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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: Akram, Muhammad | Umer Shah, Syed Muhammad | Allahviranloo, Tofigh
Article Type: Research Article
Abstract: Transportation Problems (TP) have multiple applications in supply chain management to reduce costs. Efficient methods have been developed to address TP when all factors, including supply, demand, and unit transportation costs, are precisely known. However, due to uncertainty in practical applications, it is necessary to study TP in an uncertain environment. In this paper, we define the Trapezoidal Fermatean Fuzzy Number (TrFFN) and its arithmetic operations. Then we introduce a new approach to solve TP, where transportation cost, supply, and demand are treated as TrFFN, and we call it Fermatean Fuzzy TP (FFTP). We illustrate the feasibility and superiority of …this method with two application examples, and compare the performance of this method with existing methods. Furthermore, the advantages of the proposed method over existing methods are described to address TP in uncertain environments. Show more
Keywords: Trapezoidal Fermatean fuzzy sets, linear programming problem, transportation problem, supply and demand
DOI: 10.3233/JIFS-221959
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 309-328, 2023
Authors: Rakesh, V. | Balamurugan, R.
Article Type: Research Article
Abstract: Recently, Induction motor (IM) become the most prevalent machine type and finds an applications in many fields such as industries, electric cars etc., A typical IMD system includes IM, power controller, converter and measurement sensors. The effective performance of the IM indirectly depends upon the sensors connected with IMD. Recently, sensor fault diagnosis plays a vital role in IMD control. Thus, this work formulated a unique methodology using current vector determined from the stator currents of IM to identify sensor failures. ANN topology is incorporated to detect the Sensor failure. MATLAB software is utilized to verify the efficacy of the …suggested topology. To demonstrate the practicality of this technology, experimental verification is carried out. The efficiency of the proposed approach for IM drives is demonstrated by both simulation and experimental findings. From the obtained results, it is proven that this technique detects the failure of the sensors within less time duration (about 0.25 ms). Hence, it can be effectively utilized in automobile industry. Show more
Keywords: Induction motor, ANN, fault detection, current sensor, speed sensor, sensor failure
DOI: 10.3233/JIFS-221998
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 329-339, 2023
Authors: Yan, Zhang | Miyuan, Zhang | Yajun, Wang | Xibiao, Cai | Yanjun, Li
Article Type: Research Article
Abstract: Since the BP neural network has poor performance and unstable learning rate in the maximum power point tracking (MPPT) algorithm of photovoltaic (PV) system, an adaptive particle swarm optimization BP neural network-fuzzy control PV MPPT algorithm (APSO-BP-FLC) is proposed in this paper. First, the inertia weight, learning factor and acceleration factor of particle swarm optimization (PSO) are self-updating, and the mutation operator is adopted to initialize the position of each particle. Second, the APSO algorithm is used to update the optimal weight threshold of BP neural network, where the input layer is irradiation and temperature, and the output layer is …the maximum power point (MPP) voltage. Third, the fuzzy logical control (FLC) is employed to adjust the duty cycle of Boost converter. The inputs of FLC are voltage difference and duty ratio D(n-1) at the previous time, and the output is duty ratio D(n). Moreover, D(n-1) is optimized by |dP/dU| to improve the search range of FLC. The irradiation, temperature and MPP voltage of PV cell are adopted as the datasets for simulation in a city in Shaanxi province, China. Simulation results show that the proposed MPPT algorithm is superior to the APSO-BP, FLC and perturbation and observation (P&O) algorithm with tracking performance, steady state oscillation rate and efficiency. In addition, the efficiency of proposed MPPT algorithm is improved by 0.37%, 6.2%, and 6.8% as compared to APSO-BP, FLC and P&O algorithm. Show more
Keywords: Adaptive particle swarm optimization algorithm (APSO), BP neural network, fuzzy control, PV power generation, MPPT
DOI: 10.3233/JIFS-213387
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 341-351, 2023
Authors: Vajravelu, Ashok | Selvan, K.S. Tamil | Jamil, Muhammad Mahadi Bin Abdul | Jude, Anitha | Diez, Isabel de la Torre
Article Type: Research Article
Abstract: Wireless Capsule Endoscopy (WCE) allows direct visual inspecting of the full digestive system of the patient without invasion and pain, at the price of a long examination by physicians of a large number of photographs. This research presents a new approach to color extraction to differentiate bleeding frames from normal ones and locate more bleeding areas. We have a dual-system suggestion. We use entire color information on the WCE pictures and the pixel-represented clustering approach to get the clustered centers that characterize WCE pictures as words. Then we evaluate the status of a WCE framework using the nearby SVM and …K methods (KNN). The classification performance is 95.75% accurate for the AUC 0.9771% and validates the exciting performance for bleeding classification provided by the suggested approach. Second, we present a two-step approach for extracting saliency maps to emphasize bleeding locations with a distinct color channel mixer to build a first-stage salience map. The second stage salience map was taken with optical contrast.We locate bleeding spots following a suitable fusion approach and threshold. Quantitative and qualitative studies demonstrate that our approaches can correctly distinguish bleeding sites from neighborhoods. Show more
Keywords: Bleeding classification and region detection, words-based color histograms, wireless capsule endoscopy
DOI: 10.3233/JIFS-213099
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 353-364, 2023
Authors: Zhao, Xiaohan | Zhu, Liangkuan | Wu, Bowen
Article Type: Research Article
Abstract: Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, an improved mayfly algorithm (IMA)-based color image segmentation method is proposed. Tent mapping initializes the female mayfly population to increase population diversity. Lévy flight is introduced in the wedding dance iterative formulation to make IMA jump from the local optimal solution quickly. Two nonlinear coefficients were designed to speed up the convergence of the algorithm. To better verify the effectiveness, eight benchmark functions are used to test the performance of …IMA. The average fitness value, standard deviation, and Wilcoxon rank sum test are used as evaluation metrics. The results show that IMA outperforms the comparison algorithm in terms of search accuracy. Furthermore, Kapur entropy is used as the fitness function of IMA to determine the segmentation threshold. 10 Berkeley images are segmented. The best fitness value, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and other indexes are used to evaluate the effect of segmented images. The results show that the IMA segmentation method improves the segmentation accuracy of color images and obtains higher quality segmented images. Show more
Keywords: Non-linear attraction coefficients, Tent chaotic mapping, Lévy flight, color image segmentation, mayfly algorithm
DOI: 10.3233/JIFS-221161
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 365-380, 2023
Authors: Hemam, Sofiane Mounine | Hioual, Ouided | Hioual, Ouassila
Article Type: Research Article
Abstract: In the last decade, the considerable increase of the cloud services use has led to the need to have search and selection techniques that match both the requirements of end users and those of the system. Indeed, to select a cloud service that meet the needs of both system and user is a challenge, due to the several conflicting criteria problem for the user on one hand, and for the system, i.e., the load balancing between Virtual Machines (VMs), on the second hand. Therefore, the main challenge, in this context, is how to ensure the user requirements by maintaining the …system performance constraint. To deal with this challenge, we present in this paper an approach based on the cloud service replication on one or more VMs when the number of the user requests will be important at a given moment. This allows better load balancing between VMs by distrusting the users’ requests over them. In addition, it allows to select the best cloud service according to the users need. However, the cloud services replication introduces the problem of the storage space saturation. Thus, our second contribution is to select and delete the cloud service replicas without degradation of the load balancing. The two proposed contributions are based on the MCDM techniques in order to select the VMs that can receive the replica of the cloud service and to select those, which their storage space is overloaded in order to delete the replica cloud service. The experimental results, based on Cloudsim simulator, show that our proposal can effectively achieve good performance (load balancing) and improve the response time. Show more
Keywords: Load balancing, dynamic, replication, deletion, Markov chain, TOPSIS method
DOI: 10.3233/JIFS-221989
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 381-393, 2023
Authors: Wang, Xindi | Xu, Zeshui | Qin, Yong
Article Type: Research Article
Abstract: In this paper, we establish a chance constrained model for the priority of hesitant fuzzy preference relation based on the idea of statistical distribution for preference information as stochastic variables with unknown distribution. Inspired by the idea of conditional value-at-risk (CVaR) robust optimization, a deterministic convex reformulation is proposed for tackling the chance constrained problem. The existing state-of-the-art methods usually assume that the probability density function of preference information is known a priori, such as Gaussian distribution. However, it is generally over-conservatism. On the contrary, our proposed method provides a tractable second-order cone (SOC) reformulation for the chance constrained problem …with the first and second moments, which is easy to handle and calculate. We also analyze the weight acquisition problem of hesitant fuzzy preference relation with unknown distribution preference using the SOC programming method, and obtain the priority weight with its approximately equivalent computationally tractable conic optimization model. A case study is conducted which shows that the proposed method achieves a good general conclusion by comparing it with the optimization method under Gaussian distribution. In addition, this method can also get better decision support for incomplete preference information. Show more
Keywords: Hesitant fuzzy preference relation, unknown distribution, CVaR, SOC, incomplete preference information
DOI: 10.3233/JIFS-220472
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 395-408, 2023
Authors: Shally, | Kumar, Sunil | Gupta, Punit
Article Type: Research Article
Abstract: The proliferation of cloud computing infrastructure has increased the energy demand remarkably. Energy-efficient resource management is essential for running a cost effective and environment friendly data center. Virtual Machine (VM) consolidation is a well-accepted method for reducing the energy consumption of the cloud data center. Quality of service is an equally important aspect of cloud services. VM migrations caused by consolidation often cause degradation in QoS. These two parameters have been dealt with individually in most research and very few addressed both energy efficiency and QoS simultaneously. We have proposed a new E nergy and Q oS E fficient (EQSE) …VM selection and placement method for improving the energy efficiency along with quality of service (QoS). VM selection and placement are two critical steps of VM consolidation. EQSE uses Resource Gap Minimization (RGM) algorithm for VM selection and Utilization-Aware Best-Fit Decreasing (UABFD) algorithm for placement of these VMs. EQSE along with dynamic thresholds reduces energy consumption and improves the quality of service by reducing the number of VM migrations. CloudSim simulation performed on PlanetLab data establishes the superiority of the proposed method compared to the existing state of the art methods of VM consolidation. Show more
Keywords: Energy efficient method, resource gap minimization, EQSE, energy efficient cloud data center, SLA aware resource management
DOI: 10.3233/JIFS-220535
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 409-419, 2023
Authors: Rahman, K. | Iqbal, Q.
Article Type: Research Article
Abstract: The aim of the paper is to introduce some complex Einstein aggregation operators for aggregating the different complex Pythagorean fuzzy sets (CPFSs) by considering the dependency between the pairs of its membership degrees. In the existing studies of fuzzy and its extensions, the uncertainties present in the data are handled with the help of degrees of membership that are the subset of real numbers, which may also loss some valuable data and hence consequently affect the decision results. A modification to these, complex Pythagorean fuzzy set handles the uncertainties with the degree whose ranges are extended from real subset to …the complex subset with unit disc and hence handle the two dimensional information in a single set. Thus motivated by this and this paper we present some novel Einstein aggregation operators, namely complex Pythagorean fuzzy Einstein weighted averaging (CPFEWA) operator, complex Pythagorean fuzzy Einstein ordered weighted averaging (CPFEOWA) operator, complex Pythagorean fuzzy Einstein hybrid averaging (CPFEHA) operator, induced complex Pythagorean fuzzy Einstein ordered weighted averaging (I-CPFEOWA) operator, and induced complex Pythagorean fuzzy Einstein hybrid averaging (I-CPFEHA) operator. Also develop some of their desirable properties. Furthermore, based on these operators a multi-attribute group decision making problems developed. An illustrative example related to the selection of the best alternative is considered to show the effectiveness, of the novel developed methods. Show more
Keywords: Einstein operational laws, CPFEWA operator, CPFEOWA operator, CPFEHA operator, I-CPFEOWA operator, I-CPFEHA operator, decision-making problem
DOI: 10.3233/JIFS-221538
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 421-453, 2023
Authors: Yu, Song | Tan, Weimin | Zhang, Chengming | Tang, Chao | Cai, Lihong | Hu, Dong
Article Type: Research Article
Abstract: Considering the power transformers fault diagnosis model has unstable performance and prone to over-fitting, we propose a transformers fault diagnosis model based on a meta-learning approach to kernel extreme learning machine with opposition-based learning sparrow search algorithm optimization (Meta-OSSA-KELM) in this paper. Its learning proceeds in two steps. Firstly, the base-learner KELMs is trained on the disjoint training subset. Then, meta-learner KELM is trained with the hidden codes of training set in base-learner KELMs that have been trained. In this paper, chaotic mapping and opposition-based learning are integrated into Sparrow search algorithm(SSA) and used it to optimize each learner. We …simulate this model with measured dissolved gas analysis(DGA) data, the results show that compared with PSO and SSA, opposition-based learning sparrow search algorithm(OSSA) has better global search-ability on the optimization for the proposed model. In addition, compared with Adaboost.M1, BPNN, SVM and KELM, Meta-OSSA-KELM has a higher average accuracy (90.9% vs 78.5%, 74.0%, 76.9%, 76.9%) and a lower standard deviation (1.56×10–2 vs 4.21×10–2 , 10.5×10–2 , 3.7×10–2 , 2.18×10–2 ) in simulation tests for 30 times. It is shown that the proposed model is a stable and better performance transformers fault diagnosis method. Show more
Keywords: Power transformers fault diagnosis, KELM, SSA, meta-learning
DOI: 10.3233/JIFS-211862
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 455-466, 2023
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