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

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

Authors: Alzubi, Jafar A. | Jain, Rachna | Kathuria, Abhishek | Khandelwal, Anjali | Saxena, Anmol | Singh, Anubhav

Article Type: Research Article

Abstract: The paper presents a Collaborative Adversarial Network (CAN) model for paraphrase identification, which is a collaborative network holding generator that is pitted against an adversarial network called discriminator. There has been tremendous research work and countless examinations done on sentence similarity demonstration. Learning and identifying the constant highlights, specifically in various areas and domains is the main focus of paraphrase identification. It Involves the capture of regular highlights between two sentences and the community-oriented learning upon traditional ill-disposed and adversarial learning for common feature extraction. The model outperforms the MaLSTM model, which is the baseline model, and also proves to …be comparable to many of the state-of-the-art techniques. Show more

Keywords: Paraphrase identification, text classification, adversarial networks, LSTM, NLP

DOI: 10.3233/JIFS-191933

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1021-1032, 2020

Authors: Hua, Shaoyang | Wang, Congqing | Wu, Xuewei

Article Type: Research Article

Abstract: Neural decoding is a technology to analyze intentions produced by neural activities, which has important applications in military, medical, entertainment and so on. As a typical application, decoding electromyogram (EMG) signals into corresponding gestures is an important content. In order to improve the accuracy of EMG signals recognition, researchers often extract effective features from EMG signals and classify gestures by constructing a reasonable classifier. However, because of the stochasticity of the signals, this method is not robust enough. This paper proposes a convolutional neural network (CNN) based on feature fusion, which can automatically learn and classify features from time-domain(TD) and …frequency-domain(FD). To make full use of information, two fusion methods are used and compared. Experiments show that the proposed fusion methods are superior to the traditional algorithm for both normal people and amputees, and have better performance compared with CNN method using only one kind of information. Show more

Keywords: Convolutional neural network (CNN), gestures recognition, neural decoding, surface electromyogram (sEMG)

DOI: 10.3233/JIFS-191964

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1033-1044, 2020

Authors: Zhou, Shuang | Zhang, Jianguo | Zhang, Lei | You, Lingfei

Article Type: Research Article

Abstract: In traditional mechanism reliability analysis, probability theory or statistical approaches are employed. However, these methods cannot be used under lack of data and great epistemic uncertainty. In this paper, an advanced mechanism reliability analysis method is put forward based on uncertain measure. To satisfy the subadditivity of epistemic uncertainties, a novel uncertainty quantification method based on uncertainty theory is proposed for mechanism reliability analysis. Then, a point kinematic reliability analysis method combined with uncertain measure is presented to calculate the kinematic uncertainty reliability of motion mechanism at each time instant. Three models are developed for estimating kinematic uncertainty reliability. Furthermore, …first-order Taylor series expansion is used to solve nonlinear limit state functions. A new kinematic uncertainty reliability index (KURI) is presented based on normal uncertainty distribution. Finally, by applying the proposed method to a numerical experiment, the trend of uncertainty reliability was found to be consistent with the traditional method. The two practical engineering applications show that the presented method are more reasonable compared with the classical approaches when the information of design parameters is insufficient. Show more

Keywords: Uncertainty quantification, mechanism reliability, reliability index, uncertainty theory, belief reliability

DOI: 10.3233/JIFS-191970

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1045-1059, 2020

Authors: Santos, Laércio Ives | D’Angelo, Marcos Flávio Silveira Vasconcelos | Cosme, Luciana Balieiro | de Oliveira, Heveraldo Rodrigues | Mendes, João Batista | Ekel, Petr Ya.

Article Type: Research Article

Abstract: Falls in the elderly are a public health problem because this population tends to have a longer recovery time and consequently longer hospital beds. Studies show that 84% of falls in hospital rooms occur near the bed, that led to strategies to prevent falls in the elderly population have been studied. In this context, this paper presents a schema for the detection and emission of bed exit alerts in the elderly. This schema uses signals derived from RFID sensors processed by a model based on Intelligent Swarm and Fuzzy Sets. The main contribution of this study is the use of …a Membership Windows that reduces the effects of missclassification of other strategies. The proposed work evaluated a data set containing 14 elderly aged between 66 and 86 years divided into two rooms. The results show that the presented approach improves the precision and recall in environments with greater uncertainty of classification. Show more

Keywords: Bed exit alarms, elderly care, intelligent swarm, fuzzy sets

DOI: 10.3233/JIFS-191971

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1061-1072, 2020

Authors: Rosyida, Isnaini | Widodo, | Indrati, Ch. Rini | Indriati, Diari

Article Type: Research Article

Abstract: We use the notion of fuzzy chromatic number (FCN) of fuzzy graphs based on fuzzy independent vertex sets introduced in 2015. Let G ˜ 1 be a path fuzzy graph and G ˜ 2 be any fuzzy graphs where their vertex sets are disjoint. Let G ˜ = G ˜ 1 □ G ˜ 2 be a cartesian product of G ˜ 1 and G ˜ 2 …. In this paper, we construct formula for FCN of G ˜ 1 □ G ˜ 2 and verify connection between maximum of FCN of both fuzzy graphs and FCN of their cartesian product. Also, we create an algorithm to determine FCN of the cartesian product according to the properties obtained. The last two statements show novelties of the present work. Evaluation of the algorithm is presented in the experimental results. Show more

Keywords: Fuzzy chromatic number, cartesian product, path, fuzzy graph, algorithm

DOI: 10.3233/JIFS-191982

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1073-1080, 2020

Authors: Ahmad, Ali | Koam, Ali N.A.

Article Type: Research Article

Abstract: The structures of many molecules such as dendrimers, alkanes and acyclic molecules are like trees. Rooted trees have wide applications in chemical graph theory such as enumeration and encoding of chemical structures. Structures of chemical compounds can be systematized in form of chemical and empirical formulae through mathematical means. Chemists have a long tradition of using atomic valences (vertex degrees) to find molecular structures graphically. In structural chemistry number of graph applications exist. This paper reflects the work on the following indices: first general Zagreb index M α , general Randić connectivity index R α , general …sum-connectivity index χ α , atom-bond connectivity index ABC , geometric-arithmetic index GA , fourth atom-bond connectivity index ABC 4 , fifth geometric-arithmetic index GA 5 , hyper-Zagreb index HM  (G ), first multiple Zagreb index PM 1  (G ), second multiple Zagreb index PM 2  (G ) and Zagreb polynomials M 1  (G , x ) , M 1  (G , x ) for line graph of complete m -ary tree. Show more

Keywords: Topological indices, line graph, complete m-ary trees

DOI: 10.3233/JIFS-191992

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1081-1088, 2020

Authors: Dai, Songsong

Article Type: Research Article

Abstract: The symmetric implicational methods for fuzzy reasoning characterizes the solution B * (A * ) of the formula (A  → 1 B ) → 2  (A *  → 1 B * ) for the fuzzy modus ponens (fuzzy modus tollens), where →1 and →2 are two different implications. In this study, we provide a predicate formal representation of the solution for the symmetric implicational methods based on the LΠ formal logic system, including detailed logic proofs. We bring the symmetric implicational methods within a logical framework and provide a sound logic foundation for the symmetric implicational methods of fuzzy reasoning.

Keywords: Fuzzy reasoning, symmetric implicational method, LΠ logic

DOI: 10.3233/JIFS-191998

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1089-1095, 2020

Authors: Mújica-Vargas, Dante

Article Type: Research Article

Abstract: In brain medical imaging, magnetic resonance is an important and effective means to support the computer aided diagnosis. Notwithstanding, inherent conditions such as atypical information, artifacts and vaguely delimited boundaries between existing tissues can hinder the segmentation task. A popular method to carry out this process is through Fuzzy C-Means algorithm, as well as its variants. These include the Intuitionistic Fuzzy C-Means algorithm, which is found suitable for brain magnetic resonance image segmentation, since it incorporates the advantage of intuitionistic fuzzy sets theory to handle the uncertainty. Most clustering algorithms depend of customized hand-crafted features as well as an appropriate …initialization process; this last aspect is a mandatory pre-requisite for convergence of the algorithm. In order to develop the brain image segmentation, in this paper we enhance the Intuitionistic Fuzzy C-Means performance by means of Robust Statistics. Explicitly, a non-parametric German-McClure Redescending M-Estimator is used at the initialization and clustering stages, it behaves such as a robust location estimator when the centroid vector is computed, and as a weighting when the membership matrix is updated. The fusion of both paradigms allows us to propose a clustering algorithm that develops efficiently the segmentation of magnetic resonance images, with the important merit of reduce the iteration required to converge. The robustness and effectiveness of this proposal is verified by experiments on simulated and real brain images. Show more

Keywords: Brain MRI image segmentation, intuitionistic fuzzy C-means, German-McClure redescending M-estimator

DOI: 10.3233/JIFS-192005

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1097-1108, 2020

Authors: Ding, Jianhua | Zhang, Zhiqiang

Article Type: Research Article

Abstract: Bayesian statistical inference is an important method of mathematical statistics in which both sample information and prior information are employed. Traditionally, it is often assumed that the sample observations from the population are observed precisely and characterized by crisp values. However, in many cases, the sample observations are collected in an imprecise way and characterized by uncertain values. In this paper, based on uncertain theory, we propose three kinds of uncertain Bayesian statistical inference including Bayesian point estimation, Bayesian interval estimation and Bayesian hypothesis test. Some numerical examples of uncertain Bayesian inference are presented to illustrate the proposed methods.

Keywords: Bayes’ theorem, uncertain variables, uncertain theory, uncertainty Bayesian statistical inference

DOI: 10.3233/JIFS-192014

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1109-1117, 2020

Authors: Song, Chenyang | Xu, Zeshui | Zhang, Yixin

Article Type: Research Article

Abstract: The k-Nearest Neighbor (k-NN) is one of the simplest intelligent algorithms in the field of pattern recognition and classification. The increasing complexity of practical applications brings more uncertainty and fuzziness. In this paper, we take advantage of the Dempster-Shafer evidence theory (D-S evidence theory) and the hesitant fuzzy set (HFS) in depicting uncertain preference and information, and develop the evidence k-Nearest Neighbor (Ek-NN) under the hesitant fuzzy environment. The fruit fly optimization algorithm (FOA) is adopted to determine the most appropriate value of k in Ek-NN, and a specific implementation process of the optimized Ek-NN based on FOA is also …provided. Moreover, two numerical examples about classification problems are presented to evaluate the performance of the proposed method. Comparative analysis and sensitivity analysis are further conducted to illustrate the advantages of the optimized Ek-NN based on FOA under the hesitant fuzzy environment. Show more

Keywords: k-Nearest neighbor, dempster-shafer evidence theory, hesitant fuzzy set, fruit fly optimization algorithm

DOI: 10.3233/JIFS-192026

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1119-1129, 2020