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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Sun, Guangling | Hu, Haoqi | Zhang, Xinpeng | Lu, Xiaofeng
Article Type: Research Article
Abstract: Universal Adversarial Perturbations(UAPs), which are image-agnostic adversarial perturbations, have been demonstrated to successfully deceive computer vision models. Proposed UAPs in the case of data-dependent, use the internal layers’ activation or the output layer’s decision values as supervision. In this paper, we use both of them to drive the supervised learning of UAP, termed as fully supervised UAP(FS-UAP), and design a progressive optimization strategy to solve the FS-UAP. Specifically, we define an internal layers supervised objective relying on multiple major internal layers’ activation to estimate the deviations of adversarial examples from legitimate examples. We also define an output layer supervised objective …relying on the logits of output layer to evaluate attacking degrees. In addition, we use the UAP found by previous stage as the initial solution of the next stage so as to progressively optimize the UAP stage-wise. We use seven networks and ImageNet dataset to evaluate the proposed FS-UAP, and provide an in-depth analysis for the latent factors affecting the performance of universal attacks. The experimental results show that our FS-UAP (i) has powerful capability of cheating CNNs (ii) has superior transfer-ability across models and weak data-dependent (iii) is appropriate for both untarget and target attacks. Show more
Keywords: Deep learning models, universal adversarial perturbations, fully supervised, progressive optimization
DOI: 10.3233/JIFS-210728
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 4959-4968, 2022
Authors: ul Haq, Ehtasham | Ahmad, Ishfaq | Hussain, Abid | Almanjahie, Ibrahim M.
Article Type: Research Article
Abstract: In the present simulation-based study, a novel parent-centric real-coded crossover operator is introduced with a unique probabilistic aspect of the mixture distribution. Moreover, the mixture distribution is a co-integration of double Pareto and Laplace probability distributions with various parameters. The key objective of the newly proposed methodology is to obtained optimal solutions for complex multimodal optimization problems. Hence, for its global comparison, the newly proposed mixture distribution crossover operator (MDX) is compared with double Pareto (DPX), Laplace (LX), and simulated binary (SBX) crossover operators within the conjunction of three mutation operators (MTPM, PM, and NUM). After a descriptive comparison, a …Quade multiple comparison test is also administered to examine its statistical significance. Furthermore, the performance of the genetic algorithm (GA) is also examined on a set of twenty-one unconstraint benchmark functions with diverse features. The empirical results of the simulation-based study reveal that the mixture-based crossover operator obtained a substantial dominance over all considered crossover operators in terms of computational complexity, robustness, scalability, and capability of exploration and exploitation. Moreover, the Quade multiple comparison test also showed a significant superiority with graphical authentication of the performance index (PI). Show more
Keywords: Genetic algorithms, two-component mixture model, real coded crossover, Quade test, Performance Index
DOI: 10.3233/JIFS-210886
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 4969-4985, 2022
Authors: Piegat, Andrzej | Pluciński, Marcin
Article Type: Research Article
Abstract: The paper presents the inclusion principle of fuzzy arithmetic results. This principle explains what features should have the span of the result of calculations realized with use of the fuzzy arithmetic. If some kind of fuzzy arithmetic provides results that do not comply with this principle, it means that the arithmetic has incomplete reliability, has errors in its theoretical assumptions and should either be verified or rejected. The principle contributes to the ordering of fuzzy arithmetic rules and thus to its practical applicability.
Keywords: Fuzzy arithmetic, interval arithmetic, multidimensional fuzzy arithmetic, principle of results inclusion
DOI: 10.3233/JIFS-210980
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 4987-4998, 2022
Authors: Gong, Weijun | Wang, Chaoqing | Jia, Jinlu | Qian, Yurong | Fan, Yingying
Article Type: Research Article
Abstract: Facial expression recognition (FER) has been one of the research focuses in recent years due to its significance in human-computer interactions. However, there are still challenges in the field of FER caused by the diversity and variation of facial expressions in real scenes, the singleness of feature type and the lack of enough discriminant features cannot effectively improve the recognition performance. To solve these problems, we propose a Multi-feature Fusion Network (MFNet) with dual-branch based on deep learning. Firstly, the MFNet uses the pyramid parallel multiscale residual network structure with progressive max-pooling of channel attention to extract multi-level facial features …and enhance the discrimination of features; In the meantime, a shallow Gabor convolutional network is designed to enhance the adaptation of learned features to the orientation and scale changes and improve the ability to capture local details features; Finally, the maximum expression features obtained by the above two networks are fused to make more effective expression recognition. Experiments on three public large-scale wild FER datasets (RAF-DB, FERPlus, and AffectNet) show that our MFNet has a superior recognition performance than other recognition methods. Show more
Keywords: Facial expression recognition, multi-feature fusion, feature extraction, deep learning
DOI: 10.3233/JIFS-211021
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 4999-5011, 2022
Authors: Luo, Minxia | Xu, Donghui
Article Type: Research Article
Abstract: In this paper, the concept of α (x , y )-interval-valued pointwise sustaining degree based on the left-continuous t -representable t -norms is put forward. And then, as a general extension based on the interval-valued pointwise sustaining degree, the interval-valued α (x , y )-full implication triple I method model, the interval-valued α (x , y )-quintuple implication principle models and the interval-valued α (x , y )-similarity measure method models are given. Moreover, the interval-valued R -type α (x , y )-fuzzy reasoning solutions with triple I method, quintuple implication principle and similarity measure method …are given. Some existing results are special cases of the main conclusions in this paper. Show more
Keywords: Interval-valued pointwise staining degree, left-continuous t-representable t-norms, Triple I method, quintuple implication principle, similarity measure method
DOI: 10.3233/JIFS-211076
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5013-5029, 2022
Authors: Miao, Yujie | Zhu, Shiping | Huang, Hua | Li, Junxian | Wei, Xiao | Ma, Lingkai | Pu, Jing
Article Type: Research Article
Abstract: With the development of convolutional neural networks, aiming at the problem of low efficiency and low accuracy in the process of wood species recognition, a recognition method using an improved convolutional neural network is proposed in this article. First, a large-scale wood dataset was constructed based on the WOOD-AUTH dataset and the data collected. Then, a new model named W_IMCNN was constructed based on Inception and mobilenetV3 networks for wood species identification. Experimental results showed that compared with other models, the proposed model had better recognition performance, such as shorter training time and higher recognition accuracy. In the data set …constructed by us, the accuracy of the test set reaches 96.4%. We used WOOD-AUTH dataset to evaluate the model, and the recognition accuracy reached 98.8%. Compared with state-of-the-art methods, the effectiveness of the W_IMCNN were confirmed. Show more
Keywords: Wood species, images, inception, mobileNetV3, convolutional neural networks
DOI: 10.3233/JIFS-211097
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5031-5040, 2022
Authors: Tong, Zhao | Chen, Hongjian | Liu, Bilan | Cai, Jinhui | Cai, Shuo
Article Type: Research Article
Abstract: In recent years, solving combinatorial optimization problems involves more complications, high dimensions, and multi-objective considerations. Combining the advantages of other evolutionary algorithms to enhance the performance of a unique evolutionary algorithm and form a new hybrid heuristic algorithm has become a way to strengthen the performance of the algorithm effectively. However, the intelligent hybrid heuristic algorithm destroys the integrity, universality, and robustness of the original algorithm to a certain extent and increases its time complexity. This paper implements a new idea “ML to choose heuristics” (a heuristic algorithm combined with machine learning technology) which uses the Q-learning method to learn …different strategies in genetic algorithm. Moreover, a selection-based hyper-heuristic algorithm is obtained that can guide the algorithm to make decisions at different time nodes to select appropriate strategies. The algorithm is the hybrid strategy using Q-learning on StudGA (HSQ-StudGA). The experimental results show that among the 14 standard test functions, the evolutionary algorithm guided by Q-learning can effectively improve the quality of arithmetic solution. Under the premise of not changing the evolutionary structure of the algorithm, the hyper-heuristic algorithm represents a new method to solve combinatorial optimization problems. Show more
Keywords: Arithmetic solution, combinatorial optimization, genetic algorithm, hyper-heuristic algorithm, Q-learning
DOI: 10.3233/JIFS-211250
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5041-5053, 2022
Authors: Xue, Guangming | Lin, Funing | Liu, Heng | Li, Shenggang
Article Type: Research Article
Abstract: This paper explores the prescribed performance tracking control problem of nonlinear systems with triangular structure. To obtain the desired transient performance and precise estimations of uncertain terms, the techniques of neural network control, sliding mode control and composite learning control are incorporated into the proposed control method. The presented control strategy can ensure the tracking error converges to a prescribed small residual set. Compared with the persistent excitation condition required in the conventional adaptive control, the interval excitation condition needed in the proposed control approach is weak, which guarantees that the radial basis function neural networks approximate the unknown nonlinear …terms more accurately. Finally, two simulation examples are exploited to manifest the effectiveness of the proposed approach. Show more
Keywords: Composite learning, prescribed performance, sliding mode control, neural network approximation, prediction error
DOI: 10.3233/JIFS-211310
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5055-5067, 2022
Authors: Zheng, Rong | Jia, Heming | Wang, Shuang | Liu, Qingxin
Article Type: Research Article
Abstract: Slime mould algorithm (SMA) is a new metaheuristic algorithm proposed in 2020, which has attracted extensive attention from scholars. Similar to other optimization algorithms, SMA also has the drawbacks of slow convergence rate and being trapped in local optimum at times. Therefore, the enhanced SMA named as ESMA is presented in this paper for solving global optimization problems. Two effective methods composed of multiple mutation strategy (MMS) and restart mechanism (RM) are embedded into the original SMA. MMS is utilized to increase the population diversity, and the RM is used to avoid the local optimum. To verify the ESMA’s performance, …twenty-three classical benchmark functions are employed, as well as three well-known engineering design problems, including welded beam design, pressure vessel design and speed reducer design. Several famous optimization algorithms are also chosen for comparison. Experimental results show that the ESMA outperforms other optimization algorithms in most of the test functions with faster convergence speed and higher solution accuracy, which indicates the merits of proposed ESMA. The results of Wilcoxon signed-rank test also reveal that ESMA is significant superior to other comparative optimization algorithms. Moreover, the results of three constrained engineering design problems demonstrate that ESMA is better than comparative algorithms. Show more
Keywords: Slime mould algorithm, multiple mutation strategy, restart mechanism, global optimization, optimization algorithm
DOI: 10.3233/JIFS-211408
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5069-5083, 2022
Authors: Huang, Junhui | Shao, Dangguo | Liu, Han | Xiang, Yan | Ma, Lei | Yi, Sanli | Xu, Hui
Article Type: Research Article
Abstract: Automatic segmentation of Magnetic Resonance Imaging (MRI), which bases on Residual U-Net (ResU-Net), helps radiologists to quickly assess the condition. However, the ResU-Net structure requires a large number of parameters and storage model space. It is not convenient to apply to mobile MRI device. To solve this problem, Depthwise Separable Convolution and Squeeze-and-Excitation Residual U-Networks (DSRU-Net) is proposed to segment MRI. Squeeze-and-Excitation method is a channel attention mechanism. The proposed method is conducive to simplify ResU-Net model, making ResU-Net more convenient to be applied to mobile MRI device. The fuzzy comprehensive evaluation method, which includes three evaluation factors are that …the required parameters of the model, the value of Dice Similarity Coefficient (DSC), and the value of Hausdorff Distance (HD), is used to evaluate the test results of the proposed method on the MICCAI 2012 Prostate MR Image Segmentation (PROMISE12) challenge dataset and Automatic Cardiac Diagnosis Challenge (ACDC) dataset. The fuzzy comprehensive evaluation values obtained by the proposed method in 5 PROMISE12 samples and 15 ACDC samples are 0.9889 and 0.9652, respectively. Combining the average results of the two datasets, the proposed method has the best effect in balancing the accuracy of segmentation and the amount of model parameters. Show more
Keywords: Depthwise separable convolution, channel attention mechanism, residual U-Net, MRI, segmentation
DOI: 10.3233/JIFS-211424
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5085-5095, 2022
Authors: Uma, K. | Sathya Bama, B. | Sabarinathan, D. | Mansoor Roomi, S. Md.
Article Type: Research Article
Abstract: Plant species identification is essential for healthy survival as well as the preservation and protection of biodiversity. Manual identification is time-consuming, hence to address this issue deep learning algorithms for automated plant species identification have been developed. A Novel Architecture comprising of EfficientB4Net, Convolutional Block Attention Module (CBAM) and Residual Block Decoder is proposed to act as Autoencoder for identification and retrieval of twenty distinct groups of medicinal plants, widely available in southern India. The EfficientB4 encoder compresses and encodes the input features along with channel and spatial features to the Residual Block Decoder for efficient learning. Residual Block Decoders …work to reconstruct the data from the encoded form to be as close to the original input as possible, by eliminating noise. The information-rich encoded features and the global features from the CBAM are transferred to the fully connected layer and stored in the database for retrieval of the plants. When a query image is received, the encoded feature of the query image and the database images are compared using similarity measurement, and the related images are retrieved. From the retrieved images, the query image is identified and the experimental results clearly show that the proposed method has achieved 95% accuracy when compared with other methods. Show more
Keywords: EfficientB4Net, convolutional block attention module, residual block decoder, autoencoder, fully connected layer
DOI: 10.3233/JIFS-211426
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5097-5112, 2022
Authors: Panda, S. | Dash, J.K. | Panda, G.B.
Article Type: Research Article
Abstract: Integral of a stochastic process with respect to Brownian motion is called Ito integral. Here the stochastic process and Brownian motion are random as well as fuzzy. Hence the Ito integral is fuzzy Ito integral. This paper deals with the properties of fuzzy Ito integral for simple adapted process with respect to fuzzy Brownian motion. The quadratic variance and covariance of FII are discussed. The concept of fuzzy simple adapted process, fuzzy martingale, fuzzy functions are used to derive the properties of fuzzy Ito integrals.
Keywords: Fuzzy Ito integral(FII), fuzzy Brownian motion(FBM), fuzzy simple adapted process(FSAP), quadratic variance and covariance of fuzzy Ito integral, fuzzy martingale(FM)
DOI: 10.3233/JIFS-211478
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5113-5124, 2022
Authors: Qian, Zichen | Zhao, Chihang | Zhang, Bailing | Lin, Shengmei | Hua, Liru | Li, Hao | Ma, Xiaogang | Ma, Teng | Wang, Xinliang
Article Type: Research Article
Abstract: Classification of vehicle types using surveillance images is a challenging task in Intelligent Transportation Systems (ITS). In this paper, Convolutional Neural Networks for Vehicle types classification are comparatively studied. Firstly, GoogLeNet, ResNet50 and InceptionV4 are exploited as baselines for comparison. Secondly, we proposed a new network architecture based on GoogLeNet, ResNet50 and InceptionV4, named Fused Deep Convolutional Neural Networks (FDCNN), to take advantage of the ‘Inception’ module on parameter optimization and ‘Residual’ module on avoiding gradient vanishing, and applied the model to vehicle types classification. Thirdly, we created a vehicle dataset under the conditions of complicated and varied weather and …lighting conditions, and conducted comparative experiments using the SEU vehicle dataset. Experimental results show much better performance of the proposed FDCNN with RMSprop optimizer on recognizing vehicle types. Specifically, the average classification accuracies of six vehicle types, such as truck, coach, sedan, minivan, pickup and SUV, are over 96.8%. Among the six classes of vehicle types, sedan is the most difficult to classify and the proposed FDCNN achieved over 93.81% accuracy in comparative experiments. Show more
Keywords: Vehicle types, convolutional neural networks, fused deep convolutional neural networks, intelligent transportation systems
DOI: 10.3233/JIFS-211505
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5125-5137, 2022
Authors: Brikaa, M.G. | Zheng, Zhoushun | Dagestani, Abd Alwahed | Ammar, El-Saeed | AlNemer, Ghada | Zakarya, M.
Article Type: Research Article
Abstract: The principal objective of this article is to develop an effective approach to solve matrix games with payoffs of single-valued trapezoidal neutrosophic numbers (SVTNNs). In this approach, the concepts and suitable ranking function of SVTNNs are defined. Hereby, the optimal strategies and game values for both players can be determined by solving the parameterized mathematical programming problems, which are obtained from two novel auxiliary SVTNNs programming problems based on the proposed Ambika approach. In this approach, it is verified that any matrix game with SVTNN payoffs always has a SVTNN game value. Moreover, an application example is examined to verify …the effectiveness and superiority of the developed algorithm. Finally, a comparison analysis between the proposed and the existing approaches is conducted to expose the advantages of our work. Show more
Keywords: Matrix games, neutrosophic set, mathematical programming, trapezoidal neutrosophic number, ambika approach
DOI: 10.3233/JIFS-211604
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5139-5153, 2022
Authors: Xu, Yan | Wang, Yanyun | Huang, Jiani | Qin, Hong
Article Type: Research Article
Abstract: Traditional visual SLAM algorithms run robustly under the assumption of a static environment, but always fail in dynamic scenes, since moving objects will impair camera pose tracking. Given this, this paper presents an efficient semantic dynamic SLAM (ESD-SLAM), which is suitable for dynamic scenarios. Based on the ORB-SLAM2 framework, the ESD-SLAM we proposed employs lightweight semantic segmentation network FcHarDNet to extract semantic information, and uses the region growing algorithm to optimize the semantic segmentation boundary. Then dynamic objects are removed by combining semantic information with multi-view geometry, and it further improves the localization accuracy. Combining semantic information and depth information, …a dense point cloud map of static scene is constructed to serve the planning task of mobile robot. We conduct the experiments on the public TUM RGB-D dataset and in the real-world environment. Experimental results show that the proposed algorithm can improve the performance of the ORB-SLAM2 system in dynamic scenes, and significantly improve the real-time performance compared with other same type dynamic SLAM algorithms. Show more
Keywords: Visual SLAM, dynamic scenarios, multi-view geometry, lightweight semantic segmentation
DOI: 10.3233/JIFS-211615
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5155-5164, 2022
Authors: Hamidi, Mohammad | Faraji, Fatemeh
Article Type: Research Article
Abstract: In this paper we introduce the concept of (weak) fuzzy subsupermodules based on (thin) supermodules different from fuzzy subhypermodules. In this study, the concept of α -cuts play a main role for constructing of extended (weak) fuzzy subsupermodules. In final, we introduce a notation of residual quotients of (weak) fuzzy subsupermodules and obtain some conditions to be a (weak) fuzzy subsupermodule. Also obtained some applied results in residual quotients of (weak) fuzzy subsupermodules of superrings as specially subsupermodules.
Keywords: Superrings, (thin) supermodule, (weak) fuzzy subsupermodules
DOI: 10.3233/JIFS-211655
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5165-5176, 2022
Authors: Hu, Kekun | Dong, Gang | Zhao, Yaqian | Li, Rengang | Jiang, Dongdong | Chao, Yinyin | Liu, Haiwei | Ge, Yuan
Article Type: Research Article
Abstract: Vertex classification is an important graph mining technique and has important applications in fields such as social recommendation and e-Commerce recommendation. Existing classification methods fail to make full use of the graph topology to improve the classification performance. To alleviate it, we propose a D ual G raph W avelet neural N etwork composed of two identical graph wavelet neural networks sharing network parameters. These two networks are integrated with a semi-supervised loss function and carry out supervised learning and unsupervised learning on two matrixes representing the graph topology extracted from the same graph dataset, respectively. One matrix embeds the …local consistency information and the other the global consistency information. To reduce the computational complexity of the convolution operation of the graph wavelet neural network, we design an approximate scheme based on the first type Chebyshev polynomial. Experimental results show that the proposed network significantly outperforms the state-of-the-art approaches for vertex classification on all three benchmark datasets and the proposed approximation scheme is validated for datasets with low vertex average degree when the approximation order is small. Show more
Keywords: Big graph mining, vertex classification, semi-supervised learning, graph convolutional networks, graph wavelet transform
DOI: 10.3233/JIFS-211729
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5177-5188, 2022
Authors: Gao, Pei
Article Type: Research Article
Abstract: The results of the nation-wide public college English tests and some other English tests held by the college itself are very valuable data for the assessment of the English teaching level. In the light of the selected and processed data and some other auxiliary approaches, the writer suggests that a modernized monitoring system be established to improve English teaching quality. Under this system, teachers’ and students’ English level can be evaluated in an objective way. Consequently, the subsequent measures such as teachers’ promotion as well as students’ development and employment and the evaluation of the quality of test-papers can be …taken scientifically. We can solve the above issues with help of multi-attribute group decision making (MAGDM) method. Depending on the VIKOR steps and given intuitionistic fuzzy sets (IFSs), this paper devises the IF-VIKOR to assess the teaching quality of college English. In addition, the weights of attribute are derived through CRITIC method. Then, the VIKOR method is extended to IFSs to derive the order of each alternative. Therefore, all alternatives could be ranked and the best one can be identified. Eventually, we give an example of college English teaching quality evaluation, according to some comparison with other methods to make an analysis, the results show that the method proposed in such paper is effective and easy to compute. Show more
Keywords: Multi-attribute group decision-making (MAGDM), Intuitionistic fuzzy sets (IFSs), VIKOR method, CRITIC model, teaching quality
DOI: 10.3233/JIFS-211749
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5189-5197, 2022
Authors: Li, Jiwen | Lan, Fengchong | Chen, Jiqing
Article Type: Research Article
Abstract: In view of the disadvantages of the existing pose estimation algorithm, which has low real-time performance and the positioning accuracy will be greatly reduced in dynamic scene, a compound deep learning and parallel computing algorithm (DP-PE) is proposed. The detection algorithm based on deep learning is used to detect dynamic objects in the environment, and the dynamic feature points are removed before the matching of feature points to reduce the impact of dynamic objects on the positioning accuracy; A method for distinguishing “pseudo-dynamic objects” is proposed to solve the problem that the stationary vehicles and pedestrians in the environment are …regarded as dynamic objects. The parallel computing framework for feature point extraction and matching is established on CPU-GPU heterogeneous platform to speed up DP-PE; In the localization part of DP-PE, we propose a 3D interior point detection strategy to achieve parallel search of map points, and the saturated linear kernel function is used to act on reprojection error to realize the parallelization of pose optimization. We verify the algorithm on KITTI dataset, the experimental results show that average speedup ratio of feature point extraction and matching is 6.5 times, and the overall computational efficiency of DP-PE is about 7 times higher than that before acceleration, which can realize high precision and efficient pose estimation in dynamic scene. Show more
Keywords: Intelligent vehicle, visual pose estimation, deep learning, parallel computing
DOI: 10.3233/JIFS-211771
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5199-5213, 2022
Authors: Xu, Juncai | Zhang, Jingkui | Shen, Zhenzhong
Article Type: Research Article
Abstract: Conventional intelligent recognition methods highly depend on artificial feature extraction and expert knowledge to recognize concrete structures’ internal defects. For solving this problem, one intelligent recognition method for internal concrete structure defects is proposed, based on a one-dimensional, convolutional neural network (1D-CNN). First, the impact echo detection signals are acquired, to establish the training and testing of samples for various internal concrete structure defects. Then, the convolutional network structure is used to achieve the adaptive, hierarchical extraction of the impact echo signals’ features. Finally, the Softmax classifier is used to provide the diagnosis result at the output end. The experimental …results of four types of internal defects (including voids, water-filled, imperfect solids, and sound) show that the 1D-CNN classifier, with the predicted signals as the training set, enables the successful identification of the internal defects of the concrete structure and achieves more than 90% defect-recognition accuracy. In addition, the 1D-CNN classifier has strong anti-interference ability and feasibility in practical applications. This work improves the performance of ‘impact echo’ in identifying internal defects in concrete and realizes the intelligent analysis of impact echo signals. Show more
Keywords: Impact echo, concrete structure, convolutional neural network, adaptive hierarchy
DOI: 10.3233/JIFS-211784
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5215-5226, 2022
Authors: Cai, Jianxian | Dai, Xun | Gao, Zhitao | Shi, Yan
Article Type: Research Article
Abstract: Seismic data obtained from seismic stations are the major source of the information used to forecast earthquakes. With the growth in the number of seismic stations, the size of the dataset has also increased. Traditionally, STA/LTA and AIC method have been applied to process seismic data. However, the enormous size of the dataset reduces accuracy and increases the rate of missed detection of the P and S wave phase when using these traditional methods. To tackle these issues, we introduce the novel U-net-Bidirectional Long-Term Memory Deep Network (UBDN) which can automatically and accurately identify the P and S wave phases …from seismic data. The U-net based UBDN strongly maintains the U-net’s high accuracy in edge detection for extracting seismic phase features. Meanwhile, it also reduces the missed detection rate by applying the Bidirectional Long Short-Term Memory (Bi-LSTM) mode that processes timing signals to establish the relationship between seismic phase features. Experimental results using the Stanford University seismic dataset and data from the 2008 Wenchuan earthquake aftershock confirm that the proposed UBDN method is very accurate and has a lower rate of missed phase detection, outperforming solutions that adapt traditional methods by an order of magnitude in terms of error percentage. Show more
Keywords: U-net, bidirectional long short term memory, phase identification, wenchuan aftershocks
DOI: 10.3233/JIFS-211792
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5227-5236, 2022
Authors: Xiao, Liming | Huang, Guangquan | Zhang, Genbao
Article Type: Research Article
Abstract: Failure mode and effects analysis (FMEA) is an effective tool utilized in various fields for discovering and eliminating potential failures in products and services, which is usually implemented based on experts’ linguistic assessments. However, incomprehensive weigh information of risk factors and experts, lacking the consideration of experts’ randomness and hesitation, and incomplete risk factor system is essential challenges for the traditional FMEA model. Therefore, to properly handle these challenges and further enhance the performance of the traditional FMEA, this study develops a new FMEA strategy for assessing and ranking failures’ risks. First, a novel concept of intuitionistic fuzzy clouds (IFCs) …is developed by combining the merits of the intuitionistic fuzzy set theory and the cloud model theory in manipulating uncertain information. Some basic operations and the Minkowski-type distance measure of IFCs are also presented and discussed. Further, in the proposed FMEA model, two combination weighting methods are developed to determine the synthetic weights of experts and risk factors, respectively, which consider subjectivity and objectivity simultaneously. In addition, maintenance (M) is considered as a new risk factor to enrich the assessment factor system and facilitate a more reasonable risk assessment result. Finally, a case study is implemented along with comparisons to demonstrate the feasibility and superiority of the presented FMEA model. Show more
Keywords: Failure mode and effects analysis (FMEA), intuitionistic fuzzy set theory, cloud model, risk analysis, technique for order performance by similarity to ideal solution (TOPSIS)
DOI: 10.3233/JIFS-211793
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5237-5263, 2022
Authors: Ding, Xiong | Gao, Jinding
Article Type: Research Article
Abstract: In addition to the tragic loss of life, precious natural and personal property, forest fires pose a huge threat to ecologically healthy forests and environmental protection. A forest-fire-identification is explored in this article. Because the color space model FCS brings a high false alarm rate in flame recognition, this paper proposes an improved flame recognition color space (IFCS) based on chaos theory and k-medoids particle swarm optimization algorithm. The use of IFCS color space for flame recognition can ensure simple and fast calculations and more prominent flame/non-flame pixel color attribute difference characteristics when it is compared to FCS. The IFCS …flame recognition color space is obtained by using methods such as initializing the particles in the chaotic sequence, adaptively adjusting the inertia weight, dynamic nonlinear adjustment of the learning factor, and jumping out of the local optimum from the chaotic search. In the IFCS flame color space, the binary image is obtained by the classic Otsu threshold method, and the Flame Recognition algorithm (IOFR) algorithm is established based on IFCS and Otsu. The experimental results show that based on the FCS flame recognition algorithm, the IOFR algorithm effectively reduces the flame misjudgment rate. Show more
Keywords: Forest fire prevention, flame recognition, fire color space, chaos, k-medoids, particle swarm optimization (PSO)
DOI: 10.3233/JIFS-211816
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5265-5281, 2022
Authors: Zhang, Qiang
Article Type: Research Article
Abstract: Unmanned vehicles need to gather the surrounding information comprehensively. Perception of automotive information is one of the important information. In the field of automotive perception, the stereo vision plays a vital role and stere-vision can calculate the length, width, and height, making the object more specific. However, under the existing technology, it is impossible to obtain accurate detection in a complex environment by relying on a single sensor. Therefore, it is particularly important to study the calibration technology based on multi-sensor fusion. This paper proposes a method based on feature point pair matching. Two rectangular planks are used to …extract the 3D point cloud of the edge of the board in stereo vision and LiDAR coordinate systems, which is then used to obtain the corner coordinates. Finally, the Kabsch algorithm is used to solve the coordinate transformation between the paired feature points, and a clustering method is used to remove outliers from the multiple measurements and obtain the average value. By setting up an experiment, this method can be implemented on the Nvidia Jetson Tx2 embedded development board, and accurate registration parameters can be obtained, thus verifying the theoretical method’s feasibility. It finishes calibration of the LiDAR and binocular camera based on present methods. The result shows that, it can reduce the effects of noise, and acquire registration parameters accurately of LiDAR and cameras. Compared with the approved method of the same type, our proposed method has less errors and good practical value. Show more
Keywords: Target, calibration, 3D LiDAR, binocular camera, unmanned vehicles
DOI: 10.3233/JIFS-211827
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5283-5290, 2022
Authors: Mahmood, Tahir | Izatmand, | Ali, Zeeshan | Panityakul, Thammarat
Article Type: Research Article
Abstract: In the real decision process, an important problem is how to express the attribute value more efficiently and accurately. In the real world, because of the complexity of decision-making problems and the fuzziness of decision-making environments, it is not enough to express attribute values of alternatives by exact values. For this managing with such sorts of issues, the principle of Linear Diophantine uncertain linguistic set is a valuable and capable technique to manage awkward and inconsistent information in everyday life problems. In this manuscript, we propose the original idea of Linear Diophantine uncertain linguistic set and elaborated their essential laws. …Additionally, to determine the association among any numbers of attributes, we elaborated the Linear Diophantine uncertain linguistic arithmetic Heronian mean operator, Linear Diophantine uncertain linguistic weighted arithmetic Heronian mean operator, Linear Diophantine uncertain linguistic geometric Heronian mean operator, Linear Diophantine uncertain linguistic weighted geometric Heronian mean operator, and their properties are also discovered. By using these operators, we utilize the multi-attribute decision-making procedure by using elaborated operators. To determine the consistency and validity of the elaborated operators, we illustrate some examples by using explored operators. Finally, the superiority and comparative analysis of the elaborated operators with some existing operators are also determined and justified with the help of a graphical point of view. Show more
Keywords: Linear Diophantine uncertain linguisticsets, arithmetic/geometric Heronian mean operators, decision-making methods
DOI: 10.3233/JIFS-211839
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5291-5319, 2022
Authors: Zhang, Yan | Li, Shiyu | Deng, Yang | Chen, Honggen | Yan, Xin | Li, Jing
Article Type: Research Article
Abstract: This paper develops a joint decision-making model approach to preventive maintenance and SPC (statistical process control) with delayed monitoring considered. The proposal of delayed monitoring policy postpones the sampling process till a scheduled time and contributes to six renewal scenarios of the production process, where maintenance actions are triggered by scheduled duration of preventive maintenance or the alert of X ¯ chart for monitoring the shift of process mean resulted by deterioration of equipment. By analyzing the evolution of the system in different scenarios, a mathematical model is given to minimize the expected cost …per unit time by optimizing values of five variables (scheduled duration without monitoring, scheduled duration of preventive maintenance, sample size, sampling interval and control limit). The results of a numerical example indicate that the hourly cost of the proposed model is lower than the model that delayed monitoring is not considered when the system has a low hazard rate during the early period. Finally, a sensitivity analysis is performed to demonstrate the effect of model parameters. Show more
Keywords: Repairable system, delayed monitoring, preventive maintenance, statistical process control, joint economic design
DOI: 10.3233/JIFS-211853
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5321-5334, 2022
Authors: Rajeswari, G. | Ithaya Rani, P.
Article Type: Research Article
Abstract: Facial occlusions like sunglasses, masks, caps etc. have severe consequences when reconstructing the partially occluded regions of a facial image. This paper proposes a novel hybrid machine learning approach for occlusion removal based on Structural Similarity Index Measure (SSIM) and Principal Component Analysis (PCA), called SSIM_PCA. The proposed system comprises two stages. In the first stage, a Face Similar Matrix (FSM) guided by the Structural Similarity Index Measure is generated to provide the necessary information to recover from the lost regions of the face image. The FSM generates Related Face (RF) images similar to the probe image. In the second …stage, these RF images are considered as related information and used as input data to generate eigenspaces using PCA to reconstruct the occluded face region exploiting the relationship between the occluded region and related face images, which contain relevant data to recover from the occluded area. Experimental results with five standard datasets viz. Caspeal-R1, IMFDB, and FEI have proven that the proposed method works well under illumination changes and occlusion of facial images. Show more
Keywords: Face recognition, SSIM, eigenspaces, PCA, FSM, related face
DOI: 10.3233/JIFS-211890
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5335-5350, 2022
Authors: Mishra, Rohit | Malviya, Shrikant | Ghosh, Rudra Chandra | Tiwary, Uma Shanker
Article Type: Research Article
Abstract: Impreciseness and uncertainty are the fabrics that make life interesting. For decades, human beings have developed strategies to cope with uncertainties and automate them. In personnel selection for the I.T. field, selectors often find it very difficult to select candidates by going through a set of resumes containing similar kinds of skills. Hence the selection task becomes a fuzzy decision making with the uncertainty involved. A combination of fuzzy clustering and Interval Type-2 fuzzy sets (IT2FS) is proposed in such scenarios. An experiment is conducted over a resume dataset containing fifteen hundred resumes for a particular job description. Firstly, Fuzzy …C-means clustering (FCM) is applied for selective clustering, while decision-making under uncertainty is carried through IT2FS. The candidates in the selected cluster are given a score for ranking as per the skillset criteria. The final decision for shortlisting the resumes is carried through IT2FS. The model shows an average accuracy of 88.2% with an F1-score of 0.76 compared to (K-means + IT2FS) model with an F1-score of 0.72. Thus, the proposed model performs better while decision-making under uncertainty. Show more
Keywords: Personnel selection, fuzzy clustering, interval Type-2 fuzzy sets, decision making, resume shortlisting
DOI: 10.3233/JIFS-211892
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5351-5359, 2022
Authors: Işık, Gürkan | Kaya, İhsan
Article Type: Research Article
Abstract: Defectiveness of items is generally considered as a certain value in acceptance sampling plans (ASPs). It is clear that, it may not be certainly known in some real-case problems. Uncertainties of the inspection process such as measurement errors, inspectors’ hesitancies or vagueness of the process etc. should be taken into account to obtain more reliable results. The fuzzy set theory (FST) is one of the best methods to overcome these problems. There are some studies in the literature formulating the ASPs with the help of FST. Deciding the right membership functions of the fuzzy sets (FSs) has a vital importance …on the quality of the uncertainty modeling. Additionally, the fuzzy set extensions have been offered to model more complicated uncertainties to achieve better modeling. As one of these extensions, type-2 fuzzy sets (T2FSs) gives an ability to model uncertainty in situations where it is not possible to determine exact membership function parameters. In this study, single and double ASPs based on interval T2FSs (IT2FSs) have been designed for binomial and Poisson distributions. Thus, it becomes possible to make more flexible, sensitive and descriptive sensitivity analyzes. The main characteristic functions of ASPs have been derived and the suggested formulations have been illustrated on a comparative application from manufacturing process. Results allowing for more comprehensive analysis as against to the traditional and T1FSs based plans have been obtained. Show more
Keywords: Acceptance sampling plans, binomial distribution, fuzzy sets, interval type-2 fuzzy sets, poisson distribution
DOI: 10.3233/JIFS-211915
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5361-5373, 2022
Authors: Zhang, Lei | Pan, Jiaxing | Xia, Pengfei | Wei, Chuyuan | Jing, Changfeng | Guo, Maozu | Guo, Quansheng
Article Type: Research Article
Abstract: With the increasing number of motor vehicles, exhaust emission has become a major source of urban pollution. Most studies are limited to the prediction of pollutant concentration, which cannot clearly indicate the change of pollution emissions and regional relationship. In this paper, we propose an emission propagation model of vehicle source pollution based on complex network in order to intelligently mine the interaction and propagation rules hidden behind dynamic spatiotemporal data. First, aiming at the problems of low resolution and insufficient data volume of vehicle emission data, a high-resolution pollution emission data is generated based on the COPERT (Computer Program …to Calculate Emissions from Road Transport). For study the influence of causality between regions, a propagation model is designed based on the convergent cross mapping method to transform the emission time series into a complex network. In addition, we propose a novel key node mining algorithm using hybrid local and global information to identify areas of heavy pollution. Experimental results on real datasets demonstrate that the spread of pollution follows certain rules and is also affected by regional influences. Moreover, the proposed algorithm is superior to the state-of-the-art methods. Show more
Keywords: Vehicle source pollution, complex network, propagation characteristic, convergent cross mapping, key node mining
DOI: 10.3233/JIFS-211921
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5375-5384, 2022
Authors: Zhao, Hua | Zhang, PeiXin | Liang, Yue | Amos, Sitenda
Article Type: Research Article
Abstract: As a new research task, suggestion mining increasingly gained attention in recent years. However, it is still open and challenging due to complex semantics, large diversity of domains, and the absence of large labeled and balanced datasets. More importantly, most of the research is focused on English in-domain suggestion mining. But as compared to English, Chinese suggestion has more abundant expression forms, showing many different characteristics, so it is the necessity to carry out suggestions mining research in Chinese environment. In this work, the performances of several classification models for Chinese suggestion mining were compared. Firstly, a Chinese suggestion mining …corpus was constructed for open domain, and then trained several models for the suggestion mining, which included both traditional machine learning models (feature engineering-based models) and deep learning models. Our results demonstrated that these models can successfully classify Chinese sentences into two classes: suggestion and non-suggestion. The results of this study can guide future research in Chinese open domain suggestion mining. Show more
Keywords: Suggestion mining, Chinese open domain, Text classification
DOI: 10.3233/JIFS-211932
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5385-5398, 2022
Authors: Li, Shuailong | Zhang, Wei | Zhang, Huiwen | Zhang, Xin | Leng, Yuquan
Article Type: Research Article
Abstract: Model-free reinforcement learning methods have successfully been applied to practical applications such as decision-making problems in Atari games. However, these methods have inherent shortcomings, such as a high variance and low sample efficiency. To improve the policy performance and sample efficiency of model-free reinforcement learning, we propose proximal policy optimization with model-based methods (PPOMM), a fusion method of both model-based and model-free reinforcement learning. PPOMM not only considers the information of past experience but also the prediction information of the future state. PPOMM adds the information of the next state to the objective function of the proximal policy optimization (PPO) …algorithm through a model-based method. This method uses two components to optimize the policy: the error of PPO and the error of model-based reinforcement learning. We use the latter to optimize a latent transition model and predict the information of the next state. For most games, this method outperforms the state-of-the-art PPO algorithm when we evaluate across 49 Atari games in the Arcade Learning Environment (ALE). The experimental results show that PPOMM performs better or the same as the original algorithm in 33 games. Show more
Keywords: Model-based reinforcement learning, model-free reinforcement learning, policy optimization method
DOI: 10.3233/JIFS-211935
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5399-5410, 2022
Authors: Shabana Parveen, M. | Bhuvaneswari, P.T.V.
Article Type: Research Article
Abstract: Wireless Sensor Network (WSN) is a self-structured network containing small, energy-constrained wireless nodes that act together to accomplish difficult tasks. Wearable sensors, one of the WSNs play a significant role in healthcare applications, especially patient monitoring. With a miniature size, wearable sensors have less space dedicated for energy sources. So it is important for wearable sensors to be manufactured as energy efficient and reliable and it must ensure quality of service in providing the data. Remote health care monitoring has two limitations such as adoption of mobility and the usage of low power consumption devices. To overcome these limitations, appropriate …routing protocol can be used in Low Power Lossy Networks (LLNs). IPV6 Routing Protocol for Low Power Lossy Networks (RPL) is one of the routing protocols standardized to be applied in Internet of things network with wireless sensors. The current research article investigates the performance of RPL with three Objective Functions (OF), Minimum Rank with Hysteresis Objective Function (MRHOF) with Energy as metric, MRHOF with Expected Transmission count (ETX) as metrics and Objective Function zero(OF0) with hop count as metric, in elderly health care monitoring system. The study considered two scenarios case 1 has all static nodes while case 2 has few dynamic nodes. The performance was evaluated in terms of metrics control overhead, convergence time, Packet Delivery Ratio (PDR), Latency and energy consumption and the OF optimum for reliability, mobility and energy consumption is determined. The results of the simulation showed that, in mobile scenario OF0 converged at a fast rate than the MRHOF, which increases the life time. OF0 also consumed the least energy and it increased the life time of the node. As far as PDR is concerned, OF0 had low PDR when the nodes were mobile and ETX performed well. Show more
Keywords: RPL, health care, objective function, energy consumption, COOJA
DOI: 10.3233/JIFS-211943
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5411-5426, 2022
Authors: Ait Benali, B. | Mihi, S. | Ait Mlouk, A. | El Bazi, I. | Laachfoubi, N.
Article Type: Research Article
Abstract: Named Entity Recognition (NER) is a vitally important task of Natural Language Processing (NLP), which aims at finding named entities in natural language text and classifying them into predefined categories such as persons (PER), places (LOC), organizations (ORG), and so on. In the Arabic context, the current NER approaches based on deep learning are mainly based on word embedding or character-level embedding as input. However, using a single granularity representation has problems with out-of-vocabulary (OOV), word embedding errors, and relatively simple semantic content. This paper presents a multi-headed self-attention mechanism implemented in the BiLSTM-CRF neural network structure to recognize Arabic …named entities on social media using two embeddings. Unlike other state-of-the-art approaches, this approach combines character and word embedding at the embedding layer, and the attention mechanism calculates the similarity over the entire sequence of characters and captures local context information. The proposed approach better recognized NEs in Dialect Arabic, reaching an F1 value of 74.15% on Darwish’s dataset (a publicly available Arabic NER benchmark for social media). According to our knowledge, our findings outperform the current state-of-the-art models for Arabic Named Entity Recognition on social media. Show more
Keywords: Arabic named entity recognition (ANER), natural language processing (NLP), multi-head self-attention, BiLSTM, CRF, dialect arabic, social media
DOI: 10.3233/JIFS-211944
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5427-5436, 2022
Authors: Wang, Zeng | Liu, Weidong | Yang, Minglang
Article Type: Research Article
Abstract: As the main part of design display and evaluation, product three-dimensional (3D) form is the core object in affective product design. However, previous research has not yet addressed the development of technical models and method involving complete 3D surface data, and thus cannot guarantee the quality of affective product design. By using the techniques of triangular mesh model, spherical harmonic and conditional variational auto-encoder, this paper proposes a data-driven affective product design method composed of several technical models using complete 3D surface data. These models include: mathematical model for quantifying 3D form, recognition model for recognizing customer’s affective responses, and …generative model for generating new 3D forms. For affective product design, the mathematical model achieves the acquisition and processing of complete 3D surface data, the recognition model improves the objectivity and accuracy of recognition by integrating the 3D form data into the calculation process of emotion recognition, and the generative model realizes the automatic generation of new 3D forms in response to emotional data based on the recognition results. Each model provides technical support for realizing the acquisition, processing and generation of complete 3D surface data of product form, and ensures the systematicness and completeness of the proposed method for the affective product design involving 3D form innovation. The feasibility of the method is verified by an example of car design, and the results show that it is an effective affective product design method involving 3D form innovation. Show more
Keywords: Affective product form design, complete three-dimensional surface data, emotion recognition, spherical harmonic, conditional variational auto-encoder
DOI: 10.3233/JIFS-211947
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5437-5455, 2022
Authors: He, Peng | Zhou, Gang | Liu, Hongbo | Xia, Yi | Wang, Ling
Article Type: Research Article
Abstract: Knowledge Graph (KG) embedding approaches have been proved effective to infer new facts for a KG based on the existing ones–a problem known as KG completion. However, most of them have focused on static KGs, in fact, relational facts in KGs often show temporal dynamics, e.g., the fact (US, has president, Barack Obama, [2009–2017]) is only valid from 2009 to 2017. Therefore, utilizing available time information to develop temporal KG embedding models is an increasingly important problem. In this paper, we propose a new hyperplane-based time-aware KG embedding model for temporal KG completion. By employing the method of time-specific …hyperplanes, our model could explicitly incorporate time information in the entity-relation space to predict missing elements in the KG more effectively, especially temporal scopes for facts with missing time information. Moreover, in order to model and infer four important relation patterns including symmetry, antisymmetry, inversion and composition, we map facts happened at the same time into a polar coordinate system. During training procedure, a time-enhanced negative sampling strategy is proposed to get more effective negative samples. Experimental results on datasets extracted from real-world temporal KGs show that our model significantly outperforms existing state-of-the-art approaches for the KG completion task. Show more
Keywords: Temporal knowledge graph, knowledge graph completion, knowledge graph embedding
DOI: 10.3233/JIFS-211950
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5457-5469, 2022
Authors: Jahromi, Alireza Fakharzadeh | Hajiloei, Mehdi | Dehghani, Yeganeh | Lahoninezhad, Sara
Article Type: Research Article
Abstract: To overcome curse of dimensionality for outlier detecting in high dimensional dataset, axis-parallel subspace (SOD) and angle-based outlier detection (ABOD) methods were presented. These methods are also friendly used distance-based to detect outliers. In this regard, based on the reality of fuzzy data for explaining the world phenomena, this paper introduces an extended version of both methods for fuzzy dataset. First, the basic concepts of both methods are explained. Next we provide two metrics based on Euclidean and analytic distance to measure distance between fuzzy objects; also Cosine similarity measure formula for calculating the cosine of angle between two difference …vectors in high-dimensional fuzzy dataset is illustrated. Then the algorithms to determine outliers of fuzzy datasets by using these metrics and Cosine similarity measure, based on ABOD and SOD algorithms, are presented. Some numerical experimental examples are also presented, in which both real and synthesis datasets are used, For a real numerical examination, we have applied proposed algorithms to data from 15 Iranian petrochemical companies in a fully fuzzy environment. The obtained results show the significant properties of the new methods in detecting outliers. Show more
Keywords: Outlier detection, angle-based outlier detection, axis-parallel subspace, fuzzy number, cosine-similarity
DOI: 10.3233/JIFS-211955
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5471-5481, 2022
Authors: Yang, Hong | Wang, Fan | Gong, Zengtai
Article Type: Research Article
Abstract: Based on the granular derivative and the horizontal membership function of fuzzy-number-valued-function, the existence and uniqueness of solutions of two-point boundary value problem (BVP) for a class of second-order linear fuzzy ordinary differential equations are given, including the homogeneous BVP, the semi-homogeneous BVP and the non-homogeneous BVP. However, this is somewhat different from ordinary differential equations. In fact, we can think of it simply as the transformation from a number to a set, or we can think of it macroscopically as the transformation from a point to a plane. At the same time, appropriate examples are given to illustrate this …conclusion. Show more
Keywords: Fuzzy numbers, Fuzzy differential equations, Granular differentiability, The Horizontal membership function, Relative-distance-measure (RDM) fuzzy interval arithmetic
DOI: 10.3233/JIFS-211958
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5483-5499, 2022
Authors: Jin, Zhen-yu | Yan, Cong-hua
Article Type: Research Article
Abstract: The main purpose of this paper is to study initial and final Hutton type of linear fuzzifying uniformities. The detailed characterizations of initial and final linear fuzzifying uniformities are obtained. In addition, the boundedness, complete boundedness and T 2 separation axiom of initial linear fuzzifying uniform spaces are investigated. Some examples with respect to initial and final linear fuzzifying uniformities are also provided.
Keywords: Initial linear fuzzifying uniformities, final linear fuzzifying uniformities, boundedness, complete boundedness, T2 separation axiom
DOI: 10.3233/JIFS-211960
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5501-5509, 2022
Authors: Zhang, Zunhao | Zhang, Junxia | Tian, Wei | Li, Yang | Song, Yahui | Zeng, Qi
Article Type: Research Article
Abstract: The increasing market demand for milk powder has not only promoted the production capacity of milk powder, but also increased the impact on the environment. Therefore, it is very important to study the relationship between the environmental impact of milk powder spray drying (MPSD) system and system-related parameters and identify the key parameters to improve the efficiency of the sustainable improvement of the system. Treed Gaussian Process (TGP) and Standardized Regression Coefficients (SRC)methods are used to analyze the sensitivity of the system to environmental impacts. The results show that the inlet air temperature of the drying tower has the greatest …impact on the environment of the system, accounting for about 82%, followed by the atomization pressure and the feed pump speed, accounting for about 9% and 8% respectively. Moreover, not only the environmental performance of the system should be improved, but also the quality of milk powder should be guaranteed when optimizing the parameters such as the inlet air temperature of drying tower. This study can help the manufacturers of milk powder and related equipment to determine the priority of improving the system from the perspective of environmental protection. Show more
Keywords: Sensitivity analysis, treed Gaussian process, standardized regression coefficients, spray drying, environmental impact
DOI: 10.3233/JIFS-211961
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5511-5522, 2022
Authors: Yu, Hui | Li, Jun-qing | Chen, Xiao-Long | Zhang, Wei-meng
Article Type: Research Article
Abstract: During recent years, the outpatient scheduling problem has attracted much attention from both academic and medical fields. This paper considers the outpatient scheduling problem as an extension of the flexible job shop scheduling problem (FJSP), where each patient is considered as one job. Two realistic constraints, i.e., switching and preparation times of patients are considered simultaneously. To solve the outpatient scheduling problem, a hybrid imperialist competitive algorithm (HICA) is proposed. In the proposed algorithm, first, the mutation strategy with different mutation probabilities is utilized to generate feasible and efficient solutions. Then, the diversified assimilation strategy is developed. The enhanced global …search heuristic, which includes the simulated annealing (SA) algorithm and estimation of distribution algorithm (EDA), is adopted in the assimilation strategy to improve the global search ability of the algorithm.?Moreover, four kinds of neighborhood search strategies are introduced to?generate new?promising?solutions.?Finally, the empires invasion strategy?is?proposed to?increase the diversity of the population. To verify the performance of the proposed HICA, four efficient algorithms, including imperialist competitive algorithm, improved genetic algorithm, EDA, and modified artificial immune algorithm, are selected for detailed comparisons. The simulation results confirm that the proposed algorithm can solve the outpatient scheduling problem with high efficiency. Show more
Keywords: Flexible job shop scheduling, outpatient scheduling, hybrid imperialist competitive algorithm, neighborhood search strategies
DOI: 10.3233/JIFS-212024
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5523-5536, 2022
Authors: Aggarwal, Eshika | Mohanty, B.K.
Article Type: Research Article
Abstract: An outranking procedure for Multi-Attribute Decision-Making (MADM) problems is introduced in our work that acts as a decision-aid in recommending the products to the buyers. The buyer’s product assessment is taken as Interval-Valued Intuitionistic Fuzzy Sets (IVIFS) in each attribute. The confidence level that is implicit in the buyer’s product rating is explicated in the proposed work using fuzzy entropy. As the confidence level of the buyer on the product assessment is for both satisfaction and reluctance, it is suitably distributed in membership and non-membership parts of IVIFS. Our work generates a dominance matrix that represents partial or full dominance …of one product over another after scoring the products that are unified with buyer’s confidence. The proposed work suggests the product ranking after ascertaining the buyer’s flexibility. An algorithm is written in our work to validate the procedure developed. We have compared our work with other similar works to highlight the benefits of the proposed work. A numerical example is illustrated to highlight the procedure developed. Show more
Keywords: Interval-valued Intuitionistic fuzzy sets, confidence, partial dominance matrix, outranking, flexibility behaviour
DOI: 10.3233/JIFS-212026
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5537-5551, 2022
Authors: Zhang, Min | Yang, Haijie | Li, Pengfei | Jiang, Ming
Article Type: Research Article
Abstract: Human pose estimation is still a challenging task in computer vision, especially in the case of camera view transformation, joints occlusions and overlapping, the task will be of ever-increasing difficulty to achieve success. Most existing methods pass the input through a network, which typically consists of high-to-low resolution sub-networks that are connected in series. Still, during the up-sampling process, the spatial relationships and details might be lost. This paper designs a parallel atrous convolutional network with body structure constraints (PAC-BCNet) to address the problem. Among the mentioned techniques, the parallel atrous convolution (PAC) is constructed to deal with scale changes …by connecting multiple different atrous convolution sub-networks in parallel. And it is used to extract features from different scales without reducing the resolution. Besides, the body structure constraints (BC), which enhance the correlation between each keypoint, are constructed to obtain better spatial relationships of the body by designing keypoints constraints sets and improving the loss function. In this work, a comparative experiment of the serial atrous convolution, the parallel atrous convolution, the ablation study with and without body structure constraints are conducted, which reasonably proves the effectiveness of the approach. The model is evaluated on two widely used human pose estimation benchmarks (MPII and LSP). The method achieves better performance on both datasets. Show more
Keywords: Computer vision, human pose estimation, parallel atrous convolution, body structure constraints
DOI: 10.3233/JIFS-212061
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5553-5563, 2022
Authors: Huang, Rui-Lu | Deng, Min-hui | Li, Yong-yi | Wang, Jian-qiang | Li, Jun-Bo
Article Type: Research Article
Abstract: With the attention of people to environmental and health issues, health-care waste (HCW) management has become one of the focus of researchers. The selection of appropriate HCW treatment technology is vital to the survival and development of human beings. In the assessment process of HCW disposal alternative, the evaluation information given by decision makers (DMs) often has uncertainty and ambiguity. The expression, transformation and integration of this information need to be further studied. We develop an applicable decision support framework of HCW treatment technology to provide reference for relevant staff. Firstly, the evaluation information of DMs is represented by interval …2-tuple linguistic term sets (ITLTs). To effectively express qualitative information, the cloud model theory is used to process the linguistic information, a novel concept of interval 2-tuple linguistic integrated cloud (ITLIC) is proposed, and the relevant operations, distance measure and possibility degree of ITLICs are defined. Moreover, a weighted Heronian mean (HM) operator based ITLIC is presented to fuse cloud information. Secondly, the HCW treatment technology decision support model based on the BWM and PROMETHEE is established. Finally, the proposed model is demonstrated through an empirical example, and the effectiveness and feasibility of the model is verified by comparison with extant methods. Show more
Keywords: Cloud model theory, decision support framework, interval 2-tuple linguistic information, health-care waste management
DOI: 10.3233/JIFS-212065
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5565-5590, 2022
Authors: Priyadharshini, A. | Chitra, S.
Article Type: Research Article
Abstract: Lung cancer is one of the most commonly occurring diseases that ranked in the top of the present survey. Advancements in the medical field enable non-invasive methods of computerised diagnosis procedures and detection processes. Deep learning methods are already in evaluation by keeping the deep analysis on improving segmentation accuracy and prediction accuracy etc. The classification of tumour type depends on the quality of segmentation work and feature mappings. In this paper, we developed a robust model that classifies the types of tumours with improved accuracy but is also capable of detecting the early stages of cancer by detecting the …unique hidden points of the image intensity in the lung images, etc. The system is comprised of a novel relative convergence technique for feature extraction technique to extract the infected area and its characteristic pixels to evaluate a unique feature mapping vector. The MSB feature mapping vectors are analysed with Hybrid Regress Fuzzy Net. The final result on whether a tumour is present in the CT image or normal depends on the three individual decisions made by the three algorithms mentioned. The accuracy of each algorithm is also considered for the probable decision-making. The performance measure of the entire proposed Hybrid Regress Net is evaluated through Accuracy, Precision, Recall and F1Score etc. Show more
Keywords: Lung tumor detection, nero-fuzzy logic, Image processing, medical imaging, machine learning
DOI: 10.3233/JIFS-212071
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5591-5604, 2022
Authors: Sanjay, Chintakindi | Alsamhan, Ali | Abidi, Mustufa Haider
Article Type: Research Article
Abstract: Manufacturing companies are focusing on continuous process development to thrive in today’s quality-conscious market. It is particularly relevant to investigate machining processes for advanced materials such as superalloys. Drilling is a major operation that is used in the majority of manufacturing processes. Hence, this research work is focused on investigating the drilling performance of the Monel K500. The output responses under consideration are metal removal rate (MRR), surface roughness, and tool wear. Various contemporary techniques were utilized in this work, namely machine learning methods, artificial neural networks, principal component analysis, and grey relation analysis using uncoated, coated, and HSS (high-speed …steel) drills. After annealing, the softened material can be easily machined to increase the MRR and decrease tool wear and surface roughness. The experimental results show that, after annealing, the surface roughness values for HSS drills have been reduced by 23.86%, uncoated drills by 27.29%, and coated drills by 29.27%, respectively. Moreover, tool wear values for HSS drills decreased by 28.51%, uncoated drills by 34.7%, and coated drills by 33.71%, based on the relative error approach. MRR values for HSS drills increased by 20.51 %, uncoated drills by 23.08%, and coated drills by 23.5%, respectively. For PCA (principal component analysis), feed (47%), and for GRA (gray relation analysis), feed (40.1%) will be the significant parameter followed by speed, and both methods have identified the same experimental run values for optimization of cutting parameters. The theoretical values were predicted using machine learning methods, which utilized the Python language using the Google Colab and then validated with experimental values. The predicted values obtained by the decision tree are close to the measured values as compared to support vector regression and K-nearest neighbor based on relative error. The estimated values obtained by the ANN (artificial neural networks) approach, using Easy NN plus software, match well with the actual values, with a slight deviation. Show more
Keywords: Monel K500, principal component analysis, grey relation analysis with S/N ratio, machine learning methods, ANN
DOI: 10.3233/JIFS-212087
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5605-5625, 2022
Authors: Pan, Lujia | Kalander, Marcus | Wang, Pinghui
Article Type: Research Article
Abstract: Classification algorithms are widely applied to predict failures and detect anomalies in various application areas. It is common to assume that the data and labels are correct when training, but this is challenging to guarantee in the real world. If there are erroneous labels in the training data, a model can easily overfit to these, resulting in poor performance. How to handle label noise has been previously researched, however, few works focus on label noise in anomaly detection. In this work, we propose LDAAD, a novel algorithm framework for label de-noising for anomaly detection that combines unsupervised learning and semi-supervised …learning methods. Specifically, we apply anomaly detection to partition the training data into low-risk and high-risk sets. We subsequently build upon ideas from cross-validation and train multiple classification models on segments of the low-risk data. The models are used both to relabel the samples in the high-risk set and to filter the low-risk samples. Finally, we merge the two sets to obtain a final sample set with more confident labels. We evaluate LDAAD on multiple real-world datasets and show that LDAAD achieves robust results that outperform the benchmark methods. Specifically, LDAAD achieves a 5% accuracy improvement over the second-best method for symmetric noise while having a minimal detrimental impact when no label noise is present. Show more
Keywords: Label noise, anomaly detection, ensemble learning, semi-supervised learning
DOI: 10.3233/JIFS-212096
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5627-5637, 2022
Authors: Borza, Mojtaba | Rambely, Azmin Sham
Article Type: Research Article
Abstract: In the multi-objective programming problem (MOPP), finding an efficient solution is challenging and partially encompasses some difficulties in practice. This paper presents an approach to address the multi-objective linear fractional programing problem with fuzzy coefficients (FMOLFPP). In the method, at first, the concept of α - cuts is used to change the fuzzy numbers into intervals. Therefore, the fuzzy problem is further changed into an interval-valued linear fractional programming problem (IVLFPP). Afterward, this problem is transformed into a linear programming problem (LPP) using a parametric approach and the weighted sum method. It is proven that the solution resulted from the …LPP is at least a weakly ɛ - efficient solution. Two examples are given to illustrate the method. Show more
Keywords: Efficient solution, fuzzy numbers, fuzzy programming, interval arithmetic, weighted sum approach
DOI: 10.3233/JIFS-212105
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5639-5652, 2022
Authors: Zhao, Hang | Chu, Jianjie | Mo, Rong | Chen, Chen | Ding, Ning
Article Type: Research Article
Abstract: At present, high-speed trains have become popular modern transportation. As a significant part of the high-speed train riding activity, the stowing and unloading luggage task has its characteristics. To comprehensively and reasonably evaluate passenger comfort of the stowing and unloading luggage task in high-speed trains. In this paper, passenger behavior characteristics are firstly analyzed by the author, the theoretical architecture of passenger comfort evaluation is constructed with the perspective of product aesthetics and ergonomics, and then the process of the passenger comfort evaluation is put forward. Secondly, a combination of Rough Number (RN) and Decision Making Trial and Evaluation Laboratory …(DEMATEL) (i.e. R-DEMATEL) is utilized to solve the centrality degree of comfort influencing factors and determine comfort evaluation indexes. Furthermore, the passenger comfort evaluation model with Fuzzy Neural Network (FNN) is constructed and trained. After that, the sample data of the evaluation are collected through the simulated experiment of the stowing and unloading luggage task, and they are trained with FNN comparing to Back Propagation Neural Network (BPNN). Eventually, the result of examples testing is verified that the effectiveness of the proposed method. Show more
Keywords: Comfort evaluation, stowing and unloading luggage, Rough-DEMATEL (R-DEMATEL), FNN, high-speed trains
DOI: 10.3233/JIFS-212109
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5653-5665, 2022
Authors: Senthilkumar, D. | George Washington, D. | Reshmy, A.K. | Noornisha, M.
Article Type: Research Article
Abstract: Predicting the quality of water is a very important issue in an ecosystem and it can be used to control the increase of water contamination. Also, water quality prediction is a prominent complex non-linear multi-target learning problem and extracting a relevant subset of features from a large number of features with multiple targets is a challenging task. Existing water quality prediction model not focused on multi-target learning process simultaneously and not identifying the non-linear relationship between the features and target variables. Therefore, this study proposes a multi-task learning method dealing with multi-target regression using non-linear machine learning technique. Finally, experiments …are conducted to build a prediction model based on the proposed methods to evaluate accuracy on water quality dataset. The experimental results indicate that our method increases the overall accuracy of the experimental dataset compared with the existing methods with the reduced number of significant features. Show more
Keywords: Water quality prediction, multi-target, non-linear, MARS, CART
DOI: 10.3233/JIFS-212117
Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5667-5679, 2022
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