Journal of Intelligent & Fuzzy Systems - Volume 39, issue 5

Authors: Zhang, Xianquan | Yang, Ju | Dong, Yu | Yu, Chunqiang | Tang, Zhenjun

Article Type: Research Article

Abstract: Most data hiding methods have limitations in resisting cropping and noise attacks. Aiming at this problem, a robust data hiding with multiple backups and optimized reference matrix is proposed in this paper. Specifically, secret data is divided into a set of groups and multiple backups of each group data are generated according to the number of backups. The cover image is divided into several blocks. A reference matrix is constructed by four constraints to assist data hiding and data extraction. The proposed method aims to extract exactly at least one backup of each group data so that the correct backups …can construct the secret data well if the stego-image is corrupted. Experimental results show that the proposed algorithm is robust to cropping and noise attacks. Show more

Keywords: Data hiding, anti-cropping, anti-noise, multi-backup data, data security

DOI: 10.3233/JIFS-200089

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6965-6977, 2020

Authors: El Atik, Abd El Fattah A. | wahba, Ashgan S.

Article Type: Research Article

Abstract: Rough set theory is used in simple directed graphs to study nano topology. Adjacent vertices was used in digraphs only to define their neighborhoods. Four types of neighborhood systems for vertices are introduced in this article which depend on both adjacent vertices and associated edges. Additionally, the generalization of some notions presented by Pawlak and Lellis Thivagar and some of their properties are investigated. Finally, we present a new model of a blood circulation system of the human heart based on blood paths. Also, different kinds of topological separation axioms are presented and studied between vertices and edges of the …heart blood circulation model. Show more

Keywords: Graph theory, Rough sets, Nano topology, Human heart, Separation axioms

DOI: 10.3233/JIFS-200126

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6979-6992, 2020

Authors: Han, Lu | Su, Zhi | Lin, Jing

Article Type: Research Article

Abstract: Ever increasing ordinal variables are being collected by the Personal Credit Reference System in China, however this system suffers from analysis of this kind of data, which cannot be calculated by Euclidean distance. In this study, we put forward a hybrid KNN algorithm based on Sugeno measure, and we prove that the error of this algorithm is smaller than that of Euclidean distance, furthermore, we use real data obtained from the Personal Credit Reference System to perform experiments and get the user’s initial portrait. Through the comparisons with Kmeans algorithm and other different distance measures in KNN algorithm, we find …that the hybrid KNN algorithm is more suitable for clustering personal credit data. Show more

Keywords: Hybrid KNN clustering, personal credit reference system, Sugeno measure, user’s portrait

DOI: 10.3233/JIFS-200191

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6993-7004, 2020

Authors: Zhu, Zhanlong | Liu, Yongjun | Wang, Yuan

Article Type: Research Article

Abstract: Adding spatial penalty to fuzzy C-means (FCM) model is an important way to reduce the influence of noise in image segmentation. However, these improved algorithms easily cause segmentation failures when the image has the characteristics of unequal cluster sizes. Besides, they often fall into local optimal solutions if the initial cluster centers are improper. This paper presents a noise robust hybrid algorithm for segmenting image with unequal cluster sizes based on chaotic crow search algorithm and improved fuzzy c-means to overcome the above defects. Firstly, each size of clusters is integrated into the objective function of noise detecting fuzzy c-means …algorithm (NDFCM), which can reduces the contribution of larger clusters to objective function and then the new membership degree and cluster centers are deduced. Secondly, a new expression called compactness, representing the pixel distribution of each cluster, is introduced into the iteration process of clustering. Thirdly, we use two- paths to seek the optimal solutions in each step of iteration: one path is produced by the chaotic crow search algorithm and the other is originated by gradient method. Furthermore, the better solutions of the two-paths go to next generation until the end of the iteration. Finally, the experiments on the synthetic and non–destructive testing (NDT) images show that the proposed algorithm behaves well in noise robustness and segmentation performance. Show more

Keywords: Image segmentation, fuzzy clustering, chaotic crow search algorithm, unequal cluster sizes

DOI: 10.3233/JIFS-200197

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7005-7020, 2020

Authors: Li, Feng

Article Type: Research Article

Abstract: Mining maximal frequent patterns is significant in many fields, but the mining efficiency is often low. The bottleneck lies in too many candidate subgraphs and extensive subgraph isomorphism tests. In this paper we propose an efficient mining algorithm. There are two key ideas behind the proposed methods. The first is to divide each edge of every certain graph (converted from equivalent uncertain graph) and build search tree, avoiding too many candidate subgraphs. The second is to search the tree built in the first step in order, avoiding extensive subgraph isomorphism tests. The evaluation of our approach demonstrates the significant cost …savings with respect to the state-of-the-art approach not only on the real-world datasets as well as on synthetic uncertain graph databases. Show more

Keywords: Uncertain graph, maximal frequent pattern, data mining

DOI: 10.3233/JIFS-200237

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7021-7033, 2020

Authors: Parsa, Navid | Bahmani-Firouzi, Bahman | Niknam, Taher

Article Type: Research Article

Abstract: Distribution automation is well recognized as an effective solution to enhance the reliability and efficiency of these grids in a timely manner. This paper introduces an effective probabilistic operation framework for the automated distribution networks (ADNs) incorporating the plug-in electric vehicles (PEVs) charging/discharging schemes in the presence of different renewable energy sources (RESs). To this end, this paper pursues four different strategic approaches. Firstly, an effective fuzzy based probabilistic method is proposed to model the forecast error in the wind and solar units well as the load demand through the cloud theory. Secondly, an appropriate framework is devised to model …the PEVs random behaviour considering their essential parameters such as the charging/discharging rate and arrival/departure time to/from the parking lots (PLs), the discharging level at driving mode on the road and the effects of battery degradation. As the third goal, an appropriate objective function which can consider automation indices including the social welfare and reliability is considered. Since the operation problem is a nonlinear continuous non-numerical problem, it requires an applicable and effective optimization algorithm which is regarded as the fourth goal of this paper. In this regard, a new θ -modified bat algorithm is introduced to find the optimal solution of the problem. The proposed model is simulated and examined on the IEEE 69-bus standard test system wherein results reveal the effectiveness and applicability of the proposed operation management framework. Show more

Keywords: Automated distribution networks, reliability, electric vehicles, renewable energy sources, optimization and operation management

DOI: 10.3233/JIFS-200246

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7035-7051, 2020

Authors: Saini, Jagriti | Dutta, Maitreyee | Marques, Gonçalo

Article Type: Research Article

Abstract: Indoor air pollution (IAP) has become a serious concern for developing countries around the world. As human beings spend most of their time indoors, pollution exposure causes a significant impact on their health and well-being. Long term exposure to particulate matter (PM) leads to the risk of chronic health issues such as respiratory disease, lung cancer, cardiovascular disease. In India, around 200 million people use fuel for cooking and heating needs; out of which 0.4% use biogas; 0.1% electricity; 1.5% lignite, coal or charcoal; 2.9% kerosene; 8.9% cow dung cake; 28.6% liquified petroleum gas and 49% use firewood. Almost 70% …of the Indian population lives in rural areas, and 80% of those households rely on biomass fuels for routine needs. With 1.3 million deaths per year, poor air quality is the second largest killer in India. Forecasting of indoor air quality (IAQ) can guide building occupants to take prompt actions for ventilation and management on useful time. This paper proposes prediction of IAQ using Keras optimizers and compares their prediction performance. The model is trained using real-time data collected from a cafeteria in the Chandigarh city using IoT sensor network. The main contribution of this paper is to provide a comparative study on the implementation of seven Keras Optimizers for IAQ prediction. The results show that SGD optimizer outperforms other optimizers to ensure adequate and reliable predictions with mean square error = 0.19, mean absolute error = 0.34, root mean square error = 0.43, R2 score = 0.999555, mean absolute percentage error = 1.21665%, and accuracy = 98.87%. Show more

Keywords: Indoor air quality, pollutants, prediction system, optimizers

DOI: 10.3233/JIFS-200259

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7053-7069, 2020

Authors: Zhao, Ruirui | Luo, Minxia | Li, Shenggang

Article Type: Research Article

Abstract: The theory of single valued neutrosophic sets, which is a generalization of intuitionistic fuzzy sets, is more capable of dealing with inconsistent information in practice. In this paper, we propose reverse triple I method under single valued neutrosophic environment. Firstly, we give the definitions of single valued neutrosophic t-representation t-norms and single valued neutrosophic residual implications. Secondly, we develop a formula for calculating single valued neutrosophic residual implications. Then we propose reverse triple I method based on left-continuous single valued neutrosophic t-representation t-norms and its solutions. Lastly, we discuss the robustness of reverse triple I method based on the proposed …similarity measure. Show more

Keywords: Single valued neutrosophic sets, similarity measure, reverse triple I method

DOI: 10.3233/JIFS-200265

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7071-7083, 2020

Authors: Liu, Shuqi | Shao, Mingwen | Liu, Xinping

Article Type: Research Article

Abstract: In recent years, deep neural networks have made significant progress in image classification, object detection and face recognition. However, they still have the problem of misclassification when facing adversarial examples. In order to address security issue and improve the robustness of the neural network, we propose a novel defense network based on generative adversarial network (GAN). The distribution of clean - and adversarial examples are matched to solve the mentioned problem. This guides the network to remove invisible noise accurately, and restore the adversarial example to a clean example to achieve the effect of defense. In addition, in order to …maintain the classification accuracy of clean examples and improve the fidelity of neural network, we input clean examples into proposed network for denoising. Our method can effectively remove the noise of the adversarial examples, so that the denoised adversarial examples can be correctly classified. In this paper, extensive experiments are conducted on five benchmark datasets, namely MNIST, Fashion-MNIST, CIFAR10, CIFAR100 and ImageNet. Moreover, six mainstream attack methods are adopted to test the robustness of our defense method including FGSM, PGD, MIM, JSMA, CW and Deep-Fool. Results show that our method has strong defensive capabilities against the tested attack methods, which confirms the effectiveness of the proposed method. Show more

Keywords: Deep neural network, generative adversarial network, adversarial example, defense

DOI: 10.3233/JIFS-200280

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7085-7095, 2020

Authors: Nasr Saleh, Hayel | Imdad, Mohammad | Khan, Idrees | Hasanuzzaman, Md

Article Type: Research Article

Abstract: In the present article, inspired by the work of Jleli et al. [J. Inequal. Appl. 2014, 38 (2014)] and [J. Inequal. Appl. 2014, 439 (2014)] in metric spaces, we proposed a new class of contractive mappings termed as: fuzzy Θ f -contractive mappings by using an auxiliary function Θ f  : (0, 1) → (0, 1) satisfying suitable properties. This class has further been weakened by defining the class of fuzzy Θ f -weak contractive mappings to realize yet another class of contractive mappings. Thereafter, these two newly introduced classes of contractive mappings are utilized to establish some fixed point …theorems in M -complete fuzzy metric spaces (in the sense of George and Veeramani). In support of our newly obtained results, we provide some examples besides furnishing applications to dynamic programming. Show more

Keywords: Fixed point, fuzzy Θf-contractive mappings, fuzzy Θf-weak contractive mappings, fuzzy metric space, dynamic programming

DOI: 10.3233/JIFS-200319

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7097-7106, 2020

Authors: Chuanchao, Zhang

Article Type: Research Article

Abstract: In view of the characteristics with big data, high feature dimension, and dynamic for a large-scale intuitionistic fuzzy information systems, this paper integrates intuitionistic fuzzy rough sets and generalized dynamic sampling theory, proposes a generalized attribute reduction algorithm based on similarity relation of intuitionistic fuzzy rough sets and dynamic reduction. It uses dynamic reduction sampling theory to divide a big data set into small data sets and relative positive domain cardinality instead of dependency degree as decision-making condition, and obtains reduction attributes of big intuitionistic fuzzy decision information systems, and achieves the goal of extracting key features and fault diagnosis. …The innovation of this paper is that it integrates generalized dynamic reduction and intuitionistic fuzzy rough set, and solves the problem of big data set which cannot be solved by intuitionistic fuzzy rough set. Taking an actual data as an example, the scientificity, rationality and effectiveness of the algorithm are verified from the aspects of stability, diagnostic accuracy, optimization ability and time complexity. Compared with similar algorithms, the advantages of the proposed algorithm for big data processing are confirmed. Show more

Keywords: Intuitionistic fuzzy rough set, similarity relation, relative positive domain, generalized dynamic reduction, large fuzzy decision information system, attribute reduction

DOI: 10.3233/JIFS-200347

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7107-7122, 2020

Authors: Maryum, Ilsa | Nawaz, Waqas | Ud Din, Amad

Article Type: Research Article

Abstract: Non-uniformity in medical procedures, expensive medical treatments, and the shortage of medicines in different areas are health care problems in our country. This paper aims to resolve that problem by developing a web-based-application called Hospital Management Society (HMS) based on a novel Dynamic Optimized Fuzzy C-mean Clustering and Association Rule Mining (DOFCCARM). The purpose of HMS is to enhance the hospitals (and clinics) by regulating, overseeing and accrediting them to bring uniformity in health care facilities, to make the medical treatment cost effective, to find common diseases in a particular age and area, and to help government in identifying the …areas facing the shortage of licensed medicines. Therefore, HMS creates a single platform for both the doctors of central hospital (CH) and the doctors of member hospitals (MH). The CH provides clinical practice guidelines for various diseases. A team of doctors at CH evaluate the medical treatment provided by MH. If a hospital fails to maintain the standard then HMS blacklists such hospital. In our approach, we take a range of values to distinct successive partitions and generate a parallel membership function to make fuzzy sets of patients report, rather than single partitioning point. We determine the effectiveness of our approach through experiments on a dataset. The results revealed the most common age, symptoms and location for a particular disease and shortage of particular medicine in a specific area. Show more

Keywords: Fuzzy C-mean, association rule mining, hospital management society, intelligent system

DOI: 10.3233/JIFS-200349

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7123-7134, 2020

Authors: Li, Shugang | Zhu, Lirong | Zhu, Boyi | Wang, Ru | Zheng, Lingling | Yu, Zhaoxu | Lu, Hanyu

Article Type: Research Article

Abstract: 3D printing is the important part of the emerging industry, and the accurate prediction of technology hot spots (THS) in the 3D printing industry is crucial for the strategic technology planning. The patents of the THS are always in the minority and have outlier characteristics, so the existing single and rigid models cannot accurately and robustly predict the THS. In order to make up for the shortcomings of the existing research, this study proposes a model for robust composite attraction indicator (MRCAI), which avoids the impact of outlier patents on prediction accuracy depending on not only extracting the patent attraction …indicators (AIs) but also constructing the robust composite attraction indicator (CAI) according to the rough consensus of predicted results of CAIs with high generalization. Specifically, firstly, this study selects the patent AIs from the four dimensions of the attraction: technology group attraction, state attraction, enterprise attraction and inventor attraction. Secondly, in order to completely describe the attraction features of patent, AIs are directly and indirectly integrated into CAIs. Thirdly, we reduce the influence of outlier patents on prediction accuracy from two aspects: on the one hand, we initially select the CAIs with good generalization performance based on the prediction error fluctuation range. On the other hand, we build the robust CAIs by calculating the consensus of CAIs with high generalization performance based on the rough set. Fourthly, the 3D printing industry technology attention matrix is constructed to map the effective technology strategic planning based on predicted patent backward citation count by MRCAI in the short, medium and long term. Finally, the experimental results on 3D printing patent data show that MRCAI can effectively improve the efficiency in dealing with samples with outlier patents and has strong flexibility and robustness in predicting the THS in 3D printing industry. Show more

Keywords: Technology hot spots, outlier samples, robust CAI, 3D printing, technology attention matrix

DOI: 10.3233/JIFS-200404

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7135-7149, 2020

Authors: Lio, Waichon | Jia, Lifen

Article Type: Research Article

Abstract: Since the practical production is not continuously available and sometimes suffers unexpected breakdowns, this paper applies uncertainty theory to introducing an uncertain production risk process with breakdowns to handle the production problem with uncertain cycle times (consisting of uncertain on-times and uncertain off-times) and uncertain production amounts. The concept of shortage index of the uncertain production risk process with breakdowns is provided and some formulas for the calculation are given. Furthermore, the shortage time of the uncertain production risk process with breakdowns is proposed and its uncertainty distribution is obtained. Finally, some numerical examples are revealed.

Keywords: Production, risk process, uncertainty theory, uncertain renewal process

DOI: 10.3233/JIFS-200453

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7151-7160, 2020

Authors: Alberto Morales-Rosales, Luis | Algredo-Badillo, Ignacio | Lobato-Baez, Mariana | Hernández-Gracidas, Carlos | Rodríguez-Rangel, Héctor

Article Type: Research Article

Abstract: In this research, we implement an intelligent quantitative model to assess a specific qualitative intelligence scale in children between 5 and 8 years old, based on augmented reality and the well known WISC-IV test. The output of the model is a cognitive factor associated with the analogical reasoning level of the child, and the ulterior analysis of the evaluation measure is intended to serve as an aid for the teacher to discover problems related to the child’s ability to solve visual analogies. A quantitative approach to assess analogical reasoning is suitable to avoid ambiguous evaluations of qualitative results. Also, given …that the assessment employs a visual WISC subtest, it constitutes a non-verbal evaluation. Finally, the fact that the model is based on an intelligent approach guarantees that the assessment process is impartial, based on the quantitative scores obtained, instead of an interpretation of the results. The purpose of this work is to give evidence that a computer-aided adaption, employing augmented reality and a Fuzzy Petri Net, for the WISC test, will improve the teaching-learning process in children ranges from 5 to 8 years old. A case study is analyzed, where both the paper-based and the augmented reality versions are applied to five children with Spanish as their native tongue. We show the feasibility and potentiality of implementing the test in a multimedia version to provide teachers with a more reliable resource for the diagnosis and treatment of possible learning deficiencies in the child regarding disambiguation, non-verbality, and impartiality. Show more

Keywords: Intelligent quantitative model, analogical reasoning, WISC-IV test, augmented reality learning environment, computer-aided assessments

DOI: 10.3233/JIFS-200588

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7161-7175, 2020

Authors: Vijayabalaji, Srinivasan | Balaji, Parthasarathy | Ramesh, Adhimoolam

Article Type: Research Article

Abstract: The impetus of this paper is to broaden the structure of linguistic soft set (LSS) to a new domain namely sigmoid valued fuzzy soft set (SVFSS). Some operating laws on SVFSS are also provided. Using the complement concept on SVFSS we define maximum rejection. This maximum rejection paves a way for defining a new similarity measure on SVFSS termed as maximum likely ratio (MLR). A new MCGDM algorithm for SVFSS is proposed using MLR. An illustrative example of haze equipment problem on sigmoid valued fuzzy soft set setting is also given. A comparative analysis of our approach with the existing …approaches are also presented to justify our work. Show more

Keywords: Sigmoid valued fuzzy soft set, maximum rejection, maximum likely ratio, generalized maximum likely ratio, weighted maximum likely ratio

DOI: 10.3233/JIFS-200594

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7177-7187, 2020

Authors: Al-Zoubi, Ahmad | Tatas, Konstantinos | Kyriacou, Costas

Article Type: Research Article

Abstract: Heterogeneous systems featuring multiple kinds of processors are becoming increasingly attractive due to their high performance and energy savings over their homogeneous counterparts. With the OpenCL as a unified programming language providing program portability across different types of accelerators, finding the best task-to-device mapping will be the key to achieve such a high performance. We introduce in this work the design of a fuzzy logic classifier and the evaluation of its performance in classifying OpenCL workloads in a CPU-GPU-FPGA heterogeneous environment based on carefully analyzed kernel features. The classifier is designed as part of a scheduling scheme. Results demonstrate substantial …improvement in accuracy when compared to other classifiers such as the K-Nearest- Neighbor (KNN), Support-Vector-Machine (SVM), Random-Forest (RF), Naïve-Bayes (NB) and the Bayes-Network (BN) with low computational complexity, facilitating run-time operation. Show more

Keywords: Fuzzy Logic, Heterogeneous, Classification, OpenCL

DOI: 10.3233/JIFS-200616

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7189-7202, 2020

Authors: Ontiveros-Robles, Emanuel | Castillo, Oscar | Melin, Patricia

Article Type: Research Article

Abstract: In recent years, successful applications of singleton fuzzy inference systems have been made in a plethora of different kinds of problems, for example in the areas of control, digital image processing, time series prediction, fault detection and classification. However, there exists another relatively less explored approach, which is the use of non-singleton fuzzy inference systems. This approach offers an interesting way for handling uncertainty in complex problems by considering inputs with uncertainty, while the conventional Fuzzy Systems have their inputs with crisp values (singleton systems). Non-singleton systems have as inputs Type-1 membership functions, and this difference increases the complexity of …the fuzzification, but provides the systems with additional non-linearities and robustness. The main limitations of using a non-singleton fuzzy inference system is that it requires an additional computational overhead and are usually more difficult to apply in some problems. Based on these limitations, we propose in this work an approach for efficiently processing non-singleton fuzzy systems. To verify the advantages of the proposed approach we consider the case of general type-2 fuzzy systems with non-singleton inputs and their application in the classification area. The main contribution of the paper is the implementation of non-singleton General Type-2 Fuzzy Inference Systems for the classification task, aiming at analyzing its potential advantage in classification problems. In the present paper we propose that the use of non-singleton inputs in Type-2 Fuzzy Classifiers can improve the classification rate and based on the realized experiments we can observe that General Type-2 Fuzzy Classifiers, but with non-singleton fuzzification, obtain better results in comparison with respect to their singleton counterparts. Show more

Keywords: Type-2 fuzzy classifiers, Type-2 fuzzy logic, non-singleton

DOI: 10.3233/JIFS-200639

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7203-7215, 2020

Authors: Gao, Wenjing | Zhang, Wenjun | Gao, Haiyan | Zhu, Yonghua

Article Type: Research Article

Abstract: The increasing tendency of people expressing opinions via images online has motivated the development of automatic assessment of sentiment from visual contents. Based on the observation that visual sentiment is conveyed through many visual elements in images, we put forward to tackle visual sentiment analysis under multiple instance learning (MIL) formulation. We propose a deep multiple clustered instance learning formulation, under which a deep multiple clustered instance learning network (DMCILN) is constructed for visual sentiment analysis. Specifically, the input image is converted into a bag of instances through visual instance generation module, which is composed of a pre-trained convolutional neural …network (CNN) and two adaptation layers. Then, a fuzzy c-means routing algorithm is introduced for generating clustered instances as semantic mid-level representation to bridge the instance-to-bag gap. To explore the relationships between clustered instances and bags, we construct an attention based MIL pooling layer for representing bag features. A multi-head mechanism is integrated to form MIL ensembles, which enables to weigh the contribution of each clustered instance in different subspaces for generating more robust bag representation. Finally, we conduct extensive experiments on several datasets, and the experimental results verify the feasibility of our proposed approach for visual sentiment analysis. Show more

Keywords: Visual sentiment analysis, deep multiple clustered instance learning, fuzzy c-means routing, multi-head mechanism

DOI: 10.3233/JIFS-200675

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7217-7231, 2020

Authors: Yoseph, Fahed | Heikkilä, Markku

Article Type: Research Article

Abstract: Market Intelligence is knowledge extracted from numerous data sources, both internal and external, to provide a holistic view of the market and to support decision-making. Association Rules Mining provides powerful data mining techniques for identifying associations and co-occurrences in large databases. Market Basket Analysis (MBA) uses ARM to gain insights from heterogeneous consumer shopping patterns and examines the effects of marketing initiatives. As Artificial Intelligence (AI) more and more finds its way to marketing, it entails fundamental changes in the skills-set required by marketers. For MBA, AI provides important ways to improve both the outcomes of the market basket analysis …and the performance of the analysis process. In this study we demonstrate the effects of AI on MBA by our proposed new MBA model where results of computational intelligence are used in data preprocessing, in market segmentation and in finding market trends. We show with point-of-sale (POS) data of a small, local retailer that our proposed “Åbo algorithm” MBA model increases mining performance/intelligence and extract important marketing insights to assess both demand dynamics and product popularity trends. Additionally, the results show how, as related to the 80/20 percent rule, 78% of revenue is derived 16% of the product assortment. Show more

Keywords: Association rules mining, artificial intelligence, market intelligence, small and medium-sized retailer

DOI: 10.3233/JIFS-200707

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7233-7246, 2020

Authors: Xiao, Lu | Zhang, Siqi | Wei, Guiwu | Wu, Jiang | Wei, Cun | Guo, Yanfeng | Wei, Yu

Article Type: Research Article

Abstract: Since people around the world have gradually attached importance to resource conservation, various countries are actively taking measures to promote environmental protection and sustainable development. Green supply chain management (GSCM) have emerged in this context. Thus, in this essay, a novel intuitionistic fuzzy multiple attribute group decision making (MAGDM) method is designed to tackle this issue. First of all, CRITIC (Criteria Importance Through Inter-criteria Correlation) method is utilized to determine the weights of criteria. Later, the conventional Taxonomy method is extended to the intuitionistic fuzzy environment to compute the value of development attribute of each supplier. Then, the optimal one …can be determined. Eventually, an application about green supplier selection in steel industry is presented, and a comparative analysis is made to demonstrate the superiority of the proposed method. The main features of the proposed algorithm are that they provide a practical solution for selecting GSCM and presents an objective weighting method to enhance the effectiveness of the algorithm. Show more

Keywords: Multiple attribute group decision making (MAGDM), green supply chain management (GSCM), intuitionistic fuzzy sets (IFSs), taxonomy method, CRITIC method, steel industry

DOI: 10.3233/JIFS-200709

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7247-7258, 2020

Authors: Pan, Xingguang | Wang, Shitong

Article Type: Research Article

Abstract: The feature reduction fuzzy c-means (FRFCM) algorithm has been proven to be effective for clustering data with redundant/unimportant feature(s). However, the FRFCM algorithm still has the following disadvantages. 1) The FRFCM uses the mean-to-variance-ratio (MVR) index to measure the feature importance of a dataset, but this index is affected by data normalization, i.e., a large MVR value of original feature(s) may become small if the data are normalized, and vice versa. Moreover, the MVR value(s) of the important feature(s) of a dataset may not necessarily be large. 2) The feature weights obtained by the FRFCM are sensitive to the initial …cluster centers and initial feature weights. 3) The FRFCM algorithm may be unable to assign the proper weights to the features of a dataset. Thus, in the feature reduction learning process, important features may be discarded, but unimportant features may be retained. These disadvantages can cause the FRFCM algorithm to discard important feature components. In addition, the threshold for the selection of the important feature(s) of the FRFCM may not be easy to determine. To mitigate the disadvantages of the FRFCM algorithm, we first devise a new index, named the marginal kurtosis measure (MKM), to measure the importance of each feature in a dataset. Then, a novel and robust feature reduction fuzzy c-means clustering algorithm called the FRFCM-MKM, which incorporates the marginal kurtosis measure into the FRFCM, is proposed. Furthermore, an accurate threshold is introduced to select important feature(s) and discard unimportant feature(s). Experiments on synthetic and real-world datasets demonstrate that the FRFCM-MKM is effective and efficient. Show more

Keywords: Fuzzy c-means, feature reduction learning, marginal kurtosis measure, mean-to-variance ratio

DOI: 10.3233/JIFS-200714

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7259-7279, 2020

Authors: He, Tongze | Guo, Caili | Chu, Yunfei | Yang, Yang | Wang, Yanjun

Article Type: Research Article

Abstract: Community Question Answering (CQA) websites has become an important channel for people to acquire knowledge. In CQA, one key issue is to recommend users with high expertise and willingness to answer the given questions, i.e., expert recommendation. However, a lot of existing methods consider the expert recommendation problem in a static context, ignoring that the real-world CQA websites are dynamic, with users’ interest and expertise changing over time. Although some methods that utilize time information have been proposed, their performance improvement can be limited due to fact that they fail they fail to consider the dynamic change of both user …interests and expertise. To solve these problems, we propose a deep learning based framework for expert recommendation to exploit user interest and expertise in a dynamic environment. For user interest, we leverage Long Short-Term Memory (LSTM) to model user’s short-term interest so as to capture the dynamic change of users’ interests. For user expertise, we design user expertise network, which leverages feedback on users’ historical behavior to estimate their expertise on new question. We propose two methods in user expertise network according to whether the dynamic property of expertise is considered. The experimental results on a large-scale dataset from a real-world CQA site demonstrate the superior performance of our method. Show more

Keywords: Expert recommendation, user modeling, neural network, community question answering

DOI: 10.3233/JIFS-200729

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7281-7292, 2020

Authors: Xu, Junxiang | Zhang, Jin | Guo, Jingni

Article Type: Research Article

Abstract: Taking into account the uncertainties of the factors of in-transit transportation cost, hub transshipment cost, hub construction cost, in-transit transportation time, hub transshipment time, and demand, this study uses triangular fuzzy numbers, expected value criteria, and distribution of credibility measure to minimise the total transportation cost of the hub-and-spoke road-rail combined transport (RRCT) network and the maximum transportation limit time between the origin and destination of the network. Firstly, a non-linear programming mathematical model is constructed for the regional hub-and-spoke RRCT network based on uncertain cost-time-demand. Then, an improved genetic algorithm is designed to obtain an optimized scheme. The algorithm …uses genetic algorithm to search the global space, and uses two local search methods, i.e. shift and exchange, to search the local space. Finally, the RRCT network along the Yaan-Linzhi section of the Sichuan-Tibet Railway is used as the research object to verify the applicability and effectiveness of the regional hub-and-spoke RRCT network model and the algorithm proposed in the study. Show more

Keywords: Road-rail combined transport, hub-and-spoke network, uncertain factor, improved genetic algorithm, Sichuan-Tibet Railway

DOI: 10.3233/JIFS-200748

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7293-7313, 2020

Authors: Wei, Lixin | Zhang, JinLu | Fan, Rui | Li, Xin | Sun, Hao

Article Type: Research Article

Abstract: In this article, an effective method, called an adaptive covariance strategy based on reference points (RPCMA-ES) is proposed for multi-objective optimization. In the proposed algorithm, search space is divided into independent sub-regions by calculating the angle between the objective vector and the reference vector. The reference vectors can be used not only to decompose the original multi-objective optimization problem into a number of single-objective subproblems, but also to elucidate user preferences to target a preferred subset of the whole Pareto front (PF). In this respect, any single objective optimizers can be easily used in this algorithm framework. Inspired by the …multi-objective estimation of distribution algorithms, covariance matrix adaptation evolution strategy (CMA-ES) is involved in RPCMA-ES. A state-of-the-art optimizer for single-objective continuous functions is the CMA-ES, which has proven to be able to strike a good balance between the exploration and the exploitation of search space. Furthermore, in order to avoid falling into local optimality and make the new mean closer to the optimal solution, chaos operator is added based on CMA-ES. By comparing it with four state-of-the-art multi-objective optimization algorithms, the simulation results show that the proposed algorithm is competitive and effective in terms of convergence and distribution. Show more

Keywords: Multi-objective optimization problem, Reference point, Covariance matrix adaptation evolutionary strategy, Chaos operator

DOI: 10.3233/JIFS-200749

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7315-7332, 2020

Authors: Zuo, Mingcheng | Dai, Guangming

Article Type: Research Article

Abstract: When optimizing complicated engineering design problems, the search spaces are usually extremely nonlinear, leading to the great difficulty of finding optima. To deal with this challenge, this paper introduces a parallel learning-selection-based global optimization framework (P-lsGOF), which can divide the global search space to numbers of sub-spaces along the variables learned from the principal component analysis. The core search algorithm, named memory-based adaptive differential evolution algorithm (MADE), is parallel implemented in all sub-spaces. MADE is an adaptive differential evolution algorithm with the selective memory supplement and shielding of successful control parameters. The efficiency of MADE on CEC2017 unconstrained problems and …CEC2011 real-world problems is illustrated by comparing with recently published state-of-the-art variants of success-history based adaptative differential evolution algorithm with linear population size reduction (L-SHADE) The performance of P-lsGOF on CEC2011 problems shows that the optimized results by individually conducting MADE can be further improved. Show more

Keywords: Parallel optimization framework, real-world problems, learning-based differential evolution

DOI: 10.3233/JIFS-200753

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7333-7361, 2020

Authors: Chen, Chen | Ma, Feng | Liu, Jialun | Negenborn, Rudy R. | Liu, Yuanchang | Yan, Xinping

Article Type: Research Article

Abstract: Human experience is regarded as an indispensable part of artificial intelligence in the process of controlling or decision making for autonomous cargo ships. In this paper, a novel Deep Q-Network-based (DQN) approach is proposed, which performs satisfactorily in controlling a cargo ship automatically without any human experience. At the very beginning, we use the model of KRISO Very Large Crude Carrier (KVLCC2) to describe a cargo ship. To manipulate this ship has to conquer great inertia and relatively insufficient driving force. Subsequently, customary waterways, regulations, conventions are described with Artificial Potential Field and value-functions in DQN. Based on this, the …artificial intelligence of planning and controlling a cargo ship can be obtained by undertaking sufficient training, which can control the ship directly, while avoiding collisions, keeping its position in the middle of the route as much as possible. In simulation experiments, it is demonstrated that such an approach performs better than manual works and other traditional methods in most conditions, which makes the proposed method a promising solution in improving the autonomy level of cargo ships. Show more

Keywords: Deep Q-network, reinforcement learning, artificial intelligence, autonomous ships

DOI: 10.3233/JIFS-200754

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7363-7379, 2020

Authors: Hashmi, Masooma Raza | Riaz, Muhammad | Smarandache, Florentin

Article Type: Research Article

Abstract: This manuscript contributes a progressive mathematical model for the analysis of novel coronavirus (COVID-19) and improvement of the victim from COVID-19 with some suitable circumstances. We investigate the innovative approach of the m-polar neutrosophic set (MPNS) to deal with the hesitations and obscurities of objects and rational thinking in decision-making obstacles. In this article, we propose the generalized weighted aggregation and generalized Einstein weighted aggregation operators in the context of m-polar neutrosophic numbers (MPNNs). The motivational aim of this paper is that we present a case study based on data amalgamation for the diagnosis of COVID-19 and examine with the …help of MPN-data. By using the proposed technique on generalized operators, we discuss the recovery of the victim with the time factor, proper medication, and some suitable circumstances. Ultimately, we present the advantages and productiveness of the proposed algorithm under the influence of parameter ð to the recovery results. The versatility and superiority of the proposed methodology with some existing approaches can be observed by the comparative analysis. Show more

Keywords: m-polar neutrosphic set (MPNS), m-polar neutrosophic generalized weighted aggregation (MPNGWA) operator, m-polar neutrosophic generalized Einstein weighted aggregation (MPNGEWA) operator, multi-criteria decision-making (MCDM) for medical diagnosis, Recovery of patient, comparative analysis

DOI: 10.3233/JIFS-200761

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7381-7401, 2020

Authors: Huang, Yangke | Wang, Zhiming

Article Type: Research Article

Abstract: Network pruning has been widely used to reduce the high computational cost of deep convolutional neural networks(CNNs). The dominant pruning methods, channel pruning, removes filters in layers based on their importance or sparsity training. But these methods often give limited acceleration ratio and encounter difficulties when pruning CNNs with skip connections. Block pruning methods take a sequence of consecutive layers (e.g., Conv-BN-ReLu) as a block and remove entire block each time. However, previous methods usually introduce new parameters to help pruning and lead additional parameters and extra computations. This work proposes a novel multi-granularity pruning approach that combines block pruning …with channel pruning (BPCP). The block pruning (BP) module remove blocks by directly searches the redundant blocks with gradient descent and leaves no extra parameters in final models, which is friendly to hardware optimization. The channel pruning (CP) module remove redundant channels based on importance criteria and handles CNNs with skip connections properly, which further improves the overall compression ratio. As a result, for CIFAR10, BPCP reduces the number of parameters and MACs of a ResNet56 model up to 78.9% and 80.3% respectively with <3% accuracy drop. In terms of speed, it gives a 3.17 acceleration ratio. Our code has been made available at https://github.com/Pokemon-Huang/BPCP . Show more

Keywords: Neural network compression, network pruning, residual networks

DOI: 10.3233/JIFS-200771

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7403-7410, 2020

Authors: Nataraj, Sathees Kumar | Paulraj, M. P. | Bin Abdullah, Ahmad Nazri | Bin Yaacob, Sazali

Article Type: Research Article

Abstract: In this paper, a speech-to-text translation model has been developed for Malaysian speakers based on 41 classes of Phonemes. A simple data acquisition algorithm has been used to develop a MATLAB graphical user interface (GUI) for recording the isolated word speech signals from 35 non-native Malaysian speakers. The collected database consists of 86 words with 41 classes of phoneme based on Affricatives, Diphthongs, Fricatives, Liquid, Nasals, Semivowels and Glides, Stop and Vowels. The speech samples are preprocessed to eliminate the undesirable artifacts and the fuzzy voice classifier has been employed to classify the samples into voiced sequence and unvoiced sequence. …The voiced sequences are divided into frame segments and for each frame, the Linear Predictive co-efficients features are obtained from the voiced sequence. Then the feature sets are formed by deriving the LPC features from all the extracted voiced sequences, and used for classification. The isolated words chosen based on the phonemes are associated with the extracted features to establish classification system input-output mapping. The data are then normalized and randomized to rearrange the values into definite range. The Multilayer Neural Network (MLNN) model has been developed with four combinations of input and hidden activation functions. The neural network models are trained with 60%, 70% and 80% of the total data samples. The neural network architecture was aimed at creating a robust model with 60%, 70%, and 80% of the feature set with 25 trials. The trained network model is validated by simulating the network with the remaining 40%, 30%, and 20% of the set. The reliability of trained network models were compared by measuring true-positive, false-negative, and network classification accuracy. The LPC features show better discrimination and the MLNN neural network models trained using the LPC spectral band features gives better recognition. Show more

Keywords: Fuzzy voice classifier, Malaysian English pronunciation, linear predictive coefficients (LPCC), neural network models (MLNN).

DOI: 10.3233/JIFS-200780

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7411-7429, 2020

Authors: Vijayabalaji, Srinivasan | Balaji, Parthasarathy

Article Type: Research Article

Abstract: In 1982, Pawlak set up a fresh approach to deal with uncertainties namely rough set theory, Multiple-Criteria Decision Making (MCDM) first traced by Benjamin Franklin in 17th century. Several researchers did significant contribution to MCDM thereafter. An assignment problem involves what happens to the effective function when each of a number of sources is associated with the same number of destinations. Using MCDM, Rough matrices and Assignment model we are inducing an idea to pick Best’11 in all three formats (Test, One Day Internationals (ODI), Twenty20 International matches (T20I)) in the game of cricket with players from two nationals. Using …the existing data, we are providing best batting position for any player to maximize team’s run. In addition, based on the preprocessing of informations, we are bringing some new indices to pick Indian squad for the 2019 World Cup cricket held in England from May 2019 to July 2019. After making a selection from our framework, we will compare the list of selected players by Board of Cricket Control Board in India (BCCI) and giveaway the percentage of similarity between the our selection against BCCI’s selection. We pick 11 players after selecting 15 players from 24 players to formulate the assignment model and offer the best batting order to optimize team’s run. Show more

Keywords: Rough set, rough matrix, information systems, MCDM, best’11, assignment problem

DOI: 10.3233/JIFS-200784

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7431-7447, 2020

Authors: George Fernandez, I. | Arokia Renjith, J.

Article Type: Research Article

Abstract: Cloud computing technology is playing a major role in the industry and real-life, for providing fast services such as data sharing and allocating the cloud resources that are paid and truly required. In this scenario, the cloud users are scheduled according to the rule-based systems for attempting to automate the matching between computing requirements and resources. Even though, the majority auto-scaling algorithms only helped as indicators for simple resource utilization and also not considered both cloud user needs and budget concerns. For this purpose, we propose a new model which is the combination of auto-scaling algorithms, resource allocation and scheduling …for allocating the appropriate resources and scheduled them. This model consists of three new algorithms namely Grey Wolf Optimization and Fuzzy rules based Resource allocation and Scheduling Algorithm (GWOFRSA), Auto-Scaling Algorithm for Cloud based Web Application (ASACWA) and Auto-Scaling Algorithm for handling Distributed Computing Tasks (ASADCT). Here, we introduce new auto-scaling algorithms for enhancing the performance of cloud services. In this work, the optimization technique is used to predict the cloud server workload, resource requirements and it also uses fuzzy rules for monitoring the resource utilization and the size of virtual machine allocation process. According to the workload prediction, the completion time is estimated for each cloud server. The experiments are conducted by using a simulator called CloudSim environment of Java programming and compared with the existing works available in this direction in terms of resource utilization and enhance the cloud performance with better Quality of Service of Virtual Machine allocation, Missed Deadline, Demand Satisfaction, Power Utilization, CPU Load and throughput. Show more

Keywords: Grey Wolf Optimization, resource allocation, scheduling, auto-scaling, virtual machine, cloud computing and performance

DOI: 10.3233/JIFS-200787

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7449-7467, 2020

Authors: Liu, Peide | Akram, Muhammad | Sattar, Aqsa

Article Type: Research Article

Abstract: The complex q-rung orthopair fuzzy set (Cq-ROFS), an efficient generalization of complex intuitionistic fuzzy set (CIFS) and complex Pythagorean fuzzy set (CPFS), is potent tool to handle the two-dimensional information and has larger ability to translate the more uncertainty of human judgment then CPFS as it relaxes the constrains of CPFS and thus the space of allowable orthopair increases. To solve the multi-criteria decision making (MCDM) problem by considering that criteria are at the same priority level may affect the results because in realistic situations the priority level of criteria is different. In this manuscript, we propose some useful prioritized …AOs under Cq-ROF environment by considering the prioritization among attributes. We develop two prioritized AOs, namely complex q-rung orthropair fuzzy prioritized weighted averaging (C-qROFPWA) operator and complex q-rung orthropair fuzzy prioritized weighted geometric (Cq-ROFPWG) operator. We also consider their desirable properties and two special cases with their detailed proofs. Moreover, we investigate a new technique to solve the MCDM problem by initiating an algorithm along with flowchart on the bases of proposed operators. Further, we solve a practical example to reveal the importance of proposed AOs. Finally, we apply the existing operators on the same data to compare our computed result to check the superiority and validity of our proposed operators. Show more

Keywords: Complex q-rung orthopair fuzzy set, prioritized weighted averaging operator, prioritized weighted geometric operator, decision making

DOI: 10.3233/JIFS-200789

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7469-7493, 2020

Authors: Xia, Daoxun | Guo, Fang | Liu, Haojie | Yu, Sheng

Article Type: Research Article

Abstract: The recent successful methods of person re-identification (person Re-ID) involving deep learning have mostly adopted supervised learning algorithms, which require large amounts of manually labelled data to achieve good performance. However, there are two important unresolved problems, dataset annotation is an expensive and time-consuming process, and the performance of recognition model is seriously affected by visual change. In this paper, we primarily study an unsupervised method for learning visual invariant features using networks with temporal coherence for person Re-ID; this method exploits unlabelled data to learn expressions from video. In addition, we propose an unsupervised learning integration framework for pedestrian …detection and person Re-ID for practical applications in natural scenarios. In order to prove the performance of the unsupervised person re-identification algorithm based on visual invariance features, the experimental results were verified on the iLIDS-VID, PRID2011 and MARS datasets, and a better performance of 57.5% (R-1) and 73.9% (R-5) was achieved on the iLIDS-VID and MARS datasets, respectively. The efficiency of the algorithm was validated by using BING + R-CNN as the pedestrian detector, and the person Re-ID system achieved a computation speed of 0.09s per frame on the PRW dataset. Show more

Keywords: Person re-identification, unsupervised learning, pedestrian detection, object recognition, visual invariant features

DOI: 10.3233/JIFS-200793

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7495-7503, 2020

Authors: Li, Meng | Zhao, Yifei | Xiong, Xinglong | Ma, Yuzhao

Article Type: Research Article

Abstract: Synchronous delivery with different vehicles, as an emerging concept of the delivery network, improves the efficiency of the modern logistics system significantly, which gradually gives birth to a new issue: the traveling salesman problem with drone (TSP-D). In this paper, we propose a one-truck-multiple-drone (OTMD) model on the base of the TSP-D. Compared with the traditional one-truck-one-drone (OTOD) and multiple drones models, our scheme introduces a united objective function into the optimization calculation. In terms of the proposed multiple levels iterative theory, we can compute the optimal synchronous delivery network that takes both the total delivery time and the number …of drones into consideration. Four types of customer distributions are employed to investigate the OTMD model and its associated calculation approaches. Comparing the parameters of the optimal network in different delivery models, we study the relationship among the total delivery time, customer distribution and the number of serving drones. These simulation results verify the feasibility and practicality of the OTMD, and demonstrate the features of optimization calculation with different customer distributions, being beneficial to improve the efficiency of the model logistics system. Show more

Keywords: Traveling salesman problem with drone (TSP-D), one-truck-multiple-drone (OTMD) model, optimization calculation, modern logistics system

DOI: 10.3233/JIFS-200818

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7505-7519, 2020

Authors: Senthilkumar, G. | Chitra, M.P.

Article Type: Research Article

Abstract: In the recent years increase in computer and mobile user’s, data storage has become a priority in all fields. Large- and Small-Scale businesses today thrive on their data and they spent a huge amount of money to maintain this data. Cloud Storage provides on– demand availability of IT services via Large Distributed Data Centers over High Speed Networks. Network Virtualization is been considered as a recent proliferation in cloud computing which emerges as a Multifaceted method towards future internet by facilitating shared resources. Provisioning of the Virtual Network is considered to be a major challenge in terms of creating NP …hard problems, minimization of workflow processing time under control resource etc. In order to cope up with the challenges our work has proposed an Ensemble Dynamic Optimization based on Inverse Adaptive Heuristic Critic (IAHC) for overcoming the virtual network provisioning in cloud computing. Our approach gets observed from Expert Observation and provides an approximate solution when various workflows arrives online at various Window Time (WT). It also provides an Optimal Policy for predicting the effect of Resource Allocation of one task for Present as well as Future time Windows. In order to the above approaches it also avoids the high sample complexity and maintains the cost while scaling up to provide Resource Provision. Therefore, our work achieves an adequate policy towards Resource Allocation, reduces the Cost as well as Energy Consumption and deals with real time uncertainties to avoid the Virtual Network provisioning. Show more

Keywords: Inverse adaptive heuristic critic, dynamic optimization, reward feature, network virtualization, user resource allocation

DOI: 10.3233/JIFS-200823

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7521-7535, 2020

Authors: Mahmood, Asma | Abbas, Mujahid

Article Type: Research Article

Abstract: The aim of this paper is to construct a matrix of interpersonal influences employing TOPSIS and then to apply the matrix in influence model and doubly extended TOPSIS. Entries of that matrix are obtained from coefficients of relative closeness. Such a systematically constructed matrix performs better than the direct influence matrix because of the consideration of alternatives under certain criteria/attributes. Implementation of such influence matrix improves an influence model and group decision process. In this paper, TOPSIS is used for individual as well as group decisions. Once the decisions are reached by individuals with the help of TOPSIS, then coefficients …of relative closeness are obtained and matrix of interpersonal influences is constructed. This matrix is used in influence model and to construct the influenced decision matrices. These influenced decision matrices are aggregated to get the collective decision. This strategy is based on the fact that the decisions taken by individuals affect their collective decision in future. Show more

Keywords: Group decision making, social influence networks, multi criteria decision making, TOPSIS

DOI: 10.3233/JIFS-200833

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7537-7546, 2020

Authors: Jin, Chen | Xu, Zeshui | Wang, Jinwei

Article Type: Research Article

Abstract: With the rapid development of economy and industrialization, environmental problems, especially haze pollution, are being more and more serious. When assessing the economic losses caused by haze, although the traditional quantitative method can show the amount of economic losses visually, there are also some inaccuracies in the calculation process. Based on the situation, we propose a new method called uncertain probabilistic linguistic analytic hierarchy process (UPL-AHP), which combines traditional analytic hierarchy process with uncertain probabilistic linguistic term sets to process decision information in complex problems. Firstly, we propose the concept of uncertain probabilistic linguistic comparison matrix. Then, a new approach …is given to check and improve the consistency of an uncertain probabilistic linguistic comparison matrix. After that, we introduce the application of UPL-AHP in group decision making. Finally, the proposed method is used to analyze a practical case concerning the economic losses of haze. Some relevant policy recommendations are given based on the results. Show more

Keywords: Haze pollution, economic losses, probabilistic linguistic term set, comparison matrix, analytic hierarchy process, uncertainty

DOI: 10.3233/JIFS-200834

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7547-7569, 2020

Authors: Peng, Xindong | Smarandache, Florentin

Article Type: Research Article

Abstract: The rare earth industry is a crucial strategic industry that is related to the national economy and national security. In the context of economic globalization, international competition is becoming increasingly fierce, and the rare earth industry is facing a more severe survival and development environment than ever before. Although China is the greatest world’s rare earth country in rare earth reserves, production, consumption and export volume, it is not a rare earth power. The rare earth industry has no right to speak in the international market. The comparative advantage is weakening and the security of rare earth industry appears. Therefore, …studying the rare earth industry security has important theoretical and practical significance. When measuring the China’s rare earth industry security, the primary problem involves tremendous uncertainty. Neutrosophic soft set (NSS), depicted by the parameterized form of truth membership, falsity membership and indeterminacy membership, is a more serviceable pattern for capturing uncertainty. In this paper, five dimensions of rare earth industry security are identified and then prioritized against twelve different criteria relevant to structure, organization, layout, policy and ecological aspects of industry security. Then, the objective weight is computed by CRITIC (Criteria Importance Through Inter-criteria Correlation) method while the integrated weight is determined by concurrently revealing subjective weight and objective weight. Later, neutrosophic soft decision making method based CoCoSo (Combined Compromise Solution) is explored for settling the issue of low discrimination. Lastly, the feasibility and validity of the developed algorithm is verified by the issue of China’s rare earth industry security evaluation. Show more

Keywords: Rare earth industry security, neutrosophic soft set, CoCoSo, CRITIC

DOI: 10.3233/JIFS-200847

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7571-7585, 2020

Authors: Zhang, Li | Cheng, Shufeng | Liu, Peide

Article Type: Research Article

Abstract: Probability multi-valued neutrosophic sets (PMVNSs) can better describe the incomplete and indeterminate evaluation information, and the ELECTRE method can rank the alternatives in the light of the outranking relations among criteria. To combine their advantages, this paper introduces an extended ELECTRE method to address multi-criteria group decision-making (MCGDM) problems with the information of PMVNSs. Firstly, we introduce the definitions of PMVNSs and the classical ELECTRE method, discuss the ELECTRE-based outranking relations for PMVNSs and analyze some properties of them. Furthermore, the probability multi-valued neutrosophic ELECTRE method is developed to address MCGDM problems based on the proposed distance measure and outranking …relations for PMVNSs. Finally, a typical example for logistics outsourcing provider selection is devoted to demonstrate the feasibility of the proposed approach. Moreover, the same example-based comparisons with other existing methods are carried out, the results show our proposed approach outperforms the existing methods in solving the MCGDM problems with PMVNSs. Show more

Keywords: ELECTRE, outranking relations, probability multi-valued neutrosophic sets, MCGDM

DOI: 10.3233/JIFS-200861

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7587-7604, 2020

Authors: Elavarasan, Dhivya | Vincent, Durai Raj

Article Type: Research Article

Abstract: The development in science and technical intelligence has incited to represent an extensive amount ofdata from various fields of agriculture. Therefore an objective rises up for the examination of the available data and integrating with processes like crop enhancement, yield prediction, examination of plant infections etc. Machine learning has up surged with tremendous processing techniques to perceive new contingencies in the multi-disciplinary agrarian advancements. In this pa- per a novel hybrid regression algorithm, reinforced extreme gradient boosting is proposed which displays essentially improved execution over traditional machine learning algorithms like artificial neural networks, deep Q-Network, gradient boosting, ran- dom forest …and decision tree. Extreme gradient boosting constructs new models, which are essentially, decision trees learning from the mistakes of their predecessors by optimizing the gradient descent loss function. The proposed hybrid model performs reinforcement learning at every node during the node splitting process of the decision tree construction. This leads to effective utilizationofthesamplesbyselectingtheappropriatesplitattributeforenhancedperformance. Model’sperformanceisevaluated by means of Mean Square Error, Root Mean Square Error, Mean Absolute Error, and Coefficient of Determination. To assure a fair assessment of the results, the model assessment is performed on both training and test dataset. The regression diagnostic plots from residuals and the results obtained evidently delineates the fact that proposed hybrid approach performs better with reduced error measure and improved accuracy of 94.15% over the other machine learning algorithms. Also the performance of probability density function for the proposed model delineates that, it can preserve the actual distributional characteristics of the original crop yield data more approximately when compared to the other experimented machine learning models. Show more

Keywords: Crop yield prediction, reinforcement learning, extreme gradient boosting, intelligent agrarian application

DOI: 10.3233/JIFS-200862

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7605-7620, 2020

Authors: Zhao, Tao | Li, Haodong | Dian, Songyi

Article Type: Research Article

Abstract: In this paper, we propose a method to assess the collision risk and a strategy to avoid the collision for solving the problem of dynamic real-time collision avoidance between robots when a multi-robot system is applied to perform a given task collaboratively and cooperatively. The collision risk assessment method is based on the moving direction and position of robots, and the collision avoidance strategy is based on the artificial potential field (APF) and the fuzzy inference system (FIS). The traditional artificial potential field (TAPF) has the problem of the local minimum, which will be optimized by improving the repulsive field …function. To adjust the speed of the robot adaptively and improve the security performance of the system, the FIS is used to plan the speed of robots. The hybridization of the improved artificial potential field (IAPF) and the FIS will make each robot safely and quickly find a collision-free path from the starting position to the target position in a completely unknown environment. The simulation results show that the strategy is effective and useful for collision avoidance in multi-robot systems. Show more

Keywords: Multi-robot, collision avoidance, path planning, improved artificial potential field, fuzzy inference system

DOI: 10.3233/JIFS-200869

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7621-7637, 2020

Authors: Wang, Hongyan | Huang, Zhi | Lu, Jinbo

Article Type: Research Article

Abstract: In this paper, by replacing the integral mass flow equation to fractional-order mass flow equation, the fractional-order mathematical model of 2DOF pneumatic-hydraulic upper limb rehabilitation training system is established. A new 2DOF fractional-order fuzzy PID (FOFPID) controller is designed, to provides a new reference for improving the control accuracy of the pneumatic system. In the design of the controller, the weight parameters of the input terms are transformed into the weight parameters of the error, and the input, which are analyzed to improve the accuracy of the controller design. The parameters of the control system are determined by multi-objective particle …swarm optimization. To prove the effectiveness of the proposed control method, the experimental research was carried out by building the experimental platform of pneumatic-hydraulic upper limb rehabilitation training system. The results show that the 2DOF FOFPID controller has better performance than other designed controllers under different working conditions. Show more

Keywords: Pneumatic-hydraulic drive, rehabilitation training system, fractional-order modeling, fractional-order fuzzy PID control

DOI: 10.3233/JIFS-200891

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7639-7651, 2020

Authors: Kumar, Ranjan | Edalatpanah, SA | Mohapatra, Hitesh

Article Type: Research Article

Abstract: There are different conditions where SPP play a vital role. However, there are various conditions, where we have to face with uncertain parameters such as variation of cost, time and so on. So to remove this uncertainty, Yang et al. [1 ] “[Journal of Intelligent & Fuzzy Systems, 32(1), 197-205”] have proposed the fuzzy reliable shortest path problem under mixed fuzzy environment and claimed that it is better to use their proposed method as compared to the existing method i.e., “[Hassanzadeh et al.; A genetic algorithm for solving fuzzy shortest path problems with mixed fuzzy arc lengths, Mathematical and Computer Modeling, …57(2013) 84-99”  [2 ]]. The aim of this note is, to highlight the shortcoming that is carried out in Yang et al. [1 ] article. They have used some mathematical incorrect assumptions under the mixed fuzzy domain, which is not true in a fuzzy environment. Show more

Keywords: normal fuzzy number, Shortest path problem (SPP), fuzzy shortest path problem (FSPP), mixed fuzzy environment

DOI: 10.3233/JIFS-200923

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7653-7656, 2020

Authors: Zhou, Linyong | You, Shanping | Ren, Bimo | Yu, Xuhong | Xie, Xiaoyao

Article Type: Research Article

Abstract: Pulsars are highly magnetized, rotating neutron stars with small volume and high density. The discovery of pulsars is of great significance in the fields of physics and astronomy. With the development of artificial intelligent, image recognition models based on deep learning are increasingly utilized for pulsar candidate identification. However, pulsar candidate datasets are characterized by unbalance and lack of positive samples, which has contributed the traditional methods to fall into poor performance and model bias. To this end, a general image recognition model based on adversarial training is proposed. A generator, a classifier, and two discriminators are included in the …model. Theoretical analysis demonstrates that the model has a unique optimal solution, and the classifier happens to be the inference network of the generator. Therefore, the samples produced by the generator significantly augment the diversity of training data. When the model reaches equilibrium, it can not only predict labels for unseen data, but also generate controllable samples. In experiments, we split part of data from MNIST for training. The results reveal that the model not only behaves better classification performance than CNN, but also has better controllability than CGAN and ACGAN. Then, the model is applied to pulsar candidate dataset HTRU and FAST. The results exhibit that, compared with CNN model, the F-score has increased by 1.99% and 3.67%, and the Recall has also increased by 6.28% and 8.59% respectively. Show more

Keywords: Generative adversarial nets, convolutional neural network, unbalanced dataset, pulsar candidate identification

DOI: 10.3233/JIFS-200925

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7657-7669, 2020

Authors: Liu, Xuning | Zhang, Guoying | Zhang, Zixian

Article Type: Research Article

Abstract: The feature selection of influencing factors of coal and gas outbursts is of great significance for presenting the most discriminative features and improving prediction performance of a classifier, the paper presents an effective hybrid feature selection and modified outbursts classifier framework which aims at solving exiting coal and gas outbursts prediction problems. First, a measurement standard based on maximum information coefficient(MIC) is employed to identify the wide correlations between two variables; Second, based on a ranking procedure using non-dominated sorting genetic algorithm(NSGAII), maximum relevance minimum redundancy(MRMR) algorithm is subsequently performed to find out candidate feature set highly related to the …class label and uncorrelated with each other; Third, random forest(RF) is employed to search the optimal feature subset from the candidate feature set, then the optimal feature subset that influences the classification performance of coal and gas outbursts is obtained; Finally, an improved classifier model has been proposed that combines gradient boosting decision tree(GBDT) and k-nearest neighbor(KNN) for outbursts prediction. In the modified classifier model, the GBDT is utilized to assign different weights to features, then the weighted features are input into the KNN to verify the effectiveness of proposed method on coal and gas outbursts dataset. The experimental results conclude that our proposed scheme is effective in the number of feature and prediction accuracy when compared with other related state-of-the-art prediction models based on feature selection for coal and gas outbursts. Show more

Keywords: Coal and gas outbursts, Maximum information coefficient, Non-dominated sorting genetic algorithm, Maximum relevance minimum redundancy, Random forest, Gradient boosting decision tree, K-nearest neighbor

DOI: 10.3233/JIFS-200937

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7671-7691, 2020

Authors: Guo, Jingni | Xu, Junxiang | Liao, Wei

Article Type: Research Article

Abstract: The multimodal transport network in the region with complex environment and being easily affected by disturbance factors is used as the research object in our work. The characteristics of the cascading failure of such multimodal transport network were analyzed. From the perspective of network load redistribution, the risk control methods for the cascading failure of the multimodal transport network were investigated. This research aims to solve the problem that traditional load redistribution methods usually ignore the original-destination (OD) constraint and uncertain risks. The conditional value-at-risk (CVaR) was improved based on the Bureau of Public Roads (BPR) road impedance function to …quantify the uncertainty of the disturbance factors. A nonlinear programming model was established with the generalized travel time as the objective function. A parallelly-running cellular ant colony algorithm was designed to solve the model. Empirical analysis was conducted on the multimodal transport network in Sichuan-Tibet region of China. The results of the empirical analysis verified the applicability of the proposed load redistribution method to such kind of regions and the effectiveness of the algorithm. This research provides theoretical basis and practical reference for the risk control of the cascading failure of multimodal transport networks in some regions. Show more

Keywords: Uncertain disturbance, multimodal transport network, risk control, load redistribution, cellular ant colony algorithm

DOI: 10.3233/JIFS-200968

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7693-7704, 2020

Authors: Kachouei, Mohammad | Ebrahimnejad, Ali | Bagherzadeh-Valami, Hadi

Article Type: Research Article

Abstract: Data Envelopment Analysis (DEA) is a non-parametric approach based on linear programming for evaluating the performance of decision making units (DMUs) with multiple inputs and multiple outputs. The lack of the ability to generate the actual weights, not considering the impact of undesirable outputs in the evaluation process and the measuring of efficiencies of DMUs based upon precise observations are three main drawbacks of the conventional DEA models. This paper proposes a novel approach for finding the common set of weights (CSW) to compute efficiencies in DEA model with undesirable outputs when the data are represented by fuzzy numbers. The …proposed approach is based on fuzzy arithmetic which formulates the fuzzy additive DEA model as a linear programing problem and gives fuzzy efficiencies of all DMUs based on resulting CSW. We demonstrate the applicability of the proposed model with a simple numerical example. Finally, in the context of performance management, an application of banking industry in Iran is presented for analyzing the influence of fuzzy data and depicting the impact of undesirable outputs over the efficiency results. Show more

Keywords: Data envelopment analysis, undesirable outputs, fuzzy numbers, common set of weights

DOI: 10.3233/JIFS-201022

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7705-7722, 2020

Authors: Ali, Mohamed R. | Hadhoud, Adel R. | Ma, Wen-Xiu

Article Type: Research Article

Abstract:  In this approximation study, a nonlinear singular periodic model in nuclear physics is solved by using the Hermite wavelets (HW) technique coupled with a numerical iteration technique such as the Newton Raphson (NR) one for solving the resulting nonlinear system. The stimulation of offering this numerical work comes from the aim of introducing a consistent framework that has as effective structures as Hermite wavelets. Two numerical examples of the singular periodic model in nuclear physics have been investigated to observe the robustness, proficiency, and stability of the designed scheme. The proposed outcomes of the HW technique are compared with available …numerical solutions that established fitness of the designed procedure through performance evaluated on a multiple execution. Show more

Keywords: Singular periodic systems in nuclear physics, Hermite wavelets, hybrid approach, Gaussian formula of integration, collocation technique

DOI: 10.3233/JIFS-201045

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7723-7731, 2020

Authors: Cao, Jing | Xu, Xuan-hua | Dai, Fei | Pan, Bin

Article Type: Research Article

Abstract: This study uses opinion dynamics to explore the influence of extremists in the consensus process of large group decision-making. When moderates are exposed to extremists, their risk preference will be affected. By using the opinion leader theory for reference, the influence model of extremists is constructed. To better study the influence of extremists, the similarity of risk preference between extremists and moderates is modeled to measure their similarity degree. From this model, for every moderate, the extremists are divided into two groups: homogeneous group and heterogeneous group. Finally, the risk preference evolution model is structured by considering that moderates change …their risk preference dynamically according to their initial preference, their attitude towards the homogeneous groups, and the heterogeneous groups. Finding from data analysis shows that moderates with high acceptance toward the influence of extremists are more likely to reach group consensus. It is also found that the preference trend of moderates with a certain degree of acceptance toward heterogeneous groups fluctuates with a ‘W’ shape. This study bridges the gap between opinion dynamics and group decision making. Meanwhile, the model inspires new explanations and new perspectives for the group consensus process. Show more

Keywords: Extremists, opinion dynamics, group emergency decision-making, group consensus, risk preference evolution

DOI: 10.3233/JIFS-201106

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7733-7746, 2020