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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: Rao, Juan | Peng, Ling | Rao, Jingjing | Cao, Xiaofen
Article Type: Research Article
Abstract: The evaluation of college physical education (PE) teaching quality is an indispensable part of the teaching process. Building a scientific, comprehensive, reasonable and effective evaluation system is crucial to improving the quality of college PE classroom teaching. This process is not easy, and requires long-term efforts and persistence. The PE teaching quality evaluation in Colleges and Universities is frequently viewed as the multiple attribute decision making (MADM) issue. In such paper, Taxonomy method is designed for MADM under double-valued neutrosophic sets (DVNSs). First, the score function of DVNSs and Criteria Importance Through Intercriteria Correlation (CRITIC) method is used to derive …the attribute weights. Second, then, the optimal choice is obtained through calculating the smallest double-valued neutrosophic number (DVNN) development attribute values from the DVNN positive ideal solution (DVNNPIS). Finally, a numerical example for PE teaching quality evaluation is given to illustrate the built method. Show more
Keywords: Multiple attribute decision making (MADM), double-valued neutrosophic sets (DVNSs), taxonomy method, CRITIC method, PE teaching quality
DOI: 10.3233/JIFS-230118
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10581-10590, 2023
Authors: Guo, Tianlong | Shen, Derong | Kou, Yue | Nie, Tiezheng
Article Type: Research Article
Abstract: Multi-view clustering that integrates the complementary information from different views for better clustering is a fundamental topic in data engineering. Most existing methods learn latent representations first, and then obtain the final result via post-processing. These two-step strategies may lead to sub-optimal clustering. The existing one-step methods are based on spectral clustering, which is inefficient. To address these problems, we propose a Multi-view fusion guided Matrix factorization based One-step subspace Clustering (MMOC) to perform clustering on multi-view data efficiently and effectively in one step. Specifically, we first propose a matrix factorization based multi-view fusion representation method, which adopts efficient matrix …factorization instead of time-consuming spectral representation to reduce the computational complexity. Then we propose a self-supervised weight learning strategy to distinguish the importance of different views, which considers both the gradient and the learning rate to make the learned weights closer to the real situation. Finally, we propose a one-step framework of MMOC, which effectively reduces the information loss by integrating data representation, multi-view data fusion, and clustering into one step. We conduct experiments on 5 real-world datasets. The experimental results show the effectiveness and the efficiency of our MMOC method in comparison with state-of-the-art methods. Show more
Keywords: multi-view clustering, matrix factorization, weight learning, subspace clustering
DOI: 10.3233/JIFS-224578
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10591-10604, 2023
Authors: Sudha, V. | Shanmugam, Sathiya Priya | Anitha, D. | Raja, R.
Article Type: Research Article
Abstract: An intelligent segmentation and identification of edemas diseases constitutes a most important crucial ophthalmological issues since they provide important information for the diagnosis process in accordance to the disease severity. But diagnosing the different edema diseases using the OCT-images are considered to be daunting challenge among the researchers. The implementation of computational intelligence techniques such as machine learning, deep learning, bio inspired algorithms and image processing techniques may help the doctors for some extent in improving the automatic extraction and diagnosis process consequently improving patients’ life quality. But, these are liable to more errors and less performance, which requires further …improvisation in designing the intelligent systems for an effective classification of edema diseases. In this context, this paper proposes the hybrid intelligent framework for the identification, segmentation and classification of three types of edemas such as using the retinal optical coherence tomography (OCT) Images. In this process, Single Feed Forward Training networks (SLFTN) are integrated with Convolutional Layers whose hyperparameters are tuned by using Lion Optimization algorithm. An intensive experimentation is carried out using the Kaggle Retinal OCT Image datasets-2020 with Tensor flow and the proposed framework is trained with the different set of 84,494 images in which performance metrics such as accuracy, sensitivity, specificity, recall and f1score are calculated. Results shows the proposed system has provided satisfactory performance, reaching the average highest accuracy of 99.9% in identifying and classifying the respectively. Show more
Keywords: Machine learning, deep learning, retinal optical coherance tomography images, convolutional layers, lion optimization algorithm
DOI: 10.3233/JIFS-230128
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10605-10620, 2023
Authors: Ye, Tingqing | Zheng, Haoran
Article Type: Research Article
Abstract: Uncertain statistics is a set of mathematical techniques to collect, analyze and interpret data based on uncertainty theory. In addition, probability statistics is another set of mathematical techniques based on probability theory. In practice, when to use uncertain statistics and when to use probability statistics to model some quality depends on whether the distribution function of the quality is close enough to the actual frequency. If it is close enough, then probability statistics may be used. Otherwise, uncertain statistics is recommended. In order to illustrate it, this paper employs uncertain statistics, including uncertain time series analysis, uncertain regression analysis and …uncertain differential equation, to model the birth rate in China, and explains the reason why uncertain statistics is used instead of probability statistics by analyzing the characteristics of the residual plot. In addition, uncertain hypothesis test is used to determine whether the estimated uncertain statistical models are appropriate. Show more
Keywords: Uncertainty theory, uncertain time series analysis, uncertain regression analysis, uncertain differential equation, birth rate
DOI: 10.3233/JIFS-230179
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10621-10632, 2023
Authors: Khan, Asghar | Aslam, Muhammad | Iqbal, Quaid
Article Type: Research Article
Abstract: Many unknowable elements make it difficult to measure cyclone disasters, traditional methods are insufficient to measure these factors. Fuzzy set theory and its expansions are effective ways to measure these uncertainties for these kinds of uncertainty. An evaluation of the cyclone disaster’s spatial vulnerability is necessary in order to build disaster damage reduction methods. In real life, we may come into a hesitant environment when making decisions. To explore such environments, we introduce hesitant fuzzy set (HFS) into Fermatean fuzzy set (FFS) and extend the existing research effort on FFSs in light of the effective tool of HFSs for expressing …the hesitant condition. In this study, we develop a comprehensive tropical cyclone disaster assessment by applying Fermatean hesitant fuzzy (FHF) information. In this paper, various unique aggregation strategies for the analysis of decision-making problems are introduced. As a result, Fermatean hesitant fuzzy average (FHFWA), Fermatean hesitant fuzzy ordered weighted average (FHFOWA), Fermatean hesitant fuzzy weighted geometric (FHFWG), and Fermatean hesitant fuzzy ordered weighted geometric (FHFOWG) operators have been developed. We also go over some of the most important features of these operators. Furthermore, we establish an algorithm for addressing a multiple attribute decision-making issue employing Fermatean hesitant fuzzy data by using these operators. and attribute prioritizing. A real-world problem of cyclone disaster damages in several parts of Pakistan is explored to test the applicability of these operators. In the final section, we expand the TOPSIS approach to a Fermatean hesitant fuzzy environment and compare the outcomes of the extended TOPSIS method with operators established in the FHF-environment. Show more
Keywords: Cyclone disaster, FHFSs, Aggregation operatos, TOPSIS method, MADM
DOI: 10.3233/JIFS-222144
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10633-10660, 2023
Authors: Zou, Yuan
Article Type: Research Article
Abstract: Bayesian decision models use probability theory as a commonly technique to handling uncertainty and arise in a variety of important practical applications for estimation and prediction as well as offering decision support. But the deficiencies mainly manifest in the two aspects: First, it is often difficult to avoid subjective judgment in the process of quantization of priori probabilities. Second, applying point-valued probabilities in Bayesian decision making is insufficient to capture non-stochastically stable information. Soft set theory as an emerging mathematical tool for dealing with uncertainty has yielded fruitful results. One of the key concepts involved in the theory named soft …probability which is as an immediate measurement over a statistical base can be capable of dealing with various types of stochastic phenomena including not stochastically stable phenomena, has been recently introduced to represent statistical characteristics of a given sample in a more natural and direct manner. Motivated by the work, this paper proposes a hybrid methodology that integrates soft probability and Bayesian decision theory to provide decision support when stochastically stable samples and exact values of probabilities are not available. According to the fact that soft probability is as a special case of interval probability which is mathematically proved in the paper, thus the proposed methodology is thereby consistent with Bayesian decision model with interval probability. In order to demonstrate the proof of concept, the proposed methodology has been applied to a numerical case study regarding medical diagnosis. Show more
Keywords: Soft probability, interval probability, Bayes rule, interval numbers, possibility degree
DOI: 10.3233/JIFS-223020
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10661-10673, 2023
Authors: Qureshi, Saima Siraj | He, Jingsha | Qureshi, Sirajuddin | Zhu, Nafei | Zardari, Zulfiqar Ali | Mahmood, Tariq | Wajahat, Ahsan
Article Type: Research Article
Abstract: The intelligence-embedded devices also called the Internet of Things, have heterogeneous characteristics and generate enormous data. To oblige the expansion of smart devices in the age of data, software-defined networking (SDN) has emerged as a promising cost-effective, scalable, versatile solution for IoT services. However, the proliferation and pervasiveness of IoT devices also bring some serious security concerns of cyber threats and attacks. This work presents a real-time intrusion detection and mitigation solution for SDN-enabled IoT infrastructure to provide autonomous security in high-traffic IoT networks in the 5G and beyond future. The suggested approach used an ensemble of Convolution Long short-term …memory + Bidirectional Long short-term memory (ConvLSTM2D+BLSTM) at the SDN network layer to automate flow feature extraction and classification. For evolving distributed denial of service (DDoS) attacks, the testing is conducted on the CICIDS2017 dataset. The experimental results showed that the proposed security approach could detect and mitigate threats in real-time with high accuracy of 99.69% rate, 99.93% precision, and 99.65% F1-score by performing 10-fold cross-validation. Show more
Keywords: Convolutional Neural Network, Deep Neural Network, Deep learning, Distributed Denial of Service (DDoS), Internet of things, Long Short-term Memory, Security, Software-defined Network
DOI: 10.3233/JIFS-220932
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10675-10687, 2023
Authors: Guo, Xiaoyong | Zhang, Kai | Peng, Jiahan | Chen, Xiaoyan | Guo, Guangjie
Article Type: Research Article
Abstract: This paper proposes that the task of single-image low-light enhancement can be accomplished by a straightforward method named Opt2Ada. It contains a series of pixel-level operations, including an opt imized illuminance channel decomposition, an ada ptive illumination enhancement, and an ada ptive global scaling. Opt2Ada is traditional and it does not rely on architecture engineering, super-parameter tuning, or specific training dataset. Its parameters are generic and it has better generalization capability than existing data-driven methods. For evaluation, both the full-reference, non-reference, and semantic metrics are calculated. Extensive experiments on real-world low-light images demonstrate the superiority of Opt2Ada over recent traditional …and deep learning algorithms. Due to its flexibility and effectiveness, Opt2Ada can be deployed as a pre-processing subroutine for high-level computer vision applications. Show more
Keywords: Low-light image enhancement, Image processing, Traditional method
DOI: 10.3233/JIFS-222644
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10689-10702, 2023
Authors: Akbaba, Ümmügülsün | Hikmet Değer, Ali
Article Type: Research Article
Abstract: In this study, new matrices which produce the Pell and Pell-Lucas numbers are given. By using these matrices, new identities and relations related to the Pell and Pell-Lucas numbers are obtained.
Keywords: Pell numbers, Pell-Lucas numbers, matrices
DOI: 10.3233/JIFS-222957
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10703-10707, 2023
Authors: He, Zihang | Zhao, Kaiyan | Li, Bohan | Li, Yong
Article Type: Research Article
Abstract: This paper proposes an approach that regulates the confidence of predicted boxes for corner-based detection methods. Corner-based methods have achieved state-of-the-art performance on MS-COCO by predicting corners and grouping them to generate boxes. However, the box confidence is simply defined to be the average score of grouped corners, ignoring the score and tag discrepancy between them. The discrepancy may lead to the generation of more false positives (FPs) since a larger discrepancy often means that the grouped corners less likely belong to the same object. Observing this, this paper proposes introducing the discrepancy of corners (DoC) to decrease the box …confidence. Also, the score and location of center (SLoC) of a detection box is utilized to further finely regulate the confidence. DoC and SLoC can effectively reduce FPs and missings and hence improve the detection performance without changing any model parameter. Experimental results on MS-COCO also show improvements. Show more
Keywords: Object detection, anchor-free, corner-based
DOI: 10.3233/JIFS-212804
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10709-10720, 2023
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