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

Authors: Sun, Kexin | Xin, Yuelan | Ma, Yide | Lou, Meng | Qi, Yunliang | Zhu, Jie

Article Type: Research Article

Abstract: U-Net is a commonly used deep learning model for mammogram segmentation. Despite outstanding overall performance in segmenting, U-Net still faces from two aspects of challenges: (1) the skip-connections in U-Net have limitations, which may not be able to effectively extract multi-scale features for breast masses with diverse shapes and sizes. (2) U-Net only merges low-level spatial information and high-level semantic information through concatenating, which neglects interdependencies between channels. To address these two problems, we propose the U-shape adaptive scale network (ASU-Net), which contains two modules: adaptive scale module (ASM) and feature refinement module (FRM). In each level of skip-connections, ASM …is used to adaptively adjust the receptive fields according to the different scales of the mass, which makes the network adaptively capture multi-scale features. Besides, FRM is employed to allows the decoder to capture channel-wise dependencies, which make the network can selectively emphasize the feature representation of useful channels. Two commonly used mammogram databases including the DDSM-BCRP database and the INbreast database are used to evaluate the segmentation performance of ASU-Net. Finally, ASU-Net obtains the Dice Index (DI) of 91.41% and 93.55% in the DDSM-BCRP database and the INbreast database, respectively. Show more

Keywords: Mammograms, mass segmentation, convolutional neural network, adaptive scale module, feature refinement module

DOI: 10.3233/JIFS-210393

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4205-4220, 2022

Authors: Liu, Chaojie | Lu, Jie | Fu, Wenjing | Zhou, Zhuoyi

Article Type: Research Article

Abstract: How to better evaluate the value of urban real estate is a major issue in the reform of real estate tax system. So the establishment of an accurate and efficient housing batch evaluation model is crucial in evaluating the value of housing. In this paper the second-hand housing transaction data of Zhengzhou City from 2010 to 2019 was used to model housing prices and explanatory variables by using models of Ordinary Least Square (OLS), Spatial Error Model (SEM), Geographically Weighted Regression (GWR), Geographically and Temporally Weighted Regression (GTWR), and Multiscale Geographically Weighted Regression (MGWR). And a correction method of Barrier …Line and Access Point (BLAAP) was constructed, and compared with three correction methods previously studied: Buffer Area (BA), Euclidean Distance (ED), and Non-Euclidean Distance, Travel Distance (ND, TT). The results showed: The fitting degree of GWR, MGWR and GTWR by BLAAP was 0.03–0.07 higher than by ND. The fitting degree of MGWR was the highest (0.883) by BLAAP but the smallest by Akaike Information Criterion (AIC), and 88.3% of second-hand housing data could be well interpreted by the model. Show more

Keywords: Housing price, big data, MGWR, GTWR, BLAAP, batch evaluation model

DOI: 10.3233/JIFS-210917

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4221-4240, 2022

Authors: Xiang, Chen | Xing, Wang | Hubiao, Zhang | Yuheng, Xu | You, Chen | Xiaotian, Wu

Article Type: Research Article

Abstract: Threat evaluation (TE) is essential in battlefield situation awareness and military decision-making. The current processing methods for uncertain information are not effective enough for their excessive subjectivity and difficulty to obtain detailed information about enemy weapons. In order to optimize TE on uncertain information, an approach based on interval Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and the interval SD-G1 (SD standard deviation) method is proposed in this article. By interval SD-G1 method, interval number comprehensive weights can be calculated by combining subjective and objective weights. Specifically, the subjective weight is calculated by interval G1 method, …which is an extension of G1 method into interval numbers. And the objective weight is calculated by interval SD method, which is an extension of SD method with the mean and SD of the interval array defined in this paper. Sample evaluation results show that with the interval SD-G1 method, weights of target threat attributes can be better calculated, and the approach combining interval TOPSIS and interval SD-G1 can lead to more reasonable results. Additionally, the mean and SD of interval arrays can provide a reference for other fields such as interval analysis and decision-making. Show more

Keywords: Interval number, multiple attribute decision making (MADM), interval TOPSIS, comprehensive weight, threat evaluation

DOI: 10.3233/JIFS-210945

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4241-4257, 2022

Authors: Zhou, Weibin | Chen, Tao | Huang, Huafang | Sheng, Chang | Wang, Yangfeng | Wang, Yang | Zhang, Daqiang

Article Type: Research Article

Abstract: Iris segmentation is one of the most important steps in iris recognition. The current iris segmentation network is based on convolutional neural network (CNN). Among these methods, there are still problems with the segmentation networks such as high complexity, insufficient accuracy, etc. To solve these problems, an improved low complexity DenseUnet is proposed to this paper based on U-net for acquiring a high-accuracy iris segmentation network. In this network, the improvements are as follows: (1) Design a dense block module that contains five convolutional layers and all convolutions are dilated convolutions aimed at enhancing feature extraction; (2) Except for the …last convolutional layer, all convolutional layers output feature maps are set to the number 64, and this operation is to reduce the amounts of parameters without affecting the segmentation accuracy; (3) The solution proposed to this paper has low complexity and provides the possibility for the deployment of portable mobile devices. DenseUnet is used on the dataset of IITD, CASIA V4.0 and UBIRIS V2.0 during the experimental stage. The results of the experiments have shown that the iris segmentation network proposed in this paper has a better performance than existing algorithms. Show more

Keywords: Iris segmentation, iris recognition, CNN, U-net, low complexity

DOI: 10.3233/JIFS-211396

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4259-4275, 2022

Authors: Sworna Kokila, M.L. | Gomathi, V.

Article Type: Research Article

Abstract: Automatic Person Re-identification by video surveillance is commonly used in different applications. Perhaps the human uniqueness criteria for tracking the presence of the same person across multiple camera views and a person’s growth identification is extremely challenging. To solve the above problem, we propose an efficient Auto Track Regression System (ATRF) based on a deep learning technique that uses an eminent representation strategy along with recognition. In this work, the Auto Wiley Detective (AWD) approach is proposed for the representation of features that can collect valuable information by monitoring individuals. After obtaining important information on the characteristics, it is possible …to define the personal growth identity of the generation. The OPVC (Original Pick Virtual Classifier) is used for accurate classification of the queried person from a dense area by utilizing features of a person’s growth identity extracted from feature extraction by the Auto Wiley Detection Method. The proposed Originated Pick Virtual Classifier (OPVC) uses Platt scaling (originated pick) on probit regression (virtual) to train the featured data set for accurate person re-identification, which is boosted by the Karush–Kuhn–Tucker (KKT) conditions to reduce false re-identification. Since the gallery information is trained using the Backpropagation method and smoothened analysis through approximated output, the Auto Wiley Detection Method proficiently detects the required information automatically. This also helps to detect the person query image from the database, which contains a vast collection of video images based on the similarity features identified in the query image and the detailed features extracted from the query image. The classification is completed automatically, and then the Person Re-Identification from the databases is performed accurately and efficiently. Henceforth, the proposed work effectively extracts reliable height and age estimates with improved flexibility and individual re-identifying capabilities. Show more

Keywords: Auto track regression framework, auto wiley detection, originated pick virtual classifier

DOI: 10.3233/JIFS-201977

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4277-4294, 2022

Article Type: Retraction

DOI: 10.3233/JIFS-219267

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4295-4295, 2022

Article Type: Retraction

DOI: 10.3233/JIFS-219268

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4297-4297, 2022