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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: Feng, Kan | Yang, Ke | Shi, Haopeng | Jia, Najuan | Zhang, Pingjuan
Article Type: Research Article
Abstract: Overload service in the communication network of smart substation will cause congestion, resulting in low overload service throughput, high congestion rate and long congestion control time in the average smart substation. A congestion control method for overloaded services in smart substations with high concurrent users is proposed. According to the characteristics of overload service request of smart substation, the mathematical model of the algorithm is defined by describing the overload service request of smart substation on the basis of network topology model. Combined with the wavelength rotation strategy, the congestion rate of overloaded services in smart substations is reduced, and …the throughput rate of overloaded services in smart substations is improved. Considering the factors of high concurrent users, by judging and feeding back the congestion of the overloaded services of smart substations, the congestion control of overloaded services of smart substations under high concurrent users is realized. The experimental results show that the proposed method has better effect and scalability in the congestion control of the overloaded service of the smart substation, and can effectively shorten the congestion control time of the overloaded service of the smart substation. Show more
Keywords: High concurrent users, smart substation, wavelength rotation strategy, overload service, congestion control, D_WA algorithm, overload service congestion model, congestion degree value, interest packet forwarding rate
DOI: 10.3233/JIFS-224276
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3895-3906, 2024
Authors: Wu, Zhongyi | Liu, Weidong | Zheng, Weijie
Article Type: Research Article
Abstract: This research presents a novel model for optimizing process information in manufacturing steps through the utilization of Process Constituent Elements (PCE), with the aim of enhancing the effectiveness of product process information design. To achieve this objective, a systematic analysis is conducted on six dimensions: input, output, resources, value-adding activities, environment, and process control and inspection content. In addition, specific attributes of PCE are investigated, and an improved FP-growth algorithm is employed to extract the optimized structural expressions of typical PCE, thus determining specific expression requirements. The PCE and their attribute relationships are organized into modular mapping rules, resulting in …an optimized representation structure based on a polychromatic set approach. The effectiveness of this approach is quantitatively assessed by developing a comprehensive quality indicator evaluation system for process information and using a fuzzy comprehensive evaluation model for analysis. Show more
Keywords: Process constituent elements, process design, optimization, data mining, process quality, fuzzy evaluation
DOI: 10.3233/JIFS-231198
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3907-3932, 2024
Authors: Siva Senthil, D. | Sivarani, T.S.
Article Type: Research Article
Abstract: Detecting abnormal events in surveillance involves identifying unexpected behavior through video analysis. This involves recognizing patterns or deviations from normal behavior and taking actions to mitigate potential risks. However, the distribution of data can change over time, leading to concept drift, which can make it challenging to accurately detect abnormal events. To address this issue, a new approach using a global density network (GDN) has been proposed. The GDN allows for more efficient identification of object distributions in surveillance videos, leading to improved accuracy in abnormal event detection. The proposed method combines features extracted by a backbone network with a …global density joined network (GDJN), which refines density features using dilated convolutional networks. A multistage long short-term memory (LSTM) network is then used to classify abnormal events. The experimental results are conducted on two datasets, UMN and UCSD Ped2. The achieved F1 scores were 93.42 and 94.46 respectively, with corresponding AUC values of 93.5 and 94.8. Show more
Keywords: Keywords:Video analysis, abnormal event detection, GDN, GDJN, LSTM, deep learning
DOI: 10.3233/JIFS-232177
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3933-3944, 2024
Authors: Li, Weidong | Fan, Jinsheng | Li, Zhenying | Wang, Chisheng | Zhang, Xuehai | Duan, Jinlong
Article Type: Research Article
Abstract: The accuracy of predicting river-suspended sediment concentration (SSC) is crucial for evaluating the functional lifespan of reservoirs, analyzing river geomorphological evolution, and assessing riverbed stability. In this study, we aim to develop new models for SSC prediction at two hydrological stations near Puerto Rico, USA, by integrating the bacterial foraging optimization algorithm and adaptive neural fuzzy inference network (ANFIS). The models comprise ANFIS with grid partition (ANFIS-GP), ANFIS with subtractive clustering (ANFIS-SC), and ANFIS with fuzzy c-means clustering (ANFIS-FCM). Additionally, we employ an artificial neural network (ANN) and the sediment rating curve (SRC) for predicting daily series data of flow …discharge-suspended sediment concentration (SSC). Different scenarios are considered based on varying input and output variables, leading to predictions for four distinct scenarios. At the Rio Valenciano Station, the MRSE values for ANFIS-BFO, ANFIS-FCM, ANFIS-GP, ANFIS-SC, ANN, and SRC are 2.2172, 2.5389, 2.6627, 2.7549, 2.7994, and 3.7882, respectively. For the Quebrada Blanca Station, the MRSE values for ANFIS-BFO, ANFIS-FCM, ANFIS-SC, ANFIS-GP, ANN, and SRC are 0.8295, 0.8664, 0.8964, 0.9110, 0.9684, and 1.6742, respectively. It can be inferred that ANFIS-BFO exhibits superior prediction results compared to all other models. Furthermore, ANFIS-SC and ANFIS-FCM demonstrate slightly better prediction performance than ANFIS-GP. In comparison to ANN, ANFIS-GP, ANFIS-SC, and ANFIS-FCM exhibit slightly superior prediction performance. Show more
Keywords: ANFIS, ANN, bacterial foraging optimization algorithm, modeling, suspended sediment
DOI: 10.3233/JIFS-232277
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3945-3961, 2024
Authors: Sahapudeen, Farjana Farvin | Krishna Mohan, S.
Article Type: Research Article
Abstract: Patients with lung cancer can only be diagnosed and treated surgically. Early detection of lung cancer through medical imaging could save numerous lives. Adding advanced techniques to conventional tests that offer high accuracy in diagnosing lung cancer is essential. U-Net has excelled in diversified tasks involving the segmentation of medical image datasets. A significant challenge remains in determining the ideal combination of hyper parameters for designing an optimized U-Net for detailed image segmentation. In our work, we suggested a technique for automatically generating evolutionary U-Nets to detect and segregate lung cancer anomalies. We used three distinct datasets, namely the LIDC-IRDC …Dataset, Luna 16 Dataset, and Kaggle Dataset, for training the proposed work on lung images. Our results, examined with six distinct evaluation criteria used for medical image segmentation, consistently demonstrated the highest performance. More specifically, the GA-UNet outperforms conventional approaches in terms of an impressive accuracy rate of 97.5% and a Dice similarity coefficient (DSC) of 92.3%. Show more
Keywords: Genetic programming, deep learning, attention blocks, residual network, UNets, optimized U-Net
DOI: 10.3233/JIFS-233006
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3963-3974, 2024
Authors: Demirtaş, Naime | Dalkılıç, Orhan | Riaz, Muhammad | Al-Quran, Ashraf
Article Type: Research Article
Abstract: Introduction: The soft set theory has drawn the attention of many researchers, particularly for dealing with uncertainty in decision-making problems. Despite its remarkable advantages, the soft set theory has only been used to tackle decision-making problems that aim to choose the best option. However, there exist different forms of decision-making problems that involve different forms of uncertainty. Methods: In this study, we present various algorithms based on the soft set theory in order to handle the cases where one has different uncertainty forms in decision-making problems. Some new concepts such as object code, personal object code, parameter significance …weight and new distance measures have been introduced to the literature for the construction of these algorithms. Furthermore, we show the application results of those algorithms and provide several examples. Results and Conclusions: As a result, a comparison among the application results of the algorithms implies that the best objects might not always yield the most efficient outcomes. Show more
Keywords: Soft set, D-metric space, parametric distance, algorithm, decision making
DOI: 10.3233/JIFS-234481
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3975-3985, 2024
Authors: Li, Zhixin | Liu, Hao | Huan, Zhan | Liang, Jiuzhen
Article Type: Research Article
Abstract: Human activity recognition (HAR) plays a crucial role in remotely monitoring the health of the elderly. Human annotation is time-consuming and expensive, especially for abstract sensor data. Contrastive learning can extract robust features from weakly annotated data to promote the development of sensor-based HAR. However, current research mainly focuses on the exploration of data augmentation methods and pre-trained models, disregarding the impact of data quality on label effort for fine-tuning. This paper proposes a novel active contrastive coding model that focuses on using an active query strategy to evenly select small, high-quality samples in downstream tasks to complete the update …of the pre-trained model. The proposed uncertainty-based balanced query strategy mines the most indistinguishable hard samples according to the data posterior probability in the unlabeled sample pool, and imposes class balance constraints to ensure equilibrium in the labeled sample pool. Extensive experiments have shown that the proposed method consistently outperforms several state-of-the-art baselines on four mainstream HAR benchmark datasets (UCI, WISDM, MotionSense, and USCHAD). With approximately only 10% labeled samples, our method achieves impressive F1-scores of 98.54%, 99.34%, 98.46%, and 87.74%, respectively. Show more
Keywords: Contrastive learning, active learning, human activity recognition, hard sample mining, mobile medical system
DOI: 10.3233/JIFS-234804
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3987-3999, 2024
Authors: Zeng, Zengpei
Article Type: Research Article
Abstract: Visual communication design, as a type of artistic and three-dimensional design behavior, helps to spread visual behavior by designing it. The rapid development of new media technology has provided rich channels and vast space for visual communication design, and the elements and modes of visual communication design are constantly being updated, better promoting the development of visual communication technology. The Teaching quality evaluation of visual communication design based on the cultivation of innovative and creative abilities based on the cultivation of innovative and creative abilities is a multiple-attribute decision-making (MADM). In this paper, some calculating laws on IVIFSs, Hamacher sum, …Hamacher product are introduced, and the induced interval-valued intuitionistic fuzzy Hamacher interactive hybrid weighted averaging (I-IVIFHIHWA) operator is proposed based on the interval-valued intuitionistic fuzzy Hamacher interactive hybrid weighted averaging (IVIFHIHWA) operator and induced ordered weighted averaging (I-OWA) operator. Meanwhile, some ideal properties of I-IVIFHIHWA operator are studied. Then, the I-IVIFHIHWA operator is employed to cope with the MADM under IVIFSs. Finally, an example for Teaching quality evaluation of visual communication design based on the cultivation of innovative and creative abilities is employed to test the I-IVIFHIHWA operator. Thus, the main research aim of this paper is concluded as follows: [1 ] the I-IVIFHIHWA operator is constructed based on classical IOWA operator; [2 ] the I-IVIFHIHWA operator is put forward to cope with the MADM under IVIFSs; [3 ] an empirical example for Teaching quality evaluation of visual communication design based on the cultivation of innovative and creative abilities has been put forward to show the I-IVIFHIHWA operator. Show more
Keywords: Multiple-attribute decision-making (madm), interval-valued intuitionistic fuzzy sets (ivifss), i-ivifhihwa operator, quality evaluation
DOI: 10.3233/JIFS-235960
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4001-4013, 2024
Authors: Li, Fang | Li, Pengfei
Article Type: Research Article
Abstract: Currently, the digital economy continues to deepen its development, and it has become a consensus among all sectors as the direction of global future development. Digital finance, as a fleet in the wave of digital economy, is rapidly heading towards the sunny shore of benefiting the public and serving entities driven by the digital technology engine. Xinwang Bank is a fast boat in the digital finance fleet, always adhering to the principle of technology, building an open platform, and actively promoting the construction of an open, shared, and secure digital credit ecosystem from three levels: institutional, industry, and ecological, to …assist in the development of the digital economy. The digital commercial bank security evaluation is a classical multiple attribute group decision making (MAGDM) problems. Recently, the Evaluation based on Distance from Average Solution (EDAS) method has been employed to manage MAGDM issues. The intuitionistic fuzzy sets (IFSs) are used as a tool for portraying uncertain information during the digital commercial bank security evaluation. In this paper, the intuitionistic fuzzy nunmber EDAS (IFN-EDAS) method is cultivated to manage the MAGDM based on Hamming distance and Euclidean distance under IFSs. In the end, a numerical case study for digital commercial bank security evaluation is supplied to validate the proposed method. The main contributions of this paper are outlined: (1) the EDAS method has been extended to IFSs based on Hamming distance and Euclidean distance; (2) the CRITIC method is used to derive weight based on Hamming distance and Euclidean distance under IFSs. (3) the IFN-EDAS method based on Hamming distance and Euclidean distance is founded to manage the MAGDM based on the Hamming distance and Euclidean distance under IFSs; (4) a numerical case study for digital commercial bank security evaluation and some comparative analysis is supplied to validate the proposed method. Show more
Keywords: Multiple attribute group decision making (MAGDM), intuitionistic fuzzy sets (IFSs), EDAS method, CRITIC method, digital commercial bank security evaluation
DOI: 10.3233/JIFS-236058
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4015-4027, 2024
Authors: Zhang, Bin
Article Type: Research Article
Abstract: In recent years, e-commerce live streaming and short video marketing supported by big data and artificial intelligence technology have flourished, adding new sales models for e-commerce products to mass consumption, promoting the multimodal development of the e-commerce industry, giving new impetus and connotation to economic and social development, and being an effective means to achieve high-quality development in the new era. The effectiveness evaluation of short video marketing strategies is a multiple-attribute group decision-making (MAGDM) problem. Recently, the Exponential TODIM technique and Combined Compromise Solution (CoCoSo) technique has been employed to cope with MAGDM issues. The interval-valued Pythagorean fuzzy sets …(IVPFSs) are employed as a tool for characterizing uncertain information during the effectiveness evaluation of short video marketing strategies. In this paper, the interval-valued Pythagorean fuzzy Exponential TODIM (ExpTODIM) (IVPF-ExpTODIM-CoCoSo) technique is constructed to solve the MAGDM under IVPFSs. In the end, a numerical case study for effectiveness evaluation of short video marketing strategies is given to validate the proposed technique. The main contributions of this paper are outlined: (1) the Exp-TODIM and CoCoSo technique has been extended to IVPFSs; (2) Information Entropy is employed to manage the weight values under IVPFSs. (3) the IVPF-ExpTODIM-CoCoSo technique is founded to implement the MAGDM under IVPFSs; (4) a numerical case study for effectiveness evaluation of short video marketing strategies and some comparative analysis is supplied to verify the IVPF-ExpTODIM-CoCoSo technique. Show more
Keywords: Multiple attribute group decision making (MAGDM), interval-valued Pythagorean fuzzy sets (IVPFSs), Exponential TODIM (ExpTODIM) technique, CoCoSo technique, effectiveness evaluation
DOI: 10.3233/JIFS-236767
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4029-4042, 2024
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: The rapid development of Internet of Things (IoT) technology has enabled the emergence of the Internet of Medical Things (IoMT), especially in body area network applications. To protect sensitive medical data, it is essential to ensure privacy preservation and detect intrusions in this context. This study proposes a novel intrusion detection system that protects the privacy of IoMT networks, specifically in the context of body area networks. For feature extraction, the system employs a recurrent U-Net autoencoder algorithm, which effectively captures temporal dependencies in IoMT data. In addition, privacy is protected through the combination of data anonymization techniques and data …classification using Principal Component Analysis (PCA). Combining the recurrent U-Net autoencoder algorithm, privacy preservation mechanisms, and PCA-based data classification, the proposed system architecture comprises the U-Net autoencoder algorithm. The proposed method is superior to existing approaches in terms of accuracy, precision, recall, F-measure, and classification loss, as demonstrated by experimental evaluations. This research contributes to the field of privacy protection and intrusion detection in IoMT, specifically in body area network applications. Show more
Keywords: Biomedical, Internet of Medical Things, intrusion detection, privacy preservation, recurrent neural networks, U-Net
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
Authors: Tamizharasi, A. | Ezhumalai, P.
Article Type: Research Article
Abstract: A novel approach to enhance software testing through intelligent test case selection is proposed in this work. The proposed method combines feature extraction, clustering, and a hybrid optimization algorithm to improve testing effectiveness while reducing resource overhead. It employs a context encoder to extract relevant features from software code, enhancing the accuracy of subsequent testing. Through the use of Fuzzy C-means (FCM) clustering, the test cases are classified into groups, streamlining the testing process by identifying similar cases. To optimize feature selection, a Hybrid Whale Optimized Crow Search Algorithm (HWOCSA), which intelligently combines the strengths of both Whale Optimization Algorithm …(WOA) and Crow Search Algorithm (CSA) is introduced. This hybrid approach mitigates limitations while maximizing the selection of pertinent features for testing. The ultimate contribution of this work lies in the proposal of a multi-SVM classifier, which refines the test case selection process. Each classifier learns specific problem domains, generating predictions that guide the selection of test cases with unprecedented precision. Experimental results demonstrate that the proposed method achieves remarkable improvements in testing outcomes, including enhanced performance metrics, reduced computation time, and minimized training data requirements. By significantly streamlining the testing process and accurately selecting relevant test cases, this work paves the way for higher quality software updates at a reduced cost. Show more
Keywords: Context encoder, pre-processing, FCM, WOA, HWOCSA
DOI: 10.3233/JIFS-232700
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4191-4207, 2024
Authors: Dong, Yue-Fang | Fu, Wei-wei | Zhou, Zhe | Shi, Guo-Hua
Article Type: Research Article
Abstract: Relative pupillary afferent disorder (RAPD) plays a crucial role in diagnosing optic nerve dysfunction. This paper introduces an innovative equipment design with a high-speed pupil detection algorithm and a binocular independent stimulation optical path. The proposed algorithm utilizes the grayscale characteristics of the pupil region to achieve rapid and accurate pupil detection and tracking. Initially, a pupil threshold is estimated using eigenvalues, enabling the calculation of the pupil centroid. Subsequently, leveraging the unique characteristics of the pupil region, a dynamic tracking algorithm, a second-order partial derivative threshold algorithm, and a pupil diameter extraction algorithm are employed to precisely locate the …centroid. By incorporating a binocular independent stimulus light path design, the algorithm overcomes limitations associated with the current measurement equipment. The experimental results demonstrate the algorithm’s high robustness and fast detection speed, meeting the tracking speed requirement of 1250 frames per second for a single eye. These advancements have the potential to significantly enhance the diagnosis and assessment of optic nerve dysfunction. Show more
Keywords: RAPD, pupil detection, gray level features, dynamic tracking
DOI: 10.3233/JIFS-232752
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4209-4218, 2024
Authors: Ma, Xiaoqin | Liu, Jianming | Wang, Pei | Yu, Wenchang | Hu, Huanhuan
Article Type: Research Article
Abstract: Feature selection can remove data noise and redundancy and reduce computational complexity, which is vital for machine learning. Because the difference between nominal attribute values is difficult to measure, feature selection for hybrid information systems faces challenges. In addition, many existing feature selection methods are susceptible to noise, such as Fisher, LASSO, random forest, mutual information, rough-set-based methods, etc. This paper proposes some techniques that consider the above problems from the perspective of fuzzy evidence theory. Firstly, a new distance incorporating decision attributes is defined, and then a relation between fuzzy evidence theory and fuzzy β covering with an anti-noise …mechanism is established. Based on fuzzy belief and fuzzy plausibility, two robust feature selection algorithms for hybrid data are proposed in this framework. Experiments on 10 datasets of various types have shown that the proposed algorithms achieved the highest classification accuracy 11 times out of 20 experiments, significantly surpassing the performance of the other 6 state-of-the-art algorithms, achieved dimension reduction of 84.13% on seven UCI datasets and 99.90% on three large-scale gene datasets, and have a noise tolerance that is at least 6% higher than the other 6 state-of-the-art algorithms. Therefore, it can be concluded that the proposed algorithms have excellent anti-noise ability while maintaining good feature selection ability. Show more
Keywords: Feature selection, fuzzy β covering, fuzzy belief, fuzzy plausibility, hybrid information systems
DOI: 10.3233/JIFS-233070
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4219-4242, 2024
Authors: Lu, Tianjun | Zhong, Xian | Zhong, Luo
Article Type: Research Article
Abstract: Convolutional neural networks (CNNs) have received significant attention for change detection (CD) on multimodal remote sensing images, but they struggle to capture global cues due to the locality of convolution operations. In contrast, the transformer can learn global semantic information by dividing the input image into patches, adding position encodings, and utilizing the self-attention mechanism. Motivated by this, we propose mSwinUNet, a novel end-to-end multi-modal model with swin-transformer-based and U-shaped siamese network architectures for supervised CD using Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 Multispectral Imager (MSI) data. mSwinUNet contains multi-modal encoder with difference module, bottleneck, and fused decoder, and …all of them are based on swin transformer. Firstly, tokenized multi-modal bitemporal image patches are fed into multiple Siamese encoder branches to extract multi-level multi-modal difference feature maps in parallel. Subsequently, the last level multi-modal difference maps are fused to generate the smallest scale change map in the bottleneck. Then, the hierarchical decoder incorporates patch expansion and fusion operations to fuse multi-scale difference and change maps, effectively recuperating the details of the change information. Finally, the last patch expansion and a linear projection are applied to output the final change map, which preserves the identical spatial resolution as the input image. Extensive experiments have shown that mSwinUNet outperforms several the state-of-the-art multi-modal CD methods on OSCD dataset and the corresponding Sentinel-1 SAR data. Show more
Keywords: Change detection (CD), multi-modal siamese network, swin transformer, remote sensing image
DOI: 10.3233/JIFS-233868
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4243-4252, 2024
Authors: Chen, Wenda | Shi, Cao
Article Type: Research Article
Abstract: Accurate segmentation of knee cartilage in MR images is crucial for early diagnosis and treatment of knee conditions. Manual segmentation is time-consuming, leading researchers to explore automatic deep learning methods. However, the choice between 2D and 3D networks for organ segmentation remains debated. In this paper, we propose a hybrid 2D and 3D deep neural network approach, named UVNet, which combines the strengths of both techniques to enhance segmentation performance. Within this network structure, the 3D segmentation network serves as the backbone for feature extraction, while the 2D segmentation network functions as an information supplement network. Local and global MIP …images are generated by employing various maximum intensity projection modes of knee MRI volumes as input for the information supplement network. By constructing a local and global MIP feature fusion module, the supplementary information obtained from the 2D segmentation network is fully integrated into the backbone network. We assess the quality of the proposed method using the Osteoarthritis Initiative (OAI) dataset and the 2010 Grand Challenge Knee Image Segmentation (SKI-10) dataset, comparing it to the Baseline Network and other advanced 2D and 3D segmentation methods. The experiments demonstrate that UVNet achieves competitive performance in the aforementioned two cartilage segmentation tasks. Show more
Keywords: Convolutional neural network, maximum intensity projection, segmentation of knee cartilage
DOI: 10.3233/JIFS-234050
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4253-4264, 2024
Authors: Wu, Rong | Yu, Long | Tian, Shengwei | Long, Jun | Zhou, Tiejun | Wang, Bo
Article Type: Research Article
Abstract: Event Detection (ED) has long struggled with the ambiguous definition of event categories, making it challenging to accurately classify events. Previous endeavors aimed to tackle this problem by constructing prototypes for specific event categories. However, they overlooked potential correlations among instances of distinct event categories, resulting in trigger misclassifications across event types. In this research, we introduce KEPA-CRF to train enhanced event prototypes and address the issue of limited samples in few-shot event detection. By integrating external knowledge from the Glove knowledge base into the model training process, we augment synonymous examples, mitigating the problem of insufficient samples in few-shot …event detection. Additionally, through prototype adversarial training, we contrast prototypes of different event categories to augment the representational capabilities of prototype vectors. Experimental results showcase that our approach attains superior performance on the benchmark dataset FewEvent, surpassing comparative models with reduced training time. Show more
Keywords: Few-shot event detection, PA-CRF, Contrast Learning, Glove
DOI: 10.3233/JIFS-234368
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4265-4275, 2024
Authors: Sikkandar, Mohamed Yacin | Sabarunisha Begum, S. | Algamdi, Musaed Saadullah | Alanazi, Ahmed Bakhit | Alotaibi, Mashhor Shlwan N. | Alenazi, Nadr Saleh F. | AlMutairy, Habib Fallaj | Almutairi, Abdulaziz Fallaj | Almutairi, Mohammed Sulaiman
Article Type: Research Article
Abstract: Alzheimer’s disease (AD) is the predominant aetiology of dementia among the elderly population, accounting for about 60–70% of all instances of cognitive decline. Diffusion tensor imaging (DTI) is a contemporary methodology that enables the cartography of alterations in the microstructure of white matter (WM) in neurological diseases. Nevertheless, the effort of analysing substantial amounts of medical pictures poses significant challenges, prompting researchers to shift their focus towards machine learning. This approach encompasses a collection of computer algorithms that possess the ability to autonomously adjust their output to align with the desired goal. This work proposed the use of a combined …approach using Hidden Markov Model (HMM) and MR-DTI, where Diffusion Tensor Imaging (DTI) is employed as a magnetic resonance imaging technique. The purpose of this method is to forecast the occurrence of AD. Furthermore, the statistical analysis demonstrated a significant correlation between microstructural WM changes with both output in the patient groups and cognitive functioning. This finding suggests that these abnormalities in WM might potentially serve as a biomarker for AD. The proposed method is named as Graphcut Hidden MorkovModel (Graph_HMM) is evaluated on ADNI database with statistical analysis and found that it achieves 99.8% of accuracy, 96.4% of sensitivity, 97.4% of specificity and 12.3% of MSE. Show more
Keywords: Hidden Morkov Model, Alzhemier disease, prediction, segmentation, diffusion tensor imaging (DTI), statistical analysis
DOI: 10.3233/JIFS-234613
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4277-4289, 2024
Authors: Chinnamuniyandi, Maharajan | Chandran, Sowmiya | Xu, Changjin
Article Type: Research Article
Abstract: This research investigates the presence of unique solutions and quasi-uniform stability for a class of fractional-order uncertain BAM neural networks utilizing the Banach fixed point concept, the contraction mapping principle, and analysis techniques. In order to guarantee the equilibrium point of fractional-order BAM neural networks with undetermined parameters, some new adequate criteria are devised, and both time delays result in quasi-uniform stability. The acquired results, which are simple to verify in practice, enhance and extend several earlier research works in some ways. Finally, two illustrative examples are provided to show the value of the suggested outcomes.
Keywords: BAM neural networks, quasi-uniform stability, caputo fractional-order differential equation, uncertain parameters, linear matrix inequality
DOI: 10.3233/JIFS-234744
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4291-4313, 2024
Authors: Saranya, K. | Paulraj, M. | Hema, C.R. | Nithya, S.
Article Type: Research Article
Abstract: Exploring and finding Significant features for colour visualization tasks using the EEG signals is crucial in developing a robust Brain-machine Interface (BMI). The visually evoked potential carries multiple pieces of information, and finding its best feature is a tedious task. The main objective of this research is to concentrate on various linear and non-linear features which classifies the visually evoked potential when visualizing various colours for a certain period with reduced computational time and with higher accuracy. The feature extraction techniques utilized for extracting the features of EEG signals while visualizing various colours are Power Spectral Intensity (PSI), Spectral Entropy …(SE), Detrended Fluctuation analysis (DFA), Higuchi Fractal Dimension (HFD), Petrossian Fractal Dimension (PFD), Multifractal Detrended Fluctuation Analysis (MFDFA). The extracted features were classified using the Multiclass classifier using one vs rest technique Support Vector Machine algorithm. The result shows that the MFDFA method with multiclass classifier combination has achieved 97.4 percent of classification accuracy when compared with other features. Show more
Keywords: Electroencephalogram (EEG), Brain Machine Interface (BMI), Detrended Fluctuation analysis (DFA), Higuchi Fractal Dimension (HFD), Petrossian Fractal Dimension (PFD), Multifractal Detrended Fluctuation Analysis (MFDFA)
DOI: 10.3233/JIFS-235469
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4315-4324, 2024
Authors: Ma, Xiuqin | Sun, Huanling | Qin, Hongwu | Wang, Yibo | Zheng, Yan
Article Type: Research Article
Abstract: When handling complex uncertainty information for multi-attribute decision-making (MADM) problems, interval-valued Fermatean fuzzy sets (IVFFSs) are a novel and powerful tool with a wide range of prospective applications. However, existing MADM methods based on IVFFS ignore associations between attributes and are vulnerable to extreme values. Thus, this research proposes a novel MADM method based on IVFFSs. First, taking into consideration attribute relationships, we propose interval-valued Fermatean fuzzy Bonferroni mean (IVFFBM) operators and interval-valued Fermatean fuzzy weighted Bonferroni mean (IVFFWBM) operators based on IVFFSs. Further, interval-valued Fermatean fuzzy power Bonferroni mean (IVFFPBM) operator and interval-valued Fermatean fuzzy weighted power Bonferroni mean …(IVFFWPBM) operator are suggested considering the impact of extreme values. Secondly, Attribute weights are a key component of MADM. A novel method for determining attribute weights based on fuzzy entropy is developed. Finally, a novel MADM approach is proposed based on the proposed operator and weight determination method. Experimental results on one real-life case demonstrate the superiority and effectiveness of our method. Show more
Keywords: Interval-valued fermatean fuzzy set, bonferroni mean operator, multi-attribute decision making
DOI: 10.3233/JIFS-235495
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4325-4345, 2024
Authors: Gao, Wenlong | Zhi, Minqian | Ke, Yongsong | Wang, Xiaolong | Zhuo, Yun | Liu, Anping | Yang, Yi
Article Type: Research Article
Abstract: Structure learning is the core of graph model Bayesian Network learning, and the current mainstream single search algorithm has problems such as poor learning effect, fuzzy initial network, and easy falling into local optimum. In this paper, we propose a heuristic learning algorithm HC-PSO combining the HC (Hill Climbing) algorithm and PSO (Particle Swarm Optimization) algorithm, which firstly uses HC algorithm to search for locally optimal network structures, takes these networks as the initial networks, then introduces mutation operator and crossover operator, and uses PSO algorithm for global search. Meanwhile, we use the DE (Differential Evolution) strategy to select the …mutation operator and crossover operator. Finally, experiments are conducted in four different datasets to calculate BIC (Bayesian Information Criterion) and HD (Hamming Distance), and comparative analysis is made with other algorithms, the structure shows that the HC-PSO algorithm is superior in feasibility and accuracy. Show more
Keywords: Keywords. Bayesian network, structure learning, HC algorithm, PSO algorithm, DE algorithm
DOI: 10.3233/JIFS-236454
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4347-4359, 2024
Authors: Guo, Hairu | Wang, Jin’ge | Liu, Yongli | Zhang, Yudong
Article Type: Research Article
Abstract: The Aquila optimization (AO) algorithm has the drawbacks of local optimization and poor optimization accuracy when confronted with complex optimization problems. To remedy these drawbacks, this paper proposes an Enhanced aquila optimization (EAO) algorithm. To avoid elite individual from entering the local optima, the elite opposition-based learning strategy is added. To enhance the ability of balancing global exploration and local exploitation, a dynamic boundary strategy is introduced. To elevate the algorithm’s convergence rapidity and precision, an elite retention mechanism is introduced. The effectiveness of EAO is evaluated using CEC2005 benchmark functions and four benchmark images. The experimental results confirm EAO’s …viability and efficacy. The statistical results of Freidman test and the Wilcoxon rank sum test are confirmed EAO’s robustness. The proposed EAO algorithm outperforms previous algorithms and can useful for threshold optimization and pressure vessel design. Show more
Keywords: Aquila optimization algorithm, optimization function, kapur entropy, threshold optimization, pressure vessel design
DOI: 10.3233/JIFS-236804
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4361-4380, 2024
Authors: Nippatla, V. Ramanaiah | Mandava, Srihari
Article Type: Research Article
Abstract: The main contribution of this review work is to show how various control techniques are used to manage the speed of Permanent Magnet Synchronous Motor (PMSM). The PMSM’s are mostly used in electric vehicles, electric traction and high performance industrial drive applications. In this article conventional sensorless techniques are compared with machine learning techniques such as fuzzy logic, artificial neural network and neuro-fuzzy controllers to control the speed of PMSM drive based on vector control approach. The benefits of machine learning techniques used in sensorless PMSM drive are easy to design, less execution time and fast access speed control. The …various controlling techniques used in controller along with its complexity, advantages and drawbacks are discussed in this article. The above mentioned controlling techniques are implemented and simulated by using MATLAB R2019b/Simulink software based on sensorless Model Reference Adaptive System (MRAS) with the help of Field Oriented Control (FOC) strategy of PMSM drive. By comparing the all sensorless controlling techniques in simulation study, it is identified that the combination of neuro-fuzzy controller gives the best speed control performance than other controllers. Show more
Keywords: Field oriented control, fuzzy logic control, neuro-fuzzy control, PMSM drive, sensorless control
DOI: 10.3233/JIFS-222164
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4381-4395, 2024
Authors: Chen, Siting | You, Cuiling | Wu, Nan | Huang, Yan
Article Type: Research Article
Abstract: Cross-efficiency evaluation is an extension of data envelopment analysis (DEA), which can effectively distinguish between decision-making units (DMUs) through self- and peer-evaluation. The cross-efficiency of each DMU in a set of DMUs is measured in terms of intervals when the input–output data are represented by the number of intervals. Based on the interval cross-efficiency matrix, the interval entropy is defined in terms of the likelihood. Then, considering the influence of peer evaluation, the interval conditional cross-efficiency entropy is proposed and an aggregation model of the interval conditional cross-efficiency entropy is presented to create a ranking index for DMUs. Finally, a …simple example is provided to illustrate the effectiveness of the proposed method, which is applied to the evaluation of forest carbon sink efficiency in China. The results indicate that the final cross-efficiencies of all 30 provinces range from 0 to 0.6. Among these provinces, those with a relatively high efficiency include Guangdong, Guizhou, Hainan, Shandong, and Qinghai. Show more
Keywords: Data envelopment analysis, interval data, cross-efficiency, entropy, likelihood
DOI: 10.3233/JIFS-223071
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4397-4415, 2024
Authors: Huang, Zhengwei | Liu, Huayuan | Duan, Chen | Min, Jintao
Article Type: Research Article
Abstract: In the E-commerce environment, conversations between customers and businesses contain lots of useful information about customer sentiment. By mining that information, customer sentiment can be validly identified, which is helpful in accurately identifying customer needs and improving customer satisfaction. For conversational sentiment analysis, most existing approaches take contextual information into account. On this basis, we focus on the degree of association between utterances, which can more effectively capture overall and useful sentiment information in conversation. For this purpose, we propose a hybrid model to recognize customer sentiment in conversation. The model obtains utterance vectors with sentiment information through Sentiment Knowledge …Enhanced Pre-training (SKEP), then uses the bidirectional long short-term memory network (BiLSTM) to generate contextual semantic information, and further obtains customer sentiment information by applying the self-attention mechanism to focus on the degree of association between utterances. The experimental results on the JD Dialog dataset show that our model can more accurately recognize customer sentiment than other baseline models in customer service conversation. Show more
Keywords: Customer sentiment recognition, bidirectional long short-term memory network, self-attention mechanism, sentiment knowledge enhanced pre-training
DOI: 10.3233/JIFS-224183
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4417-4428, 2024
Authors: Ghiduk, Ahmed S. | Hashim, Marwa
Article Type: Research Article
Abstract: Mutation testing can evaluate the quality of the test inputs, generate test data, and simulate any test coverage criterion. Genetic algorithms and harmony search have been applied to reduce the cost of generating test inputs. Although hybridizing search algorithms enhances the efficiency of searching the solution domain, there is a shortage of applying the hybrid search techniques in mutation testing. This paper merges the genetic and harmony search algorithms to effectively generate test data to kill higher-order mutants. In addition, the performance of the proposed technique will be evaluated and compared with a stand-alone genetic algorithm and a stand-alone harmony …search algorithm through an empirical study using a set of benchmark programs. The experimental study shows that the proposed technique outperformed the compared algorithms, reaching a higher killing ratio, where the proposed approach kills 92.8% of higher-order mutants for all tested programs. In comparison, GA kills 88.7%, and HA kills 86.6%. Besides, the proposed algorithm overcame the compared algorithm in reaching a targeted killing ratio faster than the compared algorithms. HGA reduced the execution time for each program with a reduction ratio ranging from 58.9% to 89.8%. Show more
Keywords: Genetic algorithm, harmony search algorithm, higher-order mutation testing, test-data generation
DOI: 10.3233/JIFS-230226
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4429-4452, 2024
Authors: Liu, Xia | Chen, Benwei
Article Type: Research Article
Abstract: This paper defines an improved similarity degree based on inclusion degree as well as advanced information system based on interval coverage and credibility, and thus an attribute reduction framework embodying 4×2 = 8 reduct algorithms is systematically constructed for application and optimization in interval-valued decision systems. Firstly, a harmonic similarity degree is constructed by introducing interval inclusion degree and harmonic average mechanism, which has better semantic interpretation and robustness. Secondly, interval credibility degree and coverage degree are defined for information fusion, and they are combined to propose a δ -fusion condition entropy. The improved condition entropy achieves the information reinforcement and integrity …by dual quantization fusion of credibility and coverage, and it obtains measure development from granularity monotonicity to non-monotonicity. In addition, information and joint entropies are also constructed to obtain system equations. Furthermore, 8 reduct algorithms are designed by using attribute significance for heuristic searches. Finally, data experiments show that our five novel reduct algorithms are superior to the three contrast algorithms on classification performance, which also further verify the effectiveness of proposed similarity degree, information measures and attribute reductions. Show more
Keywords: Attribute reductions, interval-valued decision systems, information measurements, δ-fusion condition entropy, harmonic similarity degree, interval coverage degree and credibility degree
DOI: 10.3233/JIFS-231950
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4453-4466, 2024
Authors: Zhang, Xiang | Huang, Jianhua | Fang, Liting | Li, Qian
Article Type: Research Article
Abstract: Selecting suppliers for prefabricated components (PCs) involves a complex decision-making process, frequently relying on ambiguous information and subjective judgment. However, most existing methods use precise values to portray indicator information and overlook the uncertainty of weights and the subjective preferences of decision-makers (DMs). In order to address these limits, this paper proposes a novel approach to select suppliers of PCs. Initially, an evaluation index system for suppliers is established through literature analysis and a questionnaire survey. The system comprises six layers: product quality, price, service level, comprehensive ability, supply ability, and environmental sustainability. The group decision matrix is then constructed …using the set-valued statistical method and the prospect theory. The index weights are determined by a combination weighting method. Next, the cobweb model is introduced to analyze the disparity between the alternative and ideal solutions, describing their similarities in terms of area and shape. Lastly, cobweb similarity is employed instead of comprehensive distance, combined with the minimum sum of squares criterion, to improve the closeness algorithm and contrast the alternatives. The results demonstrate that this method facilitates a comprehensive evaluation of the benefits and drawbacks of various alternatives from diverse perspectives. Furthermore, it allows flexible adjustments based on the risk preferences of DMs, ensuring accurate and reliable decision results. Show more
Keywords: Select suppliers, risk preference, prospect theory, cobweb model, cobweb similarity
DOI: 10.3233/JIFS-232027
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4467-4479, 2024
Authors: Xu, Yi | Zhou, Meng
Article Type: Research Article
Abstract: As an important extension of classical rough sets, local rough set model can effectively process data with noise. How to effectively calculate three approximation regions, namely positive region, negative region and boundary region, is a crucial issue of local rough sets. Existing calculation methods for approximation regions are based on conditional probability, the time complexity is O (|X ||U ||C |). In order to improve the computational efficiency of three approximation regions of local rough sets, we propose a double-local conditional probability based fast calculation method. First, to improve the computational efficiency of equivalence class, we define the double-local equivalence …class. Second, based on the double-local equivalence class, we define the double-local conditional probability. Finally, given the probability thresholds and a local equivalence class, the monotonicity of double-local conditional probability is proved, on this basis, a double-local conditional probability based fast calculation method for approximation regions of local rough sets is proposed, and the time complexity is O (MAX (|X |2 |C |, |X ||X C ||C |)). Experimental results based on 9 datasets from UCI demonstrate the effectiveness of the proposed method. Show more
Keywords: Local rough sets, approximation regions, double-local equivalence class, double-local conditional probability
DOI: 10.3233/JIFS-232767
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4481-4493, 2024
Authors: Shi, Dingpu | Zhou, Jincheng | Wu, Feng | Wang, Dan | Yang, Duo | Pan, Qingna
Article Type: Research Article
Abstract: How to better grasp students’ learning preferences in the environment of rapid development of engineering and science and technology so as to guide them to high-quality learning is one of the important research topics in the field of educational technology research today. In order to achieve this goal, this paper utilizes the LDA (Latent Dirichlet Allocation) model for text mining of the survey results on the basis of a survey on students’ self-perception evaluation. The results show that the LDA model is capable of extracting terms from text, fuzzy identifying groups of students at different levels and presenting potential logical …relationships between the groups, and further analyzing the learning preferences of students at different levels for IT courses. Based on the student’s learning needs, this paper proposes recommendations for developing students’ learning effectiveness. The LDA method proposed in this paper is a feasible and effective method for assessing students’ learning dynamics as it generates cognitive content about students’ learning and allows for the timely discovery of students’ learning expectations and cutting-edge dynamics. Show more
Keywords: Latent Dirichlet Allocation model, educational data mining, self-perceptions, network modeling
DOI: 10.3233/JIFS-232971
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4495-4509, 2024
Authors: Wang, Hui | Liu, Ensheng | Wei, Hokai
Article Type: Research Article
Abstract: A machine for tunnel boring machine (TBM ) is recognized as productive equipment for tunnel construction. A dependable and precise tunnel boring machine’s performance (such as penetration rate (ROP )) prediction could reduce the cost and help choose the suitable construction method. Hence, this research develops new integrated artificial intelligence methods, i.e., biogeography-based multilayer perceptron neural network (BMLP ) and biogeography-based support vector regression (BSVR ), to forecast TBM PR . Using the biogeography-based optimization (BBO ) algorithm aims to improve the developed model’s performance by determining the optimized neuron number of hidden layers for MLP models and the …ideal values of the essential variables of SVR method. The results show that advanced methods can productively make a nonlinear relation among the ROP and its forecasters to obtain a satisfying forecast. Amongst the BMLP models with several hidden substrates, BM 5L with five hidden substrates could attain the total ranking score (TRS ) greatest rate, with root mean squared error (RMSE ) and coefficient of determination (R 2 ) equal to 0.017 and 0.9969. Simultaneously, the BSVR was the supreme model because of the fewer RMSE (0.00497 m /hr ) and a larger R 2 (0.999) compared with BMLP models. Overall, the acquired TRS s show that the BSVR outperforms the BMLP in terms of performance. As a consequence, the BSVR model may have been chosen as the suggested model if it had been able to accurately forecast the observed value even better than BM 5L . Show more
Keywords: Tunnel boring machine, penetration rate, biogeography-based multilayer perceptron neural network (BMLP), biogeography-based support vector regression (BSVR)
DOI: 10.3233/JIFS-232989
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4511-4528, 2024
Authors: Lin, Xiangyi | Luo, Hongyun | Lian, Yinghuan
Article Type: Research Article
Abstract: This research mainly evaluates the synergistic effect of “dual carbon” and high-quality economic development from four aspects: carbon reduction, pollution reduction, green expansion, and economic growth. Firstly, an indicator system of synergistic effect evaluation is constructed, and a FOPA-Cloud evaluation model is proposed based on the FOPA (Fuzzy Ordinal Priority Approach) and Cloud model. Based on the evaluation of experts’ language variables, it is calculated that a province’s “dual carbon” and high-quality economic development generally belong to a high-level synergistic effect. However, further improvement is still needed in reducing carbon, pollution reduction, and green expansion. The tedious work of pairwise …comparison can be overcome in the FOPA-Cloud model. Optimizing and solving to determine the weight of each indicator can not only determine the overall level but also analyze specific reasons, which can provide a basis for improving the synergistic effect of “dual carbon” and high-quality economic development. Show more
Keywords: Carbon peaking and carbon neutrality, high quality development, fuzzy ordinal priority approach, cloud model
DOI: 10.3233/JIFS-233119
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4529-4541, 2024
Authors: Yu, Ming | Liu, Jiali | Liu, Yi | Yan, Gang
Article Type: Research Article
Abstract: Most existing RGB-D salient object detection (SOD) methods extract features of both modalities in parallel or adopt depth features as supplementary information for unidirectional interaction from depth modality to RGB modality in the encoder stage. These methods ignore the influence of low-quality depth maps, and there is still room for improvement in effectively fusing RGB features and depth features. To address the above problems, this paper proposes a Feature Interaction Network (FINet), which performs bi-directional interaction through feature interaction module (FIM) in the encoder stage. The feature interaction module is divided into two parts: depth enhancement module (DEM) filters the …noise in the depth features through the attention mechanism; and cross enhancement module (CEM) effectively interacts RGB features and depth features. In addition, this paper proposes a two-stage cross-modal fusion strategy: high-level fusion adopts the semantic information of high level for coarse localization of salient regions, and low-level fusion makes full use of the detailed information of low level through boundary fusion, and then we progressively refine high-level and low-level cross-modal features to obtain the final saliency prediction map. Extensive experiments show that the proposed model achieves better performance than eight state-of-the-art models on five standard datasets. Show more
Keywords: RGB-D salient object detection, feature interaction, depth enhancement module, cross enhancement module, cross-modal fusion
DOI: 10.3233/JIFS-233225
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4543-4556, 2024
Authors: Jiang, Guangtian | Song, Anbin
Article Type: Research Article
Abstract: The dual probabilistic linguistic term sets (DPLTSs) are more effective than PLTSs in solving the problem of multi-attribute group decision-making (MAGDM). In this paper, an improved TOPSIS method is developed combining the TOPSIS method and projection measure of DPLTS to supplement the existing research. Firstly, considering the mathematical characteristics of DPLTS, this paper defines the concepts of the module, cosine function, and projection of DPLTS, and then proves the mathematical properties of the cosine function. Secondly, considering the uncertainty of decision-making problems, the weight-solving models are established respectively under the condition that the weight information is completely unknown and partially …known. Furthermore, a novel DPLPrj-TOPSIS approach is established based on the projection measure proposed. It involves integrating experts’ DPLTS evaluations, normalizing different DPLTSs, calculating alternatives’ relative closeness and score, etc. Secondly, the proposed method’s feasibility is demonstrated through a case study that entails selecting network promotion plans for food manufacturers. Finally, the proposed method’s effectiveness and validity are verified by comparing and analyzing it with the traditional TOPSIS method based on a distance measure and other existing decision methods. Show more
Keywords: Dual probabilistic linguistic term sets, multi-attribute group decision-making, technique for order preference by similarity to an ideal solution (TOPSIS), closeness degree
DOI: 10.3233/JIFS-233234
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4557-4572, 2024
Authors: Camgoz Akdag, Hatice | Menekse, Akin | Sahin, Fatih
Article Type: Research Article
Abstract: Cervical cancer is entirely preventable if diagnosed at an early stage; however, the current rate of cervical cancer screening participation is not very adequate, and early detection approaches are still open and demanding. Evaluating the risk levels of potential patients in a practical and economic way is crucial to direct risky candidates to screening and establishing potential treatments to conquer the disease. In this study, a machine learning-integrated fuzzy multi-criteria decision-making (MCDM) methodology is proposed to assess the cervical cancer risk levels of patients. In this context, based on behavioral criteria obtained from the publicly accessible cervical cancer behavior risk …data set from the UCI repository, the risk levels of patients are evaluated. The proposed methodology is established in three stages: In the first stage, using a machine learning technique, i.e., feature selection, the most effective criteria for predicting cervical cancer risk are selected. In the second stage, the criteria for importance through intercriteria correlation (CRITIC) method is used to assign objective importance levels to the criteria. In the third stage, the cervical cancer risk levels of candidate patients are prioritized using the technique for order preference by similarity to the ideal solution (TOPSIS) and, alternatively, the evaluation based on distance from the average solution (EDAS) techniques. The proposed methodology is developed in an interval-valued Pythagorean fuzzy atmosphere for quantifying the uncertainty in the nature of the problem. This study demonstrates that the feature selection algorithm can be efficiently utilized to determine the fundamental criteria of an MCDM problem and to aid in the early identification of cervical cancer. Show more
Keywords: Cervical cancer, machine learning, feature selection, pythagorean fuzzy, MCDM
DOI: 10.3233/JIFS-234647
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4573-4592, 2024
Authors: Shankari, R. | Leena Jasmine, J.S. | Mary Joans, S.
Article Type: Research Article
Abstract: Breast cancer poses a significant health risk for women, demanding early detection to mitigate its mortality impact. Leveraging the power of Deep Learning (DL) in medical imaging, this paper introduces a hybrid model that integrates YOLOv7 and Half UNet for feature extraction. YOLOv7 identifies and localizes potential cancerous regions, while Half UNet focuses on extracting pertinent features with its encoder-decoder structure. The fusion of these discriminative features, coupled with feature selection via Coati Optimization, ensures a comprehensive and optimized dataset. The selected features then feed into the CatBoost classification algorithm, refining parameters iteratively for precise predictions and minimizing the loss …function. Evaluation metrics, including precision, recall, specificity, and accuracy, demonstrate the model’s superior performance. Notably, the proposed model surpasses existing methods in early-stage breast cancer detection. Beyond numerical metrics, its significance lies in the potential to positively impact patient outcomes and increase survival rates. By amalgamating cutting-edge DL techniques, the model excels in identifying intricate patterns crucial for early cancer detection. The efficient fusion of YOLOv7 and Half UNet, coupled with feature optimization through Coati Optimization, sets this model apart. This research contributes to the evolving landscape of medical imaging and DL applications, emphasizing the potential for enhanced breast cancer diagnosis and improved patient prognoses. Show more
Keywords: Breast cancer prediction, YoloV7 model, HalfUNet feature extraction, feature Selection, cat Boost model, performance metrics
DOI: 10.3233/JIFS-235116
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4593-4607, 2024
Authors: Chen, Nongtian | Chen, Kai | Sun, Youchao
Article Type: Research Article
Abstract: The reliability level of general aviation fleet system directly affects the economic benefits and safe operation of general aviation fleet. In order to effectively evaluate the reliability level of general aviation fleet, using the entropy weight variable fuzzy recognition and 1D-CNN depth learning reliability evaluation method. Firstly, taking the Cessna 172 general aviation fleet as the research object, refers to the maintenance statistical analysis of general aviation fleet reliability data, and classifies the fleet reliability evaluation indexes according to the ATA100 chapter standard. Combined with index importance analysis and Delphi expert investigation, 14 key items are extracted as reliability evaluation …indexes of general aviation fleet. Secondly, using entropy weight method to obtain indexes weight objectively, and the evaluation level membership function is constructed based on variable fuzzy recognition method. Finally, a reliability evaluation model based on 1D-CNN deep learning method was established. Through training and testing the reliability data evaluation model of general aviation fleet, and comparing with the results of evaluation methods such as support vector machines. The results show that the recognition rate of the 1D-CNN deep learning method based on entropy weight variable fuzzy recognition can reach 91.95%, verifying the objective effectiveness of the evaluation method. Show more
Keywords: General aviation fleet, reliability evaluation, variable fuzzy recognition, 1D-CNN deep learning
DOI: 10.3233/JIFS-235280
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4609-4619, 2024
Authors: Ravindra Krishna Chandar, V. | Baskaran, P. | Mohanraj, G. | Karthikeyan, D.
Article Type: Research Article
Abstract: Unmanned robotics and autonomous systems (URAS) are integral components of contemporary Cyber-Physical Systems (CPS), allowing vast applications across many domains. However, due to uncertainties and ambiguous data in real-world environments, ensuring robust and efficient decision-making in URAS is difficult. By capturing and reasoning with linguistic data, fuzzy logic has emerged as a potent tool for addressing such uncertainties. Deep Iterative Fuzzy Pooling (DIFP) is a novel method proposed in this paper for improving decision-making in URAS within CPS. The DIFP integrates the capabilities of deep learning and fuzzy logic to effectively pool and aggregate information from multiple sources, thereby facilitating …more precise and trustworthy decision-making. This research presents the architecture and operational principles of DIFP and demonstrates its efficacy in various URAS scenarios through extensive simulations and experiments. The proposed method demonstrated a high-performance level, with an accuracy of 98.86%, precision of 95.30%, recall of 97.32%, F score of 96.26%, and a notably low false positive rate of 4.17%. The results show that DIFP substantially improves decision-making performance relative to conventional methods, making it a promising technique for enhancing the autonomy and dependability of URAS in CPS. Show more
Keywords: Unmanned robotics, autonomous systems, cyberphysical systems, decision-making, fuzzy logic, deep learning, iterative fuzzy pooling, information aggregation, uncertainty handling, reliability, autonomy
DOI: 10.3233/JIFS-235721
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4621-4639, 2024
Authors: Gao, Miaomiao
Article Type: Research Article
Abstract: To improve the effect of intelligent teaching in music classrooms, this paper combines the advanced music waveform iterative reconstruction algorithm to analyze the integration and reconstruction of the music curriculum. Aiming at the problem that the projection matrix occupies a large space and takes a long time to calculate in iterative reconstruction, a fast and real-time incremental method for generating a music wave matrix is proposed. The improved method avoids the judgment and comparison calculations performed by the incremental method when calculating the length and number of each voxel that the ray passes through. The research results show that the …music curriculum integration and reconstruction model based on the advanced music waveform iterative reconstruction algorithm can effectively improve the teaching effect of modern music classrooms. Show more
Keywords: Advanced iteration, reconstruction algorithm, music curriculum, integration, reconstruction
DOI: 10.3233/JIFS-236169
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4641-4655, 2024
Authors: Venkata Krishna, G.P.C. | Vivekananda Reddy, D.
Article Type: Research Article
Abstract: Ensuring data security in cloud computing is crucial due to the growing reliance on cloud-based services. Hybrid cryptography and image steganography have emerged as robust techniques to enhance data confidentiality in the cloud. In this research paper, we propose a novel algorithm, “Machine Learning-Enhanced Hybrid Cryptography and Image Steganography,” integrating these methods to provide comprehensive data protection. The algorithm employs key generation, encryption, steganography, cloud storage, data retrieval, and machine learning-based attack detection to defend against advanced cyber threats. Our experimentation demonstrates the algorithm’s effectiveness in detecting DoS attacks, data breaches, and data leakage attempts using SVM, Neural Network, Isolation …Forest, and Random Forest models. The proposed approach offers broad applicability, fortifying data security and fostering further advancements in cloud security research. Show more
Keywords: Data security, hybrid cryptography, security
DOI: 10.3233/JIFS-236229
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4657-4667, 2024
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