach not only provides acceptable results but also outperforms the traditional BWM and its type-1 fuzzy extension. Show more

Keywords: Best-worst method (BWM), hesitant fuzzy linguistic term set, hesitant interval type-2 Fuzzy BWM, interval type-2 fuzzy set, multi-attribute decision-making, qualitative decision-making

DOI: 10.3233/JIFS-202801

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11625-11652, 2021

Authors: Nguyen, Trang T.D. | Nguyen, Loan T.T. | Nguyen, Anh | Yun, Unil | Vo, Bay

Article Type: Research Article

Abstract: Spatial clustering is one of the main techniques for spatial data mining and spatial data analysis. However, existing spatial clustering methods primarily focus on points distributed in planar space with the Euclidean distance measurement. Recently, NS-DBSCAN has been developed to perform clustering of spatial point events in Network Space based on a well-known clustering algorithm, named Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The NS-DBSCAN algorithm has efficiently solved the problem of clustering network constrained spatial points. When compared to the NC_DT (Network-Constraint Delaunay Triangulation) clustering algorithm, the NS-DBSCAN algorithm efficiently solves the problem of clustering network constrained spatial …points by visualizing the intrinsic clustering structure of spatial data by constructing density ordering charts. However, the main drawback of this algorithm is when the data are processed, objects that are not specifically categorized into types of clusters cannot be removed, which is undeniably a waste of time, particularly when the dataset is large. In an attempt to have this algorithm work with great efficiency, we thus recommend removing edges that are longer than the threshold and eliminating low-density points from the density ordering table when forming clusters and also take other effective techniques into consideration. In this paper, we develop a theorem to determine the maximum length of an edge in a road segment. Based on this theorem, an algorithm is proposed to greatly improve the performance of the density-based clustering algorithm in network space (NS-DBSCAN). Experiments using our proposed algorithm carried out in collaboration with Ho Chi Minh City, Vietnam yield the same results but shows an advantage of it over NS-DBSCAN in execution time. Show more

Keywords: Spatial data mining, spatial data clustering, NS-DBSCAN, network spatial analysis

DOI: 10.3233/JIFS-202806

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11653-11670, 2021

Authors: Özgül, Ercan | Dinçer, Hasan | Yüksel, Serhat

Article Type: Research Article

Abstract: Healthy life is recognized as one of the most fundamental human rights. However, even today, millions of people around the world are forced to choose between their basic needs and fundamental rights. Half of the world’s population does not have access to the healthcare they need. Universal Health Coverage (UHC) aims to ensure that all individuals receive the quality health services they need without incurring a financial burden, and to protect them from risk factors that threaten their health. The aim of this study is to identify the significant factors to improve UHC in the countries. For this purpose, house …of quality (HoQ) approach is used in the analysis process so that both customer expectations and technical requirements are considered. Within this framework, a novel hybrid model has been proposed which has three different stages. Firstly, 3 groups of diseases and 4 clinical services for each group are determined regarding the customer needs. Secondly, these factors are weighted by using interval-valued intuitionistic hesitant 2-tuple fuzzy decision making and trial evaluation laboratory (DEMATEL). In the final stage, 9 different technical requirements are ranked by using interval-valued intuitionistic hesitant 2-tuple fuzzy technique for order preference by similarity to ideal solution (TOPSIS). Additionally, another evaluation has also been conducted by considering Spherical fuzzy sets. Similarly, a comparative analysis has also been performed with VIKOR while ranking the alternatives. It is concluded that analysis results of both evaluations are quite similar. This situation gives an information about the coherency and consistency of the analysis results. The findings indicate that treatment services in noncommunicable diseases play the most significant role in this respect. Moreover, according to the ranking results, it is concluded that strategic policies should be related to improving the social security and special physician capacity as well as decreasing the out-of-pocket payment. Show more

Keywords: House of quality, UHC, hesitant linguistic terms, DEMATEL, TOPSIS

DOI: 10.3233/JIFS-202818

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11671-11689, 2021

Authors: Liu, Rui-Lin | Yang, Hai-Long | Zhang, Li-Juan

Article Type: Research Article

Abstract: This paper studies information structures in a fuzzy β -covering information system. We introduce the concepts of a fuzzy β -covering information system and homomorphism between them, and investigate related properties. The concept of information structure of a fuzzy β -covering information system is given. We discuss the relationships between information structures from the view of dependence and separation. Then granularity measures for a fuzzy β -covering information system are studied. Finally, we discuss invariance of fuzzy β -covering information systems under homomorphism and illustrate its application on data compression.

Keywords: Fuzzy β-covering, fuzzy β-covering information system, information structure, homomorphism, invariance property

DOI: 10.3233/JIFS-202824

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11691-11716, 2021

Authors: Wang, Fubin | Liu, Peide | Wang, Peng

Article Type: Research Article

Abstract: A scientific evaluation model can be effectively used for the evaluation of regional talent development level. This paper proposes a set of scientific index systems for evaluating rural science and technology talents, which can be used for understanding the development status and level of rural science and technology talents in various regions; putting forward the corresponding talent cultivation and introduction policies, and; promoting the development of rural economic construction. Moreover, in order to avoid the shortcoming of over-subjective indicator weight in analytic hierarchy process (AHP), this paper uses the entropy weight method to determine indicator weight. Furthermore, giving the fact …that the evaluation experts may have individual personal preferences, this paper proposes an extended TODIM method based on hybrid index values, for achieving more scientific and effective evaluation results of rural science and technology talents. Finally, the proposed methods are evaluated on an actual case, where relevant analysis and suggestions are given. Show more

Keywords: Rural scientific and technological talents, TODIM method, entropy weight method, hybrid indicator, multi-attribute decision-making

DOI: 10.3233/JIFS-202847

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11717-11730, 2021

Authors: Ju, Hongmei | Zhang, Yafang | Zhao, Ye

Article Type: Research Article

Abstract: Classification problem is an important research direction in machine learning. υ -nonparallel support vector machine (υ -NPSVM) is an important classifier used to solve classification problems. It is widely used because of its structural risk minimization principle, kernel trick, and sparsity. However, when solving classification problems, υ -NPSVM will encounter the problem of sample noises and heteroscedastic noise structure, which will affect its performance. In this paper, two improvements are made on the υ -NPSVM model, and a υ-nonparallel parametric margin fuzzy support vector machine (par-υ -FNPSVM) is established. On the one hand, for the noises that may exist in …the data set, the neighbor information is used to add fuzzy membership to the samples, so that the contribution of each sample to the classification is treated differently. On the other hand, in order to reduce the effect of heteroscedastic structure, an insensitive loss function is introduced. The advantages of the new model are verified through UCI machine learning standard data set experiments. Finally, Friedman test and Bonferroni-Dunn test are used to verify the statistical significance of it. Show more

Keywords: Classification problem, sample noises, heteroscedastic noise structure, ν-nonparallel support vector machine, parameter margin, nearest neighbor fuzzy membership

DOI: 10.3233/JIFS-202869

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11731-11747, 2021

Authors: Pavan Kumar, C.S. | Dhinesh Babu, L.D.

Article Type: Research Article

Abstract: Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. …There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks. Show more

Keywords: Dementia, sentiment analysis, machine learning, FDA, feature-split, feature engineering, trapezoid membership function

DOI: 10.3233/JIFS-202874

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11749-11761, 2021

Authors: Zargari, Hamed | Zahedi, Morteza | Rahimi, Marziea

Article Type: Research Article

Abstract: Words are one of the most essential elements of expressing sentiments in context although they are not the only ones. Also, syntactic relationships between words, morphology, punctuation, and linguistic phenomena are influential. Merely considering the concept of words as isolated phenomena causes a lot of mistakes in sentiment analysis systems. So far, a large amount of research has been conducted on generating sentiment dictionaries containing only sentiment words. A number of these dictionaries have addressed the role of combinations of sentiment words, negators, and intensifiers, while almost none of them considered the heterogeneous effect of the occurrence of multiple linguistic …phenomena in sentiment compounds. Regarding the weaknesses of the existing sentiment dictionaries, in addressing the heterogeneous effect of the occurrence of multiple intensifiers, this research presents a sentiment dictionary based on the analysis of sentiment compounds including sentiment words, negators, and intensifiers by considering the multiple intensifiers relative to the sentiment word and assigning a location-based coefficient to the intensifier, which increases the covered sentiment phrase in the dictionary, and enhanced efficiency of proposed dictionary-based sentiment analysis methods up to 7% compared to the latest methods. Show more

Keywords: Sentiment analysis, sentiment dictionary, linguistic phenomena, intensifier, intensifier location

DOI: 10.3233/JIFS-202879

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11763-11776, 2021

Authors: Zhang, Duo | Nguang, Sing Kiong | Shu, Lan | Qiu, Dong

Article Type: Research Article

Abstract: This paper focuses on establishing the trilinear fuzzy seepage model with multiple fuzzy parameters for shale gas reservoirs. Different from the conventional seepage models of shale gas reservoirs, the multiple fuzzy parameters seepage model uses fuzzy numbers to describe some parameters with uncertainty. Firstly, the multiple fuzzy parameters seepage model is constructed based on fuzzy concepts. The fuzzy structure element method and the centroid method are used to solve the fuzzy seepage model and defuzzifier, respectively. Secondly, the advantages of the development fuzzy model over the conventional seepage model are discussed and illustrated through numerical examples and simulations. Finally, to …further study the seepage laws inside shale gas reservoirs, this paper explores the sensitivity of relevant main control parameters to gas production based on the development model. Show more

Keywords: Shale gas reservoirs, fuzzy parameter, fuzzy differential equation, fuzzy structural element, fuzzy modeling

DOI: 10.3233/JIFS-202898

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11777-11797, 2021

Authors: Choudhary, Anu | Raj, Kuldip | Mursaleen, M.

Article Type: Research Article

Abstract: Tauberian theorem serves the purpose to recuperate Pringsheim’s convergence of a double sequence from its (C , 1, 1) summability under some additional conditions known as Tauberian conditions. In this article, we intend to introduce some Tauberian theorems for fuzzy number sequences by using the de la Vallée Poussin mean and double difference operator of order r  . We prove that a bounded double sequence of fuzzy number which is Δ u r - convergent is ( C , 1 , 1 ) Δ u r - summable to the …same fuzzy number L  . We make an effort to develop some new slowly oscillating and Hardy-type Tauberian conditions in certain senses employing de la Vallée Poussin mean. We establish a connection between the Δ u r - Hardy type and Δ u r - slowly oscillating Tauberian condition. Finally by using these new slowly oscillating and Hardy-type Tauberian conditions, we explore some relations between ( C , 1 , 1 ) Δ u r - summable and Δ u r - convergent double fuzzy number sequences. Show more

Keywords: Fuzzy number, difference operator, double sequences, Tauberian theorem, (C, 1, 1)- summability

DOI: 10.3233/JIFS-202921

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11799-11808, 2021

Authors: Lei, Fan | Wei, Guiwu | Chen, Xudong

Article Type: Research Article

Abstract: Probabilistic double hierarchy linguistic term set (PDHLTS) can not only express the complex linguistic information that the probabilistic linguistic term set (PLTS) cannot express, but also reflect the frequency or importance of linguistic term set (LTS)that cannot be reflected by the double hierarchy linguistic term set (DHLTS). It is an effective tool to deal with multiple attribute group decision making (MAGDM) problems. Therefore, in this paper, we propose several aggregation operators which can aggregate PDHLTS information and apply them to MAGDM problems. Firstly, the basic notion of PDHLTS is reviewed, and the distance formula and algorithm of PDHLTS are defined; …then, extant weighted averaging (WA) operator, weighted geometric(WG) operator and power weighted averaging (PWA) operator, power weighted geometric(PWG) operator to PDHLTS, and establish probability double hierarchy linguistic weighted averaging (PDHLWA) operator, probability double hierarchy linguistic weighted geometric (PDHLWG) operator, probability double hierarchy linguistic power weighted averaging (PDHLPWA) operator, probability double hierarchy linguistic power weighted geometric (PDHLPWG) operator; in addition, The idempotency, boundedness and monotonicity of these aggregation operators are studied; what’s more, those aggregation operators are proposed to establish the enterprise credit self-evaluation model; Finally, compared with the available probabilistic double hierarchy linguistic MAGDM methods, the defined model is proved to be scientific and effective. Show more

Keywords: Multiple attribute group decision making (MAGDM), probabilistic double hierarchy term set (PDHLTS), aggregation operators, enterprise credit self-evaluation model

DOI: 10.3233/JIFS-202922

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11809-11828, 2021

Authors: Wu, Xinhao | Lu, Qiujun

Article Type: Research Article

Abstract: Application of quantitative methods for forecasting purposes in financial markets has attracted significant attention from researchers and managers in recent years when conventional time series forecasting models can hardly develop the inherent rules of complex nonlinear dynamic financial systems. In this paper, based on the fuzzy technique integrated with the statistical tools and artificial neural network, a new hybrid forecasting system consisting of three stages is constructed to exhibit effectively improved forecasting accuracy of financial asset price. The sum of squared errors is minimized to determine the coefficients in fitting the fuzzy autoregression model stage for formulating sample groups to …deal with data containing outliers. Fuzzy bilinear regression model introducing risk view based on quadratic programming algorithm that reflects the properties of both least squares and possibility approaches without expert knowledge is developed in the second stage. The main idea of the model considers the sub-models tracking the possible relations between the spread and the center, also linking the estimation deviation with risk degree of fitness of the model. In the third stage, fuzzy bilinear regression forecasting combining with the optimal architecture of probabilistic neural network classifiers indicates that the proposed method has great contribution to control over-wide interval financial data with a certain confidence level. Statistical validation and performance analysis using historical financial asset yield series on Shanghai Stock Exchange composite index all exhibit the effectiveness and stability of the proposed hybrid forecasting formulation compared with other forecasting methods. Show more

Keywords: Financial asset yield series forecasting, fuzzy bilinear regression, probabilistic neural network, symmetrical triangular fuzzy number, risk-neutral

DOI: 10.3233/JIFS-202927

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11829-11844, 2021

Authors: Shabir, Muhammad | Mubarak, Asad | Naz, Munazza

Article Type: Research Article

Abstract: The rough set theory is an effective method for analyzing data vagueness, while bipolar soft sets can handle data ambiguity and bipolarity in many cases. In this article, we apply Pawlak’s concept of rough sets to the bipolar soft sets and introduce the rough bipolar soft sets by defining a rough approximation of a bipolar soft set in a generalized soft approximation space. We study their structural properties and discuss how the soft binary relation affects the rough approximations of a bipolar soft set. Two sorts of bipolar soft topologies induced by soft binary relation are examined. We additionally discuss …some similarity relations between the bipolar soft sets, depending on their roughness. Such bipolar soft sets are very useful in the problems related to decision-making such as supplier selection problem, purchase problem, portfolio selection, site selection problem etc. A methodology has been introduced for this purpose and two algorithms are presented based upon the ongoing notions of foresets and aftersets respectively. These algorithms determine the best/worst choices by considering rough approximations over two universes i.e. the universe of objects and universe of parameters under a single framework of rough bipolar soft sets. Show more

Keywords: Rough sets, bipolar soft sets, rough bipolar soft sets, bipolar soft topology, decision making

DOI: 10.3233/JIFS-202958

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11845-11860, 2021

Authors: Zhu, Jia-Nian | Liu, Xu-Chong | Liu, Chong

Article Type: Research Article

Abstract: Non-equidistant non-homogenous grey model (abbreviated as NENGM (1,1, k ) model) is a grey prediction model suitable for predicting time series with non-equal intervals. It is widely used in various fields of society due to its high prediction accuracy and strong adaptability. In order to further improve the prediction accuracy of the NENGM (1,1, k ) model, the NENGM (1,1, k ) model is optimized in terms of the cumulative order and background value of the NENGM (1,1, k ) model, and a NENGM (1,1, k ) model based on double optimization is established (abbreviated as FBNENGM (1,1, k ) …model), and the whale optimization algorithm is used to solve the best parameters of the model. In order to verify the feasibility and validity of the FBNENGM (1,1, k ) model, the FBNENGM (1,1, k ) model and other four prediction models are applied to three cases respectively, and three indexes commonly used to evaluate the performance of prediction models are used to distinguish. The results show that the prediction accuracy of the FBNENGM (1,1, k ) model based on double optimization is better than other prediction models. Show more

Keywords: Grey system theory, fractional-order accumulation, grey prediction model, FBNENGM (1, 1, k) model, ·whale optimization algorithm

DOI: 10.3233/JIFS-210023

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11861-11874, 2021

Authors: Li, Haiyan | Yang, Xiangfeng

Article Type: Research Article

Abstract: Uncertain time series is chronological sequence overtime where each period is described by an uncertain variable. In this paper, we investigate the smoothly clipped absolute deviation (SCAD) penalized estimation method to determine the unknown parameters in the uncertain autoregressive model, and the autoregressive model order can be simultaneously obtained for a pre-given thresholding parameter λ . Besides, an iterative algorithm based on local quadratic approximations for finding the penalized estimators is provided. Based on the fitted autoregressive model, the forecast value and the future value’s confidence interval are given. Besides, the sum of the squared error approach to select the …optimal λ is discussed. Finally, some examples are used to validate the effectiveness of the proposed method by the comparative analysis. Show more

Keywords: Uncertain variable, uncertain autoregressive model, SCAD penalty

DOI: 10.3233/JIFS-210031

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11875-11885, 2021

Authors: Bashir, Humera | Zahid, Zohaib | Kashif, Agha | Zafar, Sohail | Liu, Jia-Bao

Article Type: Research Article

Abstract: The 2-metric resolvability is an extension of metric resolvability in graphs having several applications in intelligent systems for example network optimization, robot navigation and sensor networking. Rotationally symmetric graphs are important in intelligent networks due to uniform rate of data transformation to all nodes. In this article, 2-metric dimension of rotationally symmetric plane graphs R n , S n and T n is computed and found to be independent of the number of vertices.

Keywords: 2-metric dimension, rotationally symmetric, plane graphs

DOI: 10.3233/JIFS-210040

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11887-11895, 2021

Authors: Chen, Lei | Han, Jun | Tian, Feng

Article Type: Research Article

Abstract: Infrared (IR) images can distinguish targets from their backgrounds based on difference in thermal radiation, whereas visible images can provide texture details with high spatial resolution. The fusion of the IR and visible images has many advantages and can be applied to applications such as target detection and recognition. This paper proposes a two-layer generative adversarial network (GAN) to fuse these two types of images. In the first layer, the network generate fused images using two GANs: one uses the IR image as input and the visible image as ground truth, and the other with the visible as input and …the IR as ground truth. In the second layer, the network transfer one of the two fused images generated in the first layer as input and the other as ground truth to GAN to generate the final fused image. We adopt TNO and INO data sets to verify our method, and by comparing with eight objective evaluation parameters obtained by other ten methods. It is demonstrated that our method is able to achieve better performance than state-of-arts on preserving both texture details and thermal information. Show more

Keywords: IR and visible images, image fusion, generative adversarial network, deep learning

DOI: 10.3233/JIFS-210041

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11897-11913, 2021

Authors: Zhang, Shao-Yu

Article Type: Research Article

Abstract: This paper introduces a special Galois connection combined with the wedge-below relation. Furthermore, by using this tool, it is shown that the category of M -fuzzifying betweenness spaces and the category of M -fuzzifying convex spaces are isomorphic and the category of arity-2 M -fuzzifying convex spaces can be embedded in the category of M -fuzzifying interval spaces as a reflective subcategory.

Keywords: Fuzzy convex structure, fuzzy betweenness space, fuzzy interval space, arity-2 fuzzy convexity

DOI: 10.3233/JIFS-210060

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11915-11925, 2021

Authors: Oh, Ju-Mok | Kim, Yong Chan

Article Type: Research Article

Abstract: In this paper, we introduce the notions of join preserving maps using distance spaces instead of fuzzy partially ordered sets on complete co-residuated lattices. We investigate the properties of Alexandrov fuzzy topologies, distance functions, join preserving maps and upper approximation operators. Furthermore, we study their relations and examples. We prove that there exist isomorphic categories and Galois correspondences between their categories.

Keywords: Complete co-residuated lattices, distance functions, join preserving maps, upper approximation operators, Alexandrov fuzzy topologies

DOI: 10.3233/JIFS-210061

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11927-11939, 2021

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

Article Type: Research Article

Abstract: Knowledge graph reasoning or completion aims at inferring missing facts by reasoning about the information already present in the knowledge graph. In this work, we explore the problem of temporal knowledge graph reasoning that performs inference on the graph over time. Most existing reasoning models ignore the time information when learning entities and relations representations. For example, the fact (Scarlett Johansson , spouse Of , Ryan Reynolds ) was true only during 2008 - 2011. To facilitate temporal reasoning, we present TA-TransRILP , which involves temporal information by utilizing RNNs and takes advantage of Integer Linear Programming. Specifically, we …utilize a character-level long short-term memory network to encode relations with sequences of temporal tokens, and combine it with common reasoning model. To achieve more accurate reasoning, we further deploy temporal consistency constraints to basic model, which can help in assessing the validity of a fact better. We conduct entity prediction and relation prediction on YAGO11k and Wikidata12k datasets. Experimental results demonstrate that TA-TransRILP can make more accurate predictions by taking time information and temporal consistency constraints into account, and outperforms existing methods with a significant improvement about 6-8% on Hits@10. Show more

Keywords: Knowledge graph reasoning, temporal information, temporal consistency constraints, integer linear programming

DOI: 10.3233/JIFS-210064

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11941-11950, 2021

Authors: Yu, Junqi | Zhang, Tianlun | Zhao, Anjun | Xie, Yunfei

Article Type: Research Article

Abstract: Energy consumption prediction can provide reliable data support for energy scheduling and optimization of office buildings. It is difficult for traditional prediction model to achieve stable accuracy and robustness when energy consumption mode is complex and data sources are diverse. Based on such situation, this paper raised an approach containing the method of comprehensive similar day and ensemble learning. Firstly, the historical data was analyzed and calculated to obtain the similarity degree of meteorological features, time factor and precursor. Next, the entropy weight method was used to calculate comprehensive similar day and applied to the model training. Then the improved …sine cosine optimization algorithm (SCA) was applied to the optimization and parameter selection of a single model. Finally, an approach of model selection and integration based on dominance was proposed, which was compared with Support Vector Regression (SVR), Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM), with a large office building in Xi ‘an taken as an example to analysis showing that compared with the prediction accuracy, root mean square percentage error (RMSPE) in the ensemble learning model after using comprehensive similar day was reduced by about 0.15 compared with the BP model, and was reduced by about 0.05, 0.06 compared with the SVR and LSTM model. Respectively, the mean absolute percentage error (MAPE) was reduced by 12.02%, 6.51% and 5.28%. Compared with several other integration methods, integration model based on dominance reduced absolute error at all times. Accordingly, the proposed approach can effectively solve problems of low accuracy and poor robustness in traditional model and predict the building energy consumption efficaciously. Show more

Keywords: Similar day, ensemble learning, sine cosine optimization algorithm, energy consumption prediction

DOI: 10.3233/JIFS-210069

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11951-11965, 2021

Authors: Wu, Meiqin | Li, Zhuoyu | Fan, Jianping

Article Type: Research Article

Abstract: With resource shortage and environmental pollution becoming more and more serious, the development of new energy vehicles (NEVs) plays an important role. In this paper, the hybrid method of best-worst method (BWM), Multi-Objective Optimization by Ratio Analysis plus Full Multiplicative Form (MULTIMOORA), and Evaluation based on Distance from Average Solution (EDAS) is used to evaluate new energy vehicles (NEVs) and select the best new energy vehicle. BWM method is used to obtain the subjective preference weight, MULTIMOORA method is used to integrate the objective data with the subjective weight to evaluate new energy vehicles, and the final ranking of alternatives …is obtained by the EDAS method. The paper collect the data of 22 representative new energy vehicle types in China, the validity and feasibility of the method is verified. Show more

Keywords: Best-worst method, multi-objective optimization by ratio analysis plus full multiplicative form, evaluation based on distance from average solution, new energy vehicles

DOI: 10.3233/JIFS-210074

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11967-11980, 2021

Authors: Srivastava, Sangeeta | Varshney, Ashwani | Katyal, Supriya | Kaur, Ravneet | Gaur, Vibha

Article Type: Research Article

Abstract: The government has established special schools to cater to the needs of children with disabilities but they are often segregated rather than receiving equitable opportunities. Artificial Intelligence has opened new ways to promote special education with advanced learning tools. These tools enable to adapt to a typical classroom set up for all the students with or without disabilities. To ensure social equity and the same classroom experience, a coherent solution is envisioned for inclusive education. This paper aims to propose a cost-effective and integrated Smart Learning Assistance (SLA) tool for Inclusive Education using Deep Learning and Computer Vision techniques. It …comprises speech to text and sign language conversion for hearing impaired students, sign language to text conversion for speech impaired students, and Braille to text for communicating with visually impaired students. The tool assists differently-abled students to make use of various teaching-learning opportunities conferred to them and ensures convenient two-way communication with the instructor and peers in the classroom thus makes learning easier. Show more

Keywords: Inclusive classroom, image processing, computer vision, deep learning, artificial intelligence

DOI: 10.3233/JIFS-210075

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11981-11994, 2021

Authors: Jin, Jiulin | Zhu, Fuyang | You, Taijie

Article Type: Research Article

Abstract: In this paper, picture fuzzy tensor is proposed, and some related properties are studied. In the meantime, the decomposition theorem of picture fuzzy tensors is established by using picture fuzzy cutting tensors and picture fuzzy t -norm. Moreover, we propose the generalized picture fuzzy weighted interaction aggregation (GPFWIA) operator and the generalized picture fuzzy weighted interaction geometric (GPFWIG) operator. Finally, an application of picture fuzzy tensor in multi-attribute decision making (MADM) problems is presented, that is, a method is suggested to solve picture fuzzy MADM problems with multi-dimensional data characteristics. It is found that our proposed method is feasible and …effective by a typical application example. Show more

Keywords: Picture fuzzy tensor, Multi-attribute decision making (MADM), Decomposition theorem, Generalized picture fuzzy weighted interaction aggregation (GPFWIA) operator, Generalized picture fuzzy weighted interaction geometric (GPFWIG) operator

DOI: 10.3233/JIFS-210093

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11995-12009, 2021

Authors: Liu, Yaning | Han, Lin | Wang, Hexiang | Yin, Bo

Article Type: Research Article

Abstract: Papillary thyroid carcinoma (PTC) is a common carcinoma in thyroid. As many benign thyroid nodules have the papillary structure which could easily be confused with PTC in morphology. Thus, pathologists have to take a lot of time on differential diagnosis of PTC besides personal diagnostic experience and there is no doubt that it is subjective and difficult to obtain consistency among observers. To address this issue, we applied deep learning to the differential diagnosis of PTC and proposed a histological image classification method for PTC based on the Inception Residual convolutional neural network (IRCNN) and support vector machine (SVM). First, …in order to expand the dataset and solve the problem of histological image color inconsistency, a pre-processing module was constructed that included color transfer and mirror transform. Then, to alleviate overfitting of the deep learning model, we optimized the convolution neural network by combining Inception Network and Residual Network to extract image features. Finally, the SVM was trained via image features extracted by IRCNN to perform the classification task. Experimental results show effectiveness of the proposed method in the classification of PTC histological images. Show more

Keywords: papillary thyroid carcinoma, histological image classification, convolutional neural network, deep learning

DOI: 10.3233/JIFS-210100

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12011-12021, 2021

Authors: Wei, Qianjin | Wang, Chengxian | Wen, Yimin

Article Type: Research Article

Abstract: Intelligent optimization algorithm combined with rough set theory to solve minimum attribute reduction (MAR) is time consuming due to repeated evaluations of the same position. The algorithm also finds in poor solution quality because individuals are not fully explored in space. This study proposed an algorithm based on quick extraction and multi-strategy social spider optimization (QSSOAR). First, a similarity constraint strategy was called to constrain the initial state of the population. In the iterative process, an adaptive opposition-based learning (AOBL) was used to enlarge the search space. To obtain a reduction with fewer attributes, the dynamic redundancy detection (DRD) strategy …was applied to remove redundant attributes in the reduction result. Furthermore, the quick extraction strategy was introduced to avoid multiple repeated computations in this paper. By combining an array with key-value pairs, the corresponding value can be obtained by simple comparison. The proposed algorithm and four representative algorithms were compared on nine UCI datasets. The results show that the proposed algorithm performs well in reduction ability, running time, and convergence speed. Meanwhile, the results confirm the superiority of the algorithm in solving MAR. Show more

Keywords: Intelligent optimization, rough set theory, attribute reduction, social spider optimization, opposition-based learning

DOI: 10.3233/JIFS-210133

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12023-12038, 2021

Authors: Liu, Jinpei | Shao, Longlong | Zhou, Ligang | Jin, Feifei

Article Type: Research Article

Abstract: Faced with complex decision problems, distribution linguistic preference relation (DLPR) is an effective way for decision-makers (DMs) to express preference information. However, due to the complexity of the decision-making environment, DMs may not be able to provide complete linguistic distribution for all linguistic terms in DLPRs, which results in incomplete DLPRs. Therefore, in order to solve group decision-making (GDM) with incomplete DLPRs, this paper proposes expected consistency-based model and multiplicative DEA cross-efficiency. For a given incomplete DLPRs, we first propose an optimization model to obtain complete DLPR. This optimization model can evaluate the missing linguistic distribution and ensure that the …obtained DLPR has a high consistency level. And then, we develop a transformation function that can transform DLPRs into multiplicative preference relations (MPRs). Furthermore, we design an improved multiplicative DEA model to obtain the priority vector of MPR for ranking all alternatives. Finally, a numerical example is provided to show the rationality and applicability of the proposed GDM method. Show more

Keywords: Group decision making, distribution linguistic preference relation, incomplete distribution linguistic preference relation, expected consistency, multiplicative DEA cross-efficiency

DOI: 10.3233/JIFS-210148

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12039-12059, 2021

Authors: Chen, Wei | Chen, Junqiu | Xian, Yantuan

Article Type: Research Article

Abstract: It is of great significance to recognize the metallurgical entity relations in order to construct the Knowledge graph of Metallurgical Literature and to further understand the metallurgical literature. However, there are few researches on the textual entity relations in metallurgical fields either few marked Corpora. The syntactic structure of the same entity relationship category is relatively simple and has strong domain characteristics. The traditional entity relationship model can not identify the domain entity relationship well. Meanwhile the syntactic structure of the same entity relations class is relatively simple, and the syntactic structure is relatively simple in the recognition of entity …relations in metallurgy field. Furthermore, the entities with similar syntactic structure often have the same entity relations and the different words in the sentence have different contribution to the entity relations. In order to solve the mentioned problems, this paper will combine the algorithm that can highlight the syntactic structure in sentences and improve the accuracy of the model with the Algorithm that can highlight the contribution of words in sentences and the loss function level integration is carried out in the framework of small sample prototype network, so as to maximize the advantages of each algorithm and improve the accuracy –firstly, in the coding layer of the prototype network, we use the CNN algorithm which can highlight the important words in the sentences and the TreeLSTM algorithm which can parse the sentences in the text so that the syntactic relations between the words in the sentences can be acted on in the relation recognition, the sentences are coded together by two algorithms, then, the EUCLIDEAN distance loss is calculated by using this high quality coding and the prototype coding, finally, the traditional entity relation recognition model with Attention Mechanism is integrated into the loss function, further highlighting the decisive role of important words in text sentences in relation recognition and improving the generalization of the model. The results showed that compared with the traditional methods such as CNN, RNN, PCNN and Bi-LSTM, the proposed method in this paper has better performance in the case of small sample data set. Show more

Keywords: Syntactic analysis, integration learning, prototype network, entity relationship recognition

DOI: 10.3233/JIFS-210163

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12061-12073, 2021

Authors: Shi, Jinglei | Guo, Junjun | Yu, Zhengtao | Xiang, Yan

Article Type: Research Article

Abstract: Unsupervised aspect identification is a challenging task in aspect-based sentiment analysis. Traditional topic models are usually used for this task, but they are not appropriate for short texts such as product reviews. In this work, we propose an aspect identification model based on aspect vector reconstruction. A key of our model is that we make connections between sentence vectors and multi-grained aspect vectors using fuzzy k-means membership function. Furthermore, to make full use of different aspect representations in vector space, we reconstruct sentence vectors based on coarse-grained aspect vectors and fine-grained aspect vectors simultaneously. The resulting model can therefore learn …better aspect representations. Experimental results on two datasets from different domains show that our proposed model can outperform a few baselines in terms of aspect identification and topic coherence of the extracted aspect terms. Show more

Keywords: Aspect identification, text clustering, topic coherence, membership function, aspect extraction

DOI: 10.3233/JIFS-210175

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12075-12085, 2021

Authors: Chen, Zhe | Zhong, Peisi | Liu, Mei | Sun, Hongyuan | Shang, Kai

Article Type: Research Article

Abstract: This work aims to help the designers to make decisions in the early stage of new product development. Design concept evaluation is very critical in design process, it may affect the later stages. However, facing to uncertain circumstance, mostly, the raw data in early stage are subjective and imprecise. This work proposes a novel approach to solve this problem. The whole work is based on rough numbers, Shannon entropy, technique for order performance by similarity to ideal solution method and preference selection index method. Firstly, rough numbers and Shannon entropy are integrated to determine the weight of evaluation criteria based …on their interrelationships. After that, a novel technique for order performance by similarity to ideal solution method improved by rough numbers and preference selection index method is proposed to evaluate and rank the alternatives. Then, a comparative case is carried out with proposed method and two other methods in this study. The comparation of evaluation processes indicates that the proposed method’s advantage. Compared the other methods, proposed approach is objective, simple and do not need additional input. The results of three methods are similar. It means that the proposed method is not only effective and efficient in design concept evaluation, but also can save time and cost in the early stage of new product development. Show more

Keywords: Rough numbers, TOPSIS-PSI, shannon entropy, design concept evaluation

DOI: 10.3233/JIFS-210184

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12087-12099, 2021

Authors: Vivek, S. | Mathew, Sunil C.

Article Type: Research Article

Abstract: This paper studies the closure and interior operators in LM -fuzzy topological spaces. The algebraic structures associated with various collections of closed sets and open sets are identified. Further, certain lattices formed by these algebraic structures are obtained and some lattice theoretic properties of the same are investigated. Corresponding to every element in M , the study associates a lattice of monoids which is determined by various types of closed sets and open sets.

Keywords: LM-fuzzy topology, Closure operator, Lattice, Monoid, 54A40

DOI: 10.3233/JIFS-210195

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12101-12109, 2021

Authors: Wang, Rui | Jia, Zhaohong | Li, Kai

Article Type: Research Article

Abstract: In this paper, a problem of scheduling jobs with different sizes and fuzzy processing times (FPT) on non-identical parallel batch machines to minimize makespan is investigated. Moreover, the processing time (PT) of each batch is subject to the location-based learning and total-PT-based deterioration effect. Since this is an NP-hard combinatorial optimization problem, an improved intelligent algorithm based on fruit fly optimization algorithm (IFOA) is proposed. To verify the performance of the algorithm, the IFOA is compared with three state-of-the-art algorithms. The comparative results demonstrate that the proposed IFOA outperforms the other compared algorithms.

Keywords: Evolutionary algorithms, combinatorial optimization, fuzzy sets, scheduling

DOI: 10.3233/JIFS-210196

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12111-12124, 2021

Authors: Guo, Jiong | Lei, Deming | Li, Ming

Article Type: Research Article

Abstract: Energy-efficient flexible job shop scheduling problems (EFJSP) have been investigated fully; however, energy-related objectives often have lower importance than other ones in many real-life situations and this case is hardly considered in the previous works. In this study, EFJSP with sequence-dependent setup times (SDST) is considered, in which total tardiness and makespan are given higher importance than total energy consumption. A two-phase imperialist competitive algorithm (TPICA) is proposed. The importance difference among objectives is implemented by treating all objectives equally in the first phase and making energy consumption not to exceed a diminishing threshold in the second phase. A dynamical …differentiating assimilation and a novel imperialist competition with the enforced search are implemented. Extensive experiments are conducted and the computational results show that TPICA is very competitive for EFJSP with SDST. Show more

Keywords: Flexible job shop scheduling, energy-efficient scheduling, imperialist competitive algorithm, sequence-dependent setup times

DOI: 10.3233/JIFS-210198

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12125-12137, 2021

Authors: Cao, Xin-Zi | Luo, Sheng-Zhou | Li, Jing-Cong | Pan, Jia-Hui

Article Type: Research Article

Abstract: The grade and stage of bladder tumors is an essential key for diagnosing and treating bladder cancer. This study proposed an automated bladder tumor prediction system to automatically assess the bladder tumor grade and stage automatically on Magnetic Resonance Imaging (MRI) images. The system included three modules: tumor segmentation, feature extraction and prediction. We proposed a U-ResNet network that automatically extracts morphological and texture features for detecting tumor regions. These features were used in support vector machine (SVM) classifiers to predict the grade and stage. Our proposed method segmented the tumor area and predicted the grade and stage more accurately …compared to different methods in our experiments on MRI images. The accuracy of bladder tumor grade prediction was about 70%, and the accuracy of the data set was about 77.5%. The extensive experiments demonstrated the usefulness and effectiveness of our method. Show more

Keywords: Bladder tumor segmentation, U-ResNet network, grade and stage, feature extraction, support vector machine

DOI: 10.3233/JIFS-210263

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12139-12150, 2021

Authors: Xu, Zhixuan | Chen, Caikou | Han, Guojiang | Gao, Jun

Article Type: Research Article

Abstract: As a successful improvement on Low Rank Representation (LRR), Latent Low Rank Representation (LatLRR) has been one of the state-of-the-art models for subspace clustering due to the capability of discovering the low dimensional subspace structures of data, especially when the data samples are insufficient and/or extremely corrupted. However, the LatLRR method does not consider the nonlinear geometric structures within data, which leads to the loss of the locality information among data in the learning phase. Moreover, the coefficients of the learnt representation matrix can be negative, which lack the interpretability. To solve the above drawbacks of LatLRR, this paper introduces …Laplacian, sparsity and non-negativity to LatLRR model and proposes a novel subspace clustering method, termed latent low rank representation with non-negative, sparse and laplacian constraints (NNSLLatLRR), in which we jointly take into account non-negativity, sparsity and laplacian properties of the learnt representation. As a result, the NNSLLatLRR can not only capture the global low dimensional structure and intrinsic non-linear geometric information of the data, but also enhance the interpretability of the learnt representation. Extensive experiments on two face benchmark datasets and a handwritten digit dataset show that our proposed method outperforms existing state-of-the-art subspace clustering methods. Show more

Keywords: Subspace clustering, low rank representation, latent low rank representation, non-negative sparse laplacian constraints

DOI: 10.3233/JIFS-210274

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12151-12165, 2021

Authors: Liu, Luping | Wang, Meiling | He, Xiaohai | Qing, Linbo | Zhang, Jin

Article Type: Research Article

Abstract: Joint extraction of entities and relations from unstructured text is an essential step in constructing a knowledge base. However, relational facts in these texts are often complicated, where most of them contain overlapping triplets, making the joint extraction task still challenging. This paper proposes a novel Sequence-to-Sequence (Seq2Seq) framework to handle the overlapping issue, which models the triplet extraction as a sequence generation task. Specifically, a unique cascade structure is proposed to connect transformer and pointer network to extract entities and relations jointly. By this means, sequences can be generated in triplet-level and it speeds up the decoding process. Besides, …a syntax-guided encoder is applied to integrate the sentence’s syntax structure into the transformer encoder explicitly, which helps the encoder pay more accurate attention to the syntax-related words. Extensive experiments were conducted on three public datasets, named NYT24, NYT29, and WebNLG, and the results show the validity of this model by comparing with various baselines. In addition, a pre-trained BERT model is also employed as the encoder. Then it comes up to excellent performance that the F1 scores on the three datasets surpass the strongest baseline by 5.7%, 5.6%, and 4.4%. Show more

Keywords: Information extraction, sequence to sequence, transformer network, pointer network, syntax-guided attention network

DOI: 10.3233/JIFS-210281

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12167-12183, 2021

Authors: Gao, Xiue | Jiang, Panling | Xie, Wenxue | Chen, Yufeng | Zhou, Shengbin | Chen, Bo

Article Type: Research Article

Abstract: Decision fusion is an effective way to resolve the conflict of diagnosis results. Aiming at the problem that Dempster-Shafer (DS) theory deals with the high conflict of evidence and produces wrong results, a decision fusion algorithm for fault diagnosis based on closeness and DS theory is proposed. Firstly, the relevant concepts of DS theory are introduced, and the normal distribution membership function is used as the evidence closeness. Secondly, the harmonic average is introduced, and the weight of each evidence is established according to the product of closeness of each evidence and its harmonic average. Thirdly, the weight of conflicting …evidence is regularized, and the final decision fusion result is obtained by using the Dempster’s rule. Lastly, the simulation and application examples are designed. Simulation and application results show that the method can effectively reduce the impact of diagnostic information conflicts and improve the accuracy of decision fusion. What’s more, the method considers the overall average distribution of evidence in the identification framework, it can reduce evidence conflicts while preserving important diagnostic information. Show more

Keywords: Fault diagnosis, decision fusion, DS theory, closeness, harmonic average

DOI: 10.3233/JIFS-210283

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12185-12194, 2021

Authors: Chen, Ting-Yu

Article Type: Research Article

Abstract: The purpose of this paper is to evolve a novel area-based Pythagorean fuzzy decision model via an approach-oriented measure and an avoidance-oriented measure in support of multiple criteria decision analysis involving intricate uncertainty of Pythagorean fuzziness. Pythagorean membership grades embedded in a Pythagorean fuzzy set is featured by tensible functions of membership, non-membership, indeterminacy, strength, and direction, which delivers flexibility and adaptability in manipulating higher-order uncertainties. However, a well-defined ordered structure is never popular in real-life issues, seldom seen in Pythagorean fuzzy circumstances. Consider that point operators can make a systematic allocation of the indeterminacy composition contained in Pythagorean fuzzy …information. This paper exploits the codomains of the point operations (i.e., the quantities that express the extents of point operators) to launch new measurements of approach orientation and avoidance orientation for performance ratings. This paper employs such measurements to develop an area-based performance index and an area-based comprehensive index for conducting a decision analysis. The applications and comparative analyses of the advanced area-based approach to some decision-making problems concerning sustainable recycling partner selection, company investment decisions, stock investment decisions, and working capital financing decisions give support to methodological advantages and practical effectiveness. Show more

Keywords: Area-based Pythagorean fuzzy decision model, approach-oriented measure, avoidance-oriented measure, multiple criteria decision analysis, Pythagorean fuzziness

DOI: 10.3233/JIFS-210290

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12195-12213, 2021

Authors: Ghafarokhi, Omid Izadi | Moattari, Mazda | Forouzantabar, Ahmad

Article Type: Research Article

Abstract: With the development of the wide-area monitoring system (WAMS), power system operators are capable of providing an accurate and fast estimation of time-varying load parameters. This study proposes a spatial-temporal deep network-based new attention concept to capture the dynamic and static patterns of electrical load consumption through modeling complicated and non-stationary interdependencies between time sequences. The designed deep attention-based network benefits from long short-term memory (LSTM) based component to learning temporal features in time and frequency-domains as encoder-decoder based recurrent neural network. Furthermore, to inherently learn spatial features, a convolutional neural network (CNN) based attention mechanism is developed. Besides, this …paper develops a loss function based on a pseudo-Huber concept to enhance the robustness of the proposed network in noisy conditions as well as improve the training performance. The simulation results on IEEE 68-bus demonstrates the effectiveness and superiority of the proposed network through comparison with several previously presented and state-of-the-art methods. Show more

Keywords: Composite load modeling, deep attention neural network, encoder-decoder, long short-term memory, convolutional neural network, wide-area monitoring system

DOI: 10.3233/JIFS-210296

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12215-12226, 2021

Authors: Gasmi, Ibtissem | Azizi, Mohamed Walid | Seridi-Bouchelaghem, Hassina | Azizi, Nabiha | Belhaouari, Samir Brahim

Article Type: Research Article

Abstract: Context-Aware Recommender System (CARS) suggests more relevant services by adapting them to the user’s specific context situation. Nevertheless, the use of many contextual factors can increase data sparsity while few context parameters fail to introduce the contextual effects in recommendations. Moreover, several CARSs are based on similarity algorithms, such as cosine and Pearson correlation coefficients. These methods are not very effective in the sparse datasets. This paper presents a context-aware model to integrate contextual factors into prediction process when there are insufficient co-rated items. The proposed algorithm uses Latent Dirichlet Allocation (LDA) to learn the latent interests of users from …the textual descriptions of items. Then, it integrates both the explicit contextual factors and their degree of importance in the prediction process by introducing a weighting function. Indeed, the PSO algorithm is employed to learn and optimize weights of these features. The results on the Movielens 1 M dataset show that the proposed model can achieve an F-measure of 45.51% with precision as 68.64%. Furthermore, the enhancement in MAE and RMSE can respectively reach 41.63% and 39.69% compared with the state-of-the-art techniques. Show more

Keywords: Collaborative filtering, context, topic modeling, PSO, LDA, sparsity problem

DOI: 10.3233/JIFS-210331

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12227-12242, 2021

Authors: Xu, Kaijie | E, Hanyu | Quan, Yinghui | Cui, Ye | Nie, Weike

Article Type: Research Article

Abstract: In this study, we develop a novel clustering with double fuzzy factors to enhance the performance of the granulation-degranulation mechanism, with which a fuzzy rule-based model is designed and demonstrated to be an enhanced one. The essence of the developed scheme is to optimize the construction of the information granules so as to eventually improve the performance of the fuzzy rule-based models. In the design process, a prototype matrix is defined to express the Fuzzy C-Means based granulation-degranulation mechanism in a clear manner. We assume that the dataset degranulated from the formed information granules is equal to the original numerical …dataset. Then, a clustering method with double fuzzy factors is derived. We also present a detailed mathematical proof for the proposed approach. Subsequently, on the basis of the enhanced version of the granulation-degranulation mechanism, we design a granular fuzzy model. The whole design is mainly focused on an efficient application of the fuzzy clustering to build information granules used in fuzzy rule-based models. Comprehensive experimental studies demonstrate the performance of the proposed scheme. Show more

Keywords: Partition matrix, granulation-degranulation mechanism, information granules, fuzzy clustering, rule-based models, prototype matrix

DOI: 10.3233/JIFS-210336

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12243-12252, 2021

Authors: Ahkouk, Karam | Machkour, Mustapha | Majhadi, Khadija | Mama, Rachid

Article Type: Research Article

Abstract: In the last decade, many intelligent interfaces and layers have been suggested to allow the use of relational databases and extraction of the content using only the natural language. However most of them struggle when exposed to new databases. In this article, we present SQLSketch, a sketch-based network for generating SQL queries to address the problem of automatically translate Natural Languages questions to SQL using the related databases schemas. We argue that the previous models that use full or partial sequence-to-sequence structure in the decoding phase can, in fact, have counter-effect on the generation operation and came up with more …loss of the context or the meaning of the user question. In this regard, we use a full sketch-based structure that decouples the generation process into many small prediction modules. The SQLSketch is evaluated against GreatSQL, a new cross-domain, large-scale and balanced dataset for the Natural Language to SQL translation task. For a long-term aim of making better models and contributing in adding more improvements to the semantic parsing tasks, we propose the GreatSQL dataset as the first balanced cross-domain corpus that includes 45,969 pairs of natural language questions and their corresponding SQL queries in addition to simplified and well structured ground-truth annotations. We establish results for SQLSketch using GreatSQL dataset and compare the performance against two popular types of models that represent the sequential and partial-sketch based approaches. Experimental result shows that SQLSketch outperforms the baseline models by 13% in exact matching accuracy and achieve a score of 23.9% to be the new state-of-the-art model on GreatSQL. Show more

Keywords: Natural language processing, text to SQL translation, database interfaces, natural language translation, machine translation

DOI: 10.3233/JIFS-210359

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12253-12263, 2021

Authors: Zhong, Xianyou | Gao, Xiang | Mei, Quan | Huang, Tianwei | Zhao, Xiao

Article Type: Research Article

Abstract: Gear fault vibration signals are commonly non-stationary, and useful fault information is often buried in heavy noise, which makes it difficult to extract gear fault features. How to select the suitable fault frequency bands is the key to gear fault diagnosis. To address the above problems, a method combining the improved minimum entropy deconvolution (MED) and accugram, named IMEDA, is proposed for extracting gear fault features. Firstly, a selection index based on permutation entropy (PE) and correlation coefficient is defined. Then, the optimal filter length can be effectively selected by the step-length searching method using the proposed index as objective …function, and the improved MED is employed to preprocess the gear vibration signals. Finally, the accugram analysis is performed for the preprocessed signals to obtain the optimal frequency band, and the fault characteristic frequencies are extracted from the square envelope spectrum of the signals in the optimal band. The method is validated by gear experimental data with gear wear-out failure. The analysis results demonstrate that the proposed method owns superior effect by comparing with the fast kurtogram (FK), MED combined with FK (MED-FK), accugram and infogram. Show more

Keywords: Minimum entropy deconvolution, accugram, frequency band selection, fault feature extraction

DOI: 10.3233/JIFS-210405

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12265-12282, 2021

Authors: An, Qing | Tang, Ruoli | Su, Hongfeng | Zhang, Jun | Li, Xin

Article Type: Research Article

Abstract: Due to the promising performance on energy-saving, the building integrated photovoltaic system (BIPV) has found an increasingly wide utilization in modern cities. For a large-scale PV array installed on the facades of a super high-rise building, the environmental conditions (e.g., the irradiance, temperature, sunlight angle etc.) are always complex and dynamic. As a result, the PV configuration and maximum power point tracking (MPPT) methodology are of great importance for both the operational safety and efficiency. In this study, some famous PV configurations are comprehensively tested under complex shading conditions in BIPV application, and a robust configuration for large-scale BIPV system …based on the total-cross-tied (TCT) circuit connection is developed. Then, by analyzing and extracting the feature variables of environment parameters, a novel fast MPPT methodology based on extreme learning machine (ELM) is proposed. Finally, the proposed configuration and its MPPT methodology are verified by simulation experiments. Experimental results show that the proposed configuration performs efficient on most of the complex shading conditions, and the ELM-based intelligent MPPT methodology can also obtain promising performance on response speed and tracking accuracy. Show more

Keywords: Building integrated photovoltaic system, maximum power point tracking, PV configuration, intelligent control, extreme learning machine

DOI: 10.3233/JIFS-210424

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12283-12300, 2021

Authors: Li, Huanhuan | Ji, Ying | Qu, Shaojian

Article Type: Research Article

Abstract: Decision-makers usually have a variety of unsure situations in the environment of group decision-making. In this paper, we resolve this difficulty by constructing two-stage stochastic integrated adjustment deviations and consensus models (iADCMs). By introducing the minimum cost consensus models (MCCMs) with costs direction constraints and stochastic programming, we develop three types of iADCMs with an uncertainty of asymmetric costs and initial opinions. The factors of directional constraints, compromise limits and free adjustment thresholds previously thought to affect consensus separately are considered in the proposed models. Different from the previous consensus models, the resulting iADCMs are solved by designing an appropriate …L-shaped algorithm. On the application in the negotiations on Grains to Green Programs (GTGP) in China, the proposed models are demonstrated to be more robust. The proposed iADCMs are compared to the MCCMs in an asymmetric costs context. The contrasting outcomes show that the two-stage stochastic iADCMs with no-cost threshold have the smallest total costs. Moreover, based on the case study, we give a sensitivity analysis of the uncertainty of asymmetric adjustment cost. Finally, conclusion and future research prospects are provided. Show more

Keywords: Two-stage stochastic integrated adjustment deviations and consensus model, directional constraints, uncertain adjustment costs, uncertainty initial opinions, L-shaped algorithm

DOI: 10.3233/JIFS-210443

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12301-12319, 2021

Authors: Al-Tarawneh, Ahmed | Al-Saraireh, Ja’afer

Article Type: Research Article

Abstract: Twitter is one of the most popular platforms used to share and post ideas. Hackers and anonymous attackers use these platforms maliciously, and their behavior can be used to predict the risk of future attacks, by gathering and classifying hackers’ tweets using machine-learning techniques. Previous approaches for detecting infected tweets are based on human efforts or text analysis, thus they are limited to capturing the hidden text between tweet lines. The main aim of this research paper is to enhance the efficiency of hacker detection for the Twitter platform using the complex networks technique with adapted machine learning algorithms. This …work presents a methodology that collects a list of users with their followers who are sharing their posts that have similar interests from a hackers’ community on Twitter. The list is built based on a set of suggested keywords that are the commonly used terms by hackers in their tweets. After that, a complex network is generated for all users to find relations among them in terms of network centrality, closeness, and betweenness. After extracting these values, a dataset of the most influential users in the hacker community is assembled. Subsequently, tweets belonging to users in the extracted dataset are gathered and classified into positive and negative classes. The output of this process is utilized with a machine learning process by applying different algorithms. This research build and investigate an accurate dataset containing real users who belong to a hackers’ community. Correctly, classified instances were measured for accuracy using the average values of K-nearest neighbor, Naive Bayes, Random Tree, and the support vector machine techniques, demonstrating about 90% and 88% accuracy for cross-validation and percentage split respectively. Consequently, the proposed network cyber Twitter model is able to detect hackers, and determine if tweets pose a risk to future institutions and individuals to provide early warning of possible attacks. Show more

Keywords: Tweets, hacking, prediction, twitter, social networks

DOI: 10.3233/JIFS-210458

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12321-12337, 2021

Authors: Ling, Chunyan | Lu, Zhenzhou

Article Type: Research Article

Abstract: To measure the effects of the fuzzy inputs on structural safety degree, this paper establishes the failure credibility-based global sensitivity by the fuzzy expected value of the absolute difference between the unconditional failure credibility and conditional one. To establish the failure credibility-based global sensitivity, the conditional failure credibility is firstly defined according to the original definition of conditional event and the relationship among the possibility, necessity and credibility, in which no extra assumption is introduced. After that, the equivalent expression of the failure credibility is deduced, on which the Bayesian transformation of the conditional failure credibility is obtained in this …paper. Then, a single-loop method based on the sequential quadratic programming is applied to efficiently estimate the defined failure credibility-based global sensitivity. According to the result of the constructed failure credibility-based global sensitivity, designers can pay more attentions to the more important fuzzy inputs to have a better control of the structural safety degree. The presented examples demonstrate the feasibility of the constructed failure credibility-based global sensitivity and the efficiency of the proposed solution. Show more

Keywords: Fuzzy input, failure credibility, global sensitivity, fuzzy expected value, conditional failure credibility, sequential quadratic programming

DOI: 10.3233/JIFS-210461

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12339-12359, 2021

Authors: Song, Xudong | Zhu, Dajie | Liang, Pan | An, Lu

Article Type: Research Article

Abstract: Although the existing transfer learning method based on deep learning can realize bearing fault diagnosis under variable load working conditions, it is difficult to obtain bearing fault data and the training data of fault diagnosis model is insufficient£¬which leads to the low accuracy and generalization ability of fault diagnosis model, A fault diagnosis method based on improved elastic net transfer learning under variable load working conditions is proposed. The improved elastic net transfer learning is used to suppress the over fitting and improve the training efficiency of the model, and the long short-term memory network is introduced to train the …fault diagnosis model, then a small amount of target domain data is used to fine tune the model parameters. Finally, the fault diagnosis model under variable load working conditions based on improved elastic net transfer learning is constructed. Finally, through model experiments and comparison with conventional deep learning fault diagnosis models such as long short-term memory network (LSTM), gated recurrent unit (GRU) and Bi-LSTM, it shows that the proposed method has higher accuracy and better generalization ability, which verifies the effectiveness of the method. Show more

Keywords: Elastic net, fault diagnosis, LSTM, transfer learning

DOI: 10.3233/JIFS-210503

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12361-12369, 2021

Authors: Zou, Yuan | Yang, Daoli | Pan, Yuchen

Article Type: Research Article

Abstract: Gross domestic product (GDP) is the most widely-used tool for measuring the overall situation of a country’s economic activity within a specified period of time. A more accurate forecasting of GDP based on standardized procedures with known samples available is conducive to guide decision making of government, enterprises and individuals. This study devotes to enhance the accuracy regarding GDP forecasting with given sample of historical data. To achieve this purpose, the study incorporates artificial neural network (ANN) into grey Markov chain model to modify the residual error, thus develops a novel hybrid model called grey Markov chain with ANN error …correction (abbreviated as GMCM_ANN), which assembles the advantages of three components to fit nonlinear forecasting with limited sample sizes. The new model has been tested by adopting the historical data, which includes the original GDP data of the United States, Japan, China and India from 2000 to 2019, and also provides predications on four countries’ GDP up to 2022. Four models including autoregressive integrated moving average model, back-propagation neural network, the traditional GM(1,1) and grey Markov chain model are as benchmarks for comparison of the predicted accuracy and application scope. The obtained results are satisfactory and indicate superior forecasting performance of the proposed approach in terms of accuracy and universality. Show more

Keywords: Gross domestic product, grey Markov chain, artificial neural network, residual correction, forecasting

DOI: 10.3233/JIFS-210509

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12371-12381, 2021

Authors: Noon, Serosh Karim | Amjad, Muhammad | Ali Qureshi, Muhammad | Mannan, Abdul

Article Type: Research Article

Abstract: Cotton is an important commodity because of its use in various industries across the globe. It is grown in many countries and is imported/exported as a cash crop due to its large utility. However, cotton yield is adversely affected by the existence of pests, viruses and pathogenic bacteria, etc. For the last one decade or so, several image processing/deep learning-based automatic plant leaf disease recognition methods have been developed but, unfortunately, they rarely address the cotton leaf diseases. The proposed work presents a simple yet efficient deep learning-based framework to recognize cotton leaf diseases. The proposed model is capable of …achieving the near ideal accuracy with early convergence to save computational cost of training. Further, due to the unavailability of publicly available datasets for this crop, a dataset is also collected comprising of three diseases namely curl virus, bacterial blight and fusarium wilt in addition to the healthy leaf Images. These images were collected from the Internet and fields of Southern Punjab region in Pakistan where the cotton crop is grown on thousands of acres every year and is exported to the Europe and the US either as a raw material or in the form of knitted industrial/domestic products. Experimental results have shown that almost all variants of our proposed deep learning framework have shown remarkably good recognition accuracy and precision. However, proposed EfficientNet-B0 model achieves 99.95% accuracy in only 152 seconds with best generalization and fast inference. Show more

Keywords: Cotton leaf disease, efficientnet, mobilenet, deep leaning, agriculture

DOI: 10.3233/JIFS-210516

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12383-12398, 2021

Authors: Abughazalah, Nabilah | Khan, Majid | Munir, Noor | Zafar, Amna

Article Type: Research Article

Abstract: In this article, we have designed a new scheme for the construction of the nonlinear confusion component. Our mechanism uses the notion of a semigroup, Inverse LA-semigroup, and various other loops. With the help of these mathematical structures, we can easily build our confusion component namely substitution boxes (S-boxes) without having specialized structures. We authenticate our proposed methodology by incorporating the available cryptographic benchmarks. Moreover, we have utilized the technique for order of preference by similarity to ideal solution (TOPSIS) to select the best nonlinear confusion component. With the aid of this multi-criteria decision-making (MCDM), one can easily select the …best possible confusion component while selecting among various available nonlinear confusion components. Show more

Keywords: Nonlinear confusion component, semigroup, loop, TOPSIS, MCDM

DOI: 10.3233/JIFS-210524

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12399-12410, 2021

Authors: Wang, H.Y. | Wang, J.S. | Zhu, L.F.

Article Type: Research Article

Abstract: Fuzzy C-means (FCM) clustering algorithm is a widely used method in data mining. However, there is a big limitation that the predefined number of clustering must be given. So it is very important to find an optimal number of clusters. Therefore, a new validity function of FCM clustering algorithm is proposed to verify the validity of the clustering results. This function is defined based on the intra-class compactness and inter-class separation from the fuzzy membership matrix, the data similarity between classes and the geometric structure of the data set, whose minimum value represents the optimal clustering partition result. The proposed …clustering validity function and seven traditional clustering validity functions are experimentally verified on four artificial data sets and six UCI data sets. The simulation results show that the proposed validity function can obtain the optimal clustering number of the data set more accurately, and can still find the more accurate clustering number under the condition of changing the fuzzy weighted index, which has strong adaptability and robustness. Show more

Keywords: Fuzzy C-means clustering algorithm, clustering validity function, membership matrix, intra-class compactness, inter-class separation

DOI: 10.3233/JIFS-210555

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12411-12432, 2021

Authors: Yavuz, Enes

Article Type: Research Article

Abstract: We define statistical Cesàro and statistical logarithmic summability methods of sequences in intuitionistic fuzzy normed spaces(IFNS ) and give slowly oscillating type and Hardy type Tauberian conditions under which statistical Cesàro summability and statistical logarithmic summability imply convergence in IFNS . Besides, we obtain analogous results for the higher order summability methods as corollaries. Also, two theorems concerning the convergence of statistically convergent sequences in IFNS are proved in the paper.

Keywords: Intuitionistic fuzzy normed space, tauberian theorem, cesàro and logarithmic summability methods, statistical convergence, slow oscillation, 03E72, 40A05, 40G05, 40G15, 40E05

DOI: 10.3233/JIFS-210596

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12433-12442, 2021

Authors: Wang, Fang | Li, Hai-Mei | Li, Yan-Lai | Wu, Ai-Ping

Article Type: Research Article

Abstract: Quality function deployment (QFD) is a customer-oriented tool for developing products. Based on the idea of the best-worst method (BWM), a novel model is developed to determine the relative importance ratings (RIRs) of customer requirements (CRs) with interval grey linguistic (IGL) information, which plays a significant role in QFD. CRs are rated with IGL variables, and the degree of greyness degree function that can be used to handle the IGL variables is defined based on the power utility function. Then, considering customer heterogeneity, a model is constructed to derive the RIRs of CRs by following the logic of the BWM. …Finally, a case study of 5 G smartphone development is provided to verify the validity and the feasibility of the proposed method. Show more

Keywords: Customer requirements, QFD, interval grey linguistic, best-worst method, utility function

DOI: 10.3233/JIFS-210799

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12443-12458, 2021

Authors: Lu, Hanchuan | Khalil, Ahmed Mostafa | Alharbi, W. | El-Gayar, M. A.

Article Type: Research Article

Abstract:  In this article, we propose a novel concept of the generalized picture fuzzy soft set by combining the picture fuzzy soft set and the fuzzy parameter set. For possible applications, we explain five kinds of operations (e.g., subset, equal, union, intersection, and complement) based on generalized picture fuzzy soft sets. Then, we establish several theoretical operations of generalized picture fuzzy soft sets. In addition, we present the new type by using the AND operation of the generalized picture fuzzy soft set for fuzzy decision-making and clarify its applicability with a numerical example. Finally, we give a comparison between the picture …fuzzy soft set theory and the generalized picture fuzzy soft set theory. It is shown that our proposed (i.e., generalized picture fuzzy soft set theory) is viable and provide decision makers a more mathematical insight before making decisions on their options. Show more

Keywords: Picture fuzzy set, soft set, generalized picture fuzzy soft set, Algorithm 1, Algorithm 2, decision-making

DOI: 10.3233/JIFS-201706

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12459-12475, 2021

Authors: Hamdoun, Hala | Sagheer, Alaa | Youness, Hassan

Article Type: Research Article

Abstract: Machine learning methods have been adopted in the literature as contenders to conventional methods to solve the energy time series forecasting (TSF) problems. Recently, deep learning methods have been emerged in the artificial intelligence field attaining astonishing performance in a wide range of applications. Yet, the evidence about their performance in to solve the energy TSF problems, in terms of accuracy and computational requirements, is scanty. Most of the review articles that handle the energy TSF problem are systematic reviews, however, a qualitative and quantitative study for the energy TSF problem is not yet available in the literature. The purpose …of this paper is twofold, first it provides a comprehensive analytical assessment for conventional, machine learning, and deep learning methods that can be utilized to solve various energy TSF problems. Second, the paper carries out an empirical assessment for many selected methods through three real-world datasets. These datasets related to electrical energy consumption problem, natural gas problem, and electric power consumption of an individual household problem. The first two problems are univariate TSF and the third problem is a multivariate TSF. Compared to both conventional and machine learning contenders, the deep learning methods attain a significant improvement in terms of accuracy and forecasting horizons examined. In the meantime, their computational requirements are notably greater than other contenders. Eventually, the paper identifies a number of challenges, potential research directions, and recommendations to the research community may serve as a basis for further research in the energy forecasting domain. Show more

Keywords: Energy time series forecasting, conventional forecasting methods, machine learning, deep learning, energy management systems

DOI: 10.3233/JIFS-201717

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12477-12502, 2021

Authors: Zhang, Na | Yan, Shuli | Fang, Zhigeng | Yang, Baohua

Article Type: Research Article

Abstract: In view of the situation that tasks or activities in the GERT model may have multiple realizations, this paper explores the time dependence of each repeated execution node under the condition of fuzzy information, and studies the characteristics of the z-tag fuzzy GERT model and its analytic algorithm. Firstly, the F-GERT model related to the number of executions of activities is defined, and the simplified rules, related properties and theorems of the network model are examined. Secondly, solving algorithm, conditional moment generating function and process arrival time of the F-GERT model for repeated execution time are studied. Finally, the application …of F-GERT queuing system based on element execution time in weapon equipment management is discussed. The feasibility and effectiveness of the model and algorithm are verified by the practical application of the project. Show more

Keywords: Project management, GERT model, fuzzy information, z-tag, moment generating function, network structure

DOI: 10.3233/JIFS-201731

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 12503-12519, 2021