Journal of Intelligent & Fuzzy Systems - Volume 39, issue 1

Authors: Naeem, Khalid | Riaz, Muhammad | Afzal, Deeba

Article Type: Research Article

Abstract: The inspiration behind this article is to introduce the notions of fuzzy neutrosophic soft σ -algebra ( fns σ -algebra), fuzzy neutrosophic soft measure ( fns measure) and fns outer measure using the concepts of fuzzy sets, soft sets, neutrosophic sets and soft σ -algebra. A number of related results along with elaborative examples are also included. We render an algorithm based upon fns -mapping to deal with imprecise data utilizing mean proportional operator and employ it on multi-criteria group decision making (MCGDM) …problem to exhibit its efficacy. Show more

Keywords: 𝔉𝔑𝔖 σ-algebra, 𝔉𝔑𝔖-measure, 𝔉𝔑𝔖 outer measure, MCGDM

DOI: 10.3233/JIFS-191062

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 277-287, 2020

Authors: Zhang, Wenyu | Ding, Jiepin | Wang, Yan | Zhang, Shuai | Zhuang, Xiaoyu

Article Type: Research Article

Abstract: The flexible multi-task scheduling problem has been extensively investigated in manufacturing systems, and its objectives are often related to the quality of manufacturing services. However, energy-related objectives along with workload balance have rarely been considered. Thus, a novel bi-objective optimization model is proposed to achieve green manufacturing. The Pareto-based fitness evaluation is employed to make a trade-off between total energy consumption and workload balance. Intermediate buffers are also considered, making the model more practical and more complicated. To solve the proposed model, a new three-stage genetic algorithm (3S-GA) is presented. A Pareto-based adaptive population size method is proposed to maintain …the diversity of the population and ensure the convergence rate. To cope with the subtask sequencing complexity, a real-time sequence scheduling heuristic is explored to effectively initialize the subtask sequence to save the energy in manufacturing systems, which is designed by minimizing the standby time according to the laxity of subtasks. After a series of experimental designs based on the Taguchi method, a suitable parameter combination of the 3S-GA is utilized. Further, computational experiments based on five instances demonstrate that the 3S-GA outperforms other four baseline algorithms taken from the literature in solving the proposed bi-objective optimization model. Show more

Keywords: Flexible multi-task scheduling, Energy consumption, Intermediate buffer, Bi-objective optimization, Pareto, Genetic algorithm

DOI: 10.3233/JIFS-191072

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 289-304, 2020

Authors: Zhao, Feng | Xie, Min | Liu, Hanqiang | Fan, Jiulun | Lan, Rong | Xie, Wen | Zheng, Yue

Article Type: Research Article

Abstract: Multilevel thresholding is one of the effective image segmentation methods. However, it faces three big challenges: (1) how to adaptively determine the number of multiple thresholds; (2) how to overcome the sensitivity to image noise; (3) how to perform multilevel thresholding under several segmentation requirements. In order to solve these problems, an adaptive multilevel thresholding algorithm based on multiobjective artificial bee colony optimization (AMT-MABCO) segmentation is presented for noisy image in this paper. To improve the robustness of AMT-MABCO to image noise, a line intercept histogram which considers both the intensity and coordinate information in the neighborhood of the pixels …is firstly utilized to define a novel between-class variance function as one fitness function. Then, an interval-valued fuzzy entropy function is constructed as another fitness function to deal with the blurred characteristic in images. AMT-MABCO tries to obtain a compromising multilevel thresholding result under these two segmentation requirements. To adaptively determine the number of thresholds, a grouping population initialization and evaluation strategies are proposed in AMT-MABCO. Furthermore, two novel search equations are constructed in AMT-MABCO to generate candidate solutions in the employed bees and onlookers phases, respectively. Experimental results show that AMT-MABCO outperforms state-of-the-art thresholding methods in noise robustness and segmentation performance. Show more

Keywords: Image segmentation, multi-objective optimization, artificial bee colony, multilevel thresholding, interval-valued fuzzy information

DOI: 10.3233/JIFS-191083

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 305-323, 2020

Authors: Yang, RouJian | Jin, LeSheng | Paternain, Daniel | Yager, Ronald R. | Mesiar, Radko | Bustince, Humberto

Article Type: Research Article

Abstract: In decision making, very often the data collected are with different extents of uncertainty. The recently introduced concept, Basic Uncertain Information (BUI), serves as one ideal information representation to well model involved uncertainties with different extents. This study discusses some methods of BUI aggregation by proposing some uncertainty transformations for them. Based on some previously obtained results, we at first define IOWA operator with poset valued input vector and inducing vector. The work then defines the concept of uncertain system, on which we can further introduce the multi-layer uncertainty transformation for BUI. Subsequently, we formally introduce MUT_IOWA aggregation procedure, which …has good potential to more and wider application areas. A numerical example is also offered along with some simple usage of it in decision making. Show more

Keywords: Aggregation function, BUI aggregation, decision making, evaluation, OWA operators, uncertain decision making

DOI: 10.3233/JIFS-191106

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 325-332, 2020

Authors: Yu, Shujuan | Liu, Danlei | Zhu, Wenfeng | Zhang, Yun | Zhao, Shengmei

Article Type: Research Article

Abstract: Text classification is a fundamental task in Nature Language Processing(NLP). However, with the challenge of complex semantic information, how to extract useful features becomes a critical issue. Different from other traditional methods, we propose a new model based on two parallel RNNs architecture, which captures context information through LSTM and GRU respectively and simultaneously. Motivated by the siamese network, our proposed architecture generates attention matrix through calculating similarity between the parallel captured context information, which ensures the effectiveness of extracted features and further improves classification results. We evaluate our proposed model on six text classification tasks. The result of experiments …shows that the ABLGCNN model proposed in this paper has the faster convergence speed and the higher precision than other models. Show more

Keywords: Long short term memory, gated recurrent unit, convolutional neural network, attention mechanism, text classification

DOI: 10.3233/JIFS-191171

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 333-340, 2020

Authors: Patil, Varsha H. | Bhavsar, Swati A. | Patil, Aboli H.

Article Type: Research Article

Abstract: Digital Bibliography and Library Project dataset is a collection of bibliographic records of computer science publications of various authors and co-authors. It contains approximately 1.5 million bibliographic records. An algorithm for an author’s information retrieval is developed to retrieve details of specific author publications and correlation among authors. Further performance of an author is measured with parameters like consistency, contribution factor, stability, cooperativeness, and solidity. The work presented is tested on the DBLP dataset. Experimental results clearly support the claim that it works efficiently for retrieving specific author-publication records and its analysis with respect to suggested parameters.

Keywords: Author, consistency, contribution factor, cooperativeness, DBLP, graph, publication stability, solidity

DOI: 10.3233/JIFS-191289

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 341-353, 2020

Authors: Szeto, Pok Man | Parvin, Hamid | Mahmoudi, Mohammad Reza | Tuan, Bui Anh | Pho, Kim-Hung

Article Type: Research Article

Abstract: Features play an important role in image processing. But as not all features are comparable, relative features emerged. From the beginning, low-level features, extracted by experts, have been employed to create difficult models for learning the problem of relative attribute. Knowing these models are limited in generality of their applicability, deep learning models can be employed instead of them. A deep artificial neural network framework has been suggested for the task of relative attribute prediction in this article. The paper suggests to use a convolutional artificial neural network for learning the mentioned attributes through a peripheral auxiliary layer, called also …a ranking layer, which is able to learn how to rank the images. A suitable ranking cost function is used to train the whole network in an end-to-end manner. The suggested method through this paper is experimentally superior to the state of the art methods on some well-known benchmarks. The experimental results indicate that the proposed method is capable of learning the problem of relative attribute. Show more

Keywords: Image processing, relative features, deep learning, deep features

DOI: 10.3233/JIFS-191292

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 355-369, 2020

Authors: Xu, Shuzhen | Wang, Jin | Zhu, Qing

Article Type: Research Article

Abstract: Motivated by a widely studied computer vision task: image inpainting, we became interested in a less concerned problem image outpainting. By which, contents beyond the image boundaries may be extrapolated. In recent years, deep learning methods have achieved remarkable improvements in image inpainting, these techniques can be considered to be applied to image outpainting as solutions. However, many of these inpainting methods generate image blocks generally resulting in blur or smooth. Recently, hallucinating edges for the missing holes before completion has been proved to be a state-of-the-art image inpainting method. Refer to the aforementioned method, we propose a three-phase outpainting …model that consists of an edge generation phase, an image expansion phase and a refinement phase. In order to depict the edge lines more accurately, we adopt a comparatively effective focal loss for edge prediction. An optimization stage with a refinement network is also added since large portions outside the image need to be inferred, and discriminator in this stage works on a decreased patch size with a coarse-to-fine fashion. In addition, with recursive outpainting, an image could be expanded arbitrarily. Experiments show that an image can be effectively expanded by our method, and our outpainting method of predicting edges and then coloring is generally superior to other methods both quantitatively and qualitatively. Show more

Keywords: Outpainting, edge detector, generative adversarial network, focal loss

DOI: 10.3233/JIFS-191310

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 371-381, 2020

Authors: Alkouri, Abd Ulzeez M. J. | Massa’deh, Morad Oqla | Ali, Mabruka

Article Type: Research Article

Abstract: Many factors with the perspective of bipolarity in the traditional Chinese food system “Yin and Yang food system” manipulate with types of food simultaneously to have a balanced body. This research studies the multiple attributes decision making (MADM) problem that measuring the “bipolarity of periodic” variation in bipolar information with an illustration example in order to find an optimal nutrition program for a person X . To convey this type of data to a mathematical formula and vice versa without losing the full meaning of human knowledge, we use bipolar fuzzy set in a complex geometry by extending the range …of bipolar fuzzy set to the realm of a complex number. This extension needs to be successful to study and introduce intensely a new mathematical structure called a bipolar complex fuzzy set (BCFS) with its properties. Ranges of values are extended to [0, 1] e iα [0,1]  and [-1, 0] e iα [-1,0] for both positive and negative membership functions, respectively, as a replacement for [-1, 0] × [0, 1] , as in the bipolar fuzzy set. The main benefit of BCFS that the amplitude and phase terms of BCFSs can convey bipolar fuzzy information. Moreover, the formal definition of BCF distance measure and illustration application are introduced. Some basic mathematical operations on BCFS are also proposed and study its properties with arithmetical examples. Show more

Keywords: Bipolar fuzzy set, bipolar complex fuzzy distance, bipolar fuzzy complement, union and intersection, fuzzy set, complex fuzzy set

DOI: 10.3233/JIFS-191350

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 383-397, 2020

Authors: Saplioglu, Kemal | Ozturk, Tulay Sugra Kucukerdem | Acar, Ramazan

Article Type: Research Article

Abstract: Due to recent increasing water demand, planning and projecting of water resources has resulted in increased costs. There-fore, it is important to obtain optimum results in project planning. In this study, dimensioning of open channels used espe-cially for irrigation purposes has been studied using a particle swarm optimization algorithm to investigate optimum base width, channel height, and slope angles. The results are summarized in graphs and tables. In the study, it was found that the optimum slope angle varied between 0 . 20⌣ 0 . 450 . Furthermore, it was found that increasing the slope angle significant-ly increased costs. Finally, the increase in flow …increases costs but the rate of increase diminishes. Show more

Keywords: Particle swarm optimization, open channels, dimension

DOI: 10.3233/JIFS-191355

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 399-405, 2020

Authors: Dheenathayalan, S. | Paramasivan, B.

Article Type: Research Article

Abstract: Delay Tolerant Network (DTN) is an array of protocols which work together to enable a standardized way to communicate between nodes is store-carry-and-forward approach. In DTN, nodes help to convey message even with connectivity problem. Instead of using the own resource to pass a message to other nodes, a relay node is preferred which discovers the sink node with a greater probability of transferring messages on its own. A condition named node’s selfishness is thus raised and it surely degrades network performance, which is more obvious in urban environment. To handle such problem, an incentive-based routing algorithm is generated by …using the concept Selfishness-Enhanced Reliable Forwarding (SERF) to remove node selfishness and Direct Diffusion to omit duplicate messages present in relay node. This algorithm finds the probability of messages received by the relay node with reference to the usage of resources of sender node. Concurrently, buffer management policy is maintained, by setting the threshold of message copies according to the resource consumption of the source node when it generates a message. The result reveals that the proposed algorithm is better to the existing algorithms with regard to the performance metrics such as, delivery ratio, the average delay, and network overhead. Show more

Keywords: DTN, buffer management, incentive mechanism, Direct Diffusion, SERF

DOI: 10.3233/JIFS-191409

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 407-419, 2020

Authors: Al-Kasassbeh, Mouhammd | Almseidin, Mohammad | Alrfou, Khaled | Kovacs, Szilveszter

Article Type: Research Article

Abstract: Recently, the Internet of Things (IoT) has been used in technology for different aspects to increase the efficiency and comfort of human life. Protecting the IoT infrastructure is not a straightforward task. There is an urgent need to handle different attack scenarios within the IoT smart environment. Attackers continuously targeted the modern aspects of technology, and trying abusing these technologies using complex attack scenarios such as Botnet attacks. Botnet attacks considered a serious challenge faces of the IoT smart environment. In this paper, we introduce a novel idea that capable of supporting the detecting of IoT-Botnet attack and in meanwhile …to avoid the issues associated with the deficiencies of the knowledge-based representation and the binary decision. This paper aims to introduce a detection approach for the IoT-BotNet attack by using the Fuzzy Rule Interpolation (FRI). The FRI reasoning methods added a benefit to enhance the robustness of fuzzy systems and effectively reduce the system’s complexity. These benefits help the Intrusion Detection System (IDS) to generate more realistic and comprehensive alerts. The proposed approach was applied to an open-source BoT-IoT dataset from the Cyber Range Lab of the center of UNSW Canberra Cyber. The proposed approach was tested, evaluated and obtained a 95.4% detection rate. Moreover, it effectively smooth the boundary between normal and IoT-BotNet traffics because of its fuzzy-nature, as well as, it had the ability to generate the required IDS alert in case of the deficiencies of the knowledge-based representation. Show more

Keywords: Internet of things, fuzzy rule interpolation, botnet attack, intrusion detection system

DOI: 10.3233/JIFS-191432

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 421-431, 2020

Authors: Niandong, Liao | Yanqi, Song | Sheng, Su | Xianshen, Huang | Haoliang, Ma

Article Type: Research Article

Abstract: Aiming at the problems of excessive dependence on manual work, low detection accuracy and poor real-time performance of current probe flow anomaly detection in power system network security detection, a detection method for calculating information entropy of probe flow and random forest classification is proposed. Firstly, the network probe stream data are captured and aggregated in real-time to extract network stream metadata. Secondly, by calculating Pearson correlation coefficient and maximum mutual information coefficient, feature selection of network metadata is carried out. Finally, the information entropy and stochastic forest algorithm are combined to detect the anomaly of probe traffic based on …the selected key feature groups, and the probe traffic is accurately classified by multiple incremental learning. The results show that the proposed method can quickly locate the abnormal position of probe traffic and analyze the abnormal points, which greatly reduces the workload of application platform for power system security monitoring, and has high detection accuracy. It effectively improves the reliability and early warning ability of power system network security. Show more

Keywords: Power system, flow detection, network probe, random forest algorithms

DOI: 10.3233/JIFS-191448

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 433-447, 2020

Authors: Holdon, Liviu-Constantin

Article Type: Research Article

Abstract: The variety of De Morgan residuated lattices includes important subvarieties of residuated lattices such as Boolean algebras, MV-algebras, BL-algebras, Stonean residuated lattices, MTL-algebras and involution residuated lattices (see L.C. Holdon [7 ]). X. Zhu, J. Yang and A. Borumand Saeid [16 ] used a special family of extreme fuzzy filters F on a BL-algebra L , they constructed a uniform structure ( L , K ) , and then the part K induced a uniform topology τ F in …L  . Also, they proved that the pair ( L , τ F ) is a topological BL-algebra, and some properties of ( L , τ F ) were investigated. Inspired by their study, in this paper, we define the family of extreme fuzzy ideals I on a De Morgan residuated lattice L , we construct a uniform structure (L , K ) , and then the part K induce a uniform topology τ I in L  . We prove that the pair ( L , τ I ) is a Topological De Morgan Residuated lattice, and some properties of ( L , τ I ) are investigated. In particular, we show that ( L , τ I ) is a first-countable, zero-dimensional, disconnected and completely regular space. Finally, we give some characterizations of topological properties of ( L , τ I ) . We note that, since ideals and filters are dual in BL-algebras (see C. Lele and J. B. Nganou [12 ]), a study on extreme fuzzy ideals in BL-algebras follows by duality, but in the framework of De Morgan residuated lattices, which is a larger class than BL-algebras, the duality between ideals and filters does not hold, so the study of extreme fuzzy ideals in De Morgan residuated lattices becomes interesting from algebraic and topological point of view, and the results of X. Zhu, J. Yang and A. Borumand Saeid [16 ] become particular cases of our theory. Show more

Keywords: De Morgan residuated lattice, obstinate ideal, extreme fuzzy ideal

DOI: 10.3233/JIFS-191474

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 449-461, 2020

Authors: Ahmad, Niaz | Mehmood, Nayyar | Kumam, Poom

Article Type: Research Article

Abstract:  In this article we introduce the notion of fuzzy measure of noncompactness. We define the fuzzy condensing and fuzzy k -set contractions using fuzzy measure of noncompactness. An extension of Stanislaw Szulfa’s fixed point theorem for a self-operator on a closed bounded and convex subset of a Banach space has been proved. Using the defined multivalued k -set contractions, generalizations of Kakutani-Fan and Krasnoselskii type theorems have been proved. For applications the existence result for solutions of a fractional Caputo-Fabrizio anti-periodic boundary value problem has been proved. We give some examples to validate our results.

Keywords: fuzzy measure of noncompactness, Szulfa’s theorem, K-Fan theorem, multivalued fuzzy condensing operators, multivalued fuzzy k-set contractions, Krasnoselskii’s theorem, fixed points

DOI: 10.3233/JIFS-191496

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 463-474, 2020

Authors: Liu, Meng Ke | Xin, Xiao Long

Article Type: Research Article

Abstract: In this paper, we study the generating formula of filters in pseudo equality algebras, also we introduce prelinear pseudo equality algebras and divisible pseudo equality algebras, and then we investigate some characterizations of them. We focus on algebraic structures of the set F  (X ) of all filters in pseudo equality algebras and obtain that F  (X ) can form a Heyting algebra. Moreover, we give the notions of some types of filters ((positive) implicative filters, fantastic filters) in pseudo equality algebras and investigate their properties. Finally, we discuss the relations among these filters.

Keywords: Pseudo equality algebra, prelinear pseudo equality algebra, divisible pseudo equality algebra, (positive) implicative filter, fantastic filter, strong normal filter

DOI: 10.3233/JIFS-191512

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 475-487, 2020

Authors: Kalanat, Nasrin | Khanshan, Alireza | Khanjari, Eynollah

Article Type: Research Article

Abstract: Knowledge discovery and data mining provide an array of solutions for real-world problems. When facing business requirements, the ultimate goal of knowledge discovery is not the knowledge itself but rather making the gained knowledge practical. Consequently, the models and patterns found by the mining methods often require post-processing. To this end, actionable knowledge discovery has been introduced which is developed to extract actionable knowledge from data. The output of actionable knowledge discovery is a set of actions that help the domain expert to gain the desired outcome. Such a process where a set of actions are extracted is called action …extraction. One of the challenges of action extraction is to incorporate causal dependencies among the variables to find actions with higher effectiveness compared to when no such dependencies are used. The goal of this paper is to dive into the lesser studied subject of “action discovery in social networks” and intends to extract actions by utilizing the casual structures discovered from such data. Furthermore, in order to capture the underlying information within a social network, we extract the corresponding structural features. We propose a method called SF-ICE-CREAM (Social Features included Inductive Causation Enabled Causal Relationship-based Economical Action Mining) to overcome the challenges introduced above. This method uses structural features to find the underlying causal structures within a social network and incorporates them into the action extraction process. Show more

Keywords: Actionable knowledge discovery, action extraction, causal network, feature extraction

DOI: 10.3233/JIFS-191519

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 489-501, 2020

Authors: Mahmoodirad, Ali | Niroomand, Sadegh | Shafiee, Mehdi

Article Type: Research Article

Abstract: A new closed loop supply chain network design problem is considered in this study. In comparison to the literature, the problem considers a more complete set of stages in the network e.g. simultaneous consideration of supplier, plant, distribution center, customer, and collection, recovering, recycling, and disposal centers. The problem makes location, capacity, allocation, demand mode and material/product flow decisions for optimizing net benefit including sales revenue, fixed establishing cost of facilities, transportation cost, material purchasing cost, production cost, and inventory holding cost. As a novelty, for the first time multi-mode demand satisfaction is considered in a closed loop supply chain …problem. As another contribution, in order to be close to real-world situations, the problem is tackled in a fuzzy environment by using trapezoidal fuzzy parameters which yield a trapezoidal fuzzy objective function value. As solution methodology, considering the fuzzy objective function, the problem is reformulated as a multi-objective fuzzy mixed integer linear problem and is crisped using credibility measure of the fuzzy constraints. Finally, the crisp multi-objective version of the problem is solved by several hybrid fuzzy programming approaches to obtain a good efficient solution. Applying several numerical test problems, the SO method performs better than other approaches. Show more

Keywords: Closed loop supply chain, fuzzy set theory, multi-objective optimization, credibility constrained modeling

DOI: 10.3233/JIFS-191528

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 503-524, 2020

Authors: Li, Guang | Mahmoudi, Mohammad Reza | Qasem, Sultan Noman | Tuan, Bui Anh | Pho, Kim-Hung

Article Type: Research Article

Abstract: During the last decade, ensemble clustering has been the subject of many researches in data mining. In ensemble clustering, several basic partitions are first generated and then a function is used for the clustering aggregation in order to create a final partition that is similar to all of the basic partitions as much as possible. Ensemble clustering has been proposed to enhance efficiency, strength, reliability, and stability of the clustering. A common slogan concerning the ensemble clustering techniques is that “the model combining several poorer models is better than a stronger model”. Here at this paper, an ensemble clustering method …is proposed using the basic k-means clustering method as its base clustering algorithm. Also, this study could raise the diversity of consensus by adopting some measures. Although our clustering ensemble approach has the strengths of kmeans, such as its efficacy and low complexity, it lacks the drawbacks which the kmeans suffers from; such as its problem in detection of clusters that are not uniformly distributed or in the circular shape. In the empirical studies, we test the proposed ensemble clustering algorithm as well as the other up-to-date cluster ensembles on different data-sets. Based on the experimental results, our cluster ensemble method is stronger than the recent competitor cluster ensemble algorithms and is the most up-to-date clustering method available. Show more

Keywords: Graph representation, cluster ensemble, kmeans clustering, small cluster

DOI: 10.3233/JIFS-191530

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 525-542, 2020

Authors: Malarvel, Muthukumaran | Nayak, Soumya Ranjan

Article Type: Research Article

Abstract: Edge detection and segmentation are the two main approaches being used since last three decades for successful image analysis in remote sensing domain. Although many intensive studies were undertaken, they all were limited to high-resolution aerial images and none addressed this problem exhaustively. The purpose of this study was to investigate both edge detection and segmentation by employing a novel hybrid method combining probability density function and partial differential equation to obtain accurate estimations. The newly proposed method is implemented in two phases: the first phase deals with smoothening that include improved kernel density estimation (KDE) with anisotropic diffusion coefficient …function kernel with both adaptive bandwidth and constant threshold selection using Shannon entropy, in addition to a weighting parameter of 3 × 3 window for lower probability of the whole image in diffusion function; whereas in the second phase, edge detection and segmentation are dealt with by incorporating two prominent techniques, namely diffusion coefficient equation and six-sigma control limit. We carried out a cross-sectional analysis using different datasets such as SIPI database and ground truth images for smoothing, edging and segmentation. Afterward, the results were compared with the other state-of-the-art techniques. Finally, the performance measures of the implemented technique were evaluated by means of entropy, fractal dimension, and an equivalent number of looks for smoothened images, by the Pratt metric for edge detection, and in the case of segmentation, misclassification error was considered. The experimental results demonstrated that the proposed scheme outperforms its counterparts in all aspects. Hence, the proposed hybrid scheme is better and robust, and results in accurate estimation for the given datasets. Show more

Keywords: Kernel density estimation, anisotropic diffusion, fractal dimension, aerial image, image smoothing, segmentation, edge detection

DOI: 10.3233/JIFS-191547

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 543-560, 2020

Authors: Wang, Jun | Tang, Fangcheng | Shang, Xiaopu | Xu, Yuan | Bai, Kaiyuan | Yan, Yusheng

Article Type: Research Article

Abstract: The recently proposed q -rung orthopair fuzzy sets (q -ROFSs) have been proved to be an effective tool to describe decision makers’ evaluation information and this paper attempts to propose a new multi-attribute group decision-making (MAGDM) method with q -rung orthopair fuzzy information. First of all, we propose a new score function of q -rung orthopair fuzzy numbers (q -ROFNs) by taking the hesitancy degree into account. When considering to fuse q -ROFNs, this paper tries to propose some novel aggregation operators. The power geometric (PG) operator has the ability of reducing or eliminating the bad influence of decision makers’ …unreasonable assessments on final decision results. Hence, we extend PG to q -ROFSs and propose the q -ROF power geometric operator and its weighted form. The most prominent advantage of dual Muirhead mean (DMM) is that it can capture the interrelationships among any numbers of input arguments. To take full advantages of PG and DMM, we further combine PG with DMM within q -rung orthopair fuzzy environment and propose the q -rung orthopair fuzzy power dual Muirhead mean, and q -rung orthopair fuzzy weighted power dual Muirhead mean operators. The proposed operators can reduce the negative effects of unreasonable evaluations on the decision results, and simultaneously take the interrelationship among any numbers of input arguments into account. In addition, we propose a new MAGDM method based on the proposed aggregation operators. Finally, we provide numerical examples to demonstrate the validity and merits of the proposed method. Show more

Keywords: q-rung orthopair fuzzy set, power geometric operator, dual muirhead mean, q-rung orthopair fuzzy power dual muirhead mean, novel score function, multi-attribute group decision-making

DOI: 10.3233/JIFS-191552

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 561-580, 2020

Authors: Li, Hongxu | Yang, Yang | Yin, Songyi

Article Type: Research Article

Abstract: The q -rung orthopair fuzzy set is a significant part of the existing orthopair fuzzy sets, whose advantage is to more comprehensively describe uncertain information. For q -rung orthopair fuzzy sets, the correlation between them is generally measured by the correlation coefficient. In order to express the positive and negative correlations of q -rung orthopair fuzzy sets simultaneously from a statistical perspective, and to reflect the attitude of decision makers, in this paper, two new correlation coefficients of q -rung orthopair fuzzy sets are proposed and investigated. Firstly, a λ -variance-based correlation coefficient of q -rung orthopair fuzzy sets is …proposed from the statistical viewpoint. Secondly, a λ -matching-function-based correlation coefficient of q -rung orthopair fuzzy sets is defined from the perspective of vector calculation. In the end, an example of clustering analysis is presented to verify the feasibility and superiority of the proposed correlation coefficients by comparing with other existing correlation coefficient of q -rung orthopair fuzzy sets. It can be seen from the clustering results that the two new λ -correlation coefficients not only consider the positive or negative correlation at the same time, but also can be dynamically adjusted according to the needs of decision makers. Furthermore, clustering results using λ -variance-based and λ -matching-function-based correlation coefficients converge faster than clustering results using the existing correlation coefficient in the q -rung orthopair fuzzy environment. Show more

Keywords: q-rung orthopair fuzzy set, correlation coefficient, clustering analysis

DOI: 10.3233/JIFS-191553

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 581-591, 2020

Authors: Li, Guanrong | Qiu, Jianjun | Yang, Xiaopeng

Article Type: Research Article

Abstract:  Considering the application in wireless communication basic-station (terminal) system, we investigate the weighted minimax programming subject to two-sides fuzzy relation inequalities with max-product composition in this paper. By establishing the maximum solution and the discrimination matrix of the inequalities system, we give the sufficient and necessary condition that the inequalities system is consistent and further obtain the structure of the solution set. We develop a solution matrix approach method for solving the proposed problem and further develop a step-by-step algorithm for carrying out the method. The theory analysis and numerical example indicate that the algorithm is feasible and efficient.

Keywords: Two-sides fuzzy relation inequalities, max-product composition, weighted minimax programming, solution matrix approach, nonlinear optimization

DOI: 10.3233/JIFS-191565

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 593-605, 2020

Authors: Zhou, Yafu | Wang, Hantao | Li, Linhui | Lian, Jing

Article Type: Research Article

Abstract: The efficiency and control accuracy of Interior Permanent Magnet Synchronous Motor (IPMSM) are the main factors affecting performance. Manual calibration has the disadvantage of high work intensity, long calibration period and high technical requirement, which leads to low calibration accuracy and motor efficiency. Thus, a novel calibration method based on Deep Deterministic Policy Gradient (DDPG) and Long Short-Term Memory (LSTM) is proposed. By constructing a deep reinforcement learning network, the self-optimization of the optimal working point under any working condition is realized, and the MAP for IPMSM in full speed-torque range is obtained. The method can be used to quickly …realize the optimal matching of d-q axis current with arbitrary stator current. It focuses on solving the problem of motor overheating caused by long adjustment time of manually calibrated MAP when the motor is overloaded, to realize fast calibration in overload area. Moreover, the method reduces the dependence on the motor parameters and increases the adaptability of the calibration MAP data to the operating conditions. The simulation and bench test indicate that the method can meet the response requirements of motor torque, and results reveal that the motor efficiency is greatly improved. Show more

Keywords: Interior permanent magnet synchronous motor, deep reinforcement learning, bench calibration, optimal control, optimal efficiency

DOI: 10.3233/JIFS-191567

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 607-626, 2020

Authors: Liu, Peide | Zhang, Pei

Article Type: Research Article

Abstract: A normal wiggly hesitant fuzzy set (NWHFS) is a powerful and useful tool to dig the potential indeterminacy of decision makers (DMs) in the process of expressing their preferences, which can be considered as an extended form of the traditional hesitant fuzzy set (HFS). The NWHFSs can not only retain the original hesitant fuzzy information completely, but also explore potential uncertainty of theses information. TODIM is an effective method to capture the psychological behavior based on prospect theory. Considering the advantages of NWHFS and TODIM method, in this paper, we define the distance measure of any two normal wiggly hesitant …fuzzy elements (NWHFEs), and put forward an extended normal wiggly hesitant fuzzy TODIM (NWHF-TODIM) approach to handle multiple attribute decision making (MADM) problems with normal wiggly hesitant fuzzy (NWHF) information. Then we use the extended NWHF-TODIM method to rank alternatives and select an ideal one. Lastly, we compare it with two existing approaches to verify the rationality and validity of the proposed approach. Show more

Keywords: Normal wiggly hesitant fuzzy sets, multiple attribute decision making, TODIM

DOI: 10.3233/JIFS-191569

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 627-644, 2020

Authors: Kesicioğlu, M. Nesibe | Şamlı, Esra

Article Type: Research Article

Abstract: In this paper, orders based on uni-nullnorms on bounded lattices are introduced and discussed. By this way, the existing orders in the literature induced by t-norms, t-conorms, uninorms and nullnorms are extended to much more general form. The relationships between the orders induced by uni-nullnorms and the orders induced by their underlying t-norms, t-conorms, uninorms and nullnorms are presented. A necessary and sufficient condition making a bounded lattice again a lattice with respect to the orders based on uni-nullnorms is given. Also, the relationships between the partially ordered sets based on the orders induced by t-norms and induced by their …N-dual t-conorms and conjugate t-norms, which are special uni-nullnorms, are investigated. Show more

Keywords: Uninorm, bounded lattice, partial order, uni-nullnorm

DOI: 10.3233/JIFS-191583

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 645-663, 2020

Authors: Pundhir, Sandhya | Ghose, Udayan | Bisht, Upasana

Article Type: Research Article

Abstract: One of the momentous transformation performed by an artificial neural network (ANN), Support Vector Machine (SVM), Radial basis Function (RBF) and many other machine learning method is the application of activation function. MyAct the proposed activation method is used here with various ANN architectures for link prediction, classification and general prediction. Statistical properties of data used here to prove the effectiveness of proposed activation function MyAct over other popular activation methods. A data dependent transfer method is developed, which is pioneer in its own way. This proves to be an unified formulation for the robust and generalised learning for the …classification, link prediction and regression problem types. Classification is done with Iris dataset using ANN with different activation method and results are compared. Improved results are achieved when MyAct used with Tailored Deep Feed Forward Artificial Neural Network (TDFFANN), simple Artificial Neural Network and Deep Artificial Neural Network. Aim here is to develop a novel activation method which work with positive data, negative data, small size data, big size data, skewed data or corrupt data. An attempt is made to cover complete versatile behaviour of data. Currently not a single activation method can work well on all above mentioned data. Results obtained using MyAct on the datasets used here proves it to be a good choice in comparison to logsig, tansig and other popular activation methods for classification and link prediction. Satisfactory improvement is achieved by using data length as well as negative range values in the prediction done by proposed method. MyAct had 22% better standard deviation than ReLU (Rectified Linear unit) and 36. 28% better standard deviation than ELU (Exponential linear unit). MyAct has 2. 6% better accuracy in regression error than Swiss method and 2. 5% better accuracy in regression error than ELU. Other results are discussed in the paper. Show more

Keywords: Artificial neural network, activation function, feedforward neural network, deep learning, ink prediction, machine learning

DOI: 10.3233/JIFS-191618

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 665-677, 2020

Authors: Rao, Xiansheng | Liu, Keyu | Song, Jingjing | Yang, Xibei | Qian, Yuhua

Article Type: Research Article

Abstract:  Presently, the Gaussian kernel approach has been widely accepted for measuring the similarities among samples and then constructing various fuzzy rough sets. Notably, the considered parameter plays a crucial role in deriving Gaussian kernel based similarities. This is mainly because different parameters will generate different scales of the similarities. From this point of view, different parameters may result in different fuzzy rough approximations and the corresponding reducts. Generally speaking, to search a parameterized reduct with better generalization performance, a naive approach can be designed by repeating the process of computing reduct through using different parameters. Obviously, it is very time-consuming. …To fill such a gap, an acceleration approach is proposed which aims to reduce the elapsed time of searching reducts based on different parameters. The main mechanism of our proposed approach is to take the variation of the used parameters into account, and then the process of finding reduct under current parameter can be realized based on the previous parameter related reduct. The experimental results over 16 UCI data sets, which are obtained by testing different Gaussian kernel based fuzzy rough sets, demonstrate that our proposed acceleration strategy not only can significantly reduce the time consumption of finding reducts in terms of different parameters, but also will not lead to poorer classification performance and significant variation of length of the obtained reducts by comparing with the results obtained by the naive process. This study suggests technical support for quickly finding reducts of parameterized fuzzy rough sets. Show more

Keywords: Acceleration strategy, attribute reduction, fuzzy dependency, fuzzy rough set, Gaussian kernel

DOI: 10.3233/JIFS-191633

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 679-695, 2020

Authors: Mahajan, Rutal | Zaveri, Mukesh

Article Type: Research Article

Abstract: Human beings often use figurative language during communication to express their thoughts. Uncovering the meaning out of figurative language is not as simple as literal language. Humor identification is considered to be an important linguistic device for sentiment analysis of figurative text because it can often change the sentiments of the text. Moreover, during verbal communication people use facial expressions, gestures and other modalities to convey their feeling and to automatically understand the meaning out of figurative sentences using these modalities is part of computer vision and digital image processing. It is difficult for written sentences where facial expressions, gestures, …other modalities, and emotions are absent and so it is an interesting question of research. Humor is a figurative device and a creative linguistic phenomenon. To understand the meaning of humor, we need to correctly understand the mood and emotions conveyed in the text, which is beyond the semantics of literal language communication. In this work, we have addressed these issues of understanding the emotions using affect-based information from text with various well established machine learning classifiers. We have exploited various affective content that inhibits the emotions and feeling of a writer such as emoticons, writing styles like punctuation, capitalization, sentiment words and so on. The proposed affect-based humor identification model is evaluated on the SemEval 2017 HashTagWars dataset and yelp review dataset with different types of the experimental configuration. This evaluates the effectiveness of the proposed humor identification model with different types of features. Show more

Keywords: Humor identification, affective computing, natural language processing, machine learning

DOI: 10.3233/JIFS-191648

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 697-708, 2020

Authors: Alzubi, Maen | Kovacs, Szilveszter

Article Type: Research Article

Abstract: Fuzzy Rule Interpolation (FRI) is an important technique for implementing inference with sparse fuzzy rule-bases. Even if a given observation has no overlap with the antecedent of any rule from the rule-base, FRI may still conclude a conclusion. This paper introduces a new method called “Incircle FRI” for fuzzy interpolation which is based on the incircle of a triangular fuzzy number. The suggested method is defined for triangular CNF fuzzy sets, for a single antecedent universe and two surrounding rules from the rule-base. The paper also extends the suggested “Incircle FRI” to trapezoidal, and hexagonal shaped fuzzy sets by decomposing …their shapes to multiple triangulars. The generated conclusion is also a CNF fuzzy set. The performance of the suggested method is evaluated based on numerical examples and a comprehensive comparison to other current FRI methods. Show more

Keywords: Fuzzy interpolative reasoning, sparse fuzzy rule-based systems, incircle triangular fuzzy numbers, incircle FRI method

DOI: 10.3233/JIFS-191660

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 709-729, 2020

Authors: Kadian, Ratika | Kumar, Satish

Article Type: Research Article

Abstract: In this communication, we have characterized the sum of two general measures associated with two distributions with discrete random variables as well as fuzzy sets. One of these measures is logarithmic, while other contains the power of variables, named as joint representation of Renyi’s-Tsallis divergence measure which implies that the proposed measure is equal to the constant time the sum of Renyi’s and Tsallis divergence measure. Besides the validation of the proposed measures, some of its major properties are also discussed for probability distributions and fuzzy sets. The performance of the proposed measure is contrasted with other existing measures in …the literature. Some illustrative examples are solved in the context of pattern recognition and fault detection problem which demonstrate the practicality and adequacy of measure between fuzzy sets. Show more

Keywords: Renyi’s-Tsallis divergence measure, convex function, fuzzy set, fuzzy divergence measure, pattern recognition, fault detection, 94A15, 94A24, 26D15

DOI: 10.3233/JIFS-191689

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 731-752, 2020

Authors: Shahbazi, Zeinab | Byun, Yung-Cheol

Article Type: Research Article

Abstract: Topic modeling for short texts is a challenging and interesting problem in the machine learning and knowledge discovery domains. Nowadays, millions of documents published on the internet from various sources. Internet websites are full of various topics and information, but there is a lot of similarity between topics, contents, and total quality of sources, which causes data repetition and gives the user the same information. Another issue is data sparsity and ambiguity because the length of the short text is limited, which causes unsatisfactory results and give irrelevant results to end-users. All these mentioned issues in short texts made an …interesting topic for researchers to use machine learning and knowledge discovery techniques to discover underlying topics from a massive amount of data. In this paper, we propose a combination of deep reinforcement learning (RL) and semantics-assisted non-negative matrix factorization model to extract meaningful and underlying topics from short document contents. The main objective of this work is to reduce the problem of repetitive information and data sparsity in short texts to help the users to get meaningful and relevant contents. Furthermore, our propose model reviews an issue of the Seq2Seq approach based on the reinforcement learning perspective and provides a combination of reinforcement learning and SeaNMF formulation using the block coordinate descent algorithm. Moreover, we compare different real-world datasets by using numerical calculation and present a couple of state-of-art models to get better performance on short text document topic modeling. Based on experimental results and comparative analysis, our propose model outperforms the state of art techniques in terms of short document topic modeling. Show more

Keywords: Topic modeling, knowledge discovery, short text, non-negative matrix factorization, machine learning

DOI: 10.3233/JIFS-191690

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 753-770, 2020

Authors: Aslam, Muhammad Shamrooz

Article Type: Research Article

Abstract: This paper deals with the problem of quantized state feedback H ∞ control for T-S fuzzy systems with appearing the communication delay under stochastic nonlinearity. To accomplish the objective a uniform framework for effective bandwidth utilization is employed to design co-design method. First of all, the co-design method is proposed such that the data can be communicated according to some logic function. Then, we implemented the measurement size-reduction scheme, using the logarithmic quantization. Additionally, we provided the impact of co-design method and quantization, on the original model of networked control systems (NCSs) is redeveloped as a new structure of …hybrid-triggered NCSs with network induced delay. Moreover, Lyapunov-Krasovskii functional is considered to grantee the closed-loop for stochastic stability analysis of the T-S fuzzy system. The solvability of Lyapunov-Krasovskii functional results in the formation of Linear matrix inequalities. The solution of Linear matrix inequalities leads to the controller gains to perform simulations to validate the proposed scheme. Show more

Keywords: Communication-delay, lyapunov-krasovskii functionals, quantizer, co-design method

DOI: 10.3233/JIFS-191708

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 771-788, 2020

Authors: Asadzadeh, Mohammad Sina | Rezaei, Gholam Reza | Jamalzadeh, Javad

Article Type: Research Article

Abstract: In the recent years, many authors have used a single method for equipping algebraic structures with uniformities which are induced by families of algebraic objects. This paper is devoted to a description of this well-known method in general, and provides insight into those results which are obtained using the method. In fact, we prove that the uniform topology induced by this method coincides with a partition topology generated by an equivalence relation, and illustrate the logic behind the continuity of algebraic operations in these kinds of uniform topologies. Furthermore, the main topological properties of the partition topology induced by a …congruence relation are presented. As an application, we explain why many results obtained from this method are trivial. These results have been collected from the works of several mathematicians on more than twenty different algebraic systems over the course of two decades. Show more

Keywords: Algebraic structure, Bl-algebra, uniform structure, partition space, partition uniformity

DOI: 10.3233/JIFS-191709

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 789-793, 2020

Authors: Antony Rosewelt, L. | Arokia Renjit, J.

Article Type: Research Article

Abstract: This paper proposes a new content recommendation system which combines the newly proposed embedded feature selection method and the new Fuzzy Temporal Logic based Decision Tree incorporated Convolutional Neural Network classifier. The newly proposed embedded feature selection called Fuzzy Decision Tree and Weighted Gini-Index based Feature Selection Algorithm (FDTWGI-FSA) that contains the existing incorporated the Fuzzy Decision Tree (FDT) and the Weighted Gini-index based Feature Selection Algorithm (WGIFSA) for getting optimized feature subset. Moreover, an enhanced CNN and Fuzzy Temporal Decision Tree for performing the deep learning process which is able to identify the exact e-content from the huge volume …of data with the help of the recommended features by the proposed embedded feature selection method. The exact e-content can be identified after performing the five-layer network structure for extracting the relevant features and it also can be classified by applying the Fuzzy Temporal Decision Tree for the e-learners. Finally, the proposed content recommendation system provides exact content to the e-learners according to their level of understanding and it also satisfies them by providing the exact high level contents. The experiments have been conducted for evaluating the proposed content recommendation system and compared with the existing classifier including the standard CNN. Show more

Keywords: Classification, deep learning, feature selection (FS), fuzzy logic, weighted genetic algorithm (WGA)

DOI: 10.3233/JIFS-191721

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 795-808, 2020

Authors: Zhang, Qiang | Hu, Junhua | Feng, Jinfu | Liu, An

Article Type: Research Article

Abstract: As an extension of the intuitionistic fuzzy set, the Pythagorean fuzzy set can depict uncertain information more effectively, so it has been well applied in multiple criteria decision making problems. At present, the multiple criteria decision making methods using the Pythagorean fuzzy set are generally ranked based on the aggregation operator or the distance measure, ignoring the important tool of the similarity measure. Therefore, this paper proposes several new similarity measures of the Pythagorean fuzzy set and applies them to multiple criteria decision making problems. Firstly, several new similarity measures of the Pythagorean fuzzy set are proposed, and their properties …are discussed. Then, based on the weighted similarity measures, the multiple criteria decision making method is proposed. Finally, the accuracy and reliability of the new similarity measures and the proposed multiple criteria decision making method are verified by the simulation cases. Show more

Keywords: Intuitionistic fuzzy set, Pythagorean fuzzy set, similarity measure, multiple criteria decision making

DOI: 10.3233/JIFS-191723

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 809-820, 2020

Authors: Chen, Zi-yu | Peng, Juan-juan | Wang, Xiao-kang | Zhang, Hong-Yu | Wang, Jian-qiang

Article Type: Research Article

Abstract: Solar energy, as a major and least-cost renewable resource, has attracted extensive attention of experts and scholars. However, the establishment of the power station is time-consuming and costly. And once selected, it is difficult to change. So it is crucial to choose the appropriate site of power station. This paper combines data analysis with multi-criteria group decision-making to solve this problem. First of all, K-means clustering method is selected to process the data according to the characteristics of the data. Secondly, the results obtained by K-means method are represented by probabilistic linguistic term sets. Thirdly, Bonferroni Mean operator is used …to adjust the weight of the criterion, which considers the consensus among experts. Fourthly, Technique for Order Preference by Similarity to Ideal Solution method is employed to rank the alternatives and select the best one. Finally, sensitivity analysis, comparison analysis and simulation are carried out to further confirm the robustness and advantage of the model. This model can help decision makers to better understand the basic situation of power station sites, make the right decisions, and improve some candidate sites according to the results. Show more

Keywords: Multi-criteria group decision-making, site selection, probabilistic linguistic term sets, K-means, bonferroni mean operator, sensitivity analysis

DOI: 10.3233/JIFS-191739

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 821-840, 2020

Authors: Liu, Zhe | Jia, Lifen

Article Type: Research Article

Abstract: As a type of differential equations driven by Liu process, uncertain delay differential equations (UDDEs) model dynamic systems with after-effects or memories in uncertain environment by incorporating time delay terms. Because it is natural for UDDEs to incorporate some unknown parameters, how to estimate them is a crucial problem in practice. This paper undertakes this issue by applying the method of moments based on discrete observations of solutions. With the Euler difference form of UDDEs, a function with respect to unknown parameters is proved to follow a standard normal uncertainty distribution. The moment estimations for unknown parameters are obtained by …solving a system of equations which uses sample moments to approximate population moments. Analytic solutions for some types of UDDEs are derived. Numerical examples show that estimations give small biases and standard deviations as long as time steps are not too large. Applications to population growth models further illustrate the practicability of our method. Show more

Keywords: Uncertain differential equation, parameter estimation, moments method, uncertainty theory

DOI: 10.3233/JIFS-191751

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 841-849, 2020

Authors: Aydemir, Salih Berkan | Yilmaz Gunduz, Sevcan

Article Type: Research Article

Abstract: Algebraic operations are used effectively in decision-making problems. Especially, Dombi, Hamacher and Einstein algebraic operators are used frequently in the decision-making field. On the other hand, it is known that aggregation operators affect the decision-making process in decision-making problems. In this paper, we used Dombi operations to develop some Fermatean fuzzy aggregation operators. Arithmetic and geometric analysis of each aggregation method were performed. We defined the following operators: Fermatean fuzzy Dombi weighted average operator, Fermatean fuzzy Dombi weighted geometric operator, Fermatean fuzzy Dombi ordered weighted average operator, Fermatean fuzzy Dombi ordered weighted geometric operator, Fermatean fuzzy Dombi hybrid weighted average …operator, Fermatean fuzzy Dombi hybrid weighted geometric operator. Also, an analysis was performed for the beta value of the Dombi parameter. Properties of proposed operators were presented, and operators were defined on Fermatean fuzzy sets. Finally, proposed operators were compared with the existing aggregation operators. To understand the impact of the proposed operators on the decision-making process, Fermatean fuzzy TOPSIS was established. Show more

Keywords: Fermatean fuzzy sets, multi-criteria decision making, TOPSIS, Dombi operations, Fermatean fuzzy aggregation operators

DOI: 10.3233/JIFS-191763

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 851-869, 2020

Authors: Qiu, Chenye

Article Type: Research Article

Abstract: Feature selection is a crucial data pre-processing step in classification problems. The wrapper approach is widely used due to their good classification performance. However, it is very computational expensive due to the cross validation scheme in the evaluation phase. In order to solve this problem, this paper proposes a novel hybrid two-stage feature selection method based on differential evolution (HTSDE). In the first stage, a cluster validity index named DB index is employed to evaluate the feature subset and the wrapper approach in used in the second stage to improve the classification accuracy of the feature subsets. In order to …find global optimal feature subsets, different trail vector generation strategies of DE are used in the two stages where the first stage focuses on global exploration and the second stage emphasizes fast convergence. The hybrid method is able to combine the advantages of both DB index and wrapper approach and improve the computational efficiency of the wrapper approach while maintaining the classification performance. HTSDE is compared with several state-of-the-art feature selection methods on 12 datasets. Experimental results show the proposed HTSDE achieves higher classification accuracy than both wrapper and filter approaches. Moreover, its computational cost is much less than those wrapper approaches. Show more

Keywords: Feature selection, cluster validity index, wrapper approach, differential evolution, trial vector generation strategy

DOI: 10.3233/JIFS-191765

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 871-884, 2020

Authors: Singla, Nikita | Sadawarti, Harsh | Singla, Jimmy | Kaur, Balwinder

Article Type: Research Article

Abstract: In this research work, a new multilayer fuzzy inference system is proposed for diagnosis of renal cancer. This proposed automated diagnosis of renal cancer using multilayer Mamdani fuzzy inference system can help to classify the different stages of renal cancer such as no cancer, stage 1, stage 2, stage 3 or stage 4 cancer. This expert system has four input variables at layer 1 and similarly seven input variables at layer 2. At layer 1, the input variables are smoking, dialysis, occupational exposure and genetic or hereditary that recognize the output conditions of renal or kidney to be normal or …to have renal cancer. The further input variables for layer 2 are haematuria (blood in urine), red blood cell count, flank pain, tumor size, Von Hippel-Lindau gene, high blood pressure and trichloroethylene exposure that reveal the output condition of kidney such as stage 1 cancer, stage 2 cancer, stage 3 cancer or stage 4 cancer. The novelty in this research work is development of multilayer fuzzy inference system that deals with fuzzy values, uncertain and ambiguous data to detect the stage of renal cancer by using two layers. This paper presents an analysis of results accurately using the proposed expert system to model the renal cancer process with medical expert advice. The confidence indicator for this proposed expert system is 95%. Show more

Keywords: Artificial intelligence, fuzzy inference system, renal cancer

DOI: 10.3233/JIFS-191785

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 885-898, 2020

Authors: Sun, Baofeng | Zhang, Xinkang | Qiao, Hai | Li, Gendao | Chen, Yifei

Article Type: Research Article

Abstract: The efficient operation of Intelligent Warehousing System does not rely on individual resource scheduling in stages but multi-type resources collaborative scheduling. In this paper, a collaborative scheduling model for stackers, automated guided vehicles and picking workstations in outbound process is abstracted into a hybrid flow-shop scheduling problem within an automated warehouse scene. Considering the impacts of uncertain factors related to scheduling, the objective function of this model is minimizing the makespan based on the triangular fuzzy processing time. A genetic algorithm is designed to obtain feasible solution of this model with the form of vector coding and the approach of …ranking fuzzy numbers. Example analysis shows that the validity of the model and algorithm is verified. Within different resource allocation schemes, their evaluating indexes are significantly different, which are the likely completion time of system operation, the capability coordination degree and the initial investment. Furthermore, the increase of picking workstations is contributed much more to reducing the likely completion time and to improving the capability coordination degree than that of automated guided vehicles. Show more

Keywords: Automated warehouse, fuzzy processing time, collaborative scheduling, genetic algorithm, capability coordination degree

DOI: 10.3233/JIFS-191827

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 899-910, 2020

Authors: Liu, Liming | Chu, Maoxiang | Gong, Rongfen | Qi, Xinyu

Article Type: Research Article

Abstract: In this paper, we propose a nonparallel support vector machine with pinball loss (Pin-NPSVM) that deals with the noise sensitivity and resampling instability of NPSVM. More specifically, we redefine a pinball loss funtion and build a pair of quantile hyper-planes. Each quantile hyper-plane is constructed by using the new pinball loss instead of ɛ -insensitive loss, which makes the new classification model be insensitive to noise samples, especially for feature noise samples around the decision boundary. Moreover, instead of hinge loss, Pin-NPSVM also builds a pair of decision boundaries based on traditional pinball loss, which further improves the anti-nosie ability …of the classification model. In a word, Pin-NPSVM not only inherits the characteristics of the nonparallel optimal hyper-planes, but also has a consistent model with Pin-SVM, which can process noise data well. Finally, numerical experimental results show that the Pin-NPSVM has more obvious advantages than other models in classification performance, especially for noise datasets. Show more

Keywords: Pattern classification, nonparallel support vector machine, pinball loss, anti-noise

DOI: 10.3233/JIFS-191845

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 911-923, 2020

Authors: Kim, Kwang Baek | Kim, Gwang Ha | Song, Doo Heon | Park, Hyun Jun | Kim, Chang Won

Article Type: Research Article

Abstract: Background: Hepatorenal index (HRI) has been an efficient and simple quantified measure in distinction between normal and abnormalities of diagnosing fatty liver. However, considering the clinical significance, the diagnosis of severity stage is more important and single HRI cutoff may not be enough. Also, the segmentation of Liver/Kidney area should be automatic to get rid of operator subjectivity from ultrasonography analysis. Method: Double-layered Fuzzy C-Means (DFCM) pixel clustering method is proposed to extract the target area of analysis automatically. HRI and other shape related variables of Liver intensity distribution such as the skewness, the kurtosis, and the coefficient …of variance (CV) are automatically computed for the fatty liver severity stage classification. Result: From fifty ultrasound images obtained from regular health checkup with 24 normal, 12 mild, 11 moderate, 3 severe stage determined by three different radiologists, the proposed DFCM automatically extracts the region of interests(ROI) and generates a set of statistically significant variables including HRI, the skewness, the kurtosis, the coefficient of variance of liver intensity distribution as well as liver echogenicity. In severity stage classification, the echogenicity of the liver and distribution shape variables such as the skewness and the kurtosis are better predictors than HRI based on our simple decision tree learning analysis. Conclusion: For better diagnosis of fatty liver severity stages, we need better set of features than the single HRI cutoff. Better machine learning structures are necessary in this severity stage classification problem with automatic segmentation method proposed in this paper. Show more

Keywords: Fatty liver severity classification, Fuzzy c-means, Self-organizing map, Hepatorenal index, Decision tree

DOI: 10.3233/JIFS-191850

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 925-936, 2020

Authors: Zhao, Tao | Xiang, Yunfang | Dian, Songyi | Guo, Rui | Li, Shengchuan

Article Type: Research Article

Abstract: This paper focuses on the path planning of mobile robot. Fuzzy logic is employed to deal with the uncertainty in the process of path planning. The hierarchical interval type-2 fuzzy method is obtained by combining the hierarchical fuzzy and interval type-2 fuzzy method, which is used in the path planning of mobile robot. Hierarchical fuzzy structure can simplify complex system and get fuzzy rules more easily. For multi input system, it can also solve the problem of rule explosion. Compared with type-1 fuzzy, interval type-2 fuzzy can better deal with the uncertainty in the process of path planning. Finally, in …order to get a better path, genetic algorithm is used to optimize the membership function in the fuzzy path planner. Through the simulation experiment, the proposed hierarchical type-2 fuzzy planning method can effectively solve the path planning problem. Compared with the type-1 fuzzy method, the interval type-2 fuzzy method and the hierarchical type-1 fuzzy method, the proposed method obtains better results. Show more

Keywords: Mobile robot, path planning, interval type-2 fuzzy, hierarchical fuzzy, genetic optimization

DOI: 10.3233/JIFS-191864

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 937-948, 2020

Authors: Luo, Dandan | Zeng, Shouzhen | Yu, Guansheng

Article Type: Research Article

Abstract: The power average (PA) operator can reduce the influence of unreasonable information given by biased decision makers effectively, while Heronian mean (HM) operator can take into account the correlation information between attribute variables in multiple attribute decision making (MADM). Pythagorean fuzzy set (PFS) is a useful tool to handle uncertain information, which has been widely applied in kinds of areas. In order to better infuse the Pythagorean fuzzy evaluation, in this paper we unify the advantages of the PA operator and HM operator, and present the Pythagorean fuzzy power Heronian mean (PFPHM) operator and the Pythagorean fuzzy weighted power Heronian …mean (PFWPHM) operator. Some merits of the developed operators are further explored. Furthermore, on the basis of the PFWPHM operator, an approach for MADM under PFS situation is presented. Finally, a numerical case concerning investment company selection is illustrated to demonstrate the availability and feasibility of the developed approach. Show more

Keywords: Pythagorean fuzzy set, HM operator, PA operator, multiple attribute decision making, investment selection

DOI: 10.3233/JIFS-191905

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 949-959, 2020

Authors: Berlin, S. Jeba | John, Mala

Article Type: Research Article

Abstract: Though deep learning networks have proven ability to perform video analytics in complex environments, there is an increased attention towards the development of compact networks which would facilitate edge processing and the result of which have yielded high performance compressed deep learning networks such as, MobileNet, PWCNet and BindsNet. In the work proposed herein, a dual network configuration is used for human action recognition, wherein, the MobileNet captures the spatial appearance of the action sequences and the PWCNet is used to extract the motion vectors. A novel Spiking Neural Network (SNN) based configuration is used as the classifier and the …SNN implementation is based on BindsNet. The proposed configuration is experimentally validated on challenging datasets, viz., HMDB51 and UCF101. The experimental results demonstrate that the proposed work is superior to the state-of-the-art techniques and comparable in few cases. Show more

Keywords: MobileNet, PWCNet, BindsNet, diehl and cook nodes, spiking neural network

DOI: 10.3233/JIFS-191914

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 961-973, 2020

Authors: Lian, Jing | Wang, Zhenghao | Li, Linhui | Zhou, Yafu | Yin, Yuhang | Li, Lei

Article Type: Research Article

Abstract: Object detection and tracking are critical and challenging problems in vehicle environment perception systems, and have received broad attention in recent years. A novel detection and tracking algorithm taking both accuracy and real-time performance into account is proposed in this paper. First, we employ a fusion algorithm based on stereo vision and deep learning in object detection, which achieves high accuracy using two complementary algorithms. Then, a prediction-association algorithm which uses a Kalman filter and Hungarian assignment for multiple object tracking is employed for object tracking. In addition, a detection and tracking framework based on stereo vision improves the robustness …of environmental perception system. Experimental results demonstrate that the proposed algorithm has high accuracy and can meet the real-time performance requirement. Show more

Keywords: Environmental perception, stereo vision, deep network, multiple object tracking

DOI: 10.3233/JIFS-191917

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 975-986, 2020

Authors: Rached, Taciana Saad | Vieira, Maria de Fátima Queiroz | Santos, Danilo | Perkusich, Angelo | Almeida, Hyggo

Article Type: Research Article

Abstract: In this article, we propose a method to recognize human emotions based on user context and brain signals. We evaluated the method through an experiment during which individuals performed tasks using a simulator for electrical power systems operator training. We collected user context through log data retrieval and brain signals using an Electroencephalography (EEG) portable monitor. The experimental results demonstrated that the method could be successfully applied to recognize the emotional states based on EEG signals and user context.

Keywords: Emotion recognition, electroencephalography, signal processing, context-awareness

DOI: 10.3233/JIFS-191923

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 987-1003, 2020

Authors: Thao, Nguyen Xuan | Smarandache, Florentin

Article Type: Research Article

Abstract: The single-valued neutrosophic set (SVNS) is an extension of the fuzzy set and intuitionistic fuzzy set. This is a useful tool to deal with uncertain and inconsistent information. In the information theory, the distance measure, entropy measure and similarity measures have an important role. Several entropy measures of SVNSs have been proposed and applied in many real problems. But they have some restriction in practice and in the academic study. The similarity measures induced from entropy were studied and gave interesting results. In this paper, we introduce a new entropy measure concept based on the SVNS, which overcomes the restriction …of existing entropy measures. At the same time, we also investigate some similarity measures which are induced from new entropy measures and apply them to propose the multi-criteria decision making (MCDM) model in selecting the supplier. Show more

Keywords: Entropy of SVNS, similarity measure of SVNS, MCDM

DOI: 10.3233/JIFS-191929

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1005-1019, 2020