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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: Qin, Yong | Xu, Zeshui | Wang, Xinxin | Škare, Marinko
Article Type: Research Article
Abstract: Fuzzy decision-making is increasingly becoming a pivotal approach to solving complex and intricate issues in tourism and hospitality management. The primary objective of this study is to unveil the developmental status, key themes and research trends within fuzzy decision-making in tourism and hospitality management (FDMTH) using Bibliometrix, CorText Manager and VOSviewer tools. As such, we conduct a comprehensive bibliometric and content-wise analysis of selected 341 publications concerning FDMTH. For one thing, we use valuable bibliometric indicators to conduct a general feature peek and performance analysis of the audited corpus. The research findings reveal a sustained scholarly interest in FDMTH. As …a critical player, Pappas, Nikolaos leads the volume of publications. The Journal of Intelligent & Fuzzy Systems stands out as the preferred outlet for FDMTH research. For another, the contingency matrix and bump graph modules are employed to detect the knowledge flow and intellectual connections in FDMTH. The results of network mapping tentatively identify geographic and thematic biases in FDMTH. More importantly, bibliographic coupling analysis reveals four specific themes, namely multi-criteria decision-making and evaluation, factors identification, fuzzy programming and forecasting, and fuzzy intelligence. Our pioneer work will contribute to the present understanding of the complexity and interdisciplinarity of FDMTH. Show more
Keywords: Fuzzy decision-making, tourism and hospitality, CorText manager, intellectual connections, thematic bias
DOI: 10.3233/JIFS-236618
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4955-4980, 2024
Authors: Yang, Xiangfei | Zhang, Faping | Wei, Jianfeng
Article Type: Research Article
Abstract: To improve the accuracy of fault diagnosis for recoil systems under multiple operating conditions, a fuzzy RBF neural network (Radial Basis Function, RBF) fault diagnosis method based on knowledge and data fusion is proposed. A kinetic model for the recoil system is first established to describe the system’s behavior. Next, fuzzy RBF neural network is used to establish the relationship between abnormal operating parameters and fault causes, achieving a fault cause diagnosis accurately based on the integration of expert experience knowledge and system operation data. A study case demonstrate that the algorithm has strong knowledge and data fusion capabilities and …can effectively identify faults in recoil system. Show more
Keywords: Fuzzy method, RBF neural network, knowledge and data fusion, the recoil system, fault diagnosis
DOI: 10.3233/JIFS-230683
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4981-4994, 2024
Authors: Senthil Kumar, V. | Aruna, R. | Varalatchoumy, M. | Manikannan, P. | Santhana Krishnan, T. | Usha Rani, B. | Kumar, Ashok | Rajaram, A.
Article Type: Research Article
Abstract: As the world embraces the transition towards renewable energy, the optimization of solar power plants becomes paramount. In this research, we present a comprehensive framework that leverages advanced analytical methodologies to address critical operational challenges and elevate the efficiency of solar power generation. Our framework encompasses data preprocessing, time series analysis, anomaly detection, and equipment performance assessment, synergistically combining their strengths to offer a holistic solution. The heart of our proposed approach lies in the precision and efficacy of anomaly detection. We introduce two powerful techniques—LSTM Autoencoder and Isolation Forest—to identify anomalies and equipment underperformance. Through meticulous evaluation, we …showcase their comparative performance, revealing the nuanced strengths of each. Visualizations depict the model’s proficiency in pinpointing anomalies, with LSTM Autoencoder emerging as a standout performer, adept at capturing even subtle deviations from expected patterns. Our research extends beyond detection to equip stakeholders with real-time insights. The visualization of daily yield trends uncovers potential data anomalies, enabling timely intervention and rectification. Additionally, we address equipment failures by harnessing random forest modeling to establish a robust relationship between irradiance, temperature, and DC power. This approach provides a powerful tool for real-time condition monitoring and fault detection, enabling proactive maintenance and enhancing operational resilience. Show more
DOI: 10.3233/JIFS-235578
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4995-5011, 2024
Authors: Sivakumaran, V. | Sankar, K.
Article Type: Research Article
Abstract: Consider a simple graph G = 〈V , E 〉 with n vertices. Define a function f from vertex set of G to set of integers 1, 2, …, n subject to the condition that there exists an integer k > 0 such that the sum of adjacent labels of each vertex in G is equal to k . In this paper, we prove that the graph K n - ⌊ n 2 ⌋ e has DML for all n ≥ 1, decomposition of distance magic graph K 2n - {ne …} into n ( n - 1 ) 2 edge distinct copies of C 4 for all n ≥ 2 and DML of join of complete graphs gives unique mathematical model and it will be a useful model in big data analysis. Show more
Keywords: Distance magic labeling, magic constant, decomposition of distance magic graph, join of complete graphs, big data analysis
DOI: 10.3233/JIFS-224511
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5013-5020, 2024
Authors: Wang, Xiaoying | Chen, Xiaohai | Zhang, Zhongwen | He, Haisheng
Article Type: Research Article
Abstract: Intelligent Transportation Systems (ITS) have experienced significant growth over the past decade thanks to advances in control, communication, and information technology applied to vehicles, roads, and traffic control systems. Vehicle type classification plays a vital role in implementing ITS because of its ability to collect useful traffic information, enable future development of transport infrastructures, and increase human comfort. As a branch of machine learning, deep learning represents a frontier for artificial intelligence, which seeks to be closer to its primary goal. Deep learning is a powerful tool for classifying vehicle types because it can capture complex traffic data characteristics and …learn from large amounts of data. This means that it can be used to accurately classify traffic data and generate valuable insights that can be used to improve traffic management. Researchers have successfully adopted these algorithms as a solution to propose optimal vehicle-type classification strategies. This paper highlights the role of deep learning algorithms in solving the vehicle type classification problem, reviewing the state-of-the-art approaches in this field. Show more
Keywords: Transportation systems, ITS, vehicle type, deep learning, optimization
DOI: 10.3233/JIFS-233302
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5021-5032, 2024
Authors: Subbaian, Santhi | Balasubramanian, Anand | Marimuthu, Murugan | Chandrasekaran, Suresh | Muthusaravanan, Gokila
Article Type: Research Article
Abstract: Coconut farming is a significant agricultural activity in South India, but the coconut trees face challenges due to adverse weather conditions and environmental factors. These challenges include various leaf diseases and pest infestations. Identifying and locating these issues can be difficult because of the large foliage and shading provided by the coconut trees. Recent research has shown that Computer Vision algorithms are becoming increasingly important for solving problems related to object identification and detection. So, in this work, the YOLOv4 algorithm was employed to detect and pinpoint diseases and infections in coconut leaves from images. The YOLOv4 model incorporates advanced …features such as cross-stage partial connections, spatial pyramid pooling, contextual feature selection, and path-based aggregation. These features enhance the model’s ability to efficiently identify issues such as yellowing and drying of leaves, pest infections, and leaf flaccidity in coconut leaf images taken in various environmental conditions. Furthermore, the model’s predictive accuracy was enhanced through multi-scale feature detection, PANet feature learning, and adaptive bounding boxes. These improvements resulted in an impressive 88% F1-Score and an 85% Mean Average Precision. The model demonstrates its effectiveness and robustness even when dealing with medium-resolution images, offering improved accuracy and speed in disease and pest detection on coconut leaves. Show more
Keywords: Coconut leaf disease, YOLO v4, precision agriculture, pest, faster RCNN, YOLO-SPP
DOI: 10.3233/JIFS-233831
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5033-5045, 2024
Authors: Zhang, Mingming | Wu, Qingling
Article Type: Research Article
Abstract: High-performance concrete (HPC) is a specialized type of concrete designed to meet stringent performance and uniformity standards that are difficult to achieve with conventional materials and standard mixing, placing, and curing methods. The testing process to determine the mechanical properties of HPC specimens is complex and time-consuming, and making improvements can be difficult after the test result does not meet the required properties. Anticipating concrete characteristics is a pivotal facet in the realm of High-Performance Concrete (HPC) manufacturing. Machine learning (ML)-driven models emerge as a promising avenue to tackle this formidable task within this context. This research endeavors to employ …a synergy of ML hybrid and ensemble frameworks for the prognostication of the mechanical attributes within HPC, encompassing compressive strength (CS), slump (SL), and flexural strength (FS). The formulation of these hybrid and ensemble constructs was executed through the integration of Support Vector Regression (SVR) with three distinct meta-heuristic algorithms: Prairie Dog Optimization (PDO), Pelican Optimization Algorithm (POA), and Mountain Gazelle Optimizer (MGO). Some criteria evaluators were used in the training, validation, and testing phases to assess the robustness of the established models, and the best model was proposed for practical applications through comparative analysis of the results. As a result, the hybrid and ensemble models were the potential methods to predict concrete properties accurately and efficiently, thereby reducing the need for expensive and time-consuming testing procedures. In general, the ensemble model, i.e., SVPPM, had a more suitable performance with high values of R2 equal to 0.989 (MPa), 0.984 (mm), and 0.992 (MPa) and RMSE = 3.82 (MPa), 9.5 (mm), and 0.30 (MPa) for CS, SL, FS compared to other models, respectively. Show more
Keywords: High-Performance dynamic properties, support vector regression, prairie dog optimization, pelican optimization algorithm, mountain gazelle optimizer
DOI: 10.3233/JIFS-234125
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5047-5072, 2024
Authors: Wang, Haomiao | Li, Yibin | Jiang, Mingshun | Zhang, Faye
Article Type: Research Article
Abstract: Domain adaptation (DA) technology has the ability to solve fault diagnosis (FD) problems under variable operating conditions. However, DA technology faces two issues: (1) in general, vibration signals inevitably contain noise, which makes it difficult to extract discriminant features.(2) there are unknown fault types in target domain. These issues will lead to poor diagnostic performance. To solve above issues, a new cross-domain open-set transfer FD method called feature improvement adversarial network (FIAN) is proposed in this article. Specifically, to alleviate noise interference, a feature improvement module (FIM) is proposed and embedded into the backbone convolutional neural network to form new …feature extractor. FIM uses soft threshold function to enhance important information and suppresses redundant information. Furthermore,open-set DA by back-propagation (OSBP) is introduced into FIAN. OSBP can predict the probability that a target domain sample belongs to an unknown category, so that it can effectively identify unknown and known category samples. Experimental results demonstrated its effectiveness and superiority in two bearing datasets. Show more
Keywords: Fault diagnosis, rolling bearing, open-set domain adaptation, feature improvement module, adversarial network
DOI: 10.3233/JIFS-236593
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5073-5085, 2024
Authors: Chandana Mani, R.K. | Kamalakannan, J.
Article Type: Research Article
Abstract: Breast cancer (BC) is the most common cancer amongst women that threatens the health of women, initial diagnosis of BC becomes essential. Though there were several means to diagnose BC, the standard way is pathological analysis. Precise diagnosis of BC necessitates experienced histopathologists and needs more effort and time for completing this task. Recently, machine learning (ML) was successfully implemented in text classification, image recognition, and object recognition. With the emergence of computer aided diagnoses (CAD) technology, ML was effectively implemented for BC diagnosis. Histopathological image classification depends on deep learning (DL), particularly convolution neural network (CNN), which frequently needs …a large amount of labelled training models, whereas the labelled data was hard to obtain. This study develops an Aquila Optimizer(AO) with Hybrid ResNet-DenseNet Enabled Breast Cancer Classification on Histopathological Images (AOHRD-BC2HI). The proposed AOHRD-BC2HI technique inspects the histopathological images for the diagnosis of breast cancer. To accomplish this, the presented AOHRD-BC2HI technique uses hybridization of Resnet with Densenet (HRD) model for feature extraction. Moreover, the HRD method can be enforced for feature extracting procedure in which the DenseNet (feature value memory by concatenation) and ResNet (refinement of feature value by addition) were interpreted. For BC detection and classification, the DSAE model is utilized. The AO algorithm is exploited to improve the detection performance of DSAE model. The experimental validation of the presented AOHRD-BC2HI approach is tested using benchmark dataset and the results are investigated under distinct measures.Also the proposed model achieved the accuracy of 96%. The comparative result reports the improved performance of the presented AOHRD-BC2HI technique over other recent methods. Show more
Keywords: Breast cancer, histopathological images, aquila optimizer, computer aided diagnosis, deep learning
DOI: 10.3233/JIFS-236636
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5087-5102, 2024
Authors: Jyoti, | Singh, Jaspreeti | Gosain, Anjana
Article Type: Research Article
Abstract: Addressing missing values is a persistent challenge in the field of data mining. The presence of incomplete data can significantly compromise the overall data quality. Consequently, it is crucial to handle incomplete data efficiently. This paper presents a novel approach for imputing missing values that incorporates Kernelized Fuzzy C-Means (KFCM) clustering and proposes a method termed LIKFCM, which combines its benefits with Linear Interpolation (LI). The proposed LIKFCM’s performance is assessed through a comparison against nine state-of-the-art imputation techniques (mean, median, LI, EMI, KNNI, KMI, FKMI, LIFCM, and LIPFCM) across ten widely used real-world datasets from the UCI repository with …six combinations of missing ratios to assess the efficacy of the proposed imputation method. From the experimental results, it is evident that our proposed method outperforms the existing imputation methods with significant improvements in terms of RMSE & MAE for these datasets. Additionally, experiments examining the effect of missing values validate the robustness of the proposed approach by handling different missing ratios. The performance validation of the proposed approach against other state-of-the-art imputation methods has been conducted utilizing a Kendall’s W statistical test, involving a comparison of their mean ranks across different missing ratios. The outcomes indicate that LIKFCM has outperformed other imputation methods, attaining the highest rank in terms of different evaluation criteria. Show more
Keywords: Incomplete data, missing value imputation, fuzzy clustering, LI, LIKFCM
DOI: 10.3233/JIFS-236869
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5103-5123, 2024
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