Journal of Intelligent & Fuzzy Systems - Volume 46, issue 2

Authors: Ma, Junwen | Bi, Wenhao | Mao, Zeming | Zhang, An | Tang, Changhong

Article Type: Research Article

Abstract: The weaponized unmanned aerial vehicle (UAV) swarms have posed a significant threat to maritime civilian and military installations. For effective defense deployment, threat assessment has become a critical part of maritime defense decision-making. However, due to the uncertainty of threat information and the ignorance of decision-makers’ psychological behaviors, there are great challenges in obtaining a reliable and accurate threat assessment result to assist in maritime defense decision-making. To this end, this paper proposes an integrated threat assessment method for maritime defense against UAV swarms based on improved interval type-2 fuzzy best-worst method (IT2FBWM), prospect theory and VIKOR (VlseKriterijumska Optimizacija I …Kompromisno Resenje, in Serbian). Firstly, the improved IT2FBWM is designed by introducing interval type-2 fuzzy set (IT2FS) and entropy-based information to obtain attribute weights with high reliability. Then, the hybrid fuzzy scheme covering IT2FS and interval number is constructed to express the uncertainty of different types of threat information. Next, VIKOR is extended to hybrid fuzzy environment and combined with prospect theory to consider the influence of psychological behaviors of decision-makers. Finally, the improved IT2FBWM and extended VIKOR are integrated to determine the threat ranking of targets and the priority defense targets. A case study of maritime threat assessment is provided to illustrate the performance of the proposed method. Moreover, sensitivity and comparative experiments were conducted, and the results indicate that the proposed method not only obtain the reliable threat assessment result but also outperforms the other methods in terms of attribute weight determination, decision preference consideration and decision mechanism. Show more

Keywords: Threat assessment, interval type-2 fuzzy, best-worst method, prospect theory, multi-attribute decision-making

DOI: 10.3233/JIFS-231675

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4043-4061, 2024

Authors: Cai, Buqing | Tian, Shengwei | Yu, Long | Long, Jun | Zhou, Tiejun | Wang, Bo

Article Type: Research Article

Abstract: With the rapid growth of Internet penetration, identifying emergency information from network news has become increasingly significant for emergency monitoring and early warning. Although deep learning models have been commonly used in Chinese Named Entity Recognition (NER), they require a significant amount of well-labeled training data, which is difficult to obtain for emergencies. In this paper, we propose an NER model that combines bidirectional encoder representations from Transformers (BERT), bidirectional long-short-term memory (BILSTM), and conditional random field (CRF) based on adversarial training (ATBBC) to address this issue. Firstly, we constructed an emergency dataset (ED) based on the classification and coding …specifications of the national emergency platform system. Secondly, we utilized the BERT pre-training model with adversarial training to extract text features. Finally, BILSTM and CRF were used to predict the probability distribution of entity labels and decode the probability distribution into corresponding entity labels.Experiments on the ED show that our model achieves an F1-score of 85.39% on the test dataset, which proves the effectiveness of our model. Show more

Keywords: Named Entity Recognition, BERT, BILSTM, CRF, Adversarial Training

DOI: 10.3233/JIFS-232385

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4063-4076, 2024

Authors: Wang, Chuantao | Wang, Xiumin | Zhai, Jiliang | Shao, Shuo

Article Type: Research Article

Abstract: In recent years, UNet and its derivative networks have gained widespread recognition as major methods of medical image segmentation. However, networks like UNet often struggle with Point-of-Care (POC) healthcare applications due to their high number of parameters and computational complexity. To tackle these challenges, this paper introduces an efficient network designed for medical image segmentation called MCU-Net, which leverages ConvNeXt to enhance UNet. 1) Based on ConvNeXt, MCU-Net proposes the MCU Block, which employs techniques such as large kernel convolution, depth-wise separable convolution, and an inverted bottleneck design. To ensure stable segmentation performance, it also integrates global response normalization (GRN) …layers and Gaussian Error Linear Unit (GELU) activation functions. 2) Additionally, MCU-Net introduces an enhanced Multi-Scale Convolution Attention (MSCA) module after the original UNet’s skip connections, emphasizing medical image features and capturing semantic insights across multiple scales. 3)The downsampling process replaces pooling layers with convolutions, and both upsampling and downsampling stages incorporate batch normalization (BN) layers to enhance model stability during training. The experimental results demonstrate that MCU-Net, with a parameter count of 2.19 million and computational complexity of 19.73 FLOPs, outperforms other segmentation models. The overall performance of MCU-Net in medical image segmentation surpasses that of other models, achieving a Dice score of 91.8% and mIoU of 84.7% on the GlaS dataset. When compared to UNet on the BUSI dataset, MCU-Net shows an improvement of 2% in Dice and 2.9% in mIoU. Show more

Keywords: Convolution neural network, deep learning, medical image processing, semantic segmentation

DOI: 10.3233/JIFS-233232

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4077-4092, 2024

Authors: Ragul Vignesh, M. | Srihari, K. | Karthik, S.

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-234441

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4093-4104, 2024

Authors: Liu, Yongfei

Article Type: Research Article

Abstract: The improved Sparse Signal Reconstruction (SR) algorithm for Trusted Artificial Intelligence (AI) and Distributed Compressed Sensing (DCS) technology was thoroughly investigated. The study verified its effectiveness and advantages in trusted AI and DCS systems, which have significant implications for enhancing the credibility, security, and performance of signal processing and AI algorithms. The reconstruction performance was evaluated using Orthogonal Matching Pursuit (OMP), Basis Pursuit (BP), and Least Absolute Shrinkage and Selection Operator (LASSO). The analysis primarily focused on runtime, refactoring errors, and the number of successful reconstruction attempts. When K = 4, K = 6, K = 8, and K = 10, OMP outperformed BP and LASSO in terms …of successful reconstructions, demonstrating better performance and higher reconstruction precision. Show more

Keywords: Trusted artificial intelligence, distributed compressed sensing technology, sparse signal reconstruction algorithm, orthogonal matching pursuit, basis pursuit, least absolute shrinkage and selection operator

DOI: 10.3233/JIFS-234771

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4105-4118, 2024

Authors: Luo, Minxia | Gu, Xiaojing | Li, Wenling

Article Type: Research Article

Abstract: As the theory of picture fuzzy sets has been developed, more information in life can be expressed in mathematical terms. Similarity measure is a special tool for quantifying the similarity between two sets, so studying similarity measure on picture fuzzy sets has become a trending topic. This new research direction has drawn a great deal of attention from experts and has led to a number of important results which have led to significant results in a number of practical applications. By examining these new findings, we discovered that there are many studies on similarity measure of picture fuzzy sets, some …of them are deficient in solving certain problems, and such similarity measures can lead to the calculation of unreasonable data in practical applications, affecting the final results. Secondly, there is still room for research similarity measures on exponential functions. Considering these two aspects, we propose two new similarity measures based on exponential function, which not only satisfy the axiomatic definition of similarity measures, but also show reasonable computational results in practical applications. Show more

Keywords: Picture fuzzy set, similarity measure, pattern recognition, degree of confidence

DOI: 10.3233/JIFS-235571

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4119-4126, 2024

Authors: Mao, Cui

Article Type: Research Article

Abstract: With the acceleration of economic globalization, enterprises are facing fierce competition and huge challenges, requiring deep financial management transformation. In this context, the integration of industry and finance has gradually demonstrated extremely important value. The integration of industry and finance can not only effectively improve the efficiency of financial management, prevent business risks, and improve operational efficiency, but also enhance the comprehensive ability of enterprise financial management, providing a more flexible, transparent, and efficient financial management system for enterprises. The operational quality evaluation of industry-finance integration enterprises under lean management accounting is a multiple-attribute decision-making (MADM). In this paper, some …calculating laws on IVIFSs, Hamacher sum, Hamacher product are introduced, and the interval-valued intuitionistic fuzzy Hamacher interactive power averaging (IVIFHIPA) technique is proposed based on the interval-valued intuitionistic fuzzy (IVIF) Hamacher interactive weighted averaging (IVIFHIWA) technique and power average (PA) technique. Meanwhile, some ideal properties of IVIFHIPA technique are studied. Then, the IVIFHIPA technique is employed to cope with the MADM under IVIFSs. Finally, an example for operational quality evaluation of industry-finance integration enterprises under lean management accounting is employed to test the IVIFHIPA technique. Thus, the main research aim of this paper is concluded as follows: (1) the IVIFHIPA technique is constructed based on IVIFHIWA technique and classical power average (PA) technique; (2) the IVIFHIPA technique is put forward to cope with the MADM under IVIFSs; (3) an empirical example for operational quality evaluation of industry-finance integration enterprises under lean management accounting has been put forward to show the IVIFHIPA technique. Show more

Keywords: Multi-attribute decision making (MADM), Interval-valued intuitionistic fuzzy sets (IVIFSs), IVIFHIPA technique, operational quality evaluation

DOI: 10.3233/JIFS-235820

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4127-4146, 2024

Authors: Banitalebi, S. | Ahn, S.S. | Borzooei, R.A.

Article Type: Research Article

Abstract: Recently, the neutrosophic graph has been introduced as an extension of fuzzy graphs and intuitionistic fuzzy graphs, which offers more compatibility and flexibility than these two types in modeling and structuring many actual issues. In this article, using neutrosophic highly strong arc, the new notions of (totally) special irregular, highly special irregular, strongly special irregular, neighborly special irregular and special arc-irregular of neutrosophic graphs are stated. Finally, one of their utilizations relevant to offering a fixed optimization model in decision making in diverse conditions is presented. In fact,we present a decision-making problem in real-world applied example which discusses the factors …influencing a companys efficiency. The presented model is, in fact, a factor-based model wherein the impact score of each factor is divided into two types of direct and indirect influences, in which the concept of neutrosophic special dominating set plays a significant role. Show more

Keywords: Neutrosophic graph, special irregular neurosophic graph, special homomorphism, special isomorphism

DOI: 10.3233/JIFS-221785

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4147-4157, 2024

Authors: Dong, Yumin | Che, Xuanxuan | Fu, Yanying | Liu, Hengrui | Sun, Lina

Article Type: Research Article

Abstract: Previously, single classification models were mainly studied to classify human protein cell images, i.e., to identify a certain protein based on a set of different cells. However, a classifier can identify only one protein, in fact, a single cell usually consists of multiple proteins, and the proteins are not completely independent of each other. In this paper, we build a human protein cell classification model by multi-label learning. The logical relationship and distribution characteristics among the labels are analyzed to determine the different proteins contained in a set of different cells (i.e., containing multiple elements in the output space). In …this paper, using human protein image data, we conducted comparison experiments on pre-trained Xception and InceptionResnet V2 to optimize the two models in terms of data augmentation, channel settings, and model structure. The results show that the Optimized InceptionResnet V2 model achieves high performance in the classification task. The final accuracy of the Optimized InceptionResnet V2 model we obtained reached 96.1%, which is a 2.82% improvement relative to that before the optimized model. Show more

Keywords: Human protein atlas images data set, multi-label learning, deep convolutional neural network

DOI: 10.3233/JIFS-223464

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4159-4172, 2024

Authors: Kamber, Eren | Baskak, Murat

Article Type: Research Article

Abstract: In this study, it is aimed to integrate CODAS method with circular intuitionistic fuzzy sets as a new solution method for MCDM problems. Containing a radius notation with degrees of central membership and non-membership degrees is the main advantage of circular intuitionistic fuzzy in decision making. On the other side, Combinative Distance-based Assessment (CODAS) method contains many advantages such as basing on two types of distance calculations (Euclidean and Taxicab distances) comparing with other MCDM methods. When the advantages of circular intuitionistic fuzzy sets and CODAS method are considered, proposed circular intuitionistic fuzzy CODAS method (CIFS-CODAS) presents many superiorities compared …to other MCDM techniques. By this way, an application for green logistics park location selection will be handled by using CIFS-CODAS to show the validity of the methodology. After, a comparative analysis with intuitionistic fuzzy CODAS (IFS-CODAS), intuitionistic fuzzy TOPSIS (IFS-TOPSIS) and intuitionistic fuzzy EDAS (IFS-EDAS) methods will be performed for green logistics park location selection problem to confirm the robustness of presented method. Green logistics and Green Deal are also emphasized considering environmental factors as a scope of the article. Finally, the results will be evaluated in the context of the logistics sector and green logistics. Show more

Keywords: Green logistics, circular intuitionistic fuzzy sets, fuzzy, CODAS method, location selection

DOI: 10.3233/JIFS-231843

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4173-4189, 2024