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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, Wenlong | Zhi, Minqian | Ke, Yongsong | Wang, Xiaolong | Zhuo, Yun | Liu, Anping | Yang, Yi
Article Type: Research Article
Abstract: Structure learning is the core of graph model Bayesian Network learning, and the current mainstream single search algorithm has problems such as poor learning effect, fuzzy initial network, and easy falling into local optimum. In this paper, we propose a heuristic learning algorithm HC-PSO combining the HC (Hill Climbing) algorithm and PSO (Particle Swarm Optimization) algorithm, which firstly uses HC algorithm to search for locally optimal network structures, takes these networks as the initial networks, then introduces mutation operator and crossover operator, and uses PSO algorithm for global search. Meanwhile, we use the DE (Differential Evolution) strategy to select the …mutation operator and crossover operator. Finally, experiments are conducted in four different datasets to calculate BIC (Bayesian Information Criterion) and HD (Hamming Distance), and comparative analysis is made with other algorithms, the structure shows that the HC-PSO algorithm is superior in feasibility and accuracy. Show more
Keywords: Keywords. Bayesian network, structure learning, HC algorithm, PSO algorithm, DE algorithm
DOI: 10.3233/JIFS-236454
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4347-4359, 2024
Authors: Guo, Hairu | Wang, Jin’ge | Liu, Yongli | Zhang, Yudong
Article Type: Research Article
Abstract: The Aquila optimization (AO) algorithm has the drawbacks of local optimization and poor optimization accuracy when confronted with complex optimization problems. To remedy these drawbacks, this paper proposes an Enhanced aquila optimization (EAO) algorithm. To avoid elite individual from entering the local optima, the elite opposition-based learning strategy is added. To enhance the ability of balancing global exploration and local exploitation, a dynamic boundary strategy is introduced. To elevate the algorithm’s convergence rapidity and precision, an elite retention mechanism is introduced. The effectiveness of EAO is evaluated using CEC2005 benchmark functions and four benchmark images. The experimental results confirm EAO’s …viability and efficacy. The statistical results of Freidman test and the Wilcoxon rank sum test are confirmed EAO’s robustness. The proposed EAO algorithm outperforms previous algorithms and can useful for threshold optimization and pressure vessel design. Show more
Keywords: Aquila optimization algorithm, optimization function, kapur entropy, threshold optimization, pressure vessel design
DOI: 10.3233/JIFS-236804
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4361-4380, 2024
Authors: Nippatla, V. Ramanaiah | Mandava, Srihari
Article Type: Research Article
Abstract: The main contribution of this review work is to show how various control techniques are used to manage the speed of Permanent Magnet Synchronous Motor (PMSM). The PMSM’s are mostly used in electric vehicles, electric traction and high performance industrial drive applications. In this article conventional sensorless techniques are compared with machine learning techniques such as fuzzy logic, artificial neural network and neuro-fuzzy controllers to control the speed of PMSM drive based on vector control approach. The benefits of machine learning techniques used in sensorless PMSM drive are easy to design, less execution time and fast access speed control. The …various controlling techniques used in controller along with its complexity, advantages and drawbacks are discussed in this article. The above mentioned controlling techniques are implemented and simulated by using MATLAB R2019b/Simulink software based on sensorless Model Reference Adaptive System (MRAS) with the help of Field Oriented Control (FOC) strategy of PMSM drive. By comparing the all sensorless controlling techniques in simulation study, it is identified that the combination of neuro-fuzzy controller gives the best speed control performance than other controllers. Show more
Keywords: Field oriented control, fuzzy logic control, neuro-fuzzy control, PMSM drive, sensorless control
DOI: 10.3233/JIFS-222164
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4381-4395, 2024
Authors: Chen, Siting | You, Cuiling | Wu, Nan | Huang, Yan
Article Type: Research Article
Abstract: Cross-efficiency evaluation is an extension of data envelopment analysis (DEA), which can effectively distinguish between decision-making units (DMUs) through self- and peer-evaluation. The cross-efficiency of each DMU in a set of DMUs is measured in terms of intervals when the input–output data are represented by the number of intervals. Based on the interval cross-efficiency matrix, the interval entropy is defined in terms of the likelihood. Then, considering the influence of peer evaluation, the interval conditional cross-efficiency entropy is proposed and an aggregation model of the interval conditional cross-efficiency entropy is presented to create a ranking index for DMUs. Finally, a …simple example is provided to illustrate the effectiveness of the proposed method, which is applied to the evaluation of forest carbon sink efficiency in China. The results indicate that the final cross-efficiencies of all 30 provinces range from 0 to 0.6. Among these provinces, those with a relatively high efficiency include Guangdong, Guizhou, Hainan, Shandong, and Qinghai. Show more
Keywords: Data envelopment analysis, interval data, cross-efficiency, entropy, likelihood
DOI: 10.3233/JIFS-223071
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4397-4415, 2024
Authors: Huang, Zhengwei | Liu, Huayuan | Duan, Chen | Min, Jintao
Article Type: Research Article
Abstract: In the E-commerce environment, conversations between customers and businesses contain lots of useful information about customer sentiment. By mining that information, customer sentiment can be validly identified, which is helpful in accurately identifying customer needs and improving customer satisfaction. For conversational sentiment analysis, most existing approaches take contextual information into account. On this basis, we focus on the degree of association between utterances, which can more effectively capture overall and useful sentiment information in conversation. For this purpose, we propose a hybrid model to recognize customer sentiment in conversation. The model obtains utterance vectors with sentiment information through Sentiment Knowledge …Enhanced Pre-training (SKEP), then uses the bidirectional long short-term memory network (BiLSTM) to generate contextual semantic information, and further obtains customer sentiment information by applying the self-attention mechanism to focus on the degree of association between utterances. The experimental results on the JD Dialog dataset show that our model can more accurately recognize customer sentiment than other baseline models in customer service conversation. Show more
Keywords: Customer sentiment recognition, bidirectional long short-term memory network, self-attention mechanism, sentiment knowledge enhanced pre-training
DOI: 10.3233/JIFS-224183
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4417-4428, 2024
Authors: Ghiduk, Ahmed S. | Hashim, Marwa
Article Type: Research Article
Abstract: Mutation testing can evaluate the quality of the test inputs, generate test data, and simulate any test coverage criterion. Genetic algorithms and harmony search have been applied to reduce the cost of generating test inputs. Although hybridizing search algorithms enhances the efficiency of searching the solution domain, there is a shortage of applying the hybrid search techniques in mutation testing. This paper merges the genetic and harmony search algorithms to effectively generate test data to kill higher-order mutants. In addition, the performance of the proposed technique will be evaluated and compared with a stand-alone genetic algorithm and a stand-alone harmony …search algorithm through an empirical study using a set of benchmark programs. The experimental study shows that the proposed technique outperformed the compared algorithms, reaching a higher killing ratio, where the proposed approach kills 92.8% of higher-order mutants for all tested programs. In comparison, GA kills 88.7%, and HA kills 86.6%. Besides, the proposed algorithm overcame the compared algorithm in reaching a targeted killing ratio faster than the compared algorithms. HGA reduced the execution time for each program with a reduction ratio ranging from 58.9% to 89.8%. Show more
Keywords: Genetic algorithm, harmony search algorithm, higher-order mutation testing, test-data generation
DOI: 10.3233/JIFS-230226
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4429-4452, 2024
Authors: Liu, Xia | Chen, Benwei
Article Type: Research Article
Abstract: This paper defines an improved similarity degree based on inclusion degree as well as advanced information system based on interval coverage and credibility, and thus an attribute reduction framework embodying 4×2 = 8 reduct algorithms is systematically constructed for application and optimization in interval-valued decision systems. Firstly, a harmonic similarity degree is constructed by introducing interval inclusion degree and harmonic average mechanism, which has better semantic interpretation and robustness. Secondly, interval credibility degree and coverage degree are defined for information fusion, and they are combined to propose a δ -fusion condition entropy. The improved condition entropy achieves the information reinforcement and integrity …by dual quantization fusion of credibility and coverage, and it obtains measure development from granularity monotonicity to non-monotonicity. In addition, information and joint entropies are also constructed to obtain system equations. Furthermore, 8 reduct algorithms are designed by using attribute significance for heuristic searches. Finally, data experiments show that our five novel reduct algorithms are superior to the three contrast algorithms on classification performance, which also further verify the effectiveness of proposed similarity degree, information measures and attribute reductions. Show more
Keywords: Attribute reductions, interval-valued decision systems, information measurements, δ-fusion condition entropy, harmonic similarity degree, interval coverage degree and credibility degree
DOI: 10.3233/JIFS-231950
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4453-4466, 2024
Authors: Zhang, Xiang | Huang, Jianhua | Fang, Liting | Li, Qian
Article Type: Research Article
Abstract: Selecting suppliers for prefabricated components (PCs) involves a complex decision-making process, frequently relying on ambiguous information and subjective judgment. However, most existing methods use precise values to portray indicator information and overlook the uncertainty of weights and the subjective preferences of decision-makers (DMs). In order to address these limits, this paper proposes a novel approach to select suppliers of PCs. Initially, an evaluation index system for suppliers is established through literature analysis and a questionnaire survey. The system comprises six layers: product quality, price, service level, comprehensive ability, supply ability, and environmental sustainability. The group decision matrix is then constructed …using the set-valued statistical method and the prospect theory. The index weights are determined by a combination weighting method. Next, the cobweb model is introduced to analyze the disparity between the alternative and ideal solutions, describing their similarities in terms of area and shape. Lastly, cobweb similarity is employed instead of comprehensive distance, combined with the minimum sum of squares criterion, to improve the closeness algorithm and contrast the alternatives. The results demonstrate that this method facilitates a comprehensive evaluation of the benefits and drawbacks of various alternatives from diverse perspectives. Furthermore, it allows flexible adjustments based on the risk preferences of DMs, ensuring accurate and reliable decision results. Show more
Keywords: Select suppliers, risk preference, prospect theory, cobweb model, cobweb similarity
DOI: 10.3233/JIFS-232027
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4467-4479, 2024
Authors: Xu, Yi | Zhou, Meng
Article Type: Research Article
Abstract: As an important extension of classical rough sets, local rough set model can effectively process data with noise. How to effectively calculate three approximation regions, namely positive region, negative region and boundary region, is a crucial issue of local rough sets. Existing calculation methods for approximation regions are based on conditional probability, the time complexity is O (|X ||U ||C |). In order to improve the computational efficiency of three approximation regions of local rough sets, we propose a double-local conditional probability based fast calculation method. First, to improve the computational efficiency of equivalence class, we define the double-local equivalence …class. Second, based on the double-local equivalence class, we define the double-local conditional probability. Finally, given the probability thresholds and a local equivalence class, the monotonicity of double-local conditional probability is proved, on this basis, a double-local conditional probability based fast calculation method for approximation regions of local rough sets is proposed, and the time complexity is O (MAX (|X |2 |C |, |X ||X C ||C |)). Experimental results based on 9 datasets from UCI demonstrate the effectiveness of the proposed method. Show more
Keywords: Local rough sets, approximation regions, double-local equivalence class, double-local conditional probability
DOI: 10.3233/JIFS-232767
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4481-4493, 2024
Authors: Shi, Dingpu | Zhou, Jincheng | Wu, Feng | Wang, Dan | Yang, Duo | Pan, Qingna
Article Type: Research Article
Abstract: How to better grasp students’ learning preferences in the environment of rapid development of engineering and science and technology so as to guide them to high-quality learning is one of the important research topics in the field of educational technology research today. In order to achieve this goal, this paper utilizes the LDA (Latent Dirichlet Allocation) model for text mining of the survey results on the basis of a survey on students’ self-perception evaluation. The results show that the LDA model is capable of extracting terms from text, fuzzy identifying groups of students at different levels and presenting potential logical …relationships between the groups, and further analyzing the learning preferences of students at different levels for IT courses. Based on the student’s learning needs, this paper proposes recommendations for developing students’ learning effectiveness. The LDA method proposed in this paper is a feasible and effective method for assessing students’ learning dynamics as it generates cognitive content about students’ learning and allows for the timely discovery of students’ learning expectations and cutting-edge dynamics. Show more
Keywords: Latent Dirichlet Allocation model, educational data mining, self-perceptions, network modeling
DOI: 10.3233/JIFS-232971
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4495-4509, 2024
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