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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: Gao, Kelian | Yu, Fangqing
Article Type: Research Article
Abstract: With the emergence of various new teaching concepts and teaching models in colleges and universities, the original evaluation system can no longer fully meet the requirements of the current development of physical education curriculum teaching in colleges and universities. Thus, reducing the judgment and guidance of evaluation on physical education curriculum teaching shall result in the development of physical education curriculum teaching quality evaluation lagging behind the development of teaching and being in a passive position. Therefore, it is particularly important to establish a new evaluation standard system for the teaching quality of physical education courses. The teaching quality evaluation …of college physical education (PE) is viewed as multiple attribute decision-making (MADM). In this paper, an enhanced probabilistic simplified neutrosophic grey relational analysis (PSN-GRA) method is designed for MADM. Then, in the environment of probabilistic simplified neutrophil set (PSNSs), the PSN-GRA method and CRITIC method are combined to rank the alternative schemes, and a numerical example of college physical education teaching quality evaluation proves the practicability of the new method and compares it with other methods. The results show that this method is simple, effective and simple in calculation. Show more
Keywords: Multiple attributes decision making (MADM), probabilistic simplified neutrosophic sets (PSNSs), GRA method, Teaching quality evaluation
DOI: 10.3233/JIFS-231728
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5199-5210, 2023
Authors: Chang, Yung-Chia | Chang, Kuei-Hu | Chen, Wei-Ting
Article Type: Research Article
Abstract: In vehicle leasing industry which presents a great business opportunity, information completed by applicants was assessed and judged by leasing associates manually in most cases; therefore, assessment results would be affected by their personal experience of leasing associates and decisions would be further affected accordingly. There are few researches on applicant credit risk assessment due to not easy to obtain of vehicle leasing data. Further, the difficulty in vehicle leasing risk assessment is increased due to class imbalance problems in vehicle leasing data. In order to address such issue, a research on credit risk assessment in vehicle leasing industry was …conducted in this study. The great disparity in the ratio of high risk and low risk data was addressed by applying synthetic minority over-sampling technique (SMOTE). Then, classification effect of risk assessment model was improved by applying logistic regression in a two-phase manner. In the section of empirical analysis, the feasibility and effectiveness of the approach proposed in this study was validated by using data of actual vehicle leasing application cases provided by a financial institution in Taiwan. It is found that the proposed approach provided a simple yet effective way to build a credit risk assessment model for companies that provide vehicle leasing. Show more
Keywords: Credit risk assessment model, logistic regression, synthetic minority over-sampling technique, category asymmetry
DOI: 10.3233/JIFS-231344
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5211-5222, 2023
Authors: Jayagopalan, Santhosh | Alkhouli, Mahmoud | Aruna, R.
Article Type: Research Article
Abstract: Nowadays the existing legacy management-based healthcare system maintains and processes a large amount of health-related data. The widespread adoption of the Internet of Things (IoT) and its progressive development have promised the way for the development of IoT-enabled healthcare with impressive data processing and big data storage capabilities. Intelligent medical healthcare intends to offer a framework to remotely monitor users’ health-related data as the Industrial Internet of Things (IIoT) develops. Because they are stored on a cloud server, the data are still susceptible to manipulation and privacy breaches. The Keras Xception Deep Learning System (KX-DLS) with Dynamic Searchable Symmetric Encryption …(DSSE) scheme is a revolutionary IoT-based deep learning intelligent privacy-preserving system that is advantageous for digital healthcare and its functionalities to handle security-related challenges. The dataset is being used to pre-train the system, and users’ personal information is kept separate in a secure location. Without knowing any personal information about the users, we analyse health-related data stored in the cloud and build a sophisticated security framework based on a deep learning model. With the most extensive collection of security features, our framework for learning intelligent privacy preservation optimizes the system to guarantee high data integrity and few privacy breaches. As a result, it may be useful in situations where users employ mobile devices with limited resources to engage a healthcare cloud system for extensive virtual health services, and the results of this research show that it has been a better-secured model in comparison with state-of-the-art previous techniques. Show more
Keywords: Privacy-preserving, internet of things, cloud storage, keras xception deep learning system, dynamic searchable symmetric encryption scheme
DOI: 10.3233/JIFS-231713
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5223-5238, 2023
Authors: Xu, Xiao-Peng | Wang, Li
Article Type: Research Article
Abstract: Fermentation engineering is a technology that uses engineering technology to produce useful biological products for human beings by using certain functions of organisms (mainly microorganisms) and active isolated enzymes, or to directly control certain industrial production processes with the participation of microorganisms. With the progress of science and technology, fermentation technology has also developed greatly, and has entered the stage of modern fermentation engineering that can artificially control and modify microorganisms to make these microorganisms produce products for human beings. As an important part of modern biotechnology, modern fermentation engineering has a broad application prospect. The grain fermentation process quality …evaluation is a classical MADM issues. In such paper, the generalized weighted Bonferroni mean (WBM) operator is constructed for MADM with single-valued neutrosophic sets (SVNSs). Then, the generalized single-valued neutrosophic number WBM (GSVNNWBM) operator is built and then the MADM decision methods are proposed based on the GSVNNWBM operator. Finally, an example about grain fermentation process quality comprehensive evaluation and some comparative analysis were given to demonstrate the GSVNNWBM method. Show more
Keywords: Multiple attribute decision making (MADM), single-valued neutrosophic sets (SVNSs), weighted Bonferroni mean (WBM) operator, maximizing deviation method, grain fermentation process quality evaluation
DOI: 10.3233/JIFS-231978
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5239-5249, 2023
Authors: Lv, Bailin | Wang, Sijia | Xia, Kaijian | Jiang, Yizhang
Article Type: Research Article
Abstract: Machine learning methods have become an effective strategy commonly used in quantitative hedge funds, which can maximize profits and reduce investment risks to a certain extent. Traditional stock forecasting systems are usually based on a single attribute of stock data. There are two main challenges in this process: 1) Use suitable processing methods to deal with highly nonlinear time series data such as stocks. 2) Using a single class of stock data for training does not capture the correlation between other related data and the training data. Based on RBF neural network, this research introduces view weighting and collaborative learning …mechanism, and proposes a MV-RBF model. It mainly includes the following contributions: 1) By comparing the experimental results of MV-RBF model with the single-view model, its effectiveness and feasibility are verified. 2) The MV-RBF model was compared with other commonly used classification models to analyze its advantages and disadvantages. 3) Study the relevant parameters, stability and other indicators of MV-RBF model. The experimental results show that compared with the single view model and most common classification models, MV-RBF has certain improvement in the prediction accuracy. Show more
Keywords: Multi-view learning, stock price prediction, collaborative learning, view weighting mechanism, RBF neural network
DOI: 10.3233/JIFS-223202
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5251-5264, 2023
Authors: Sreekrishna, M. | Prem Jacob, T.
Article Type: Research Article
Abstract: Unstructured pathology report plays a major role in definitive cancer diagnosis. Accessing or searching unstructured textual information from the clinical pathology reports is one of the major concerns in cancer healthcare sector to provide precise medicine, analysis of cancer outcomes, providing cancer care services, accurate measurement for future prediction, treatment history, and comparative future research work. An efficient methodology has to be introduced for to extract quantitative information from the unstructured cancer data. Integrating computational intelligence in Robotic Process Automation can be done to process this data and automate repetitive activities for evaluating patients clinical pathology report. RPA-based NLP BERT …system is designed and evaluated to automatically extract information on these variables for the patients from pathology report. In order to detect tumour and outcomes from documented pathology reports, a supervised machine learning keyword based extraction algorithm was developed in which the pathology data are examined to extract keywords from 2087 reports with 1579 of data reports being processed for the development phase and 508 of data being used for evaluation. The precision recall and accuracy are calculated for organ specimens for cancer test as (0.984, 0.982, 0.9839), test methodology(0.986, 0.981,0.9956) and pathological result(0.986, 0.9938, 0.9795) were achieved. The feasibility of autonomously extracting pre-defined data from clinical narratives for cancer research were established in this work. The outcomes showed that our methodology was suitable for actual use in obtaining essential information from pathology reports. Show more
Keywords: Unstructured data, intelligent automation, bots, feature analysis, prediction, supervised learning, NLP, pathology, information extraction, diagnosis
DOI: 10.3233/JIFS-231625
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5265-5276, 2023
Authors: Wang, Jingyang
Article Type: Research Article
Abstract: Scientific research management is an important component of university management and an important guarantee for the sustainable operation of universities. A mature scientific research management system is a sharp tool to promote the discipline construction and comprehensive strength improvement of universities. Therefore, colleges and universities should actively explore new concepts, models, and mechanisms for scientific research management while continuously improving their own educational quality. Based on the analysis of the current situation of university scientific research management in China, it is of great significance to explore effective ways to improve the quality of university scientific research management in the new …era. The quality evaluation of higher education scientific research management in entrepreneurship undergraduate colleges is a classical MAGDM problems. Recently, the TODIM and VIKOR method has been used to cope with MAGDM issues. The interval-valued Pythagorean fuzzy sets (IVPFSs) are used as a tool for characterizing uncertain information during the quality evaluation of higher education scientific research management in entrepreneurship undergraduate colleges. In this manuscript, the interval-valued Pythagorean fuzzy TODIM-VIKOR (IVPF-TODIM-VIKOR) method is built to solve the MAGDM under IVPFSs. In the end, a numerical case study for quality evaluation of higher education scientific research management in entrepreneurship undergraduate colleges is given to validate the proposed method. Show more
Keywords: Multiple attribute group decision making (MAGDM), IVPFSs, TODIM, VIKOR, Higher education scientific research management
DOI: 10.3233/JIFS-232621
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5277-5289, 2023
Authors: Wang, Guodong | Chen, Zhen | Wang, Guowei
Article Type: Research Article
Abstract: The art curriculum is a compulsory course for students in the compulsory education stage, which is beneficial in cultivating students’ sentiments, cultivating their temperament and improving their intelligence. Since the new curriculum reform, the art curriculum has been implemented for nearly twenty years, and it has made great breakthroughs and gains. However, at the same time, there are many problems in the process of implementing the art curriculum. The art teaching effectiveness evaluation of primary schools is viewed as the multi-attribute decision-making (MADM). In this paper, the triangular fuzzy neutrosophic number cross-entropy (TFNN-CE) method is designed with help of cross-entropy …and triangular fuzzy neutrosophic sets (TFNSs). Furthermore, Then, TFNN-CE method is built to solve the MADM. Finally, a numerical example for art teaching effectiveness evaluation of primary schools is given and some comparisons are conducted to r illustrate advantages of the designed method. Show more
Keywords: Multiple attribute decision making (MADM) problems, triangular fuzzy neutrosophic sets (TFNSs), cross-entropy method, 2TLNN-CE method, art teaching effectiveness evaluation
DOI: 10.3233/JIFS-232638
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5291-5301, 2023
Authors: Li, Eryang | Feng, Xiangqian | Wei, Cuiping
Article Type: Research Article
Abstract: Internet of Things (IoT) technology now has a new purpose and relevance as a result of the digitalization wave. In this setting, businesses start to plan how they will use IoT technology. But some critical factors can prevent the successful deployment of IoT, and businesses must get beyond these critical factors if they want to do so. The literature review, system literature review, and Delphi technique are used to identify 15 critical factors. These critical factors are then divided into four categories: organization, technology, process, and environment. The PFN-weighted power harmonic operator is proposed with the aim of more effectively …obtaining assessment data from experts and lessening the inaccuracy of outcomes caused by information loss. The best and worst method (BWM) is used to determine the ideal weight of critical factors. Results indicate that the primary critical factors to the effective adoption of the Internet of Things are talent, resource limitations, integration complexity, technical operations, equipment power consumption, technical dependability, and data governance. This research will benefit corporate managers in recognizing the significance of the effective deployment of the Internet of Things, identifying major critical factors to this achievement, and making decisions to remove these factors. Thus, an organization may support the effective adoption of the animal Internet of Things. Show more
Keywords: Internet of things, critical factors, PFN, weighted power harmonic operator, BWM
DOI: 10.3233/JIFS-231023
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5303-5323, 2023
Authors: Karamat, Tahira | Ullah, Kifayat | Pamucar, Dragan | Akram, Maria
Article Type: Research Article
Abstract: Prioritization is usually required in problems involving multi-attribute group decision-making (MAGDM). Several strategies and procedures have been introduced in fuzzy systems to apply prioritization. This study examines the MAGDM problem in a Pythagorean fuzzy (PF) setting with varying amounts of demand for specialists and attributes. We regard the novel Aczel Alsina aggregation operators (AOs) as the most addition to fuzzy mathematics that can deal with uncertainties significantly. We suggest a few PF AOs based on Aczel Alsina t-norm and t-conorm, including the PF-prioritized Aczel Alsina averaging (PFPAAA) and PF-prioritized Aczel Alsina geometric (PFPAAG) operators. It is proven that these AOs …fulfil the aggregation criteria by investigating the properties of monotonicity, boundedness, and idempotency. The weights for prioritization are derived from the knowledge of experts, and the proposed operators can capture the phenomenon of prioritization among the aggregated arguments. The proposed AOs are then applied to assess fire extinguishers using a MAGDM technique. The importance of PFPAAA and PFPAAG operators is verified by comparing the proposed AOs with other well-known AOs. Show more
Keywords: Pythagorean fuzzy sets, prioritized aggregation operators, Aczel-Alsina t-norm and t-conorm, decision-making methods, fire extinguishers
DOI: 10.3233/JIFS-231876
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5325-5351, 2023
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