Journal of Intelligent & Fuzzy Systems - Volume 41, issue 1

Authors: Neffati, Syrine | Ben Abdellafou, Khaoula | Aljuhani, Ahamed | Taouali, Okba

Article Type: Research Article

Abstract: The development of Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) systems in the past decade has led to a remarkable advance in biomedical applications and devices. Particularly, CAM and CAD systems are employed in medical engineering, robotic surgery, clinical medicine, dentistry and other biomedical areas. Hence, the accuracy and precision of the CAD and CAM systems are extremely important for proper treatment. This work suggests a new CAD system for brain image classification by analyzing Magnetic Resonance Images (MRIs) of the brain. Firstly, we use the proposed Downsized Rank Kernel Partial Least Squares (DR-KPLS) as a feature extraction technique. Then, …we perform the classification using Support Vector Machines (SVM) and we validate with a k-fold cross validation approach. Further, we utilize the Tabu search metaheuristic approach in order to determine the optimal parameter of the kernel function. The proposed algorithm is entitled DR-KPLS+SVM. The algorithm is tested on the OASIS MRI database. The proposed kernel-based classifier is found to be better performant than the existing methods. Show more

Keywords: Dimensionality reduction, CAD system, Optimization, KLPS, classification

DOI: 10.3233/JIFS-210595

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1845-1854, 2021

Authors: Juneja, Poonam | Garg, Rachana | Kumar, Parmod

Article Type: Research Article

Abstract: The paper presents a novel method for processing uncertain data of Phasor measurement unit (PMU) modules first time in the literature using Fuzzy Reasoning Petri net (FPN). It addresses several key issues such as exploitation of Petri net representation from operating state of PMU to its failure state whereas Fuzzy logic is used to deal with the uncertain data of PMU modules. Sprouting tree, an information flow path, of PMU failure is drawn due to various components and estimation accuracy can be enhanced by integration of more truthiness input data. Fault tree diagram, Fuzzy Petri net model (FPN), production rule …sets for PMU are developed and finally degree of truthiness of proposition is computed from sprouting tree. Fuzzy logic reasoning is used for routing the sprouting tree whereas Petri net is employed for dynamics of states due to failure of modules of PMU. The fusion of two technologies is made for the dynamic response, processing and reasoning to sprouting tree information flow from operating state to unavailability of PMU. The research work is useful to pinpoint the weakness in design of modules of PMU and to assess its reliability. Show more

Keywords: Fuzzy logic system, Petri net, Phasor measurement unit, reliability, sprouting tree

DOI: 10.3233/JIFS-210602

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1855-1867, 2021

Authors: Wang, Shengwei | Li, Ping | Ji, Hao | Zhan, Yulin | Li, Honghong

Article Type: Research Article

Abstract: Intelligent algorithms using deep learning can help learn feature data with nonlinearity and uncertainty, such as time-series particle concentration data. This paper proposes an improved particle swarm optimization (IPSO) algorithm using nonlinear decreasing weights to optimize the hyperparameters, such as the number of hidden layer neurons, learning rate, and maximum number of iterations of the long short-term memory (LSTM) neural network, to predict the time series for air particulate concentration and capture its data dependence. The IPSO algorithm uses nonlinear decreasing weights to make the inertia weights nonlinearly decreasing during the iteration process to improve the convergence speed and capability …of finding the global optimization of the PSO. This study addresses the limitations of the traditional method and exhibits accurate predictions. The results of the improved algorithm reveal that the root means square, mean absolute percentage error, and mean absolute error of the IPSO-LSTM model predicted changes in six particle concentrations, which decreased by 1.59% to 5.35%, 0.25% to 3.82%, 7.82% to 13.65%, 0.7% to 3.62%, 0.01% to 3.55%, and 1.06% to 17.21%, respectively, compared with the LSTM and PSO-LSTM models. The IPSO-LSTM prediction model has higher accuracy than the other models, and its accurate prediction model is suitable for regional air quality management and effective control of the adverse effects of air pollution. Show more

Keywords: Particle concentration, particle swarm optimization, long short-term memory network, nonlinear decreasing weight, air pollution

DOI: 10.3233/JIFS-210603

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1869-1885, 2021

Authors: Liu, Fuwei | Wang, Yansen

Article Type: Research Article

Abstract: The freezing pipe fracture can cause freezing wall to thaw and even lead to major accidents such as mine flooding easily, which seriously threatens the safety in construction. Therefore, scientific and effective comprehensive risk assessment for freezing pipe fracture is of great significance. In this work, a risk assessment method is put forward based on improved AHP-Cloud model with 19 evaluation indicators. First, the multi-dimension evaluation index system and evaluation model are established, on the basis of in-depth analysis of the risk factors that may lead to accidents. Second, synthesizing the normalization process and the improved analytic hierarchy process (AHP), …the evaluation grade cloud and comprehensive evaluation cloud of freezing pipe fracture can be acquired by using the forward cloud generator. Finally, According to the max-subjection principle and the comprehensive evaluation method, we obtain the risk level of freezing pipe fracture. The model is applied to Yangcun Coal Mine. It has been verified that the risk assessment problem of freezing pipe fracture in freezing sinking can be successfully solved by the model we proposed. Above all, the study offers a new research idea for the risk management of freezing pipe fracture in freeze sinking. Show more

Keywords: Freezing pipe fracture, risk assessment, improved AHP-Cloud model, fuzzy factors, freeze sinking

DOI: 10.3233/JIFS-210608

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1887-1900, 2021

Authors: Lian, Jie

Article Type: Research Article

Abstract: In order to improve the distribution efficiency of cold chain logistics and reduce the distribution cost, an optimization model of cross-docking scheduling of cold chain logistics based on fuzzy time window is constructed. According to the complexity of cold chain logistics network, a multi-objective optimization model of cross-docking scheduling of cold chain logistics vehicle routing with fuzzy time window is established. In order to ensure the lowest total cost of cold chain logistics distribution and improve the overall customer satisfaction with service time, the Drosophila optimization algorithm is used to solve the model to obtain the optimal vehicle routing of …cross-docking scheduling optimization of cold chain logistics. The simulation test results show that: after the application of the model, the cold chain logistics distribution time is significantly shortened, the distribution cost is significantly reduced, the damage cost is reduced, the carbon emission of vehicles is reduced, and the economic and low-carbon benefits are significantly improved, which can be used as an effective tool to solve the cross-docking scheduling optimization problem of cold chain logistics. Show more

Keywords: Fuzzy time window, cold chain, logistics, cross-docking, scheduling optimization model, Drosophila optimization algorithm

DOI: 10.3233/JIFS-210611

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1901-1915, 2021

Authors: Wang, Xiaoyan | Sun, Jianbin | Zhao, Qingsong | You, Yaqian | Jiang, Jiang

Article Type: Research Article

Abstract: It is difficult for many classic classification methods to consider expert experience and classify small-sample datasets well. The evidential reasoning rule (ER rule) classifier can solve these problems. The ER rule has strong processing and comprehensive analysis abilities for diversified mixed information and can solve problems with expert experience effectively. Moreover, the initial parameters of the classifier constructed based on the ER rule can be set according to empirical knowledge instead of being trained by a large number of samples, which can help the classifier classify small-sample datasets well. However, the initial parameters of the ER rule classifier need to …be optimized, and choosing the best optimization algorithm is still a challenge. Considering these problems, the ER rule classifier with an optimization operator recommendation is proposed in this paper. First, the initial ER rule classifier is constructed based on training samples and expert experience. Second, the adjustable parameters are optimized, in which the optimization operator recommendation strategy is applied to select the best algorithm by partial samples, and then experiments with full samples are carried out. Finally, a case study on a turbofan engine degradation simulation dataset is carried out, and the results indicate that the ER rule classifier has a higher classification accuracy than other classic classifiers, which demonstrates the capability and effectiveness of the proposed ER rule classifier with an optimization operator recommendation. Show more

Keywords: Evidential reasoning rule (ER rule), optimization operator recommendation, classification, turbofan engine degradation status

DOI: 10.3233/JIFS-210629

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1917-1929, 2021

Authors: Congdong, Li | Weiming, Yang | Yinyun, Yu | Bingjun, Li

Article Type: Research Article

Abstract: In the process of product development, the identification and evaluation of important nodes is of great significance for the effective control of complex product engineering change. In order to identify and evaluate important nodes accurately, this paper proposes a method to evaluate the importance of complex product nodes. Firstly, an engineering change expression method based on multi-stage complex network is proposed. Then, the evaluation index system of important nodes of complex products is constructed. A three parameter grey relational model based on subjective and objective weights is proposed to identify and evaluate the important nodes of complex products. Finally, an …example of a large permanent magnet synchronous centrifugal compressor is analyzed. The example shows that the top nodes are node 4, 1, 7, 9 and 24. Compared with other experiments, the proposed method can effectively and reasonably evaluate the node importance of complex products. Show more

Keywords: Complex product, node importance evaluation, three-parameter interval grey number, grey relational model

DOI: 10.3233/JIFS-210635

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1931-1948, 2021

Authors: Sirbiladze, Gia | Matsaberidze, Bidzina | Ghvaberidze, Bezhan | Midodashvili, Bidzina | Mikadze, David

Article Type: Research Article

Abstract: The attributes influencing the decision-making process in planning transportation of goods from selected facilities locations in disaster zones are considered. Experts evaluate each candidate for humanitarian aid distribution centers (HADCs) (service centers) against each uncertainty factor in q-rung orthopair fuzzy sets (q-ROFS). For representation of experts’ knowledge in the input data for planning emergency service facilities locations a q-rung orthopair fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach is developed. Based on the offered fuzzy TOPSIS aggregation a new innovative objective function is introduced which maximizes a candidate HADC’s selection index and reduces HADCs opening risks …in disaster zones. The HADCs location and goods transportation problem is reduced to the bi-criteria problem of partitioning the set of customers by the set of service centers: 1) Minimization of opened HADCs and goods transportation total costs; 2) Maximization of HADCs selection index. Partitioning type transportation constraints are also constructed. Our approach for solving the constructed bi-criteria partitioning problem consists of two phases. In the first phase, based on the covering’s matrix, we generate a new matrix with columns allowing to find all possible partitioning of the demand points with the opened HADCs. In the second phase, using the generated matrix and our exact algorithm we find the partitioning –allocations of the HADCs to the centers corresponded to the Pareto-optimal solutions. The constructed model is illustrated with a numerical example. Show more

Keywords: q-rung orthopair fuzzy sets, TOPSIS, fuzzy multi-objective facility location-transportation problem, partitioning problem, Pareto-optimal solution

DOI: 10.3233/JIFS-210636

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1949-1962, 2021

Authors: Tang, Xing | Yu, Suihuai | Chu, Jianjie | Fan, Hao

Article Type: Research Article

Abstract: When the proximity sensor of a smartphone is impaired, it would easily lead to screen mistouch during conversation, which will significantly affect the user experience. However, there are relatively few studies that have been focused on the quality of user experience following sensor impairment. The purpose of this study was to compare and evaluate different machine learning models in forecasting the user’s posture during a phone call, thereby providing a compensation approach for detecting proximity to the human ear during a phone call following sensor damage. The built-in accelerometer sensors of smartphones were employed to collect posture data while users …were employing their smartphones. Three main postures (holding, moving and answering) were identified; the posture data were obtained through training and prediction using five machine learning models. The results showed that the model that utilized triaxial data had better prediction accuracy than the model that used single-axis data. Furthermore, models with time-domain features had a higher accuracy rate. Among the five models, neural networks had the best prediction accuracy (0.982). The proposed approach could be of immense benefit to the users following proximity sensor damage, and would be advantageous in the design of the smartphone, particularly in the early stages of the design process. Show more

Keywords: Accelerometer sensor, damage, posture, proximity sensor, smartphone

DOI: 10.3233/JIFS-210646

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1963-1974, 2021

Authors: Akram, Muhammad | Ullah, Inayat | Allahviranloo, Tofigh | Edalatpanah, S.A.

Article Type: Research Article

Abstract: A Pythagorean fuzzy set is a powerful model for depicting fuzziness and uncertainty. This model is more flexible and practical as compared to an intuitionistic fuzzy model. This research article presents a new model called LR -type fully Pythagorean fuzzy linear programming problem. We consider the notions of LR -type Pythagorean fuzzy number, ranking for LR -type Pythagorean fuzzy numbers and arithmetic operations for unrestricted LR -type Pythagorean fuzzy numbers. We propose a method to solve LR -type fully Pythagorean fuzzy linear programming problems with equality constraints. We describe our proposed method with numerical examples including diet problem.

Keywords: Pythagorean fuzzy linear programming problem, ranking function, LR-type Pythagorean fuzzy numbers

DOI: 10.3233/JIFS-210655

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1975-1992, 2021