Journal of Intelligent & Fuzzy Systems - Volume 42, issue 3

Authors: Wang, Weibing | Wang, Shenquan | Zhao, Shuanfeng | Lu, Zhengxiong | He, Haitao

Article Type: Research Article

Abstract: The complexity of the coalface environment determines the non-linear and fuzzy characteristics of the drum adjustment height. To overcome this challenge, this study proposes an adaptive fuzzy reasoning Petri net (AFRPN) model based on fuzzy reasoning and fuzzy Petri net (FPN) and then applies it to the intelligent adjustment height of the shearer drum. This study constructs adaptive and reasoning algorithms. The former was used to optimize the AFRPN parameters, and the latter made the AFRPN model run. AFRPN could represent rules that had non-linear and attribute mapping relationships and could adjust the parameters adaptively to improve the accuracy of …the output. Subsequently, the drum adjustment height model was established and compared to three models neural network (NN), classification and regression tree(CART) and gradient boosting decision tree (GBDT). The experimental results showed that this method is superior to other drum adjustment height methods and that AFRPN can achieve intelligent adjustment of the shearer drum height by constructing fuzzy inference rules. Show more

Keywords: Drum intelligent adjustment, fuzzy reasoning, adaptive, Petri net

DOI: 10.3233/JIFS-211193

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1767-1781, 2022

Authors: Hu, Yuanjiao | Sun, Zhaoyun | Li, Wei | Pei, Lili

Article Type: Research Article

Abstract: The rational distribution of public bicycle rental fleets is crucial for improving the efficiency of public bicycle programs. The accurate prediction of the demand for public bicycles is critical to improve bicycle utilization. To overcome the shortcomings of traditional algorithms such as low prediction accuracy and poor stability, using the 2011–2012 hourly bicycle rental data provided by the Washington City Bicycle Rental System, this study aims to develop an optimized and innovative public bicycle demand forecasting model based on grid search and eXtreme Gradient Boosting (XGBoost) algorithm. First, the feature ranking method based on machine learning models is used to …analyze feature importance on the original data. In addition, a public bicycle demand forecast model is established based on important factors affecting bicycle utilization. Finally, to predict bicycle demand accurately, this study optimizes the model parameters through a grid search (GS) algorithm and builds a new prediction model based on the optimal parameters. The results show that the optimized XGBoost model based on the grid search algorithm can predict the bicycle demand more accurately than other models. The optimized model has an R-Squared of 0.947, and a root mean squared logarithmic error of 0.495. The results can be used for the effective management and reasonable dispatch of public bicycles. Show more

Keywords: Bicycle demand forecast, feature importance, grid search algorithm, optimal parameters, eXtreme Gradient Boosting

DOI: 10.3233/JIFS-211202

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1783-1801, 2022

Authors: Zhang, Tao | Yu, Long | Tian, Shengwei

Article Type: Research Article

Abstract: In this paper, we presents an apporch for real-world human face close-up images cartoonization. We use generative adversarial network combined with an attention mechanism to convert real-world face pictures and cartoon-style images as unpaired data sets. At present, the image-to-image translation model has been able to successfully transfer style and content. However, some problems still exist in the task of cartoonizing human faces:Hunman face has many details, and the content of the image is easy to lose details after the image is translated. the quality of the image generated by the model is defective. The model in this paper uses …the generative adversarial network combined with the attention mechanism, and proposes a new generative adversarial network combined with the attention mechanism to deal with these problems. The channel attention mechanism is embedded between the upper and lower sampling layers of the generator network, to avoid increasing the complexity of the model while conveying the complete details of the underlying information. After comparing the experimental results of FID, PSNR, MSE three indicators and the size of the model parameters, the new model network proposed in this paper avoids the complexity of the model while achieving a good balance in the conversion task of style and content. Show more

Keywords: Generative adversarial networks, attention mechanism, style transfer, image cartoonization

DOI: 10.3233/JIFS-211210

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1803-1811, 2022

Authors: Zhai, Longzhen | Feng, Shaohong

Article Type: Research Article

Abstract: In order to solve the problem of finding the best evacuation route quickly and effectively, in the event of an accident, a novel evacuation route planning method is proposed based on Genetic Algorithm and Simulated Annealing algorithm in this paper. On the one hand, the simulated annealing algorithm is introduced and a simulated annealing genetic algorithm is proposed, which can effectively avoid the problem of the search process falling into the local optimal solution. On the other hand, an adaptive genetic operator is designed to achieve the purpose of maintaining population diversity. The adaptive genetic operator includes an adaptive crossover …probability operator and an adaptive mutation probability operator. Finally, the path planning simulation verification is carried out for the genetic algorithm and the improved genetic algorithm. The simulation results show that the improved method has greatly improved the path planning distance and time compared with the traditional genetic algorithm. Show more

Keywords: Genetic Algorithm (GA), stimulated annealing (SA), adaptive Algorithm, evacuation

DOI: 10.3233/JIFS-211214

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1813-1823, 2022

Authors: Akbar, Sumaiya Begum | Thanupillai, Kalaiselvi | Govindarajan, Valarmathi

Article Type: Research Article

Abstract: Bitcoin is an innovative decentralized digital currency without intermediaries. Bitcoin price prediction is a demanding need in the present situation. This paper makes an investigation on the Bitcoin price forecast with a Bi-directional Gated Recurrent Unit (GRU) time series method, combined with opinion mining based on Twitter and Reddit feeds. An hourly basis sentimental analysis through the implementation of Natural Language Processing presents a positive impact of sentimental analysis on the Bitcoin price prediction. For prediction, RNN, long-short memory, GRU has been utilized. Unidirectional and Bi-directional versions of all three networks with and without sentimental analysis were implemented for comparison. …Of all the techniques implemented Bi-directional GRU along with sentimental analysis gives a minimum RMSE and Minimum absolute percentage error of 1108.33 and 7.384%. Thus, the framework including Bi-Directional GRU along with Sentimental Analysis provides better results than the State-of-art methods. Show more

Keywords: Bitcoin, neural network, mining, GRU, RMSE, MAPE

DOI: 10.3233/JIFS-211217

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1825-1833, 2022

Authors: Tufail, Faiza | Shabir, Muhammad

Article Type: Research Article

Abstract: Bipolarity indicates the positive and negative aspects of a particular problem. The concept behind the bipolarity is that a huge range of human decision analysis is involved in bipolar subjective thoughts. The VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje) which means multicriteria optimization and compromise solution, has already become a quite popular multi-criteria decision making tool for its computational simplicity and solution accuracy. In this article, we propose a hybrid model for multi-criteria decision-making (MCDM) based on bipolar fuzzy soft β -covering based bipolar fuzzy rough sets using VIKOR technique. It consists of a suitable redesign of the VIKOR approach so …that it can use information with bipolar configurations. This method focuses on selecting and ranking from a set of feasible alternatives, and determines compromise solution for a problem with conflicting criteria to help the decision maker in reaching a final course of action. It determines the compromise ranking list based on the particular measure of closeness to the ideal solution. For illustration, the proposed technique is applied to a decision-making problems, namely, the selection of site for renewable energy project (solar power plant). A comparison of this method with another aggregation operator method and with the existing decision making algorithm Fuzzy VIKOR is also presented. Show more

Keywords: Bipolar fuzzy soft β-neighborhood, bipolar fuzzy soft complementry β-neighborhood, bipolar fuzzy soft β-covering, bipolar fuzzy soft β-covering based bipolar fuzzy rough set, decision-making application

DOI: 10.3233/JIFS-211223

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1835-1857, 2022

Authors: Zhao, Shuai | You, Fucheng | Chang, Wen | Zhang, Tianyu | Hu, Man

Article Type: Research Article

Abstract: The BERT pre-trained language model has achieved good results in various subtasks of natural language processing, but its performance in generating Chinese summaries is not ideal. The most intuitive reason is that the BERT model is based on character-level composition, while the Chinese language is mostly in the form of phrases. Directly fine-tuning the BERT model cannot achieve the expected effect. This paper proposes a novel summary generation model with BERT augmented by the pooling layer. In our model, we perform an average pooling operation on token embedding to improve the model’s ability to capture phrase-level semantic information. We use …LCSTS and NLPCC2017 to verify our proposed method. Experimental data shows that the average pooling model’s introduction can effectively improve the generated summary quality. Furthermore, different data needs to be set with varying pooling kernel sizes to achieve the best results through comparative analysis. In addition, our proposed method has strong generalizability. It can be applied not only to the task of generating summaries, but also to other natural language processing tasks. Show more

Keywords: Summary generation, fine-tuning bert, average pooling, transformer

DOI: 10.3233/JIFS-211229

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1859-1868, 2022

Authors: Selvaraj, Poovarasan | Chandra, E.

Article Type: Research Article

Abstract: The most challenging process in recent Speech Enhancement (SE) systems is to exclude the non-stationary noises and additive white Gaussian noise in real-time applications. Several SE techniques suggested were not successful in real-time scenarios to eliminate noises in the speech signals due to the high utilization of resources. So, a Sliding Window Empirical Mode Decomposition including a Variant of Variational Model Decomposition and Hurst (SWEMD-VVMDH) technique was developed for minimizing the difficulty in real-time applications. But this is the statistical framework that takes a long time for computations. Hence in this article, this SWEMD-VVMDH technique is extended using Deep Neural …Network (DNN) that learns the decomposed speech signals via SWEMD-VVMDH efficiently to achieve SE. At first, the noisy speech signals are decomposed into Intrinsic Mode Functions (IMFs) by the SWEMD Hurst (SWEMDH) technique. Then, the Time-Delay Estimation (TDE)-based VVMD was performed on the IMFs to elect the most relevant IMFs according to the Hurst exponent and lessen the low- as well as high-frequency noise elements in the speech signal. For each signal frame, the target features are chosen and fed to the DNN that learns these features to estimate the Ideal Ratio Mask (IRM) in a supervised manner. The abilities of DNN are enhanced for the categories of background noise, and the Signal-to-Noise Ratio (SNR) of the speech signals. Also, the noise category dimension and the SNR dimension are chosen for training and testing manifold DNNs since these are dimensions often taken into account for the SE systems. Further, the IRM in each frequency channel for all noisy signal samples is concatenated to reconstruct the noiseless speech signal. At last, the experimental outcomes exhibit considerable improvement in SE under different categories of noises. Show more

Keywords: Speech enhancement, SWEMD-VVMDH, DNN, ideal ratio mask, speech quality, speech intelligibility, generalizability

DOI: 10.3233/JIFS-211236

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1869-1883, 2022

Authors: Zhang, Yanteng | Teng, Qizhi | Qing, Linbo | Liu, Yan | He, Xiaohai

Article Type: Research Article

Abstract: Alzheimer’s disease (AD) is a degenerative brain disease and the most common cause of dementia. In recent years, with the widespread application of artificial intelligence in the medical field, various deep learning-based methods have been applied for AD detection using sMRI images. Many of these networks achieved AD vs HC (Healthy Control) classification accuracy of up to 90%but with a large number of computational parameters and floating point operations (FLOPs). In this paper, we adopt a novel ghost module, which uses a series of cheap operations of linear transformation to generate more feature maps, embedded into our designed ResNet architecture …for task of AD vs HC classification. According to experiments on the OASIS dataset, our lightweight network achieves an optimistic accuracy of 97.92%and its total parameters are dozens of times smaller than state-of-the-art deep learning networks. Our proposed AD classification network achieves better performance while the computational cost is reduced significantly. Show more

Keywords: Deep learning, ghost module, residual network, AD classification

DOI: 10.3233/JIFS-211247

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1885-1893, 2022

Authors: Jyoshna, Girika | Zia Ur Rahman, Md.

Article Type: Research Article

Abstract: Removing of noise component is an important task in all practical applications like hearing aids, speech therapy etc. In speech communication applications the speech components are contaminated with various types of noises. Separation of speech and noise component is a key issue in hearing aids, speech therapy applications. This paper demonstrates a hybrid version of singular spectrum analysis (SSA) and independent component analysis (ICA) based adaptive noise canceller (ANC) to separate noise and speech components. As ICA is not suitable for single channel sources, SSA is used to map signal channel data to multivariant data. Therefore, SSA based ICA decomposition …is used to generate reference for noise cancellation process. Variable Step based adaptive learning algorithm is used to separate noise contaminations from speech signals. To reduce computational complexity of system, sign regressor operation is applied to the data vector of the proposed adaptive learning methodology. Performance measures such as Signal to noise ratio improvement, excess mean square error and misadjustment are calculated for various considered ANCs, their values for crane noise are 29.6633 dB, – 27.4854 dB and 0.2058 respectively. Among the various adaptive learning algorithms, sign regressor based step variable method performs better than the other algorithms. Hence this learning methodology is well suited for hearing aids and speech therapy applications due to its robustness, less computational complexity and filtering ability. Show more

Keywords: Adaptive learning, computational complexity, reference generation, speech enhancement, independent component analysis

DOI: 10.3233/JIFS-211249

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1895-1906, 2022

Authors: Hao, Dong | Zhang, Runtong | Bai, Kaiyuan

Article Type: Research Article

Abstract: Online health communities (OHCs) have emerged as a significant platform for people communicating health information and self-healthcare management. In recent, the researches focusing on its performance measurement and the service quality evaluation have drawn intensive attention. Although some qualitative methods have made evaluation and analyses for the OHCs performance, the studies based on fuzzy multi-attribute decision making theory are rarely developed in the service quality evaluation of OHCs. In view of the complexity and uncertainty of evaluation mission, this paper develops an integrated evaluation approach of the OHC service quality based on the q-rung orthopair fuzzy linguistic aggregation operators. Firstly, …we propose the cross-entropy of q-rung orthopair fuzzy numbers, which is applied in solving the optimal weight of indicators by a linear programming model. Next, the q-rung orthopair fuzzy linguistic power average (q-ROFLPA) and q-rung orthopair fuzzy linguistic partitioned dual Maclaurin symmetric mean (q-ROFLPDMSM) operators are developed for aggregating the assessment information and ranking the OHCs. Based on the proposed aggregation operators, the evaluation indicator system and an evaluation framework are constructed to accomplish the service quality evaluation of OHCs. Finally, a practical evaluation case of OHCs is provided to demonstrate the reliability and advantages of the proposed approach. Show more

Keywords: Online health communities, q-rung orthopair fuzzy linguistic sets, partitioned dual Maclaurin symmetric mean, multi-attribute decision making, service quality evaluation

DOI: 10.3233/JIFS-211257

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1907-1924, 2022

Authors: An, Qingxian | Zhang, Ruiyi | Shen, Yongchang

Article Type: Research Article

Abstract: Data envelopment analysis (DEA) is widely used to evaluate the performance of a group of homogeneous decision making units (DMUs). Considering the uncertainty, interval DEA has been introduced to fit into more situations. In this paper, an interval efficiency method based on slacks-based measure is proposed to solve the uncertain problems in DEA. Firstly, the maximum and minimum efficiency values of the evaluated DMU are calculated by the furthest and closest distance from the evaluated DMU to the projection points on the Pareto-efficient frontier, respectively. Then, the AHP method is used for the full ranking of DMUs. The paper uses …the pairwise comparison relationship between each pair of DMUs to construct the interval multiplicative preference relations (IMPRs) matrix. If the matrix does not meet the consistency condition, a method to obtain consistency IMPRs is introduced. According to the consistency judgment matrix, the full ranking of DMUs can be obtained. Finally, we apply our method to the performance evaluation of 12 tourist hotels in Taipei in 2019. Show more

Keywords: Performance measurement, data envelopment analysis, interval efficiency, interval multiplicative preference relations, full ranking

DOI: 10.3233/JIFS-211292

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1925-1936, 2022

Authors: Seethappan, K. | Premalatha, K.

Article Type: Research Article

Abstract: Although there have been various researches in the detection of different figurative language, there is no single work in the automatic classification of euphemisms. Our primary work is to present a system for the automatic classification of euphemistic phrases in a document. In this research, a large dataset consisting of 100,000 sentences is collected from different resources for identifying euphemism or non-euphemism utterances. In this work, several approaches are focused to improve the euphemism classification: 1. A Combination of lexical n-gram features 2.Three Feature-weighting schemes 3.Deep learning classification algorithms. In this paper, four machine learning (J48, Random Forest, Multinomial Naïve …Bayes, and SVM) and three deep learning algorithms (Multilayer Perceptron, Convolutional Neural Network, and Long Short-Term Memory) are investigated with various combinations of features and feature weighting schemes to classify the sentences. According to our experiments, Convolutional Neural Network (CNN) achieves precision 95.43%, recall 95.06%, F-Score 95.25%, accuracy 95.26%, and Kappa 0.905 by using a combination of unigram and bigram features with TF-IDF feature weighting scheme in the classification of euphemism. These results of experiments show CNN with a strong combination of unigram and bigram features set with TF-IDF feature weighting scheme outperforms another six classification algorithms in detecting the euphemisms in our dataset. Show more

Keywords: Euphemism, TF-IDF, n-gram, Support Vector Machine, CNN

DOI: 10.3233/JIFS-211295

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1937-1948, 2022

Authors: Jan, Atif | Khan, Gul Muhammad

Article Type: Research Article

Abstract:  Identification/recognition of assault, fighting, shooting, and vandalism from video sequence using deep 2D and 3D convolutional neural networks (CNNs) is explored in this paper. Recent wave of extensive unrestricted urbanization has not only uplifted the standard of living, but has also threatened the safety of a common man leading to an extraordinary rise in crime rate. Although Closed-circuit television (CCTV) footage provides a monitoring framework, yet, it’s useless without an auto volume crime detection system. The system proposed in this work is an effort to eradicate volume crimes through accurate detection in real-time. Firstly, a fine-grained annotated dataset including instance …and activity information has been developed for real-world volume crimes. Secondly, a comparison between 3D CNN and 2D CNN network has been presented to identify the malicious event from the video sequence. This is carried out to explore the significance of spatial and temporal information present in the video for event recognition. It has been observed that 2D CNN even with lesser parameters achieved a promising classification accuracy of 91.2%and Area under the curve (AUC) of 95.2%on four classes. The system also reduces false alarm rate in comparison to state-of-the-art approaches. Show more

Keywords: Convolutional neural network, spatio-temporal features, malicious activity detection, deep learning

DOI: 10.3233/JIFS-211338

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1949-1961, 2022

Authors: Shi, Maolin | Wang, Zihao | Xu, Lizhang

Article Type: Research Article

Abstract: Data clustering based on regression relationship is able to improve the validity and reliability of the engineering data mining results. Surrogate models are widely used to evaluate the regression relationship in the process of data clustering, but there is no single surrogate model that always performs the best for all the regression relationships. To solve this issue, a fuzzy clustering algorithm based on hybrid surrogate model is proposed in this work. The proposed algorithm is based on the framework of fuzzy c -means algorithm, in which the differences between the clusters are evaluated by the regression relationship instead of Euclidean …distance. Several surrogate models are simultaneously utilized to evaluate the regression relationship through a weighting scheme. The clustering objective function is designed based on the prediction errors of multiple surrogate models, and an alternating optimization method is proposed to minimize it to obtain the memberships of data and the weights of surrogate models. The synthetic datasets are used to test single surrogate model-based fuzzy clustering algorithms to choose the surrogate models used in the proposed algorithm. It is found that support vector regression-based and response surface-based fuzzy clustering algorithms show competitive clustering performance, so support vector regression and response surface are used to construct the hybrid surrogate model in the proposed algorithm. The experimental results of synthetic datasets and engineering datasets show that the proposed algorithm can provide more competitive clustering performance compared with single surrogate model-based fuzzy clustering algorithms for the datasets with regression relationships. Show more

Keywords: Data clustering, fuzzy clustering, regression relationship, hybrid surrogate model, engineering data

DOI: 10.3233/JIFS-211340

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1963-1976, 2022

Authors: Wang, Heng | Ye, Xiang | Li, Yong

Article Type: Research Article

Abstract: Model pruning aims to reduce the parameter amount of deep neural networks while retaining the performance. Existing strategies often treat all layers equally and all layers simply share the same pruning rate. However, it is observed from our experiments that the redundancy degree differs from layer to layer. Based on this observation, this work proposes a pruning strategy depending on the layer-wise redundancy degree. Firstly, we define the redundancy degree for each layer by the norm and similarity redundancy of filters. Then a novel layer-wise strategy, Redundancy-dependent Filter Pruning (RedFiP), is proposed which prunes different proportion of filters at different …layers according to the defined redundancy degree. Since the redundancy analysis and experimental results of RedFiP show that deeper layers need fewer filters, a phase-wise strategy, Phased Filter Pruning (PFP), is proposed that divides the layers into three phases and layers in each phase share the same pruning rate. The phase-wise PFP allows the layer-wise RedFiP to be easily implemented in existing structures of deep neural networks. Experimental results show that when total parameters are pruned by 40%, RedFiP outperforms the state-of-the-art strategy FPGM-Mixed by 1.83% on CIFAR-100, and even slightly outperforms the non-pruned model by 0.11% on CIFAR-10. On ImageNet-1k, RedFiP (30%) and PFP (30%) outperform FPGM-Mixed (30%) by 1.3% and 0.8% with ResNet-18. Show more

Keywords: Filter pruning, redundancy, phase, importance

DOI: 10.3233/JIFS-211346

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1977-1990, 2022

Authors: Chen, Yong | Zhang, Tianbao | Wang, Ruojun | Cai, Lei

Article Type: Research Article

Abstract: The failure of complex engineering systems is easy to lead to disastrous consequences. To prevent the failure, it is necessary to model complex engineering systems using probabilistic techniques with limited data which is a major feature of complex engineering systems. It is a good choice to perform such modeling using Bayesian network because of its advantages in probabilistic modeling. However, few Bayesian network structural learning algorithms are designed for complex engineering systems with limited data. Therefore, an algorithm for learning the Bayesian network structure of them should be developed. Based on the process of self-purification of water, a complex engineering …system is segmented into three components according to the degree of difficulty in solving them. And then a Bayesian network learning algorithm with three components (TC), including PC algorithm, MIK algorithm which is originated by the paper through combining Mutual Information and K2 algorithm, and the Hill-Climbing method, is developed, i.e. TC algorithm. To verify its effectiveness, TC algorithm, K2 algorithm, and Max-Min Hill-Climbing are respectively used to learn Alarm network with different sizes of samples. The results imply that TC algorithm has the best performance. Finally, TC algorithm is applied to study tank spill accidents with 220 samples. Show more

Keywords: Bayesian network structural learning, algorithm, complex engineering systems, failure probability

DOI: 10.3233/JIFS-211354

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 1991-2004, 2022

Authors: Bai, Shenshen | Li, Longjie | Chen, Xiaoyun

Article Type: Research Article

Abstract: The Dempster-Shafer evidence theory has been extensively used in various applications of information fusion owing to its capability in dealing with uncertain modeling and reasoning. However, when meeting highly conflicting evidence, the classical Dempster’s combination rule may give counter-intuitive results. To address this issue, we propose a new method in this work to fuse conflicting evidence. Firstly, a new evidence distance metric, named Belief Mover’s Distance, which is inspired by the Earth Mover’s Distance, is defined to measure the difference between two pieces of evidence. Subsequently, the credibility weight and distance weight of each piece of evidence are computed according …to the Belief Mover’s Distance. Then, the final weight of each piece of evidence is generated by unifying these two weights. Finally, the classical Dempster’s rule is employed to fuse the weighted average evidence. Several examples and applications are presented to analyze the performance of the proposed method. Experimental results manifest that the proposed method is remarkably effective in comparison with other methods. Show more

Keywords: Evidence theory, conflicting evidence, combination rule, evidence distance, Belief Mover’s Distance

DOI: 10.3233/JIFS-211397

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2005-2021, 2022

Authors: Li, Fang | Zhang, Lihua | Wang, Xiao | Liu, Shihu

Article Type: Research Article

Abstract: In the existing high-order fuzzy logical relationship (FLR) based forecasting model, each FLR is used to describe the association between multiple premise observations and a consequent observation. Therefore, these FLRs concentrate on the one-step-ahead forecasting. In real applications, there exist another kind of association: the association between multiple premise observations and multiple consequent observations. For such association, the existing FLRs can’t express and ignored. To depict it, the high-order multi-point association FLR is raised in this study. The antecedent and consequent of a high-order multi-point association FLR are consisted of multiple observations. Thus, the proposed FLR reflects the influence of …multiple premise observations on the multiple consequent observations, and can be applied for multi-step-ahead forecasting with no cumulative errors. On the basis of high-order multi-point association FLR, the high-order multi-point trend association FLR is constructed, it describes the trend association in time series. By using these two new kinds of FLRs, a fuzzy time series based multi-step-ahead forecasting model is established. In this model, the multi-point (trend) association FLRs effective in capturing the associations of time series and improving forecasting accuracy. The benefits of the proposed FLRs and the superior performance of the established forecasting model are demonstrated through the experimental analysis. Show more

Keywords: Fuzzy time series, high-order multi-point association fuzzy logical relationship, high-order multi-point trend association fuzzy logical relationship, multi-step-ahead forecasting

DOI: 10.3233/JIFS-211405

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2023-2039, 2022

Authors: Zhang, Zhaojun | Lu, Rui | Zhao, Minglong | Luan, Shengyang | Bu, Ming

Article Type: Research Article

Abstract: The research of path planning method based on genetic algorithm (GA) for the mobile robot has received much attention in recent years. GA, as one evolutionary computation model, mimics the process of natural evolution and genetics. The quality of the initial population plays an essential role in improving the performance of GA. However, when GA based on a random initialization method is applied to path planning problems, it will lead to the emergence of infeasible solutions and reduce the performance of the algorithm. A novel GA with a hybrid initialization method, termed NGA, is proposed to solve this problem in …this paper. In the initial population, NGA first randomly selects three free grids as intermediate nodes. Then, a part of the population uses a random initialization method to obtain the complete path. The other part of the population obtains the complete path using a greedy-related method. Finally, according to the actual situation, the redundant nodes or duplicate paths in the path are deleted to avoid the redundant paths. In addition, the deletion operation and the reverse operation are also introduced to the NGA iteration process to prevent the algorithm from falling into the local optimum. Simulation experiments are carried out with other algorithms to verify the effectiveness of the NGA. Simulation results show that NGA is superior to other algorithms in convergence accuracy, optimization ability, and success rate. Besides, NGA can generate the optimal feasible paths in complex environments. Show more

Keywords: Path planning, mobile robot, genetic algorithm, initial population

DOI: 10.3233/JIFS-211423

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2041-2056, 2022

Authors: Huang, Yuchong | Xu, Ning | Wang, Nan | Li, Jie

Article Type: Research Article

Abstract: Through innovatively introducing the receding horizon into probabilistic model checking, an online strategy synthesis method for multi-robot systems from local automatons is proposed to complete complex tasks that are assigned to each robot. Firstly, each robot is modeled as a Markov decision process which models both probabilistic and nondeterministic behavior. Secondly, the task specification of each robot is expressed as a linear temporal logic formula. For some tasks that robots cannot complete by themselves, the collaboration requirements take the form of atomic proposition into the LTL specifications. And the LTL specifications are transformed to deterministic rabin automatons over which a …task progression metric is defined to determine the local goal states in the finite-horizon product systems. Thirdly, two horizons are set to determine the running steps in automatons and MDPs. By dynamically building local finite-horizon product systems, the collaboration strategies are synthesized iteratively for each robot to satisfy the task specifications with maximum probability. Finally, through simulation experiments in an indoor environment, the results show that the method can synthesize correct strategies online for multi-robot systems which has no restriction on the LTL operators and reduce the computational burden brought by the automaton-based approach. Show more

Keywords: Receding horizon, linear temporal logic, Markov decision process, probabilistic model checking, multi-robot collaboration

DOI: 10.3233/JIFS-211427

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2057-2069, 2022

Authors: She, Chunyan | Zeng, Shaohua

Article Type: Research Article

Abstract: Outlier detection is a hot issue in data mining, which has plenty of real-world applications. LOF (Local Outlier Factor) can capture the abnormal degree of objects in the dataset with different density levels, and many extended algorithms have been proposed in recent years. However, the LOF needs to search the nearest neighborhood of each object on the whole dataset, which greatly increases the time cost. Most of these extended algorithms only consider the distance between an object and its neighborhood, but ignore the local distribution of an object within its neighborhood, resulting in a high false-positive rate. To improve the …running speed, a rough clustering based on triple fusion is proposed, which divides a dataset into several subsets and outlier detection is performed only on each subset. Then, considering the local distribution of an object within its neighborhood, a new local outlier factor is constructed to estimate the abnormal degree of each object. Finally, the experimental results indicate that the proposed algorithm has better performance and lower running time than the others. Show more

Keywords: Outlier detection, local outlier factor, rough Clustering

DOI: 10.3233/JIFS-211433

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2071-2082, 2022

Authors: Wang, Yun | Jin, Xin | Yang, Jie | Jiang, Qian | Tang, Yue | Wang, Puming | Lee, Shin-Jye

Article Type: Research Article

Abstract: Multi-focus image fusion is a technique that integrates the focused areas in a pair or set of source images with the same scene into a fully focused image. Inspired by transfer learning, this paper proposes a novel color multi-focus image fusion method based on deep learning. First, color multi-focus source images are fed into VGG-19 network, and the parameters of convolutional layer of the VGG-19 network are then migrated to a neural network containing multilayer convolutional layers and multilayer skip-connection structures for feature extraction. Second, the initial decision maps are generated using the reconstructed feature maps of a deconvolution module. …Third, the initial decision maps are refined and processed to obtain the second decision maps, and then the source images are fused to obtain the initial fused images based on the second decision maps. Finally, the final fused image is produced by comparing the Q ABF metrics of the initial fused images. The experimental results show that the proposed method can effectively improve the segmentation performance of the focused and unfocused areas in the source images, and the generated fused images are superior in both subjective and objective metrics compared with most contrast methods. Show more

Keywords: Deep learning, feature extraction, multi-focus images fusion, neural networks, transfer learning

DOI: 10.3233/JIFS-211434

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2083-2102, 2022

Authors: Özdemir, Özgür | Akın, Emre Salih | Velioğlu, Rıza | Dalyan, Tuğba

Article Type: Research Article

Abstract: Machine translation (MT) is an important challenge in the fields of Computational Linguistics. In this study, we conducted neural machine translation (NMT) experiments on two different architectures. First, Sequence to Sequence (Seq2Seq) architecture along with a variation that utilizes attention mechanism is performed on translation task. Second, an architecture that is fully based on the self-attention mechanism, namely Transformer, is employed to perform a comprehensive comparison. Besides, the contribution of employing Byte Pair Encoding (BPE) and Gumbel Softmax distributions are examined for both architectures. The experiments are conducted on two different datasets: TED Talks that is one of the popular …benchmark datasets for NMT especially among morphologically rich languages like Turkish and WMT18 News dataset that is provided by The Third Conference on Machine Translation (WMT) for shared tasks on various aspects of machine translation. The evaluation of Turkish-to-English translations’ results demonstrate that the Transformer model with combination of BPE and Gumbel Softmax achieved 22.4 BLEU score on TED Talks and 38.7 BLUE score on WMT18 News dataset. The empirical results support that using Gumbel Softmax distribution improves the quality of translations for both architectures. Show more

Keywords: Neural machine translation, Gumbel Softmax, sequence to sequence, transformer

DOI: 10.3233/JIFS-211453

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2103-2113, 2022

Authors: Tian, Yun Bo | Ma, Zhen Ming

Article Type: Research Article

Abstract: Both Heronian mean (HM) operators and Bonferroni mean (BM) operators can capture the interrelationship between input arguments and have been a hot research topic as a useful aggregation technique in fuzzy and intuitionistic fuzzy environments. In this paper, associated with the common characters of these operators we propose the covering-based compound mean operators in fuzzy environments to capture various interrelationships between input arguments, some desirable properties and special cases of the proposed mean operators are provided. Then, conditions under which these covering-based compound mean operators can be directly used to aggregate the membership degrees and nonmembership degrees of intuitionistic fuzzy …information, are provided. In particular, novel intuitionistic fuzzy HM operators and intuitionistic fuzzy BM operators are directly derived from the classical ones. We list the detailed steps of multiple attribute decision making with the developed aggregation operators, and give a comparison of the new extensions of BM operators by this paper with the corresponding existing ones to prove the rationality and effectiveness of the proposed method. Show more

Keywords: Heronian mean operator, Bonferroni mean operator, Covering-based compound mean operator, Intuitionistic fuzzy sets, Multiple attribute decision making

DOI: 10.3233/JIFS-211457

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2115-2126, 2022

Authors: He, Fang | Zhang, Wenyu | Yan, Zhijia

Article Type: Research Article

Abstract: Credit scoring has become increasingly important for financial institutions. With the advancement of artificial intelligence, machine learning methods, especially ensemble learning methods, have become increasingly popular for credit scoring. However, the problems of imbalanced data distribution and underutilized feature information have not been well addressed sufficiently. To make the credit scoring model more adaptable to imbalanced datasets, the original model-based synthetic sampling method is extended herein to balance the datasets by generating appropriate minority samples to alleviate class overlap. To enable the credit scoring model to extract inherent correlations from features, a new bagging-based feature transformation method is proposed, which …transforms features using a tree-based algorithm and selects features using the chi-square statistic. Furthermore, a two-layer ensemble method that combines the advantages of dynamic ensemble selection and stacking is proposed to improve the classification performance of the proposed multi-stage ensemble model. Finally, four standardized datasets are used to evaluate the performance of the proposed ensemble model using six evaluation metrics. The experimental results confirm that the proposed ensemble model is effective in improving classification performance and is superior to other benchmark models. Show more

Keywords: Ensemble learning, credit scoring, synthetic sampling, feature transformation

DOI: 10.3233/JIFS-211467

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2127-2142, 2022

Authors: Shao, Dangguo | Li, Chengyao | Huang, Chusheng | An, Qing | Xiang, Yan | Guo, Junjun | He, Jianfeng

Article Type: Research Article

Abstract: Aiming at the low effectiveness of short texts feature extraction, this paper proposes a short texts classification model based on the improved Wasserstein-Latent Dirichlet Allocation (W-LDA), which is a neural network topic model based on the Wasserstein Auto-Encoder (WAE) framework. The improvements of W-LDA are as follows: Firstly, the Bag of Words (BOW) input in the W-LDA is preprocessed by Term Frequency–Inverse Document Frequency (TF-IDF); Subsequently, the prior distribution of potential topics in W-LDA is replaced from the Dirichlet distribution to the Gaussian mixture distribution, which is based on the Variational Bayesian inference; And then the sparsemax function layer is …introduced after the hidden layer inferred by the encoder network to generate a sparse document-topic distribution with better topic relevance, the improved W-LDA is named the Sparse Wasserstein-Variational Bayesian Gaussian mixture model (SW-VBGMM); Finally, the document-topic distribution generated by SW-VBGMM is input to BiGRU (Bidirectional Gating Recurrent Unit) for the deep feature extraction and the short texts classification. Experiments on three Chinese short texts datasets and one English dataset represent that our model is better than some common topic models and neural network models in the four evaluation indexes (accuracy, precision, recall, F1 value) of text classification. Show more

Keywords: Short texts classification, neural network topic model, Variational Bayesian Gaussian mixture model (VBGMM), sparsemax, BiGRU (Bidirectional Gating Recurrent Unit)

DOI: 10.3233/JIFS-211471

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2143-2155, 2022

Authors: Yang, Yaxu | Guo, Zixue | He, Zefang

Article Type: Research Article

Abstract: The occurrence of public health emergency will cause huge economic losses and casualties, which posed a huge threat to the economic and social development. In response to the emergency, a large amount of emergency relief supplies will be transported to the affected areas. Faced with this public health emergency of international concern, the concept of emergency logistics capacity and the evaluation model based on probabilistic linguistic term sets are proposed. In this paper, the emergency logistics capability evaluation is transformed into user demand evaluation, and the importance of each index of emergency logistics capability is determined by using Quality Function …Deployment (QFD) and prospect theory. Under the probabilistic language information environment, a multi-attribute decision making method is established by using TODIM method. Finally, an example is given to verify the feasibility of the proposed method. Show more

Keywords: Emergency logistics capacity, probabilistic linguistic term sets, quality function deployment (QFD)

DOI: 10.3233/JIFS-211495

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2157-2168, 2022

Authors: Zheng, Wei | Du, Qing | Fan, Yongjian | Tan, Lijuan | Xia, Chuanlin | Yang, Fengyu

Article Type: Research Article

Abstract: Personalized exercise recommendation is an important research project in the field of online learning, which can explore students’ strengths and weaknesses and tailor exercises for them. However, programming exercises differs from other disciplines or types of exercises due to the comprehensive of the exercises and the specificity of program debugging. In order to assist students in learning programming, this paper proposes a programming exercise recommendation algorithm based on knowledge structure tree (KSTER). Firstly, the algorithm provides a calculation method for quantifying students’ cognitive level to obtain their knowledge needs through individual learning-related data. Secondly, a knowledge structure tree is constructed …based on the association relationship of knowledge points, and a learning objective prediction method is proposed by combining the knowledge needs and the knowledge structure tree to represent and update the learning objective. Finally, KSTER imports a matching operator that calculates cognitive level and exercise difficulty based on learning objectives, and makes top-η recommendation for exercises. Experiments show that the proposed algorithm significantly outperforms the other algorithms in both precision and recall. The comparison experiments with real-world data demonstrate that KSTER effectively improves students’ learning efficiency. Show more

Keywords: Personalized recommendation, learning objectives, knowledge structure tree, online learning

DOI: 10.3233/JIFS-211499

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2169-2180, 2022

Authors: Dai, Tianhong | Cong, Shijie | Huang, Jianping | Zhang, Yanwen | Huang, Xinwang | Xie, Qiancheng | Sun, Chunxue | Li, Kexin

Article Type: Research Article

Abstract: In agricultural production, weed removal is an important part of crop cultivation, but inevitably, other plants compete with crops for nutrients. Only by identifying and removing weeds can the quality of the harvest be guaranteed. Therefore, the distinction between weeds and crops is particularly important. Recently, deep learning technology has also been applied to the field of botany, and achieved good results. Convolutional neural networks are widely used in deep learning because of their excellent classification effects. The purpose of this article is to find a new method of plant seedling classification. This method includes two stages: image segmentation and …image classification. The first stage is to use the improved U-Net to segment the dataset, and the second stage is to use six classification networks to classify the seedlings of the segmented dataset. The dataset used for the experiment contained 12 different types of plants, namely, 3 crops and 9 weeds. The model was evaluated by the multi-class statistical analysis of accuracy, recall, precision, and F1-score. The results show that the two-stage classification method combining the improved U-Net segmentation network and the classification network was more conducive to the classification of plant seedlings, and the classification accuracy reaches 97.7%. Show more

Keywords: Deep learning, plant seedlings classification, machine learning, U-Net

DOI: 10.3233/JIFS-211507

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2181-2191, 2022

Authors: Zhang, Yan | Yang, Gongping | Liu, Yikun | Wang, Chong | Yin, Yilong

Article Type: Research Article

Abstract: Detection of cotton bolls in the field environments is one of crucial techniques for many precision agriculture applications, including yield estimation, disease and pest recognition and automatic harvesting. Because of the complex conditions, such as different growth periods and occlusion among leaves and bolls, detection in the field environments is a task with considerable challenges. Despite this, the development of deep learning technologies have shown great potential to effectively solve this task. In this work, we propose an Improved YOLOv5 network to detect unopened cotton bolls in the field accurately and with lower cost, which combines DenseNet, attention mechanism and …Bi-FPN. Besides, we modify the architecture of the network to get larger feature maps from shallower network layers to enhance the ability of detecting bolls due to the size of cotton boll is generally small. We collect image data of cotton in Aodu Farm in Xinjiang Province, China and establish a dataset containing 616 high-resolution images. The experiment results show that the proposed method is superior to the original YOLOv5 model and other methods such as YOLOv3,SSD and FasterRCNN considering the detection accuracy, computational cost, model size and speed at the same time. The detection of cotton boll can be further applied for different purposes such as yield prediction and identification of diseases and pests in earlier stage which can effectively help farmers take effective approaches in time and reduce the crop losses and therefore increase production. Show more

Keywords: Unopened cotton boll detection, deep learning, improved YOLOv5, image data collection, Bi-FPN

DOI: 10.3233/JIFS-211514

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2193-2206, 2022

Authors: Shao, Dangguo | An, Qing | Huang, Kun | Xiang, Yan | Ma, Lei | Guo, Junjun | Yin, Runda

Article Type: Research Article

Abstract: The purpose of aspect-level sentiment analysis is to identify the contextual sentence expressions given by sentiment for some aspects. For previous works, many scholars have proved the importance of the interaction between aspects and contexts. However, most existing methods ignore or do not specifically capture the position information of the aspect targets in the sentence. Thus, we propose an aspect-level sentiment analysis based on joint aspect and position hierarchy attention mechanism network. At the same time, the model adopts a joint approach to make the model of the aspect features and the position features. On the one hand, this method …clearly captures the interaction between aspect words and context when inputting word vector information. On the other hand, this method can enhance the importance of position information in the sentence and boost the information retrieval ability of the model. Additionally, the model utilizes a hierarchical attention mechanism to extract feature information and to differentiate sentiment towards, which is similar to filtering useless information again. Experiment on the SemEval 2014 dataset represent that our model achieves better performance on aspect-level sentiment classification. Show more

Keywords: Aspect-level, position information, hierarchy attention mechanism, sentiment analysis, sentiment polarity

DOI: 10.3233/JIFS-211515

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2207-2218, 2022

Authors: Bai, Luyi | Cui, Zengmei | Duan, Xinyi | Fu, Hao

Article Type: Research Article

Abstract: With the increasing popularity of XML for data representations, there is a lot of interest in keyword query on XML. Many algorithms have been proposed for XML keyword queries. But the existing approaches fall short in their abilities to analyze the logical relationship between keywords of spatiotemporal data. To overcome this limitation, in this paper, we firstly propose the concept of query time series (QTS) according to the data revision degree. For the logical relationship of keywords in QTS, we study the intra-coupling logic relationship and the inter-coupling logic relationship separately. Then a calculation method of keyword similarity is proposed …and the best parameter in the method is found through experiment. Finally, we compare this method with others. Experimental results show that our method is superior to previous approaches. Show more

Keywords: Data revision degree, keyword search, spatiotemporal data, XML

DOI: 10.3233/JIFS-211537

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2219-2228, 2022

Authors: Shabbir, Wasif | Aijun, Li | Taimoor, Muhammad | Yuwei, Cui

Article Type: Research Article

Abstract: The problem of quick and accurate fault estimation in nonlinear systems is addressed in this article by combining the technique of radial basis function neural network (RBFNN) and global fast terminal sliding mode control (GFTSMC) concept. A new strategy to update the neural network weights, by using the global fast terminal sliding surface instead of conventional error back propagation method, is introduced to achieve real time, quick and accurate fault estimation which is critical for fault tolerant control system design. The combination of online learning ability of RBFNN, to approximate any nonlinear function, and finite time convergence property of GFTSMC …ensures quick detection and accurate estimation of faults in real time. The effectiveness of the proposed strategy is demonstrated through simulations using a nonlinear model of a commercial aircraft and considering a wide range of sensors and actuators faults. The simulation results show that the proposed method is capable of quick and accurate online fault estimation in nonlinear systems and shows improved performance as compared to conventional RBFNN and other techniques existing in literature. Show more

Keywords: Fault estimation, neural networks, global fast terminal sliding mode control

DOI: 10.3233/JIFS-211547

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2229-2245, 2022

Authors: Tan, Chao | Liu, Jie

Article Type: Research Article

Abstract: The prime focus of knowledge distillation (KD) seeks a light proxy termed student to mimic the outputs of its heavy neural networks termed teacher, and makes the student run real-time on the resource-limited devices. This paradigm requires aligning the soft logits of both teacher and student. However, few doubts whether the process of softening the logits truly give full play to the teacher-student paradigm. In this paper, we launch several analyses to delve into this issue from scratch. Subsequently, several simple yet effective functions are devised to replace the vanilla KD. The ultimate function can be an effective alternative to …its original counterparts and work well with other skills like FitNets. To claim this point, we conduct several visual tasks on individual benchmarks, and experimental results verify the potential of our proposed function in terms of performance gains. For example, when the teacher and student networks are ShuffleNetV2-1.0 and ShuffleNetV2-0.5, our proposed method achieves 40.88%top-1 error rate on Tiny ImageNet. Show more

Keywords: Neural network compression, knowledge distillation, knowledge transfer

DOI: 10.3233/JIFS-211549

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2247-2259, 2022

Authors: Zhou, Anmin | Huang, Tianyi | Huang, Cheng | Li, Dunhan | Song, Chuangchuang

Article Type: Research Article

Abstract: Python is a concise language which can be used to build lightweight tools or dynamic object-orientated applications. The various attributes of Python have made it attractive to numerous malware authors. Attackers often embed malicious shell commands into Python scripts for illegal operations. However, traditional static analysis methods are not feasible to detect this kind of attack because they focus on common features and failure in finding those malicious commands. On the other hand, dynamic analysis is not optimal in this case for its time-consuming and inefficient. In this paper, we propose PyComm, a model for detecting malicious commands in Python …scripts with multidimensional features based on machine learning, which considers both 12 statistical features and string sequences of Python source code. Meanwhile, three comparison experiments are designed to evaluate the validity of proposed method. Experimental results show that presented model has achieved an excellent performance based on those practical features and random forest (RF) algorithm, obtained an accuracy of 0.955 with a recall of 0.943. Show more

Keywords: Cyber security, malicious script detection, malicious command, machine learning, static analysis

DOI: 10.3233/JIFS-211557

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2261-2273, 2022

Authors: Li, Xiaofei | Ye, Binhua | Liu, Xinwang

Article Type: Research Article

Abstract: Linear programming is an important branch of operations research. The model is mature in theory and widely used in real life. However, various complex realistic scenarios involve fuzzy information. In this paper, we consider a fuzzy linear programming (FLP) model in which all parameters are trapezoidal interval type-2 fuzzy numbers (IT2FNs) and propose a solution method based on the nearest interval approximation and the best-worst cases (BWC) method. We prove the nearest interval approximation interval of trapezoidal IT2FNs, then the trapezoidal IT2FNs in the model are transformed into interval numbers which both upper and lower limits are interval numbers. With …the help of best-worst cases (BWC) method, the sub-models of the transformed interval linear programming model are proposed, and four sub-solutions with different specific meanings can be obtained by solving them respectively. Finally, an application example is presented to show the rationality and practical significance of the method. Show more

Keywords: Fuzzy linear programming (FLP), Trapezoidal interval type-2 fuzzy numbers (IT2FNs), Best-worst cases (BWC) method, The nearest interval approximation

DOI: 10.3233/JIFS-211568

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2275-2285, 2022

Authors: Li, Baolin | Yang, Lihua | Qian, Jie

Article Type: Research Article

Abstract: In practice, picture hesitant fuzzy sets (PHFSs) combining the picture fuzzy sets (PFSs) and hesitant fuzzy sets (HFSs) are suitable to represent more complex multi-criteria decision-making (MCDM) information. The power heronian (PH) operators, which have the merits of power average (PA) and heronian mean (HM) operators, are extended to the environment of PHFSs in this article. First, some algebraic operations of picture hesitant fuzzy numbers (PHFNs), comparative functions and distance measure are introduced. Second, two novel operators, called as picture hesitant fuzzy weighted power heronian (PHFWPH) operator and picture hesitant fuzzy weighted geometric power heronian (PHFWGPH) operator, are defined. Meanwhile, …some desirable characteristics and special instances of two operators are investigated as well. Third, a novel MCDM approach applying the proposed PH operators to handle PHFNs is explored. Lastly, to indicate the effectiveness of this novel method, an example regarding MCDM problem is conducted, as well as sensitivity and comparison analysis. Show more

Keywords: Multi-criteria decision-making, power heronian operators, picture hesitant fuzzy sets

DOI: 10.3233/JIFS-211569

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2287-2308, 2022

Authors: Zhou, Chang-Jie | Yao, Wei

Article Type: Research Article

Abstract: For a usual commutative quantale Q (does not necessarily have a unit), we propose a definition of Q -ordered sets by introducing a kind of self-adaptive self-reflexivity. We study their completeness and the related Q -modules of complete lattices. The main result is that, the complete Q -ordered sets and the Q -modules of complete lattices are categorical isomorphic.

Keywords: Commutative quantale, Q-order, Q-module, complete Q-ordered set

DOI: 10.3233/JIFS-211581

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2309-2316, 2022

Authors: Wang, Sha | Li, Teng | Liu, Zifeng | Pan, Dongbo | Zhang, Yu

Article Type: Research Article

Abstract: The embedding capacity and steganography quality are two important performance indicators of data hiding which has practical application value for copyright and intellectual property protection, public information protection and online elections. Many researches presented hiding methods to improve the performance. However, the existing data hiding methods have problems such as low embedding capacity or poor stego-image quality. This paper proposes a new method (Single Pixel Modification, SPM) to improve the performance further. The SPM (Single Pixel Modification) method embeds k secret bits into a cover-pixel with the idea that minimizing the change to cover-pixel and adopting modulus operation based …on 2k . The experimental results show that the proposed method has better performance than methods compared and the highest hiding capacity can reach 4 bits per pixel and the average PSNR of stego-images is 34.83 dB . The source code and related materials are made to public to make it easy for researchers to verify the work and stimulate further research. Show more

Keywords: Data hiding, modulus calculation, cover image, stego-image

DOI: 10.3233/JIFS-211606

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2317-2329, 2022

Authors: Zhang, Yu | Xiao, Qunli | Deng, Xinyang | Jiang, Wen

Article Type: Research Article

Abstract: The ship target recognition (STR) is greatly related to the battlefield situation awareness, which has recently gained prominence in the military domains. With the diversification and complexity of military missions, ship targets are mostly performed in the form of formations. Therefore, using the formation information to improve the accuracy of the ship target type recognition is worth studying. To effectively identify ship target type, we in this paper jointly consider the ship dynamic, formation, and feature information to propose a STR method based on Bayesian inference and evidence theory. Specifically, we first calculate the ship position distance matrix and the …directional distance matrix with the Dynamic Time Warping (DTW) and the difference-vector algorithm taken into account. Then, we use the two distance matrices to obtain the ship formation information at different distance thresholds by the hierarchical clustering method, based on which we can infer the ship type. Thirdly, formation information and other attribute information are as nodes of the Bayesian Network (BN) to infer the ship type. Afterward, we can convert the recognition results at different thresholds into body of evidences (BOEs) as multiple information sources. Finally, we fuse the BOEs to get the final recognition. The proposed method is verified in simulation battle scenario in this paper. The simulation results demonstrate that the proposed method achieves performance superiority as compared with other ship recognition methods in terms of recognition accuracy. Show more

Keywords: Ship target recognition, multi-source information, formation information, Bayesian inference, evidence theory

DOI: 10.3233/JIFS-211638

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2331-2346, 2022

Authors: Jin, Zhen-Yu | Yan, Cong-Hua

Article Type: Research Article

Abstract: In this paper, a notion of fuzzifying bornological linear spaces is introduced and the necessary and sufficient condition for fuzzifying bornologies to be compatible with linear structure is discussed. The characterizations of convergence and separation in fuzzifying bornological linear spaces are showed. In particular, some examples with respect to linear fuzzifying bornologies induced by probabilistic normed spaces and fuzzifying topological linear spaces are also provided.

Keywords: Fuzzifying bornological linear spaces, fuzzifying bornological convergence, separation, Product linear fuzzifying bornologies, quotient linear fuzzifying bornologies

DOI: 10.3233/JIFS-211644

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2347-2358, 2022

Authors: Chen, Zhixiong | Tian, Shengwei | Yu, Long | Zhang, Liqiang | Zhang, Xinyu

Article Type: Research Article

Abstract: In recent years, the research on object detection has been intensified. A large number of object detection results are applied to our daily life, which greatly facilitates our work and life. In this paper, we propose a more effective object detection neural network model ENHANCE_YOLOV4. We studied the effects of several attention mechanisms on YOLOV4, and finally concluded that spatial attention mechanism had the best effect on YOLOV4. Therefore, based on previous studies, this paper introduces Dilated Convolution and one-by-one convolution into the spatial attention mechanism to expand the receptive field and combine channel information. Compared with CBAM and BAM, …which are composed of spatial attention and channel attention, this improved spatial attention module reduces model parameters and improves detection capabilities. We built a new network model by embedding improved spatial attention module in the appropriate place in YOLOV4. And this paper proves that the detection accuracy of this network structure on the VOC data set is increased by 0.8%, and the detection accuracy on the coco data set is increased by 7%when the calculation performance is increased a little. Show more

Keywords: DCNN, object detection, spatial attention, dilated convolution, COCO

DOI: 10.3233/JIFS-211648

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2359-2368, 2022

Authors: Wu, Guoqiang | Li, Qingping

Article Type: Research Article

Abstract: Population structure changes interact with economic development, moderate population and reasonable population structure are important guarantees for sustainable social and economic development. The research ignores the specific impact of the change of population age structure on economic growth, and proposes and establishes a population economic function model based on data mining algorithm. Based on the changes of population structure in Liaoning Province in the past 20 years, Grey correlation analysis method is selected. The analysis shows that there is a close relationship between population structure and economic growth. Based on this research, the econometric method is used to construct a …multiple linear regression model to further analyze the specific impact of population structure changes on economic growth. The analysis results show that the total population of urban areas, the total number of employed people in the primary industry, the number of middle school students per 10,000 people, and the total number of employed people in the tertiary industry are the four most significant demographic indicators for the per capita GDP of the study area. There is a significant positive correlation between the total number of employed people in the tertiary industry and per capita GDP and there is a significant negative correlation between the total number of employed people in the primary industry and the number of middle school students per capita and per capita GDP. The impact of other indicators on per capita GDP is not significant. According to the conclusion, countermeasures and suggestions to ease population structure change and promote the coordinated development of population and economy in the study area are put forward. Show more

Keywords: Grey correlation analysis method, data mining algorithm, population economic function, model

DOI: 10.3233/JIFS-211663

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2369-2382, 2022

Authors: Zhou, Daxin | Qian, Yurong | Ma, Yuanyuan | Fan, Yingying | Yang, Jianeng | Tan, Fuxiang

Article Type: Research Article

Abstract: Low-illumination image restoration has been widely used in many fields. Aiming at the problem of low resolution and noise amplification in low light environment, this paper applies style transfer of CycleGAN(Cycle-Consistent Generative Adversarial Networks) to low illumination image enhancement. In the design network structure, different convolution kernels are used to extract the features from three paths, and the deep residual shrinkage network is designed to suppress the noise after convolution. The color deviation of the image can be resolved by the identity loss of CycleGAN. In the discriminator, different convolution kernels are used to extract image features from two paths. …Compared with the training and testing results of Deep-Retinex network, GLAD network, KinD and other network methods on LOL-dataset and Brightening dataset, CycleGAN based on multi-scale depth residuals contraction proposed in this experiment on LOL-dataset results image quality evaluation indicators PSNR = 24.62, NIQE = 4.9856, SSIM = 0.8628, PSNR = 27.85, NIQE = 4.7652, SSIM = 0.8753. From the visual effect and objective index, it is proved that CycleGAN based on multi-scale depth residual shrinkage has excellent performance in low illumination enhancement, detail recovery and denoising. Show more

Keywords: Style migration, cycle-consistent generative adversarial networks, depth residual shrinkage, image enhancement

DOI: 10.3233/JIFS-211664

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2383-2395, 2022

Authors: Ramalingam, Anita | Navaneethakrishnan, Subalalitha Chinnaudayar

Article Type: Research Article

Abstract: Thirukkural, a Tamil classic literature, which was written in 300 BCE is a didactic literature. Though Thirukkural comprises 1330 couplets which are organized into three sections and 133 chapters, in order to retrieve meaningful Thirukkural for a given query in search systems, a better organization of the Thirukkural is needed. This paper lays such a foundation by classifying the Thirukkural into ten new categories called superclasses that is helpful for building a better Information Retrieval (IR) system. The classifier is trained using Multinomial Naïve Bayes algorithm. Each superclass is further classified into two subcategories based on the didactic information. The …proposed classification framework is evaluated using precision, recall and F-score metrics and achieved an overall F-score of 82.33% and a comparison analysis has been done with the Support Vector Machine, Logistic Regression and Random Forest algorithms. An IR system is built on top of the proposed system and the performance comparison has been done with the Google search and a locally built keyword search. The proposed classification framework has achieved a mean average precision score of 89%, whereas the Google search and keyword search have yielded 59% and 68% respectively. Show more

Keywords: Natural language processing, text classification, information retrieval, multinomial naive bayes classifier, the Thirukkural , morphological analysis

DOI: 10.3233/JIFS-211667

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2397-2408, 2022

Authors: Wei, Pengfei | Zeng, Bi | Liao, Wenxiong

Article Type: Research Article

Abstract: Intent detection and slot filling are recognized as two very important tasks in a spoken language understanding (SLU) system. In order to model these two tasks at the same time, many joint models based on deep neural networks have been proposed recently and archived excellent results. In addition, graph neural network has made good achievements in the field of vision. Therefore, we combine these two advantages and propose a new joint model with a wheel-graph attention network (Wheel-GAT), which is able to model interrelated connections directly for single intent detection and slot filling. To construct a graph structure for utterances, …we create intent nodes, slot nodes, and directed edges. Intent nodes can provide utterance-level semantic information for slot filling, while slot nodes can also provide local keyword information for intent detection. The two tasks promote each other and carry out end-to-end training at the same time. Experiments show that our proposed approach is superior to multiple baselines on ATIS and SNIPS datasets. Besides, we also demonstrate that using bi-directional encoder representation from transformer (BERT) model further boosts the performance of the SLU task. Show more

Keywords: Spoken language understanding, graph neural network, attention mechanism, joint learning

DOI: 10.3233/JIFS-211674

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2409-2420, 2022

Authors: Hou, Long | Yu, Long | Tian, Shengwei | Zhang, Yanhan

Article Type: Research Article

Abstract: Underwater image enhancement has always been a hot spot in underwater vision research. However, due to complicated underwater environment, a lot of problems such as the color distortion and low brightness of underwater raw images are very likely to occur. In response to the above situation, we proposed a generative adversarial network that integrated multiple attention to enhance underwater images. In the generator, we introduced multi-layer dense connections and CSAM modules, of which the former could capture more detailed features and make use of previous features, while the latter could improve the utilization of the feature map. Meanwhile, we improved …the enhancement effect of the generated image by combining VGG19 content loss function and SmoothL1 loss function. Finally, we verified the effectiveness of the proposed model through qualitative and quantitative experiments, and compared the results with the performance of several latest models. The results show that the methods proposed in this paper are superior to the existing methods. Show more

Keywords: Deep learning, attentional mechanism, underwater image, image enhancement.

DOI: 10.3233/JIFS-211680

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2421-2433, 2022

Authors: Wei, Jinpeng | Qu, Shaojian | Jiang, Shan | Feng, Can | Xu, Yuting | Zhao, Xiaohui

Article Type: Research Article

Abstract: Individual opinion is one of the vital factors influencing the consensus in group decision-making, and is often uncertain. The previous studies mostly used probability distribution, interval distribution or uncertainty distribution function to describe the uncertainty of individual opinions. However, this requires an accurate understanding of the individual opinions distribution, which is often difficult to satisfy in real life. In order to overcome this shortcoming, this paper uses a robust optimization method to construct three uncertain sets to better characterize the uncertainty of individual initial opinions. In addition, we used three different aggregation operators to obtain collective opinions instead of using …fixed values. Furthermore, we applied the numerical simulations on flood disaster assessment in south China so as to evaluate the robustness of the solutions obtained by the robust consensus models that we proposed. The results showed that the proposed models are more robust than the previous models. Finally, the sensitivity analysis of uncertain parameters was discussed and compared, and the characteristics of the proposed models were revealed. Show more

Keywords: Group decision making, aggregation operator, consensus models, uncertainty set, robust optimization

DOI: 10.3233/JIFS-211704

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2435-2449, 2022

Authors: Yang, Gang | Li, Tianbin | Ma, Chunchi | Meng, Lubo | Zhang, Hang | Ma, Junjie

Article Type: Research Article

Abstract: Accurate prediction of surrounding rock grades holds great significance to tunnel construction. This paper proposed an intelligent classification method for surrounding rock based on one-dimensional convolutional neural networks (1D CNNs). Six indicators collected in some tunnel construction sites are considered, and the degree of linear correlation between these indicators has been analyzed. The improved one-hot encoding method is put forward for transforming these non-image indicators into one-dimensional structural data and avoiding the sampling error in the indicators of surrounding rock collected in the field. We found that the 1D CNNs model based on the improved one-hot encoding method can best …extract the features of surrounding rock classification indicators (in terms of both accuracy and efficiency). We applied the well-trained classification model of tunnel surrounding rock to a series of expressway tunnels in China, and the results show that our model could accurately predict the surrounding rock grade and has great application value in the construction of tunnel engineering. It provides a new research idea for the prediction of surrounding rock grades in tunnel engineering. Show more

Keywords: Tunnel engineering, surrounding rock classification, index-based classification, one-dimensional convolutional neural network, non-image data

DOI: 10.3233/JIFS-211718

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2451-2469, 2022

Authors: Liu, Mingzhou | Xu, Xin | Hu, Jing | Jiang, Qiannan

Article Type: Research Article

Abstract: Road detection algorithms with high robustness as well as timeliness are the basis for developing intelligent assisted driving systems. To improve the robustness as well as the timeliness of unstructured road detection, a new algorithm is proposed in this paper. First, for the first frame in the video, the homography matrix H is estimated based on the improved random sample consensus (RANSAC) algorithm for different regions in the image, and the features of H are automatically extracted using convolutional neural network (CNN), which in turn enables road detection. Secondly, in order to improve the rate of subsequent similar frame detection, …the color as well as texture features of the road are extracted from the detection results of the first frame, and the corresponding Gaussian mixture models (GMMs) are constructed based on Orchard-Bouman, and then the Gibbs energy function is used to achieve road detection in subsequent frames. Finally, the above algorithm is verified in a real unstructured road scene, and the experimental results show that the algorithm is 98.4% accurate and can process 58 frames per second with 1024×960 pixels. Show more

Keywords: Unstructured road detection, Gibbs energy function, CNN

DOI: 10.3233/JIFS-211733

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2471-2489, 2022

Authors: Connie, Tee | Tan, Yee Fan | Goh, Michael Kah Ong | Hon, Hock Woon | Kadim, Zulaikha | Wong, Li Pei

Article Type: Research Article

Abstract: In the recent years, Artificial Intelligence (AI) has been widely deployed in the healthcare industry. The new AI technology enables efficient and personalized healthcare systems for the public. In this paper, transfer learning with pre-trained VGGFace model is applied to identify sick symptoms based on the facial features of a person. As the deep learning model’s operation is unknown for making a decision, this paper investigates the use of Explainable AI (XAI) techniques for soliciting explanations for the predictions made by the model. Various XAI techniques including Integrated Gradient, Explainable region-based AI (XRAI) and Local Interpretable Model-Agnostic Explanations (LIME) are …studied. XAI is crucial to increase the model’s transparency and reliability for practical deployment. Experimental results demonstrate that the attribution method can give proper explanations for the decisions made by highlighting important attributes in the images. The facial features that account for positive and negative classes predictions are highlighted appropriately for effective visualization. XAI can help to increase accountability and trustworthiness of the healthcare system as it provides insights for understanding how a conclusion is derived from the AI model. Show more

Keywords: Explainable AI, health prediction, transfer learning, deep learning

DOI: 10.3233/JIFS-211737

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2491-2503, 2022

Authors: Rajagopal, Sureshkumar | Umapathy, Prabha

Article Type: Research Article

Abstract: As the move towards Grid Integrated-Photovoltaic (GI-PV) system is proposed to improve the power quality development. A novel Adaptive Neuro-Fuzzy Inference System (ANFIS) based on improved Moth Flame Optimization (MFO) algorithm is described for grid integrated approach. The solar integration of Maximum Power Point (MPP) fed into modified Switched Boost Inverter (SBI) is presented, this GI-PV connected circuit has become prominent research in a recent scenario for energy demand. Proposed MFOA-ANFIS controller has generated the duty cycle pulses to each converter circuit. The benefit of grid-tied SBI is direct control outer-loop employed to obtain MFO-ANFIS techniques. To maintain a constant …voltage DC-link is employed for inner-loop, this presence of constant DC-power to grid loads with support of MFO-ANFIS assists Proportional Integral Differential (PID) method. The results acquired by the simulation expressed that the proposed controller is addressed to maintain active and reactive power exchange, regulate DC bus-link voltages, grid voltage, and grid current. The effectiveness of the practical implication research is achieved by the output as represented as minimum grid harmonics, load current, and compensator current as verified in MATLAB/Simulink platform. Show more

Keywords: Grid Integrated-Photovoltaic, Maximum Power Point, Adaptive Neuro-Fuzzy Inference System, Moth Flame Optimization Algorithm

DOI: 10.3233/JIFS-211748

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2505-2519, 2022

Authors: Muhiuddin, G. | John, J. Catherine Grace | Elavarasan, B. | Jun, Y.B. | Porselvi, K.

Article Type: Research Article

Abstract: The concept of a hybrid structure in X -semimodules, where X is a semiring, is introduced in this paper. The notions of hybrid subsemimodule and hybrid right (resp., left) ideals are defined and discussed in semirings. We investigate the representations of hybrid subsemimodules and hybrid ideals using hybrid products. We also get some interesting results on t -pure hybrid ideals in X . Furthermore, we show how hybrid products and hybrid intersections are linked. Finally, the characterization theorem is proved in terms of hybrid …structures for fully idempotent semirings. Show more

Keywords: Hybrid semirings, hybrid X-semimodules, fully idempotent hybrid ideals, t-pure hybrid ideals, weakly regular

DOI: 10.3233/JIFS-211751

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2521-2531, 2022

Authors: Li, Wenyi | Zhang, Cuixia | Liu, Conghu | Liu, Xiao

Article Type: Research Article

Abstract: In order to improve the quality of remanufacturing assembly with uncertainty for the sustainability of remanufacturing industry, an error propagation model of the remanufacturing assembly process and its optimal control method are established. First, the state space model of error propagation is established by taking the work-in-process parameter errors of each process as the initial state of the procedure and the parameters of remanufactured parts and operation quantities as the input. Then, the quality control issue of remanufacturing assembly is transformed into a convex quadratic programming with constraints based on this model. Finally, the proposed method is used to control …the remanufactured-crankshaft assembly quality. The experimental results show that the axial-clearance consistency and the crankshaft torque are improved, and the one-time assembly success rate of a remanufactured crankshaft is increased from 96.97%to 99.24%. This study provides a theoretical model and method support for the quality control of remanufacturing assembly and has a practical effect on improving the quality of remanufactured products. Show more

Keywords: Remanufacturing, assembly, error propagation, optimal control, state space, convex quadratic programming

DOI: 10.3233/JIFS-211791

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2533-2547, 2022

Authors: Jha, Sunil Kumar | Marina, Ninoslav | Wang, Jinwei | Ahmad, Zulfiqar

Article Type: Research Article

Abstract: Machine learning approaches have a valuable contribution in improving competency in automated decision systems. Several machine learning approaches have been developed in the past studies in individual disease diagnosis prediction. The present study aims to develop a hybrid machine learning approach for diagnosis predictions of multiple diseases based on the combination of efficient feature generation, selection, and classification methods. Specifically, the combination of latent semantic analysis, ranker search, and fuzzy-rough-k-nearest neighbor has been proposed and validated in the diagnosis prediction of the primary tumor, post-operative, breast cancer, lymphography, audiology, fertility, immunotherapy, and COVID-19, etc. The performance of the proposed approach …is compared with single and other hybrid machine learning approaches in terms of accuracy, analysis time, precision, recall, F-measure, the area under ROC, and the Kappa coefficient. The proposed hybrid approach performs better than single and other hybrid approaches in the diagnosis prediction of each of the selected diseases. Precisely, the suggested approach achieved the maximum recognition accuracy of 99.12%of the primary tumor, 96.45%of breast cancer Wisconsin, 94.44%of cryotherapy, 93.81%of audiology, and significant improvement in the classification accuracy and other evaluation metrics in the recognition of the rest of the selected diseases. Besides, it handles the missing values in the dataset effectively. Show more

Keywords: Hybrid machine learning, fuzzy nearest neighbor, disease diagnosis prediction, feature generation and selection

DOI: 10.3233/JIFS-211820

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2549-2563, 2022

Authors: Yao, Jianrong | Wang, Zhongyi | Wang, Lu | Zhang, Zhebin | Jiang, Hui | Yan, Surong

Article Type: Research Article

Abstract: With the in-depth application of artificial intelligence technology in the financial field, credit scoring models constructed by machine learning algorithms have become mainstream. However, the high-dimensional and complex attribute features of the borrower pose challenges to the predictive competence of the model. This paper proposes a hybrid model with a novel feature selection method and an enhanced voting method for credit scoring. First, a novel feature selection combined method based on a genetic algorithm (FSCM-GA) is proposed, in which different classifiers are used to select features in combination with a genetic algorithm and combine them to generate an optimal …feature subset. Furthermore, an enhanced voting method (EVM) is proposed to integrate classifiers, with the aim of improving the classification results in which the prediction probability values are close to the threshold. Finally, the predictive competence of the proposed model was validated on three public datasets and five evaluation metrics (accuracy, AUC, F-score, Log loss and Brier score). The comparative experiment and significance test results confirmed the good performance and robustness of the proposed model. Show more

Keywords: Credit scoring, hybrid model, feature selection, machine learning, ensemble learning

DOI: 10.3233/JIFS-211828

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2565-2579, 2022

Authors: Li, Lin | Yu, Xiaolei | Liu, Zhenlu | Zhao, Zhimin | Wu, Chao | Zhang, Ke | Zhou, Shanhao

Article Type: Research Article

Abstract: As a non-contact automatic identification technology, Radio Frequency Identification (RFID) is of great significance to improve the simultaneous identification of multi-target. This paper designs a more efficient and accurate multi-tag reading performance measurement system based on the fusion of YOLOv3 and Elman neural network. In the machine vision subsystem, multi-tag images are collected by dual CCD and detected by neural network algorithm. The reading distance of 3D distributed multi-tag is measured by laser ranging to evaluate the reading performance of RFID system. Firstly, the multi-tag are detected by YOLOv3, which realizes the measurement of 3D coordinates, improves the prediction accuracy, …enhances the recognition ability of small targets, and improves the accuracy of 3D coordinate detection. Secondly, the relationship between the 3D coordinates and the corresponding reading distance of RFID multi-tag are modelled by Elman recurrent neural network. Finally, the reading performance of RFID multi-tag is optimized. Compared with the state-of-the-arts, the multi-tag detection rate of YOLOv3 is 17.4% higher and the time is 3.27 times higher than that of the previous template matching algorithm. In terms of reading performance, the MAPE of Elman neural network is 1.46 %, which is at least 21.43 % higher than other methods. In running time, Elman only needs 1.69s, which is at least 28.40% higher than others. Thus, the system not only improves the accuracy, but also improves the speed, which provides a new insight for the measurement and optimization of RFID performance. Show more

Keywords: RFID, neural network, Elman, YOLOv3, performance optimization

DOI: 10.3233/JIFS-211838

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2581-2594, 2022

Authors: Wu, Jian | Jin, Yuting | Zhou, Mi | Cao, Mingshuo | Liu, Yujia

Article Type: Research Article

Abstract: Sustainable supplier selection (SSS) plays an increasingly critical role in the stability and development of the organization with increasing environmental awareness. This article proposes a linguistic multiple attribute group decision-making method to select the appropriate sustainable supplier by combing Decision Making and Trial Evaluation Laboratory(DEMATEL) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). To do that, a distribution linguistic based DEMATEL technique is developed to deal with the complexity in criteria of SSS. To eliminate the inconsistency among multiple decision-makers providing the preference information of evaluation criteria, a minimum adjusting cost feedback mechanism is utilized to reach group consensus. Therefore, the …proposed weights obtaining method can not only deal with the subjectivity of evaluation criterion but also satisfy group decision-makers with different profits and backgrounds. Then, based on the evaluation matrices of supplier performance, it calculates the ranking of alternative suppliers by the VIKOR method. Hence, it can deal with the ambiguity of decision makers’ evaluation and provide the best solution for decision-makers, as a consequence, it makes the final evaluation result more feasible and operable. Finally, the effectiveness and efficiency of this method are verified based on the actual situation of ABC Company. This study proposed a linguistic multiple attribute group decision-making method to select the appropriate sustainable supplier by combing Decision Making and Trial Evaluation Laboratory(DEMATEL) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). What’s more, the proposed method considered the group consensus reaching processes. Show more

Keywords: Sustainable supplier selection, , Group decision making, DEMATEL, VIKOR, Consensus

DOI: 10.3233/JIFS-211929

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2595-2613, 2022

Authors: Guo, Dayong | Hu, Qing

Article Type: Research Article

Abstract: Aiming at the problems of low precision, slow data transmission speed and long response time of silk quality and temperature control in tobacco intelligent production line, a multi-index testing system is designed. According to the characteristics of PROFIBUS fieldbus technology, combined with PROFIBUS transmission technology, a factory level information network is formed with PROFIBUS-DP as the exchange mode. Based on the PROFIBUS technology, the dual redundancy structure of control ring network and management information ring network is adopted, and the whole network architecture is constructed by logic layering. From the point of view of building enterprise MES system, it locates …real-time production monitoring, production task receiving and production line related data collection, integrates equipment control layer, centralized monitoring layer and production management layer, and designs system function structure. The functional structure of the system, and the establishment of a number of data tables, to achieve a tobacco intelligent production line silk quality detection system design. Experimental results show that this method can effectively speed up the data transmission speed and shorten the system response time. Show more

Keywords: PROFIBUS fieldbus technology, tobacco intelligent production line, silk quality, multi-index integrated test system

DOI: 10.3233/JIFS-211936

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2615-2627, 2022

Authors: Zhou, Yinfeng | Li, Jinjin | Wang, Hongkun | Sun, Wen

Article Type: Research Article

Abstract: In knowledge space theory (KST), knowledge structure is an effective feature to evaluate individuals’ knowledge and guide future learning. How to construct knowledge structures is one of the key research problems in KST. At present, the knowledge structure has been generalized to the polytomous knowledge structure. This article mainly focuses on the special polytomous knowledge structures delineated by skills, which are called fuzzy knowledge structures. We consider how to construct fuzzy knowledge structures based on the relationship between items and skills, and how to find the learning paths for specific knowledge domains. First, we construct knowledge structures in four models, …which are the conjunctive model of skill maps, the disjunctive and conjunctive models of fuzzy skill maps, and the competency model of fuzzy skill multimaps. Second, we assess individuals’ skills and find the learning paths for the specific knowledge domains in the first three models. Finding the learning paths for a specific knowledge domain can guide learning and improve the learning efficiency of individuals. Finally, we analyze some data sets to show that the algorithms proposed are effective and applicable. These works can be applied to adaptive learning systems, which bring great convenience for assessing individuals’ knowledge and guiding future learning. Show more

Keywords: Fuzzy knowledge structure, learning path, disjunctive model, conjunctive model, competency model

DOI: 10.3233/JIFS-212018

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2629-2645, 2022

Authors: Prakash, R | Ayyar, K

Article Type: Research Article

Abstract: This paper presents an Enhanced Whale Optimization Algorithm (EWO) approach for tuning to perfection of Fractional Order Proportional Integral and integral order Controller (FOPI λ ) is used to sensorless speed control of permanent magnet Brushless DC (PMBLDC) motor under the operating dynamic condition such as (i) speed change by set speed command signal (ii) varying load conditions, (iii) integrated conditions and (iv) controller parameters uncertainty. On the other hand, it deals with a reduced THD (Total Harmonic Distortion) under dynamic operating conditions to improve the power quality for the above control system. Here present are three optimization techniques, namely …(i) Enhanced Whale Optimization (EWO), (ii) Invasive Weed Optimization (IWO), and (iii) Social Spider Optimization (SSO) for fine-tuning of the FOPI λ controller parameters with reduction of THD. The proposed optimization algorithm optimized FOPI λ controller are compared under various BLDC motor operating conditions. Based on the results of MATLAB/Simulink models, the proposed algorithms are evaluated. Here, both the simulation and the results of the experiments are validated for the proposed controller technique. It demonstrates that the effectiveness of the proposed controllers is completely validated by comparing the three intelligent optimization techniques mentioned above. The EWO optimized FOPI λ controller for speed control of sensorless PMBLDC motor clearly outperforms the other two intelligent controllers by minimizing the time domain parameters, THD, performance Indices error, convergence time, control efforts, cost function, mean and standard deviation. Show more

Keywords: BLDC motor drives, fractional order PID controller (FOPIλ), whale optimization algorithm, sensorless speed control techniques

DOI: 10.3233/JIFS-212167

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2647-2666, 2022

Authors: Murugesan, Malathi | Kaliannan, Kalaiselvi | Balraj, Shankarlal | Singaram, Kokila | Kaliannan, Thenmalar | Albert, Johny Renoald

Article Type: Research Article

Abstract: Deep learning algorithms will be used to detect lung nodule anomalies at an earlier stage. The primary goal of this effort is to properly identify lung cancer, which is critical in preserving a person’s life. Lung cancer has been a source of concern for people all around the world for decades. Several researchers presented numerous issues and solutions for various stages of a computer-aided system for diagnosing lung cancer in its early stages, as well as information about lung cancer. Computer vision is one of the field of artificial intelligence this is a better way to detect and prevent the …lung cancer. This study focuses on the stages involved in detecting lung tumor regions, namely pre-processing, segmentation, and classification models. An adaptive median filter is used in pre-processing to identify the noise. The work’s originality seeks to create a simple yet effective model for the rapid identification and U-net architecture based segmentation of lung nodules. This approach focuses on the identification and segmentation of lung cancer by detecting picture normalcy and abnormalities. Show more

Keywords: Lung cancer, pre-processing, support vector machine, deep learning, U-Net, classification accuracy

DOI: 10.3233/JIFS-212189

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2667-2679, 2022

Authors: Angappamudaliar Palanisamy, Senthil Kumar | Selvaraj, Dinesh | Ramasamy, SivaBalaKrishnan

Article Type: Research Article

Abstract: In the field of mobile robot decision making and control, path planning is an essential element as it defines the performance of the design. It is one of the hot topics in artificial intelligence and researchers pay more attention to develop an efficient model. The key requirements that must be considered while designing a navigational system for mobile robots are origin point, obstacles, destination point, path planning, and realistic decision mechanism. However, conventional systems have limitations as slow response, long planning, large turns, and unsafe factors. Aiming at the problems, this research work presents a hybrid optimized path planning model …for a mobile robot. Improved particle swarm optimization and Modified Whale optimization models are incorporated as a hybrid multi-objective approach to obtain the shortest, smoothest, and safest path for a mobile robot. Experimental results demonstrate that the proposed hybrid optimization model is suitable for mobile robot navigation for dynamic environments by obtaining a shorter, smoother, and safer path than existing algorithms. Show more

Keywords: Path planning, path optimization, hybrid optimization, mobile robot, improved particle swarm optimization

DOI: 10.3233/JIFS-211801

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2681-2693, 2022

Authors: Iqbal, M. Mohamed | Latha, K.

Article Type: Research Article

Abstract: Link prediction plays a predominant role in complex network analysis. It indicates to determine the probability of the presence of future links that depends on available information. The existing standard classical similarity indices-based link prediction models considered the neighbour nodes have a similar effect towards link probability. Nevertheless, the common neighbor nodes residing in different communities may vary in real-world networks. In this paper, a novel community information-based link prediction model has been proposed in which every neighboring node’s community information (community centrality) has been considered to predict the link between the given node pair. In the proposed model, the …given social network graph can be divided into different communities and community centrality is calculated for every derived community based on degree, closeness, and betweenness basic graph centrality measures. Afterward, the new community centrality-based similarity indices have been introduced to compute the community centralities which are applied to nine existing basic similarity indices. The empirical analysis on 13 real-world social networks datasets manifests that the proposed model yields better prediction accuracy of 97% rather than existing models. Moreover, the proposed model is parallelized efficiently to work on large complex networks using Spark GraphX Big Data-based parallel Graph processing technique and it attains a lesser execution time of 250 seconds. Show more

Keywords: Link prediction, social network, performance evaluation, prediction accuracy, parallel louvain algorithm

DOI: 10.3233/JIFS-211821

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2695-2711, 2022

Authors: Pang, Jing | Yao, Bingxue | Li, Lingqiang

Article Type: Research Article

Abstract: In this paper, we point out that Lin’s general neighborhood systems-based rough set model is an extension of Qian’s optimistic rough set model, and thus called optimistic general neighborhood systmes-based rough set model. Then we present a new rough set model based on general neighborhood systems, and prove that it is an extension of Qian’s pessimistic rough set model. Later, we study the basic properties of the proposed pessimistic rough sets, and define the serial, reflexive, symmetric, transitive and Euclidean conditions for general neighborhood systems, and explore the further properties of related rough sets. Furthermore, we apply the pessimistic general …neighborhood systems-based rough set model in the research of incomplete information system, and build a three-way decision model based on it. A simple practical example to show the effectiveness of our model is also presented. Show more

Keywords: Rough set, neighborhood system, pessimistic rough approximation operator, three-way decision

DOI: 10.3233/JIFS-211851

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2713-2725, 2022

Authors: Luo, Wentao | Feng, Pingfa | Zhang, Jianfu | Yu, Dingwen | Wu, Zhijun

Article Type: Research Article

Abstract: As the service life of the assembly equipment are short, the tightening data it produces are very limited. Therefore, data-driven assembly quality diagnosis is still a challenge task in industries. Transfer learning can be used to address small data problems. However, transfer learning has strict requirements on the training dataset, which is hard to satisfy. To solve the above problem, an Improved Deep Convolution Generative Adversarial Transfer Learning Model (IDCGAN-TM) is proposed, which integrates three modules: The generative learning module automatically produces source datasets based on small target datasets by using the improved generative-adversarial theory. The feature learning module improves …the feature extraction ability by building a lightweight deep learning model (DL). The transfer learning module consists of a pre-trained DL and a one fully connected layer to better perform the intelligent quality diagnosis on the training small sample data. A parallel computing method is adopted to obtain produced source data efficiently. Real assembly quality diagnosis cases are designed and discussed to validate the advance of the proposed model. In addition, the comparison experiments are designed to show that the proposed approach holds the better transfer diagnosis performance compared with the existing three state-of-art approaches. Show more

Keywords: Transfer learning, generative adversarial learning, small sample learning, quality diagnosis

DOI: 10.3233/JIFS-211860

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2727-2741, 2022

Authors: Sambath Kumar, K. | Rajendran, A.

Article Type: Research Article

Abstract: Manual segmentation of brain tumor is not only a tedious task that may bring human mistakes. An automatic segmentation gives results faster, and it extends the survival rate with an earlier treatment plan. So, an automatic brain tumor segmentation model, modified inception module based U-Net (IMU-Net) proposed. It takes Magnetic resonance (MR) images from the BRATS 2017 training dataset with four modalities (FLAIR, T1, T1ce, and T2). The concatenation of two series 3×3 kernels, one 5×5, and one 1×1 convolution kernels are utilized to extract the whole tumor (WT), core tumor (CT), and enhance tumor (ET). The modified inception module …(IM) collects all the relevant features and provides better segmentation results. The proposed deep learning model contains 40 convolution layers and utilizes intensity normalization and data augmentation operation for further improvement. It achieved the mean dice similarity coefficient (DSC) of 0.90, 0.77, 0.74, and the mean Intersection over Union (IOU) of 0.79, 0.70, 0.70 for WT, CT, and ET during the evaluation. Show more

Keywords: Brain tumor, automatic segmentation, deep neural network, inception, convolution

DOI: 10.3233/JIFS-211879

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2743-2754, 2022

Authors: Liu, Wei | Wang, Yuhong

Article Type: Research Article

Abstract: In view of the present situation that most aggregation methods of fuzzy preference information are extended or mixed by classical aggregation operators, which leads to the aggregation accuracy is not high. The purpose of this paper is to develop a novel method for spatial aggregation of fuzzy preference information. Thus we map the fuzzy preference information to a set of three-dimensional coordinate and construct the spatial aggregation model based on Steiner-Weber point. Then, the plant growth simulation algorithm (PGSA) algorithm is used to find the spatial aggregation point. According to the comparison and analysis of the numerical example, the aggregation …matrix established by our method is closer to the group preference matrices. Therefore, the optimal aggregation point obtained by using the optimal aggregation method based on spatial Steiner-Weber point can best represent the comprehensive opinion of the decision makers. Show more

Keywords: Fuzzy preference information, Steiner-Weber point, spatial aggregation model, aggregation operator, PGSA

DOI: 10.3233/JIFS-211913

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2755-2773, 2022

Authors: Honghong, Zhang | Xusheng, Gan | Ying, Liu | Yarong, Wu | Jingjuan, Sun | Liang, Tong | Feng, Yang

Article Type: Research Article

Abstract: To provide real-time safety assessment for low-altitude unmanned aerial vehicle (UAV) air traffic management, and to ensure the UAVs safe operation in low-altitude airspace, a risk assessment framework is proposed. It considers the accidents probability and the accidents hazards. Firstly, accidents probability model based on the System Theoretic Process Analysis-Bayesian Network (STPA-BN) algorithm is built. Potential system hazards are effectively identified and analyzed through the STPA process. The accidents cause identified based on the STPA process is taken as the root node. The relevant failure probability table is given respectively. It constitutes the BN used to analyze the system accidents …probability. This method uses a combination of qualitative and quantitative methods to calculate the accidents probability. Then, based on the UAV fall model, considering the uncertainty of the UAV operation process, the UAV fall point distribution is determined based on the Monte-Carlo method, and the impact area of the fall is calculated. Thus the system risk value is obtained. Finally, through case analysis, the validity and rationality of the proposed risk assessment framework are verified. Show more

Keywords: Unmanned aerial vehicle system, air traffic management, risk assessment

DOI: 10.3233/JIFS-211927

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2775-2792, 2022

Authors: Jeyaprakash, P. | Agees Kumar, C. | Ravi, A.

Article Type: Research Article

Abstract: Electricity is the most critical facility for humans. All traditional energy supplies are rapidly depleting. As a result, the energy resources are moved from traditional to non-conventional. In this research, mixture of two energy tools, namely wind and solar energy are used. Using a Hybrid Energy Storage System (HESS), continuous power can be provided. Electricity can be produced at a cost that is affordable. The integration of solar and wind in a hybrid system cause an increase in the system’s stability, which is the key benefit of this research. The system’s power transmission efficiency and reliability can be greatly enhanced …by integrating these two intermittent sources. When one of the energy source is unavailable or inadequate to meet load demands, the other energy source will supply the power. The major contribution in this research is that, the proposed bidirectional single-inductor multiple-port (BSIMP) converter significantly lowers the component count, smaller circuit size and lower cost, allowing HESS to be integrated into DC microgrid. Minimum number of components are used for the same number of ESs in HESS in the proposed BSIMP converter. The hybridization of battery and supercapacitor (SC) for storage purpose is more cost effective, as compared to the battery energy storage system, thus improving the battery stress and hence used for large scale grid energy storage. SC’s are accepted as backup and found very useful in delivering high power, not possible with batteries. The use of SC in addition to batteries can be one solution for achieving the low life cycle economy. The Single Objective Adaptive Firefly Algorithm (SOAFA) is introduced for optimising the Proportional-Integral (PI) controller parameters. The system cost is reduced by about 32%, with the constraints on wind turbine swept area, PV area, total battery and SC capacity with the proposed optimisation algorithm. Show more

Keywords: Bidirectional single-inductor multiple-port (BSIMP) converter, Single Objective Adaptive firefly Algorithm (SOAFA), PI controller, Hybrid Energy Storage System (HESS)

DOI: 10.3233/JIFS-212262

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2793-2808, 2022

Authors: Sha, Gang | Wu, Junsheng | Yu, Bin

Article Type: Research Article

Abstract: Purpose: at present, more and more deep learning algorithms are used to detect and segment lesions from spinal CT (Computed Tomography) images. But these algorithms usually require computers with high performance and occupy large resources, so they are not suitable for the clinical embedded and mobile devices, which only have limited computational resources and also expect a relative good performance in detecting and segmenting lesions. Methods: in this paper, we present a model based on Yolov3-tiny to detect three spinal fracture lesions, cfracture (cervical fracture), tfracture (thoracic fracture), and lfracture (lumbar fracture) with a small size model. We …construct this novel model by replacing the traditional convolutional layers in YoloV3-tiny with fire modules from SqueezeNet, so as to reduce the parameters and model size, meanwhile get accurate lesions detection. Then we remove the batch normalization layers in the fire modules after the comparative experiments, though the overall performance of fire module without batch normalization layers is slightly improved, we can reduce computation complexity and low occupations of computer resources for fast lesions detection. Results: the experiments show that the shrank model only has a size of 13 MB (almost a third of Yolov3-tiny), while the mAP (mean Average Precsion) is 91.3%, and IOU (intersection over union) is 90.7. The detection time is 0.015 second per CT image, and BFLOP/s (Billion Floating Point Operations per Second) value is less than Yolov3-tiny. Conclusion: the model we presented can be deployed in clinical embedded and mobile devices, meanwhile has a relative accurate and rapid real-time lesions detection. Show more

Keywords: Deep learning, Yolov3-tiny, shrank model, fire module, detection and location

DOI: 10.3233/JIFS-212255

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2809-2828, 2022