Journal of Intelligent & Fuzzy Systems - Volume 41, issue 2

Authors: Farwa, Shabieh | Kamran, Muhammad | Sarwar, Sundas | Kazmi, Maedah | Ahmad, Hijaz | Gepreel, Khaled A.

Article Type: Research Article

Abstract:  In this work, bipolar fuzzy parameterized sets in conjunction with soft sets are studied. This research focuses on the application of bipolar fuzzy parameterized soft sets (BFPSS ), arising from association of bipolar fuzzy parameterized sets with soft sets. Some useful operations and fundamental properties of BFPSS are presented. Mainly, we aim to design a novel and comparatively labour-saving algorithm to see the efficacy of BFPSS . We discuss an application of our algorithm in pharmaceutical decision making problem based on effectiveness and harmfulness of certain drugs. However, the algorithm is equally applicable in other decision making environments as …well where BFPSS arise. By preserving the structural implication of BFPSS , we compare our technique with a most recent algorithm to prove the significance of our method. Show more

Keywords: Fuzzy sets, bipolar fuzzy sets, soft sets, bipolar fuzzy parameterized soft sets, decision making

DOI: 10.3233/JIFS-202685

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2813-2821, 2021

Authors: Zhao, Yifan | Tian, Shuicheng

Article Type: Research Article

Abstract: In order to overcome the problems of low recognition rate and long recognition time existing in traditional methods, a method for identifying hidden disaster factors in coal mines based on Naive Bayes algorithm was proposed. The posterior probability of Bayesian network is calculated to obtain the maximum value of the posterior probability, so as to judge the categories of hidden disaster factors in coal mines. The method of combining soft and hard threshold functions is used to denoise Naive Bayes network. Combined with the structural equation of coal mine concealed disaster-causing factors, the index weight of coal mine disaster-causing factors …is calculated, and a fast identification model of disaster-causing factors is built to complete the identification. Experimental results show that the quality factors of the proposed method are all higher than 8, the recognition rate is as high as 98%, and the recognition time is basically controlled within 0.8 s. Show more

Keywords: Naive bayes algorithm, coal mine, hidden disaster factors, identification

DOI: 10.3233/JIFS-202726

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2823-2831, 2021

Authors: Fan, Jianping | Wang, Shuting | Wu, Meiqin

Article Type: Research Article

Abstract: Failure modes and effects analysis (FMEA) is a useful reliability analysis technique to identify potential failure modes in a wide range of industries. However, the conventional FMEA method is deficient in dealing with the risk evaluation and prioritization method. To overcome the shortcomings, this paper presents a new risk priority model using Best-Worst Method based on D numbers (D-BWM) and the Measurement of Alternatives and Ranking according to COmpromise Solution based on D numbers (D-MARCOS). First, D numbers are used to deal with the uncertainty of FMEA team members’ subjective judgment. Second, the distance-based method is proposed to determine the …objective weight of each team member. Then, the D-BWM was used to determine the weight of risk factors. The combination rule of D number theory combined the evaluation information of multiple members into group opinions. Finally, D-MARCOS method is proposed to obtain the risk priority of the failure modes. An example and the results of comparative analysis show the method is effective. Show more

Keywords: Failure modes and effects analysis, D numbers, BWM, MARCOS

DOI: 10.3233/JIFS-202765

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2833-2846, 2021

Authors: Luo, Damei | Li, Zhaowen | Qu, Liangdong

Article Type: Research Article

Abstract: An information system (IS) is an important mathematical tool for artificial intelligence. A fuzzy probabilistic information system (FPIS), the combination of some fuzzy relations in the same universe which satisfies the probability distribution, can be seen as an IS with fuzzy relations. A FPIS overcomes the shortcoming that rough set theory assumes elements in the universe with equal probability and leads to lose some useful information. This paper integrates the probability distribution into the fuzzy relations in a FPIS and studies its reduction. Firstly, the concept of a FPIS is introduced and its reduction is proposed. Then, the fuzzy relations …in a FPIS are divided into three categories (P -necessary, P -relatively necessary and P -unnecessary fuzzy relations) according to their importance. Next, entropy measurement for a FPIS is investigated. Moreover, some reduction algorithms are constructed. Finally, an example is given to verify the effectiveness of these proposed algorithms. Show more

Keywords: Fuzzy relation, FPIS, reduction, core, uncertainty, entropy, algorithm

DOI: 10.3233/JIFS-202783

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2847-2863, 2021

Authors: Zhang, Nan | Sheng, Yuhong | Zhang, Jing | Wang, Xiaoli

Article Type: Research Article

Abstract: In uncertainty theory, parameter estimation of uncertain differential equation is a very important research direction. The parameter estimation of multifactor uncertain differential equation needs to be solved. Multifactor uncertain differential equation is a differential equation driven by multiple Liu processes. The paper introduces two methods to solve the unknown parameters of the multifactor uncertain differential equation, they are the method of moment estimation and the method of least squares estimation. Several numerical examples are used to illustrate the proposed parameter estimation methods.

Keywords: Uncertainty theory, multifactor uncertain differential equation, parameter estimation

DOI: 10.3233/JIFS-202891

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2865-2878, 2021

Authors: Moradi Zirkohi, Majid | Lin, Tsung-Chih

Article Type: Research Article

Abstract: Interval type-2 fuzzy logic systems (IT2FLSs) have better abilities to cope with uncertainties in many applications. One major drawback of IT2FLSs is the high computational cost of the iterative Karnik-Mendel (KM) algorithms in type-reduction (TR). From the practical point of view, this prevents using IT2FLS in real-world applications. To address this issue, a novel non-iterative method called Moradi-Zirkohi-Lin (MZL) TR method is proposed for computing the centroid of an IT2FLS. This makes the practical implementation of the IT2FLSs simpler. Comparative simulation results show that the proposed method outperforms the KM TR method in terms of computational burden. Besides, closer results, …in terms of accuracy, to the KM TR method among the existing non-iterative TR methods are also achieved by the proposed TR method. Show more

Keywords: Karnik-Mendel (KM) algorithms, type-reduction, non-iterative, the centroid of an interval type-2 Fuzzy Set

DOI: 10.3233/JIFS-202913

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2879-2889, 2021

Authors: Venkataramanan, C. | Ramalingam, S. | Manikandan, A.

Article Type: Research Article

Abstract: Smart farming is one of the immense applications of Wireless Sensor Networks (WSN). Still, most of the researches have been focusing on precision agriculture using WSNs. In general, the nodes within the wireless sensor systems are self-configured. Based on the application requirement, gadgets within the region of interest collect data, prepare it, and send it to the recipient. The biggest impediments to these sensor systems are collision, restricted battery, and transmission capacity. Due to these characteristics, the node battery depletes earlier, when it starts working. Currently, agriculture depends on rain due to the lack of water resources and irrigation services. …The crop development depends totally on the factors of water, the climatic conditions of the soil, etc. In large-scale agriculture, it is exceptionally problematic to analyze all the parameters accurately throughout the growing field. In this article, high-precision architecture for large-scale agriculture has been proposed. An IoT (Internet of Things) enabled WSN has been built and installed in the respective areas to measure the physical quantities regularly. In addition, Lévy-Walk Bat (LWBA) algorithm has been proposed to optimize the collected data. The prediction accuracy of the collected data is evaluated by LWBA and then, it is compared with the existing optimization algorithms with different error solvers. It has provided the exact information regarding the whole landscape and it will help the farmers to irrigate precisely. Show more

Keywords: Data prediction, error minimization, IoT, regression, machine learning, optimization, smart agriculture, SVM, WSNs

DOI: 10.3233/JIFS-202953

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2891-2904, 2021

Authors: Cen, Shixin | Yu, Yang | Yan, Gang | Yu, Ming | Kong, Yanlei

Article Type: Research Article

Abstract: As a spontaneous facial expression, micro-expression reveals the psychological responses of human beings. However, micro-expression recognition (MER) is highly susceptible to noise interference due to the short existing time and low-intensity of facial actions. Research on facial action coding systems explores the correlation between emotional states and facial actions, which provides more discriminative features. Therefore, based on the exploration of correlation information, the goal of our work is to propose a spatiotemporal network that is robust to low-intensity muscle movements for the MER task. Firstly, a multi-scale weighted module is proposed to encode the spatial global context, which is obtained …by merging features of different resolutions preserved from the backbone network. Secondly, we propose a multi-task-based facial action learning module using the constraints of the correlation between muscle movement and micro-expressions to encode local action features. Besides, a clustering constraint term is introduced to restrict the feature distribution of similar actions to improve categories’ separability in feature space. Finally, the global context and local action features are stacked as high-quality spatial descriptions to predict micro-expressions by passing through the Convolutional Long Short-Term Memory (ConvLSTM) network. The proposed method is proved to outperform other mainstream methods through comparative experiments on the SMIC, CASME-I, and CASME-II datasets. Show more

Keywords: Micro-expression recognition, multi-scale weighted module, facial action learning module, spatiotemporal network

DOI: 10.3233/JIFS-202962

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2905-2921, 2021

Authors: Liu, Liming | Li, Ping | Chu, Maoxiang | Gao, Chuang

Article Type: Research Article

Abstract: Basic oxygen furnace (BOF) steelmaking plays an important role in steelmaking process. Hence, it is necessary to study BOF steelmaking modeling. In this paper, a novel regression algorithm is proposed by using nonparallel support vector regression with weight information (WNPSVR) for the end-point prediction of BOF steelmaking. The weight information is excavated by K -nearest neighbors (KNNs) algorithm. Since the whale optimization algorithm (WOA) has the characteristics of fast convergence speed and a few adjustment parameters, WOA is applied to optimize the parameters in the objective function of WNPSVR. Compared with traditional prediction models, WNPSVR-WOA is not easy to fall …into local minimum values and is insensitive to noise. Thus, the prediction and control of molten steel end-point information are more accurate. Experimental results verify the effectiveness and feasibility of the proposed model. Within different error bounds (0.005 wt.% for carbon content model and 10°C for temperature model), the hit rates of carbon content and temperature are 89% and 95%, respectively. Meanwhile, a double hit rate of 85% is achieved. The above results conclude that our WNPSVR-WOA has important reference value for actual BOF application and can improve the steel product quality. Moreover, WNPSVR-WOA can also be used to other fields. Show more

Keywords: Basic oxygen furnace, end-point information prediction, nonparallel support vector regression, weight information, whale optimization algorithm

DOI: 10.3233/JIFS-210007

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2923-2937, 2021

Authors: Jyothi, R.L. | Rahiman, M. Abdul

Article Type: Research Article

Abstract: Binarization is the most important stage in historical document image processing. Efficient working of character and word recognition algorithms depend on effective segmentation methods. Segmentation algorithms in turn depend on images free of noises and degradations. Most of these historical documents are illegible with degradations like bleeding through degradation, faded ink or faint characters, uneven illumination, contrast variation, etc. For effective processing of these document images, efficient binarization algorithms should be devised. Here a simple modified version of the Convolutional Neural Network (CNN) is proposed for historical document binarization. AOD-Net architecture for generating dehazed images from hazed images is modified …to create the proposed network.The new CNN model is created by incorporating Difference of Concatenation layer (DOC), Enhancement layer (EN) and Thresholding layer into AOD-Net to make it suitable for binarization of highly degraded document images. The DOC layer and EN layer work effectively in solving degradation that exists in the form of low pass noises. The complexity of working of the proposed model is reduced by decreasing the number of layers and by introducing filters in convolution layers that work with low inter-pixel dependency. This modified version of CNN works effectively with a variety of highly degraded documents when tested with the benchmark historical datasets. The main highlight of the proposed network is that it works efficiently in a generalized manner for any type of document images without further parameter tuning. Another important highlight of this method is that it can handle most of the degradation categories present in document images. In this work, the performance of the proposed model is compared with Otsu, Sauvola, and three recent Deep Learning-based models. Show more

Keywords: Binarization, historical document images, degradation, difference of concatenated convolutions, enhancement layer

DOI: 10.3233/JIFS-210015

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2939-2952, 2021

Authors: Zhang, Lingyun | Zhang, Pingjian

Article Type: Research Article

Abstract: Computational aesthetics, which uses computers to learn human aesthetic habits and ultimately replace humans in scoring images, has become a hot topic in recent years due to its wide application. Most of the initial research is to manually extract features and use classifiers such as support vector machines to score images. With the development of deep learning, traditional manual feature extraction methods are gradually replaced by convolutional neural networks to extract more comprehensive features. However, it is a huge challenge to artificially design an aesthetic neural network. Recently, Neural Architecture Search has upsurged to find suitable neural networks for many …tasks in deep learning. In this paper, we first attempt to combine Neural Architecture Search with computational aesthetics. We design and apply a customized progressive differentiable architecture search strategy to obtain a light-weighted and efficient aesthetic baseline model. In addition, we simulate the multi-person rating mechanism by outputting the distribution of the aesthetic value of the image, replacing the previous classification scheme of judging the beauty and unbeauty of the image by the threshold value, and propose a self-weighted Earth Mover’s Distance loss to better fit human subjective scoring. Based on the baseline model, we further introduce several strategies including an attention mechanism, the dilated convolution, and adaptive pooling, to enhance the performance. Finally, we design several groups of comparative experiments to demonstrate the effectiveness of our baseline aesthetic model and the introduced improvement strategies. Show more

Keywords: Artificial intelligence, deep learning, convolutional neural networks, computational aesthetics, neural architecture search

DOI: 10.3233/JIFS-210026

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2953-2967, 2021

Authors: Mohan, Prakash | Sundaram, Manikandan | Satpathy, Sambit | Das, Sanchali

Article Type: Research Article

Abstract: Techniques of data compression involve de-duplication of data that plays an important role in eliminating duplicate copies of information and has been widely employed in cloud storage to scale back the storage capacity and save information measure. A secure AES encryption de-duplication system for finding duplication with the meaning and store up it in the cloud. To protect the privacy of sensitive information whereas supporting de-duplication, The AES encryption technique and SHA-256 hashing algorithm have been utilized to encrypt the information before outsourcing. Pre-processing is completed and documents are compared and verified with the use of wordnet. Cosine similarity is …employed to see the similarity between both the documents and to perform this, a far economical VSM data structure is used. Wordnet hierarchical corpus is used to see syntax and semantics so that the identification of duplicates is done. NLTK provides a large vary of libraries and programs for symbolic and statistical natural language process (NLP) for the Python programming language that is used here for the unidentified words by cosine similarity. Within the previous strategies, cloud storage was used abundantly since similar files were allowed to store. By implementing our system, space for storing is reduced up to 85%. Since AES and SHA-256 are employed, it provides high security and efficiency. Show more

Keywords: Vector space Model, Wordnet, deduplication, cosine similarity, NLTK

DOI: 10.3233/JIFS-210038

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2969-2980, 2021

Authors: Zhang, Jin | Gu, Fu | Ji, Yangjian | Guo, Jianfeng

Article Type: Research Article

Abstract: To enable a quick and accurate access of targeted scientific and technological literature from massive stocks, here a deep content-based collaborative filtering method, namely DeepCCF, for personalized scientific and technological literature resources recommendation was proposed. By combining content-based filtering (CBF) and neural network-based collaborative filtering (NCF), the approach transforms the problem of scientific and technological literature recommendation into a binary classification task. Firstly, the word2vec is used to train the words embedding of the papers’ titles and abstracts. Secondly, an academic literature topic model is built using term frequency–inverse document frequency (TF-IDF) and word embedding. Thirdly, the search and view …history and published papers of researchers are utilized to construct the model that portrays the interests of researchers. Deep neural networks (DNNs) are then used to learn the nonlinear and complicated high-order interaction features between users and papers, and the top k recommendation list is generated by predicting the outputs of the model. The experimental results show that our proposed method can quickly and accurately capture the latent relations between the interests of researchers and the topics of paper, and be able to acquire the researchers’ preferences effectively as well. The proposed method has tremendous implications in personalized academic paper recommendation, to propel technological progress. Show more

Keywords: Scientific and technological literature resources, personalized recommendation, deep learning, recommendation algorithm

DOI: 10.3233/JIFS-210043

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2981-2996, 2021

Authors: Gao, Yin | Jia, Lifen

Article Type: Research Article

Abstract: Uncertain delay differential equations (UDDEs) charactered by Liu process can be employed to model an uncertain control system with a delay time. The stability of its solution always be a significant matter. At present, the stability in measure for UDDEs has been proposed and investigated based on the strong Lipschitz condition. In reality, the strong Lipschitz condition is so strictly and hardly applied to judge the stability in measure for UDDEs. For the sake of solving the above issue, the stability in measure based on new Lipschitz condition as a larger scale of applications is verified in this paper. In …other words, if it satisfies the strong Lipschitz condition, it must satisfy the new Lipschitz conditions. Conversely, it may not be established. An example is provided to show that it is stable in measure based on the new Lipschitz conditions, but it becomes invalid based on the strong Lipschitz condition. Moreover, a special class of UDDEs is verified to be stable in measure without any limited condition. Besides, some examples are investigated in this paper. Show more

Keywords: Stability in measure, Liu process, uncertain process, uncertain delay differential equations

DOI: 10.3233/JIFS-210089

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 2997-3009, 2021

Authors: Senniappan, Gomathi | Umapathy, Prabha

Article Type: Research Article

Abstract:  Renewable energy presently occupies a prominent position in India’s overall energy generation scheme. In the midst of numerous alternative energy resources, solar energy is widely used as it persists in large volumes with varying specification ratings. It is suited for both stand-alone and grid-coupled application. The Maximum Power Tracking (MPPT) scheme has a profound effect on the operating efficiency of a Photovoltaic (PV) power plant. This paper proposes an inventive hybridized Human Psychology Optimization-Perturb and Observation (HPO-PO) MPPT approach for obtaining the optimal duty cycle of the boost converter to harvest global maxima from a grid-connected Total Cost Tied (TCT) …configured PV array of 4080 W. The suggested method provides enriched performance both in steady-state, as well as in rapid and randomly changing weather conditions. Comparison studies of various MPPT procedures, including Perturbation and Observation (PO), Artificial Bee Colony (ABC), and Human Psychology Optimization (HPO) in MATLAB environment, illustrate the usefulness of the evoked system in meeting its goals. The suggested MPPT procedure has offered enhanced activities in terms of voltage quality, maximum power tracking capability, and converter efficiency compared to other methods. The recommended hybridized MPPT approach is experimentally validated on a hardware set-up using a 16-bit dsPIC30F2010 Digital Signal Controller in enhancing the behavior of the grid-connected PV system. Show more

Keywords: Photovoltaic (PV) systems, MPPT, perturb and observation, artificial bee colony, human psychology optimization (HPO), hybrid HPO-PO

DOI: 10.3233/JIFS-210114

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3011-3030, 2021

Authors: Xiao, Yanjun | Zhang, Zhenpeng | Liu, Zhenhao | Zhang, Zonghua | Zhou, Wei | Liu, Weiling

Article Type: Research Article

Abstract: In textile machines, the stability of warp tension is one of the decisive factors for the reliability, stability and product quality of weaving process. In order to meet the improving requirement for weaving efficiency and fabric quality, it is proposed that a fuzzy optimization integral separation PID warp tension control scheme based on process sampling to improve the warp tension control level of loom. Aiming at the problems of time-varying, nonlinear and variable coupling in the warp tension control system of loom, the forming mechanism of warp tension is modeled and analyzed, and the sampling scheme of warp tension based …on process is proposed. Based on the periodic change of warp tension at macro level and continuous fluctuation at micro level, the integral separation control and fuzzy optimization theory are introduced to optimize the control effect of the control system on the basis of classical PID control algorithm. Finally, the simulation and experiment show that the scheme can improve the tension controls performance and effectively reduce the tension error fluctuation. Show more

Keywords: Rapier loom, tension control, closed loop control, fuzzy optimization, integral separation control

DOI: 10.3233/JIFS-210124

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3031-3044, 2021

Authors: El-Bably, M. K. | Abo-Tabl, E. A.

Article Type: Research Article

Abstract: The present work proposes new styles of rough sets by using different neighborhoods which are made from a general binary relation. The proposed approximations represent a generalization to Pawlak’s rough sets and some of its generalizations, where the accuracy of these approximations is enhanced significantly. Comparisons are obtained between the methods proposed and the previous ones. Moreover, we extend the notion of “nano-topology”, which have introduced by Thivagar and Richard [49 ], to any binary relation. Besides, to demonstrate the importance of the suggested approaches for deciding on an effective tool for diagnosing lung cancer diseases, we include a medical …application of lung cancer disease to identify the most risk factors for this disease and help the doctor in decision-making. Finally, two algorithms are given for decision-making problems. These algorithms are tested on hypothetical data for comparison with already existing methods. Show more

Keywords: Neighborhoods, topology, rough sets, generalized nano-topology, attributes reduction and lung cancer disease

DOI: 10.3233/JIFS-210167

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3045-3060, 2021

Authors: Wu, Hsien-Chung

Article Type: Research Article

Abstract: The main purpose of this paper is to establish a mechanical procedure to determine the membership functions using the data collected from the economic and engineering problems. Determining the membership functions from the collected data may depend on the subjective viewpoint of decision makers. The mechanical procedure proposed in this paper can get rid of the subjective bias of decision makers. The concept of solid families is also proposed by regarding the sets in a family to be continuously varied. The desired fuzzy sets will be generated in the sense that its α -level sets will be identical to the …sets of the original family. In order to achieve this purpose, any arbitrary families will be rearranged as the nested families by applying some suitable functions to the original families that are formulated from the collected data. Show more

Keywords: Nested families, non-normal fuzzy sets, solid families

DOI: 10.3233/JIFS-210201

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3061-3082, 2021

Authors: Ling, Jie | Xiong, Su | Luo, Yu

Article Type: Research Article

Abstract: Uniform Resource Location (URL) is the network unified resource location system that specifies the location and access method of resources on the Internet. At present, malicious URL has become one of the main means of network attack. How to detect malicious URL timely and accurately has become an engaging research topic. The recent proposed deep learning-based detection models can achieve high accuracy in simulations, but several problems are exposed when they are used in real applications. These models need a balanced labeled dataset for training, while collecting large numbers of the latest labeled URL samples is difficult due to the …rapid generation of URL in the real application environment. In addition, in most randomly collected datasets, the number of benign URL samples and malicious URL samples is extremely unbalanced, as malicious URL samples are often rare. This paper proposes a semi-supervised learning malicious URL detection method based on generative adversarial network (GAN) to solve the above two problems. By utilizing the unlabeled URLs for model training in a semi-supervised way, the requirement of large numbers of labeled samples is weakened. And the imbalance problem can be relieved with the synthetic malicious URL generated by adversarial learning. Experimental results show that the proposed method outperforms the classic SVM and LSTM based methods. Specially, the proposed method can obtain high accuracy with insufficient labeled samples and unbalanced dataset. e.g., the proposed method can achieve 87.8% /91.9% detection accuracy when the number of labeled samples is reduced to 20% /40% of that of conventional methods. Show more

Keywords: Malicious URL detection, network security, deep learning, semi-supervised learning

DOI: 10.3233/JIFS-210212

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3083-3092, 2021

Authors: Angelini, Pierpaolo | Maturo, Fabrizio

Article Type: Research Article

Abstract: This paper focuses on logical aspects of choices being made by the consumer under conditions of uncertainty or certainty. Such logical aspects are found out to be the same. Choices being made by the consumer that should maximize her subjective utility are decisions studied by revealed preference theory. A finite number of possible alternatives is considered. They are mutually exclusive propositions identifying all quantitative states of nature of a consumption plan. Each proposition of it is expressed by a real number. This research work distinguishes it from its temporary truth value depending on the state of information and knowledge of …the consumer. Since each point of the consumption space of the consumer belongs to a two-dimensional convex set, this article focuses on conjoint distributions of mass. Indeed, the consumption space of the consumer is generated by all coherent summaries of a conjoint distribution of mass. Each point of her consumption space is connected with a weighted average of states of nature of two consumption plans jointly studied. They give rise to a conjoint distribution of mass. The consumer chooses a point of a two-dimensional convex set representing that bundle of goods actually demanded by her inside of her consumption space. This paper innovatively shows that it is nothing but a bilinear and disaggregate measure. It is decomposed into two real numbers, where each real number is a linear measure. In this paper, different measures are obtained. They can be disaggregate or aggregate measures, where the latter are independent of the notion of ordered pair of consumption plans. Show more

Keywords: 2-parallelepiped, α-product, temporary truth value, antisymmetric tensor, non-linear metric, linear metric

DOI: 10.3233/JIFS-210234

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3093-3105, 2021

Authors: Wu, Meiqin | Hou, Xiaoqing | Fan, Jianping

Article Type: Research Article

Abstract: As an extended model of data envelopment analysis (DEA), cross-efficiency is utilized in various fields. It applies the relative evaluation values of homogeneous decision-making units (DMUs) in ranking completely. Due to the non-uniqueness of DEA optimal weights, and the cross-efficiency methods focus on the aggregation of cross-efficiency matrix values. There is a low correction between the final efficiency evaluation value and the value assigned to the criteria weight or the value of the criteria. This paper uses the modified aggressive cross-efficiency model to calculate the weights of the decision-making units (DMUs). Then according to the consistency of peer-evaluation criteria, the …entropy weight method is used to aggregate the index weights to generate a set of common weights, and a new Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method is used to calculate the utility score of DMUs that can rank them completely. Finally, a numerical case of supplier selection is offered to illustrate the feasibility and effectiveness of the proposed method. Show more

Keywords: Cross-efficiency, group decision-making, criteria utility, MARCOS method

DOI: 10.3233/JIFS-210279

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3107-3119, 2021

Authors: Liu, Donghai | Luo, Yan

Article Type: Research Article

Abstract: Although some correlation measure of intuitionistic fuzzy sets(IFSs) have been proposed, some of them cannot express the consistency of information or satisfy the axioms of similarity measure. In this paper, we present a consensus reaching process based on the concordance correlation measure of IFSs in multi-criteria decision making problems. Firstly, we define an innovative concordance correlation measure of IFSs, which not only takes the average information deviation of IFSs into account but also overcomes the disadvantages of previous correlation measures. In addition, its properties and the relationship between the defined new concordance correlation measure and Pearson correlation coefficient of IFSs …are discussed. Secondly, considering that the classical TOPSIS method cannot be applied to the correlation measure with negative values, we continue to introduce the concept of relative concordance correlation measure and propose a consensus reaching process with minimum adjustment for an innovative behavioral TOPSIS method. Furthermore, a detailed numerical example and the comparison analyses are provided to verify the advantages of the proposed method. At last, we discuss the sensitivity and stability of the method. Show more

Keywords: Concordance correlation measure, Consensus reaching process, Intuitionistic fuzy set, Behavioral TOPSIS

DOI: 10.3233/JIFS-210343

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3121-3136, 2021

Authors: Wang, Jinfeng | Huang, Shuaihui | Jiang, Fajian | Zheng, Zhishen | Ou, Jianbin | Chen, Hao | Chen, Runjian | Wang, Wenzhong

Article Type: Research Article

Abstract: Fuzzy integral in data mining is an excellent information fusion tool. It has obvious advantages in solving the combination of features and has more successful applications in classification problems. However, with the increase of the number of features, the time complexity and space complexity of fuzzy integral will also increase exponentially. This problem limits the development of fuzzy integral. This article proposes a high-efficiency fuzzy integral—Parallel and Sparse Frame Based Fuzzy Integral (PSFI) for reducing time complexity and space complexity in the calculation of fuzzy integrals, which is based on the distributed parallel computing framework-Spark combined with the concept of …sparse storage. Aiming at the efficiency problem of the Python language, Cython programming technology is introduced in the meanwhile. Our algorithm is packaged into an algorithm library to realize a more efficient PSFI. The experiments verified the impact of the number of parallel nodes on the performance of the algorithm, test the performance of PSFI in classification, and apply PSFI on regression problems and imbalanced big data classification. The results have shown that PSFI reduces the variable storage space requirements of datasets with aplenty of features by thousands of times with the increase of computing resources. Furthermore, it is proved that PSFI has higher prediction accuracy than the classic fuzzy integral running on a single processor. Show more

Keywords: Parallel computing, sparse storage, fuzzy integral, fuzzy measure

DOI: 10.3233/JIFS-210372

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3137-3159, 2021

Authors: Qiu, Liqing | Yang, Zhongqi | Zhu, Shiwei | Gu, Chunmei | Tian, Xiangbo

Article Type: Research Article

Abstract: Influence maximization is a classic network optimization problem, which has been widely used in the field of viral marketing. The influence maximization problem aims to find a fixed number of active nodes. After a specific propagation model, the number of active nodes reaches the maximum. However, the existing influence maximization algorithms are overly pursuing certain indicators of efficiency or accuracy, which cannot be well accepted by some researchers. This paper proposes an effective algorithm to balance the accuracy and efficiency of the influence maximization problem called local two-hop search algorithm (LTHS). The core of the proposed algorithm is a node …not only be affected by one-hop neighbor nodes, but also by two-hop neighbor nodes. Firstly, this paper selects initial seed nodes according to the characteristics of the node degree. Generally, the high degree of nodes regards as influential nodes. Secondly, this paper proposes a node two-hop influence evaluate function called two-hop diffusion value (THDV), which can evaluate node influence more accurately. Furthermore, in order to seek higher efficiency, this paper proposes a method to reduce the network scale. This paper conducted full experiments on five real-world social network datasets, and compared with other four well-known algorithms. The experimental results show that the LTHS algorithm is better than the comparison algorithms in terms of efficiency and accuracy. Show more

Keywords: Social network, influence maximization, local influence, heuristic algorithm

DOI: 10.3233/JIFS-210379

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3161-3172, 2021

Authors: Yiarayong, Pairote

Article Type: Research Article

Abstract: The aim of this manuscript is to apply bipolar fuzzy sets for dealing with several kinds of theories in LA -semigroups. To begin with, we introduce the concept of 2-absorbing (quasi-2-absorbing) bipolar fuzzy ideals and strongly 2-absorbing (quasi-strongly 2-absorbing) bipolar fuzzy ideals in LA -semigroups, and investigate several related properties. In particular, we show that a bipolar fuzzy set A = ( μ A P , μ A N ) over an LA -semigroup S is weakly 2-absorbing …if and only if [ B ⊙ C ] ⊙ D ⪯ A implies B ⊙ C ⪯ A or C ⊙ D ⪯ A or B ⊙ D ⪯ A for any bipolar fuzzy sets B = ( μ B P , μ B N ) , C = ( μ C P , μ C N ) and D = ( μ D P , μ D N ) . Also, we give some characterizations of quasi-strongly 2-absorbing bipolar fuzzy ideals over an LA -semigroup S by bipolar fuzzy points. In conclusion of this paper we prove that the relationship between quasi-strongly 2-absorbing bipolar fuzzy ideals over an LA -semigroup S and quasi-2-absorbing bipolar fuzzy ideals over S . Show more

Keywords: bipolar fuzzy set, 2-absorbing bipolar fuzzy ideal, strongly 2-absorbing bipolar fuzzy ideal, ℒℒ-semigroup, quasi-2-absorbing bipolar fuzzy ideal

DOI: 10.3233/JIFS-210388

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3173-3181, 2021

Authors: Zhao, Shan | Li, Zhao

Article Type: Research Article

Abstract: The interpolation functions of interval type-2 fuzzy systems and their universal approximation are investigated in this paper. Two types of fuzzification methods are designed to construct the antecedents and consequents of the type-2 inference rules. Then the properties of the fuzzy operator and the type-reduction algorithm are used to integrate all parts of the fuzzy system. Interpolation functions of interval type-2 fuzzy systems, which are proved to be universal approximators, are obtained based on three models, namely single input and single output, double inputs and single output, and multiple inputs and single output. The proposed approach is applied to approximate …experiments of dynamic systems so as to evaluate the system performance. The system parameters are optimized by the QPSO algorithm. Experimental results for several data sets are given to show the approximation performances of the proposed interpolation functions are better than those of the interpolation function of the classical type-1 fuzzy system. Show more

Keywords: Interval type-2 fuzzy system, interval type-2 fuzzy set, interpolation function, universal approximation

DOI: 10.3233/JIFS-210435

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3183-3200, 2021

Authors: Zixian, Zhang | Xuning, Liu | Zhixiang, Li | Hongqiang, Hu

Article Type: Research Article

Abstract: The influencing factors of coal and gas outburst are complex, and now the accuracy and efficiency of outburst prediction are not high. In order to obtain the effective features from influencing factors and realize the accurate and fast dynamic prediction of coal and gas outburst, this article proposes an outburst prediction model based on the coupling of feature selection and intelligent optimization classifier. Firstly, in view of the redundancy and irrelevance of the influencing factors of coal and gas outburst, we use Boruta feature selection method to obtain the optimal feature subset from influencing factors of coal and gas outburst. …Secondly, based on Apriori association rules mining method, the internal association relationship between coal and gas outburst influencing factors is mined, and the strong association rules existing in the influencing factors and samples that affect the classification of coal and gas outburst are extracted. Finally, svm is used to classify coal and gas outburst based on the above obtained optimal feature subset and sample data, and Bayesian optimization algorithm is used to optimize the kernel parameters of svm, and the coal and gas outburst pattern recognition prediction model is established, which is compared with the existing coal and gas outburst prediction model in literatures. Compared with the method of feature selection and association rules mining alone, the proposed model achieves the highest prediction accuracy of 93% when the feature dimension is 3, which is higher than that of Apriori association rules and Boruta feature selection, and the classification accuracy is significantly improved. However, the feature dimension decreased significantly, the results show that the proposed model is better than other prediction models, which further verifies the accuracy and applicability of the coupling prediction model. Show more

Keywords: Coal and gas outburst, Feature selection, Boruta, Apriori, Bayesian optimization, SVM

DOI: 10.3233/JIFS-210466

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3201-3218, 2021

Authors: Yang, Han | Qin, Keyun

Article Type: Research Article

Abstract: The theory of three-way concept analysis has been developed into an effective tool for data analysis and knowledge discovery. In this paper, we propose neutrosophic three-way concept lattice by combining neutrosophic set with three-way concept analysis and present an approach for conflict analysis by using neutrosophic three-way concept lattice. Firstly, we propose the notion of neutrosophic formal context, in which the relationships between objects and attributes are expressed by neutrosophic numbers. Three pairs of concept derivation operators are proposed. The basic properties of object-induced and attribute-induced neutrosophic three-way concept lattices are examined. Secondly, we divide the neutrosophic formal context into …three classical formal contexts and propose the notions of the candidate neutrosophic three-way concepts and the redundant neutrosophic three-way concepts. Two approaches of constructing object-induced (attribute-induced) neutrosophic three-way concept lattices are presented by using candidate, redundant and original neutrosophic three-way concepts respectively. Finally, we apply the neutrosophic formal concept analysis to the conflict analysis and put forward the corresponding optimal strategy and the calculation method of the alliance. Show more

Keywords: Three-way concept analysis, neutrosophic set, neutrosophic three-way concept lattice, conflict analysis

DOI: 10.3233/JIFS-210481

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3219-3236, 2021

Authors: He, Yan | Wei, Guiwu | Chen, Xudong

Article Type: Research Article

Abstract: The optimal supplier selection in medical instrument industries could be considered a classical MAGDM issue. The probabilistic uncertain linguistic term sets (PULTSs) could depict uncertain information well and the Taxonomy method is appropriate to compare various alternatives according to their merits and utility degree from studied attributes. In such paper, we develop a Taxonomy method for probabilistic uncertain linguistic MAGDM (PUL-MAGDM) with the completely unknown attribute weights. Above all, the score function’s definition is utilized to derive the weights of attribute based upon the CRITIC method. In addition, the probabilistic uncertain linguistic development pattern (PULDP) is improved and the smallest …development attribute value from the positive ideal solution under PULTSs is calculated to determine the optimal alternative. In the end, taking the supplier selection in medical instrument industries as an example, we demonstrate the usage of the developed algorithms. Based on this, the comparison of methods is conducted with existing methods, such as PUL-TOPSIS method, the PULWA operator, the PUL-EDAS method and the ULWA operator. The results verify that the decision-making framework is valid and effective for supplier selection. Thus, the advantage of this designed method is that it is simple to understand and easy to compute. The designed method can also contribute to the selection of suitable alternative successfully in other selection issues. Show more

Keywords: Multiple attribute group decision making (MAGDM) issues, probabilistic uncertain linguistic sets (PULTSs), taxonomy approach, CRITIC method, supplier selection

DOI: 10.3233/JIFS-210494

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3237-3250, 2021

Authors: Amsaprabhaa, M. | Nancy Jane, Y. | Khanna Nehemiah, H.

Article Type: Research Article

Abstract: Due to the COVID-19 pandemic, countries across the globe has enforced lockdown restrictions that influence the people’s socio-economic lifecycle. The objective of this paper is to predict the communal emotion of people from different locations during the COVID-19 lockdown. The proposed work aims in developing a deep spatio-temporal analysis framework of geo-tagged tweets to predict the emotions of different topics based on location. An optimized Latent Dirichlet Allocation (LDA) approach is presented for finding the optimal hyper-parameters using grid search. A multi-class emotion classification model is then built via a Recurrent Neural Network (RNN) to predict emotions for each topic …based on locations. The proposed work is experimented with the twitter streaming API dataset. The experimental results prove that the presented LDA model-using grid search along with the RNN model for emotion classification outperforms the other state of art methods with an improved accuracy of 94.6%. Show more

Keywords: Topic modeling, latent dirichlet allocation, grid search, multi-class emotion classification, recurrent neural network

DOI: 10.3233/JIFS-210544

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3251-3264, 2021

Authors: Chen, Hongyu | Wang, Shengsheng

Article Type: Research Article

Abstract: Since the end of 2019, the COVID-19, which has swept across the world, has caused serious impacts on public health and economy. Although Reverse Transcription-Polymerase Chain Reaction (RT-PCR) is the gold standard for clinical diagnosis, it is very time-consuming and labor-intensive. At the same time, more and more people have doubted the sensitivity of RT-PCR. Therefore, Computed Tomography (CT) images are used as a substitute for RT-PCR. Powered by the research of the field of artificial intelligence, deep learning, which is a branch of machine learning, has made a great success on medical image segmentation. However, general full supervision methods …require pixel-level point-by-point annotations, which is very costly. In this paper, we put forward an image segmentation method based on weakly supervised learning for CT images of COVID-19, which can effectively segment the lung infection area and doesn’t require pixel-level labels. Our method is contrasted with another four weakly supervised learning methods in recent years, and the results have been significantly improved. Show more

Keywords: COVID-19, deep learning, weakly supervised learning, computed tomography, automated segmentation

DOI: 10.3233/JIFS-210569

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3265-3276, 2021

Authors: Sharma, Sudeep | Padhy, Prabin K.

Article Type: Research Article

Abstract:  The combination of machine learning and artificial intelligent has already proved its potential in achieving remarkable results for modeling unknown systems. These techniques commonly use enough data samples to train and optimize their architectures. In the present era, with the availability of enough storage and computation power, the machine learning based data-driven system modeling approaches are getting popular as they do not interrupt the normal system operations and work solely on collected data. This work proposes a data-driven parametric neural network technique for modeling time-delayed systems, which is demanding but challenging area of research and comes under nonlinear optimization problem. …The key contribution of this work is the inclusion of an extended B-polynomial into the network structure for estimating time-delayed first and second order system models. These type of models extensively used for addressing simulations, predictions, controlling and monitoring related issues. Also, an adaptive learning based convergence of the proposed algorithm is proved with the help of the Lyapunov stability theory. The proposed algorithm compared with existing techniques on some well-known example problems. A real practical system plant is also included for validating the proposed concept. Show more

Keywords: Identification, estimation, modeling, adaptive learning, time delays, neural networks, lyapunov theory, intelligent systems

DOI: 10.3233/JIFS-210580

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3277-3288, 2021

Authors: Zhou, Qing | Peng, Wei | Tang, Dai

Article Type: Research Article

Abstract: In many countries, outpatients generally visit a major hospital without a referral from health professionals due to the shortage of family physicians. Not knowing at which medical specialty department to register, outpatients have to wait in long queues to consult receptionists. We propose to alleviate this situation via a computer system offering an automatic recommendation of departments (ARD) to outpatients, which identifies the appropriate medical department for outpatients according to their chief complaints. Besides, ARD systems can boost the emerging services of online hospital registration and online medical diagnosis, which require that the outpatients know the correct department first. ARD …is a typical problem of text classification. Nevertheless, off-the-shelf tools for text processing may not suit ARD, because the chief complaints of outpatients are generally brief and contain much noisy information. To solve this problem, we propose ARD-K, a deep learning framework incorporating external medical knowledge sources. We also propose a dual-attention mechanism to mitigate the interference of noisy words and knowledge entities. The performance of ARD-K is compared with some off-the-shelf techniques on a real-world dataset. The results demonstrate the effectiveness of ARD-K for the automatic recommendation of departments to outpatients. Show more

Keywords: Automatic recommendation of departments, medical knowledge graph, attention mechanism, clinical text classification

DOI: 10.3233/JIFS-210599

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3289-3299, 2021

Authors: Yan, Mian | Feng, Jianghong | Xu, Su Xiu

Article Type: Research Article

Abstract: In recent years, the problem of complex multi-attribute group decision-making (MAGDM) in uncertain environments has received increasing attention. In evaluating MAGDM problems, obtaining the objective attribute weights is very important. Considering the excellent performance of intuitive fuzzy linguistic sets in dealing with uncertain information, this paper introduces a new interval-valued intuitionistic pure linguistic entropy weight (IVIPLEW) method for determining attribute weights and evaluating MAGDM problems. The IVIPLEW method considers the cases of missing values, and uses the conventional interval-valued intuitionistic pure linguistic (IVIPL) expectations to supplement the missing values. This method of dealing with missing values not only considers the …expectations of experts, but also prevents fluctuations in linguistic variables from impacting the decision results. This paper establishes an analysis framework that allows the IVIPLEW method to be applied to MAGDM problems, and presents a practical case study that illustrates the practicality and effectiveness of IVIPLEW. The results are quite satisfactory. The effectiveness of the proposed method is demonstrated through a comparison with the IVIPL information aggregation method. Furthermore, the robustness of the IVIPLEW method is verified through a sensitivity analysis. The results presented in this paper show that the IVIPLEW method is applicable to a wide range of MAGDM problems. Show more

Keywords: Interval-valued intuitionistic pure linguistic, entropy weight method, group decision-making, attribute weights

DOI: 10.3233/JIFS-210609

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3301-3316, 2021

Authors: Cheng, Haodong | Han, Meng | Zhang, Ni | Wang, Le | Li, Xiaojuan

Article Type: Research Article

Abstract: The researcher proposed the concept of Top-K high-utility itemsets mining over data streams. Users directly specify the number K of high-utility itemsets they wish to obtain for mining with no need to set a minimum utility threshold. There exist some problems in current Top-K high-utility itemsets mining algorithms over data streams including the complex construction process of the storage structure, the inefficiency of threshold raising strategies and utility pruning strategies, and large scale of the search space, etc., which still can not meet the requirement of real-time processing over data streams with limited time and memory constraints. To solve this …problem, this paper proposes an efficient algorithm based on dataset projection for mining Top-K high-utility itemsets from a data stream. A data structure CIUDataListSW is also proposed, which stores the position of the item in the transaction to effectively obtain the initial projected dataset of the item. In order to improve the projection efficiency, this paper innovates a new reorganization technology for projected transactions in common batches to maintain the sort order of transactions in the process of dataset projection. Dual pruning strategy and transaction merging mechanism are also used to further reduce search space and dataset scanning costs. In addition, based on the proposed CUDH S W structure, an efficient threshold raising strategy CUD is used, and a new threshold raising strategy CUDCB is designed to further shorten the mining time. Experimental results show that the algorithm has great advantages in running time and memory consumption, and it is especially suitable for the mining of high-utility itemsets of dense datasets. Show more

Keywords: Itemset mining, utility mining, high utility itemsets, data streams, Top-K high-utility

DOI: 10.3233/JIFS-210610

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3317-3338, 2021

Authors: Singh, Deepika | Saha, Anju | Gosain, Anjana

Article Type: Research Article

Abstract: Imbalanced dataset classification is challenging because of the severely skewed class distribution. The traditional machine learning algorithms show degraded performance for these skewed datasets. However, there are additional characteristics of a classification dataset that are not only challenging for the traditional machine learning algorithms but also increase the difficulty when constructing a model for imbalanced datasets. Data complexity metrics identify these intrinsic characteristics, which cause substantial deterioration of the learning algorithms’ performance. Though many research efforts have been made to deal with class noise, none of them focused on imbalanced datasets coupled with other intrinsic factors. This paper presents a …novel hybrid pre-processing algorithm focusing on treating the class-label noise in the imbalanced dataset, which suffers from other intrinsic factors such as class overlapping, non-linear class boundaries, small disjuncts, and borderline examples. This algorithm uses the wCM complexity metric (proposed for imbalanced dataset) to identify noisy, borderline, and other difficult instances of the dataset and then intelligently handles these instances. Experiments on synthetic datasets and real-world datasets with different levels of imbalance, noise, small disjuncts, class overlapping, and borderline examples are conducted to check the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm offers an interesting alternative to popular state-of-the-art pre-processing algorithms for effectively handling imbalanced datasets along with noise and other difficulties. Show more

Keywords: Classification, class imbalance, data complexity, overlapping, bayes error, pre-processing, learning algorithms

DOI: 10.3233/JIFS-210624

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3339-3354, 2021

Authors: Zhu, Kun | Zhang, Shuai | Zhang, Wenyu | Zhang, Zhiqiang

Article Type: Research Article

Abstract: Accurate taxi demand forecasting is significant to estimate the change of demand to further make informed decisions. Although deep learning methods have been widely applied for taxi demand forecasting, they neglect the complexity of taxi demand data and the impact of event occurrences, making it hard to effectively model the taxi demand in highly dynamic areas (e.g., areas with frequent event occurrences). Therefore, to achieve accurate and stable taxi demand forecasting in highly dynamic areas, a novel hybrid deep learning model is proposed in this study. First, to reduce the complexity of taxi demand time series, the seasonal-trend decomposition procedures …based on loess is employed to decompose the time series into three simpler components (i.e., seasonal, trend, and remainder components). Then, different forecasting methods are adopted to handle different components to obtain robust forecasting results. Moreover, considering the instability and nonlinearity of the remainder component, this study proposed to fuse the event features (in particular, text data) to capture the unusual fluctuation patterns of remainder component and solve its extreme value problem. Finally, genetic algorithm is applied to determine the optimal weights for integrating the forecasting results of three components to obtain the final taxi demand. The experimental results demonstrate the better accuracy and reliability of the proposed model compared with other baseline forecasting models. Show more

Keywords: Taxi demand forecasting, deep learning, seasonal-trend decomposition procedures based on loess, data fusion, text data, genetic algorithm

DOI: 10.3233/JIFS-210657

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3355-3371, 2021

Authors: Riaz, Muhammad | Habib, Anam | Aslam, Muhammad

Article Type: Research Article

Abstract:  A cubic bipolar fuzzy set (CBFS) is a new approach in computational intelligence and decision-making under uncertainty. This model is the generalization of bipolar fuzzy sets to deal with two-sided contrasting features which can describe the information with a bipolar fuzzy number and an interval-valued bipolar fuzzy number simultaneously. In this paper, the Dombi’s operations are analyzed for information aggregation of cubic bipolar fuzzy numbers (CBFNs). The Dombi’s operations carry the advantage of more pliability and reliability due to the existence of their operational parameters. Owing to the pliable nature of Dombi’s operators, this research work introduces new aggregation operators …named as cubic bipolar fuzzy Dombi weighted averaging (CBFDWA) operator and cubic bipolar fuzzy Dombi ordered weighted averaging (CBFDOWA) operator with ℙ -order and ℝ -order, respectively. Additionally, this paper presents some significant characteristics of suggested operators including, idempotency, boundedness and monotonicity. Moreover, a robust multi-criteria decision making (MCDM) technique is developed by using ℙ -CBFDWA and ℝ -CBFDWA operators. Based on the suggested operators a practical application is demonstrated towards MCDM under uncertainty. The comparison analysis of suggested Dombi’s operators with existing operators is also given to discuss the rationality, efficiency and applicability of these operators. Show more

Keywords: Cubic bipolar fuzzy sets, Dombi’s operations, cubic bipolar fuzzy Dombi weighted averaging operator, cubic bipolar fuzzy Dombi ordered weighted averaging operator, ℙ-order and ℝ-order operations, multi-criteria decision making

DOI: 10.3233/JIFS-210667

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3373-3393, 2021

Authors: Duan, Huiming | Huang, Jiangbo | Wang, Siqi | He, Chenglin

Article Type: Research Article

Abstract: The stock market is an important embodiment of a national economy and financial activities and has an important impact on a country, enterprises and individuals. Stock forecasting can allow investment institutions and investors to understand the trend of the stock market in advance, which is a challenging and meaningful study. First, through the impulse phenomenon of the stock market, this paper discusses the problem of stock price prediction with delay, and the impulse delay differential equation is established. Second, according to the difference between the differential and the difference, the nonlinear delay grey prediction model is established. Next, the model …parameters are estimated and the solving steps are obtained. The nonlinear parameters and delay time are optimized by the particle swarm optimization algorithm. Finally, the new model is applied to the prediction of the Shanghai stock market and the Shenzhen stock market closing indexes; the results show that the new model can effectively predict stock prices, which is much better than the existing four grey models and a time series model. Show more

Keywords: Stock price, impulse delay, grey model, forecasting

DOI: 10.3233/JIFS-210726

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3395-3413, 2021

Authors: Hu, Junying | Qian, Xiaofei | Cheng, Hao | Tan, Changchun | Liu, Xinbao

Article Type: Research Article

Abstract: Based on phase space reconstruction (PSR) and hybrid VNS-SVR model, a remaining useful life (RUL) prediction method for aircraft engines is proposed. The proposed hybrid model combines support vector regression (SVR), which has been successfully adopted for regression problems, with the variable neighborhood search (VNS). First, the phase space reconstruction is used to transform the selected one-dimensional performance sequences of aircraft engines into matrix forms, which increases the data information and improve the learning efficiency of the model effectively. Then, SVR is used to construct the prediction model. Meanwhile, a VNS algorithm is proposed to optimize the kernel parameters. Finally, …the hybrid model is used to RUL prediction of the aircraft engines. The experimental results show that the method has a good prediction performance. Show more

Keywords: Remaining useful life, support vector regression, phase space reconstruction, variable neighborhood search, aircraft engine

DOI: 10.3233/JIFS-210740

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3415-3428, 2021

Authors: Xiao, Yanjun | Han, Furong | Ding, Yvheng | Liu, Weiling

Article Type: Research Article

Abstract: The safety and stability of the rapier loom during operation directly impact the quality of the fabric. Therefore, it is of great significance to carry out fault diagnosis research on rapier looms. In order to solve the problems of low diagnosis efficiency, untimely diagnosis, and high maintenance cost of existing rapier looms in manual troubleshooting of loom failures. This paper proposes a new intelligent fault diagnosis method for rapier looms based on the fusion of expert system and fault tree. A new expert system knowledge base is formed by combining the dynamic fault tree model with the expert system knowledge …base. It solves the problem that the traditional expert system cannot achieve precise positioning in the face of complex fault types. Construct the rapier loom’s fault diagnosis model, build the intelligent diagnosis platform, and finally realize the intelligent fault diagnosis of the rapier loom. Experimental results show that the algorithm can quickly diagnose and locate rapier loom faults. Compared with the current intelligent diagnosis algorithm, the algorithm structure is simplified, which provides a theoretical basis for the broad application of intelligent fault diagnosis on rapier looms. Show more

Keywords: Rapier loom, expert system, fault tree, fault diagnosis

DOI: 10.3233/JIFS-210741

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3429-3441, 2021

Authors: Xu, Haiyan | Chang, Yuqing | Zhao, Yong | Wang, Fuli

Article Type: Research Article

Abstract: Accurate and stable wind speed forecasting is an essential means to ensure the safe and stable operation of wind power integration. Therefore, a new hybrid model was proposed to improve wind speed forecasting performance, consisting of data pre-processing, model forecasting, and error correction (EC). The specific modeling process is as follows: (a) A wind speed series was decomposed into a series of subseries with different frequencies utilizing the ensemble empirical mode decomposition (EEMD) method. Afterward, various subseries were divided into high-frequency components, intermediate-frequency components, and low-frequency components based on their sample entropies (SE). (b) Three frequency components were forecast by …separately employing the hybrid model of convolutional neural network and long short-term memory network (CNN-LSTM), long short-term memory network (LSTM), and Elman neural network. (c) Subsequently, an error sequence was further forecast using CNN-LSTM. (d) Finally, three actual datasets were used to forecast the multi-step wind speed, and the forecasting performance of the proposed model was verified. The test results show that the forecasting performance of the proposed model is better than the other 13 models in three actual datasets. Show more

Keywords: Ensemble empirical mode decomposition, long short-term memory network, elman neural network, error correction

DOI: 10.3233/JIFS-210779

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3443-3462, 2021

Authors: Luo, Jun | Tian, Qin | Xu, Meng

Article Type: Research Article

Abstract: Aiming at the disadvantages of slow convergence and the premature phenomenon of the butterfly optimization algorithm (BOA), this paper proposes a modified BOA (MBOA) called reverse guidance butterfly optimization algorithm integrated with information cross-sharing. First, the quasi-opposition concept is employed in the global search phase that lacks local exploitation capabilities to broaden the search space. Second, the neighborhood search weight factor is added in the local search stage to balance exploration and exploitation. Finally, the information cross-sharing mechanism is introduced to enhance the ability of the algorithm to jump out of the local optima. The proposed MBOA is tested in …fourteen benchmark functions and three constrained engineering problems. The series of experimental results indicate that MBOA shows better performance in terms of convergence speed, convergence accuracy, stability as well as robustness. Show more

Keywords: Butterfly optimization algorithm, benchmark function, information cross-sharing, neighborhood search weight factor, reverse guidance

DOI: 10.3233/JIFS-210815

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3463-3484, 2021

Authors: Zhang, Lijun | Duan, Lixiang | Hong, Xiaocui | Liu, Xiangyu | Zhang, Xinyun

Article Type: Research Article

Abstract: Machinery operates well under normal conditions in most cases; far fewer samples are collected in a fault state (minority samples) than in a normal state, resulting in an imbalance of samples. Common machine learning algorithms such as deep neural networks require a significant amount of data during training to avoid overfitting. These models often fail to detect minority samples when the input samples are imbalanced, which results in missed diagnoses of equipment faults. As an effective method to enhance minority samples, Deep Convolution Generative Adversarial Network (DCGAN) does not fundamentally address the problem of unstable Generative Adversarial Network (GAN) training. …This study proposes an improved DCGAN model with improved stability and sample balance for achieving greater classification accuracy over minority samples. First, spectral normalization is performed on each convolutional layer, improving stability in the DCGAN discriminator. Then, the improved DCGAN model is trained to generate new samples that are different from the original samples but with a similar distribution when the Nash equilibrium is reached. Four indices—Inception Score (IS), Fréchet Inception Distance Score (FID), Peak Signal to Noise Ratio (PSNR), and Structural Similarity (SSIM)—were used to quantitatively evaluate of the generated images. Finally, the Balance Degree of Samples (BDS) index was proposed, and the new samples are proportionally added to the original samples to improve sample balance, resulting in the formation of several groups of datasets with different balance degrees, and Convolutional Neural Network (CNN) models are used to classify these samples. With experimental analysis on the reciprocating compressor, the variance of lost data is found to be less than 1% of the original value, representing an increase in stabilityof the model to generate diverse and high-quality sample images, as compared with that of the unmodified model. The classification accuracy exceeds 95% and tends to remain stable when the balance degree of samples is greater than 80%. Show more

Keywords: Imbalanced, data enhancement, fault diagnosis, DCGAN, CNN

DOI: 10.3233/JIFS-210843

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3485-3498, 2021

Authors: Moussa, Mona M. | Shoitan, Rasha | Abdallah, Mohamed S.

Article Type: Research Article

Abstract: Finding the common objects in a set of images is considered one of the recent challenges in different computer vision tasks. Most of the conventional methods have proposed unsupervised and weakly supervised co-localization methods to find the common objects; however, these methods require producing a huge amount of region proposals. This paper tackles this problem by exploiting supervised learning benefits to localize the common object in a set of unlabeled images containing multiple objects or with no common objects. Two stages are proposed to localize the common objects: the candidate box generation stage and the matching and clustering stage. In …the candidate box generation stage, the objects are localized and surrounded by the bounding boxes. The matching and clustering stage is applied on the generated bounding boxes and creates a distance matrix based on a trained Siamese network to reflect the matching percentage. Hierarchical clustering uses the generated distance matrix to find the common objects and create clusters for each one. The proposed method is trained on PASCAL VOC 2007 dataset; on the other hand, it is assessed by applying different experiments on PASCAL VOC 2007 6×2 and Object Discovery datasets, respectively. The results reveal that the proposed method outperforms the conventional methods by 8% to 40% in terms of corloc metric. Show more

Keywords: Object localization, Siamese network, hierarchical clustering, and convolutional neural networks (CNNs)

DOI: 10.3233/JIFS-210854

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3499-3508, 2021

Authors: Meixin, Huang | Caixia, Liu

Article Type: Research Article

Abstract: Fractional order grey model is effective in describing the uncertainty of the system. In this paper, we propose a novel variable-order fractional discrete grey model (short for VOFDGM(1,1)) by combining the discrete grey model and variable-order fractional accumulation, which is a more general form of the DGM(1,1). The detailed modeling procedure of the presented model is first systematically studied, in particular, matrix perturbation theory is used to prove the validity in terms of the stability of the model, and then, the model parameters are optimized by the whale optimization algorithm. The accuracy of the proposed model is verified by comparing …it with classical models on six data sequences with different forms. Finally, the model is applied to predict the electricity consumption of Beijing and Liaoning Province of China, and the results show that the model has a better prediction performance compared with the other four commonly-used grey models. To the best of our knowledge, this is the first time that the variable-order fractional accumulation is introduced into the discrete grey model, which greatly increases the prediction accuracy of the DGM(1,1) and extends the application range of grey models. Show more

Keywords: Grey model, variable-order fractional accumulation, whale optimization algorithm, electricity consumption

DOI: 10.3233/JIFS-210871

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3509-3522, 2021

Authors: Li, Shuhao | Sun, Qiang | Liu, Shupei

Article Type: Research Article

Abstract: In recent years, supply chain risk management has been followed with interest due to the short life cycle of products. How to identify risk indicators can help evaluate risks on supply chains. Commonly adopted methods such as Fuzzy to determine the level of risks have limitations. In this paper, a framework of supply chain risk evaluation is first proposed and risk indicators are identified by theoretical surveys from 35 keywords and empirical analysis from 448 questionnaires. Moreover, both linguistic risk assessment model and Cloud model are used to evaluate risks of supply chain. The Cloud model evaluation results are between …general risk and high risk but closer to high risk. In addition, Cloud expected value of risk is 6.54 which is within the high-risk range, and evaluation results are also high risk. It is shown that when the weights are the same, the cloud model can determine the priority of risk indicators, and reflect volatility and randomness comparing with other evaluation methods. Show more

Keywords: Cloud model, supply chain risk management, word frequency, risk identification, risk evaluation

DOI: 10.3233/JIFS-210883

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3523-3540, 2021

Authors: Chen, Xinghao | Zhou, Bin

Article Type: Research Article

Abstract: Path planning is the basis and prerequisite for unmanned aerial vehicle (UAV) to perform tasks, and it is important to achieve precise location in path planning. This paper focuses on solving the UAV path planning problem under the constraint of system positioning error. Some nodes can re-initiate the accumulated flight error to zero and this type of scenario can be modeled as the resource-constrained shortest path problem with re-initialization (RCSPP-R). The additional re-initiation conditions expand the set of viable paths for the original constrained shortest path problem and increasing the search cost. To solve the problem, an effective preprocessing method …is proposed to reduce the network nodes. At the same time, a relaxed pruning strategy is introduced into the traditional Pulse algorithm to reduce the search space and avoid more redundant calculations on unfavorable scalable nodes by the proposed heuristic search strategy. To evaluate the accuracy and effectiveness of the proposed algorithm, some numerical experiments were carried out. The results indicate that the three strategies can reduce the search space by 99%, 97% and 80%, respectively, and in the case of a large network, the heuristic algorithm combining the three strategies can improve the efficiency by an average of 80% compared to some classical solution. Show more

Keywords: UAV path planing, constraints shortest paths, resource re-initialized, pulse algorithm, heuristics

DOI: 10.3233/JIFS-210901

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3541-3553, 2021

Authors: Farag, Hania H. | Said, Lamiaa A. A. | Rizk, Mohamed R. M. | Ahmed, Magdy Abd ElAzim

Article Type: Research Article

Abstract: COVID-19 has been considered as a global pandemic. Recently, researchers are using deep learning networks for medical diseases’ diagnosis. Some of these researches focuses on optimizing deep learning neural networks for enhancing the network accuracy. Optimizing the Convolutional Neural Network includes testing various networks which are obtained through manually configuring their hyperparameters, then the configuration with the highest accuracy is implemented. Each time a different database is used, a different combination of the hyperparameters is required. This paper introduces two COVID-19 diagnosing systems using both Residual Network and Xception Network optimized by random search in the purpose of finding optimal …models that give better diagnosis rates for COVID-19. The proposed systems showed that hyperparameters tuning for the ResNet and the Xception Net using random search optimization give more accurate results than other techniques with accuracies 99.27536% and 100 % respectively. We can conclude that hyperparameters tuning using random search optimization for either the tuned Residual Network or the tuned Xception Network gives better accuracies than other techniques diagnosing COVID-19. Show more

Keywords: Convolutional neural network, hyperparameter, residual network, xception network, random search optimization

DOI: 10.3233/JIFS-210925

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3555-3571, 2021

Authors: Song, Qinyu | Ni, Yaodong | Ralescu, Dan

Article Type: Research Article

Abstract: The customer demands of various products bring a challenge for manufacturers. They have to design customized products while maintaining economies of scale and low costs. In this paper, to address this challenge, four approaches are argued to help companies find out the optimal solutions of products’ performance and the maximum profit: (i) only platform modularity without component sharing (ii) only component sharing without platform modularity, (iii) using both platform modularity and component sharing to develop products, or iv) the products are developed individually from a given unshared components set. A theoretical model is proposed and the most profitable approach is …found to develop a whole new product family when uncertainty exists in the customer demand and economies of scale with pre-defined parameters. We find that, when consumers’ valuation is considered, the manufacturer may prefer to adopt platform or component sharing individually rather than combining them because the performance of high-end products using platform and component sharing strategies is worse than that using two strategies separately. If platform and component sharing are adopted, the high-end product is under designed, but the manufacturer can benefit from economies of scale. When economies of scale of the platform are greater than or equal to that of component sharing, the optimal performance level of low-end products under platform strategy is lower than that under component sharing strategy. Finally, the detailed numerical analysis provides support for the feasibility and effectiveness of the model. Show more

Keywords: Platform, component sharing, uncertainty theory

DOI: 10.3233/JIFS-210957

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3573-3589, 2021

Authors: Chen, Jing

Article Type: Research Article

Abstract: Efficient and reliable fresh agricultural products supply chain is the key to meet the demand of consumers for fresh agricultural products, and also the guarantee for suppliers to realize their economic benefits. Therefore, a multi-dimensional analysis model of agricultural products supply chain competition based on fuzzy mean value is proposed. Firstly, the information distribution model of multi-dimensional analysis of agricultural product supply chain competition is proposed. On this basis, the multi-dimensional analysis information scheduling fusion of agricultural product supply chain competition is processed. Then, the application of mean value fuzzy in agricultural product supply chain is analyzed. According to the …identification module of agricultural product information code, the fuzzy comprehensive evaluation model of supply chain and the mean fuzzy analytic hierarchy process, the competition of agricultural product supply chain is established Dimension analysis model. The experimental results show that the performance score of agricultural product supply chain is higher, the accuracy of supply chain information diagnosis is higher, and the clustering of agricultural product supply chain information diagnosis is better. Show more

Keywords: Mean fuzzy, agricultural product supply chain, competition multidimensional analysis model, mean fuzzy analytic hierarchy process

DOI: 10.3233/JIFS-210962

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3591-3602, 2021

Authors: Dong, Jin | Wang, Jian | Chen, Sen

Article Type: Research Article

Abstract: Manufacturing industry is the foundation of a country’s economic development and prosperity. At present, the data in manufacturing enterprises have the problems of weak correlation and high redundancy, which can be solved effectively by knowledge graph. In this paper, a method of knowledge graph construction in manufacturing domain based on knowledge enhanced word embedding model is proposed. The main contributions are as follows: (1) At the algorithmic level, this paper proposes KEWE-BERT, an end-to-end model for joint entity and relation extraction, which superimposes the token embedding and knowledge embedding output by BERT and TransR so as to improve the effect …of knowledge extraction; (2) At the application level, knowledge representation model ManuOnto and dataset ManuDT are constructed based on real manufacturing scenarios, and KEWE-BERT is used to construct knowledge graph from them. The knowledge graph constructed has rich semantic relations, which can be applied in actual production environment. Other than that, KEWE-BERT can extract effective knowledge and patterns from redundant texts in the enterprise, which providing a solution for enterprise data management. Show more

Keywords: BERT, knowledge graph construction, TransR, manufacturing, knowledge extraction

DOI: 10.3233/JIFS-210982

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3603-3613, 2021

Authors: Guo, Wenbo | Huang, Cheng | Niu, Weina | Fang, Yong

Article Type: Research Article

Abstract: In the software development process, many developers learn from code snippets in the open-source community to implement specific functions. However, few people think about whether these code have vulnerabilities, which provides channels for developing unsafe programs. To this end, this paper constructs a source code snippets vulnerability mining system named PyVul based on deep learning to automatically detect the security of code snippets in the open source community. PyVul builds abstract syntax tree (AST) for the source code to extract its code feature, and then introduces the bidirectional long-term short-term memory (BiLSTM) neural network algorithm to detect vulnerability codes. If …it is vulnerable code, the further constructed a multi-classification model could analyze the context discussion contents in associated threads, to classify the code vulnerability type based the content description. Compared with traditional detection methods, this method can identify vulnerable code and classify vulnerability type. The accuracy of the proposed model can reach 85%. PyVul also found 138 vulnerable code snippets in the real public open-source community. In the future, it can be used in the open-source community for vulnerable code auditing to assist users in safe development. Show more

Keywords: Open-source community, vulnerability mining, content analysis, BiLSTM

DOI: 10.3233/JIFS-211011

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3615-3628, 2021

Authors: Atmaca, S. | Zorlutuna, I.

Article Type: Research Article

Abstract: In 2020, r-near topological spaces on Near Approximation Spaces were introduced by Atmaca [1 ]. In this study, we introduce the concept of continuity on r-near topological spaces and examine some properties of it.

Keywords: Near set, r-near topology, continuity

DOI: 10.3233/JIFS-211017

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3629-3633, 2021

Authors: Shi, Zhanhong | Zhang, Dinghai

Article Type: Research Article

Abstract: Attribute significance is very important in multiple-attribute decision-making (MADM) problems. In a MADM problem, the significance of attributes is often different. In order to overcome the shortcoming that attribute significance is usually given artificially. The purpose of this paper is to give attribute significance computation formulas based on inclusion degree. We note that in the real-world application, there is a lot of incomplete information due to the error of data measurement, the limitation of data understanding and data acquisition, etc. Firstly, we give a general description and the definition of incomplete information systems. We then establish the tolerance relation for …incomplete linguistic information system, with the tolerance classes and inclusion degree, significance of attribute is proposed and the corresponding computation formula is obtained. Subsequently, for incomplete fuzzy information system and incomplete interval-valued fuzzy information system, the dominance relation and interval dominance relation is established, respectively. And the dominance class and interval dominance class of an element are got as well. With the help of inclusion degree, the computation formulas of attribute significance for incomplete fuzzy information system and incomplete interval-valued fuzzy information system are also obtained. At the same time, results show that the reduction of attribute set can be obtained by computing the significance of attributes in these incomplete information systems. Finally, as the applications of attribute significance, the attribute significance is viewed as attribute weights to solve MADM problems and the corresponding TOPSIS methods for three incomplete information systems are proposed. The numerical examples are also employed to illustrate the feasibility and effectiveness of the proposed approaches. Show more

Keywords: MADM, incomplete information systems, dominance relation, attribute significance

DOI: 10.3233/JIFS-211046

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3635-3651, 2021

Authors: Wang, Zhenjie | Cui, Wenxia | Jin, Wenbin

Article Type: Research Article

Abstract: This paper mainly considers the finite-time synchronization problem of fuzzy inertial cellular neural networks (FICNNs) with time-varying delays. By constructing the suitable Lyapunov functional, and using integral inequality techniques, several sufficient criteria have been proposed to ensure the finite-time synchronization for the addressed (FICNNs). Without applying the known finite-time stability theorem, which is widely used to solve the finite-time synchronization problems for (FICNNs). In this paper, the proposed method is relatively convenient to solve finite-time synchronization problem of the addressed system, this paper extends the research works on the finite-time synchronization of (FICNNs). Finally, numerical simulations illustrated verify the effectiveness …of the proposed results. Show more

Keywords: Finite-time synchronization, complex networks, time-varying delays, integral inequality

DOI: 10.3233/JIFS-211065

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3653-3666, 2021

Authors: Zhang, Xilong | Han, Meng | Wu, Hongxin | Li, Muhang | Chen, Zhiqiang

Article Type: Research Article

Abstract: With the rapid development of information technology, data streams in various fields are showing the characteristics of rapid arrival, complex structure and timely processing. Complex types of data streams make the classification performance worse. However, ensemble classification has become one of the main methods of processing data streams. Ensemble classification performance is better than traditional single classifiers. This article introduces the ensemble classification algorithms of complex data streams for the first time. Then overview analyzes the advantages and disadvantages of these algorithms for steady-state, concept drift, imbalanced, multi-label and multi-instance data streams. At the same time, the application fields of …data streams are also introduced which summarizes the ensemble algorithms processing text, graph and big data streams. Moreover, it comprehensively summarizes the verification technology, evaluation indicators and open source platforms of complex data streams mining algorithms. Finally, the challenges and future research directions of ensemble learning algorithms dealing with uncertain, multi-type, delayed, multi-type concept drift data streams are given. Show more

Keywords: Overview, ensemble classification, complex data streams, evaluation technology, domain data streams

DOI: 10.3233/JIFS-211100

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3667-3695, 2021

Authors: Ye, Aihui | Zhang, Runtong | Wu, Pei | Xing, Yuping

Article Type: Research Article

Abstract: Since the information quality in the online health community is very important for users to obtain valuable health information, information quality evaluation is a necessary research that involves a multi-attribute decision-making (MADM) problem. However, few researches have been done to address both the construction of evaluation criteria and the expression and processing of fuzzy information, especially in online health community. This paper proposes a novel evaluation framework of information service quality combined principal component analysis (PCA) method with the TOPSIS method under q-rung orthopair fuzzy set (q-ROFS) environment. An accurate evaluation criteria system is optimized by the PCA method, and …the q-ROF TOPSIS method is proposed to process larger space of fuzzy evaluation information and overcome information loss and information distortion, in which a new distance measure between q-ROFSs is defined and an entropy weight model is initiated to determine the unknown weight of attribute. Moreover, a numerical example is performed to prove the practicability and superiority of the method through comparative analysis, which gives clear results of information quality evaluation of four online health communities. This research ends with fuzzy decision-making theory and application, and provides references for standardizing and improving the information quality of online health communities. Show more

Keywords: q-Rung orthopair fuzzy set, TOPSIS method, multi-attribute decision-making, entropy measure, information quality evaluation

DOI: 10.3233/JIFS-211123

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3697-3714, 2021

Authors: Goraghani, Simin Saidi | Ali Borzooei, Rajab | Ahn, Sun Shin

Article Type: Research Article

Abstract: In recent years, A. Di Nola et al. studied the notions of MV -semiring and semimodules and investigated related results [9, 10, 12, 26 ]. Now in this paper, by using an MV -semiring and an MV -algebra, we introduce the new definition of MV -semimodule, study basic properties and find some examples. Then we study A -ideals on MV -semimodules and Q -ideals on MV -semirings, and by using them, we study the quotient structures of MV -semimodule. Finally, we present the notions of prime A -ideal, torsion free MV -semimodule and annihilator on MV -semimodule and we study …the relations among them. Show more

Keywords: MV-semiring, MV-algebra, MV-module, MV-semimodule, Q-ideal, prime A-ideal, 06D35, 16Y60

DOI: 10.3233/JIFS-211130

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3715-3726, 2021

Authors: Preethi, P. | Asokan, R. | Thillaiarasu, N. | Saravanan, T.

Article Type: Research Article

Abstract: Classical Handwriting recognition systems depend on manual feature extraction with a lot of previous domain knowledge. It’s difficult to train an optical character recognition system based on these requirements. Deep learning approaches are at the centre of handwriting recognition research, which has yielded breakthrough results in recent years. However, the rapid growth in the amount of handwritten data combined with the availability of enormous processing power necessitates an increase in recognition accuracy and warrants further investigation. Convolutional Neural Networks (CNNs) are extremely good at perceiving the structure of handwritten characters in ways that allow for the automatic extraction of distinct …features, making CNN the best method for solving handwriting recognition problems. In this research work, a novel CNN has built to modify the network structure with Orthogonal Learning Chaotic Grey Wolf Optimization (CNN-OLCGWO). This modification is adopted for evolutionarily optimizing the number of hyper-parameters. This proposed optimizer predicts the optimal values from the fitness computation and shows better efficiency when compared to various other conventional approaches. The ultimate target of this work is to endeavour a suitable path towards digitalization by offering superior accuracy and better computation. Here, MATLAB 2018b has been used as the simulation environment to measure metrics like accuracy, recall, precision, and F-measure. The proposed CNN- OLCGWO offers a superior trade-off in contrary to prevailing approaches. Show more

Keywords: Convolutional neural networks, grey wolf optimization, orthogonal learning, chaotic map, digit recognition

DOI: 10.3233/JIFS-211242

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3727-3737, 2021

Authors: Zheng, Jian | Wang, Jianfeng | Chen, Yanping | Chen, Shuping | Chen, Jingjin | Zhong, Wenlong | Wu, Wenling

Article Type: Research Article

Abstract: Neural networks can approximate data because of owning many compact non-linear layers. In high-dimensional space, due to the curse of dimensionality, data distribution becomes sparse, causing that it is difficulty to provide sufficient information. Hence, the task becomes even harder if neural networks approximate data in high-dimensional space. To address this issue, according to the Lipschitz condition, the two deviations, i.e., the deviation of the neural networks trained using high-dimensional functions, and the deviation of high-dimensional functions approximation data, are derived. This purpose of doing this is to improve the ability of approximation high-dimensional space using neural networks. Experimental results …show that the neural networks trained using high-dimensional functions outperforms that of using data in the capability of approximation data in high-dimensional space. We find that the neural networks trained using high-dimensional functions more suitable for high-dimensional space than that of using data, so that there is no need to retain sufficient data for neural networks training. Our findings suggests that in high-dimensional space, by tuning hidden layers of neural networks, this is hard to have substantial positive effects on improving precision of approximation data. Show more

Keywords: Data sparsity, high-dimensional function, high-dimensional space, neural networks

DOI: 10.3233/JIFS-211417

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3739-3750, 2021

Authors: Wang, Jih-Chang | Chen, Ting-Yu

Article Type: Research Article

Abstract: The theory involving T-spherical fuzziness provides an exceptionally good tool to efficiently manipulate the impreciseness, equivocation, and vagueness inherent in multiple criteria assessment and decision-making processes. By exploiting the notions of score functions and distance measures for complex T-spherical fuzzy information, this paper aims to propound an innovational T-spherical fuzzy ELECTRE (ELimination Et Choice Translating REality) approach to handling intricate and convoluted evaluation problems. Several newly-created score functions are employed from the comparative perspective to constitute a core procedure concerning concordance and discordance determination in the current T-spherical fuzzy ELECTRE method. By the agency of a realistic application, this paper …appraises the usefulness and efficacy of available score functions in the advanced ELECTRE mechanism under T-spherical fuzzy uncertainties. This paper incorporates two forms of Minkowski distance measures into the core procedure; moreover, the effectuality of the advocated measure in differentiating T-spherical fuzzy information is validated. The effectiveness outcomes of the evolved method have been investigated through the medium of an investment decision regarding potential company options for extending the business scope. The real-world application also explores the comparative advantages of distinct score functions in tackling multiple criteria decision-making tasks. Finally, this paper puts forward a conclusion and future research directions. Show more

Keywords: T-spherical fuzziness, multiple criteria assessment, decision-making, score function, T-spherical fuzzy elimination and choice translating reality (ELECTRE)

DOI: 10.3233/JIFS-211431

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3751-3770, 2021

Authors: More, Sujeet | Singla, Jimmy

Article Type: Research Article

Abstract: Deep learning has shown outstanding efficiency in medical image segmentation. Segmentation of knee tissues is an important task for early diagnosis of rheumatoid arthritis (RA) with selecting variant features. Automated segmentation and feature extraction of knee tissues are desirable for faster and reliable analysis of large datasets and further diagnosis. In this paper a novel architecture called as Discrete-MultiResUNet, which is a combination of discrete wavelet transform (DWT) with MultiResUNet architecture is applied for feature extraction and segmentation, respectively. This hybrid architecture captures more prominent features from the knee magnetic resonance image efficiently with segmenting vital knee tissues. The hybrid …model is evaluated on the knee MR dataset demonstrating outperforming performance compared with baseline models. The model achieves excellent segmentation performance accuracy of 96.77% with a dice coefficient of 98%. Show more

Keywords: MultiResUNet, discrete wavelet transform, dice similarity coefficient, rheumatoid arthritis, segmentation

DOI: 10.3233/JIFS-211459

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3771-3781, 2021

Authors: Zhang, Shanshan | Gao, Hui | Wei, Guiwu | Chen, Xudong

Article Type: Research Article

Abstract: The Multi-attribute group decision making (MAGDM) problem is an interesting everyday problem full of complexity and ambiguity. As an extended form of fuzzy sets, intuitionistic fuzzy sets (IFSs) can provide decision-makers (DMs) with a wider range of preferences for MAGDM. The grey relational analysis (GRA) is an effective method for dealing with MAGDM problems. However, in view of the incomplete and asymmetric information and the influence of DMs’ psychological factors on the decision result, we develop a new model that GRA method based on cumulative prospect theory (CPT) under the intuitionistic fuzzy environment. Moreover, the weight of attribute is calculated …by entropy weight, so as to distinguish the importance level of attributes, which greatly improves the credibility of the selected scheme. simultaneously, the proposed method is used to the selection of optimal green suppliers for testifying the availability of this new model and the final comparison between this new method and the existing methods further verify the reliability. In addition, the proposed method provides some references for other selection problems. Show more

Keywords: Multi-attribute group decision making (MAGDM), grey relational analysis (GRA) method, cumulative prospect theory (CPT), intuitionistic fuzzy sets (IFSs)

DOI: 10.3233/JIFS-211461

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3783-3795, 2021

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

Article Type: Research Article

Abstract: Knowledge graph reasoning or completion aims at inferring missing facts based on existing ones in a knowledge graph. In this work, we focus on the problem of open-world knowledge graph reasoning—a task that reasons about entities which are absent from KG at training time (unseen entities). Unfortunately, the performance of most existing reasoning methods on this problem turns out to be unsatisfactory. Recently, some works use graph convolutional networks to obtain the embeddings of unseen entities for prediction tasks. Graph convolutional networks gather information from the entity’s neighborhood, however, they neglect the unequal natures of neighboring nodes. To resolve this …issue, we present an attention-based method named as NAKGR, which leverages neighborhood information to generate entities and relations representations. The proposed model is an encoder-decoder architecture. Specifically, the encoder devises an graph attention mechanism to aggregate neighboring nodes’ information with a weighted combination. The decoder employs an energy function to predict the plausibility for each triplets. Benchmark experiments show that NAKGR achieves significant improvements on the open-world reasoning tasks. In addition, our model also performs well on the closed-world reasoning tasks. Show more

Keywords: Open-world knowledge graph reasoning, neighborhood information, graph attention networks, knowledge representation learning

DOI: 10.3233/JIFS-211889

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3797-3808, 2021

Authors: Xie, Ning | Chen, Dengkai | Fan, Yu | Zhu, Mengya

Article Type: Research Article

Abstract: In the development of product design, one of the elements of market competition for products is to meet the Kansei needs of users. Compared to features, users pay more attention to whether products can match their emotions, which is Kansei needs. The product developers are eager to get the Kansei needs of users more accurately and conveniently. This paper takes the computer cloud platform as the carrier and based on the collaborative filtering algorithm. We used personalized double matrix recommendation algorithm as the core, and the adjectives dimensionality reduction method to filter the image tags to simplify the users’ rating …process and improve the recommendation efficiency. Finally, we construct a Kansei needs acquisition model to quickly and easily obtain the Kansei needs of users. We verify the model using the air purifier as a subject. The results of the case show that the model can find out the user’s Kansei needs more quickly. When the data is more, the prediction will be more accurate and timely. Show more

Keywords: Kansei needs, image tags, double matrix recommendation algorithm, adjectives dimensionality reduction, cloud platform

DOI: 10.3233/JIFS-191241

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3809-3820, 2021

Authors: Li, Dong | Wang, Yuejiao | Li, Muhao | Sun, Xin | Pan, Jingchang | Ma, Jun

Article Type: Research Article

Abstract: In the real world, a large number of social systems can be modeled as signed social networks including both positive and negative relationships. Influence maximization in signed social networks is an interesting and significant research direction, which has gained some attention. All of existing studies mainly focused on positive influence maximization (PIM) problem. The goal of the PIM problem is to select the seed set with maximum positive influence in signed social networks. However, the selected seed set with maximum positive influence may also has a large amount of negative influence, which will cause bad effects in the real applications. …Therefore, maximizing purely positive influence is not the final and best goal in signed social networks. In this paper, we introduce the concept of net positive influence and propose the net positive influence maximization (NPIM) problem for signed social networks, to select the seed set with as much positive influence as possible and as less negative influence as possible. Additionally, we prove that the objective function of NPIM problem under polarity-related independent cascade model is non-monotone and non-submodular, which means the traditional greedy algorithm is not applicable to the NPIM problem. Thus, we propose an improved R-Greedy algorithm to solve the NPIM problem. Extensive experiments on two Epinions and Slashdot datasets indicate the differences between positive influence and net positive influence, and also demonstrate that our proposed solution performs better than the state-of-the-art methods in terms of promoting net positive influence diffusion in less running time. Show more

Keywords: Influence maximization, signed social networks, net positive influence, polarity

DOI: 10.3233/JIFS-191908

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3821-3832, 2021

Authors: Adak, Sudip | Mahapatra, G.S.

Article Type: Research Article

Abstract: This paper develops a fuzzy two-layer supply chain for manufacturer and retailer with defective and non-defective types of products. The manufacturer produces up to a specific time, including faulty and non-defective items, and after the screening, the non-defective item sends to the retailer. The retailer’s strategy is to do the screening of items received from the manufacturer; subsequently, the perfect quality items are used to fulfill the customer’s demand, and the defective items are reworked. The retailer considers that customer demand is time and reliability dependent. The supply chain considers probabilistic deterioration for the manufacturer and retailers along with the …strategies such as production rate, unit production cost, cost of idle time of manufacturer, screening, rework, etc. The optimum average profit of the integrated model is evaluated for both the cases crisp and fuzzy environments. Managerial insights and the effect of changes in the parameters’ values on the optimal inventory policy under fuzziness are presented. Show more

Keywords: Two-layer supply chain, deterioration, imperfect items, trapezoidal fuzzy number, reliability, advertisement

DOI: 10.3233/JIFS-200562

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3833-3847, 2021

Authors: Dai, Ziwei | Zhang, Zhiyong | Chen, Mingzhou

Article Type: Research Article

Abstract: Task scheduling is important in cloud manufacturing because of customers’ increasingly individualized demands. However, when various changes occur, a previous optimal schedule may become non-optimal or even infeasible owing to the uncertainty of the real manufacturing environment where dynamic task arrival over time is a vital source. In this paper, we propose a novel collaborative task scheduling (CTS) model dealing with new task arrival which considers multi-supply chain collaboration. We present an improved multi-population biogeography-based optimization (IMPBBO) algorithm that uses a matrix-based solution representation and integrates the multi-population strategy, local search for the best solution, and the collaboration mechanism, for …determining the optimal schedule. A series of experiments are conducted for verifying the effectiveness of the IMPBBO algorithm for solving the CTS model by comparing it with five other algorithms. The experimental results concerning average best values obtained by the IMPBBO algorithm are better than that obtained by comparison algorithms for 41 out of 45 cases, showing its superior performance. Wilcoxon-test has been employed to strengthen the fact that IMPBBO algorithm performs better than five comparison algorithms. Show more

Keywords: Cloud manufacturing, task scheduling, multi-supply chain collaboration, new task arrival, biogeography-based optimization

DOI: 10.3233/JIFS-201066

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3849-3872, 2021

Authors: Mohammadi Moghadam, Hooman | Foroozan, Hossein | Gheisarnejad, Meysam | Khooban, Mohammad-Hassan

Article Type: Research Article

Abstract: Recently, the Digital Twin (DT) technology, which joints the physical environment and virtual space, has drawn more attention in industry and research academic plans. In general, the virtual model representations of the physical objects are created in the DT manner to simulates the characteristics and behaviors of the real-word system. Applying a supervisory system not only can reduce the failures of components, but also preserve the overall costs associated with the system at a minimum. This paper reviews the DT applications in the power system, while its advantages in wind turbines, solar panels, power electronic converter, and shipboard electrical system …will be briefly discussed. The potential benefits of contemporary technologies to ameliorate the DT in the industry are studied. Besides, it provides a great technique to assess and analyze system performance. As a basis for DT, various new emerging developments as an example of artificial intelligence (AI), big data, the internet of things (IoT), and 5 G are reviewed. Show more

Keywords: Index Terms: Digital Twin (DT), Artificial Intelligence (AI), ship power system, big data, Internet of Things (IoT)

DOI: 10.3233/JIFS-201885

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3873-3893, 2021

Authors: Liu, Peide | Zhang, Pei

Article Type: Research Article

Abstract: A normal wiggly hesitant fuzzy set is a very useful tool to mine the potential uncertain information given by decision makers, which is considered as an extension of hesitant fuzzy set and can improve the effectiveness of decision making. Power average operator can relieve the impact on decision result of unreasonable data, and the generalized Maclaurin symmetric mean operator (GMSM) is an extension of Maclaurin symmetric mean operator with wider range of applications, which can consider the relationship among decision attributes. By integrating the advantages of them, in this paper, we develop the normal wiggly hesitant fuzzy power GMSM (NWHFPGMSM) …operator and its weighted form based on the distance measure of two normal wiggly hesitant fuzzy elements, then we further discuss their properties and some special cases. Thus, a new multi-attribute decision making method based on the NWHFPGMSM operator under normal wiggly hesitant fuzzy environment is proposed. Finally, we select some examples to illustrate the effectiveness and superiority of the proposed method in this paper through comparison and analysis with other methods. Show more

Keywords: Normal wiggly hesitant fuzzy set, power average operator, generalized maclaurin symmetric mean operator, multi-attribute decision making

DOI: 10.3233/JIFS-202112

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3895-3920, 2021

Authors: Mohammady Talvar, Houshyar | Haj Seyyed Javadi, Hamid | Navidi, Hamidreza | Rezakhani, Afshin

Article Type: Research Article

Abstract: IoT-based network systems use a modern architecture called fog computing, In which data providing data services is economical with low latency. This paper tends to solve the challenge of resource allocation in fog computing. Solving the resource allocation challenge leads to increased profits, economic savings, and optimal computing systems use. Here resource allocation is improved by making use of the combined algorithm Nash equilibrium and auction. In the proposed method, each player is assigned a matrix. Each player matrix includes fog nodes (FNs), data service subscribers (DSSs), and data service operators (DSOs). Each player generates the best strategy based on …the other players strategy in all stages of the algorithm. The simulation results show that FNs profit in the combined Nash and Auction equilibrium algorithms is superior to the Stackelberg game algorithm. Show more

Keywords: Fog computing, resource allocation, IoT, nash equilibrium, auction algorithm

DOI: 10.3233/JIFS-202122

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3921-3932, 2021

Authors: Adu, Kwabena | Yu, Yongbin | Cai, Jingye | Mensah, Patrick Kwabena | Owusu-Agyemang, Kwabena

Article Type: Research Article

Abstract: Convolutional neural networks (CNNs) for automatic classification and medical image diagnosis have recently displayed a remarkable performance. However, the CNNs fail to recognize original images rotated and oriented differently, limiting their performance. This paper presents a new capsule network (CapsNet) based framework known as the multi-lane atrous feature fusion capsule network (MLAF-CapsNet) for brain tumor type classification. The MLAF-CapsNet consists of atrous and CLAHE, where the atrous increases receptive fields and maintains spatial representation, whereas the CLAHE is used as a base layer that uses an improved adaptive histogram equalization (AHE) to enhance the input images. The proposed method is …evaluated using whole-brain tumor and segmented tumor datasets. The efficiency performance of the two datasets is explored and compared. The experimental results of the MLAF-CapsNet show better accuracies (93.40% and 96.60%) and precisions (94.21% and 96.55%) in feature extraction based on the original images from the two datasets than the traditional CapsNet (78.93% and 97.30%). Based on the two datasets’ augmentation, the proposed method achieved the best accuracy (98.48% and 98.82%) and precisions (98.88% and 98.58%) in extracting features compared to the traditional CapsNet. Our results indicate that the proposed method can successfully improve brain tumor classification problems and support radiologists in medical diagnostics. Show more

Keywords: Brain tumor classification, capsule networks, deep neural network, atrous convolution, dynamic routing

DOI: 10.3233/JIFS-202261

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3933-3950, 2021

Authors: Geng, Kaifeng | Ye, Chunming

Article Type: Research Article

Abstract: Facing the worsening environmental problems, green manufacturing and sustainable development have attracted much attention. Aiming at the energy-efficient distributed re-entrant hybrid flow shop scheduling problem considering the customer order constraints (EDORHFSP) under Time-of-Use (TOU) electricity price, a mathematical model is established to minimize the maximum completion time and total consumption energy cost. In the study, some customer orders require production in multiple factories and jobs belonging to the same customer order must be processed in one factory. Firstly, a memetic algorithm (MA) was proposed to solve the problem. To improve the performance of the algorithm, encoding and decoding methods, energy …cost saving procedure, three heuristic rules about the population initialization and some neighborhood search methods are designed. Then, Taguchi method is adopted to research the influence of parameters setting. Lastly, numerical experiments demonstrate the effectiveness and superiority of MA for the EDORHFSP. Show more

Keywords: Energy-efficient, memetic algorithm, Time-of-Use electricity price, distributed re-entrant hybrid flow shop scheduling, customer order constraints

DOI: 10.3233/JIFS-202963

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3951-3971, 2021

Authors: Chen, Lifang | Wei, Mengru

Article Type: Research Article

Abstract: With the popularity of depth sensors and 3D scanners, 3D point cloud has developed rapidly. 3D scene understanding based on deep learning has become a research hotspot. However, many existing networks failed to fully consider the local structures of point clouds, limiting their abilities to exploit the complicated relationships between points. In this paper, we propose Enriching Local Features Network (ELF-Net), which enriches local features of point clouds. We propose Local Points Encoding Module (LPEM) and Feature Concatenate Module (FCM) in our network. Specifically, LPEM is designed to encode the information of eight orientations and 3D coordinate information of local …points. We stack the encoding units to achieve multi-scale representation, which is conducive to obtaining robustness and capturing details of the network. In Set Abstraction (SA) module, we apply farthest point sampling (FPS) method to sample the initial points and ball query method is used to group the neighboring points within a radius. FCM is designed to update the representations of local points by applying graph attention mechanism in local regions, which aims to enrich neighboring point feature representations. Finally, our network also proposes a new multivariate loss function, which combines the Center Loss function and Cross Entropy loss function to act on the classification branch. Experimental results show the effectiveness of our proposed network on ModelNet40 (achieves 92.35% accuracy), ScanNet (achieves 85.46% accuracy) and S3DIS (achieves 86.4% accuracy) datasets. Show more

Keywords: Point cloud classification and segmentation, local points encoding module, feature concatenate module, multivariate loss function

DOI: 10.3233/JIFS-210065

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3973-3983, 2021

Authors: Wan, Quan | Wu, Lin | Yu, Zhengtao

Article Type: Research Article

Abstract: Initial results of neural architecture search (NAS) in natural language processing (NLP) have been achieved, but the search space of most NAS methods is based on the simplest recurrent cell and thus does not consider the modeling of long sequences. The remote information tends to disappear gradually when the input sequence is long, resulting in poor model performance. In this paper, we present an approach based on dual cells to search for a better-performing network architecture. We construct a search space that is more compatible with language modeling tasks by adding an information storage cell inside the search cell, so …that we can make better use of the remote information of the sequence and improve the performance of the model. The language model searched by our method achieves better results than those of the baseline method on the Penn Treebank data set and WikiText-2 data set. Show more

Keywords: Neural architecture search, natural language processing, recurrent neural network

DOI: 10.3233/JIFS-210207

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3985-3992, 2021

Authors: Mu, Tianshi | Lin, Kequan | Zhang, Huabing | Wang, Jian

Article Type: Research Article

Abstract: Deep learning is gaining significant traction in a wide range of areas. Whereas, recent studies have demonstrated that deep learning exhibits the fatal weakness on adversarial examples. Due to the black-box nature and un-transparency problem of deep learning, it is difficult to explain the reason for the existence of adversarial examples and also hard to defend against them. This study focuses on improving the adversarial robustness of convolutional neural networks. We first explore how adversarial examples behave inside the network through visualization. We find that adversarial examples produce perturbations in hidden activations, which forms an amplification effect to fool the …network. Motivated by this observation, we propose an approach, termed as sanitizing hidden activations, to help the network correctly recognize adversarial examples by eliminating or reducing the perturbations in hidden activations. To demonstrate the effectiveness of our approach, we conduct experiments on three widely used datasets: MNIST, CIFAR-10 and ImageNet, and also compare with state-of-the-art defense techniques. The experimental results show that our sanitizing approach is more generalized to defend against different kinds of attacks and can effectively improve the adversarial robustness of convolutional neural networks. Show more

Keywords: Adversarial examples, sanitizing hidden activations, adversarial robustness, convolutional neural networks

DOI: 10.3233/JIFS-210371

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3993-4003, 2021

Authors: Kazemi, Mohsen | Niknam, Taher | Bahmani-Firouzi, Bahman | Nafar, Mehdi

Article Type: Research Article

Abstract: This paper uses the coordinated energy management strategy for different sources and storages in the framework of flexible grid-connected energy hubs that participate in the day-ahead (DA) energy and reserve markets. In the base problem, this method maximizes the difference between the expected revenue of hubs gained by selling energy and reserve power in the proposed markets and the expected cost of lost flexibility (COLF). Also, it is subject to linearized optimal power flow (LOPF) equations in the electricity, gas and district heating systems, as well as hub constraints including different sources, storages and reserve models. This problem contains uncertainties …of load, market price, reserve requirement, renewable power and hub mobile storages parameters. Therefore, the hybrid stochastic/robust optimization (HSRO) is suitable to model these uncertain parameters and obtain robust capability for the hub to improve the system flexibility. Accordingly, the bounded uncertainty-based robust optimization (BURO) is used in this paper to model the uncertainty of hub mobile storages to achieve the hub robust potential in improving the system flexibility, and other uncertain parameters are modeled according to scenario-based stochastic programming (SBSP). Finally, the proposed strategy is implemented on a standard test system. The obtained numerical results confirm the capability of the suggested scheme in improving the economic status of sources and storages using the coordinated energy management strategy in the form of an energy hub, as well as enhancing economic conditions, operation, and flexibility of energy networks thanks to hubs for having access to optimal scheduling. Show more

Keywords: Coordinated energy management, cost of lost flexibility, energy and reserve market, flexible grid-connected energy hub, hybrid stochastic/robust optimization

DOI: 10.3233/JIFS-201284

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4005-4020, 2021

Authors: Faragallah, Osama S. | Muhammed, Abdullah N. | Taha, Taha S. | Geweid, Gamal G.N.

Article Type: Research Article

Abstract: This paper presents a new approach to the multi-modal medical image fusion based on Principal Component Analysis (PCA) and Singular value decomposition (SVD).The main objective of the proposed approach is to facilitate its implementation on a hardware unit, so it works effectively at run time. To evaluate the presented approach, it was tested in fusing four different cases of a registered CT and MRI images. Eleven quality metrics (including Mutual Information and Universal Image Quality Index) were used in evaluating the fused image obtained by the proposed approach, and compare it with the images obtained by the other fusion approaches. …In experiments, the quality metrics shows that the fused image obtained by the presented approach has better quality result and it proved effective in medical image fusion especially in MRI and CT images. It also indicates that the paper approach had reduced the processing time and the memory required during the fusion process, and leads to very cheap and fast hardware implementation of the presented approach. Show more

Keywords: Image fusion, PCA, SVD, medical images, fusion

DOI: 10.3233/JIFS-202884

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4021-4033, 2021

Authors: Gao, Jinding

Article Type: Research Article

Abstract: In order to solve some function optimization problems, Population Dynamics Optimization Algorithm under Microbial Control in Contaminated Environment (PDO-MCCE) is proposed by adopting a population dynamics model with microbial treatment in a polluted environment. In this algorithm, individuals are automatically divided into normal populations and mutant populations. The number of individuals in each category is automatically calculated and adjusted according to the population dynamics model, it solves the problem of artificially determining the number of individuals. There are 7 operators in the algorithm, they realize the information exchange between individuals the information exchange within and between populations, the information diffusion …of strong individuals and the transmission of environmental information are realized to individuals, the number of individuals are increased or decreased to ensure that the algorithm has global convergence. The periodic increase of the number of individuals in the mutant population can greatly increase the probability of the search jumping out of the local optimal solution trap. In the iterative calculation, the algorithm only deals with 3/500∼1/10 of the number of individual features at a time, the time complexity is reduced greatly. In order to assess the scalability, efficiency and robustness of the proposed algorithm, the experiments have been carried out on realistic, synthetic and random benchmarks with different dimensions. The test case shows that the PDO-MCCE algorithm has better performance and is suitable for solving some optimization problems with higher dimensions. Show more

Keywords: Swarm intelligence optimization algorithm, population dynamics, environmental pollution, microbial control

DOI: 10.3233/JIFS-210127

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4035-4049, 2021

Authors: Rampriya, R.S. | Sabarinathan, | Suganya, R.

Article Type: Research Article

Abstract: In the near future, combo of UAV (Unmanned Aerial Vehicle) and computer vision will play a vital role in monitoring the condition of the railroad periodically to ensure passenger safety. The most significant module involved in railroad visual processing is obstacle detection, in which caution is obstacle fallen near track gage inside or outside. This leads to the importance of detecting and segment the railroad as three key regions, such as gage inside, rails, and background. Traditional railroad segmentation methods depend on either manual feature selection or expensive dedicated devices such as Lidar, which is typically less reliable in railroad …semantic segmentation. Also, cameras mounted on moving vehicles like a drone can produce high-resolution images, so segmenting precise pixel information from those aerial images has been challenging due to the railroad surroundings chaos. RSNet is a multi-level feature fusion algorithm for segmenting railroad aerial images captured by UAV and proposes an attention-based efficient convolutional encoder for feature extraction, which is robust and computationally efficient and modified residual decoder for segmentation which considers only essential features and produces less overhead with higher performance even in real-time railroad drone imagery. The network is trained and tested on a railroad scenic view segmentation dataset (RSSD), which we have built from real-time UAV images and achieves 0.973 dice coefficient and 0.94 jaccard on test data that exhibits better results compared to the existing approaches like a residual unit and residual squeeze net. Show more

Keywords: Railroad aerial images, efficient net, modified residual net, attention layer, semantic segmentation

DOI: 10.3233/JIFS-210349

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4051-4068, 2021

Authors: Pan, Hongguang | Wen, Fan | Huang, Xiangdong | Lei, Xinyu | Yang, Xiaoling

Article Type: Research Article

Abstract: In the field of super-resolution image reconstruction, as a learning-based method, deep plug-and-play super-resolution (DPSR) algorithm can be used to find the blur kernel by using the existing blind deblurring methods. However, DPSR is not flexible enough in processing images with high- and low-frequency information. Considering a channel attention mechanism can distinguish low-frequency information and features in low-resolution images, in this paper, we firstly introduce this mechanism and design a new residual channel attention networks (RCAN); then the RCAN is adopted to replace deep feature extraction part in DPSR to achieve the adaptive adjustment of channel characteristics. Through four test …experiments based on Set5, Set14, Urban100 and BSD100 datasets, we find that, under different blur kernels and different scale factors, the average peak signal to noise ratio (PSNR) and structural similarity (SSIM) values of our proposed method increase by 0.31dB and 0.55%, respectively; under different noise levels, the average PSNR and SSIM values increase by 0.26dB and 0.51%, respectively. Show more

Keywords: image reconstruction, channel attention mechanism, residual channel attention networks, blur kernel

DOI: 10.3233/JIFS-202696

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4069-4078, 2021

Authors: Ma, Lina | Xu, Fengju | Wang, Lihua | Taslima, Akther

Article Type: Research Article

Abstract: Capital enrichment (CE) results from capital flows, which reflect the capital distribution among different regions and industries. This paper constructs the evaluation model of resource allocation efficiency from the perspective of capital and innovation resources. It expounds on CE’s theoretical mechanism by using the panel data from 2011 to 2018 for system GMM estimation. It finds that the manufacturing capital allocation efficiency (CAE) and innovation resource allocation efficiency (IRAE) show a volatile development trend. Both static and dynamic panel models show that there is a significant U-shaped curvilinear relationship between CE and CAE, CE and IRAE. CE’s inhibitory effect on …CAE and IRAE decreases with the improvement of CE until it exceeds the critical value of 8.27 and 8.93. After that, its impact on CAE and IRAE changes from negative to positive. Show more

Keywords: China’s manufacturing industry, capital enrichment, capital allocation efficiency, innovation resource allocation efficiency

DOI: 10.3233/JIFS-202856

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4079-4095, 2021

Authors: Rodríguez, Antonio

Article Type: Research Article

Abstract: Taking concepts from supply management, we developed a specification-assessment-compliance approach to obtain a transparent multi-criteria decision-making method. We designed an artificial-neuron-like node that allows the implementation of networks to represent hierarchies of evaluation criteria. A new graphical model based on functions in the unit segment uses the concept of Pythagorean fuzzy set (PFS). The specification PFSs’ entropies modulate the widths of one-sided triangular fuzzy numbers (TFNs) with positive slopes that become the evaluation nodes’ activation functions. All the specifications refer to the same point to facilitate the evaluation and ensure coherence. One-sided TFNs with negative slopes biunivocally represent the assessment …PFSs at the input layer of the network. A risk case study on the options for the outsourcing of an information technology development project shows the proposed method’s implementation. We compare the results with those of the application of two other previous methods. Show more

Keywords: Multi-criteria decision-making, Pythagorean fuzzy set, triangular fuzzy number, artificial neuron, information entropy

DOI: 10.3233/JIFS-210029

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4097-4108, 2021

Authors: Riaz, Muhammad | Garg, Harish | Farid, Hafiz Muhammad Athar | Aslam, Muhammad

Article Type: Research Article

Abstract: The low-carbon supply chain management is big a challenge for the researchers due to the rapid increase in global warming and environmental concerns. With the advancement of the environmental concerns and social economy, it is an unavoidable choice for a business to achieve sustainable growth for low-carbon supply chain management. Since the root of the chain depends upon the supplier selection and choosing an excellent low-carbon supply. Green supplier selection is one of the most crucial activities in low-carbon supply chain management, it is critical to develop rigorous requirements and a system for selection in low-carbon green supply chain management …(LCGSCM). A q-rung orthopair fuzzy number (q-ROFN) is pair of membership degree (MD) and non-membership degrees (NMD) which is reliable to address uncertainties in the various real-life problems. This article sets out a decision analysis approach for interactions between MDs and NMDs with the help of q-ROFNs. For this objective, we develop new aggregation operators (AOs) named as, q-rung orthopair fuzzy interaction weighted averaging (q-ROFIWA) operator, q-rung orthopair fuzzy interaction ordered weighted averaging (q-ROFIOWA) operator, q-rung orthopair fuzzy interaction hybrid averaging (q-ROFIHA) operator, q-rung orthopair fuzzy interaction weighted geometric (q-ROFIWG) operator, q-rung orthopair fuzzy interaction ordered weighted geometric (q-ROFIOWG) operator and q-rung orthopair fuzzy interaction hybrid geometric (q-ROFIHG) operator. These AOs define an advanced approach for information fusion and modeling uncertainties in multi-criteria decision-making (MCDM). At the end, a robust MCDM approach based on newly developed AOs is developed. Some significant properties of these AOS are analyzed and the efficiency of the developed approach is assessed with a practical application towards sustainable low-carbon green supply chain management. Show more

Keywords: MCDM, Aggregation operators, interaction relation, low-carbon green supply chain management

DOI: 10.3233/JIFS-210506

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4109-4126, 2021

Authors: Balamurugan, S. | Nageswari, S.

Article Type: Research Article

Abstract: Modular Multilevel Converter (MMC) plays a vital role in high voltage industries because of its high rating power conversion. Due to its usage in high voltage rating power conversion and switched capacitor usage in MMC structure, there arises a problem of unbalanced capacitor voltage, which causes circulating current and disturbance in output current regulation. To manage these problematic parameters, a FOPID (Fractional Order Proportional Integral Derivative) controller has been utilized, due to its dynamic tracking and fast response. Secondly, the gain values of FOPID are not efficient, and they are optimized for each control group at all times of MMC …working conditions. To provide a dynamic gain value by considering the dynamic change of error tracking parameters, Wild Spider Foraging Optimization (WSFO) algorithm has been developed based on the foraging behaviour of wild spider searching food (gain values) in view of changing the error of tracking parameters. The proposed algorithm has been evaluated in MATLAB Simulink by modeling the MMC structure with FOPID controller. The parameters of FOPID are optimized by bio-inspired algorithms like WSFO, Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). The outcomes of the proposed WSFO-FOPID provide minimum circulating current and effectively balance the capacitor voltage in MMC. When the effectiveness of the results has been verified with the existing ABC and PSO optimization approaches, the proposed algorithm outperforms. Show more

Keywords: Modular multilevel converter, wild spider foraging optimization, circulating current, total harmonic distortion

DOI: 10.3233/JIFS-210528

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4127-4147, 2021

Authors: Li, JX | Zhao, H | Zhu, S.P | Huang, H. | Miao, YJ | Jiang, ZY

Article Type: Research Article

Abstract: The classification of fresh tobacco leaves during the picking process plays an important role in the subsequent roasting. In this paper, a lightweight convolutional neural network is used to detect the maturity of tobacco leaves quickly. Fresh tobacco leaves in the datasets are divided into 3 categories by the picking position, and each category is divided into 4 maturity levels and finally gets 12 types of tobacco leaves with different maturity. To ensure the lightweight of the model, the new network is based on the MobileNetV2 to establish. By utilizing shortcut operation, the shallow network information is preserved, and network …degradation is suppressed. In the tobacco leaf datasets we obtained, the improved network has superior performance and compared with other classic networks, the model size and the number of operations have been reduced. Show more

Keywords: Tobacco classification, lightweight network, MobileNetV2, shortcut

DOI: 10.3233/JIFS-210640

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4149-4158, 2021

Authors: Lei, Deming | Xi, Bingjie

Article Type: Research Article

Abstract: Distributed scheduling has attracted much attention in recent years; however, distributed scheduling problem with uncertainty is seldom considered. In this study, fuzzy distributed two-stage hybrid flow shop scheduling problem (FDTHFSP) with sequence-dependent setup time is addressed and a diversified teaching-learning-based optimization (DTLBO) algorithm is applied to optimize fuzzy makespan and total agreement index. In DTLBO, multiple classes are constructed and categorized into two types according to class quality. Different combinations of global search and neighborhood search are used in two kind of classes. A temporary class with multiple teachers is built based on Pareto rank and difference index and evolved …in a new way. Computational experiments are conducted and results demonstrate that the main strategies of DTLBO are effective and DTLBO has promising advantages on solving the considered problem. Show more

Keywords: Two-stage hybrid flow shop scheduling, distributed scheduling, fuzzy scheduling, teaching-learning-based optimization

DOI: 10.3233/JIFS-210764

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4159-4173, 2021

Authors: Yan, Zhiyu | Lv, Shuang

Article Type: Research Article

Abstract: Accurate prediction of traffic flow is of great significance for alleviating urban traffic congestions. Most previous studies used historical traffic data, in which only one model or algorithm was adopted by the whole prediction space and the differences in various regions were ignored. In this context, based on time and space heterogeneity, a Classification and Regression Trees-K-Nearest Neighbor (CART-KNN) Hybrid Prediction model was proposed to predict short-term taxi demand. Firstly, a concentric partitioning method was applied to divide the test area into discrete small areas according to its boarding density level. Then the CART model was used to divide the …dataset of each area according to its temporal characteristics, and KNN was established for each subset by using the corresponding boarding density data to estimate the parameters of the KNN model. Finally, the proposed method was tested on the New York City Taxi and Limousine Commission (TLC) data, and the traditional KNN model, backpropagation (BP) neural network, long-short term memory model (LSTM) were used to compare with the proposed CART-KNN model. The selected models were used to predict the demand for taxis in New York City, and the Kriging Interpolation was used to obtain all the regional predictions. From the results, it can be suggested that the proposed CART-KNN model performed better than other general models by showing smaller mean absolute percentage error (MAPE) and root mean square error (RMSE) value. The improvement of prediction accuracy of CART-KNN model is helpful to understand the regional demand pattern to partition the boarding density data from the time and space dimensions. The partition method can be extended into many models using traffic data. Show more

Keywords: Short-term taxi demand forecast, CART-KNN hybrid prediction model, spatial and temporal heterogeneity, concentric partitioning, time series

DOI: 10.3233/JIFS-210872

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4175-4186, 2021

Authors: Ge, Yang | Xin, Ding

Article Type: Research Article

Abstract: In the task of Person re-identification (reID), the range of motion of pedestrians often spans multiple camera areas, and their motion direction and behavior cannot be constrained, and irrelevant people or objects in different scenes will also obtain target pedestrian information for us Cause interference. At the same time, the surveillance system also has many characteristics such as a fixed shooting angle of a single camera, different angles between different cameras, and low image resolution. These characteristics make the task of Person re-identification difficult. This paper proposes a Multi-level Feature Extraction Network (MFEN) based on SEResNet-50. Extracting richer and more …diverse pedestrian features from poor-quality images will effectively improve the re-identification ability of the network, and MFEN can obtain Multistage key features in the image through the Feature Re-extraction Method (FRM) proposed in this paper. Experiments show that compared with AANet-50, MFEN has 3.85% /0.71% improvements of mAP/ Rank-1 on the Market1501 dataset, and 2.74% /1.28% improvements of mAP/ Rank-1 on the DukeMTMC-reID dataset. Show more

Keywords: Person re-identification, full supervision, attention mechanism, multi-level feature extraction network, feature re-extraction method

DOI: 10.3233/JIFS-211456

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 4187-4201, 2021