Journal of Intelligent & Fuzzy Systems - Volume 45, issue 2

Authors: Han, Puyu | Yao, Wei | Jiang, Xian

Article Type: Research Article

Abstract: A λ-subset, or a [0,λ]-valued fuzzy subset, is a mapping from a nonempty set to the interval [0,λ]. In this paper, we use the notion of hemimetrics, a kind of distance functions, as the basic structure to define and study fuzzy rough set model of λ-subsets by using the usual addition and subtraction of real numbers. We define a pair of fuzzy upper/lower approximation operators and investigate their properties and interrelations. These two operators have nice logical descriptions by using the related Lukasiewicz logical systems. We show that upper definable sets, lower definable sets and definable sets are equivalent, and …they form an Alexandrov fuzzy topology. A processing of a λ-subset via fuzzy upper/lower approximation operators can actually considered as a processing of the related image, and thus has potential applications in image processing. Show more

Keywords: Hemimetric, λ-subset, fuzzy rough set, fuzzy upper/lower approximation operator, definable set

DOI: 10.3233/JIFS-213049

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 1883-1889, 2023

Authors: Saidi Sief, Ali

Article Type: Research Article

Abstract: Here, we develop a fuzzy controller using a new online self-adapting design. The objective of this work is to control a nonlinear process by using a one-dimensional input rule variable, instead of error and error variation . The initial limits of the fuzzy logic membership functions are mostly depend on experiments and previous knowledge of the dynamic process behaviors. Generally, the membership function parameters have a significant impact on control signal amplitude and, consequently on the convergence and stability of the controller-plant system. The proposed technique determines the limits of the antecedent membership functions online using the k th …and k  - 1th outputs of the controlled plant and reference model, respectively. Meanwhile, the limits of the consequent membership functions are calculated using error and error variation. This approach ensures: (i) that the input/output variables have the required fuzzy space, (ii) the controlled plant follows the desired reference model, and (iii) the control signal amplitude is within acceptable limits. Additionally, (iiii ) it takes into account the dynamic variability of the process and the existence of an overshoot. The membership function parameters are updated continuously through a self-adapting procedure, ensuring improved control performance. Ultimately, the proposed approach is improved using two nonlinear systems. Show more

Keywords: Self-adapting fuzzy controller, dynamic membership functions, nonlinear systems, fuzzy experts systems, FLC

DOI: 10.3233/JIFS-222142

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 1891-1908, 2023

Authors: Shi, Chenchen | Zhang, Changlun | Deng, Lulu | He, Qiang | Wang, Hengyou | Huo, Lianzhi

Article Type: Research Article

Abstract: Data driven deep learning methods have become the mainstream method of building extraction from remote sensing images. In this paper, deep learning algorithm is used to classify and extract buildings from remote sensing images of rural areas around the Great Wall in the suburbs of Beijing captured by unmanned aerial vehicles. Aiming at the shortcomings of the current mainstream instance segmentation algorithm Mask R-CNN in feature fusion and poor prediction of instance mask boundaries, this paper proposes a boundary optimization algorithm for building instance segmentation based on discrete wavelet transform. Firstly, the discrete wavelet transform is introduced into the segmentation …task branch of Mask R-CNN algorithm to extract the low-frequency and high-frequency information of the real mask, in which the high-frequency information includes the boundary information. Secondly, the pixel by pixel prediction of the mask turns into the learning of the low-frequency and high-frequency information of the real mask. The learning of the high-frequency information helps the segmentation network to learn the boundary features better. Finally, using the reversibility of discrete wavelet transform, the low-frequency and high-frequency information of the learned mask is inversely transformed to reconstruct the final mask. The improved algorithm is evaluated on the dataset COCO, and applied to the automatic extraction of buildings. The DWT Mask R-CNN algorithm model achieved 70.2% segmentation accuracy and 71.4% detection accuracy, which were improved by 1% and 0.7% respectively compared with the Mask R-CNN and Cascade Mask R-CNN models. The experimental results show that the instance segmentation edge optimization algorithm combined with wavelet transform has achieved better results on the segmentation boundary, improved the poor effect of mask edge detection and achieved higher detection accuracy, and can accurately extract village buildings. Show more

Keywords: Instance segmentation, Mask R-CNN, wavelet transform, building

DOI: 10.3233/JIFS-222312

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 1909-1922, 2023

Authors: Li, Ruisong | Hu, Yanrong | Liu, Hongjiu

Article Type: Research Article

Abstract: We studied China’s Common Prosperity process by assessing and comparing the level of Common Prosperity in different regions of China and made some beneficial recommendations to government departments. The research data comes from the China Statistical Yearbook , which includes data from 31 provinces and cities from 2015 to 2020. According to the relevant research, eleven evaluation indicators were selected. We combined GRA with the TOPSIS method for scoring and the K-means clustering algorithm for dividing the GRA-TOPSIS scoring results into three evaluation levels. Then, the convolutional neural network model was used to predict and simulate the level of common …prosperity. Taking 2020 as an example, the results show: (1) From 2015 to 2020, China’s Common Prosperity level reached its highest point in 2020. Due to the impact of COVID-19 in 2019, the scores of 31 regions are generally lower than in the previous four years. The situation changed in 2020; (2) In terms of regional distribution, the economic development of Beijing, Shanghai, and other eastern regions is relatively good, with a higher degree of Common Prosperity than that of other regions; (3) The average prediction accuracy is high in our model. It can be close to 100%, indicating that the model has a good prediction effect. In addition, we made recommendations based on the research results, which have good references for actively promoting common prosperity. Show more

Keywords: The level of China’s common prosperity, gray relational analysis method, cluster analysis, convolutional neural network

DOI: 10.3233/JIFS-222442

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 1923-1937, 2023

Authors: Huang, Yuling | Song, Yunlin

Article Type: Research Article

Abstract: Recently, the algorithmic trading of financial assets is rapidly developing with the rise of deep learning. In particular, deep reinforcement learning, as a combination of deep learning and reinforcement learning, stands out among many approaches in the field of decision-making because of its high performance, strong generalization, and high fitting ability. In this paper, we attempt to propose a hybrid method of recurrent reinforcement learning (RRL) and deep learning to figure out the algorithmic trading problem of determining the optimal trading position in the daily trading activities of the stock market. We adopt deep neural network (DNN), long short-term memory …neural network (LSTM), and bidirectional long short-term memory neural network (BiLSTM) to automatically extract higher-level abstract feature information from sequential trading data, respectively, and then generate optimal trading strategies by interacting with the environment in a reinforcement learning framework. In particular, the BiLSTM consisting of two LSTM models with opposite directions is able to make full use of the information from both directions in attempting to capture more effective information. In experiments, the daily data of Dow Jones, S&P500, and NASDAQ (from Jan-01, 2005 to Dec-31, 2020) are applied to verify the performance of the newly proposed DNN-RL, LSTM-RL, and BiLSTM-RL trading systems. Experimental results show that the proposed methods significantly outperform the benchmark methods, such as RRL and Buy and Hold, with higher scalability and better robustness. Especially, BiLSTM-RL performs better than other methods. Show more

Keywords: Reinforcement learning, deep learning, trading strategy, Sharpe ratio

DOI: 10.3233/JIFS-223101

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 1939-1951, 2023

Authors: Lu, MeiLi | Lin, Chin-Tsai

Article Type: Research Article

Abstract: Chinese firms are actively investing in movie ticketing Apps, but there is no unified model for how to evaluate such investments, which can hinder investment decision-making into such ticketing Apps. Currently, there is limited research on the criteria for investors to select the best Chinese movie ticketing Apps. To fill this gap, the present study proposes a three-stage evaluation model for investments in these specific Apps. First, it constructs a new hierarchy for movie ticketing App networks via the Modified Delphi Method (MDM), which consists of 5 criteria and 20 sub-criteria. Second, we apply the Analytic Network Process (ANP) to …calculate the weight of the criteria and sub-criteria, finding that information is the most important, followed by system, trust, service, and word-of-mouth (WOM) in that order. Finally, three movie ticketing Apps are utilized as alternatives, and the best alternative is selected by Techniques for Order Preference Similarity Ideal Solution (TOPSIS) to verify the model’s feasibility. The results herein offer theoretical and practical insights for the development and promotion of movie ticketing Apps and provide a reference for investors to formulate relevant financing strategies. Show more

Keywords: Investment selection, movie ticketing App, ANP, TOPSIS

DOI: 10.3233/JIFS-223566

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 1953-1969, 2023

Authors: Fan, Yuwei | Shi, Lei | Yuan, Lu

Article Type: Research Article

Abstract: In the present day, online users are incentivized to engage in short text-based communication. These short texts harbor a significant amount of implicit information, including opinions, topics, and emotions, which are of notable value for both exploration and analysis. By alleviating the sparsity in short texts, topic models can be used to discover topics from large collections of short texts. While there is a large body of surveys focused on topic modeling, but only a few of them have focused on the short texts. This paper presents a comprehensive overview of topic modeling methods for short texts from a novel …perspective. Firstly, it discusses short text probabilistic topic models and outlines the directions in which they can be improved. Secondly, it explores short text neural topic models, which can be categorized into three groups based on their underlying structures. In addition, this paper provides a detailed investigation of embedding methods in topic modeling. Moreover, various applications and corresponding works are surveyed, with a focus on short texts. The commonly used public corpora and evaluation indicators for topic modeling are also summarized. Finally, the advantages and disadvantages of short text topic modeling are discussed in detail, and future research directions are proposed. Show more

Keywords: Short text, probabilistic topic model, neural topic model, word embeddings, deep learning

DOI: 10.3233/JIFS-223834

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 1971-1990, 2023

Authors: Wang, Renjie | Tan, Fei | Yang, Kunlong | Hao, Yuwen | Li, Fengguo | Yu, Xiaoyuan

Article Type: Research Article

Abstract: With the development of convolutional neural networks, many improved algorithms have been successively proposed to promote the accuracy of dense crowd counting. However, these algorithms are deployed with expensive computing resources, which is unbearable for small devices such as embedded systems with limited computing resources. To realize the real-time counting on the small devices, it is of great significance how to trade off the computation cost and processing accuracy of the dense crowd-counting algorithm. Thus, we propose a lightweight dense crowd-counting algorithm (LCNNet) to improve this issue. Specifically, the proposed LCNNet consists of two subnetworks, a feature extraction subnetwork, and …a regression subnetwork, with a bottleneck depth-separable convolution with a residuals module as the basic module. The LCNNet effectively improves computational efficiency and reduces the computational cost, which can be performed on small devices. Extensive evaluations on four benchmark datasets well demonstrate the effectiveness of the proposed LCNNet for dense crowd-counting models. Meanwhile, the proposed LCNNet can maintain a comparable level of computational accuracy and computational cost on Vehicle counting datasets. Show more

Keywords: Crowd counting, lightweight, depth-separable convolution, highly congested scenes

DOI: 10.3233/JIFS-224081

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 1991-2004, 2023

Authors: Mashak, Neda Pirzad | Akbarizadeh, Gholamreza | Farshidi, Ebrahim

Article Type: Research Article

Abstract: Since prostate cancer is one of the most important causes of death in today’s society, the investigation of why and how to diagnose and predict it has received much attention from researchers. The cooperation of computer and medical experts provides a new solution in analyzing these data and obtaining useful and practical models, which is deep learning. In fact, deep learning as one of the most important tools for analyzing data and discovering relationships between them and predicting the occurrence of events is one of the practical tools of researchers in this way. This study segments and classifies prostate cancer …using a deep learning approach and architectures tested in the ImageNet dataset and based on a method to identify factors affecting this disease. In the proposed method, after increasing the number of data based on removing dominant noises in MRI images, image segmentation using a network based on deep learning called faster R-CNN, and then feature extraction and classification with architecture Various deep learning networks have reached the appropriate accuracy and speed in detection and classification. The aim of this study is to reduce unnecessary biopsies and to choose and plan treatment to help the doctor and the patient. Achieving the minimum error in the diagnosis of malignant lesion with a criterion called Sensitivity of 93.54% and AUC equal to 95% with the ResNet50 architecture has achieved the goal of this research. Show more

Keywords: Magnetic Resonance Imaging (MRI), prostate cancer prediction, morphological properties, deep learning architecture, transfer learning, Receiver Operating Characteristic (ROC) Curve

DOI: 10.3233/JIFS-224274

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2005-2017, 2023

Authors: Xiu, Zhen-Yu | Jiang, Yu-Xiu

Article Type: Research Article

Abstract: In this article, we present new methods for constructing uninorms on bounded lattices under the additional constraints and prove that some of these constraints are sufficient and necessary for the uninorms. Moreover, some illustrative examples for the construction of uninorms are provided. At last, we show that the additional constraints on t -norms (t -conorms) and t -subnorms (t -subconorms) of some uninorms in the literature are exactly sufficient and necessary.

Keywords: Bounded lattices, t-norms, t-subnorms, uninorms

DOI: 10.3233/JIFS-224537

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2019-2030, 2023