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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: Saeed, Maha Mohammed | Al-Ghour, Samer | Mehmood, Arif | Al-Shomrani, Mohammed M. | Park, Choonkil | Lee, Jung Rye
Article Type: Research Article
Abstract: This work investigates the new notion of operators, including the interior operator, exterior operator and closure operator in bipolar vague soft topological spaces. On the basis of these notions few results are addressed in bipolar vague soft topological spaces. Lastly, the intriguing concept is that of a sequence’s limit and on the basis of this concept few more results are addressed in bipolar vague soft topological spaces.
Keywords: Bipolar vague soft set, bipolar vague soft operations, bipolar vague soft topological space, bipolar vague soft α-open sets, bipolar vague soft α-close sets, bipolar vague soft operators, bipolar vague soft equence
DOI: 10.3233/JIFS-220498
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1099-1116, 2023
Authors: Miao, Yong | Liu, Zedong | Zhuang, Zijing | Yan, Xiaofeng
Article Type: Research Article
Abstract: The most significant parameter in groundwater movement in stones is capillary water absorption. Specifying the capillary water absorption (CWP ) of rocks needs hard and laborious experimental work, while prediction models can reduce the cost and required time. To this aim, different rock specimens were gathered from various rocks. For the prediction processes, the hybrid adaptive neuro-fuzzy inference system (ANFIS) models also were proposed to determine the optimal value of two constituent parameters of the ANFIS, which the particle swarm optimization (PSO) and whale optimization algorithm (WOA) algorithm applied to the ANFIS for this aim. Results present that ANFIS processes …have passable accomplishment in forecasting the CWA with R 2 larger than 0.832 and 0.917 for the training and testing data, respectively, a good connection among actual and anticipated values. Considering developed models, the ANFIS model optimized with WOA performs better than another model in training and testing datasets. In the training dataset, the value of R2 and RRSE is 0.917 and 29.29% for the WOA-ANFIS model, while the PSO-ANFIS model is 0.911 and 30.50%, respectively. Overall, it is clear that WOA-ANFIS can be recognized as the proposed model, which shows its capability to find the optimal value of two constituent parameters of the ANFIS. Show more
Keywords: Capillary water absorption, building stones, prediction, adaptive neuro-fuzzy inference system, hybrid ANFIS
DOI: 10.3233/JIFS-220640
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1117-1127, 2023
Authors: Mohammed, Awsan | Ghaithan, Ahmed | Al-Yami, Fahad
Article Type: Research Article
Abstract: The oil and gas industry is one of the harshest environments on reinforced concrete structures. Enhancing the reliability of these industries has been identified as a critical goal to meet anticipated production targets and maintain competitiveness. The purpose of this paper is to rank and prioritize risk factors on reinforced concrete structural systems in the oil and gas industry to reduce failures and improve system reliability. The risk factors influencing reinforced concrete systems are identified based on the experts interviewed who specialized in risk analysis. In this paper, a risk assessment approach based on a hybrid fuzzy failure mode and …effect analysis is developed in order to rank the factors and improve the process of reinforced concrete maintenance prioritization. The developed approach is also compared with the other two methods; namely, conventional failure mode and effect analysis (FMEA) and grey rational analysis (GRA) integrated with FMEA. The three developed approaches are designed to acquire the highest risk priority number (RPN) values; conventional RPN, GRA-FMEA RPN, and Fuzzy-FMEA RPN. These values will be utilized as the focus of improvements to reduce the possibility of some kind of failure occurring a second time and improve the deteriorated reinforced concrete structure to minimize the likelihood of failures. The results revealed that high-risk systems include the compression train, steam turbine, and combustion gas turbine generator, while the majority require maintenance of the supporting concrete foundation as soon as second-degree deterioration occurs. Furthermore, the results indicated that the Fuzzy FMEA approach was appropriate for assessing deteriorated reinforced concrete structures.. This work represents a step forward in the development of a tool that can be used to assess the risk of degraded concrete structures and improve their integrity through proper monitoring and maintenance. Show more
Keywords: Risk assessment, concrete structures, oil and gas industry, fuzzy FMEA, grey rational analysis
DOI: 10.3233/JIFS-221328
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1129-1151, 2023
Authors: Shabbir, Wasif | Aijun, Li | Taimoor, Muhammad | Yuwei, Cui
Article Type: Research Article
Abstract: Flight performance of unmanned aerial vehicles (UAVs) strongly depends on implemented attitude tracking control. For designing better controllers, nonlinear control design techniques are often opted instead of control design based on linearized models. Uncertainty in nonlinear dynamics estimation may arise due to inaccuracies in aerodynamic derivatives and simplifications/assumptions made during the derivation of nonlinear models. This paper considers attitude tracking control of fixed-wing UAVs having uncertain dynamics and corrupted gyro sensor outputs. An integral chain differentiator (ICD) is used to provide the analytical redundancy to the gyros used to measure the angular rates. Two control design schemes are proposed, a …neuro-fuzzy adaptive sliding mode control (NFASMC) and an ICD approximation-based fuzzy adaptive sliding mode control (ICD-FASMC). In NFASMC, the uncertain part of the dynamics is estimated using an adaptive radial basis function neural network. Gyro sensor output errors are estimated in real-time, using ICD based error estimation scheme and used in the control law along with the sensor’s corrupted outputs. In ICD-FASMC, the uncertain dynamics and angular rates of UAV are estimated using the ICD such that the requirement of the gyro sensor outputs for control design is bypassed. The switching gain of the designed controllers is made adaptive using fuzzy logic to mitigate the chattering effect. The stability of the proposed controllers is proved using the Lyapunov approach. The proposed schemes are implemented using a nonlinear simulation of a fixed-wing UAV. Simulation results are presented to show the effectiveness of the proposed techniques. Show more
Keywords: Neural network, tracking control, sliding mode control, fuzzy logic, UAV, sensors
DOI: 10.3233/JIFS-222630
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1153-1168, 2023
Authors: Subramanian, Kannimuthu | Kandhasamy, Premalatha
Article Type: Research Article
Abstract: Mining high utility itemsets (HUIs) from transaction databases is one of the current research areas in the data mining field. HUI mining finds itemsets whose utility meets a predefined threshold. It enables users to quantify the usefulness or preferences of products by utilizing different values. Since utility mining approaches do not satisfy the downward closure property, the cost of candidate generation for HUI mining in terms of time and memory space is excessive. This paper presents Genetic Algorithm based Particle Swarm Optimization (GA-PSO), which can efficiently prune down the number of candidates and optimally acquire the complete set of high …utility itemsets. The proposed algorithm’s performance is assessed using the synthetic dataset T20.I6.D100K and the real-time supermarket dataset, which comprises 38765 transactions and 167 unique products. It performs very effectively in terms of time and memory on large databases constituted of small transactions, which are challenging for existing high utility itemsets mining algorithms to manage. Experiments on real-world applications show the importance of high utility itemsets in business decisions, as well as the distinction between frequent and high utility itemsets. Show more
Keywords: Data mining, high utility itemset, genetic algorithm, particle swarm optimization, stagnation
DOI: 10.3233/JIFS-220871
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1169-1189, 2023
Authors: Riaz, Muhammad | Jamil, Nimra
Article Type: Research Article
Abstract: The idea of a cubic bipolar fuzzy set (CBFS ) is a new hybrid extension of the cubic set (CS) and the bipolar fuzzy set (BFS). A CBFS is a strong model to deal with bipolarity and fuzziness in terms of positive membership grades (PMGs) and negative membership grades (NMGs). A positive interval and a positive numbers represent a PMG to express the degree of belongingness of a specific property, and a negative interval and a negative number represent a NMG which defines the degree of non-belongingness of the specific property (or satisfaction level of its counter property). The …aim of this paper is to define the cubic bipolar fuzzy topology under P-order (CBFS P topology) as well as the cubic bipolar fuzzy topology under R-order (CBFS R topology). We investigate certain properties and results of CBFS P topology and CBFS R topology. Topological structures on CBFSs are helping in the development of new artificial intelligence (AI) techniques for healthcare domain strategies and investigating various critical diseases. Such techniques allow for the early detection and investigation of diseases, assisting clinicians in minimizing the possible risk factors. An extended linear assignment model (LAM) and superiority and inferiority ranking method (SIR method) are proposed for healthcare diagnosis based on newly developed structures. The proposed LAM and SIR method are successfully applied for investigation of critical diseases. Moreover, we discuss a comparison analysis of investigations made by suggested techniques with some existing approaches. Show more
Keywords: Cubic bipolar fuzzy set, cubic bipolar fuzzy topology, computational intelligence, linear assignment model, SIR method, healthcare
DOI: 10.3233/JIFS-222224
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1191-1212, 2023
Authors: Carmalatta, J. | Diwakaran, S. | Uma Maheswari, P. | Raja, S. | Robinson, Y. Harold | Julie, E. Golden | Kumar, Raghvendra | Son, Le Hoang | Le, Chung | Tung, Nguyen Thanh | Long, Hoang Viet
Article Type: Research Article
Abstract: In Passive Clustered Wireless Sensor Networks (WSNs), energy is lost in a sensor node during the data transmission. In order to avoid the energy loss due to data transmission, a data prediction technique is implemented. In this paper, we present a new multi-point data prediction technique, in which the prediction algorithm is initially implemented at both member nodes and cluster heads. The algorithm is updated to cluster head by member nodes by tracking temporal correlation of data. Neuro-Fuzzy model is used as a predictor in both member nodes and cluster heads. The simulation is performed using MATLAB and the overall …energy in nodes seems to increase. The mean square error (MSE) value is reduced to greater extend. Show more
Keywords: Neuro-fuzzy, wireless sensor networks, clustering, cluster head, mean square error value, energy consumption.
DOI: 10.3233/JIFS-212214
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1213-1228, 2023
Authors: Esmaeili, Mahin
Article Type: Research Article
Abstract: This paper presents a new combined algorithm for the fuzzy Travelling Salesman Problem (FTSP) based on a composition of the Intelligent Water Drops (IWD) and the Electromagnetism-like (EM) algorithms. In a FTSP, the time consumed distance between cities i and j can be described by vague knowledge, such as fuzzy quantity. The main goal of FTSP is to achieve the minimum distance of Hamilton circuit of G graph, where the Hamilton circuit is a closed route of cities (i.e., nodes) of G that have been visited only once. The proposed algorithm transfers the generated responses by …the IWD to the EM, where the best answer is selected. Importantly, the computed results from both algotithm are compared and the best is accumulated. In other words, in each iteration, the best result is collected by comparison between the current and previous hierarchies until the halt condition is fulfilled. Finally, the results of the genetic algorithm (GA), IWD and EM algorithms are compared, so that the efficiency of the proposed combined IWD-EM algorithm is determined. Show more
Keywords: Fuzzy travelling salesman problem (TSP), intelligent water drops (IWD) algorithm, electromagnetism-like (EM) algorithm, genetic algorithm (GA)
DOI: 10.3233/JIFS-213121
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1229-1240, 2023
Authors: Wu, Meiqin | Chen, Ruixin | Fan, Jianping
Article Type: Research Article
Abstract: Multi-criteria decision-making methods often include attributes with uncertain nature in practical applications, single-valued neutrosophic set is an important approach to solve above problem. QUALIFLEX method is a traditional decision method that makes decision by comparing different permutations of alternatives. In this paper, QUALIFLEX method is developed to solve the MCDM problem with the element of decision matrix is the single-valued neutrosophic number. Besides, since the defects of the original QUALIFLEX method about fusing information of different attributes, this paper uses Dempster-Shafer theory of evidence to integrate the information about weight and alternatives. Finally, by comparing the result with other MCDM …methods, we find that the new method can not only obtain reasonable results, but also explain the decision results by probability theory. This paper not only develops the traditional MCDM method, but also a meaningful attempt to apply AI algorithm in MCDM method. Show more
Keywords: Dempster-Shafer theory of evidence, QUALIFLEX, Single-valued neutrosophic set, multiple criteria decision making (MCDM), evidential reasoning
DOI: 10.3233/JIFS-220194
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1241-1256, 2023
Authors: Liu, Yitong | Mu, Xuewen
Article Type: Research Article
Abstract: A new neural network is proposed to solve the second-order cone constrained variational inequality (SOCCVI) problems. Instead of the smoothed Fishcer-Burmeister function, a smooth regularized Chen-Harker-Kanzow-Smale (CHKS) function is used to handle relevant complementarity conditions. By using a neural network approach based on the CHKS function, the KKT conditions corresponding to the SOCCVI are solved. Some stability properties of the neural network can be verified by the Lyapunov method. When the parameters of the neural network are different, the achieved convergence speed will also vary. Further by controlling the corresponding parameters, the neural network can achieve a faster convergence speed …than a classical model. Numerical simulations are applied to examine the computing capability of the neural network as well as the influence of parameters on it. Show more
Keywords: Neural network, Second-order cone, Variational inequality, CHKS function, Lyapunov method
DOI: 10.3233/JIFS-220972
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1257-1268, 2023
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