Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Purchase individual online access for 1 year to this journal.
Price: EUR 315.00Impact Factor 2024: 1.7
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: Liang, Meishe | Mi, Jusheng | Feng, Tao | Jin, Chenxia
Article Type: Research Article
Abstract: Knowledge acquisition in intuitionistic fuzzy information systems is of importance because those fuzzy information systems are often encountered in many real-life problems. Formal concept analysis is a simple and effective tool for knowledge acquisition. However, there is still little work on introducing knowledge acquisition methods based on formal concept analysis into intuitionistic fuzzy information systems. This paper mainly extends the formal concept theory into intuitionistic fuzzy information systems. Firstly, two pairs of adjoint mappings are defined in intuitionistic fuzzy formal contexts. It is verified that both pairs of adjoint mappings form Galois connections. Secondly, two types of intuitionistic fuzzy concept …lattices are constructed. After that, we also present the main theorems and propositions of the intuitionistic fuzzy concept lattices. Thirdly, we deeply discuss the attribute characteristics for type-1 generalized one-sided intuitionistic fuzzy concept lattice. Furthermore, a discernibility matrix-based algorithm is proposed for attribute reduction and the effectiveness of this algorithm is demonstrated by a practical example. The construction of intuitionistic fuzzy conceptS is meaningful for the complex and fuzzy information in real life. Show more
Keywords: Formal concept analysis, attribute reduction, galois connection, intuitionistic fuzzy formal context
DOI: 10.3233/JIFS-202719
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3561-3573, 2022
Authors: Zhang, Nan | Xue, Xiaoming | Jiang, Wei | Gu, Yuanhui | Shi, Liping | Chen, Xiaogang | Zhou, Jianzhong
Article Type: Research Article
Abstract: This paper proposes a novel Takagi–Sugeno fuzzy model identification method by combining fuzzy c-regression model clustering (FCRM), least squares support vector machine (LSSVM) and intelligent optimization algorithm. Firstly, in order to improve the performance of FCRM for the complex nonlinear dataset, in this paper the method of FCRM based on LSSVM (FCRM-LSSVM) is proposed to discover the data structure and obtain the antecedent parameters. And then, a newly developed intelligent optimization algorithm by hybridizing Harris hawks optimization and moth-flame optimization algorithm (IHHOMFO) is proposed to further optimize the antecedent membership function parameters obtained by the FCRM-LSSVM. Finally, the proposed novel …T-S fuzzy model identification combines FCRM, LSSVM and IHHOMFO for solving actual model identification problems. Experiments on five different datasets demonstrate that the proposed method is more efficient than conventional methods, such as T-S model identification based on fuzzy c-means (FCM), FCRM and FCRM-LSSVM, in standard measurement indexes. This study thus demonstrates that the proposed method is a credible and competitive fuzzy model identification method. The novel method contributes not only to the theoretical aspects of fuzzy model, but is also widely applicable in data mining, image recognition and prediction problems. Show more
Keywords: T-S fuzzy model, fuzzy c-regression model, least squares support vector machine, hybrid optimization algorithm
DOI: 10.3233/JIFS-211093
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3575-3598, 2022
Authors: Yang, Junfeng | Huang, Yuwen | Guo, Yubin | Huang, Fuxian | Li, Jing
Article Type: Research Article
Abstract: Although some methods of feature extraction for photoplethysmography (PPG) biometric recognition have been extensively studied, effectiveness of local features, time cost of feature extraction, and robust identification for small-scale data remain challenging. To address these issues, we proposed a feature-extraction method of PPG biometrics combining singular value decomposition with local mean decomposition and time-domain parameters. First, we used the singular-value-decomposition method to de-noise the original PPG data. Second, we extracted the local-mean-decomposition-based and time-domain features, which are fused into a concatenated feature. Finally, we combined the concatenated feature with four classifiers for classification and decision-making. Extensive experiments on the three …datasets have shown that the waveform of the PPG signal de-noised by singular value decomposition was smoother and more regular, the concatenated feature had strong inter-subject distinguishability and intra-subject similarity, and the concatenated feature combined with a random-forest classifier was the best and could achieve 99.40%, 99.88%, and 99.56% recognition rates on the respective datasets. The method is competitive with several state-of-the-art methods. Show more
Keywords: PPG biometrics, singular value decomposition, local mean decomposition, time-domain parameters
DOI: 10.3233/JIFS-212086
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3599-3610, 2022
Authors: Jiang, Weiwei | Luo, Jiayun
Article Type: Research Article
Abstract: Drought is a serious natural disaster that has a long duration and a wide range of influences. To decrease drought-induced losses, drought prediction is the basis of corresponding drought prevention and disaster reduction measures. While this problem has been studied in the literature, it remains unknown whether drought can be precisely predicted with machine learning models using weather data. To answer this question, a real-world public dataset is leveraged in this study, and different drought levels are predicted using the last 90 days of 18 meteorological indicators as the predictors. In a comprehensive approach, 16 machine learning models and 16 …deep learning models are evaluated and compared. The results show that no single model can achieve the best performance for all evaluation metrics simultaneously, which indicates that the drought prediction problem is still challenging. As benchmarks for further studies, the code and results are publicly available in a GitHub repository. Show more
Keywords: Drought prediction, weather data, machine learning, deep learning
DOI: 10.3233/JIFS-212748
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3611-3626, 2022
Authors: Yue, Xiaofeng | Ma, Guoyuan | Gao, Xueliang | Lu, Yucheng
Article Type: Research Article
Abstract: The surface inspection of strip steel defects plays a vital role in the industry, and it has attracted widespread attention in the industry. In this paper, an improved sparrow search algorithm (WMR-SSA) with intelligent weighting factors and mutation operators is proposed, WMR-SSA can balance the development capability of the algorithm based on the number of iterations. In addition, WMR-SSA enhances the local search capability of the algorithm through mutation operators. At the same time, the algorithm determines the initial position of the population by random walk to enhance the diversity of the population. The WMR-SSA algorithm is compared with GA, …PSO, CS, GWO, BSA, and original SSA, and the experiment proves that the WMR-SSA algorithm is better than other algorithms. In this study, WMR-SSA is combined with BP neural network and implemented for the classification of defective strip images. The accuracy and stability of WMR-SSA-BP are effectively demonstrated experimentally by comparing it with classifiers optimized by other intelligent algorithms. Show more
Keywords: Defect detection, sparrow search algorithm (SSA), intelligent weighting factor, mutation operator, random walk
DOI: 10.3233/JIFS-212883
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3627-3653, 2022
Authors: Zheng, Ting | Li, Shangze | Zhang, Luyan
Article Type: Research Article
Abstract: The silicon dioxide is the hardest part to melt among the iron tailing components, the melting behavior of iron tailing can be represented by the melting behavior of silicon dioxide. Estimating the real-time melting rate of silicon dioxide in the time sequence provide guidance for the tailing addition and heat compensation in the process of slag cotton preparation, also indirectly improved the direct fiber forming technology of blast furnace slag. The position of silicon dioxide particles in the high-temperature molten pool during the melting process is changing constantly, using a strong weighted distance centroid algorithm to rack the centroid position …of silicon dioxide particles during the melting process, and present the motion trail of centroid of silicon dioxide. In the paper, extracting indexes which represent the edge outline characteristics of silicon dioxide during the melting process of silicon dioxide using Snake active contour algorithm combined with Sobel operator, include shape, perimeter and area. Using the extracted skeleton characteristics, a three-dimensional skeleton generation model is created. From the skeleton data, estimating the volume of silicon dioxide and determine the parameter formula for the actual melting rate of silicon dioxide. The silicon dioxide melting rate at each moment is calculated by numerical simulation. The results of the Hough test circle and the silicon dioxide melting rate are verified. The rationality of the model is further determined. Show more
Keywords: Silicon dioxide melting, active contour, star skeleton, depth estimation, machine vision
DOI: 10.3233/JIFS-212971
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3655-3677, 2022
Authors: Almseidin, Mohammad | Al-Sawwa, Jamil | Alkasassbeh, Mouhammd
Article Type: Research Article
Abstract: Nowadays, with the rapid increase in the number of applications and networks, the number of cyber multi-step attacks has been increasing exponentially. Thus, the need for a reliable and acceptable Intrusion Detection System (IDS) solution is becoming urgent to protect the networks and devices. However, implementing a robust IDS needs a reliable and up-to-date dataset in order to capture the behaviors of the new types of attacks especially a multi-step attack. In this paper, a new benchmark Multi-Step Cyber-Attack Dataset (MSCAD) is introduced. MSCAD includes two multi-step scenarios; the first scenario is a password cracking attack, and the second attack …scenario is a volume-based Distributed Denial of Service (DDoS) attack. The MSCAD was assessed in two manners; firstly, the MSCAD was used to train IDS. Then, the performance of IDS was evaluated in terms of G-mean and Area Under Curve (AUC). Secondly, the MSCAD was compared with other free open-source and public datasets based on the latest keys criteria of a dataset evaluation framework. The results show that IDS-based MSCAD achieved the best performance with G-mean 0.83 and obtained good accuracy to detect the attacks. Besides, the MSCAD successfully passing twelve keys criteria. Show more
Keywords: Intrusion detection system (IDS), multi-step cyber-attacks, machine learning, resampling algorithms, intrusion datasets
DOI: 10.3233/JIFS-213247
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3679-3694, 2022
Authors: Subarna, T.G. | Sukumar, P.
Article Type: Research Article
Abstract: Earlier detection of cervical cancer in women can save their lives before a chronic development. The accurate detection in cancer tissues of cervix in the human body is very important. In this article, cervical images were classified into either affected or healthy images using deep learning architecture. The proposed approach was designed with the modules of Edge detector, complex wavelet transform, feature derivation and Convolutional Neural Networks (CNN) architecture with segmentation. The edge pixels in the source cervical image were detected using Kirsch’s edge detector, the Complex Wavelet Transform (CWT) was there used to decompose the edge detected cervical images …into number of sub bands. Local Derivative Pattern (LDP) and statistical features were computed from the decomposed sub bands and feature map was constructed using the computed features. The featured map along with the source cervical image was fed into the Cervical Ensemble Network (CEENET) model for classifying of cervical images into the classes healthy or cancer (affected). Show more
Keywords: Cervix, deep learning, CNN, cervical image, cancer
DOI: 10.3233/JIFS-220173
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3695-3707, 2022
Authors: Cao, Jing | Xu, Xuan-hua | Chen, Yudi | Ji, Wenying
Article Type: Research Article
Abstract: During and after an emergency event, multiple organizations with various specialties are involved in consensus decision-making to reduce the loss of lives and property in a timely manner. However, timely, high-consensus decision-making is challenging due to communication barriers between participating organizations. Thus, this study generalizes a conceptual communication network considering communication barriers by reviewing multiple historical emergencies and proposes a quantitative communication network model by integrating an opinion dynamics model and social network analysis (SNA). An illustrative example is provided by simulating two emergency decision-making scenarios to verify the proposed model. A case study of the 2013 Qingdao oil pipeline …explosion is presented to demonstrate the feasibility and applicability of the proposed model. The results of the case study indicate that the proposed model can accurately quantify the impact of communication barriers on the opinion formation time. This research provides a quantitative toolkit for understanding and improving decision-making performance in various emergencies. Show more
Keywords: Interorganizational communication network, communication barriers, opinion formation, emergency decision-making
DOI: 10.3233/JIFS-212102
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3709-3726, 2022
Authors: Parimala, V. | Devarajan, K.
Article Type: Research Article
Abstract: The recent decade has seen a rapid evolution of communication technologies and standards with the ultimate goal of providing global users with seamless connectivity a data access. Conventional methods of communication have been completely replaced by state-of-the-art hand-held gadgets and portable devices that enable users to communicate at high transmission rates. However, as high-end devices and gadgets become more popular and their demand for operating frequency which is essentially the Radio Frequency (RF) band in the EM (Electro Magnetic) spectrum tends to force the limits to the higher end of the RF spectrum. Due to the limitation of RF band …availability, a spectrum is constructed for the requesting user for promising solution, and a difficult task. The emerging cognitive radio networks are a set of intelligent tools and scheme of identify the vacant spots in the band through effective sensing and allocating the spectrum to the requesting users. A modified cluster-based model has been proposed as part of extensive research on spectrum sensing. In the proposed work, a two-phase clustering model in the form of modified Fuzzy C-Means (FCM), and K-Means clustering is used, in which FCM is used as a training module on the channel features. K-Means is effectively used as an unsupervised classifier model. The proposed classification model was tested in a densely populated cognitive radio network compared to standard methods such as SVM (Support Vector Machine), FCM, and K-Means. Superior performance in terms of quality metrics like 90% classification accuracy, 91% spectral utility 90% are notable findings of this research work. Show more
Keywords: Cognitive radio network, clustering, spectrum utilization, support vector machine, fuzzy C-means K-means
DOI: 10.3233/JIFS-212863
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 3727-3740, 2022
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl