Journal of Intelligent & Fuzzy Systems - Volume 40, issue 1

Authors: Oner, Tahsin | Katican, Tugce | Saeid, Arsham Borumand

Article Type: Research Article

Abstract: The aim of this study is to introduce fuzzy filters of Sheffer stroke Hilbert algebra. After defining fuzzy filters of Sheffer stroke Hilbert algebra, it is shown that a quotient structure of this algebra is described by its fuzzy filter. In addition to this, the level filter of a Sheffer stroke Hilbert algebra is determined by its fuzzy filter. Some fuzzy filters of a Sheffer stroke Hilbert algebra are defined by a homomorphism. Normal and maximal fuzzy filters of a Sheffer stroke Hilbert algebra and the relation between them are presented. By giving the Cartesian product of fuzzy filters of …a Sheffer stroke Hilbert algebra, various properties are examined. Show more

Keywords: (Sheffer stroke) Hilbert algebra, (normal) fuzzy filter

DOI: 10.3233/JIFS-200760

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 759-772, 2021

Authors: Liu, Shuai | Xu, Ying | Guo, Lingming | Shao, Meng | Yue, Guodong | An, Dong

Article Type: Research Article

Abstract: Tens of thousands of work-related injuries and deaths are reported in the construction industry each year, and a high percentage of them are due to construction workers not wearing safety equipment. In order to address this safety issue, it is particularly necessary to automatically identify people and detect the safety characteristics of personnel at the same time in the prefabricated building. Therefore, this paper proposes a depth feature detection algorithm based on the Extended-YOLOv3 model. On the basis of the YOLOv3 network, a security feature recognition network and a feature transmission network are added to achieve the purpose of detecting …security features while identifying personnel. Firstly, a security feature recognition network is added side by side on the basis of the YOLOv3 network to analyze the wearing characteristics of construction workers. Secondly, the S-SPP module is added to the object detection and feature recognition network to broaden the features of the deep network and help the network extract more useful features from the high-resolution input image. Finally, a special feature transmission network is designed to transfer features between the construction worker detection network and the security feature recognition network, so that the two networks can obtain feature information from the other network respectively. Compared with YOLOv3 algorithm, Extended-YOLOv3 in this paper adds security feature recognition and feature transmission functions, and adds S-SPP module to the object detection and feature recognition network. The experimental results show that the Extended-YOLOv3 algorithm is 1.3% better than the YOLOV3 algorithm in AP index. Show more

Keywords: YOLOv3, target detection, depth feature extraction, S-SPP module, deep learning

DOI: 10.3233/JIFS-200778

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 773-786, 2021

Authors: Saravanan, G. | Yuvaraj, N.

Article Type: Research Article

Abstract: Mobile Cloud Computing (MCC) addresses the drawbacks of Mobile Users (MU) where the in-depth evaluation of mobile applications is transferred to a centralized cloud via a wireless medium to reduce load, therefore optimizing resources. In this paper, we consider the resource (i.e., bandwidth and memory) allocation problem to support mobile applications in a MCC environment. In such an environment, Mobile Cloud Service Providers (MCSPs) form a coalition to create a resource pool to share their resources with the Mobile Cloud Users. To enhance the welfare of the MCSPs, a method for optimal resource allocation to the mobile users called, Poisson …Linear Deep Resource Allocation (PL-DRA) is designed. For resource allocation between mobile users, we formulate and solve optimization models to acquire an optimal number of application instances while meeting the requirements of mobile users. For optimal application instances, the Poisson Distributed Queuing model is designed. The distributed resource management is designed as a multithreaded model where parallel computation is provided. Next, a Linear Gradient Deep Resource Allocation (LG-DRA) model is designed based on the constraints, bandwidth, and memory to allocate mobile user instances. This model combines the advantage of both decision making (i.e. Linear Programming) and perception ability (i.e. Deep Resource Allocation). Besides, a Stochastic Gradient Learning is utilized to address mobile user scalability. The simulation results show that the Poisson queuing strategy based on the improved Deep Learning algorithm has better performance in response time, response overhead, and energy consumption than other algorithms. Show more

Keywords: Mobile cloud computing, mobile cloud service providers, mobile cloud users, poisson linear, stochastic, deep neural resource allocation

DOI: 10.3233/JIFS-200799

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 787-797, 2021

Authors: Xu, Xiaoyun | Wu, Jingzheng | Yang, Mutian | Luo, Tianyue | Meng, Qianru | Li, Weiheng | Wu, Yanjun

Article Type: Research Article

Abstract: As the scale of software systems continues expanding, software architecture is receiving more and more attention as the blueprint for the complex software system. An outstanding architecture requires a lot of professional experience and expertise. In current practice, architects try to find solutions manually, which is time-consuming and error-prone because of the knowledge barrier between newcomers and experienced architects. The problem can be solved by easing the process of apply experience from prominent architects. To this end, this paper proposes a novel graph-embedding-based method, AI-CTO, to automatically suggest software stack solutions according to the knowledge and experience of prominent architects. …Firstly, AI-CTO converts existing industry experience to knowledge, i.e., knowledge graph. Secondly, the knowledge graph is embedded in a low-dimensional vector space. Then, the entity vectors are used to predict valuable software stack solutions by an SVM model. We evaluate AI-CTO with two case studies and compare its solutions with the software stacks of large companies. The experiment results show that AI-CTO can find effective and correct stack solutions and it outperforms other baseline methods. Show more

Keywords: Knowledge graph, graph embedding, software architecture

DOI: 10.3233/JIFS-200899

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 799-812, 2021

Authors: Kazemi, Sajad | Mavi, Reza Kiani | Emrouznejad, Ali | Kiani Mavi, Neda

Article Type: Research Article

Abstract: Data Envelopment Analysis (DEA) is the most popular mathematical approach to assess efficiency of decision-making units (DMUs). In complex organizations, DMUs face a heterogeneous condition regarding environmental factors which affect their efficiencies. When there are a large number of objects, non-homogeneity of DMUs significantly influences their efficiency scores that leads to unfair ranking of DMUs. The aim of this study is to deal with non-homogeneous DMUs by implementing a clustering technique for further efficiency analysis. This paper proposes a common set of weights (CSW) model with ideal point method to develop an identical weight vector for all DMUs. This study …proposes a framework to measuring efficiency of complex organizations, such as banks, that have several operational styles or various objectives. The proposed framework helps managers and decision makers (1) to identify environmental components influencing the efficiency of DMUs, (2) to use a fuzzy equivalence relation approach proposed here to cluster the DMUs to homogenized groups, (3) to produce a common set of weights (CSWs) for all DMUs with the model developed here that considers fuzzy data within each cluster, and finally (4) to calculate the efficiency score and overall ranking of DMUs within each cluster. Show more

Keywords: Data envelopment analysis, fuzzy DEA, non-homogeneous, clustering, common set of weights (CSW)

DOI: 10.3233/JIFS-200962

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 813-832, 2021

Authors: Khan, Y. A. | Chu, Y. M. | Abbas, S. Z.

Article Type: Research Article

Abstract: This paper investigates governments’ performance in the country. We achieved this objective differently. We employed an inverse method of assessment, with the utilization of factor copula modeling technique, to study the dependence relationship of exchange rates returns as auxiliary variables, the performance of political and army government tenures in the country in the last two decades are evaluated. Through factor analysis, common factors for the exchange rate are obtained. The analysis shows that conditioned on the common factors, the dependence amongst the elected currencies are strongly asymmetric in most of the tenures except the term of Pakistan Muslim League-Nawaz, and …condition on common factor Clayton copula demonstrating hypothesis is more suitable. However, we perceive high left tail reliance among foreign currency returns during Pakistan Muslim League-Nawaz tenure, and the condition on common factor Gumbel copula molding assumption is more appropriate. We are signifying the foulest government performance in the country among all occupancies under consideration. Show more

Keywords: Factor analysis, asymmetric, clayton copula, exchange rate, gumbel copula

DOI: 10.3233/JIFS-200996

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 833-847, 2021

Authors: Saleem, Nasir | Khattak, Muhammad Irfan | Al-Hasan, Mu’ath | Jan, Atif

Article Type: Research Article

Abstract: Speech enhancement is a very important problem in various speech processing applications. Recently, supervised speech enhancement using deep learning approaches to estimate a time-frequency mask have proved remarkable performance gain. In this paper, we have proposed time-frequency masking-based supervised speech enhancement method for improving intelligibility and quality of the noisy speech. We believe that a large performance gain can be achieved if deep neural networks (DNNs) are layer-wise pre-trained by stacking Gaussian-Bernoulli Restricted Boltzmann Machine (GB-RBM). The proposed DNN is called as Gaussian-Bernoulli Deep Belief Network (GB-DBN) and are optimized by minimizing errors between the estimated and pre-defined masks. Non-linear …Mel-Scale weighted mean square error (LMW-MSE ) loss function is used as training criterion. We have examined the performance of the proposed pre-training scheme using different DNNs which are established on three time-frequency masks comprised of the ideal amplitude mask (IAM), ideal ratio mask (IRM), and phase sensitive mask (PSM). The results in different noisy conditions demonstrated that when DNNs are pre-trained by the proposed scheme provided a persistent performance gain in terms of the perceived speech intelligibility and quality. Also, the proposed pre-training scheme is effective and robust in noisy training data. Show more

Keywords: Supervised speech enhancement, deep learning, deep belief networks, restricted boltzmann machine, intelligibility, quality

DOI: 10.3233/JIFS-201014

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 849-864, 2021

Authors: Gong, Zengtai | Wang, Junhu

Article Type: Research Article

Abstract: Up to now, there have been a lot of research results about multi-attribute decision making problems by fuzzy graph theory. However, there are few investigations about multi-attribute decision making problems under the background of indecisiveness. The main reason is that the difference of cognition and the complexity of thinking by decision makers, for the same question have different opinions. In this paper, we proposed a hesitant fuzzy hypergraph model based on hesitant fuzzy sets and fuzzy hypergraphs. At the same time, some basic graph operations of hesitant fuzzy hypergraphs are investigated and several equivalence relationship between hesitant fuzzy hypergraphs, hesitant …fuzzy formal concept analysis and hesitant fuzzy information systems are discussed. Since granular computing can deal with multi-attribute decision-making problems well, we considered the hesitant fuzzy hypergraph model of granular computing, and established an algorithm of multi-attribute decision-making problem based on hesitant fuzzy hypergraph model. Finally an example is given to illustrate the effectiveness of the algorithm. Show more

Keywords: Hesitant fuzzy sets, hesitant fuzzy graph, hesitant fuzzy hypergraph, granular computing, graph decision

DOI: 10.3233/JIFS-201016

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 865-875, 2021

Authors: Hourali, Samira | Zahedi, Morteza | Fateh, Mansour

Article Type: Research Article

Abstract: Coreference resolution is critical for improving the performance of all text-based systems including information extraction, document summarization, machine translation, and question-answering. Most of coreference resolution solutions rely on using knowledge resources like lexical knowledge, syntactic knowledge, world knowledge and semantic knowledge. This paper presents a new knowledge-based coreference resolution model using neural network architecture. It uses XLNet embeddings as input and does not rely on any syntactic or dependency parsers. For more efficient span representation and mention detection, we used entity-level information. Mentions were extracted from the text with an unhand engineered mention detector, and the features were extracted from …a deep neural network. We also propose a nonlinear multi-criteria ranking model to rank the candidate antecedents. This model simultaneously determines the total score of alternatives and the weight of the features in order to speed up the process of ranking alternatives. Compared to the state-of-the-art models, the simulation results showed significant improvements on the English CoNLL-2012 shared task (+6.4 F1). Moreover, we achieved 96.1% F1 score on the n2c2 medical dataset. Show more

Keywords: Natural language processing, coreference resolution, knowledge management, entity level information, neural network, multi-criteria ranking model

DOI: 10.3233/JIFS-201050

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 877-892, 2021

Authors: Mannar Mannan, J. | Sindhanai Selvan, K. | Mohemmed Yousuf, R.

Article Type: Research Article

Abstract: Massive digital documents on Internet leading to use e-learning, and it becomes an emerging field of research due to the massive growth of internet users. E-learning requires suitable document ranking method to avoid navigating to the next Search Engine Result Page (SERP) frequently. The existing document ranking methods are lacking to rank the documents independently based on the conceptual contents. This paper proposes a novel method for ranking the documents independently based on the different classification of term it contains. In this approach, the terms are classified into five categories such as (1) direct query term, (2) expanded terms, (3) …semantically related term, (4) supporting terms and (5) stop words. The query has been expanded using domain ontology to acquire more semantic terms for better understanding of user query. The semantic weight has been applied independently over different categories of terms in a document for ranking. The document with the highest augmented value in each category of terms has been ranked first. Remaining documents are ranked in the same way and are arranged in the descending order. The WordNet tool is utilized as a knowledge base and Wu and Palmer semantic distance method have applied for measuring semantic distance between the query and document terms for ranking the terms. The experiments show that the performance of the proposed document ranking method for e-learning retrieved better document compared with existing document ranking methods. Show more

Keywords: Ontology, semantic ranking, classification, information retrieval

DOI: 10.3233/JIFS-201070

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 893-905, 2021

Authors: Demirkiran, Emin T. | Pak, Muhammet Y. | Cekik, Rasim

Article Type: Research Article

Abstract: Recommender systems have recently become a significant part of e-commerce applications. Through the different types of recommender systems, collaborative filtering is the most popular and successful recommender system for providing recommendations. Recent studies have shown that using multi-criteria ratings helps the system to know the customers better. However, bringing multi aspects to collaborative filtering causes new challenges such as scalability and sparsity. Additionally, revealing the relation between criteria is yet another optimization problem. Hence, increasing the accuracy in prediction is a challenge. In this paper, an aggregation-function based multi-criteria collaborative filtering system using Rough Sets Theory is proposed as a …novel approach. Rough Sets Theory is used to uncover the relationship between the overall criterion and the individual criteria. Experimental results show that the proposed model (RoughMCCF) successfully improves the predictive accuracy without compromising on online performance. Show more

Keywords: Accuracy, multi-criteria collaborative filtering, recommender systems, rough sets theory

DOI: 10.3233/JIFS-201073

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 907-917, 2021

Authors: Karimzadeh Parizi, Morteza | Keynia, Farshid | Khatibi bardsiri, Amid

Article Type: Research Article

Abstract: Success of metaheuristic algorithms depends on the efficient balance between of exploration and exploitation phases. Any optimization algorithm requires a combination of diverse exploration and proper exploitation to avoid local optima. This paper proposes a new improved version of the Woodpecker Mating Algorithm (WMA), based on opposition-based learning, known as the OWMA aiming to develop exploration and exploitation capacities and establish a simultaneous balance between these two phases. This improvement consists of three major mechanisms, the first of which is the new Distance Opposition-based Learning (DOBL) mechanism for improving exploration, diversity, and convergence. The second mechanism is the allocation of …local memory of personal experiences of search agents for developing the exploitation capacity. The third mechanism is the use of a self-regulatory and dynamic method for setting the Hα parameter to improve the Running Away function (RA) performance. The ability of the proposed algorithm to solve 23 benchmark mathematical functions was evaluated and compared to that of a series of the latest and most popular metaheuristic methods reviewed in the research literature. The proposed algorithm is also used as a Multi-Layer Perceptron (MLP) neural network trainer to solve the classification problem on four biomedical datasets and three function approximation datasets. In addition, the OWMA algorithm was evaluated in five optimization problems constrained by the real world. The simulation results proved the superior and promising performance of the proposed algorithm in the majority of evaluations. The results prove the superiority and promising performance of the proposed algorithm in solving very complicated optimization problems. Show more

Keywords: Optimization, metaheuristic, woodpecker mating algorithm, distance opposition-based learning

DOI: 10.3233/JIFS-201075

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 919-946, 2021

Authors: Mostafa, Samih M.

Article Type: Research Article

Abstract: Data preprocessing is a necessary core in data mining. Preprocessing involves handling missing values, outlier and noise removal, data normalization, etc. The problem with existing methods which handle missing values is that they deal with the whole data ignoring the characteristics of the data (e.g., similarities and differences between cases). This paper focuses on handling the missing values using machine learning methods taking into account the characteristics of the data. The proposed preprocessing method clusters the data, then imputes the missing values in each cluster depending on the data belong to this cluster rather than the whole data. The author …performed a comparative study of the proposed method and ten popular imputation methods namely mean, median, mode, KNN, IterativeImputer, IterativeSVD, Softimpute, Mice, Forimp, and Missforest. The experiments were done on four datasets with different number of clusters, sizes, and shapes. The empirical study showed better effectiveness from the point of view of imputation time, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2 score) (i.e., the similarity of the original removed value to the imputed one). Show more

Keywords: Data preprocessing, missing data, imputation, missingness mechanisms

DOI: 10.3233/JIFS-201077

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 947-972, 2021

Authors: El-Sharkasy, M. M.

Article Type: Research Article

Abstract: Topological concepts play an important role in applications and solving real-life problems. Among of these concepts are neighbourhood and minimal structure. In this paper, we introduce a new space-based on a generalized system with a binary relation on a nonempty set by using the concept of a minimal structure, which is called a minimal structure approximation space (briefly, MSAS ), and study some of its properties. Also, we compare the advantages of MSAS with neighbourhood approximation space which are based on the same starting point, and apply the concept of MSAS in some examples of chemistry to extraction …and reduct the information. Finally, we investigate the concepts of the separation axioms on MSAS and study some of its properties in the information system as the process of approximation of information. Show more

Keywords: Rough set, approximation space, minimal structure, topological space, T0, T1 and T2, 54A05, 54C55, 54E05

DOI: 10.3233/JIFS-201090

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 973-982, 2021

Authors: Li, Huan | Tang, Pengyi | Ma, Yuechao

Article Type: Research Article

Abstract: In this paper, a class of observer-based sliding mode controller is designed, and the finite-time H ∞ control problem of uncertain T-S fuzzy systems with time-varying is studied. Firstly, an integral-type sliding surface function with time-delay is devised based on the state estimator, and sufficient criteria of finite-time bounded and finite-time H ∞ bounded can be obtained for the T-S systems. Moreover, the proposed sliding mode control law is integrated to ensure the dynamics of controlled system into the sliding surface in a finite-time interval. Then, according to the linear matrix inequalities (LMIs), the desired gain matrices of …fuzzy sliding mode controller and state estimator are derived. Finally, effectiveness gives some illustrative examples may be used to display the value of the current proposed method as well as a significant improvement. Show more

Keywords: Finite-time H∞ control, T-S fuzzy system, sliding mode, time-varying delay, linear matrix inequalities (LIMs)

DOI: 10.3233/JIFS-201091

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 983-999, 2021

Authors: Chen, Yen-Liang | Chi, Fang-Chi

Article Type: Research Article

Abstract: In the rough set theory proposed by Pawlak, the concept of reduct is very important. The reduct is the minimum attribute set that preserves the partition of the universe. A great deal of research in the past has attempted to reduce the representation of the original table. The advantage of using a reduced representation table is that it can summarize the original table so that it retains the original knowledge without distortion. However, using reduct to summarize tables may encounter the problem of the table still being too large, so users will be overwhelmed by too much information. To solve …this problem, this article considers how to further reduce the size of the table without causing too much distortion to the original knowledge. Therefore, we set an upper limit for information distortion, which represents the maximum degree of information distortion we allow. Under this upper limit of distortion, we seek to find the summary table with the highest compression. This paper proposes two algorithms. The first is to find all summary tables that satisfy the maximum distortion constraint, while the second is to further select the summary table with the greatest degree of compression from these tables. Show more

Keywords: Rough set, reduct, attribute reduction, information system, summarization

DOI: 10.3233/JIFS-201160

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1001-1015, 2021

Authors: Wu, Ziheng | Li, Cong | Zhou, Fang | Liu, Lei

Article Type: Research Article

Abstract: Fuzzy C-means clustering algorithm (FCM) is an effective approach for clustering. However, in most existing FCM type frameworks, only in-cluster compactness is taken into account, whereas the between-cluster separability is overlooked. In this paper, to enhance the clustering, by incorporating the feature weighting and data weighting method, we put forward a new weighted fuzzy C-means clustering approach considering between-cluster separability, in which for achieving good compactness and separability, making the in-cluster distances as small as possible and making the between-cluster distances as large as possible, the in-cluster distances and between-cluster distances are taken into account; To achieve the optimal clustering …result, the iterative formulas of the feature weights, membership degrees, data weights and cluster centers are obtained by maximizing the in-cluster compactness and the between-cluster separability. Experiments on real-world datasets were carried out, the results showed that the new approach could obtain promising performance. Show more

Keywords: Fuzzy C-means, data weighting, feature weighting, between-cluster separability

DOI: 10.3233/JIFS-201178

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1017-1024, 2021

Authors: Mensah, Patrick Kwabena | Weyori, Benjamin Asubam | Ayidzoe, Mighty Abra

Article Type: Research Article

Abstract: Capsule Networks (CapsNets) excel on simple image recognition problems. However, they fail to perform on complex images with high similarity and background objects. This paper proposes Local Binary Pattern (LBP) k-means routing and evaluates its performance on three publicly available plant disease datasets containing images with high similarity and background objects. The proposed routing algorithm adopts the squared Euclidean distance, sigmoid function, and a ‘simple-squash’ in place of dot product, SoftMax normalizer, and the squashing function found respectively in the dynamic routing algorithm. Extensive experiments conducted on the three datasets showed that the proposed model achieves consistent improvement in test …accuracy across the three datasets as well as allowing an increase in the number of routing iterations with no performance degradation. The proposed model outperformed a baseline CapsNet by 8.37% on the tomato dataset with an overall test accuracy of 98.80%, comparable to state-of-the-art models on the same datasets. Show more

Keywords: Capsule network, convolutional neural network, plant disease, classification, activation maps

DOI: 10.3233/JIFS-201226

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1025-1036, 2021

Authors: Wu, Deyin | Li, Yonghong

Article Type: Research Article

Abstract: In this paper, we research a class of axioms in closed G-V fuzzy matroids. The main research method is to transform fuzzy matroids into matroids. First, we study many properties of the basis family of induced matroids, and define a new mapping which can reflect the relationship between bases of induced matroids of a G-V fuzzy matroid. Second, we discuss the new mapping, and reveal the relationship and properties among the fundamental sequence, the induced basis family and the new mapping of a G-V fuzzy matroid. From these relationships and properties, we extract four key attributes: normativity property, inclusion property, …exchange property, and right surjection. Finally, we propose and prove “the induced basis axioms for a closed G-V fuzzy matroid” by these key attributes. With the help of these axioms, a closed G-V fuzzy matroid can be uniquely determined by a finite number sequence, a subset family and a mapping on this subset family when they satisfy above four attributes, and vice versa. Show more

Keywords: Matroids, fuzzy matroids, fundamental sequences, induced matroids, induced basis families, induced basis family mappings

DOI: 10.3233/JIFS-201227

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1037-1049, 2021

Authors: Das, Kousik | Naseem, Usman | Samanta, Sovan | Khan, Shah Khalid | De, Kajal

Article Type: Research Article

Abstract: In the recent phenomenon of social networks, both online and offline, two nodes may be connected, but they may not follow each other. Thus there are two separate links to be given to capture the notion. Directed links are given if the nodes follow each other, and undirected links represent the regular connections (without following). Thus, this network may have both types of relationships/ links simultaneously. This type of network can be represented by mixed graphs. But, uncertainties in following and connectedness exist in complex systems. To capture the uncertainties, fuzzy mixed graphs are introduced in this article. Some operations, …completeness, and regularity and few other properties of fuzzy mixed graphs are explained. Representation of fuzzy mixed graphs as matrix and isomorphism theorems on fuzzy mixed graphs are developed. A network of COVID19 affected areas in India are assumed, and central regions are identified as per the proposed theory. Show more

Keywords: Fuzzy mixed graphs, fuzzy mixed degree, adjacency matrices, isomorphism, COVID19

DOI: 10.3233/JIFS-201249

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1051-1064, 2021

Authors: Bai, Luyi | Li, Nan | Bai, Huilei

Article Type: Research Article

Abstract: With the growing importance of the fuzzy spatiotemporal data in information application, there is an increasing need for researching on the integration method of multi-source heterogeneous fuzzy spatiotemporal data. In this paper, we first propose a fuzzy spatiotemporal RDF graph model based on RDF (Resource Description Framework) that proposed by the World Wide Web Consortium (W3C) to represent data in triples (subject, predicate, object). Secondly, we analyze and classify the related heterogeneous problems of multi-source heterogeneous fuzzy spatiotemporal data, and use the fuzzy spatiotemporal RDF graph model to define the corresponding rules to solve these heterogeneous problems. In addition, based …on the characteristics of RDF triples, we analyze the heterogeneous problem of multi-source heterogeneous fuzzy spatiotemporal data integration in RDF triples, and provide the integration methods FRDFG in this paper. Finally, we report our experiments results to validate our approach and show its significant superiority. Show more

Keywords: RDF, multi-source heterogeneous fuzzy spatiotemporal data, data integration

DOI: 10.3233/JIFS-201258

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1065-1082, 2021

Authors: Stefenon, Stéfano Frizzo | Kasburg, Christopher | Freire, Roberto Zanetti | Silva Ferreira, Fernanda Cristina | Bertol, Douglas Wildgrube | Nied, Ademir

Article Type: Research Article

Abstract: The generation of electric energy by photovoltaic (PV) panels depends on many parameters, one of them is the sun’s angle of incidence. By using solar active trackers, it is possible to maximize generation capacity through real-time positioning. However, if the engines that update the position of the panels use more energy than the difference in efficiency, the solar tracker system becomes ineffective. In this way, a time series forecasting method can be assumed to determine the generation capacity in a pre-established horizon prediction to evaluate if a position update would provide efficient results. Among a wide range of algorithms that …can be used in forecasting, this work considered a Neuro-Fuzzy Inference System due to its combined advantages such as smoothness property from Fuzzy systems and adaptability property from neural networks structures. Focusing on time series forecasting, this article presents a model and evaluates the solar prediction capacity using the Wavelet Neuro-Fuzzy algorithm, where Wavelets were included in the model for feature extraction. In this sense, this paper aims to evaluate whether it is possible to obtain reasonable accuracy using a hybrid model for electric power generation forecasting considering solar trackers. The main contributions of this work are related to the efficiency improvement of PV panels. By assuming a hybrid computational model, it is possible to make a forecast and determine if the use of solar tracking is interesting during certain periods. Finally, the proposed model showed promising results when compared to traditional Nonlinear autoregressive model structures. Show more

Keywords: Photovoltaic panels, Neuro-Fuzzy inference system, time series forecasting, wavelets, solar trackers

DOI: 10.3233/JIFS-201279

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1083-1096, 2021

Authors: Muhiuddin, G.

Article Type: Research Article

Abstract: In this paper, neutrosophic N -structures are applied to p -ideals of BCI -algebras. In fact, we introduce the notion of neutrosophic N -p -ideal in BCI -algebras, and investigate several properties. Further, we present characterizations of neutrosophic N -p -ideal. Moreover, we consider relations between a neutrosophic N -ideal and a neutrosophic N -p -ideal. Also, we provide conditions for a neutrosophic N -ideal to be a neutrosophic N -p -ideal. …Furthermore, it is proved that the neutrosophic N -structure Q N G over Q is a neutrosophic N p -ideal of Q ⇔ G is a p-ideal of Q where G is a non-empty subset of a BCI -algebras Q . Show more

Keywords: BCI-algebra, p-ideal, neutrosophic set, neutrosophic 𝒩-ideal, neutrosophic 𝒩p-ideal

DOI: 10.3233/JIFS-201309

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1097-1105, 2021

Authors: Liu, Peide | Akram, Muhammad | Bashir, Ayesha

Article Type: Research Article

Abstract: This article puts forward an innovative notion of complex picture fuzzy set (CPFS) which is particularly an extension and a generalization of picture fuzzy sets (PFSs) by the addition of phase term in the description of PFSs. The uniqueness of CPFS lies in the capability to manage the uncertainty and periodicity, simultaneously, due to the presence of phase term which broadens the range of CPFS from a real plane to the complex plane of unit disk. We describe and verify the fundamental operations and properties of CPFSs. We introduce the aggregation operators, namely; complex picture fuzzy power averaging and complex …picture fuzzy power geometric operators in CPFSs environment, based on weighted and ordered weighted averaging and geometric operators. We construct multi-criteria decision making (MCDM) problem, using these operators and describe a numerical example to illustrate the validity and competence of this article. Finally, we discuss the advantages of this generalized concept of aggregation technique and analyze a comparative study to demonstrate the superiority and consistency of our model. Show more

Keywords: Complex picture fuzzy set, power aggregation operators, multi-criteria decision making

DOI: 10.3233/JIFS-201385

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1107-1128, 2021

Authors: Azhar, Kamran | Zafar, Sohail | Kashif, Agha | Zahid, Zohaib

Article Type: Research Article

Abstract: Fault-tolerant resolving partition is natural extension of resolving partitions which have many applications in different areas of computer sciences for example sensor networking, intelligent systems, optimization and robot navigation. For a nontrivial connected graph G  (V  (G ) , E  (G )), the partition representation of vertex v with respect to an ordered partition Π  = {S i  : 1 ≤ i  ≤ k } of V  (G ) is the k -vector r ( v | Π ) = ( d ( v , S i ) ) i = 1 k , where, d  (v …, S i ) = min {d  (v , x ) |x  ∈ S i }, for i  ∈ {1, 2, …, k }. A partition Π is said to be fault-tolerant partition resolving set of G if r  (u |Π ) and r  (v |Π ) differ by at least two places for all u  ≠ v  ∈ V  (G ). A fault-tolerant partition resolving set of minimum cardinality is called the fault-tolerant partition basis of G and its cardinality the fault-tolerant partition dimension of G denoted by P ( G ) . In this article, we will compute fault-tolerant partition dimension of families of tadpole and necklace graphs. Show more

Keywords: Tadpole graph, necklace graph, partition dimension, fault-tolerant partition dimension

DOI: 10.3233/JIFS-201390

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1129-1135, 2021

Authors: Lu, Xiangyang | Li, Hengyi | Li, Xiaoquan | Xu, Juncai

Article Type: Research Article

Abstract: The security of a train becomes a more critical issue as the train’s speed and the complexity of the railway conditions increases. It is especially true when the train runs on a curved radius rail when the lateral force between the train and the rail is less stable. The rail’s side grinding is a significant problem that affects the train’s safety, especially when the train passes through small radial sections in mountainous areas. The intelligent rail lubrication system is critical to enhancing rails’ safety and efficiency and reducing grease pollution along rail lines. This system is modeled with a force …analysis of train curve motion and numerical simulation of wear power. The lubrication system is constructed with hardware and software. Based on fuzzy group analysis, this system and the adaptive Proportional Integration Differential (PID) controller is presented to improve the lubricative effects. The system test results show that the quality of lubrication control using this system is efficacious; the control convergence is more reliable than the conventional PID controller. Show more

Keywords: Security, curve rail, adaptive PID, lubrication system

DOI: 10.3233/JIFS-201398

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1137-1146, 2021

Authors: Cheng, Yali | Li, Yonghong | Yang, Jie

Article Type: Research Article

Abstract: Linguistic intuitionistic fuzzy sets can qualitatively rather than quantitatively express data in the form of membership degree. But quantitative tools are required to handle qualitative information. Therefore, an improved linguistic scale function, which can more accurately manifest the subjective feelings of decision-makers, is employed to deal with linguistic intuitionistic information. Subsequently, due to some commonly used distance measures do not comprehensively evaluate the information of linguistic intuitionistic fuzzy sets, an improved distance measure of linguistic intuitionistic fuzzy sets is designed. It considers the cross-evaluation information to get more realistic reasoning results. In addition, a new similarity measure defined by nonlinear …Gaussian diffusion model is proposed, which can provide different response scales for different information between various schemes. The properties of these measures are also studied in detail. On this basis, a method in linguistic intuitionistic fuzzy environment is developed to handle multi-attribute decision-making problems. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method and the influence of the parameters is analyzed. Show more

Keywords: Linguistic intuitionistic fuzzy set, linguistic scale function, distance measure, similarity measure, multi-attribute decision-making

DOI: 10.3233/JIFS-201429

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1147-1160, 2021

Authors: Zhang, Yang | Ye, Jianmu

Article Type: Research Article

Abstract: As an important psychological feature that affects the strategic decision-making of top management team (TMT), risk preference exerts important impact on the failure of innovation. Taking Chinese pharmaceutical listed companies from 2011 to 2017 as research samples, this paper uses principal component analysis and probit regression model to empirically test the impact of TMT risk preference on technological innovation failure of firms from the perspective of government intervention. It is found that TMT’s risk preference has a significantly positive impact on the failure of firms’ technological innovation. The regulatory role of government intervention between TMT’s risk preference and failure of …firms’ technological innovation is an inverted U-shaped relationship. Low or high level of government intervention will promote the positive impact of TMT’s risk preference on the failure of technological innovation, and increase the risk of technological innovation failure. The findings of this study contribute to: (1) broaden the research perspective of technological innovation failure. (2) The innovation failure rate can be reduced by constructing or optimizing the TMT and the government intervening in firm innovation through reasonable and perfect technology innovation guidance policies. (3) expand the research means of innovation failure, making the results more convincing. Show more

Keywords: TMT, Risk preference, Failure of technological innovation, Government intervention

DOI: 10.3233/JIFS-201516

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1161-1173, 2021

Authors: Shalaby, Raafat | Ammar, Hossam Hassan | Azar, Ahmad Taher | Mahmoud, Mohamed I.

Article Type: Research Article

Abstract: This paper seeks to improve the efficiency of photovoltaic (PV) water pumping system using Fractional-order Fuzzy Maximum Power Point Tracking (FoF-MPPT) control and Gray Wolf Optimization (GWO) technique. The fractional calculus has been used to provide an enhanced model of PV water pumping system to, accurately, describe its nonlinear characteristics. Moreover, three metaheuristic optimizers are applied to tune the parameters of the proposed FoF-MPPT, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and the GWO. The FoF-MPPT is intensively tested and compared to the Perturb and Observe (PO), the Incremental Conductance (INC) and the FL-MPPT controllers. A MATLAB-Simscape based physical …model of the PV water pumping system has been developed and simulated for different control techniques with the proposed optimization algorithms. The response of the PV water pumping systems is evaluated under rapidly changing weather conditions to prove the effectiveness of the optimized FoF-MPPT compared to the conventional algorithms. The reliability of the comparative study has been emphasized in terms of several transient tracking and steady- state performance indices under different operating conditions. The simulation results show the effective performance of the proposed metaheuristic optimized FL-MPPT and FoF-MPPT control under different climatic conditions with disturbance rejection and robustness analysis. Show more

Keywords: Optimal Fractional Order Control, Fractional Order Systems, optimal control, Maximum Power Point Tracking

DOI: 10.3233/JIFS-201538

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1175-1190, 2021

Authors: Verma, Rajkumar

Article Type: Research Article

Abstract: The development of information measures associated with fuzzy and intuitionistic fuzzy sets is an important research area from the past few decades. Divergence and entropy are two significant information measures in the intuitionistic fuzzy set (IFS) theory, which have gained wider attention from researchers due to their extensive applications in different areas. In the literature, the existing information measures for IFSs have some drawbacks, which make them irrelevant to use in application areas. In order to obtain more robust and flexible information measures for IFSs, the present work develops and studies some parametric information measures under the intuitionistic fuzzy environment. …First, the paper reviews the existing intuitionistic fuzzy divergence measures in detail with their shortcomings and then proposes four new order-α divergence measures between two IFSs. It is worth mentioning that the developed divergence measures satisfy several elegant mathematical properties. Second, we define four new entropy measures called order-α intuitionistic fuzzy entropy measures in order to quantify the fuzziness associated with an IFS. We prove basic and advanced properties of the order-α intuitionistic fuzzy entropy measures for justifying their validity. The paper shows that the introduced measures include various existing fuzzy and intuitionistic fuzzy information measures as their special cases. Further, utilizing the conventional multi-attributive border approximation area comparison (MABAC) model, we develop an intuitionistic fuzzy MABAC method to solve real-life multiple attribute group decision-making problems. Finally, the proposed method is demonstrated by using a practical application of personnel selection. Show more

Keywords: Divergence measure, entropy measure, intuitionistic fuzzy set, MABAC model, personnel selection

DOI: 10.3233/JIFS-201540

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1191-1217, 2021

Authors: Singh, Lovepreet | Huang, He | Bordoloi, Sanandam | Garg, Ankit | Jiang, Mingjie

Article Type: Research Article

Abstract: Images of green infrastructure (gardens, green corridor, green roofs and grasslands) large area can be captured and processed to provide spatial and temporal variation in colours of plant leaves. This may indicate average variation in plant growth over large urban landscape (community gardens, green corridor etc). Towards this direction, this short technical note explores development of a simple automated machine learning program that can accurately segregate colors from plant leaves. In this newly developed program, a machine learning algorithm has been modified and adapted to give the proportion of different colors present in a leaf. Python script is developed for …an image processing. For validation, experiments are conducted in green house to grow Axonopus compressus . Script first extracts different RGB (Red Green and Blue) colors present in the leaf using the K-means clustering algorithm. Appropriate centroids required for the clusters of leaf colors are formed by the K-means algorithm. The new program provides saves computation time and gives output in form of different colors proportion as a CSV (Comma-Separated Values) file. This study is the first step towards the demonstration of using automated programs for the segregation of colors from the leaf in order to access the growth of the plant in an urban landscape. Show more

Keywords: Color segregation, K-means algorithm, automation, cluster analysis, python script

DOI: 10.3233/JIFS-201542

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1219-1243, 2021

Authors: Wang, Siqi | Wei, Guiwu | Wu, Jiang | Wei, Cun | Guo, Yanfeng

Article Type: Research Article

Abstract: Probabilistic linguistic term sets are used to express uncertain decision information in multiple attribute group decision making problems. For probabilistic linguistic multiple attribute group decision making (MAGDM) with weight determined by CRITIC (Criteria Importance Through Intercriteria Correlation) method, the probabilistic linguistic grey relational projection method is proposed in this paper. Firstly, the correlation coefficient among attributes and standard deviation of each attribute are utilized to compute the attributes weights. Then the most ideal alternative is decided by means of counting the grey relational projection (GRP) from probabilistic linguistic positive ideal solution and probabilistic linguistic negative ideal solution. In the end, …a numerical example for site selection of hospital constructions is applied to further account for the extended method. The result demonstrates the availability of the proposed method and it can be used in other fields which refers to problems of selection. Show more

Keywords: Multiple attribute group decision making; probabilistic linguistic term sets, CRITIC method, grey relational projection method, site selection of hospital constructions

DOI: 10.3233/JIFS-201543

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1245-1259, 2021

Authors: Sameri, E. | Borzooei, R.A.

Article Type: Research Article

Abstract: In this paper, we generalize the concept of extended order algebras in order to get a new algebraic structure called “extended implicative groupoid”. First, we define the notions of pre-weak extended, weak extended, right extended and left extended implicative groupoid. Then we introduce the concept of extended implicative groupoid by using these notions. In addition, the special properties of these structures, such as the existence of MacNeille completion and adjoint product are studied. Finally, we prove that the class of symmetrical associative complete distributive extended implicative groupoids, coincides with complete residuated lattices.

Keywords: Extended order algebra, extended implicative groupoid, MacNeille completion, residuated lattice, 06F99, 18B40

DOI: 10.3233/JIFS-201575

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1261-1275, 2021

Authors: Jin, Zhen-yu | Yan, Cong-hua

Article Type: Research Article

Abstract: Motivated by the concept of lattice-bornological vector spaces of J. Paseka, S. Solovyov and M. Stehlík, which extends bornological vector spaces to the fuzzy setting over a complete lattice, this paper continues to study the theory of L -bornological vector spaces. The specific description of L -bornological vector spaces is presented, some properties of Lowen functors between the category of bornological vector spaces and the category of L -bornological vector spaces are discussed. In addition, the notions and some properties of L -Mackey convergence and separation in L -bornological vector spaces are showed. The equivalent characterization of separation in L …-bornological vector spaces in terms of L -Mackey convergence is obtained in particular. Show more

Keywords: L-bornological vector spaces, L-Mackey convergence, Separation, The product L-bornological vector spaces, The quotient L-bornological vector spaces

DOI: 10.3233/JIFS-201599

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1277-1285, 2021

Authors: Akram, Muhammad | Naz, Sumera | Shahzadi, Sundas | Ziaa, Faiza

Article Type: Research Article

Abstract:  q -Rung orthopair fuzzy sets (q-ROFSs), originally proposed by Yager, can powerfully modify the range of indication of decision information by changing a parameter q based on the different hesitation degree, and the dual hesitant q-rung orthopair fuzzy set (DHq-ROFS), a new technique to consider human’s hesitance, can be more substantial of dealing with real multi-attribute decision making (MADM) problems. Inspired by DHq-ROFSs, in this article, we extend the concept of q-rung orthopair fuzzy graphs to dual hesitant q-rung orthopair fuzzy context and introduce the innovative concept of a dual hesitant q -rung orthopair fuzzy graphs based on Hamacher …operator called dual hesitant q -rung orthopair fuzzy Hamacher graphs (DHq-ROFHGs). We propose the new concepts of geometric-arithmetic energy and atom bond connectivity energy of a DHq-ROFHG and determine its upper and lower bounds. Moreover, on the basis of the proposed concept of DHq-ROFHGs, we introduce a new approach to solve the MADM problems with dual hesitant q-rung orthopair fuzzy information. At the end, we give a numerical model related to the selection of most significant defensive factor to illustrate the applicability of the developed approach, and exhibit its viability. Comparative analysis is conducted and the superiorities are illustrated. Show more

Keywords: Dual hesitant q-rung orthopair fuzzy graph, Hamacher operator, Geometric-arithmetic energy, Atom bond connectivity energy, Multi-attribute decision making

DOI: 10.3233/JIFS-201605

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1287-1307, 2021

Authors: Saqib, Muhammad | Akram, Muhammad | Bashir, Shahida | Allahviranloo, Tofigh

Article Type: Research Article

Abstract: Differential equations occur in many fields of science, engineering and social science as it is a natural way of modeling uncertain dynamical systems. A bipolar fuzzy set model is useful mathematical tool for addressing uncertainty which is an extension of fuzzy set model. In this paper, we study differential equations in bipolar fuzzy environment. We introduce the concept gH -derivative of bipolar fuzzy valued function. We present some properties of gH -differentiability of bipolar fuzzy valued function by considering different types of differentiability. We consider bipolar fuzzy Taylor expansion. By using Taylor expansion, Euler method is presented for solving bipolar …fuzzy initial value problems. We discuss convergence analysis of proposed method. We describe some numerical examples to see the convergence and stability of the method and compute global truncation error. From numerical results, we see that for small step size Euler method converges to exact solution. Show more

Keywords: Generalized Hukuhara derivative, Bipolar Fuzzy Taylor expansion, bipolar fuzzy initial value problem, Euler method method, convergence analysis, 34A07

DOI: 10.3233/JIFS-201619

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1309-1341, 2021

Authors: Mahboob, Aamir | Rashid, Tabasam

Article Type: Research Article

Abstract:  In this paper, a multistage decision-making problem concerning uncertainty and ambiguity is discussed using Pythagorean fuzzy sets. Complement Pythagorean fuzzy membership grades and their properties are also considered. Using the definition of an alpha-level set, we introduce the multistage decision-making problems, where the possibility theory and satisfaction grades are declared with the help of Pythagorean membership grades. Pythagorean multistage decision-making is an uncertain theory, where decision-maker has only one opportunity to choose the scenario under the combination of Pythagorean possibility and satisfaction grades at each stage. According to the selection of criteria, a series of decision points are concluded. The …payoff collaborates with these decision points at each stage. The multistage decision-making using Pythagorean fuzzy sets is the scenario-based theory in place of other theories like lottery-based theory etc. The results have been calculated using multistage Pythagorean fuzzy sets in which the decision-maker has only one chance to select the optimal solution. The TOPSIS technique has been applied and the comparison between these two techniques is highlighted. Show more

Keywords: Pythagorean fuzzy sets, Dynamic Programming, Multistage decision making, TOPSIS

DOI: 10.3233/JIFS-201661

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1343-1356, 2021

Authors: Xu, Junxiang | Guo, Jingni | Zhang, Jin | Sun, Yongdong | Liu, Weihua | Ma, Hui

Article Type: Retraction

Abstract: This article has been retracted, and the online PDF has been watermarked “RETRACTED”. The retraction notice is available at http://doi.org/10.3233/IFS-219216 .

DOI: 10.3233/JIFS-201693

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1357-1366, 2021

Authors: Akram, Muhammad | Alsulami, Samirah | Karaaslan, Faruk | Khan, Ayesha

Article Type: Research Article

Abstract: A q-rung orthopair fuzzy set (q -ROFS) is more practical and powerful than intuitionistic fuzzy set (IFS) and Pythagorean fuzzy set (PFS) to model uncertainty in various decision-making problems. In this research article, we introduce the notion of q -rung orthopair fuzzy Hamacher graphs (q -ROFHGs). We utilize the Hamacher operators because they are flexible and parameterized in decision making. We determine the energy of q -ROFHGs as well as the energy of splitting and shadow q -ROFHGs. In addition, we propose the Randić energy of q -ROFHG and its some substantial results. Further, we present the idea of q …-rung orthopair fuzzy Hamacher digraphs (q -ROFHDGs). We solve a decision-making numerical example related to the selection of best housing society for investment by calculating the energy and Randić energy of q -ROFHDGs and an algorithm to exhibit the applicability of the presented concepts in decision making. Finally, we present the conclusion. Show more

Keywords: q-rung orthopair fuzzy Hamacher graphs, Energy, Splitting graph, Shadow graph, Randić energy

DOI: 10.3233/JIFS-201700

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1367-1390, 2021

Authors: Chen, Dan | Yang, Xiangfeng

Article Type: Research Article

Abstract: The objective of uncertain time series analysis is to explore the relationship between the imprecise observation data over time and to predict future values, where these data are uncertain variables in the sense of uncertainty theory. In this paper, the method of maximum likelihood is used to estimate the unknown parameters in the uncertain autoregressive model, and the unknown parameters of uncertainty distributions of the disturbance terms are simultaneously obtained. Based on the fitted autoregressive model, the forecast value and confidence interval of the future data are derived. Besides, the mean squared error is proposed to measure the goodness of …fit among different estimation methods, and an algorithm is introduced. Finally, the comparative analysis of the least squares, least absolute deviations, and maximum likelihood estimations are given, and two examples are presented to verify the feasibility of this approach. Show more

Keywords: Uncertain variable, autoregressive model, maximum likelihood estimation, mean squared error

DOI: 10.3233/JIFS-201724

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1391-1399, 2021

Authors: Shahzadi, Gulfam | Akram, Muhammad

Article Type: Research Article

Abstract: With the rapid increase of COVID-19, mostly people are facing antivirus mask shortages. It is necessary to select a good antivirus mask and make it useful for everyone. For maximize the efficacy of the antivirus masks, we propose a decision support algorithm based on the concept of Fermatean fuzzy soft set (FFS f S ). The basic purpose of this article is to introduce the notion of FFS f S to deal with problems involving uncertainty and complexity corresponding to various parameters. Here, the valuable properties of FFS f S are merged with the Yager …operator to propose four new operators, namely, Fermatean fuzzy soft Yager weighted average (FFS f YWA ), Fermatean fuzzy soft Yager ordered weighted average (FFS f YOWA ), Fermatean fuzzy soft Yager weighted geometric (FFS f YWG ) and Fermatean fuzzy soft Yager ordered weighted geometric (FFS f YOWG ) operators. The fundamental properties of proposed operators are discussed. For the importance of proposed operators, a multi-attribute group decision-making (MAGDM) strategy is presented along with an application for the selection of an antivirus mask over the COVID-19 pandemic. The comparison with existing operators shows that existing operators cannot deal with data involving parametric study but developed operators have the ability to deal decision-making problems using parameterized information. Show more

Keywords: Fermatean fuzzy soft numbers, Yager operators, Aggregation operators, Antivirus mask selection, TOPSIS method

DOI: 10.3233/JIFS-201760

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1401-1416, 2021

Authors: Khan, Jalaluddin | Li, Jian Ping | Haq, Amin Ul | Khan, Ghufran Ahmad | Ahmad, Sultan | Abdullah Alghamdi, Abdulrahman | Golilarz, Noorbakhsh Amiri

Article Type: Research Article

Abstract: The emerging technologies with IoT (Internet of Things) systems are elevated as a prototype and combination of the smart connectivity ecosystem. These ecosystems are appropriately connected in a smart healthcare system which are generating finest monitoring activities among the patients, well-organized diagnosis process, intensive support and care against the traditional healthcare operations. But facilitating these highly technological adaptations, the preserving personal information of the patients are on the risk with data leakage and privacy theft in the current revolution. Concerning secure protection and privacy theft of the patient’s information. We emphasized this paper on secure monitoring with the help of …intelligently recorded summary’s keyframe extraction and applied two rounds lightweight cosine-transform encryption. This article includes firstly, a regimented process of keyframe extraction which is employed to retrieve meaningful frames of image through visual sensor with sending alert (quick notice) to authority. Secondly, employed two rounds of lightweight cosine-transform encryption operation of agreed (detected) keyframes to endure security and safety for the further any kinds of attacks from the adversary. The combined methodology corroborates highly usefulness with engendering appropriate results, little execution of encryption time (0.2277-0.2607), information entropy (7.9996), correlation coefficient (0.0010), robustness (NPCR 99.6383, UACI 33.3516), uniform histogram deviation (R 0.0359, G 0.0492, B 0.0582) and other well adopted secure ideology than any other keyframe or image encryption approaches. Furthermore, this incorporating method can effectively reduce vital communication cost, bandwidth issues, storage, data transmission cost and effective timely judicious analysis over the occurred activities and keep protection by using effective encryption methodology to remain attack free from any attacker or adversary, and provide confidentiality about patient’s privacy in the smart healthcare system. Show more

Keywords: Internet of things, security, privacy, secure surveillance, image encryption

DOI: 10.3233/JIFS-201770

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1417-1442, 2021

Authors: Dinçer, Hasan | Baykal, Elif | Yüksel, Serhat

Article Type: Research Article

Abstract: The study aims to propose a novel model to define the role of spiritual leadership on the ethical climate for the banking industry. There are mainly three different stages in this model. Firstly, the criteria of each factor are selected with correlation coefficients by considering the balanced scorecard (BSC)-based linguistic evaluations. After that, these criteria are weighted by using interval type-2 (IT2) fuzzy decision-making trial and evaluation laboratory (DEMATEL). The third and the final stage aims to rank 5 biggest banks of Turkey which are quoted in İstanbul Stock Exchange. Within this framework, IT2 fuzzy technique for order preference by …similarity to ideal solution (TOPSIS) approach is considered. The findings demonstrate that the spiritual leadership has a significant influence on the ethical climate. Altruistic love is the most important spiritual leadership dimension to improve ethical climate in the organization. On the other side, it is also concluded that private banks in Turkey are the most successful with respect to the ethical climate. The results give an idea that spiritual leader contributes to the improvement of the ties of love and respect among employees. The main reason is that altruistic love improves the judgement and sensitivity competencies of the ethical so that employees tend to be working in a more ethical way. Show more

Keywords: Interval Type-2 fuzzy sets, balanced scorecard, DEMATEL, TOPSIS, ethical climate, spiritual leadership

DOI: 10.3233/JIFS-201840

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1443-1455, 2021

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

Article Type: Research Article

Abstract: Classification problem is an important research direction in machine learning. Nonparallel support vector machine (NPSVM) is an important classifier used to solve classification problems. It is widely used because of its structural risk minimization principle, kernel trick, and sparsity. When solving multi-class classification problems, NPSVM will encounter the problem of sample noises, low discrimination speed and unrecognized regions, which will affect its performance. In this paper, based on the multi-class NPSVM model, two improvements are made, and a directed acyclic graph fuzzy nonparallel support vector machine (DAG-F-NPSVM) model is established. On the one hand, for the noises that may exist …in the data set, the density information is used to add fuzzy membership to the samples, so that the contribution of each samples to the classification is treated differently. On the other hand, in order to reduce the decision time and solve the problem of unrecognized regions, the theory of directed acyclic graph (DAG) is introduced. Finally, the advantages of the new model in classification accuracy and decision speed is verified through UCI machine learning standard data set experiments. Finally, Friedman test and Bonferroni-Dunn test are used to verify the statistical significance of this new method. Show more

Keywords: Multi-class classification problem, nonparallel support vector machine, fuzzy membership, directed acyclic graph

DOI: 10.3233/JIFS-201847

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1457-1470, 2021

Authors: Mao, Jin | Yang, Lei | Liu, Kai | Du, Jinfu | Cui, Yahui

Article Type: Research Article

Abstract: In the following process, in order to improve the driving safety and road utilization of the adaptive cruise control (ACC) system, a variable time headway spacing strategy was studied. In view of the fact that the variable spacing strategy cannot adapt to the complex and variable deceleration conditions, an improved variable time headway strategy is proposed, which changes with the deceleration time and deceleration of the preceding vehicle. Based on this, the upper controller of adaptive cruise control based on model predictive control is designed, and numerical simulation of the variable time headway spacing strategy is performed, which verifies the …effectiveness of the improved variable time headway strategy. The results show that the spacing strategy proposed in this paper can more smoothly keep up with the preceding vehicle, and improve driving safety, comfort and road utilization. Show more

Keywords: Adaptive cruise control, variable time headway, spacing strategy, model predictive control

DOI: 10.3233/JIFS-202107

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1471-1479, 2021

Authors: Deng, Geng | Xie, Yaoguo | Wang, Xindong | Fu, Qiang

Article Type: Research Article

Abstract: Many classification problems contain shape information from input features, such as monotonic, convex, and concave. In this research, we propose a new classifier, called Shape-Restricted Support Vector Machine (SR-SVM), which takes the component-wise shape information to enhance classification accuracy. There exists vast research literature on monotonic classification covering monotonic or ordinal shapes. Our proposed classifier extends to handle convex and concave types of features, and combinations of these types. While standard SVM uses linear separating hyperplanes, our novel SR-SVM essentially constructs non-parametric and nonlinear separating planes subject to component-wise shape restrictions. We formulate SR-SVM classifier as a convex optimization …problem and solve it using an active-set algorithm. The approach applies basis function expansions on the input and effectively utilizes the standard SVM solver. We illustrate our methodology using simulation and real world examples, and show that SR-SVM improves the classification performance with additional shape information of input. Show more

DOI: 10.3233/JIFS-202155

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1481-1494, 2021

Authors: Wu, Yangxu | Yang, Wanting | Pan, Jinxiao | Chen, Ping

Article Type: Research Article

Abstract: Pavement crack assessment is an important indicator for evaluating road health. However, due to the dark color of the asphalt pavement and the texture characteristics of the pavement, current asphalt pavement crack detection technology cannot meet the requirements of accuracy and efficiency. In this paper, we propose an end-to-end multi-scale full convolutional neural network to achieve the semantic segmentation of cracks in road images by learning the crack characteristics in the complex fine grain background of asphalt pavement. The method uses DenseNet and deconvolution network framework to achieve pixel-level detection and fuses features learned from different scales of convolutional kernels …through a full convolutional network to obtain richer information on multi-scale features, allowing more detailed representation of crack features in high-resolution images. And the back end joins the SVM classifier to achieve crack classification after crack segmentation. Then we create a road test standard data set containing 12 cracks and evaluate it on the data. The experimental results show that the method achieves good segmentation effect for 12 types of cracks, and the crack segmentation for asphalt pavement is better than the most advanced methods. Show more

Keywords: Convolutional neural network (CNN), denseNet, deconvolution network, multi-scale full convolutional

DOI: 10.3233/JIFS-191105

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1495-1508, 2021

Authors: Song, Zekun | Li, Haodong | Shi, Jintang

Article Type: Research Article

Abstract: Service accessibility can be used to describe the travel time of passengers between different nodes, and opportunities to get transportation services in the high-speed railway (HSR) system. Based on the traditional train line planning theory, this paper introduces the transportation service accessibility index, and propose a new nonlinear passenger train line planning model, which aims to maximize the service accessibility, as well as minimize the operational cost of railway company. The model is transformed into a single-objective model, and then we design a harmony search algorithm to solve it. Finally, the model is validated by a numerical example. The results …of this model as well as the scenarios of the single-objective models for minimizing operational costs and maximizing service accessibility are compared. From the perspective of service frequency and accessibility of each nodes, we know that the proposed method can balance conflicts between average speed between large nodes and service frequency of small and medium size nodes in high-speed railway network. Show more

Keywords: High-speed railway, train line plan, service accessibility, harmony search algorithm, multi-objective optimization

DOI: 10.3233/JIFS-191866

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1509-1519, 2021

Authors: Yang, Jian | Han, Jihua | Wu, Tong | Zhang, Hao | Shang, Lixia

Article Type: Research Article

Abstract: The economic development of any country is closely linked with the consumption of energy. Therefore, international policies encourage increasing penetration of renewable energy sources (RES) into the electrical grid in order to reduce CO2 emissions and cover ever-increasing demands. However, high variance of RES complicates their integration into power systems and complicates their transition from central to distributed energy sources. On the other hand, increasing the penetration of RES in electrical networks stimulates the demand for large capacity for energy storage. This paper presents a new approach to optimize the size of on-grid renewable energy systems integrated to pumped …storage system using Salp Swarm Algorithm (SSA). This approach allows the examination of various energy sources and their combination to handle the optimal configuration of the hybrid system. The simulation and optimization process of the studied system have been carried out by MATLAB programming. The impact of the system under study on the grid is examined according to the power exchange values between the system and the grid. Moreover, different scenarios have been introduced for optimal operation. The simulation results indicate that these hybrid systems can reduce power exchange with the grid and ensure that the proposed system is economically and environmentally feasible. Furthermore, the results indicate the technical feasibility of seawater hydroelectric power plants in increasing the capacity of the electric grid to allow for high penetration of RES. Finally, the results showed that the best minimum value of the objective function is 3.9113 and showed that CO2 emission can be reduced about 29.65% per year compared to the conventional power plants. Show more

Keywords: CO2 emission, energy exchange, energy management, renewable energy, hydroelectric pumped storage

DOI: 10.3233/JIFS-192017

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1521-1536, 2021

Authors: Wu, Beng | He, Wei | Wang, Jing | Liang, Huaqing | Chen, Chong

Article Type: Research Article

Abstract: As the environment issue is put on the agenda, air pollution also concerns a lot. Nitrogen oxide (NOx) an is important factor which affects air pollution and is also the main gas emissions of the smoke and waste gas of FCC unit in petrochemical industry. It is important to accurately predict the NOx emission in advance for petrochemical industry to avoid air pollution incidents. In this paper, convolutional neural network (CNN) and long short-term memory (LSTM) are combined to predict the NOx emission in Fluid Catalytic Cracking unit (FCC unit). Convolutional-LSTM (CLSTM) is able to extract the spatial and temporal …features which are essential information in the prediction of the NOx emission. The features in the factors of production which would affect the NOx emission are extracted by CNN which prepares time series data for LSTM. The LSTM layer is connected after CNN to model the irregular trends in time series. CNN, Multi-layer perception (MLP), rand forest (RF), support vector machine (SVM) and LSTM are implemented as baseline models. The results from the proposed CLSTM model showed better performance than all the baseline models. The mean absolute error and root mean square error for CLSTM were calculated with the values of 16.8267 and 23.7089 which are the lowest among all the models. The Pearson correlation coefficient and R2 for the proposed CLSTM model are calculated with the value of 0.9263, 0.8237 which are the highest among all the models. Furthermore, the residual graphs indicate the well matched performance between the observations and the predictions. The study provides a model reference for forecasting the NOx concentration emitted by FCC unit in petrochemical industry. Show more

Keywords: Nitrogen oxides, machine learning, LSTM, CNN

DOI: 10.3233/JIFS-192086

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1537-1545, 2021

Authors: You, Shuang | Zhou, Yaping

Article Type: Research Article

Abstract: The traffic flow prediction using cellular automata (CA) is a trendy research domain that identified the potential of CA in modelling the traffic flow. CA is a technique, which utilizes the basic units for describing the overall behaviour of complicated systems. The CA model poses a benefit for defining the characteristics of traffic flow. This paper proposes a modified CA model to reveal the prediction of traffic flows at the signalised intersection. Based on the CA model, the traffic density and the average speed are computed for studying the characteristics and spatial evolution of traffic flow in signalised intersection. Moreover, …a CA model with a self-organizing traffic signal system is devised by proposing a new optimization model for controlling the traffic rules. The Sunflower Cat Optimization (SCO) algorithm is employed for efficiently predicting traffic. The SCO is designed by integrating the Sunflower optimization algorithm (SFO) and Cat swarm optimization (CSO) algorithm. Also, the fitness function is devised, which helps to guide the control rules evaluated by traffic simulation using the CA model. Thus, the cellular automaton is optimized using the SCO algorithm for predicting the traffic flows. The proposed Sunflower Cat Optimization-based cellular automata (SCO-CA) outperformed other methods with minimal travel time, distance, average traffic density, and maximal average speed. Show more

Keywords: Traffic flow prediction, signalized intersection, cellular automata, average speed, traffic density

DOI: 10.3233/JIFS-192099

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1547-1566, 2021

Authors: Xin, Xian-Wei | Song, Ji-Hua | Xue, Zhan-Ao | Peng, Wei-Ming

Article Type: Research Article

Abstract: As an important expanded of the classical formal concept, the three-way formal concept analysis integrates more information with the three-way decision theory. However, to the best of our knowledge, few scholars have studied the intuitionistic fuzzy three-way formal concept analysis. This paper proposes an intuitionistic fuzzy three-way formal concept analysis model based on the attribute correlation degree. To achieve this, we comprehensively analyze the composition of attribute correlation degree in the intuitionistic fuzzy environment, and introduce the corresponding calculation methods for different situations, as well as prove the related properties. Furthermore, we investigate the intuitionistic fuzzy three-way concept lattice ((IF3WCL) …of object-induced and attribute-induced. Then, the relationship between the IF3WCL and the positive, negative and boundary domains in the three-way decision are discussed. In addition, considering the final decision problem of boundary objects, the secondary decision strategy of boundary objects is obtained for IF3WCL. Finally, a numerical example of multinational company investment illustrates the effectiveness of the proposed model. In this paper, we systematically study the IF3WCL, and give a quantitative analysis method of formal concept decision along with its connection with three-way decision, which provides new ideas for the related research. Show more

Keywords: Intuitionistic fuzzy, attribute correlation degree, IF3WCL, secondary decision, three-way decision

DOI: 10.3233/JIFS-200002

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1567-1583, 2021

Authors: Zhongzheng, Xiao | Luktarhan, Nurbol

Article Type: Research Article

Abstract: A webshell is a common tool for network intrusion. It has the characteristics of considerable threat and good concealment. An attacker obtains the management authority of web services through the webshell to penetrate and control web applications smoothly. Because webshell and common web page features are almost identical, it can evade detection by traditional firewalls and anti-virus software. Moreover, with the application of various anti-detection feature hiding techniques to the webshell, it is difficult to detect new patterns in time based on the traditional signature matching method. Webshell detection has been proposed based on deep learning. First, a dataset is …opcoded, and the source code and opcode code features are fused. Second, the processed dataset is reduced using the SRNN and an attention mechanism, and the capsule network improves complete predictions for unknown pages. Experiments prove that the algorithm has higher detection efficiency and accuracy than traditional webshell detection methods, and it can also detect new types of webshell with a certain probability. Show more

Keywords: SRNN, Webshell, attention, CapsNet, opcode

DOI: 10.3233/JIFS-200314

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1585-1596, 2021

Authors: Bekmezci, Ilker | Ermis, Murat | Cimen, Egemen Berki

Article Type: Research Article

Abstract: Social network analysis offers an understanding of our modern world, and it affords the ability to represent, analyze and even simulate complex structures. While an unweighted model can be used for online communities, trust or friendship networks should be analyzed with weighted models. To analyze social networks, it is essential to produce realistic social models. However, there are serious differences between social network models and real-life data in terms of their fundamental statistical parameters. In this paper, a genetic algorithm (GA)-based social network improvement method is proposed to produce social networks more similar to real-life data sets. First, it creates …a social model based on existing studies in the literature, and then it improves the model with the proposed GA-based approach based on the similarity of the average degree, the k -nearest neighbor, the clustering coefficient, degree distribution and link overlap. This study can be used to model the structural and statistical properties of large-scale societies more realistically. The performance results show that our approach can reduce the dissimilarity between the created social networks and the real-life data sets in terms of their primary statistical properties. It has been shown that the proposed GA-based approach can be used effectively not only in unweighted networks but also in weighted networks. Show more

Keywords: Genetic algorithm, social network modeling, trust network, online communities

DOI: 10.3233/JIFS-200563

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1597-1608, 2021

Authors: Yang, Jie | Zhou, Wei | Li, Shuai

Article Type: Research Article

Abstract: Vague sets are a further extension of fuzzy sets. In rough set theory, target concept can be characterized by different rough approximation spaces when it is a vague concept. The uncertainty measure of vague sets in rough approximation spaces is an important issue. If the uncertainty measure is not accurate enough, different rough approximation spaces of a vague concept may possess the same result, which makes it impossible to distinguish these approximation spaces for charactering a vague concept strictly. In this paper, this problem will be solved from the perspective of similarity. Firstly, based on the similarity between vague information …granules(VIGs), we proposed an uncertainty measure with strong distinguishing ability called rough vague similarity (RVS). Furthermore, by studying the multi-granularity rough approximations of a vague concept, we reveal the change rules of RVS with the changing granularities and conclude that the RVS between any two rough approximation spaces can degenerate to granularity measure and information measure. Finally, a case study and related experiments are listed to verify that RVS possesses a better performance for reflecting differences among rough approximation spaces for describing a vague concept. Show more

Keywords: Vague sets, uncertainty measure, vague information granule, rough vague similarity, multi-granularity

DOI: 10.3233/JIFS-200611

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1609-1621, 2021

Authors: Iqbal, Shahid | Ullah Khan, Hikmat | Ishfaq, Umar | Alghobiri, Mohammed | Iqbal, Saqib

Article Type: Research Article

Abstract: The social web appears to enrich human lives by providing effective applications for online social interactions. Microblogs are one of the most important applications of the social Web. The Microbloggers who influence the social community users through their content in the form of tweets are known as the influential microbloggers. The identification of such influential microbloggers has vast applications in advertising, online marketing, corporate communication, information dissemination, etc. This paper investigates the problem of identifying influential microbloggers by proposing MIPPLA (Model to identify Influential using Productivity, Popularity and Link Analysis) model which integrates the modules of Productivity and …Popularity . The Productivity module considers a micro-blogger’s activity and the Popularity module identifies a microbloggers influence in an online social community. In addition, we modify the classic PageRank by utilizing the Twitter features such as retweet, mention, and reply for ranking the influential users. The proposed approaches are evaluated using real-world social networks. The results prove that the MIPPLA model efficiently identifies and ranks the top influential users in an effective manner as compared to the existing techniques. Show more

Keywords: Social web, online social networks, microblogs, influential users, big data, data mining

DOI: 10.3233/JIFS-201036

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1623-1637, 2021

Authors: Qahremani, E. | Allahviranloo, T. | Abbasbandy, S. | Ahmady, N.

Article Type: Research Article

Abstract: This paper is concerned with aspects of the analytical fuzzy solutions of the parabolic Volterra partial integro-differential equations under generalized Hukuhara partial differentiability and it consists of two parts. The first part of this paper deals with aspects of background knowledge in fuzzy mathematics, with emphasis on the generalized Hukuhara partial differentiability. The existence and uniqueness of the solutions of the fuzzy Volterra partial integro-differential equations by considering the type of [gH  - p ]-differentiability of solutions are proved in this part. The second part is concerned with the central themes of this paper, using the fuzzy Laplace transform method for …solving the fuzzy parabolic Volterra partial integro-differential equations with emphasis on the type of [gH  - p ]-differentiability of solution. We test the effectiveness of method by solving some fuzzy Volterra partial integro-differential equations of parabolic type. Show more

Keywords: Fuzzy laplace transform, generalized hukuhara partial differentiable, fuzzy parabolic volterra partial integro-differential equation, fuzzy triangular functions

DOI: 10.3233/JIFS-201125

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1639-1654, 2021

Authors: Cheng, Linhai | Zhang, Yu | He, Yingying | Lv, Yuejin

Article Type: Research Article

Abstract: Classical rough set theory (RST) is based on equivalence relations, and does not have an effective mechanism when the attribute value of the objects is uncertain information. However, the information in actual problems is often uncertain, and an accurate or too vague description of the information can no longer fully meet the actual needs. Interval rough number (IRN) can reflect a certain degree of certainty in the uncertainty of the data when describing the uncertainty of the data, and can enable decision makers to make decisions more in line with actual needs according to their risk preferences. However, the current …research on rough set models (RSMs) whose attribute values are interval rough numbers is still very scarce, and they cannot analyze the interval rough number information system (IRNIS) from the perspective of similar relation. therefore, three new interval rough number rough set models (IRNRSMs) based on similar relation are proposed in this paper. Firstly, aiming at the limitations of the existing interval similarity degree (ISD), new interval similarity degree and interval rough number similarity degree (IRNSD) are proposed, and their properties are discussed. Secondly, in the IRNIS, based on the newly proposed IRNSD, three IRNRSMs based on similar class, β -maximal consistent class and β -equivalent class are proposed, and their properties are discussed. And then, the relationships between these three IRNRSMs and those between their corresponding approximation accuracies are researched. Finally, it can be found that the IRNRSM based on the β -equivalent classes has the highest approximation accuracy. Proposing new IRNRSMs based on similar relation is a meaningful contribution to extending the application range of RST. Show more

Keywords: Interval rough number, rough set model, intervals similarity degree, β-equivalent class, approximation accuracy

DOI: 10.3233/JIFS-191096

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1655-1666, 2021

Authors: Sun, Kangjian | Jia, Heming | Li, Yao | Jiang, Zichao

Article Type: Research Article

Abstract: Slime mould algorithm (SMA) is a novel metaheuristic that simulates foraging behavior of slime mould. Regarding its drawbacks and properties, a hybrid optimization (BTβ SMA) based on improved SMA is proposed to produce the higher-quality optimal results. Brownian motion and tournament selection mechanism are introduced into the basic SMA to improve the exploration capability. Moreover, a local search algorithm (Adaptive β -hill climbing, Aβ HC) is hybridized with the improved SMA. It is considered from boosting the exploitation trend. The proposed BTβ SMA algorithm is evaluated in two main phases. Firstly, the two improved hybrid variants (BTβ SMA-1 and BTβ …SMA-2) are compared with the basic SMA algorithm through 16 benchmark functions. Also, the performance of winner is further evaluated through comparisons with 7 state-of-the-art algorithms. The simulation results report fitness and computation time. The convergence curve and boxplot visualize the effects of fitness. The comparison results on the function optimization suggest that BTβ SMA is superior to competitors. Wilcoxon rank-sum test is also employed to investigate the significance of the results. Secondly, the applicability on real-world tasks is proved by solving structure engineering design problems and training multilayer perceptrons. The numerical results indicate the merits of the BTβ SMA algorithm in terms of solution precision. Show more

Keywords: Slime mould algorithm, adaptive β-hill climbing, function optimization, structure engineering design, training multilayer perceptron

DOI: 10.3233/JIFS-201755

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1667-1679, 2021

Authors: Khan, Asif | Li, Jian Ping | Haq, Amin Ul | Memon, Imran | Patel, Sarosh H. | ud. Din, Salah

Article Type: Research Article

Abstract: On-time recovery and treatment of disease is always desirable. The use of Machine learning in health-care has grown very fast to diagnosis the different kinds of diseases in the past few years. In such a diagnosis, past and real-time data are playing very crucial role in using data mining techniques. Still, we are lacking in diagnosing the emotional mental disturbance accurately in the early stages. Thus,the initial diagnosis of depression expressively stances a great problem for both,researchers and clinical professionals. We have addressed the said problem in our proposed work using Pipeline Machine Learning technique where people based on emotional …stages have been effectively classified into different groups in e-healthcare. To implement Hybrid classification, a well known machine learning multi-feature hybrid classifier is used by having the emotional stimulation in form of negative or positive people. In order to improve classification, an Ensemble Learning Algorithm is used which helps in choosing the more suitable features from the available genres-emotion data on online media. Additionally, Hold out validation method has been to split the dataset for training and testing of the predictive model. Further, performance evaluation measures have been applied to check the proposed system evaluation. This study is done on Genres-Tags MovieLens dataset. The experimental results show that applied ensemble method provides optimal classification performance by choosing the best subset of features. The said results proved the excellency of the proposed system which comes from the choosing most related features selected by the Integrated Learning algorithm. Additionally, suggested approach is used to accurately and effectively diagnose the depression in its early stage. It will help in recovery and treatment of depressed people. We conclude that use of the suggested method is highly suitable in all aspects of e-healthcare for depress stimulation. Show more

Keywords: Socialnetworking, human physci, retrieval-ranking, trendprediction, informationretrieval, ML, datascience

DOI: 10.3233/JIFS-201069

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1681-1694, 2021

Authors: Fei, Kaifang | Jiang, Minghui | Zhang, Yadan

Article Type: Research Article

Abstract: In this paper, the matters of dissipativity and finite time synchronization for memristor-based neural networks (MNNs) with mixed time-varying discontinuities are investigated. Firstly, under the framework of extending Filippov differential inclusion theory, several effective new criteria are derived. Then, the global dissipativity of Filippov solution to neural networks is proved by using generalized Halanay inequality and matrix measure method. Secondly, some novel sufficient conditions are introduced to guarantee the finite-time synchronization of the drive-response MNNs based on a simple Lyapunov function and two different feedback controllers. Finally, several numerical examples are given to verify the validity of the theoretical results.

Keywords: Memristor, dissipativity, finite time synchronization, mixed time-varying delayed, neural networks

DOI: 10.3233/JIFS-191397

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1695-1712, 2021

Authors: Kosheleva, Olga | Kreinovich, Vladik

Article Type: Book Review

DOI: 10.3233/JIFS-189471

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1713-1714, 2021

Authors: Kosheleva, Olga | Kreinovich, Vladik

Article Type: Book Review

DOI: 10.3233/JIFS-189472

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1715-1716, 2021

Authors: Kreinovich, Vladik

Article Type: Book Review

DOI: 10.3233/JIFS-189473

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 1717-1719, 2021