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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: Zhang, Yun | Zou, Xiangxiang | Yu, Shujuan | Huang, Liya | Wang, Weigang | Zhao, Shengmei | Wang, Xiumei
Article Type: Research Article
Abstract: Facial expression recognition is a current research hotspot and can be applied to computer vision fields such as human-computer interaction and affective computing. The lack of diversity and category recognition information in the neural network input may affect the performance of the network, resulting in insufficient extraction of facial expression features. In order to address the above problems, a lightweight deep convolution neural network with convolution block attention module is proposed in this paper. The implementation of the lightweight DNN relies on the use of deep separable convolution and residual blocks. The combination of the convolution block attention module and …the improved classification function can optimize the lightweight model. We use accuracy and confusion matrix to evaluate different models, ultimately achieving 71.5% and 99.5% accuracy on the Fer2013 and CK+ datasets respectively. The experimental results show that our model has good feature representation capabilities. Show more
Keywords: Facial expression recognition, deep neural network, attention mechanism, AM-Softmax
DOI: 10.3233/JIFS-212846
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5673-5683, 2022
Authors: Hussain, Azmat | Mahmood, Tahir | Ali, Muhammad Irfan | Iampan, Aiyared
Article Type: Research Article
Abstract: Recently, some improvement has been made in the dominant notion of fuzzy set that is Yager investigated the generalized concept of fuzzy set, Intuitionistic fuzzy set (IFS) and Pythagorean fuzzy set (PFS) and called it q-rung orthopair fuzzy (q-ROF) set (q-ROFS). The aim of this manuscript is to present the concept of q-ROF soft (q-ROFS t ) set (q-ROFS t S) based on the Dombi operations. Since Dombi operational parameter possess natural flexibility with the resilience of variability. Some new operational laws are defined based on hybrid study of soft sets and q-ROFS. The advantage of Dombi operational …parameter is very important to express the experts’ attitude in decision making. In this paper, we present q-ROFS t Dombi average (q-ROFS t DA) aggregation operators including q-ROFS t Dombi weighted average (q-ROFS t DWA), q-ROFS t Dombi ordered weighted average (q-ROFS t DOWA) and q-ROFS t Dombi hybrid average (q-ROFS t DHA) operators. Moreover, we investigate q-ROFS t Dombi geometric (q-ROFS t DG) aggregation operators including q-ROFS t Dombi weighted geometric (q-ROFS t DWG), q-ROFS t Dombi ordered weighted geometric (q-ROFS t DOWG), and q-ROFS t Dombi hybrid geometric (q-ROFS t DHG) operators. The basic properties of these operators are presented with detail such us Idempotency, Boundedness, Monotonicity, Shift invariance, and Homogeneity. Thus from the analysis and advantages of proposed model, it is clear that the investigated q-ROFS t DWA operator is the generalized form of IF S t DWA, PFS t DWA and q-ROFDWA operators. Similarly, the investigated q-ROFS t DWG operator is the generalized form of IF S t DWG, PFS t DWG and q-ROFDWG operators. By applying the develop approach, this manuscript contains the technique and algorithm for multicriteria decision making (MCDM). Further a numerical example is developed to illustrate the flexibility and applicability of the developed operators. Show more
Keywords: PFS, q-ROFS, Soft Sets, q-ROFStS, Dombi Operators, q-ROFSt DWA, q-ROFSt DOWA, q-ROFSt DHA, q-ROFSt DWG, q-ROFSt DOWG and q-ROFSt DHG Operator, MCDM
DOI: 10.3233/JIFS-212921
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5685-5702, 2022
Authors: Yao, Lingjuan | Feng, Zonghong | Wang, Yong
Article Type: Research Article
Abstract: In this paper, we introduce the notion of BF-contexts and show that the set of hyper-concepts of the BF-contexts is a bifinite domain. Conversely, given a bifinite domain we can obtain a BF-context such that all the hyper-concepts of it is isomorphic to the bifinite domain. Further, We obtain category equivalent to that of bifinite domains and BF-contexts.
Keywords: Rough approximable concept, BF-context, Bifinite domain, categorical equivalence
DOI: 10.3233/JIFS-212939
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5703-5708, 2022
Authors: Yue, Tan | He, Zihang | Li, Chang | Hu, Zonghai | Li, Yong
Article Type: Research Article
Abstract: The number of scientific papers has been increasing ever more rapidly. Researchers have to spend a lot of time classifying papers relevant to their study, especially into fine-grained subfields. However, almost all existing paper classification models are coarse-grained, which can not meet the needs of researchers. Observing this, we propose a lightweight fine-grained classification model for scientific paper. Dynamic weighting coefficients on feature words are incorporated into the model to improve the classification accuracy. The feature word weight is optimized by the Mean Decrease Accuracy (MDA) algorithm. Considering applicability, the lightweight processing is conducted through algorithm pruning and training sample …pruning. Comparison with mainstream models shows simultaneous improvement in accuracy and time efficiency by our model. Show more
Keywords: Artificial intelligence application, fine-grained classification, lightweight processing, machine learning, paper classification system
DOI: 10.3233/JIFS-213022
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5709-5719, 2022
Authors: Prabhu, T.N. | Karuppasamy, K.
Article Type: Research Article
Abstract: Intrusion attack is considered as the major concerns to be focussed in wireless sensor network which should be seriously viewed for identification of secure and trustworthy information processing. The various characteristics involved in Intrusion attacks should be adapted precisely since it impacts on result of the intrusion detection in terms of accuracy. PCA-based centralized approach (PCACID) and Knowledge based Intrusion Detection Strategy (KBIDS) is suggested in this research for achieving the accurateintrusion detection. Though KBIDS is involved in achieving accurate detection, the demerit is that time complexity and computational overhead are progressively more which in turn influences on the entire …network performance. Traffic Variation based Intrusion Detection System (TV-IDS) plays a major role in mitigating these issues. In addition to it, Fuzzy based mean shift clustering is also suggested for incorporating clustering feature process which influences precise clustering result with the advantage of less time complexity. The decision classifier takes its role after the assessment of data points bias variations. This variation factor helps in recognizing smaller traffic variation and not determined as irregular data. The classification is achieved by hybrid genetic neuro fuzzy classifier. The updating of ANFIS weight values is accomplished concurrently with optimal selection by means of genetic algorithm. The optimal route path is chosen by greatly utilizing the artificial bee colony algorithm. The various fitness parameters involved in this research are energy level of nodes, bandwidth, etc., for efficient data transmission successfully. MATLAB simulation platform is greatly utilized for assessment of overall results for validating that proposed TV-IDS achieves improved outcomes comparatively. Show more
Keywords: Intrusion detection, feature extraction, feature grouping, traffic variation, optimal route path selection
DOI: 10.3233/JIFS-213027
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5721-5731, 2022
Authors: Li, Fanshu | Yao, Dengfeng | Jiang, Minghu | Kang, Xinchen
Article Type: Research Article
Abstract: A new smoking behavior recognition algorithm based on a weak supervision fine-grained structure and the EficientDet network is proposed in this study to solve the poor recognition effect and lack of data samples of smoking behavior in complex situations. The proposed algorithm uses the framework of a fine-grained two-level attention model with weak supervision. First, the feature edge of the image block is detected by a structured method, and the edge is screened by non-maximum suppression to form a candidate region block. Smoking behavior can then be recognized effectively by combining the results of the object-level filter for specific objects …and the local-level filter for locating discriminant parts. Second, the object-level filter uses an improved EfficientDet network to classify prospective objects and candidate regions with strong features. The present smoking behavior recognition algorithm and coarse- and fine-grained algorithms are compared to verify the effectiveness of the algorithm. Experimental results show that the accuracy of the proposed algorithm is 93.10%, which is higher than that of the optimal smoking behavior detection algorithm by 1.7%, and the error detection rate is 3.6%. Show more
Keywords: Smoking, EfficienDet network, weakly supervised fine-grained target detection, attention mechanism, behavior recognition
DOI: 10.3233/JIFS-213042
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5733-5747, 2022
Authors: Ye, Xiang | He, Zihang | Li, Bohan | Li, Yong
Article Type: Research Article
Abstract: Geometric invariant feature representation plays an indispensable role in the field of image processing and computer vision. Recently, convolution neural networks (CNNs) have witnessed a great research progress, however CNNs do not excel at dealing with geometrically transformed images. Existing methods enhancing the ability of CNNs learning invariant feature representation rely partly on data augmentation or have a relatively weak generalization ability. This paper proposes orientation adaptive kernels (OA kernels) and orientation adaptive max pooling (OA max pooling) that comprise a new topological structure, orientation adaptive neural networks (OACNNs). OA kernels output the orientation feature maps which encode the orientation …information of images. OA max pooling max-pools the orientation feature maps by automatically rotating the pooling windows according to their orientation. OA kernels and OA max pooling together allow for the eight orientation response of images to be computed, and then the max orientation response is obtained, which is proved to be a robust rotation invariant feature representation. OACNNs are compared with state-of-the-art methods and consistently outperform them in various experiments. OACNNs demonstrate a better generalization ability, yielding a test error rate 3.14 on the rotated images but only trained on “up-right” images, which outperforms all state-of-the-art methods by a large margin. Show more
Keywords: Orientation adaptive kernel, rotation invariance, image transformation, feature extraction
DOI: 10.3233/JIFS-213051
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5749-5758, 2022
Authors: Krishnakumar, K. | Gandhi, S. Indira | Sivaranjani, C.K.
Article Type: Research Article
Abstract: Video stitching has become popular due to recent advancements in technology to provide broad views and high-resolution displays. Comprehensive view or panoramic videos and high-resolution displays are created by stitching videos captured by multiple cameras or by a single camera at different points of time. This paper proposes a video stitching technique with stabilization for moving multi-camera videos adopting the wavelet decomposition technique. This method uses only those feature points that reduce the mismatching and increase the precision in estimating the transformation from among the feature points identified by the Speed-Up Robust Features detector. This work differs from the similar …work of others in two directions. Instead of using all selected feature points for the matching purpose, only significant among them are used. Unlike others, the frames are stabilized before they are stitched. Show more
Keywords: Stitching, Stabilization, Wavelet Transform, SURF, Threshold
DOI: 10.3233/JIFS-213069
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5759-5770, 2022
Authors: Zhang, Hao | Hua, Haiyang | Liu, Tianci
Article Type: Research Article
Abstract: Most of the deep learning object detection methods based on multi-modal information fusion cannot directly control the quality of the fused images at present, because the fusion only depends on the detection results. The indirectness of control is not conducive to the target detection of the network in principle. For the sake of the problem, we propose a multimodal information cross-fusion detection method based on a generative adversarial network (CrossGAN-Detection), which is composed of GAN and a target detection network. And the target detection network acts as the second discriminator of GAN during training. Through the content loss function and …dual discriminator, directly controllable guidance is provided for the generator, which is designed to learn the relationship between different modes adaptively through cross fusion. We conduct abundant experiments on the KITTI dataset, which is the prevalent dataset in the fusion-detection field. The experimental results show that the AP of the novel method for vehicle detection achieves 96.66%, 87.15%, and 78.46% in easy, moderate, and hard categories respectively, which is improved about 7% compared to the state-of-art methods. Show more
Keywords: Target detection, multimodal data, GAN, controllable fusion
DOI: 10.3233/JIFS-213074
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5771-5782, 2022
Authors: Tian, Yu | Zong, Zhaojun | Hu, Feng
Article Type: Research Article
Abstract: Complex uncertain variables are measurable functions from uncertainty spaces to the set of complex numbers and are used to model complex uncertain quantities. In this paper, we investigate Egoroff’s theorem and Lusin’s theorem for complex uncertain sequences. For studying these theorems, we introduce two concepts: strongly order continuous and regular. And as far as we know, our results are new.
Keywords: Complex uncertain variables, Egoroff’s theorem, Lusin’s theorem
DOI: 10.3233/JIFS-213151
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5783-5792, 2022
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