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

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