Journal of Intelligent & Fuzzy Systems - Volume 46, issue 1

Authors: Tiwari, Devendra | Gupta, Anand

Article Type: Research Article

Abstract: Tables are commonly used for effective and compact representation of relational information across the data in diverse document classes like scientific papers, financial statements, newspaper articles, invoices, or product descriptions. However, table structure detection is a relatively simple process for humans, but recognizing precise table structure is still a computer vision challenge. Further, innumerable possible table layouts increase the risk of automatic topic modeling and understanding the capability of each table from the generic document. This paper develops the framework to recognize the table structure from the Compound Document Image(CDI). Initially, the bilateral filter is designed for image transformation, enhancing …CDI quality. An improved binarization-Sauvola algorithm (IBSA) is proposed to degrade the tables with uneven illumination, low contrast, and uniform background. The morphological Thinning method extracts the line from the table. The masking approach extracts the row and column from the table. Finally, the ResNet Attention model optimized over Black Widow optimization-based mutual exclusion (BWME) is developed to recognize the table structure from the document images. The UNLV, TableBank, and ICDAR-2013 table competition datasets are used to evaluate the proposed framework’s performance. Precision and accuracy are the metrics considered for evaluating the proposed framework performance. From the experimental results, the proposed framework achieved a precision value of 96.62 and the accuracy value of 94.34, which shows the effectiveness of the proposed approach’s performance. Show more

Keywords: Image transformation, table extraction, ResNet Attention model, table structure recognition

DOI: 10.3233/JIFS-232646

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1101-1114, 2024

Authors: Zhang, Yu | Shen, Bo | Zhang, Jinglin | Zhang, Zhiyuan

Article Type: Research Article

Abstract:  The task of conversational machine reading comprehension (CMRC) is an extension of single-turn machine reading comprehension to multi-turn settings, to relflect the conversational way in which people seek information. The correlations between multiple rounds of questions mean that the conversation history is critical to solving the CMRC task. However, existing CMRC models ignore the interference that arises from using excessive historical information to answer the current question when incorporating the dialogue history into the current question. In this paper, an effective Question Selection Module (QSM) is designed to select most relevant historical dialogues when answering the current question through question …coupling and coarse-to-fine matching. In addition, most existing approaches perform memory inference by stacked RNNs at context word level, without considering semantic information flowing in the direction of conversation flow. In view of this problem, we implement sequential recurrent reasoning at the turn level of the dialogue, where the turn information contains all the filtered historical semantics for the current step. We conduct experiments on two benchmark datasets, QuAC and CoQA, released by Stanford University. The results confirm that our model satisfactorily captures the valid history and performs recurrent reasoning, and our model achieves an F1-score of 83.0% on CoQA dataset and 67.8% on QuAC dataset, outperforming the best alternative model by 4.6% on CoQA and 2.7% on QuAC. Show more

Keywords: Conversational machine reading comprehension, conversation history, recurrent reasoning, attention mechanism

DOI: 10.3233/JIFS-233828

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1115-1128, 2024

Authors: Li, Ping | Ni, Zhiwei | Zhu, Xuhui | Song, Juan | Liu, Wentao

Article Type: Research Article

Abstract: The histopathological image classification method, based on deep learning, can be used to assist pathologists in cancer recognition in colon histopathology. The popularization of automatic and accurate histopathological image classification methods in this way is of great significance. However, smaller medical institutions with limited medical resources may lack colon histopathology image training sets with reliable labeled information; thus they may be unable to meet the needs of deep learning for many labeled training samples. Therefore, in this paper, the colon histopathological image set with rich label information from a certain medical institution is taken as the source domain; the colon …histopathological image set from a smaller medical institution with limited medical resources is taken as the target domain. Considering the potential differences between histopathological images obtained by different institutions, this paper proposes a classification learning framework, namely unsupervised domain adaptation with local structure preservation for colon histopathological image classification, which can learn an adaptive classifier by performing distribution alignment and preserving intra-domain local structure to predict the labels of the colon histopathological images from institutions with lower medical resources. Extensive experiments demonstrate that the proposed framework shows significant improvement in accuracy and specificity of colon histopathological images without reliable labeled information compared to models without unsupervised domain adaptation. Specifically, in an affiliated hospital in Fuyang City, Anhui Province, the classification accuracy of benign and malignant colon histopathological images reaches 96.21%. The results of comparative experiments also show promising classification performance of our method in comparison with other unsupervised domain adaptation methods. Show more

Keywords: Colon cancer, histopathological image, cross-domain classification, unsupervised domain adaptation, transfer learning

DOI: 10.3233/JIFS-234920

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1129-1142, 2024

Authors: Zhang, Kai | Wang, Yixiang | Hu, Zhicheng | Zhou, Ligang

Article Type: Research Article

Abstract: Combination forecasting is an effective tool to improve the forecasting rate by combining single forecasting methods. The purpose of this paper is to apply a new combination forecasting model to predicting the BRT crude oil price based on the dispersion degree of two triangular fuzzy numbers with the circumcenter distance and radius of the circumcircle. First, a dispersion degree of two triangular fuzzy numbers is proposed to measure the triangular fuzzy numbers with the circumcenter distance and radius of the circumcircle, which can be used to predict the fluctuating trend and is suitable for crude oil futures price. Second, three …single prediction methods (ARIMA, LSSVR and GRNN) are then presented to combine traditional statistical time set prediction with the latest machine learning time prediction methods which can strengthen the advantage and weaken the disadvantage. Finally, the practical example of crude oil price forecasting for London Brent crude futures is employed to illustrate the validity of the proposed forecasting method. The experimental results show that the proposed forecasting method produces much better forecasting performance than some existing triangular fuzzy models. The prediction error is reduced to 2.7 from 3–5 in oil price combination forecasting, in another comparison experiment the error is reduced to 0.0135 from 1. The proposed combination forecasting method, which fully capitalizes on the time sets forecasting model and intelligent algorithm, makes the triangular fuzzy prediction more accurate than before and has effective applicability. Show more

Keywords: Oil price forecasting, dispersion degree of two triangular fuzzy numbers, ARIMA, LSSVR, GRNN

DOI: 10.3233/JIFS-230741

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1143-1166, 2024

Authors: Narayanan, Badri | Muthusamy, Sreekumar

Article Type: Research Article

Abstract:  The performance of Interval type-2 fuzzy logic system (IT2FLS) can be affected by many factors including the type of reduction methodology followed and the kind of membership function applied. Further, a particular membership function is influenced by its construction, the type of optimisation and adaptiveness applied, and the learning scheme adopted. The available literature lags in providing detailed information about such factors affecting the performance of IT2FLS. In this work, an attempt has been made to comprehensively study the factors affecting the performance of IT2FLS by introducing a new trapezoidal-triangular membership function (TTMF). A real-time application of drilling operation has …been considered as an example for predicting temperature of the job, which is considered as one of the key state variables to evaluate. A detailed comparison based on membership functions (MFs) such as triangular membership function (TrMF), trapezoidal membership function (TMF), the newly introduced trapezoidal-triangular membership function (TTMF), semi-elliptic membership function (SEMF), and Gaussian membership function (GMF) has been performed and presented. Further, the average error rate obtained with two “type-reduction” methods such as “Wu-Mendel” uncertainty bounds and Center of sets type reduction (COS TR) has also been discussed. This study provides information for selecting a particular MF and “type reduction” scheme for the implementation of IT2FLS. Also, concludes that MF having fewer parameters such as GMF and SEMF possess significant advantages in terms of computation complexity compared to others. Show more

Keywords: Interval type-2 fuzzy logic system, semi-elliptic membership function, trapezoidal membership function, trapezoidal-triangular membership function, center of sets type reduction, Wu-Mendel uncertainty bound

DOI: 10.3233/JIFS-231412

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1167-1182, 2024

Authors: Fan, Jianping | Zhu, Qianwei | Wu, Meiqin

Article Type: Research Article

Abstract: Failure mode and effect analysis (FMEA) is an effective quality management tool used to improve product quality and reliability. However, with the application of FMEA, its shortcomings are exposed regarding risk assessment, weight determination, and failure mode risk prioritization. This paper proposes a new FMEA model using VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method based on the Interval-valued linguistic Z-numbers (IVLZNs). Specifically, IVLZNs and the Interval-valued linguistic Z-numbers weighted arithmetic averaging (IVLZNWAA) operator are used to evaluate and aggregate risk information of failure modes; the maximum deviation method is used to determine the weight of risk factors; the IVLZNs-VIKOR method …is used to determine the risk priority of failure modes. Then, a numerical example is given to verify the effectiveness of the proposed model. Finally, a comparative analysis is made to demonstrate the feasibility and rationality of the proposed method. Show more

Keywords: Interval-valued linguistic Z-numbers (IVLZNs), interval-valued linguistic Z-numbers weighted arithmetic averaging (IVLZNWAA) operator, failure mode and effect analysis (FMEA), VIKOR method

DOI: 10.3233/JIFS-231527

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1183-1199, 2024

Authors: Nguyen, Anh Tu | Bui, Thanh Lam | Bui, Huy Anh | Nguyen, Sy Tai | Nguyen, Xuan Thuan

Article Type: Research Article

Abstract: With the superior development of technology, mobile robots have become an essential part of humans’ daily life. Consequently, interacting and dealing with them pushes us to develop and propose different suitable Human-Robot Interaction (HRI) systems that can detect the interacted user’s actions and achieve the desired output in real-time. In this paper, we propose a closed-loop smart mechanism for two main agents: the hand gloves’ controller and the mobile robot. To be more specific, the developed model employs flex sensors to measure the curve of the finger. The sensor signals are then processed by aiding the Hedge Algebras Algorithm to …control the movement direction and customize the speed of the mobile robot via wireless communication. Numerical simulation and experiments demonstrate that the mobile robot could operate reliably, respond rapidly to control signals, and vary its speed continually based on the different finger gestures. Besides, the control results are also compared with those obtained from the traditional fuzzy controller to prove the superiority and efficiency of the proposed method. Show more

Keywords: Hedge algebras algorithm, hand gestures, mobile robot, fuzzy controller, wireless protocol

DOI: 10.3233/JIFS-232116

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1201-1212, 2024

Authors: Li, Jing | Jia, Bin | Fan, Jiulun | Yu, Haiyan | Hu, Yifan | Zhao, Feng

Article Type: Research Article

Abstract: The relative entropy fuzzy c-means (REFCM) clustering algorithm improves the robustness of the fuzzy c-means (FCM) algorithm against noise. However, its increased complexity results in slower convergence. To address this issue, we have proposed a suppressed REFCM (SREFCM) algorithm, in which a constant suppression rate, α, is selected. However, in cases where external factors, such as changes in the data structure, are present, relying on a fixed α value may result in a decline in algorithm performance, which is clearly unsuitable. Therefore, the adaptive selection of parameters is a critical step. Based on the data structure itself, this paper proposes …an algorithm for adaptive parameter selection utilizing partition entropy coefficient and alternating modified partition coefficient, and compares it to six parameter selection algorithms based on generalized rules: θ ′ type, ρ type, β type, τ type, σ type and ξ type. Empirical findings indicate that adapting parameters can enhance the partitioning capability of the algorithm while ensuring a rapid convergence rate. Show more

Keywords: Suppressed relative entropy fuzzy c-means clustering algorithm, suppression rate, partition entropy coefficient, alternating modified partition coefficient, adaptive parameter selection

DOI: 10.3233/JIFS-232999

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1213-1228, 2024

Authors: Tang, Shangjie | Zhong, Youkun

Article Type: Research Article

Abstract: The development of rural preschool education (RPE) is not only related to the healthy growth of rural preschool children, but also to social fairness and sustainable development. Therefore, the development of RPE not only involves the expansion of quantity, but also the improvement of its quality. At present, in China’s RPE, the determination of value goals There are still many obstacles in terms of source supply, institutional mechanism construction, development mode selection, and external environment construction, which make the high-quality development of RPE lack good internal motivation and external support. In view of this situation, some researchers have begun to …explore the high-quality and sustainable development of RPE differently. However, the high-quality development of RPE is a systematic reform project that needs to start from the present. From multiple perspectives such as reality and history, internal and external education, this paper examines the systematic and global nature of RPE reform and development. The development level evaluation of RPE is a MADM. In this paper, the generalized weighted Bonferroni mean (GWBM) decision operator and power average (PA) is designed to manage the MADM under single-valued neutrosophic sets (SVNSs). Then, the generalized single-valued neutrosophic number power WBM (GSVNNPWBM) decision operator is constructed and the MADM model are constructed based on GSVNNPWBM decision operator. Finally, a decision example for development level evaluation of RPE and some useful comparative studies were constructed to verify the GSVNNPWBM decision operator. Show more

Keywords: MADM, single-valued neutrosophic sets (SVNSs), GWBM operator, PA operator, development level evaluation

DOI: 10.3233/JIFS-233121

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1229-1244, 2024

Authors: Fu, Liping

Article Type: Research Article

Abstract: Today, information technology has penetrated into various fields of universities, and the development of information technology in teaching, scientific research, management, and services has become a catalyst for promoting changes in universities. In terms of teaching informatization, the Internet provides a powerful tool for knowledge dissemination and a huge platform for learning and communication between university teachers and students. Knowledge sharing has become easier, and the era of mutual interaction between teachers and students has arrived. University teachers need to quickly face this challenge, adapt to the new teaching and learning environment, improve their own literacy, enhance their information-based teaching …ability, change their teaching behavior, and thereby improve the quality of university education and meet the needs of society for talent cultivation. The informationization teaching ability evaluation of university teachers is a classical MAGDM problems. Recently, the Exponential TODIM(ExpTODIM) and (grey relational analysis) GRA method has been used to cope with MAGDM issues. The interval neutrosophic sets (INSs) are used as a tool for characterizing uncertain information during the informationization teaching ability evaluation of university teachers. In this manuscript, the interval neutrosophic number Exponential TODIM-GRA (INN-ExpTODIM-GRA) method is built to solve the MAGDM under INSs. In the end, a numerical case study for informationization teaching ability evaluation of university teachers is given to validate the proposed method. Show more

Keywords: Multiple attribute group decision making (MAGDM), interval neutrosophic sets (INSs), ExpTODIM, GRA, informationization teaching ability evaluation

DOI: 10.3233/JIFS-233192

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1245-1258, 2024