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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: Liang, Weijing | Xue, Ye | Xu, Jing
Article Type: Research Article
Abstract: With the increasing global disaster risks, constructing more inclusive, flexible, and resilient communities has become crucial for effectively carrying out disaster prevention, mitigation, and relief work. However, existing research on community resilience mostly focuses on the selection of key factors and the assessment of community resilience, lacking in-depth exploration of the interactions between factors and simulation studies of key paths. Therefore, this paper applies the Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy DEMATEL) method to select important factors of community resilience. Based on this, the maximum average difference entropy method is used to analyze the relationships and influence mechanisms among …different factors, thus identifying the key factors and key paths affecting community resilience. The Fuzzy Cognitive Map (FCM) is then used to simulate the paths. The study finds that factors of community resilience can be categorized as input, intermediary, and output types, and further analysis of their influence mechanisms reveals four key paths and four key factors. Through pathway simulation, different improvement states of community resilience are observed when triggering the input-type factors of the key paths. Therefore, under limited resources, a phased and systematic approach to enhancing community resilience should be adopted. The contribution of this study lies in providing a comprehensive analysis of factors and pathway selection methods, and through pathway simulation, it offers a scientific basis and decision support for improving and constructing community resilience in practice. Show more
Keywords: Fuzzy cognitive map, fuzzy DEMATEL, maximum average difference entropy method, community resilience, simulation analysis
DOI: 10.3233/JIFS-232234
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-23, 2024
Authors: Zhang, Shuguang | Xie, Chengyuan | Zhang, Heng | Gong, Wenzheng | Liu, Lingjie | Zhi, Xuntao
Article Type: Research Article
Abstract: Graph Convolutional Networks (GCN) are prevalent techniques in collaborative filtering recommendations. However, current GCN-based approaches for collaborative filtering recommendation have limitations in effectively embedding neighboring nodes during node and neighbor information aggregation. Furthermore, weight allocation for the user (or item) representations after convolution of each layer is too uniform. To resolve these limitations, we propose a new Graph Convolutional Collaborative Filtering recommendation method based on temporal information during the node aggregation process (TA-GCCF). The method aggregates and propagates information using Gated Recurrent Units, while dynamically updating features based on the timing and sequence of interactions between nodes and their neighbors. …Concurrently, we have developed a convolution attention coefficient to ascertain the significance of embedding at distinct layers. Experiments on three benchmark datasets show that our method significantly outperforms the comparison methods in the accuracy of prediction. Show more
Keywords: Graph convolutional neural network, collaborative filtering, recommendation, gated recurrent units, temporal information
DOI: 10.3233/JIFS-238307
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Vela-Rincón, Virna V. | Mújica-Vargas, Dante | Luna-Álvarez, Antonio | Arenas Muñiz, Andrés Antonio | Cruz-Prospero, Luis A.
Article Type: Research Article
Abstract: Image segmentation is a very studied area, looking for the best clustering of pixels. However, it is sometimes a complicated task, especially when these pixels are at the edges of regions, where there is a gradient and it is difficult to decide to which region to assign it. Hesitating fuzzy sets (HFS) better describe these situations, allowing to have multiple possible values for each element, giving more flexibility. This type of sets has been mainly applied in decision-making problems, obtaining better results than other types of fuzzy sets. This research proposes a fast and automatic method based on fuzzy hesitant …clustering (FAHFC), which does not require parameters since it is capable of determining the number of clusters, using the Calinski-Harabasz index, in which the segmentation is performed, solving the initialization problem in clustering; it also proposes an alternative to construct the HFS through the use of fuzzy relations. The experiments show superiority in terms of clustering quality and convergence over some selected state-of-the-art algorithms. Show more
Keywords: Fuzzy clustering, hesitant fuzzy sets, image segmentation, Calinski-Harabasz index
DOI: 10.3233/JIFS-219370
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Zhou, Ning | Ren, Zhihao
Article Type: Research Article
Abstract: Traffic flow prediction is a significant application of deep learning in spatio-temporal forecasting analysis. Existing research faces challenges that hinder prediction accuracy. One challenge is the inadequate capturing of spatial dependencies in traffic flow due to fixed pre-defined graph structures. Moreover, manually designed prior graphs still have limitations in extracting spatial features. Another challenge is the instability in short-term prediction performance when pre-defined graphs are completely abandoned in favor of parameter training. Additionally, ordinary RNN sequence convolution methods also struggle to capture long-term sequential patterns in large historical traffic data, leading to gradient vanishing or exploding issues. To address these …challenges, we proposes a graph convolutional network for traffic flow prediction. We combine an improved prior graph with an adaptive graph to form a dual-branch spatio-temporal neural network. In the first branch, we introduce a time graph based on rough data inference to complement the predefined static graph. In the second branch, we construct an adaptive learning framework that dynamically learns the adjacency matrix and captures global road information. By utilizing enhanced multi-scale gated convolution, we extract spatio-temporal dependencies. Our method surpasses most baseline approaches according to extensive experiments conducted on public datasets. Show more
Keywords: Traffic flow prediction, graph convolutional network, adaptive graph, rough data inference, dual-branch
DOI: 10.3233/JIFS-236819
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Milovanović, Vladimir | Aleksić, Aleksandar | Milenkov, Marjan | Sokolović, Vlada
Article Type: Research Article
Abstract: The paper aims to present a hybrid model for measuring the performance of business processes in complex organizations based on the subjective decision-making of expert teams. The subject of the research is finding ways to measure, analyze and improve the key performance indicators (KPIs) process. Obtaining the values of KPIs, which reflect the real state of the process, creates a basis for their ranking, i.e. insight into KPIs that are extremely important for the process as well as KPIs that are of lesser importance, but as such are not excluded from consideration because they are necessary for the beginning, realization …and completion of the process. The model was compiled through five phases and was tested through a case study in a real business organization, which deals with the maintenance of complex combat systems. The obtained results helped the management to take certain measures in order to improve the performance of the maintenance process. In the model, it is proposed to form two expert teams, which make assessments based on experience and express them in linguistic terms according to a predefined scale. Modeling of linguistic expressions is realized using intuitive fuzzy sets of a higher order, more precisely Fermatean fuzzy sets (FFS). Selecting KPIs, decomposing the process into sub-processes and assessing the relative importance of sub-processes is carried out by one team of experts, while another team carries out the assessment of KPIs at the level of each sub-process. Determining the relative importance of sub-processes is realized using the Delphi method extended to FFS while reaching a consensus. The measurement of process performance, i.e. the value of KPIs, is realized using Multi-Criteria Group Decision-Making (MCGDM), such as the ELECTRE method extended with FFS. The sensitivity analysis of the developed model is realized by uncertainty modeling with q-rung orthopair fuzzy sets. Show more
Keywords: Fermatean fuzzy set (FFSs), ELECTRE method, Delphi method, maintenance, performance
DOI: 10.3233/JIFS-238907
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Xin, Ling
Article Type: Research Article
Abstract: In the era of digital economy, the optimization of enterprise supply chain networks has become a key challenge, while the problems of traditional supply chains, including information asymmetry and lack of trust, seriously hinder the development of enterprise supply chain networks. This paper will use the blockchain distributed technology and the digital economy background to explore how to use the blockchain distributed technology to optimize the existing problems. Firstly, study the supply chain information sharing to develop resources to reduce costs, then use the application of block chain technology and smart contract to establish information sharing mechanism to help the …supply chain information more transparent and improve trust; secondly, use the block chain technology decentralized storage model to realize the decentralized supply chain research, and finally use the consensus method to improve the privacy protection of information, to avoid information asymmetry among users. Through experiments, it could be found that the optimization method of enterprise supply chain network based on blockchain distributed technology had a traceability accuracy of over 92.35% for the extracted products, with an average traceability accuracy of 93.791% for 10 products. Research on the transparency of different supply chain information was above 89.73% . By utilizing blockchain distributed technology, information protection in enterprise supply chains could be effectively improved; trust mechanisms could be better established; risk control effectiveness could be improved; optimization of enterprise supply chain networks could be better assisted. Show more
Keywords: Enterprise supply chain network optimization, blockchain distributed technology, digital economy, smart contracts, deep learning
DOI: 10.3233/JIFS-234664
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Cruz, Eddy Sánchez-Dela | Fuentes-Ramos, Mirta | Loeza-Mejía, Cecilia-Irene | José-Guzmán, Irahan-Otoniel
Article Type: Research Article
Abstract: Purpose: Vaginal infections are prevalent causes of gynecological consultations. This study introduces and evaluates the efficacy of four Machine Learning algorithms in detecting vaginitis cases in southern Mexico. Methods: Utilizing Simple Perceptron, Naïve Bayes, CART, and AdaBoost, we conducted classification experiments to identify four vaginitis subtypes (gardnerella, candidiasis, trichomoniasis, and chlamydia) in 600 patient cases. Results: The outcomes are promising, with a majority achieving 100% accuracy in vaginitis identification. Conclusion: The successful implementation and high accuracy of these algorithms demonstrate their potential as valuable diagnostic tools for vaginal infections, particularly in southern Mexico. It …is crucial in a region where health technology adoption lags behind, and intelligent software support is limited in gynecological diagnoses. Show more
Keywords: Machine learning, gynecological pathologies, vaginitis, local dataset, correct identification
DOI: 10.3233/JIFS-219377
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Xie, Mengtong | Chai, Huaqi
Article Type: Research Article
Abstract: A human resources management plan is presently recognised as one of the most important components of a corporate technique. This is due to the fact that its major purpose is to interact with people, who are the most precious asset that an organisation has. It is impossible for an organisation to achieve its objectives without the participation of individuals. An organisation may effectively plan as well as manage individual processes to support the organization’s objectives and adapt nimbly to any change if it has well-prepared HR techniques and an action plan for its execution. This investigation puts up a fresh …way for the board of directors of a private firm to increase their assets and advance their growth by using cloud programming that is characterised by networks. The small company resource has been improved by strengthening human resource management techniques, and the cloud SDN network is used for job scheduling using Q-convolutional reinforcement recurrent learning. The proposed technique attained Quadratic normalized square error of 60%, existing SDN attained 55%, HRM attained 58% for Synthetic dataset; for Human resources dataset propsed technique attained Quadratic normalized square error of 62%, existing SDN attained 56%, HRM attained 59%; proposed technique attained Quadratic normalized square error of 64%, existing SDN attained 58%, HRM attained 59% for dataset. Show more
Keywords: Small business management, cloud software defined networks, human resource management, task scheduling, recurrent learning
DOI: 10.3233/JIFS-235379
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Cortés-Antonio, Prometeo | Valdez, Fevrier | Melin, Patricia | Castillo, Oscar
Article Type: Research Article
Abstract: The computing with words is an approach that has unique characteristics and advantages to model cognitive processes, this article explains the relationship and difference between type-1 and type-2 fuzzy sets in the definition of linguistic values. Here, we perform a compressive review and justify because type-2 sets are more appropriate in modeling linguistic values, and a heuristic procedure by examples is carried out to define linguistic values on a continuous variable. A visual comparison of a rule-based system, when linguistic values use crips, type-1, and type-2 fuzzy sets in modeling a cognitive system.
Keywords: Type-2 and type-1 fuzzy sets, linguistic values and variables, rule-based systems, cognitive computing
DOI: 10.3233/JIFS-219368
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Li, Zhiyuan | Hu, Chunhua | Hou, Zhanshan
Article Type: Research Article
Abstract: This study goes into the complexities of innovation and entrepreneurial skills by developing a detailed linear model and exploring the essential components that make up these talents. A multi-objective function model is presented to assess the effectiveness of using and distributing educational resources in this setting. For this assessment, the study uses the grey correlation method. Through a series of experimental simulations, the study demonstrates that the optimisation approach significantly improves the utilisation and allocation efficiency of educational resources committed to innovation and entrepreneurship by 18.72% and 20.98%, respectively. This results in a more balanced resource utilisation, which helps to …enhance the allocation of educational resources. A major conclusion of this study is the correlation value of 0.3177 with ideal entrepreneurship, which indicates a high degree of excellence in innovation and entrepreneurship education reached across the population analysed.. Show more
Keywords: Linear spatial model, grey correlation, resource allocation, multi-objective optimization, innovation and entrepreneurship
DOI: 10.3233/JIFS-236992
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
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