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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
Authors: Xu, Chuannuo | Cheng, Xuezhen | Wang, Yi
Article Type: Research Article
Abstract: Rolling bearings are a key component of rotating machinery and their health directly affects the safe operation of mechanical equipment. Therefore, fault diagnose for rolling bearings is very important. The fault diagnosis process of rolling bearings includes three stages: signal decomposition, feature extraction, and pattern recognition. Variational mode decomposition (VMD) can suppress end effects, but improper parameter settings will cause information losses or excessive decomposition. In this work, an improved whale optimization algorithm (IWOA) is applied to parameter settings of VMD. Correspondingly, an IWOA-VMD signal decomposition method is proposed. The decomposed signal is combined with a Laplace score method and …classifier to remove the redundancy and noise in the feature set and obtain a low-dimensional sensitive feature subset. Then, aiming at the problem of the parameter settings of a least squares support vector machine (LSSVM) affecting the recognition performance and accuracy, a salp swarm algorithm (SSA) is used to globally optimize the penalty parameter and kernel width in the LSSVM to establish an SSA-LSSVM fault recognition model. This model is applied to the fault diagnosis of rolling bearings. In particular, rolling bearing fault samples at Case Western Reserve University are used to verify the method. The results indicate that the proposed method is effective and improves the speed and accuracy of fault diagnosis. Show more
Keywords: Least squares support vector machine, rolling fault, salp swarm algorithm, variational mode decomposition, whale algorithm
DOI: 10.3233/JIFS-236532
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4669-4680, 2024
Authors: Jothi, J. Sathiya | Chinnadurai, M.
Article Type: Research Article
Abstract: The most common type of disease that is normal among women is lung cancer. It is one of the main reasons among women, despite great efforts to prevent it through trackers. An automatic disease detection system helps doctors identify and provide accurate results, thus minimizing the mortality rate. Computer Aided Diagnosis (CAD) has minimal human intervention and produces more accurate results than humans. It will be a difficult and lengthy task that depends on the experience of the pathologists. Deep learning methods have been shown to give better results when correlated with ML and extract the best highlights from images. …The main objective of this article is to propose a deep learning technique in combination with a convolution neural network (CNN) with a chimpanzee optimization algorithm to diagnose lung cancer. Here, CNN is used for feature extraction and used for extracted feature detection. Experimental results show that the proposed system achieves 100% accuracy, 99% sensitivity, 99% recall, and 98% F1 score compared to other traditional models. As the system achieved correct results, it can help doctors easily investigate lung cancer. Show more
Keywords: Lung cancer, convolution neural network, computer aided diagnosis, chimp optimization
DOI: 10.3233/JIFS-237339
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4681-4696, 2024
Authors: Gnanasundari, P. | Sheela Sobana Rani, K.
Article Type: Research Article
Abstract: Wireless sensor networks (WSNs) are a new technology that helps with a variety of practical uses, involving healthcare and monitoring the environment. In recent years, security has been considered as important topic in WSN since it is vulnerable to several security threats. Recent works uses cryptographic techniques to ensure security in WSN. In existing works, the security methodologies require high resources but still assure low level security. To resolve this issue, this paper proposes a node validation method which is lightweight as well as assures high level security. The main idea behind this work is to integrate Blockchain technology with …WSN environment. We presented a novel Blockchain-assisted Node Validation (BlockNode) methodology for ensuring high level security. To maintain energy efficiency, the network is segregated into multiple clusters by Valid Cluster Formation (VCF) approach. In each cluster, optimum CH is selected by using type-II fuzzy algorithm. The VCF approach only allows the valid nodes which are authorized by Blockchain validation. Then, the data transmission is secured by Jacobian Curve Encryption (JCE) algorithm. For optimal route selection, Energy-aware Reinforcement Learning (ERL) algorithm is proposed. Overall, the proposed work high level security with minimum resource consumption. The experimental results obtained from NS-3.25 simulation tool confirms that the proposed work achieves better performance in security level, encryption & decryption time, delay, energy consumption, delivery ratio and throughput. Show more
Keywords: Node Validation, energy efficiency, cybersecurity, blockchain, WSN
DOI: 10.3233/JIFS-230020
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4697-4711, 2024
Authors: Chen, Zhe | Ye, Lin | Zhang, Hongli | Zhang, Yunting
Article Type: Research Article
Abstract: The purpose of the Chinese similar case matching task is to compare the similarity of two case texts with a given anchor text and find out which text is more similar to the anchor text. In the area of law, it plays an important role and has been of interest to many researchers. Previous approaches have compared legal texts only at the text semantic level, without incorporating article information of law. In addition, the position correlation of words in case texts is often important, but it has not been considered in previous approaches. This paper proposes a method which extracts …features from the semantic similarity level and from the level of related articles of law, respectively, to enable similarity comparisons of legal case texts. When similarity comparisons are made at the semantic similarity level, a novel capsule network method is proposed based on siamese structure that introduces the position correlation and the routing mechanism within the capsule network is improved so that deep text features between case pairs can be learned. When similarity comparisons are made at the level of related articles of law, related articles of law are selected and coded and interacted with the case text features to generate legal features. Experiment is conducted with a real-world legal text dataset, and the proposed model outperformed all baseline models, demonstrating effectiveness of the proposed model. Further, to confirm the generality of the improved capsule network proposed in the paper on long text datasets, this paper also carried out experiments on two long text datasets, demonstrating effectiveness of the improved capsule network proposed in the model. Show more
Keywords: Chinese similar case matching, integrating articles of law, capsule network, dynamic routing with position correlation
DOI: 10.3233/JIFS-232185
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4713-4731, 2024
Authors: Ramasamy, Karthikeyan | Sundaramurthy, Arivoli | Vaithiyalingam, Chitra
Article Type: Research Article
Abstract: The primary goal is to enhance the PSN by maintaining stable and consistent MGS operation and reestablishing stable operating conditions after generational interruptions. The artificial neural network is created using a bio-inspired optimization algorithm, such as particle swarm optimization, second generation particle swarm optimization, and new model particle swarm optimization., which directs the evolutionary learning process to determine the most optimal solution. For the best result, the ANN and bio-inspired algorithm (BIANN) are coupled. The suggested BIANN-based controller is made comprised of an internal current and an external power loop. The proper PI gain parameter is tuned using BIANN, allowing …the MGS to be stable. Three PSOs are used to investigate the suggested method, and the Matlab Simulink platform is used to create the fitness functions. The results are examined and contrasted. The new model’s particle swarm optimization provides MGS functioning and stability that is largely accurate and reliable. Show more
Keywords: Engineering optimization, Micro-grid, BIANN, stability assessment, mathematical model
DOI: 10.3233/JIFS-233112
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4733-4744, 2024
Authors: Chen, Minghao | Wang, Shuai | Zhang, Jiazhong
Article Type: Research Article
Abstract: The influence maximization problem is one of the hot research topics in the field of complex networks in recent years. The so-called influence maximization problem is how to select the seed set that propagates the largest amount of information on a given network. In practical applications, networks are often exposed to complicated environments, and both link-specific and node-specific attacks can have a significant impact on the network’s propagation performance. Several pilot studies have revealed the crux of the robust influence maximization problem, but the current work available is not comprehensive. On the one hand, existing studies only consider the case …that the network structure is stable or under link-specific attacks, and few researches have concentrated on the case when the network structure is under node-specific attacks. On the other hand, the current algorithm fails to combine the information of the search process well to solve the robust influence maximization problem. Aiming at these deficiencies, in this paper, a metric for evaluating the robust influence performance of seeds under node-specific attacks is developed. Guided by this, a genetic algorithm (GA) maintaining the principle of diversity concern (DC) to solve the Robust Influence Maximization (RIM) problem is designed, called DC-GA-RIM. DC-GA-RIM contains several problem-orientated operators and fully considers diverse information in the optimization process, which significantly improves the search ability of the algorithm. The effectiveness of DC-GA-RIM in solving the RIM problem is demonstrated on a variety of networks. The superiority of this algorithm over other approaches is shown. Show more
Keywords: Complex networks, influence maximization problem, robustness, optimization, genetic algorithm
DOI: 10.3233/JIFS-233222
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4745-4759, 2024
Authors: Wang, Xiaoli | Wang, Chongguo | Shi, Gang
Article Type: Research Article
Abstract: As a complex uncertain differential equation, how to solve the multi-dimensional uncertain differential equation is a complicated and difficult problem. This paper will be devoted to the α-path of some special multi-dimensional uncertain differential equations, namely, multi-factor uncertain differential equations, nested uncertain differential equations and multi-factor nested uncertain differential equations. The α-path method is used to study the numerical solution problems of the above three special multi-dimensional uncertain differential equations. At the same time, the inverse uncertainty distributions and expected values of these three special multi-dimensional uncertain differential equations are also obtained. At last, the numerical algorithm examples are given …to verify it. Show more
Keywords: Uncertainty theory, multi-dimensional uncertain differential equation, α-path, numerical algorithm
DOI: 10.3233/JIFS-234432
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4761-4776, 2024
Authors: Wu, Huimin
Article Type: Research Article
Abstract: Text summarization (TS) plays a crucial role in natural language processing (NLP) by automatically condensing and capturing key information from text documents. Its significance extends to diverse fields, including engineering, healthcare, and others, where it offers substantial time and resource savings. However, manual summarization is a laborious task, prompting the need for automated text summarization systems. In this paper, we propose a novel strategy for extractive summarization that leverages a generative adversarial network (GAN)-based method and Bidirectional Encoder Representations from Transformers (BERT) word embedding. BERT, a transformer-based architecture, processes sentence bidirectionally, considering both preceding and following words. This contextual understanding …empowers BERT to generate word representations that carry a deeper meaning and accurately reflect their usage within specific contexts. Our method adopts a generator and discriminator within the GAN framework. The generator assesses the likelihood of each sentence in the summary while the discriminator evaluates the generated summary. To extract meaningful features in parallel, we introduce three dilated convolution layers in the generator and discriminator. Dilated convolution allows for capturing a larger context and incorporating long-range dependencies. By introducing gaps between filter weights, dilated convolution expands the receptive field, enabling the model to consider a broader context of words. To encourage the generator to explore diverse sentence combinations that lead to high-quality summaries, we introduce various noises to each document within our proposed GAN. This approach allows the generator to learn from a range of sentence permutations and select the most suitable ones. We evaluate the performance of our proposed model using the CNN/Daily Mail dataset. The results, measured using the ROUGE metric, demonstrate the superiority of our approach compared to other tested methods. This confirms the effectiveness of our GAN-based strategy, which integrates dilated convolution layers, BERT word embedding, and a generator-discriminator framework in achieving enhanced extractive summarization performance. Show more
Keywords: Automated extractive summarization, generative adversarial network, bidirectional encoder representations from transformers, dilated convolution layer
DOI: 10.3233/JIFS-234709
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4777-4790, 2024
Authors: Adnan, R. Syed Aamir | Kumaravel, R.
Article Type: Research Article
Abstract: Weather Forecasting is very essential as it is helpful in saving lives and materials by predicting disasters such as cyclonic storms, tsunamis, extreme rainfall, etc. Within the defined range of rainfall rate approximation, this study investigates the application of Fuzzy Logic (FL) to forecast rainfall using the Interval Type –2 Fuzzy Inference System (IT2FIS). Environmental parameters which influence rainfall have been applied in this analysis and every implementation is carried out using MATLAB 9.13. The performance of IT2FIS model is compared with the actual data. Correlation coefficient (R 2 ) and Root Mean-Squared Error (RMSE) have been used to evaluate …the performance metrics of the proposed model. The results suggest that the IT2FIS model can capture the dynamic behavior of rainfall data and generate reasonable results, implying that it might be beneficial in long-term rainfall prediction. Show more
Keywords: Weather forecasting, fuzzy logic, interval type –2 Fuzzy Inference System, Environmental parameters
DOI: 10.3233/JIFS-235828
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4791-4802, 2024
Authors: Paul, Ann Rija | Grace Mary Kanaga, E.
Article Type: Research Article
Abstract: In this new era of intelligence and automation, it is important to develop intelligent software to analyse traffic data and detect abnormal activities occurring in the public. Information from GPS, Surveillance cameras, traffic management systems etc will be helpful for the researchers to develop such algorithms. In this research work, we propose a method to detect traffic accidents and used a deep convolutional neural network (D-CNN) and Centroid based vehicle tracking algorithm for vehicle detection. Overlapping bounding boxes and speed of the vehicle are considered for collision detection. The vehicle is tracked using a centroid tracking algorithm to find acceleration, …speed and trajectory values of each vehicle in the continuous frames. The trajectory and angle change after the collision can be used to classify the accidents. The result shows a detection accuracy of 99% in such a way outperforms the other latest methods. The results from the proposed method can be used in several accident reconstruction softwares like PC crash, ARPro etc. Show more
Keywords: Vehicle tracking, surveillance, collision detection, trajectory and angle of intersection, deep convolutional neural network
DOI: 10.3233/JIFS-235911
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4803-4816, 2024
Authors: Zhang, Kun | Zhou, Yu | Long, Haixia | Wu, Shulei | Wang, Chaoyang | Hong, Haizhuang | Fu, Xixi | Wang, Haifeng
Article Type: Research Article
Abstract: The complexity of marine information types, data diversity, data collection difficulties and other aspects makes the network security of marine information management more and more prominent, and has become a major issue affecting the stability of the country and society, so it is urgent to establish a marine information management network security system. Traditional network security technology adopts a passive approach and cannot actively detect viruses, trojans, and other hidden objects in the network. Antivirus software would only be used when attacked. If the risk of network attack is too great, the consequences would be unimaginable. This paper designed a …marine information management network security system based on artificial intelligence embedded technology, which improved the efficiency of marine information security management. This paper also applied the embedded technology of AI to the network security management, and proposed the k-means clustering algorithm (K-Means) of AI, which can greatly improve the network security. The experimental results in this paper showed that the intrusion detection rates of System 1 and System 2 were 56.3% and 78.3% respectively when the number of viruses was 50 at 30M, and 65.5% and 80.1% respectively when the number of viruses was 50 at 60M. It showed that the intrusion detection rate of System 2 was higher both at 30M and 60M. Show more
Keywords: Artificial intelligence, K-means clustering algorithm, marine information management, network security
DOI: 10.3233/JIFS-236018
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4817-4827, 2024
Authors: Haripriya, V. | Vishal Gupta, Mohan | Nadkarni, Nikita | Malik, Suraj | Yadav, Aditya | Joshi, Apoorva
Article Type: Research Article
Abstract: From online social networks to life-or-death security systems, multimedia files (photos, movies, and audio recordings) have grown common in today’s digital culture. Protecting people, businesses and infrastructure requires strict adherence to the encryption and decryption of multimedia data. We suggested an Ensemble Whale Optimized Recurrent Neural Network (EWO-RNN)used in this study to overcome these issues. With the help of this study, multimedia security will be evaluated in more accurate and comprehensive manner. Smarter decisions and proactive security measures may follow as a result of this. To increase the system quality and the overall performance, the collected data is pre-processed for …normalized data by using Min-Max Normalization. Pre-processed data is extracted by using Kernel Principle Component Analysis (K-PCA). The EWO-RNN evaluates the effectiveness and efficiency of an approach by analyzing the performance of Accuracy (97.85%), Precision (92.2%), F1-score (96.1%), Mean Square Error (MSE) (0.086), Root Mean Square (RMSE) (0.12%) and Sensitivity (95%). The Enhanced Radial Base Deep Learning Algorithm for Predicting Multimedia Security Issues proposes a solution with improved resilience, accuracy, generalization, and decision-making capabilities. In a dynamic and evolving digital environment, this makes the algorithm a viable tool for multimedia security assessments. Show more
Keywords: Multimedia, security issues, ensemble whale optimized recurrent neural network (EWO-RNN), min-max normalization, kernel principle component analysis (K-PCA)
DOI: 10.3233/JIFS-237041
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4829-4840, 2024
Authors: Jayachandran, A. | Ganesh, S.
Article Type: Research Article
Abstract: Microaneurysms, tiny, circular red dots that occur in retinal fundus images, are one of the earliest symptoms of diabetic retinopathy. Because microaneurysms are small and delicate, detecting them can be difficult. Their small size and cunning character make automatic detection of them difficult. In this study, a novel encoder-decoder network is proposed to segment the MAs automatically and accurately. The encoder part mainly consists of three parts: a low-level feature extraction module composed of a dense connectivity block (Dense Block), a High-resolution Block (HR Block), and an Atrous Spatial Pyramid Pooling (ASPP) module, of which the latter two modules are …used to extract high-level information. Therefore, the network is named a Multi-Level Features based Deep Convolutional Neural Network (MF-DCNN). The proposed decoder takes advantage of the multi-scale features from the encoder to predict MA regions. Compared with the existing methods on three datasets, it is proved that the proposed method is better than the current excellent methods in the segmentation results of the normal and abnormal fundus. In the case of fewer network parameters, MF-DCNN achieves better prediction performance on intersection over union (IoU), dice similarity coefficient (DSC), and other evaluation metrics. MF-DCNN is lightweight and able to use multi-scale features to predict MA regions. It can be used to automatically segment the MA and assist in computer-aided diagnosis. Show more
Keywords: Microaneurysm detection, fundus images, segmentation, features
DOI: 10.3233/JIFS-230154
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4841-4857, 2024
Authors: Long, Zuqiang | Luo, Zelong | Wang, Yunmeng
Article Type: Research Article
Abstract: The analytical structure plays an important role in system design and stability analysis of FLC. Structure analysis of traditional IT2 TS FLCs using Zadeh min operator and KM algorithm requires multiple IC dividing, which results in complex calculation and cumbersome parameter adjustment. This article proposes a new IT2 TS FLC by adopting product-type operator and NT algorithm. The proposed controller has such advantages: 1)use product-type operator to skip the partitioning in fuzzy inference process;2) use NT algorithm to avoid determining switching points and sorting rule consequents in type-reduction process. Then, the controller is proved to be universal approximator and sufficient …condition is deduced. Finally, we derive the analytical structure of the controller by substituting the parameters, and study the relationship between the uncertainty parameter θ and the analytical structure when the rule consequents are symmetric or asymmetric. Both the computational costs during operation and the computational workload for structural analysis can be reduced significantly by using the new FLC. Show more
Keywords: NT algorithm, IT2 TS FLC, analytical structure, universal approximation
DOI: 10.3233/JIFS-231866
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4859-4867, 2024
Authors: Yang, Hui | Wang, Yi-Na
Article Type: Research Article
Abstract: In this paper, we provide some new characterizations of L -convex systems. For this purpose, we first introduce the concept of partial hull operators and establish the categorical relationship between partial hull operators and convex systems. Then we abstract the relationship between a subset and its partially convex hull in convex system to a binary relation, called enclosed relation. Moreover, we prove that the enclosed relations are equivalent to convex systems. Subsequently, we generalize the concept of partial hull operators and enclosed relations to the fuzzy case, which will be called L -partial hull operators and L -enclosed relations respectively. …Finally we explore the categorical isomorphisms between them. Show more
Keywords: L-convex systems, partially L-convex hull operators, L-partial hull operators, L-enclosed relations
DOI: 10.3233/JIFS-232243
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4869-4879, 2024
Authors: dos Santos Lima, Matheus | Kich, Victor Augusto | Steinmetz, Raul | Tello Gamarra, Daniel Fernando
Article Type: Research Article
Abstract: The present study focuses on the implementation of Deep Reinforcement Learning (Deep-RL) techniques for a parallel manipulator robot, specifically the Delta Robot, within a simulated setting. We introduced a simulation framework designed to guide the Delta Robot’s end-effector to a designated spatial point accurately. Within this environment, the robotic agent undergoes a learning process grounded in trial and error. It garners positive rewards for successful predictions regarding the next action and faces negative repercussions for inaccuracies. Through this iterative learning mechanism, the robot refines its strategies, thereby establishing improved decision-making rules based on the ever-evolving environment states. Our investigation delved …into three distinct Deep-RL algorithms: the Deep Q-Network Algorithm (DQN), the Double Deep Q-Network (DDQN), and the Trust Region Policy Optimization Algorithm (TRPO). All three methodologies were adept at addressing the challenge presented, and a comprehensive discussion of the findings is encapsulated in the subsequent sections of the paper. Show more
Keywords: Deep reinforcement learning, parallel robots, delta robot
DOI: 10.3233/JIFS-232795
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4881-4894, 2024
Authors: Gao, Kaihan | Ju, Yiwei | Li, Shuai | Yang, Xuebing | Zhang, Wensheng | Li, Guoqing
Article Type: Research Article
Abstract: Recent advances in high-throughput electron microscopy (EM) have revolutionized the examination of microstructures by enabling fast EM image generation. However, accurately segmenting EM images remains challenging due to inherent characteristics, including low contrast and subtle grayscale variations. Moreover, as manually annotated EM images are limited, it is usually impractical to utilize deep learning techniques for EM image segmentation. To address these challenges, the pyramid multiscale channel attention network (PmcaNet) is specifically designed. PmcaNet employs a convolutional neural network-based architecture and a multiscale feature pyramid to effectively capture global context information, enhancing its ability to comprehend the intricate structures within EM …images. To enable the rapid extraction of channel-wise dependencies, a novel attention module is introduced to enhance the representation of intricate nonlinear features within the images. The performance of PmcaNet is evaluated on two general EM image segmentation datasets as well as a homemade dataset of superalloy materials, regarding pixel-wise accuracy and mean intersection over union (mIoU) as evaluation metrics. Extensive experiments demonstrate that PmcaNet outperforms other models on the ISBI 2012 dataset, achieving 87.85% pixel-wise accuracy and 73.11% mean intersection over union (mIoU), while also advancing results on the Kathuri and SEM-material datasets. Show more
Keywords: Electron microscopy, image segmentation, convolutional neural network, multiscale feature pyramid
DOI: 10.3233/JIFS-235138
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4895-4907, 2024
Authors: Shi, Liqing | Xiong, Taiping | Cui, Gengshen | Pan, Minghua | Zhu, Zhiguo | Cheng, Wei
Article Type: Research Article
Abstract: In order to accurately estimate the disparity of ill-posed regions, such as weak texture and occlusion regions, we propose DSPANet, a stereo matching network that incorporates a dual-stream pyramid module and a channel and spatial attention module. The dual-stream pyramid module captures numerous complementary features from different layers by utilizing multi-resolution inputs and feature extraction blocks. This approach enables the learning of local detailed features at various scales. These features at various scales are then combined to calculate the stereo matching cost. By incorporating channel and spatial attention module into the feature extraction process to obtain richer and more concise …contextual information, the matching cost can be constructed more accurately, which provides powerful conditions for subsequent cost aggregation. In the cost aggregation stage, we utilize the stacked hourglass module for both encoding and decoding. Additionally, we incorporate 3D global attention upsampling during the decoding stage, which enables high-level features to provide guidance information to low-level features in a simple way. We evaluate our proposed method on the Scene Flow dataset, as well as the KITTI2012 and KITTI2015 datasets. The experimental results demonstrate that our DSPANet achieves superior performance and effectively enhances the matching results in ill-posed regions. Our code has been implemented using PyTorch and will be released after paper publication at https://github.com/Shi-LiQing/DSPANet . Show more
Keywords: Stereo matching, dual-stream pyramid, attention mechanism, binocular vision
DOI: 10.3233/JIFS-235415
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4909-4922, 2024
Authors: Zhang, Baishun | Su, Xue
Article Type: Research Article
Abstract: In practical applications of machine learning, only part of data is labeled because the cost of assessing class label is relatively high. Measure of uncertainty is abbreviated as MU. This paper explores MU for partially labeled real-valued data via a discernibility relation. First, a decision information system with partially labeled real-valued data (p-RVDIS) is separated into two decision information systems: one is the decision information system with labeled real-valued data (l-RVDIS) and the other is the decision information system with unlabeled real-valued data (u-RVDIS). Then, based on a discernibility relation, dependence function, conditional information entropy and conditional information amount, four …degrees of importance on an attribute subset in a p-RVDIS are defined. They are calculated by taking the weighted sum of l-RVDIS and u-RVDIS based on the missing rate, which can be considered as four MUs for a p-RVDIS. Combining l-RVDIS and u-RVDIS provides a more accurate assessment of the importance and classification ability of attribute subsets in a p-RVDIS. This is precisely the novelty of this paper. Finally, experimental analysis on several datasets verify the effectiveness of these MUs. These findings will contribute to the comprehension of the essence of the uncertainty in a p-RVDIS. Show more
Keywords: Partially labeled real-valued data, p-RVDIS, Discernibility relation, Uncertainty, Measure
DOI: 10.3233/JIFS-236958
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4923-4940, 2024
Authors: Sun, Ruixia | Xiao, Ping | Tang, Wei | Chen, Long | Chen, Dandan
Article Type: Research Article
Abstract: In order to further enhance energy conservation and emission reduction, the header lifting structure of a harvester is studied. First, a double electric pushrod structure is used to replace the oil cylinder and air cylinder lifting structure of a traditional header to reduce fuel consumption and harmful gas emission. Furthermore, a mathematical model and a simulation model of the electric pushrod are established. To enhance the control effect of the header lifting structure, an improved version of the traditional gray wolf optimization (GWO) algorithm is designed. The nonlinear convergence factor, Kent chaotic mapping and convergence surrounding and spiral updating operations …are introduced to increase the convergence speed and optimization accuracy of this algorithm. The improved GWO (IGWO) algorithm is applied to optimize the proportional-integral-derivative (PID) controller of the double pushrod coordinated control system. Then, a new IGWO-PID control algorithm is also designed. The cross-coupling control strategy of header’s double pushrods is then studied. Results of the simulation and bench test show that the IGWO-PID control algorithm and the cross-coupling control strategy can effectively enhance controlling effect of the harvester header. The left and right pushrods can achieve good synchronous and coordinated movements. Show more
Keywords: Harvester header, electric pushrod, IGWO-PID control algorithm, cross-coupled collocated control
DOI: 10.3233/JIFS-231193
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4941-4953, 2024
Authors: Qin, Yong | Xu, Zeshui | Wang, Xinxin | Škare, Marinko
Article Type: Research Article
Abstract: Fuzzy decision-making is increasingly becoming a pivotal approach to solving complex and intricate issues in tourism and hospitality management. The primary objective of this study is to unveil the developmental status, key themes and research trends within fuzzy decision-making in tourism and hospitality management (FDMTH) using Bibliometrix, CorText Manager and VOSviewer tools. As such, we conduct a comprehensive bibliometric and content-wise analysis of selected 341 publications concerning FDMTH. For one thing, we use valuable bibliometric indicators to conduct a general feature peek and performance analysis of the audited corpus. The research findings reveal a sustained scholarly interest in FDMTH. As …a critical player, Pappas, Nikolaos leads the volume of publications. The Journal of Intelligent & Fuzzy Systems stands out as the preferred outlet for FDMTH research. For another, the contingency matrix and bump graph modules are employed to detect the knowledge flow and intellectual connections in FDMTH. The results of network mapping tentatively identify geographic and thematic biases in FDMTH. More importantly, bibliographic coupling analysis reveals four specific themes, namely multi-criteria decision-making and evaluation, factors identification, fuzzy programming and forecasting, and fuzzy intelligence. Our pioneer work will contribute to the present understanding of the complexity and interdisciplinarity of FDMTH. Show more
Keywords: Fuzzy decision-making, tourism and hospitality, CorText manager, intellectual connections, thematic bias
DOI: 10.3233/JIFS-236618
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4955-4980, 2024
Authors: Yang, Xiangfei | Zhang, Faping | Wei, Jianfeng
Article Type: Research Article
Abstract: To improve the accuracy of fault diagnosis for recoil systems under multiple operating conditions, a fuzzy RBF neural network (Radial Basis Function, RBF) fault diagnosis method based on knowledge and data fusion is proposed. A kinetic model for the recoil system is first established to describe the system’s behavior. Next, fuzzy RBF neural network is used to establish the relationship between abnormal operating parameters and fault causes, achieving a fault cause diagnosis accurately based on the integration of expert experience knowledge and system operation data. A study case demonstrate that the algorithm has strong knowledge and data fusion capabilities and …can effectively identify faults in recoil system. Show more
Keywords: Fuzzy method, RBF neural network, knowledge and data fusion, the recoil system, fault diagnosis
DOI: 10.3233/JIFS-230683
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4981-4994, 2024
Authors: Senthil Kumar, V. | Aruna, R. | Varalatchoumy, M. | Manikannan, P. | Santhana Krishnan, T. | Usha Rani, B. | Kumar, Ashok | Rajaram, A.
Article Type: Research Article
Abstract: As the world embraces the transition towards renewable energy, the optimization of solar power plants becomes paramount. In this research, we present a comprehensive framework that leverages advanced analytical methodologies to address critical operational challenges and elevate the efficiency of solar power generation. Our framework encompasses data preprocessing, time series analysis, anomaly detection, and equipment performance assessment, synergistically combining their strengths to offer a holistic solution. The heart of our proposed approach lies in the precision and efficacy of anomaly detection. We introduce two powerful techniques—LSTM Autoencoder and Isolation Forest—to identify anomalies and equipment underperformance. Through meticulous evaluation, we …showcase their comparative performance, revealing the nuanced strengths of each. Visualizations depict the model’s proficiency in pinpointing anomalies, with LSTM Autoencoder emerging as a standout performer, adept at capturing even subtle deviations from expected patterns. Our research extends beyond detection to equip stakeholders with real-time insights. The visualization of daily yield trends uncovers potential data anomalies, enabling timely intervention and rectification. Additionally, we address equipment failures by harnessing random forest modeling to establish a robust relationship between irradiance, temperature, and DC power. This approach provides a powerful tool for real-time condition monitoring and fault detection, enabling proactive maintenance and enhancing operational resilience. Show more
DOI: 10.3233/JIFS-235578
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4995-5011, 2024
Authors: Sivakumaran, V. | Sankar, K.
Article Type: Research Article
Abstract: Consider a simple graph G = 〈V , E 〉 with n vertices. Define a function f from vertex set of G to set of integers 1, 2, …, n subject to the condition that there exists an integer k > 0 such that the sum of adjacent labels of each vertex in G is equal to k . In this paper, we prove that the graph K n - ⌊ n 2 ⌋ e has DML for all n ≥ 1, decomposition of distance magic graph K 2n - {ne …} into n ( n - 1 ) 2 edge distinct copies of C 4 for all n ≥ 2 and DML of join of complete graphs gives unique mathematical model and it will be a useful model in big data analysis. Show more
Keywords: Distance magic labeling, magic constant, decomposition of distance magic graph, join of complete graphs, big data analysis
DOI: 10.3233/JIFS-224511
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5013-5020, 2024
Authors: Wang, Xiaoying | Chen, Xiaohai | Zhang, Zhongwen | He, Haisheng
Article Type: Research Article
Abstract: Intelligent Transportation Systems (ITS) have experienced significant growth over the past decade thanks to advances in control, communication, and information technology applied to vehicles, roads, and traffic control systems. Vehicle type classification plays a vital role in implementing ITS because of its ability to collect useful traffic information, enable future development of transport infrastructures, and increase human comfort. As a branch of machine learning, deep learning represents a frontier for artificial intelligence, which seeks to be closer to its primary goal. Deep learning is a powerful tool for classifying vehicle types because it can capture complex traffic data characteristics and …learn from large amounts of data. This means that it can be used to accurately classify traffic data and generate valuable insights that can be used to improve traffic management. Researchers have successfully adopted these algorithms as a solution to propose optimal vehicle-type classification strategies. This paper highlights the role of deep learning algorithms in solving the vehicle type classification problem, reviewing the state-of-the-art approaches in this field. Show more
Keywords: Transportation systems, ITS, vehicle type, deep learning, optimization
DOI: 10.3233/JIFS-233302
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5021-5032, 2024
Authors: Subbaian, Santhi | Balasubramanian, Anand | Marimuthu, Murugan | Chandrasekaran, Suresh | Muthusaravanan, Gokila
Article Type: Research Article
Abstract: Coconut farming is a significant agricultural activity in South India, but the coconut trees face challenges due to adverse weather conditions and environmental factors. These challenges include various leaf diseases and pest infestations. Identifying and locating these issues can be difficult because of the large foliage and shading provided by the coconut trees. Recent research has shown that Computer Vision algorithms are becoming increasingly important for solving problems related to object identification and detection. So, in this work, the YOLOv4 algorithm was employed to detect and pinpoint diseases and infections in coconut leaves from images. The YOLOv4 model incorporates advanced …features such as cross-stage partial connections, spatial pyramid pooling, contextual feature selection, and path-based aggregation. These features enhance the model’s ability to efficiently identify issues such as yellowing and drying of leaves, pest infections, and leaf flaccidity in coconut leaf images taken in various environmental conditions. Furthermore, the model’s predictive accuracy was enhanced through multi-scale feature detection, PANet feature learning, and adaptive bounding boxes. These improvements resulted in an impressive 88% F1-Score and an 85% Mean Average Precision. The model demonstrates its effectiveness and robustness even when dealing with medium-resolution images, offering improved accuracy and speed in disease and pest detection on coconut leaves. Show more
Keywords: Coconut leaf disease, YOLO v4, precision agriculture, pest, faster RCNN, YOLO-SPP
DOI: 10.3233/JIFS-233831
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5033-5045, 2024
Authors: Zhang, Mingming | Wu, Qingling
Article Type: Research Article
Abstract: High-performance concrete (HPC) is a specialized type of concrete designed to meet stringent performance and uniformity standards that are difficult to achieve with conventional materials and standard mixing, placing, and curing methods. The testing process to determine the mechanical properties of HPC specimens is complex and time-consuming, and making improvements can be difficult after the test result does not meet the required properties. Anticipating concrete characteristics is a pivotal facet in the realm of High-Performance Concrete (HPC) manufacturing. Machine learning (ML)-driven models emerge as a promising avenue to tackle this formidable task within this context. This research endeavors to employ …a synergy of ML hybrid and ensemble frameworks for the prognostication of the mechanical attributes within HPC, encompassing compressive strength (CS), slump (SL), and flexural strength (FS). The formulation of these hybrid and ensemble constructs was executed through the integration of Support Vector Regression (SVR) with three distinct meta-heuristic algorithms: Prairie Dog Optimization (PDO), Pelican Optimization Algorithm (POA), and Mountain Gazelle Optimizer (MGO). Some criteria evaluators were used in the training, validation, and testing phases to assess the robustness of the established models, and the best model was proposed for practical applications through comparative analysis of the results. As a result, the hybrid and ensemble models were the potential methods to predict concrete properties accurately and efficiently, thereby reducing the need for expensive and time-consuming testing procedures. In general, the ensemble model, i.e., SVPPM, had a more suitable performance with high values of R2 equal to 0.989 (MPa), 0.984 (mm), and 0.992 (MPa) and RMSE = 3.82 (MPa), 9.5 (mm), and 0.30 (MPa) for CS, SL, FS compared to other models, respectively. Show more
Keywords: High-Performance dynamic properties, support vector regression, prairie dog optimization, pelican optimization algorithm, mountain gazelle optimizer
DOI: 10.3233/JIFS-234125
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5047-5072, 2024
Authors: Wang, Haomiao | Li, Yibin | Jiang, Mingshun | Zhang, Faye
Article Type: Research Article
Abstract: Domain adaptation (DA) technology has the ability to solve fault diagnosis (FD) problems under variable operating conditions. However, DA technology faces two issues: (1) in general, vibration signals inevitably contain noise, which makes it difficult to extract discriminant features.(2) there are unknown fault types in target domain. These issues will lead to poor diagnostic performance. To solve above issues, a new cross-domain open-set transfer FD method called feature improvement adversarial network (FIAN) is proposed in this article. Specifically, to alleviate noise interference, a feature improvement module (FIM) is proposed and embedded into the backbone convolutional neural network to form new …feature extractor. FIM uses soft threshold function to enhance important information and suppresses redundant information. Furthermore,open-set DA by back-propagation (OSBP) is introduced into FIAN. OSBP can predict the probability that a target domain sample belongs to an unknown category, so that it can effectively identify unknown and known category samples. Experimental results demonstrated its effectiveness and superiority in two bearing datasets. Show more
Keywords: Fault diagnosis, rolling bearing, open-set domain adaptation, feature improvement module, adversarial network
DOI: 10.3233/JIFS-236593
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5073-5085, 2024
Authors: Chandana Mani, R.K. | Kamalakannan, J.
Article Type: Research Article
Abstract: Breast cancer (BC) is the most common cancer amongst women that threatens the health of women, initial diagnosis of BC becomes essential. Though there were several means to diagnose BC, the standard way is pathological analysis. Precise diagnosis of BC necessitates experienced histopathologists and needs more effort and time for completing this task. Recently, machine learning (ML) was successfully implemented in text classification, image recognition, and object recognition. With the emergence of computer aided diagnoses (CAD) technology, ML was effectively implemented for BC diagnosis. Histopathological image classification depends on deep learning (DL), particularly convolution neural network (CNN), which frequently needs …a large amount of labelled training models, whereas the labelled data was hard to obtain. This study develops an Aquila Optimizer(AO) with Hybrid ResNet-DenseNet Enabled Breast Cancer Classification on Histopathological Images (AOHRD-BC2HI). The proposed AOHRD-BC2HI technique inspects the histopathological images for the diagnosis of breast cancer. To accomplish this, the presented AOHRD-BC2HI technique uses hybridization of Resnet with Densenet (HRD) model for feature extraction. Moreover, the HRD method can be enforced for feature extracting procedure in which the DenseNet (feature value memory by concatenation) and ResNet (refinement of feature value by addition) were interpreted. For BC detection and classification, the DSAE model is utilized. The AO algorithm is exploited to improve the detection performance of DSAE model. The experimental validation of the presented AOHRD-BC2HI approach is tested using benchmark dataset and the results are investigated under distinct measures.Also the proposed model achieved the accuracy of 96%. The comparative result reports the improved performance of the presented AOHRD-BC2HI technique over other recent methods. Show more
Keywords: Breast cancer, histopathological images, aquila optimizer, computer aided diagnosis, deep learning
DOI: 10.3233/JIFS-236636
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5087-5102, 2024
Authors: Jyoti, | Singh, Jaspreeti | Gosain, Anjana
Article Type: Research Article
Abstract: Addressing missing values is a persistent challenge in the field of data mining. The presence of incomplete data can significantly compromise the overall data quality. Consequently, it is crucial to handle incomplete data efficiently. This paper presents a novel approach for imputing missing values that incorporates Kernelized Fuzzy C-Means (KFCM) clustering and proposes a method termed LIKFCM, which combines its benefits with Linear Interpolation (LI). The proposed LIKFCM’s performance is assessed through a comparison against nine state-of-the-art imputation techniques (mean, median, LI, EMI, KNNI, KMI, FKMI, LIFCM, and LIPFCM) across ten widely used real-world datasets from the UCI repository with …six combinations of missing ratios to assess the efficacy of the proposed imputation method. From the experimental results, it is evident that our proposed method outperforms the existing imputation methods with significant improvements in terms of RMSE & MAE for these datasets. Additionally, experiments examining the effect of missing values validate the robustness of the proposed approach by handling different missing ratios. The performance validation of the proposed approach against other state-of-the-art imputation methods has been conducted utilizing a Kendall’s W statistical test, involving a comparison of their mean ranks across different missing ratios. The outcomes indicate that LIKFCM has outperformed other imputation methods, attaining the highest rank in terms of different evaluation criteria. Show more
Keywords: Incomplete data, missing value imputation, fuzzy clustering, LI, LIKFCM
DOI: 10.3233/JIFS-236869
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5103-5123, 2024
Authors: Zhang, Xiang | Huang, Jianhua
Article Type: Research Article
Abstract: The occurrence of safety incidents for existing glass curtain walls (EGCWs) pronounced menace to the security of both lives and property. Undertaking safety assessment for EGCWs carries essential practical significance. However, current fuzzy evaluation methods overlook the uncertainty of indicator weights and the intricacies of rank attribution. In response, this paper proposes a novel approach to the safety assessment of EGCWs. This research establishes a framework of evaluation indicators for EGCWs and divides the safety ranks of each indicator into four tiers: Safe, Mild risk, Moderate risk, and High risk. Quantitative and qualitative indicators are quantified via the variable fuzzy …cloud algorithm and cloud model. The information cloud combination weighting method is introduced to determine the weight clouds of indicators. Finally, a two-dimensional assessment result is derived using an improved fuzzy comprehensive evaluation method and fuzzy entropy. The exemplified outcomes demonstrate that this approach captures the safety status of evaluation subjects based on risk ranks, and fuzzy entropy addresses two issues: inconsistent level attribution and the comparison of identical risk ranks. The appraisal method further unveils the safety details of EGCWs, with findings that align consistently with the actual situation. Show more
Keywords: Safety evaluation, existing glass curtain wall, variable fuzzy cloud algorithm, cloud model, fuzzy entropy
DOI: 10.3233/JIFS-237414
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5125-5137, 2024
Authors: Fu, Zhiyu | Fu, Zhihui
Article Type: Research Article
Abstract: Textural translations from diverse foreign languages require reference corpora for providing error-free readability. This applies to any foreign language example English or any other language translation to Japanese. Therefore Japanese corpus repository verifies the consistency of the translated texts, words, and sentences for its readability. This article introduces a Semantic Translation Model (STM) using Fuzzy Control (FC) for supporting the aforementioned fact. The proposed model analyzes the translated word semantics based on its sentence occurrence and meaning. These two factors are analyzed using two-level fuzzy control; the first level identifies the word placement/occurrence-based readability and the second level identifies the …meaning retention. If any inconsistency is observed in the first level, the second is not carried out preventing readability errors. The fuzzy control process relies on near-to-same Japanese corpus inputs for improving readability. If the case fails then a new word replacement or displacement of the semantics is enforced. Therefore the fuzzy control levels are expanded based on different word occurrences, preventing time complexity. Show more
Keywords: Japanese translation, fuzzy control, readability, semantic analysis
DOI: 10.3233/JIFS-234575
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5139-5153, 2024
Authors: Zhao, Weisen
Article Type: Research Article
Abstract: Electrochemical energy storage battery fault prediction and diagnosis can provide timely feedback and accurate judgment for the battery management system(BMS), so that this enables timely adoption of appropriate measures to rectify the faults, thereby ensuring the long-term operation and high efficiency of the energy storage battery system. Based on the idea of data driven, this paper applies the Long-Short Term Memory(LSTM) algorithm in the field of artificial intelligence to establish the fault prediction model of energy storage battery, which can realize the prediction of the voltage difference over-limit fault according to the operation data of the energy storage battery, and …introduce the parameter of the difference between maximum voltage and minimum voltage(DMM) at the cluster level to quantitatively determine whether the battery cluster has a fault. It provides powerful guidance and effective methods for the safe and stable operation of electrochemical energy storage power stations. Show more
Keywords: Fault prediction, data driven, LSTM, artificial intelligence
DOI: 10.3233/JIFS-235726
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5155-5164, 2024
Authors: Zheng, Guangyuan | Cheng, Chen | Dong, Xinling | Liu, Yi
Article Type: Research Article
Abstract: Acceleration and deceleration control, as one of the key technologies in high-speed CNC system, directly affects the machining efficiency, the stability of machining process and the error of machining follow. Therefore, it is necessary to study and explore new acceleration and deceleration control methods in high-speed CNC system to ensure the smooth feed and improve the machining accuracy. Therefore, the acceleration and deceleration control algorithm of NC system based on deep reinforcement learning and single chip microcomputer is studied. The theoretical basis of deep reinforcement learning is analyzed, and the acceleration of acceleration and deceleration is calculated based on the …linear acceleration and deceleration control. The whole integer operation of single chip microcomputer is used to estimate the value range of each step. The variation characteristics of trajectory motion are predicted so that acceleration and deceleration can be processed across program segments. The velocity of transfer point is calculated by the rate of change of feed velocity vector, and the speed of multi-program is smoothed by adjusting the allowable contour error. Based on the proximal strategy optimization algorithm in deep reinforcement learning, the acceleration and deceleration control model of CNC system is established to realize the acceleration and deceleration control. The experimental results show that the proposed algorithm has better control effect, shorter time and smaller interpolation error, which can ensure the NC system to feed smoothly at high speed. Show more
Keywords: Deep reinforcement learning, single chip microcomputer, near-end strategy optimization algorithm, CNC system, acceleration and deceleration control, Smooth processing
DOI: 10.3233/JIFS-238195
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5165-5174, 2024
Authors: Yi, Yangyang | Yu, Long | Tian, Shengwei | Gao, Xuezhuang | Li, Jie | Zhao, Xingang
Article Type: Research Article
Abstract: In recent years, 3D object detection based on LiDAR point clouds is a key component of autonomous driving. In pursuit of enhancing the accuracy of 3D point cloud feature extraction and point cloud detection, this paper introduces a novel 3D object detection model, termed as Graph Self-Attention-RCNN (GA-RCNN). This model is designed to integrate voxel information and point location information, enhancing the quality of 3D object proposals while maintaining contextual accuracy. The first stage rectifies the previous approach that relied on local features for preselected boxes, overlooking crucial global contextual information. An improved method is suggested in this work, utilizing …BEV to capture long-range dependencies via a cross-attention mechanism. The second stage addresses the overreliance on local neighborhood point feature extraction. The Graph Self-Attention Pooling method is proposed, characterized by its dynamic computation of contribution weights for inputs. This enhances the model’s flexibility and generalization performance. Extensive evaluations on KITTI and Waymo datasets demonstrate GA-RCNN’s superior accuracy compared to other methods, affirming its efficacy in 3D object detection. Show more
Keywords: 3D object detection, Point clouds, deep learning
DOI: 10.3233/JIFS-234024
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5175-5189, 2024
Authors: he, Jia-long | zhang, Xiao-Lin | wang, Yong-Ping | gu, Rui-Chun | liu, Li-xin | xu, En-Hui
Article Type: Research Article
Abstract: Although deep learning models show powerful performance, they are still easily deceived by adversarial samples. Some methods for generating adversarial samples have the drawback of high time loss, which is problematic for adversarial training, and the existing adversarial training methods are difficult to adapt to the dynamic nature of the model, so it is still challenging to study an efficient adversarial training method. In this paper, we propose an adversarial training method, the core of which is the improved adversarial sample generation method AGFAT for adversarial training and the improved dynamic adversarial training method AGFAT-DAT. AGFAT uses a word frequency-based …approach to identify significant words, filter replacement candidates, and use an efficient semantic constraint module as a means to reduce the time of adversarial sample generation; AGFAT-DAT is a dynamic adversarial training approach that uses a cyclic attack on the model after adversarial training and generates adversarial samples for adversarial training again. It is demonstrated that the proposed method can significantly reduce the generation time of adversarial samples, and the adversarial-trained model can also effectively defend against other types of word-level adversarial attacks. Show more
Keywords: Text classification, adversarial samples, adversarial training
DOI: 10.3233/JIFS-234034
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5191-5202, 2024
Authors: Fan, Kun | Zhang, Dingran | Lv, Yuanyuan | Zhou, Lang | Qu, Hua
Article Type: Research Article
Abstract: In order to solve the problem of discrete manufacturing customization and personalized production scheduling, considering the influence of manual labor on processing time, we propose a multi-objective Hybrid Job-shop Scheduling with Multiprocessor Task(HJSMT) problem with cooperative effect model. Based on the actual production, two optimization objectives are set, i. e. minimizing the maximum completion time and the total tardiness. Firstly, considering the situation where workers’ cooperation reduces job processing time, the cooperative effect of workers co-processing is considered by referring to the learning effect curve in the model. Subsequently, we develop an Improved Non-dominated Sorting Genetic Algorithm-II (INSGA-II) to solve …the multi-objective HJSMT problem by improving Precedence Operation Crossover (POX) and Multiple Mutations (MM) operations. Finally, the scheduling results and the C values are compared with other algorithms to verify the effectiveness of the algorithm. Simultaneously, the multi-objective HJSMT problem with the cooperative effect is solved by the INSGA-II algorithm, and the experimental results also demonstrate the superior performance of the algorithm. Show more
Keywords: Hybrid job-shop scheduling, multiprocessor task, cooperative effect, multi-objective optimization, improved non-dominated sorting genetic algorithm-II
DOI: 10.3233/JIFS-235047
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5203-5217, 2024
Authors: Guo, Lixin
Article Type: Research Article
Abstract: We analyze the properties and characteristics of the information structure in incomplete lattice-valued information system (ILIS), we redefine the information structure and the dependence and information distance between the two information structures. In addition, in order to evaluate the uncertainty of ILIS, the concepts of granular measure and entropy measure are expounded, including information granulation, information quantity, rough entropy and information entropy. Finally, we carry out numerical experiments to verify the feasibility of the method, and carry out effective statistical analysis. These results are conducive to the establishment of granular computing framework in ILIS.
Keywords: Granular computing, incomplete lattice-valued information system, information distance, information granule, information structure
DOI: 10.3233/JIFS-235777
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5219-5237, 2024
Authors: Li, Chunhua | Zhu, Ying | Zhan, Xiaoqin | Huang, Huawei
Article Type: Research Article
Abstract: Type B semigroups are described as the generalized inverse semigroups in the range of abundant semigroup. Motivated by studying fuzzy congruences in inverse semigroups, and as a continuation of N. Kuroki’s work in inverse semigroups and our work in abundant semigroups in terms of fuzzy subsets, this paper considers fuzzy admissible congruences on some classes of type B semigroups. Our main purpose is to show when a fuzzy admissible congruence on a type B semigroup with E -properties is E -properties preserving. In particular, we get some sufficient and necessary conditions for some classes of type B semigroups to be …primitive, E -unitary and E -reflexive, respectively. As an application, we extend our results to the cases of inverse semigroups. Show more
Keywords: Fuzzy admissible congruences, type B semigroups, E-unitary, primitive, E-reflexive
DOI: 10.3233/JIFS-230383
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5239-5247, 2024
Authors: You, Xingye | Mao, Jian | Liu, Jingming | Huang, Kai
Article Type: Research Article
Abstract: Conducted electromagnetic emissions from interconnecting cables in computer systems can lead to internal information leakage and cause information security problems. However, unintentionally leaked EM signals are characterized by low signal-to-noise ratio and random noise, making it difficult to recover the original signal. In this paper, we propose a denoising model (S-DnCNN) based on an improved DnCNN to better recover the original signal. The network structure consists of three parts: feature mapping generation, low-dimensional feature extraction, and original reconstruction. To improve the noise extraction capability, we use Leaky ReLU as the activation function of the CNN, and introduce a residual structure …and a convolutional attention module. The residual structure uses residual hopping to implicitly remove potentially clean images by hidden layer operations, thus training noisy data to recover clean data. We construct a one-dimensional selective convolution kernel (SKConv1d) and fuse it with local paths to form a feature extraction network, which improves the performance of the network. The experimental results show that our proposed method can preserve the details in the effective signal during denoising and shows good generalization to complex SNR data. Show more
Keywords: Information security, electromagnetic information leakage, feature extraction, low signal-to-noise ratio, denoising
DOI: 10.3233/JIFS-232371
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5249-5261, 2024
Authors: Pu, Shihua | Liu, Zuohua
Article Type: Research Article
Abstract: Under the highly valued environment of intelligent breeding, rapid and accurate detection of pigs in the breeding process can scientifically monitor the health of pigs and improve the welfare level of pigs. At present, the methods of live pig detection cannot complete the detection task in real time and accurately, so a pig detection model named TR-YOLO is proposed. Using cameras to collect data at the pig breeding site in Rongchang District, Chongqing City, LabelImg software is used to mark the position of pigs in the image, and data augmentation methods are used to expand the data samples, thus constructing …a pig dataset. The lightweight YOLOv5n is selected as the baseline detection model. In order to complete the pig detection task more accurately, a C3DW module constructed by depth wise separable convolution with large convolution kernels is used to replace the C3 module in YOLOv5n, which enhances the receptive field of the whole detection model; a C3TR module constructed by Transformer structure is used to extract more refined global feature information. Contrast with the baseline model YOLOv5n, the new detection model does not increase additional computational load, and improves the accuracy of detection by 1.6 percentage points. Compared with other lightweight detection models, the new detection model has corresponding advantages in terms of parameter quantity, computational load, detection accuracy and so on. It can detect pigs in feeding more accurately while satisfying the real-time performance of target detection, providing an effective method for live monitoring and analysis of pigs at the production site. Show more
Keywords: Pig, deep learning, target detection, detection network, transformer
DOI: 10.3233/JIFS-236674
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5263-5273, 2024
Authors: Li, Hai | Gao, Mingjian | Lin, Zhizhan | Peng, Jian | Xie, Liangzhen | Ma, Junjie
Article Type: Research Article
Abstract: Background: Atrial fibrillation (AF), one of the most prevalent heart rhythm disorders, may lead to thromboembolism, heart failure, and sudden death. However, the mechanism of AF has not yet been fully explained. Objective: This study aims to identify novel gene signatures and to investigate the potential therapeutic targets of AF with an integrated bioinformatic approach. Methods: The gene expression and methylation datasets of AF were obtained through the Gene Expression Omnibus (GEO) database. Subsequently, a set of differentially expressed genes and differential methylation sites were identified. Gene functional annotation analysis was conducted to explore the potential …function of differentially-methylated/expressed genes. Then, we constructed a PPI network and TF–miRNA–mRNA network. Finally, weighted gene co-expression network analysis (WGCNA) was presented to study critical modules of AF. Results: Seven hypomethylated-high expression genes and nine hypermethylated-low expression genes were acquired from AF patients. Functional enrichment results indicated that the differentially-methylated/expressed genes were mainly concentrated in decidualization, maternal placenta development, regulation of nitric-oxide synthase activity, and osteoclast differentiation. Based on the results of the PPI, we defined 4 key genes namely FHL2, STC2, ALPK3, and RAP1GAP2 as the core genes, playing essential roles in the TF-miRNA-mRNA network. In the end, we constructed two co-expression modules that highly correlated with AF-related phenotype. Conclusion: In our study, we found critical genes for AF that might help understand the molecular changes in AF. Show more
Keywords: Atrial fibrillation, differentially expressed genes, DNA methylation, hub genes, miRNA, co-expression analysis
DOI: 10.3233/JIFS-234306
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5275-5285, 2024
Authors: Xhafaj, Evgjeni | Qendraj, Daniela Halidini | Salillari, Denisa
Article Type: Research Article
Abstract: The adoption of online banking is a big challenge as well as an emergent paradigm which is evolving quickly. The study develops a new exploration model that is used to determine significant constructs of the UTAUT theory that influence the adoption of online-banking. A study was conducted through an online survey of online banking users. Three methods were used; firstly, PLS-SEM was used to determine which of the constructs have a significant impact on behavioral intention to use e-banking, secondly, an incorporated neural network model was used to classify the relative impact of significant variables obtained from PLS-SEM, and finally …a hybrid procedure is incorporated from PLS-SEM to initialize the Fuzzy TOPSIS. The findings of the research paper constitute the ranked results and a comparison between them. The results of PLS-SEM and ANN analysis showed the same ranking for the constructs, while the decision making method Fuzzy TOPSIS introduced some changes in the ranking. This study presents valuable insights for the banking system to bring effective projects that increase the possibilities of using online banking. Show more
Keywords: PLS-SEM, ANN, online banking, fuzzy TOPSIS, UTAUT
DOI: 10.3233/JIFS-235388
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5287-5297, 2024
Authors: Zheng, Xuehui | Wang, Jun | Gao, You
Article Type: Research Article
Abstract: Selecting appropriate Cluster Heads (CHs) can significantly enhance the lifetime of the wireless sensor networks (WSNs). Fuzzy logic is an effective approach for CH election. However, existing fuzzy-logic-based CH election methods usually require a large number of fuzzy rules, making the CH election procedure inefficiency. In this study, a data-driven CH election method is proposed based on a compact set of fuzzy rules, which are learned by group sparse Takagi-Sugeno-Kang (GS-TSK) fuzzy system. Specifically, five linguistic variables were first used as features to describe the status of sensor nodes. After that, a compact set of fuzzy rules were learned by …GS-TSK, and they were then used to predict the chance of each sensor node becoming a CH. Based on the selected CHs, the clusters are generated. Simulation results show that the GS-TSK can select CHs with fewer rules more accurately. Besides, by using the proposed DD-FLC, an average improvement of WSN was shown in terms of first node dead (FND), 10% of nodes dead (10PND), quarter of nodes dead (QND), half of nodes dead (HND). Show more
Keywords: Wireless sensor network, sparse learning, TSK fuzzy system, GS-TSK
DOI: 10.3233/JIFS-224252
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5299-5311, 2024
Authors: Rani, R.M. | Dwarakanath, B. | Kathiravan, M. | Murugesan, S. | Bharathiraja, N. | Vinoth Kumar, M.
Article Type: Research Article
Abstract: Liver cancer is a leading cause of death worldwide and poses a significant challenge to physicians in terms of accurate diagnosis and treatment. AI-powered segmentation and classification algorithms can play a vital role in assisting physicians in detecting and diagnosing liver tumors. However, liver tumor classification is a difficult task due to factors such as noise, non-homogeneity, and significant appearance variations in cancerous tissue. In this study, we propose a novel approach to automatically segmenting and classifying liver tumors. Our proposed framework comprises three main components: a preprocessing unit to enhance picture contrast, a Masked Recurrent Convolutional Neural Network (RCNN) …for liver segmentation, and a pixel-wise classification unit for identifying abnormalities in the liver. When our models are applied to the challenging MICCAI’2027 liver tumor segmentation (LITS) database, we achieve Dice similarity coefficients of 96% and 98% for liver segmentation and lesion identification, respectively. We also demonstrate the efficiency of our proposed framework by comparing it with similar strategies for tumor segmentations. The proposed approach achieved high accuracy, sensitivity, specificity, and F1 score parameters for liver segmentation and lesion identification. These results were evaluated using the Dice similarity coefficient and compared with similar strategies for tumor segmentation. Our approach holds promise for improving the accuracy and speed of liver tumor detection and diagnosis, which could have significant implications for patient outcomes. Show more
Keywords: Liver segmentation, classification, deep learning, and mask RCNN
DOI: 10.3233/JIFS-232195
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5313-5328, 2024
Authors: Mohananthini, N. | Rajeshkumar, K. | Ananth, C.
Article Type: Research Article
Abstract: Heart disease (HD) is a leading cause of mortality worldwide, emphasizing the need for accurate and efficient detection and classification methods. Recently, Blockchain (BC) provides seamless and secure sharing of heart disease data amongst healthcare providers, specialists, and researchers. This allows collaborative efforts, data exchange, and integration of diverse datasets, leading to a more comprehensive analysis and accurate detection of heart diseases. BC provides a decentralized and tamper-proof platform for storing sensitive patient data related to heart disease. This ensures the integrity and security of the data, reducing the risk of unauthorized access or data manipulation. Therefore, this study presents …a new blockchain-assisted heart disease detection and classification model with feature selection with optimal fuzzy logic (BHDDC-FSOFL) technique. The presented BHDDC-FSOFL technique uses BC technology to store healthcare data securely. In addition, the disease detection module encompasses the design of biogeography teaching and learning-based optimization (BTLBO) algorithm for feature selection (FS) procedure. Moreover, an adaptive neuro-fuzzy inference system (ANFIS) classifier can be exploited for HD detection and classification. Furthermore, the ebola search optimization (ESO) algorithm is used for the parameter tuning of the ANFIS classifier. The integration of ANFIS classifier enables the modeling of uncertainty and imprecision in HD data, while metaheuristic algorithms aid in optimizing the classification process. Additionally, the utilization of BC technology ensures secure and transparent storage and sharing of healthcare data. To demonstrate the enhanced HD classification results of the BHDDC-FSOFL technique, a detailed experimental analysis was made on the HD dataset. The extensive result analysis pointed out the improved performance of the BHDDC-FSOFL technique compared to recent approaches in terms of different measures. Therefore, the proposed model offers a reliable and privacy-enhancing solution for healthcare providers and patients in a BC-assisted healthcare environment. Show more
Keywords: Heart disease detection, healthcare, blockchain, security, fuzzy logic, feature selection
DOI: 10.3233/JIFS-232902
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5329-5342, 2024
Authors: Adar, Tuba | Delice, Elif Kılıç | Delice, Orhan
Article Type: Research Article
Abstract: Accurate and rapid diagnosis is a significant factor in reducing incidence rate; especially when the number of people inflicted with a disease is considerably high. In the healthcare sector, the decision-making process might be a complex and error-prone one due to excessive workload, negligence, time restrictions, incorrect or incomplete evaluation of medical reports and analyses, and lack of experience as well as insufficient knowledge and skills. Clinical decision support systems (CDSSs) are those developed to improve effectiveness of decisions by supporting physicians’ decision-making process regarding their patients. In this study, a new artificial intelligence-based CDSS and a user-friendly interface for …this system were developed to ensure rapid and accurate detection of pandemic diseases. The proposed CDSS, which is called panCdss, uses hybrid models consisting of the Convolutional Neural Network (CNN) model and Machine Learning (ML) methods in order to detect covid-19 from lung computed tomography (CT) images. Transfer Learning (TL) models were used to detect monkeypox from skin lesion images and covid-19 from chest X-Ray images. The results obtained from these models were evaluated according to accuracy, precision, recall and F1-score performance metrics. Of these models, the ones with the highest classification performance were used in the panCdss. The highest classification values obtained for each dataset were as follows: % 91.71 accuracy, % 92.07 precision, % 90.29 recall and % 91.71 F1-score for covid-19 CT dataset by using CNN+RF hybrid model; % 99.56 accuracy, % 100 precision, % 99.12 recall and % 99.55 F1-score for covid-19 X-ray dataset by using VGG16 model; and % 90.38 accuracy, % 93.32 precision, % 88.11 recall and % 90.64 F1-score for monkeypox dataset by using MobileNetV2. It is believed that panCdss can be successfully employed for rapid and accurate classification of pandemic diseases and can help reduce physicians’ workload. Furthermore, the study showed that the proposed CDSS is an adaptable, flexible and dynamic system that can be practiced not only for the detection of pandemic diseases but also for other diseases. To the authors’ knowledge, this proposed CDSS is the first CDSS developed for pandemic disease detection. Show more
Keywords: Clinical decision support system, artificial intelligence, deep learning, user interface, pandemic diseases
DOI: 10.3233/JIFS-232477
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5343-5358, 2024
Authors: Qin, Xiwen | Ji, Xing | Zhang, Siqi | Xu, Dingxin
Article Type: Research Article
Abstract: The emergence of credit has generated a wealth of data on consumer lending behavior. In recent years, financial institutions have also started to use such data to make informed lending decisions based on fine-grained customer data, but conventional risk assessment models are inadequate in meeting the risk control requirements of the financial industry. Therefore, this paper proposes a credit scoring ensemble model incorporating fuzzy clustering particle swarm optimization (PSO) algorithm to obtain better credit risk prediction capability. First, a weighted outlier detection method based on the Induced Ordered Weighted Average Operator is proposed to preprocess the data to reduce noisy …data’s misleading effect on model training. Then, an undersampling method combined with fuzzy clustering PSO is proposed to overcome the negative effect of category imbalance on model training by resampling the data. In addition, a hyperparameter optimization framework is introduced to adaptively adjust important parameters in the ensemble model considering the impact of parameter settings on the training performance of the model. Based on the evaluation metrics of F-score, AUC, and Kappa coefficient, an empirical analysis was conducted on five credit risk datasets. The results show that the proposed method outperforms the comparative model with an improvement of 10% to 50% in terms of F-score and AUC. The highest achieved F-score is 0.9488, and the maximum AUC is 0.9807, demonstrating the effectiveness of the proposed method. The kappa coefficient results indicate a high level of consistency in the predicted classification results of the model. Show more
Keywords: Credit scoring, improved PSO, Fuzzy C-means, undersampling, ensemble model
DOI: 10.3233/JIFS-233334
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5359-5376, 2024
Authors: Wu, Yixun | Wang, Taiyu | Gu, Runze | Liu, Chao | Xu, Boqiang
Article Type: Research Article
Abstract: In order to address the problem of decreased accuracy in vehicle object detection models when facing low-light conditions in nighttime environments, this paper proposes a method to enhance the accuracy and precision of object detection by using the image translation technology based on the Generative Adversarial Network (GAN) in the field of computer vision, specifically the CycleGAN, from the perspective of improving the training set of object detection models. This is achieved by transforming the existing well-established daytime vehicle dataset into a nighttime vehicle dataset. The proposed method adopts a comparative experimental approach to obtain translation models with different degrees …of fitting by changing the training set capacity, and selects the optimal model based on the evaluation of the effect. The translated dataset is then used to train the YOLO-v5-based object detection model, and the quality of the nighttime dataset is evaluated through the evaluation of annotation confidence and effectiveness. The research results indicate that utilizing the translated nighttime vehicle dataset for training the object detection model can increase the area under the PR curve and the peak F1 score by 10.4% and 9% respectively. This approach improves the annotation accuracy and precision of vehicle object detection models in nighttime environments without requiring additional labeling of vehicles in monitoring videos. Show more
Keywords: Vehicle object detection, CycleGAN, nighttime vehicle image dataset, deep learning, machine vision
DOI: 10.3233/JIFS-233899
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5377-5389, 2024
Authors: Li, Jie
Article Type: Research Article
Abstract: In response to the evolving landscape of the modern era, the requirements for engineering audit have undergone significant changes. To achieve efficient audit tasks and obtain accurate and reliable results, the integration of machine learning and wireless network technology has become essential, leading to the emergence of digital and information-based audit modes. This paper focuses on the development of a digital audit system that combines engineering audit management fusion with machine learning and wireless network technology. Such an approach reflects the dynamic shift in internal audit functions and objectives, providing clear guidelines for the future of digital audit management. By …harnessing the power of machine learning and wireless networks, the digital audit system effectively addresses challenges associated with data management, sharing, exchange, and security during the audit process. Through seamless integration, it enables comprehensive electronic and digital management of internal and audit business processes. This research explores the platform’s functionalities and its potential application, using actual audit data for analysis. The proposed digital audit system showcases superior real-time data querying performance, heightened accuracy in checks, and enhanced retrieval capabilities. The simulation results validate the system’s efficacy, highlighting its ability to deliver true and dependable audit outcomes. By embracing digital transformation, the engineering audit field can harness the potential of cutting-edge technologies, thus paving the way for a more efficient, reliable, and future-ready approach to audit management. Show more
Keywords: Machine learning, wireless network technology, digital engineering audit, audit management strategy
DOI: 10.3233/JIFS-230759
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5391-5403, 2024
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