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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: Kalaimathi, M. | Balamurugan, B.J. | Nagar, Atulya K.
Article Type: Research Article
Abstract: Let G = (V , E ) be a simple graph. A 1-1 function f : V → ℕ , where ℕ is the set of natural numbers, is said to induce a k -Zumkeller graph G if the induced edge function f * : E → ℕ defined by f * (xy ) = f (x ) f (y ) satisfies the following conditions:(i) f * (xy ) is a Zumkeller number for every xy ∈ E . (ii) The total number …of distinct Zumkeller numbers on the edges of G is k . A Mycielski transformation of a graph is a larger graph having more vertices and edges. In this article, the Mycielski transformation of a graphs such as path, cycle and star graphs have been computed and their k -Zumkeller graphs have been investigated by reducing the number of distinct Zumkeller numbers. AMS Subject Classification: 05C78 f * (xy ) is a Zumkeller number for every xy ∈ E . The total number of distinct Zumkeller numbers on the edges of G is k . Show more
Keywords: Zumkeller numbers, k-Zumkeller graph, Mycielski transformation
DOI: 10.3233/JIFS-231095
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7923-7932, 2024
Authors: Xiao, Yanjun | Pei, Eryue | Shi, Linhan | Peng, Kai | Liu, Weiling
Article Type: Research Article
Abstract: In order to solve the problem that Switched Reluctance Motor (SRM) generates torque pulsation phenomenon during operation, which reduces the stability of loom spindle operation, this paper proposes and designs a multi-algorithm fusion-based SRM control strategy from the point of view of control strategy research. Combined with the operating characteristics of the loom, the causes of SRM torque pulsation are analyzed from the point of view of SRM control strategy, and combined with the spindle control indexes, the voltage chopper control and torque distribution function are introduced to construct the SRM control strategy scheme for the loom. On this basis, …an optimization strategy based on the fusion of fuzzy control algorithm, particle swarm algorithm and simulated annealing algorithm is proposed to optimize the torque distribution function, and the algorithmic process of SRM control strategy is verified through comparative tests. The results show that the control strategy can make its torque pulsation reduced to less than 10%, the speed rise time is less than 0.1 s, and the relative error of the speed is less than 0.05%, which meets the index requirements of the spindle drive. This proves that the SRM torque pulsation can be reduced by the multi-algorithm fusion control strategy without increasing the hardware cost, which provides a useful reference for solving the SRM torque pulsation problem under the requirement of low cost. Show more
Keywords: Rapier loom, switched reluctance motor, torque distribution function, multi-algorithm fusion
DOI: 10.3233/JIFS-233138
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7933-7957, 2024
Authors: Zakaria, Aliya Syaffa | Shafi, Muhammad Ammar | Mohd Zim, Mohd Arif | Musa, Aisya Natasya
Article Type: Research Article
Abstract: Lung cancer constituted 12.2% of newly diagnosed cancer cases globally in 2020. The high fatality rate of the condition is attributed to delayed diagnosis and inadequate symptom recognition. In Malaysia, the incidence of lung cancer is estimated to be 1 in 60 males and 1 in 138 females, with a median age of 70 years or above. Most lung cancer cases were detected during advanced stages, specifically stages III and IV, with a prevalence exceeding 90% for both genders. In Malaysia, most patients are diagnosed in stages III and IV, which are associated with a lower likelihood of long-term survival. …Many cases are identified at a late stage, characterized by significant tumor expansion or the spread of cancer cells to areas that cannot be treated surgically. Malaysians are unaware of cancer symptoms; hence the situation is common. To improve survival and reduce mortality, Malaysians must recognize the symptoms of lung cancer. Fuzzy linear regression and multiple linear regression models have been compared to predict high-risk lung cancer symptoms in Malaysia. The fuzzy linear regression model analyses secondary data, eliminates irrelevant information and enhances precision in the results. Lung cancer patients at Al-Sultan Abdullah Hospital (UiTM Hospital) in Selangor provided data for this study. Data from 124 lung cancer patients were analyzed using Microsoft Excel, SPSS, and MATLAB. To improve data accuracy, the study used cross-validation measurement error (MSE and RMSE). According to data analysis, hemoptysis and chest pain are high-risk symptoms with MSE and RMSE values of 1.549 and 1.245, respectively. Show more
Keywords: Lung cancer, symptoms of lung cancer, fuzzy linear regression, prediction data, statistical error
DOI: 10.3233/JIFS-233714
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7959-7968, 2024
Authors: Xiao, Yanjun | Li, Shifang | Zhang, Kun | Zhang, Yameng | Xiao, Yanchun
Article Type: Research Article
Abstract: Recovering low-quality waste heat using industrial waste heat is challenging, and the reuse technology needs to erupt. Moreover, the gas source of low-quality waste heat is relatively volatile, which makes it challenging to keep the actual working condition of the plant stable. Therefore, it is inspiring to research the robustness of root-waste heat power generation processed measurement and control system to improve the stability of the plant operation. Hence, in this paper, we have applied uncertainty theory to analyze it and formulate the uncertainty model based on the Bode diagram. We also proposed a control method based on the uncertainty …model, which combines robust control and internal model control to make the roots waste heat power generation system operate stably under the effect of external disturbances and changes of internal structure or parameters in actual operation. Experimental results show that the robust internal model control method has a speed deviation of no more than 7.9 r/min compared with the PID control method. The adjustment time to track the set value does not exceed 73.1 seconds within the allowed fluctuation range. The fluctuation variance is 30.95% of that of the PID controller. The dynamic performance is better, with strong anti-interference capability and significantly improved tracking performance. It ensures the stability of the roots-type waste heat utilization system, which is essential for future intelligent grid-connected power generation. Show more
Keywords: Waste heat power generation, uncertain theory, robust internal model control, roots power machine
DOI: 10.3233/JIFS-234416
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7969-7987, 2024
Authors: Lei, Fan | Cai, Qiang | Wei, Guiwu
Article Type: Research Article
Abstract: The development and application of blockchain provides technical support for supply chain technological innovation and industrial innovation. Integrating the decentralized, independent, open, traceable and tamper-proof features of the blockchain into the supply chain can effectively improve the problems of unstable supply chain structure, low security, low privacy, low collaboration ability and high operating costs. Establishing probabilistic double hierarchy linguistic multi-attribute decision-making (PDHL-MADM) model to evaluate the performance of blockchain is an effective measure to optimize blockchain performance and improve supply chain stability. Therefore, this thesis first takes the processing efficiency, cost, security performance, update and improvement ability as evaluation attributes. …Then the IDOCRIW weight method is used to calculate the objective weight of attributes. Based on Aczel-Alsina t-norm (AATN) and Aczel-Alsina t-conorm (AATCN), four operations of probabilistic double hierarchy linguistic term set (PDHLTS) are defined, and PDHLAAWA operator, PDHLAAOWA operator, PDHLAAHA operator, PDHLAAHM operator, PDHLAAWHM operator and their dual operators are proposed, and a series of corresponding PDHL operator models are constructed. In addition, the sensitivity and stability of this series of operator models are analyzed in depth. Finally, the new model proposed in this thesis is compared with the existing model to verify its scientific and superiority. Show more
Keywords: Probabilistic double hierarchy linguistic term set (PDHLTS), Multi-attribute decision-making (MADM), PDHLAAWA operator and PDHLAAWHM operator, evaluate the performance of blockchain
DOI: 10.3233/JIFS-235215
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 7989-8024, 2024
Authors: Qiao, Junfeng | Peng, Lin | Zhou, Aihua | Pan, Sen | Yang, Pei | Xu, Min | Shen, Xiaofeng | Chen, Jingde | Gu, Hua
Article Type: Research Article
Abstract: This paper proposes a method of beforehand prediction of electric equipment faults based on chain-linked recurrent neural network algorithm, which takes the operating parameters of power equipment and other relevant environmental factors as inputs, and takes the fault characteristics as output judgment marks, and constructs a machine learning training model to realize the prediction of power equipment faults. The neural network algorithm adopted in this paper adopts a tree structure. Each sub-node can transfer information with its multiple superior nodes, so that the correlation between the data of the front and back nodes can be obtained, which meets the needs …of the equipment fault prediction model. Considering that the occurrence of power transformer faults is sudden and greatly affected by changes in the surrounding environment, the input of prediction algorithms should consider more environmental factors. This method takes the historical data of various parameters including meteorological phenomena, geography data, and temperature of adjacent equipment and facilities as the training sample set, improves the learning model, gives the trend curve of each index, and gives a prompt at its threshold to ensure the prediction accuracy and give the index prediction. Show more
Keywords: Recurrent neural network, power equipment fault prediction, index trend curve, fault feature sample set, power supply reliability
DOI: 10.3233/JIFS-236459
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8025-8035, 2024
Authors: Kamala Devi, K. | Raja Sekar, J.
Article Type: Research Article
Abstract: Breast cancer has been life-threatening for many years as it is the common cause of fatality among women. The challenges of screening such tumors through manual approaches can be overcome by computer-aided diagnosis, which aids radiologists in making precise decisions. The selection of significant features is crucial for the estimation of prediction accuracy. This work proposes a hybrid Genetic Algorithm (GA) and Honey Badger Algorithm (HBA) based Deep Neural Network (DNN), HGAHBA-DNN for the concurrent optimal features selection and parameter optimization; further, the optimal features and parameters extracted are fed into the DNN for the prediction of the breast cancer. …It fuses the benefits of HBA with parallel processing and efficient feedback with GA’s excellent global convergent rate during the processing stages. The aforementioned method is evaluated on the Wisconsin Original Breast Cancer (WOBC), Wisconsin Diagnostic Breast Cancer (WDBC), and the Surveillance, Epidemiology, and End Results (SEER) datasets. Subsequently, the performance is validated using several metrics like accuracy, precision, Recall, and F1-score. The experimental result shows that HGAHBA-DNN obtains accuracy of 99.42%, 99.84%, and 92.44% for the WOBC, WDBC, and SEER datasets respectively, which is much superior to the other state-of-the-art methods. Show more
Keywords: Breast cancer prediction, DNN, feature selection, genetic algorithm, honey badger algorithm, parameter optimization
DOI: 10.3233/JIFS-236577
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8037-8048, 2024
Authors: Hou, Yuntong | Shang, Shuye | Cao, Shengxi | Liu, Zhengjia
Article Type: Research Article
Abstract: A robust muscle fatigue algorithm plays a pivotal role in depicting the degree of muscle fatigue in both time-series EMG signal graphs and spectral graphs, aligning with human perception. While the fuzzy approximate entropy (fApEn ) algorithm has been enhanced from the foundation of approximate entropy (ApEn ) through the incorporation of fuzzy affiliation, concerns persist regarding the threshold value and the algorithm’s application range. This study extracts EMG signals across varied time durations and head-down angles, employing enhanced signal preprocessing techniques and optimizing the fApEn algorithm. Furthermore, real-time fatigue perceptions of subjects were recorded using the rating of …perceived exertion. Experimental outcomes reveal that the EMG signal, post-wavelet analysis preprocessing, demonstrates promising noise reduction capabilities. Notably, the fApEn algorithm exhibits considerable enhancements through the identification of an optimal threshold using the gradient descent algorithm and a machine learning strategy. Show more
Keywords: EMG, muscle fatigue, fuzzy approximate entropy, wavelet transform, machine learning
DOI: 10.3233/JIFS-237293
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8049-8063, 2024
Authors: Tian, Huaqiang | Yu, Long | Tian, Shengwei | Long, Jun | Zhou, Tiejun | Wang, Bo | Li, Yuhuan
Article Type: Research Article
Abstract: A spect-B ased S entiment A nalysis (ABSA ) has been the focus of increasing study in recent years. Previous research has demonstrated that incorporating syntactic information, such as dependency trees, can enhance ABSA performance. Despite the widespread use of metaphors in daily life to express emotions more vividly, few studies have integrated this literary device into ABSA. In this paper, we propose a novel ABSA model that utilizes M etaphor I dentification P rocedure (MIP ) to encode both the sentence and aspect word as a single unit, thereby overcoming these limitations. Our experimental results demonstrate that our model …achieves competitive performance in ABSA. Show more
Keywords: Aspect-based sentiment analysis, metaphorical sentiment analysis, transformer, deep learning
DOI: 10.3233/JIFS-233077
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8065-8074, 2024
Authors: Ding, Huafeng | Shang, Junyan | Zhou, Guohua
Article Type: Research Article
Abstract: Emotional state recognition is an important part of emotional research. Compared to non-physiological signals, the electroencephalogram (EEG) signals can truly and objectively reflect a person’s emotional state. To explore the multi-frequency band emotional information and address the noise problem of EEG signals, this paper proposes a robust multi-frequency band joint dictionary learning with low-rank representation (RMBDLL). Based on the dictionary learning, the technologies of sparse and low-rank representation are jointly integrated to reveal the intrinsic connections and discriminative information of EEG multi-frequency band. RMBDLL consists of robust dictionary learning and intra-class/inter-class local constraint learning. In robust dictionary learning part, RMBDLL …separates complex noise in EEG signals and establishes clean sub-dictionaries on each frequency band to improve the robustness of the model. In this case, different frequency data obtains the same encoding coefficients according to the consistency of emotional state recognition. In intra-class/inter-class local constraint learning part, RMBDLL introduces a regularization term composed of intra-class and inter-class local constraints, which are constructed from the local structural information of dictionary atoms, resulting in intra-class similarity and inter-class difference of EEG multi-frequency bands. The effectiveness of RMBDLL is verified on the SEED dataset with different noises. The experimental results show that the RMBDLL algorithm can maintain the discriminative local structure in the training samples and achieve good recognition performance on noisy EEG emotion datasets. Show more
Keywords: Multi-frequency band, dictionary learning, electroencephalogram, noise data, low-rank representation
DOI: 10.3233/JIFS-233753
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8075-8088, 2024
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