Journal of Intelligent & Fuzzy Systems - Volume 42, issue 4

Authors: Zhu, Xiaowei | Han, Yu | Li, Shichong | Wang, Xinyin

Article Type: Research Article

Abstract: With the rapid growth of social network users, the social network has accumulated massive social network topics. However, due to the randomness of content, it becomes sparse and noisy, accompanied by many daily chats and meaningless topics, which brings challenges to bursty topics discovery. To deal with these problems, this paper proposes the spatial-temporal topic model with sparse prior and recurrent neural networks (RNN) prior for bursty topic discovering (ST-SRTM). The semantic relationship of words is learned through RNN to alleviate the sparsity. The spatial-temporal areas information is introduced to focus on bursty topics for further weakening the semantic sparsity …of social network context. Besides, we introduced the “Spike and Slab” prior to decouple the sparseness and smoothness. Simultaneously, we realized the automatic discovery of social network bursts by introducing the burstiness of words as the prior and binary switching variables. We constructed multiple sets of comparative experiments to verify the performance of ST-SRTM by leveraging different evaluation indicators on real Sina Weibo data sets. The experimental results confirm the superiority of our ST-SRTM. Show more

Keywords: Social network, bursty topic, topic model, RNN, sparse prior

DOI: 10.3233/JIFS-212135

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3909-3922, 2022

Authors: Rashwan, Rashwan A. | Hammad, Hasanen A. | Nafea, A.

Article Type: Research Article

Abstract: In this manuscript, the concept of a cyclic tripled type fuzzy cone contraction mapping in the setting of fuzzy cone metric spaces is introduced. Also, some theoretical results concerned with tripled fixed points are given without a mixed monotone property in the mentioned space. Moreover, under this concept, some strong tripled fixed point results are obtained. Ultimately, to support the theoretical results non-trivial examples are listed and the existence of a unique solution to a system of integral equations is presented as an application.

Keywords: Strong tripled fixed point, fuzzy cone metric space, contraction condition, integral equation, cyclic tripled type fuzzy cone contraction mapping

DOI: 10.3233/JIFS-212188

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3923-3943, 2022

Authors: Wang, Yini | Wang, Sichun

Article Type: Research Article

Abstract: Fuzzy relation is one of the main research contents of fuzzy set theory. This paper obtains some results on fuzzy relations by studying relationships between fuzzy relations and their uncertainty measurement. The concepts of equality, dependence, partial dependence and independence between fuzzy relations are first introduced. Then, uncertainty measurement for a fuzzy relation is investigated by using dependence between fuzzy relations. Moreover, the basic properties of uncertainty measurement are obtained. Next, effectiveness analysis is carried out. Finally, an application of the proposed measures in attribute reduction for heterogeneous data is given. These results will be helpful for understanding the essence …of a fuzzy relation. Show more

Keywords: Fuzzy relation, dependence, uncertainty, measurement, attribute reduction, heterogeneous data

DOI: 10.3233/JIFS-212215

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3945-3961, 2022

Authors: Su, Nan | Lin, Zhishuo | You, Wenlong | Zheng, Nan | Ma, Kun

Article Type: Research Article

Abstract: Management of garbage classification is a general term for a series of activities to sort, store and transport garbage into public resources according to certain regulations or standards. Current garbage classification systems have several drawbacks, such as inability to identify multiple garbage categories, and high dependence on the surrounding environment. To address these issues, this paper has proposed the Real Time Multi-Modal Garbage classification System (abbreviated as RMGCS). It consists of two sub systems: an indoor garbage classification applet (abbreviated as IGCA) and an outdoor garbage classification system (abbreviated as OGCS). IGCA provides users with three methods of garbage classification, …and OGCS provides users with outdoor real-time multi-target garbage classification and can dynamically update the recognition model. RMGCS achieves real-time, accurate, and multimodal classification. Finally, the experiments with RMGCS show that our approaches are effective and efficient. Show more

Keywords: Garbage classification, multi-modality, picture recognition, real-time video recognition

DOI: 10.3233/JIFS-212225

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3963-3973, 2022

Authors: Li, Dong | Gong, Lanlan | Liu, Shulin | Sun, Xin | Gu, Ming | Qian, Kun

Article Type: Research Article

Abstract: The traditional batch learning classification methods need to obtain all kinds of data once before training. This makes them unable to recognize the data from the unseen types and cannot continuously enhance their classification ability through learning the testing data in the testing process, because they lack continual learning ability. Inspired by the continual learning mechanism of the biological immune system (BIS), this paper proposed a continual learning classification method with single-label memory cells (S-CLCM). The type of testing data is identified by memory cells, and the data type from unseen types is determined by an affinity threshold. New memory …cells are cultivated continuously by learning the testing data to enhance the classification ability of S-CLCM gradually. Every memory cell has the same size and a unique type. It becomes a standard batch learning classification method or a standard clustering method under certain conditions. Take the experiments on twenty benchmark datasets to estimate its classification performance and possible superiority. Results show S-CLCM has good performance when it becomes a standard batch learning classification method, and S-CLCM is superior to the other classical classification algorithms when the data from unseen types or new labeled data appear during the testing process. It can improve the classification accuracy by up to 33%, and by at least 14%. Show more

Keywords: Classification, continual learning, biological immune system, machine learning, artificial immune algorithm

DOI: 10.3233/JIFS-212226

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3975-3991, 2022

Authors: Yang, Jie | Luo, Tian | Zeng, Lijuan | Jin, Xin

Article Type: Research Article

Abstract: Neighborhood rough sets (NRS) are the extended model of the classical rough sets. The NRS describe the target concept by upper and lower neighborhood approximation boundaries. However, the method of approximately describing the uncertain target concept with existed neighborhood information granules is not given. To solve this problem, the cost-sensitive approximation model of the NRS is proposed in this paper, and its related properties are analyzed. To obtain the optimal approximation granular layer, the cost-sensitive progressive mechanism is proposed by considering user requirements. The case study shows that the reasonable granular layer and its approximation can be obtained under certain …constraints, which is suitable for cost-sensitive application scenarios. The experimental results show that the advantage of the proposed approximation model, moreover, the decision cost of the NRS approximation model will monotonically decrease with granularity being finer. Show more

Keywords: Neighborhood rough sets, approximation model, cost-sensitive, Granular layer selection

DOI: 10.3233/JIFS-212234

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3993-4003, 2022

Authors: Yu, Zhiqiang | Huang, Yuxin | Guo, Junjun

Article Type: Research Article

Abstract: It has been shown that the performance of neural machine translation (NMT) drops starkly in low-resource conditions. Thai-Lao is a typical low-resource language pair of tiny parallel corpus, leading to suboptimal NMT performance on it. However, Thai and Lao have considerable similarities in linguistic morphology and have bilingual lexicon which is relatively easy to obtain. To use this feature, we first build a bilingual similarity lexicon composed of pairs of similar words. Then we propose a novel NMT architecture to leverage the similarity between Thai and Lao. Specifically, besides the prevailing sentence encoder, we introduce an extra similarity lexicon encoder …into the conventional encoder-decoder architecture, by which the semantic information carried by the similarity lexicon can be represented. We further provide a simple mechanism in the decoder to balance the information representations delivered from the input sentence and the similarity lexicon. Our approach can fully exploit linguistic similarity carried by the similarity lexicon to improve translation quality. Experimental results demonstrate that our approach achieves significant improvements over the state-of-the-art Transformer baseline system and previous similar works. Show more

Keywords: Neural machine translation, Thai-Lao, linguistic similarity, structure improving, lexicon

DOI: 10.3233/JIFS-212236

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4005-4014, 2022

Authors: Binh, Nguyen Thanh | Hien, Nguyen Mong | Tin, Dang Thanh

Article Type: Research Article

Abstract: The central retinal artery and its branches supply blood to the inner retina. Vascular manifestations in the retina indirectly reflect the vascular changes and damage in organs such as the heart, kidneys, and brain because of the similar vascular structure of these organs. The diabetic retinopathy and risk of stroke are caused by increased venular caliber. The degrees of these diseases depend on the changes of arterioles and venules. The ratio between the calibers of arterioles and venules (AVR) is various. AVR is considered as the useful diagnostic indicator of different associated health problems. However, the task is not easy …because of the lack of information of the features being used to classify the retinal vessels as arterioles and venules. This paper proposed a method to classify the retinal vessels into the arterioles and venules based on improving U-Net architecture and graph cuts. The accuracy of the proposed method is about 97.6%. The results of the proposed method are better than the other methods in RITE dataset and AVRDB dataset. Show more

Keywords: Arterioles, venules, U-Net architecture, graph cuts, retinal blood vessels

DOI: 10.3233/JIFS-212259

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4015-4026, 2022

Authors: Zhong, Xianyou | Xia, Tianyi | Zhao, Yankun | Zhao, Xiao

Article Type: Research Article

Abstract: The weak fault characteristics of rolling bearings are difficult to identify due to strong background noise. To address this issue, a bearing fault detection scheme combining swarm decomposition (SWD) and frequency-weighted energy operator (FWEO) is presented. First, SWD is applied to decompose the bearing fault signal into single mode components. Then, a new evaluation index termed LEP is constructed by combining the advantages of envelope entropy, Pearson correlation coefficient and L-kurtosis, and it is utilized to choose the sensitive component containing the richest bearing fault characteristics. Finally, FWEO is employed for extracting the bearing fault features from the sensitive component. …Simulation and experimental analyses indicate that the LEP index has better performance than the L-kurtosis index in determining the sensitive component. The method has the effect of suppressing noise and enhancing impulse characteristics, which is superior to the SWD-based envelope demodulation method. Show more

Keywords: Swarm decomposition, frequency-weighted energy operator, fault diagnosis, rolling bearing

DOI: 10.3233/JIFS-212305

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4027-4039, 2022

Authors: Guo, Wenbin | Zhang, Juan

Article Type: Research Article

Abstract: This article propose s a network that is mainly used to deal with a single image polluted by raindrops in rainy weather to get a clean image without raindrops. In the existing solutions, most of the methods rely on paired images, that is, the rain image and the real image without rain in the same scene. However, in many cases, the paired images are difficult to obtain, which makes it impossible to apply the raindrop removal network in many scenarios. Therefore this article proposes a semi-supervised rain-removing network apply to unpaired images. The model contains two parts: a supervised network …and an unsupervised network. After the model is trained, the unsupervised network does not require paired images and it can get a clean image without raindrops. In particular, our network can perform training on paired and unpaired samples. The experimental results show that the best results are achieved not only on the supervised rain-removing network, but also on the unsupervised rain-removing network. Show more

Keywords: Rain removal, raindrop detection, semi-supervised learning, image restoration, shared weight

DOI: 10.3233/JIFS-212342

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4041-4049, 2022