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

Authors: Gou, Zhinan | Li, Yan

Article Type: Research Article

Abstract: With the development of the web 2.0 communities, information retrieval has been widely applied based on the collaborative tagging system. However, a user issues a query that is often a brief query with only one or two keywords, which leads to a series of problems like inaccurate query words, information overload and information disorientation. The query expansion addresses this issue by reformulating each search query with additional words. By analyzing the limitation of existing query expansion methods in folksonomy, this paper proposes a novel query expansion method, based on user profile and topic model, for search in folksonomy. In detail, …topic model is constructed by variational antoencoder with Word2Vec firstly. Then, query expansion is conducted by user profile and topic model. Finally, the proposed method is evaluated by a real dataset. Evaluation results show that the proposed method outperforms the baseline methods. Show more

Keywords: Query expansion, user profile, topic model, Word2Vec

DOI: 10.3233/JIFS-210508

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1701-1711, 2021

Authors: Zeng, Detian | Shi, Jingjia | Zhan, Jun | Liu, Shu

Article Type: Research Article

Abstract: To use the electromagnetic chuck to precisely absorb industrial parts in manufacturing, this paper presents a hybrid algorithm for grasping pose optimization, especially for the part with a large surface area and irregular shape. The hybrid algorithm is based on the Gaussian distribution sampling and the hybrid particle swarm optimization (PSO). The Gaussian distribution sampling based on the geometric center point is used to initialize the population, and the dynamic Alpha-stable mutation enhances the global optimization capability of the hybrid algorithm. Compared with other algorithms, the experimental results show that ours achieves the best results on the dataset presented in …this work. Moreover, the time cost of the hybrid algorithm is near a fifth of the conventional PSO in the discovery of optimal grasping pose. In summary, the proposed algorithm satisfies the real-time requirements in industrial production and still has the highest success rate, which has been deployed on the actual production line of SANY Group. Show more

Keywords: Particle swarm optimization, Gaussian distribution, alpha-stable distribution, grasping pose

DOI: 10.3233/JIFS-210520

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1713-1726, 2021

Authors: Li, Yingxin | Li, Shihua | Peng, Shuangyun | Zhao, Shoulu | Yang, Wenxian | Qiu, Lidan

Article Type: Research Article

Abstract: Changes in plateau body lake water are an important indicator of global ecosystem changes, and a timely and accurate grasp of this change information can provide a scientific reference for the formulation of relevant policies. The traditional fuzzy C-means clustering (FCM) algorithm takes into account the ambiguity of the classification of the ground object pixels but does not consider the rich spectral information of the neighboring pixels and is very sensitive to the background noise” of the remote sensing image, resulting in low water extraction accuracy. Aiming to compensate for the shortcomings of the traditional FCM algorithm, this paper proposes …an improved FCM algorithm. This algorithm replaces the Euclidean distance of the traditional FCM algorithm with a combination of the Mahalanobis distance and spectral angle matching (SAM) to fully take into account the spectral information of neighboring pixels and improve the clustering accuracy. The study selected Sentinel-2 images of the Fuxian Lake and Xingyun Lake basins during normal, wet, and dry periods as the data source. Under the same conditions, the clustering accuracy was compared with the traditional FCM algorithm, improved FCM algorithm, K-means clustering method and iterative self-organizing data analysis (ISODATA) clustering method. The experimental results show that the improved FCM algorithm has a higher water extraction accuracy than the traditional FCM algorithm, K-means clustering method and ISODATA clustering method. The kappa coefficient and overall accuracy (OA) of the improved FCM algorithm can be increased by 5.56%–9.45% and 2.66%–5.32%, respectively, and the omission error and commission error can be reduced by 1.72%–4.55% and 12.14%–22.10%, respectively. When the improved FCM algorithm is used, the extraction accuracy is higher for plateau deep lakes than for plateau shallow lakes, and the extraction effect for lakes with poor water environments is more significant than that of other methods. The improved FCM algorithm better maintains the integrity of the water boundary and overcomes the influence of a certain number of mountain shadows and urban building pixels on the clustering results. Show more

Keywords: Remote sensing, fuzzy clustering, FCM algorithm, mahalanobis distance, spectral angle matching

DOI: 10.3233/JIFS-210526

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1727-1740, 2021

Authors: Nan, TaiBen | Zhang, Haidong | He, Yanping

Article Type: Research Article

Abstract: The overwhelming majority of existing decision-making methods combined with the Pythagorean fuzzy set (PFS) are based on aggregation operators, and their logical foundation is imperfect. Therefore, we attempt to establish two decision-making methods based on the Pythagorean fuzzy multiple I method. This paper is devoted to the discussion of the full implication multiple I method based on the PFS. We first propose the concepts of Pythagorean t-norm, Pythagorean t-conorm, residual Pythagorean fuzzy implication operator (RPFIO), Pythagorean fuzzy biresiduum, and the degree of similarity between PFSs based on the Pythagorean fuzzy biresiduum. In addition, the full implication multiple I method for …Pythagorean fuzzy modus ponens (PFMP) is established, and the reversibility and continuity properties of the full implication multiple I method of PFMP are analyzed. Finally, a practical problem is discussed to demonstrate the effectiveness of the Pythagorean fuzzy full implication multiple I method in a decision-making problem. The advantages of the new method over existing methods are also explained. Overall, the proposed methods are based on logical reasoning, so they can more accurately and completely express decision information. Show more

Keywords: Full implication multiple I method, PFS, RPFIO, decision-making problem

DOI: 10.3233/JIFS-210527

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1741-1755, 2021

Authors: Karbasaki, M. Miri | Balooch Shahryari, M. R. | Sedaghatfar, O.

Article Type: Research Article

Abstract: This article identifies and presents the generalized difference (g-difference) of fuzzy numbers, Fréchet and Gâteaux generalized differentiability (g-differentiability) for fuzzy multi-dimensional mapping which consists of a new concept, fuzzy g-(continuous linear) function; Moreover, the relationship between Fréchet and Gâteaux g-differentiability is studied and shown. The concepts of directional and partial g-differentiability are further framed and the relationship of which will the aforementioned concepts are also explored. Furthermore, characterization is pointed out for Fréchet and Gâteaux g-differentiability; based on level-set and through differentiability of endpoints real-valued functions a characterization is also offered and explored for directional and partial g-differentiability. The sufficient …condition for Fréchet and Gâteaux g-differentiability, directional and partial g-differentiability based on level-set and through employing level-wise gH-differentiability (LgH-differentiability) is expressed. Finally, to illustrate the ability and reliability of the aforementioned concepts we have solved some application examples. Show more

Keywords: Fuzzy multi-dimensional mappings, g-(linear continuous) function, g-differentiability, Fréchet g-derivative, Gâteaux g-derivative, Directional g-derivative, Partial g-derivative

DOI: 10.3233/JIFS-210530

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1757-1775, 2021

Authors: Kalli, SivaNagiReddy | Suresh, T. | Prasanth, A. | Muthumanickam, T. | Mohanram, K.

Article Type: Research Article

Abstract: Automatic moving object detection has gained increased research interest due to its widespread applications like security provision, traffic monitoring, and various types of anomalies detection, etc. In the video surveillance system, the video is processed for the detection of motion objects in a step-by-step process. However, the detection has become complex and less effective due to various complex constraints. To obtain an effective performance in the detection of motion objects, this research work focuses to develop an automatic motion object detection system based on the statistical properties of video and supervised learning. In this paper, a novel Background Modeling mechanism …is proposed with the help of a Biased Illumination Field Fuzzy C-means algorithm to detect the moving objects more accurately. Here, the non-stationary pixels are separated from stationary pixels through the Background Subtraction. Afterward, the Biased Illumination Field Fuzzy C-means approach has accomplished to improve the segmentation accuracy through clustering under noise and varying illumination conditions. The performance of the proposed algorithm compared with conventional methods in terms of accuracy, precision, recall, and F- measure. Show more

Keywords: Background modeling, fuzzy c-means, motion object detection, video surveillance system

DOI: 10.3233/JIFS-210563

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1777-1789, 2021

Authors: Cheng, Fangmin | Yu, Suihuai | Qin, Shengfeng | Chu, Jianjie | Chen, Jian

Article Type: Research Article

Abstract: Evaluating the quality of the user experience (UX) of existing products is important for new product development. Conventional UX evaluation methods, such as questionnaire, have the disadvantages of the great subjective influence of investigators and limited number of participants. Meanwhile, online product reviews on e-commerce platforms express user evaluations of product UX. Because the reviews objectively reflect the user opinions and contain a large amount of data, they have potential as an information source for UX evaluation. In this context, this study explores how to evaluate product UX through using online product reviews. A pilot study is conducted to define …the key elements of a review. Then, a systematic method of product UX evaluation based on reviews is proposed. The method includes three parts: extraction of key elements, integration of key elements, and quantitative evaluation based on rough number. The effectiveness of the proposed method is demonstrated by a case study using reviews of a wireless vacuum cleaner. Based on the proposed method, designers can objectively evaluate the UX quality of existing products and obtain detailed suggestions for product improvement. Show more

Keywords: User experience (UX) evaluation, Online product reviews, Opinion mining, UX aspect, Product design

DOI: 10.3233/JIFS-210564

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1791-1805, 2021

Authors: Gogo, Kevin Otieno | Nderu, Lawrence | Mutua, Makau

Article Type: Research Article

Abstract: Fuzzy logic is a branch of artificial intelligence that has been used extensively in developing Fuzzy systems and models. These systems usually offer artificial intelligence based on the predictive mathematical models used; in this case linear regression mathematical model. Interval type 2 Gaussian fuzzy logic is a fuzzy logic that utilizes Gaussian upper membership function and the lower membership function, with a footprint of uncertainty in between the Gaussian membership functions. The artificial intelligence solutions predicted by these interval type 2 fuzzy systems depends on the training and the resultant linear regression mathematical model developed, which usually extract their training …data from the expert knowledge stored in their knowledge bases. The variances in the expert knowledge stored in these knowledge-bases usually affect the overall accuracy of the linear regression predictive models of these systems, due to the variances in the training data. This research therefore establishes the extent that these variances in knowledge bases affect the predictive accuracy of these models, with a case study on knowledge bases used to predict learners’ knowledge level abilities. The calculated linear regression predictive models show that for every variance in the knowledge base, there occurs a change in linear regression predictive model with an intercept value factor commensurate to the variances and their respective weights in the knowledge bases. Show more

Keywords: Interval type 2 gaussian fuzzy logic, linear regression predictive models, intelligent system models, knowledge-bases

DOI: 10.3233/JIFS-210568

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1807-1820, 2021

Authors: Deng, Min-Hui | Zhou, Xiao-Yu | Wang, Jian-Qiang | Li, Jun-Bo | Cheng, Peng-Fei

Article Type: Research Article

Abstract: The development of new energy industry is a pressing issue due to the deterioration of the environment. The selection of new energy projects is a critical problem for decision makers. Incomplete and uncertain information appears in the process of new energy project selection. Compared with other linguistic expressions, probabilistic linguistic term set (PLTS) simultaneously reflects all possible linguistic terms and their corresponding weights, which conforms to the cognitive habits of people. Thus, a multi-criteria decision-making framework under PLTS environment is constructed for energy project selection. Firstly, a normalised projection model of PLTS, which considers the distance and the angle between …two objects, is proposed to overcome the limitations of distance measurement. Secondly, a comprehensive weight-determination method combining the maximum deviation and expert scoring methods is developed to calculate the weight vector of the criteria. Furthermore, a projection-based VIKOR (Višekriterijumska optimizacija i kompromisno rešenje) method is established to select new energy projects, which can reflect the preferences of decision makers for group utility and individual regret. Finally, a numerical study on new energy project selection is performed to determine the validity and applicability of this method. Sensitive and comparative analyses are also conducted to reflect the rationality and feasibility of the method. Show more

Keywords: Multi-criteria decision-making, probabilistic linguistic term set, projection measurement, VIKOR method, new energy project selection

DOI: 10.3233/JIFS-210573

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1821-1836, 2021

Authors: Zhu, Siyu | He, Chongnan | Song, Mingjuan | Li, Linna

Article Type: Research Article

Abstract: In response to the frequent counterfeiting of Wuchang rice in the market, an effective method to identify brand rice is proposed. Taking the near-infrared spectroscopy data of a total of 373 grains of rice from the four origins (Wuchang, Shangzhi, Yanshou, and Fangzheng) as the observations, kernel principal component analysis(KPCA) was employed to reduce the dimensionality, and Fisher discriminant analysis(FDA) and k-nearest neighbor algorithm (KNN) were used to identify brand rice respectively. The effects of the two recognition methods are very good, and that of KNN is relatively better. Howerver the shortcomings of KNN are obvious. For instance, it has …only one test dimension and its test of samples is not delicate enough. In order to further improve the recognition accuracy, fuzzy k-nearest neighbor set is defined and fuzzy probability theory is employed to get a new recognition method –Two-Parameter KNN discrimination method. Compared with KNN algorithm, this method increases the examination dimension. It not only examines the proportion of the number of samples in each pattern class in the k-nearest neighbor set, but also examines the degree of similarity between the center of each pattern class and the sample to be identified. Therefore, the recognition process is more delicate and the recognition accuracy is higher. In the identification of brand rice, the discriminant accuracy of Two-Parameter KNN algorithm is significantly higher than that of FDA and that of KNN algorithm. Show more

Keywords: Brand rice, fuzzy probability, kernel principal component analysis, two-parameter k-nearest neighbor algorithm

DOI: 10.3233/JIFS-210584

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1837-1843, 2021

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

Authors: Van Nguyen, Kiet | Duy Nguyen, Nhat | Do, Phong Nguyen-Thuan | Gia-Tuan Nguyen, Anh | Nguyen, Ngan Luu-Thuy

Article Type: Research Article

Abstract: Machine Reading Comprehension has attracted significant interest in research on natural language understanding, and large-scale datasets and neural network-based methods have been developed for this task. However, most developments of resources and methods in machine reading comprehension have been investigated using two resource-rich languages, English and Chinese. This article proposes a system called ViReader for open-domain machine reading comprehension in Vietnamese by using Wikipedia as the textual knowledge source, where the answer to any particular question is a textual span derived directly from texts on Vietnamese Wikipedia. Our system combines a sentence retriever component, based on techniques of information retrieval …to extract the relevant sentences, with a transfer learning-based answer extractor trained to predict answers based on Wikipedia texts. Experiments on multiple datasets for machine reading comprehension in Vietnamese and other languages demonstrate that (1) our ViReader system is highly competitive with prevalent machine learning-based systems, and (2) multi-task learning by using a combination consisting of the sentence retriever and answer extractor is an end-to-end reading comprehension system. The sentence retriever component of our proposed system retrieves the sentences that are most likely to provide the answer response to the given question. The transfer learning-based answer extractor then reads the document from which the sentences have been retrieved, predicts the answer, and returns it to the user. The ViReader system achieves new state-of-the-art performances, with values of 70.83 % EM (exact match) and 89.54 % F1, outperforming the BERT-based system by 11.55% and 9.54% , respectively. It also obtains state-of-the-art performance on UIT-ViNewsQA (another Vietnamese dataset consisting of online health-domain news) and BiPaR (a bilingual dataset on English and Chinese novel texts). Compared with the BERT-based system, our system achieves significant improvements (in terms of F1) with 7.65% for English and 6.13% for Chinese on the BiPaR dataset. Furthermore, we build a ViReader application programming interface that programmers can employ in Artificial Intelligence applications. Show more

Keywords: Machine reading comprehension, question answering, transfer learning, sentence transformer

DOI: 10.3233/JIFS-210683

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1993-2011, 2021

Authors: Kumar, Mukul | Katyal, Nipun | Ruban, Nersisson | Lyakso, Elena | Mary Mekala, A. | Joseph Raj, Alex Noel | Maarc Richard, G.

Article Type: Research Article

Abstract: Over the years the need for differentiating various emotions from oral communication plays an important role in emotion based studies. There have been different algorithms to classify the kinds of emotion. Although there is no measure of fidelity of the emotion under consideration, which is primarily due to the reason that most of the readily available datasets that are annotated are produced by actors and not generated in real-world scenarios. Therefore, the predicted emotion lacks an important aspect called authenticity, which is whether an emotion is actual or stimulated. In this research work, we have developed a transfer learning and …style transfer based hybrid convolutional neural network algorithm to classify the emotion as well as the fidelity of the emotion. The model is trained on features extracted from a dataset that contains stimulated as well as actual utterances. We have compared the developed algorithm with conventional machine learning and deep learning techniques by few metrics like accuracy, Precision, Recall and F1 score. The developed model performs much better than the conventional machine learning and deep learning models. The research aims to dive deeper into human emotion and make a model that understands it like humans do with precision, recall, F1 score values of 0.994, 0.996, 0.995 for speech authenticity and 0.992, 0.989, 0.99 for speech emotion classification respectively. Show more

Keywords: Deep learning, speech fidelity classification, linear prediction cepstral coefficients (LPCC), mel frequency cepstral coefficients (MFCC), speech emotion recognition

DOI: 10.3233/JIFS-210711

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2013-2024, 2021

Authors: Gul, Rizwan | Shabir, Muhammad

Article Type: Research Article

Abstract: Pawlak’s rough set theory based on single granulation has been extended to multi-granulation rough set structure in recent years. Multi-granulation rough set theory has become a flouring research direction in rough set theory. In this paper, we propose the notion of (α , β )-multi-granulation bipolar fuzzified rough set ((α , β )-MGBFRSs). For this purpose, a collection of bipolar fuzzy tolerance relations has been used. In the framework of multi-granulation, we proposed two types of (α , β )-multi-granulation bipolar fuzzified rough sets model. One is called the optimistic (α , β )-multi-granulation bipolar fuzzified rough sets ((α , …β ) o -MGBFRSs) and the other is called the pessimistic (α , β )-multi-granulation bipolar fuzzified rough sets ((α , β ) p -MGBFRSs). Subsequently, a number of important structural properties and results of proposed models are investigated in detail. The relationships among the (α , β )-MGBFRSs, (α , β ) o -MGBFRSs and (α , β ) p -MGBFRSs are also established. In order to illustrate our proposed models, some examples are considered, which are helpful for applying this theory in practical issues. Moreover, several important measures associated with (α , β )-multi-granulation bipolar fuzzified rough set like the measure of accuracy , the measure of precision , and accuracy of approximation are presented. Finally, we construct a new approach to multi-criteria group decision-making method based on (α , β )-MGBFRSs, and the validity of this technique is illustrated by a practical application. Compared with the existing results, we also expound its advantages. Show more

Keywords: Rough set, multi-granulation rough approximations, bipolar fuzzy tolerance relation, (α, β)-bipolar fuzzified rough set, (α, β)-multi-granulation bipolar fuzzified rough sets, decision making method

DOI: 10.3233/JIFS-210717

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2025-2060, 2021

Authors: Sakthidasan Sankaran, K. | Gao, Xiao-Zhi

Article Type: Research Article

Abstract: Nowadays, numerous algorithms on power allocation have been proposed for maximizing the EE (Energy efficiency) and SE (Spectral efficiency) in the Distributed Antenna System (DAS). Moreover, the conservative techniques employed for power allocation seem to be problematic, due to their high computational complexity. The main objective of this paper focuses on optimizing the power allocation in order to enhance the EE and SE along with the improved antenna capacity using an effective optimization approach with the clustering model. To obtain the optimized power allocation and antenna capacity, Multi-scale resource Grasshopper Optimization Algorithm (Multi-scale resource GOA) scheme is proposed and employed. …Furthermore, clustering is developed based on the Discriminative cluster-based Expectation maximization (DC-EM) clustering algorithms, which also helps to reduce the interference rate and computational complexity. The performance analysis is made under various scenarios and circumstances. The proposed system (DAS with GOA-EM) is assessed and compared with the existing approaches in terms of both the EE and SE, which demonstrates that its superiority. Show more

Keywords: Distributed Antenna system, power allocation, energy efficiency, spectral efficiency, optimization algorithms

DOI: 10.3233/JIFS-210727

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2061-2072, 2021

Authors: Islam, Sk Rabiul | Pal, Madhumangal

Article Type: Research Article

Abstract: Topological indices have an important role in molecular chemistry, network theory, spectral graph theory and several physical worlds. Most of the topological indices are defined in a crisp graph. As fuzzy graphs are more generalization of crisp graphs, those indices have more application in fuzzy graphs also. In this article, we introduced the fuzzy hyper-Wiener index (FHWI) and studied this index for various fuzzy graphs like path, cycle, star, etc and provided some interesting bounds of FHWI for that fuzzy graph. A lower bound of FHWI is established for n -vertex connected fuzzy graph depending on strength of a strong …edges. A relation between FHWI of a tree and its maximum spanning tree is established and this index is calculated for the saturated cycle. Also, at the end of the article, an application in the share market of this index is presented. Show more

Keywords: Fuzzy graph, wiener index, hyper-wiener index, fuzzy hyper-wiener index

DOI: 10.3233/JIFS-210736

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2073-2083, 2021

Authors: Qureshi, Shahana Gajala | Shandilya, Shishir Kumar

Article Type: Research Article

Abstract: WSN (Wireless Sensor Network) is a network of devices which can transfer the data collected from an examined field via wireless links. Thus secure data transmission is required for accurate transfer of data from source to destination as data passes through various intermediate nodes. The study intends to perform shortest, secure path routing on the basis of trust through novel Hybridized Crow Whale Optimization (H-CWO) and QoS based bipartite Coverage Routing (QOS-CR) as well as to analyze the system’s performance. Nodes are randomly deployed in the network area. Initially, a trust metric formation is implemented via novel H-CWO and the …authenticated nodes are selected. Then through the secure routing protocol, Cluster head (CH) is selected to perform clustering. Neighbourhood hop prediction is executed to determine the shortest path routing and secure data transfer is performed through novel QOS-CR. The proposed system is analyzed by comparing it with various existing methods in terms of delay, throughput, energy and alive nodes. The results attained from comparative analysis revealed the efficiency of the proposed system. The proposed novel H-CWO and QOS-CR exhibited minimum delay, high throughput, energy and maximum alive nodes thereby ensuring safe transmission of data from source node to destination node. Show more

Keywords: Wireless sensor networks, trust metric, secured routing, hybrid crow whale optimization and QOS based bipartite coverage routing

DOI: 10.3233/JIFS-210766

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2085-2099, 2021

Authors: Cao, Mengxue | Lu, Laijun | Zhong, Yu

Article Type: Research Article

Abstract: How to more effectively perform anomaly detection of combination information has always been an important issue for the scholars in various fields. In order to identify and extract the geochemical anomaly information related to polymetallic mineralization in the Hunjiang area, this article uses the hybrid method that combines multivariate canonical harmonic trend analysis (MCHTA), singularity analysis with radius-areal metal amount and improved adaptive fuzzy self-organizing map (IAFSOM). First, multiple sets of combination feature information with multi-dimensional variables will be obtained through the MCHTA method, which information is considered as the initial information for the subsequent analysis. Next, the singularity analysis …method is used to process the combination concentration value to calculate the singularity indexes. Finally, the singularity indexes are classified by the IAFSOM method, and nine groups of sample data are obtained. The analysis results found that the samples information in fourth group covered most of the low α -values. The main conclusions in this study are as follows: (1) The MCHTA method can effectively detect the combination information related to geochemical anomaly; (2) The application of singularity analysis method with radius-areal metal amount can reveal the significant characteristics of mineralization combination elements; (3) IAFSOM can be used as an effective tool for the classification and identification of geochemical anomaly with combination information; (4) the hybrid method that combines MCHTA method, singularity analysis and IAFSOM model has a good indication significance in the prospecting of geochemical anomalies, and could provide a good method for geochemical prospecting. Show more

Keywords: Key words: Multivariate canonical harmonic trend analysis (MCHTA), singularity analysis with radius-areal metal amount, improved adaptive fuzzy self-organizing mapping (IAFSOM), iron polymetallic mineralization, Hunjiang district

DOI: 10.3233/JIFS-210786

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2101-2110, 2021

Authors: Song, Juan | Ni, Zhiwei | Jin, Feifei | Wu, Wenying | Li, Ping

Article Type: Research Article

Abstract: Probabilistic dual hesitant fuzzy sets (PDHFSs) have good flexibility and integrity in expressing fuzzy and uncertain information. However, some crucial problems related to PDHFSs remain unsolved, such as how to define probabilistic dual hesitant fuzzy preference relations (PDHFPRs) and solve group decision-making (GDM) problems with PDHFPRs. This paper establishes the concept of PDHFPRs and investigates consensus-based GDM methods with PDHFPRs. First, a new distance measure is proposed to quantify the difference between two PDHFPRs, which does not increase the virtual elements of membership and non-membership degrees, and can contain all distance combination of membership and non-membership elements. Therefore, the distance …calculation results are not affected by the subjectivity of decision-makers (DMs). Second, the consensus measures for PDHFPRs are proposed, which are effective tool to measure the consensus level among DMs. Moreover, two consensus-based GDM methods are proposed, which can improve the group consensus level for PDHFPRs by changing the PDHFPR with the worst consensus level or modifying the weights of DMs. Finally, the proposed methods are applied to the location selection of large-scale industrial solid waste treatment facilities. The comparison with existing methods illustrates the validity and feasibility of the proposed methods. Show more

Keywords: Group decision-making, probabilistic dual hesitant fuzzy preference relations, distance measure, consensus

DOI: 10.3233/JIFS-210796

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2111-2128, 2021

Authors: Zhang, Xiangxiang | Chang, Liu | Luo, Jingwen | Wu, Jia

Article Type: Research Article

Abstract: With the rise of the Internet of Things, the opportunistic network of portable smart devices has become a new hot spot in academic research in recent years. The mobility of nodes in opportunistic networks makes the communication links between nodes unstable, so data forwarding is an important research content in opportunistic networks. However, the traditional opportunistic network algorithm only considers the transmission of information and does not consider the social relationship between people, resulting in a low transmission rate and high network overhead. Therefore, this paper proposes an efficient data transmission model based on community clustering. According to the user’s …social relationship and the release location of the points of interest, the nodes with a high degree of interest relevance are divided into the same community. Weaken the concept of a central point in the community, and users can share information to solve the problem of excessive load on some nodes in the network and sizeable end-to-end delay. Show more

Keywords: Opportunistic social networks, community clustering, interest point, community reconstruction, data transmission, IoT system

DOI: 10.3233/JIFS-210807

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2129-2144, 2021

Authors: Zhao, Peng | Han, Baoming | Li, Dewei | Li, Yawei

Article Type: Research Article

Abstract: As a key operation for the daily maintenance of electric multiple units (EMU), the first-level maintenance operation directly affects the utilization efficiency of the EMU. The fixed operation sequence of EMU trains, the limitation of the track capacity and inconsistent arrival time of EMU trains give rise to such problems as extended waiting time, idle tracks and waste of maintenance capacity. To solve these problems and optimize the assignment of EMU-to-track, we propose a flexible job-shop sequence scheduling (Flexible-JSS) mode for the first-level maintenance of EMU trains, and a flexible sequence and tracks sharing (FSTS) model for the first-level maintenance …at electric multiple units depot (EMUD) has also been proposed in this paper. The FSTS model is designed to shorten the latest completion time after taking into account the constraints such as the train length, track capacity, the operation sequence of all EMU trains, the operation process of a single EMU train, and the train-set scheduling plan. A modified genetic algorithm is used to solve the model. The feasibility and effectiveness of the model and algorithm are verified by a real case, and the comparison with the other two fixed job-shop sequence scheduling (Fixed-JSS) modes proves that the Flexible-JSS mode can improve the efficiency and ability of the first-level maintenance at EMUD impressively. Show more

Keywords: First-level maintenance operation, flexible job-shop sequence scheduling mode, flexible sequence and tracks sharing (FSTS) model, modified genetic algorithm, the latest completion time

DOI: 10.3233/JIFS-210823

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2145-2160, 2021

Authors: Gao, Yan | Liu, Chenchen | Zhao, Liangyu | Zhang, Kun

Article Type: Research Article

Abstract: The q-rung orthopair fuzzy set is a powerful and useful tool to deal with uncertainty, but in actual decision-making process, decision-makers are usually required to analyze the actual problem dynamically. Therefore in this paper, we consider the time-series q-rung orthopair fuzzy decision making. First, we introduce the new cosine similarity measure of q-ROFS which combines the cosine similarity measure and the Euclidean distance measure. Then, we combine the advantages of projection method and grey correlation degree, establishing the nonlinear programming model to calculate the weights of attributes. Furthermore, we use the exponential decay model to get the weights formulas of …q-ROFS at different times. Then we replace the distance function with grey relational projection and extend TOPSIS method. Based on these, we propose a new MAGDM approach to deal with time-series q-rung orthopair fuzzy problem not only from the point of view of geometry but also from the point of view of algebra. Finally, we give a practical example to illustrate effectiveness and feasibility of the new method. Show more

Keywords: q-rung orthopair fuzzy set, time-series, grey correlation degree, cosine distance measure

DOI: 10.3233/JIFS-210841

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2161-2170, 2021

Authors: Zhao, Tingting | Yi, Xiaoli | Zeng, Zhiyong | Feng, Tao

Article Type: Research Article

Abstract: YTNR (Yunnan Tongbiguan Nature Reserve) is located in the westernmost part of China’s tropical regions and is the only area in China with the tropical biota of the Irrawaddy River system. The reserve has abundant tropical flora and fauna resources. In order to realize the real-time detection of wild animals in this area, this paper proposes an improved YOLO (You only look once) network. The original YOLO model can achieve higher detection accuracy, but due to the complex model structure, it cannot achieve a faster detection speed on the CPU detection platform. Therefore, the lightweight network MobileNet is introduced to …replace the backbone feature extraction network in YOLO, which realizes real-time detection on the CPU platform. In response to the difficulty in collecting wild animal image data, the research team deployed 50 high-definition cameras in the study area and conducted continuous observations for more than 1,000 hours. In the end, this research uses 1410 images of wildlife collected in the field and 1577 wildlife images from the internet to construct a research data set combined with the manual annotation of domain experts. At the same time, transfer learning is introduced to solve the problem of insufficient training data and the network is difficult to fit. The experimental results show that our model trained on a training set containing 2419 animal images has a mean average precision of 93.6% and an FPS (Frame Per Second) of 3.8 under the CPU. Compared with YOLO, the mean average precision is increased by 7.7%, and the FPS value is increased by 3. Show more

Keywords: Wildlife detection, YOLO, transfer learning, MobileNet, PANet

DOI: 10.3233/JIFS-210859

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2171-2181, 2021

Authors: Wang, Qian | Zhao, Wenfang | Ren, Jiadong

Article Type: Research Article

Abstract: Intrusion Detection System (IDS) can reduce the losses caused by intrusion behaviors and protect users’ information security. The effectiveness of IDS depends on the performance of the algorithm used in identifying intrusions. And traditional machine learning algorithms are limited to deal with the intrusion data with the characteristics of high-dimensionality, nonlinearity and imbalance. Therefore, this paper proposes an I ntrusion D etection algorithm based on I mage E nhanced C onvolutional N eural N etwork (ID-IE-CNN ). Firstly, based on the image processing technology of deep learning, oversampling method is used to increase the amount of original data to achieve …data balance. Secondly, the one-dimensional data is converted into two-dimensional image data, the convolutional layer and the pooling layer are used to extract the main features of the image to reduce the data dimensionality. Thirdly, the Tanh function is introduced as an activation function to fit nonlinear data, a fully connected layer is used to integrate local information, and the generalization ability of the prediction model is improved by the Dropout method. Finally, the Softmax classifier is used to predict the behavior of intrusion detection. This paper uses the KDDCup99 data set and compares with other competitive algorithms. Both in the performance of binary classification and multi-classification, ID-IE-CNN is better than the compared algorithms, which verifies its superiority. Show more

Keywords: Intrusion detection, convolutional neural network, image enhancement

DOI: 10.3233/JIFS-210863

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2183-2194, 2021

Authors: Niu, Guo | Ma, Zhengming

Article Type: Research Article

Abstract: Locally Linear Embedding (LLE) is honored as the first algorithm of manifold learning. Generally speaking, the relation between a data and its nearest neighbors is nonlinear and LLE only extracts its linear part. Therefore, local nonlinear embedding is an important direction of improvement to LLE. However, any attempt in this direction may lead to a significant increase in computational complexity. In this paper, a novel algorithm called local quasi-linear embedding (LQLE) is proposed. In our LQLE, each high-dimensional data vector is first expanded by using Kronecker product. The expanded vector contains not only the components of the original vector, but …also the polynomials of its components. Then, each expanded vector of high dimensional data is linearly approximated with the expanded vectors of its nearest neighbors. In this way, the proposed LQLE achieves a certain degree of local nonlinearity and learns the data dimensionality reduction results under the principle of keeping local nonlinearity unchanged. More importantly, LQLE does not increase computation complexity by only replacing the data vectors with their Kronecker product expansions in the original LLE program. Experimental results between our proposed methods and four comparison algorithms on various datasets demonstrate the well performance of the proposed methods. Show more

Keywords: Dimensionality reduction, locally linear embedding, local quasi-linear

DOI: 10.3233/JIFS-210891

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2195-2205, 2021

Authors: Ramalingeswar, J.T. | Subramanian, K.

Article Type: Research Article

Abstract: The effective coordination of solar photovoltaic (solar PV) with Electrical Vehicles (EV) can substantially improve the micro grid(MG) stability and economic benefits. This paper presents a novel Energy Management System (EMS) that synchronizes EV storage with Solar PV and load variability. Reducing grid dependency and energy cost of the MGs are the key objectives of the proposed EMS. A smart EV prioritization based control strategy is developed using fuzzy controller. Probabilistic approach is designed to estimate the EV usage expectancy in the near time zone that helps smart decision on choosing EVs. Minimizing battery degradation and maximizing EV storage exploitation …are the key objectives of EV prioritization. On the other hand, Water Filling Algorithm (WFA) is used for Optimal Storage Distribution (OSD) in each zone of energy need for load flattening. The proposed EMS is implemented in a real time on-grid MG scenario and different case studies have been investigated to realize the impact of proposed EMS. A comprehensive cost analysis has been conducted and the efficacy of the proposed EMS is analysed. Show more

Keywords: Solar PV, EV storage, WFA, load flattening, EV ranking

DOI: 10.3233/JIFS-210930

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2207-2223, 2021

Authors: Tian, Jinghui | Han, Dongying | Xiao, Lifeng | Shi, Peiming

Article Type: Research Article

Abstract: With the innovation and development of detection technology, various types of sensors are installed to monitor the operating status of equipment in modern industry. Compared with the same type of sensors for monitoring, heterogeneous sensors can collect more comprehensive complementary fault information. Due to the large distribution differences and serious noise pollution of heterogeneous sensor data collected in industrial sites, this brings certain challenges to the development of heterogeneous data fusion strategies. In view of the large distribution difference in the feature spatial of heterogeneous data and the difficulty of effective fusion of fault information, this paper presents a multi-scale …deep coupling convolutional neural network (MDCN), which is used to map the heterogeneous fault information from different feature spaces to the common spaces for full fusion. Specifically, a multi-scale convolution module (MSC) with multiple filters of different sizes is adopted to extract multi-scale fault features of heterogeneous sensor data. Then, the maximum mean discrepancy (MMD) is applied to measure the distance between different spatial features in the coupling layer, and the common failure information in the heterogeneous data is mined by minimizing MMD to fuse effectively in order to identify the failure state of the device. The validity of this method is verified by the data collected on a first-level parallel gearbox mixed fault experiment platform. Show more

Keywords: Fault diagnosis, information fusion, maximum mean difference, convolutional neural network

DOI: 10.3233/JIFS-210932

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2225-2238, 2021

Authors: Yan, Wei | Ding, Yuhan

Article Type: Research Article

Abstract: With the rapid development of Semantic Web, the retrieval of RDF data has become a research hotspot. As the main method of data retrieval, keyword search has attracted much attention because of its simple operation. The existing RDF keyword search methods mainly search directly on RDF graph, which is no longer applicable to RDF knowledge graph. Firstly, we propose to transform RDF knowledge graph data into type graph to prune the search space. Then based on type graph, we extract frequent search patterns and establish a list from frequent search patterns to pattern instances. Finally, we propose a method of …the Bloom coding, which can be used to quickly judge whether the information our need is in frequent search patterns. The experiments show that our approach outperforms the state-of-the-art methods on both accuracy and response time. Show more

Keywords: RDF knowledge graph, keyword, type graph, frequent search pattern, Bloom coding

DOI: 10.3233/JIFS-210950

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2239-2253, 2021

Authors: Chen, Lei | Han, Jun | Tian, Feng

Article Type: Research Article

Abstract: Fusing the infrared (IR) and visible images has many advantages and can be applied to applications such as target detection and recognition. Colors can give more accurate and distinct features, but the low resolution and low contrast of fused images make this a challenge task. In this paper, we proposed a method based on parallel generative adversarial networks (GANs) to address the challenge. We used IR image, visible image and fusion image as ground truth of ‘L’, ‘a’ and ‘b’ of the Lab model. Through the parallel GANs, we can gain the Lab data which can be converted to RGB …image. We adopt TNO and RoadScene data sets to verify our method, and compare with five objective evaluation parameters obtained by other three methods based on deep learning (DL). It is demonstrated that the proposed approach is able to achieve better performance against state-of-arts methods. Show more

Keywords: IR and visible images, image fusion, generative adversarial network, lab

DOI: 10.3233/JIFS-210987

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2255-2264, 2021

Authors: Firouzkouhi, Narjes | Amini, Abbas | Cheng, Chun | Soleymani, Mehdi | Davvaz, Bijan

Article Type: Research Article

Abstract: Inspired by fuzzy hyperalgebras and fuzzy polynomial function (term function), some homomorphism properties of fundamental relation on fuzzy hyperalgebras are conveyed. The obtained relations of fuzzy hyperalgebra are utilized for certain applications, i.e., biological phenomena and genetics along with some elucidatory examples presenting various aspects of fuzzy hyperalgebras. Then, by considering the definition of identities (weak and strong) as a class of fuzzy polynomial function, the smallest equivalence relation (fundamental relation) is obtained which is an important tool for fuzzy hyperalgebraic systems. Through the characterization of these equivalence relations of a fuzzy hyperalgebra, we assign the smallest equivalence relation …α i 1 i 2 ∗ on a fuzzy hyperalgebra via identities where the factor hyperalgebra is a universal algebra. We extend and improve the identities on fuzzy hyperalgebras and characterize the smallest equivalence relation α J ∗ on the set of strong identities. Show more

Keywords: Fuzzy hyperalgebra, fuzzy polynomial function, identity, fundamental relation, universal algebra, homomorphism

DOI: 10.3233/JIFS-210994

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2265-2274, 2021

Authors: Jia, Zhifu | Liu, Xinsheng

Article Type: Research Article

Abstract: In this paper, we propose complex uncertain differential equations (CUDEs) based on uncertainty theory. In order to describe the evolution of complex uncertain phenomenon related to belief degrees, we apply the complex Liu process to CUDEs. Firstly, we pose a concept of a linear CUDE and prove that homogeneous linear CUDE and general linear CUDE have solutions. Then, we prove existence and uniqueness theorem of a special CUDE. Further, we design a numerical algorithm to obtain inverse uncertainty distribution of the solution. Finally, as an application, we analyse the inverse uncertainty distributions of time integral of CUDEs and design numerical …algorithms to obtain inverse uncertainty distributions of time integral. Show more

Keywords: Complex uncertain differential equations, existence and uniqueness theorem, time integral

DOI: 10.3233/JIFS-211030

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2275-2289, 2021

Authors: Sha, Gang | Wu, Junsheng | Yu, Bin

Article Type: Research Article

Abstract: With the development of computer technology, more and more deep learning algorithms are widely used in medical image processing. Viewing CT images is a very usual and important way in diagnosing spinal fracture diseases, but correctly reading CT images and effectively segmenting spinal lesions or not is deeply depended on doctors’ clinical experiences. In this paper, we present a method of combining U-net, dense blocks and dilated convolution to segment lesions objectively, so as to give a help in diagnosing spinal diseases and provide a reference clinically. First, we preprocess and augment CT images of spinal lesions. Second, we present …the DenseU-net network model consists of dense blocks and U-net to raise the depth of training network. Third, we introduce dilated convolution into DenseU-net to construct proposed DDU-net(Dilated Dense U-net), in order to raise receptive field of CT images for getting more lesions information. The experiments show that DDU-net has a good segmentation performance of spinal lesions, which can build a solid foundation for both doctors and patients. Show more

Keywords: Deep learning, Segmentation, Dense U-net, DDU-net(Dilated Dense U-net)

DOI: 10.3233/JIFS-211063

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2291-2304, 2021

Authors: Wu, Xiu-Yun | Liao, Chun-Yan | Zhao, Yan-Hui

Article Type: Research Article

Abstract: In this paper, the notion of (L , M )- fuzzy convex derived hull spaces is introduced. It is proved that the category of (L , M )- fuzzy convex derived hull spaces is isomorphic to the category of (L , M )- fuzzy convex spaces and the category of (L , M )- fuzzy convex enclosed relation spaces. Based on this, the notion of (L , M )- fuzzy restricted convex derived hull spaces is introduced. It is further proved that the category of (L , M )- fuzzy restricted convex derived hull spaces is isomorphic to the category …of (L , M )- fuzzy restricted convex hull spaces. Show more

Keywords: (L, M)- fuzzy convex space, (L, M)- fuzzy convex enclosed relation space, (L, M)- fuzzy convex derived hull space, (L, M)- fuzzy restricted convex hull space

DOI: 10.3233/JIFS-211115

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2305-2317, 2021

Authors: Dündar, Erdinç | Ulusu, Uğur

Article Type: Research Article

Abstract: The authors of the present paper, firstly, investigated relations between the notions of rough convergence and classical convergence, and studied on some properties of the rough convergence notion which the set of rough limit points and rough cluster points of a sequence of functions defined on amenable semigroups. Then, they examined the dependence of r -limit LIMr f of a fixed function f  ∈ G on varying parameter r .

Keywords: Rough convergence, rough limit point, rough cluster point, Folner sequence, amenable semigroups

DOI: 10.3233/JIFS-211167

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2319-2324, 2021

Authors: Khan, Majid | Haj Ismail, Abd Al Karim | Ishaque, Iqra | Hussain, Iqtadar

Article Type: Research Article

Abstract: Substitution boxes (S-boxes) are among the most widely recognized and fundamental component of most modern block ciphers. This is on the grounds that they can give a cipher fortifying properties to oppose known and possible cryptanalytic assaults. We have suggested a novel tool to select nonlinear confusion component. This nonlinear confusion component added confusion capability which describes to make the connection among the key and the cipher as complex and engaging as possible. The confusion can be obtained by using substitution box (S-box) and complex scrambling algorithm that relies on key and the input (plaintext). Various statistical and cryptographic characteristics …were introduced to measure the strength of substitution boxes (S-boxes). With the help of the present objective weight methods and ranking technique, we can select an ideal S-box among other constructed confusion component to make our encryption algorithm secure and robust against various cryptographic attacks. Show more

Keywords: Simple additive weighting, entropy weighting, S-boxes

DOI: 10.3233/JIFS-211176

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2325-2338, 2021

Authors: Xiao, Yanjun | Liang, Shitong | Wang, Xiaolei | Jiang, Yunfeng | Liu, Weiling | Sun, Lingyu

Article Type: Research Article

Abstract: The abnormal vibration of the loom spindle will seriously affect the quality of the textile. Based on the inherent embedded control system of the rapier loom, this paper develops an embedded system that monitors and analyzes the vibration signal of the spindle to determine the cause of the spindle failure. The system improves the traditional vibration sensor signal acquisition method, design the sensor peripheral auxiliary circuit and vibration signal conditioning circuit, and design the data storage and communication module so that the system has the characteristics of low cost, strong flexibility and scalability. The embedded algorithm program of Fast Fourier …transform is developed, optimized, and is applied to embedded platform, therefore the system can analyze the characteristics of vibration signal in frequency domain. Finally, back propagation neural network (BPNN) is introduced to investigate and match the relationship between the vibration spectrum characteristics and fault types of the loom spindle. The extracted back propagation (BP) learning result is a mathematical mapping formula, which enables the embedded system to analyze and determine the cause of vibration fault by using this formula. System design is conducive to improving the level of production intelligence and reducing personnel costs in the production process. Show more

Keywords: Loom, vibration detection, embedded system, back propagation neural network, Fast Fourier transform

DOI: 10.3233/JIFS-211269

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2339-2356, 2021

Authors: Chen, Chuanming | Ye, Zhen | Hu, Fan | Gong, Shan | Sun, Liping | Yu, Qingying

Article Type: Research Article

Abstract: Existing trajectory-clustering methods do not consider road-network connectivity, road directionality, and real path length while measuring the similarity between different road-network trajectories. This paper proposes a trajectory-clustering method based on road-network-sensitive features, which can solve the problem of similarity metrics among trajectories in the road network, and effectively combine their local and overall similarity features. First, the method performs the primary clustering of trajectories based on the overall vehicle motion trends. Then, the map-matched trajectories are clustered based on the road segment density, connectivity, and corner characteristics. Finally, clustering is then merged based on the multi-area similarity measure. The visualization …and experimental results on real road-network trajectories show that the proposed method is more effective and comprehensive than existing methods, and more suitable for urban road planning, public transportation planning, and congested road detection. Show more

Keywords: Trajectory clustering, map matching, road-network trajectory, trajectory similarity

DOI: 10.3233/JIFS-211270

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2357-2375, 2021

Authors: Aziz, Fehmi | Tahir, Faheem | Midhat, Sadia | Naz, Shafaq | Qureshi, Naveeda Akhtar

Article Type: Research Article

Abstract:  Present study is an interdisciplinary approach towards rapid and efficient medical diagnosis. The research articulated on data set of cross-sectional study of pregnant females dwelling rural area of Pakistan. The prognosis of gestational wellbeing followed through analyzing heterogenic medical information to develop a holistic picture of ongoing pregnancy. Therefore, for rapid medical diagnosis and precision in decision-making, Fuzzy Soft Set (denoted as FSS) theory selected to develop an algorithm. The algorithm constructed as single point, multipoint and cumulative diagnosis for predicting health status with respect of Hemoglobin, Body Mass Index and Random Glucose Concentration (Respectively denoted as Hb, BMI and …RGC) of subjects under study. We successfully proposed novel approach for complex modeling and provision of algorithm for medical diagnosis. The algorithms successfully dealt with analyzing diversely attributed detailed medical tests/reports as input. The output of complex modeling effectively served efficient decision-making in predicting gestational wellbeing. Show more

Keywords: Medical diagnosis, fuzzy set, soft set, BMI, maternal anemia

DOI: 10.3233/JIFS-190452

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2377-2385, 2021

Authors: Zhou, Jia-Jia | Li, Xiang-Yang

Article Type: Research Article

Abstract:  In present paper, we put forward four types of hesitant fuzzy β covering rough sets (HFβ CRSs) by uniting covering based rough sets (CBRSs) and hesitant fuzzy sets (HFSs). We firstly originate hesitant fuzzy β covering of the universe, which can induce two types of neighborhood to produce four types of HFβ CRSs. We then make further efforts to probe into the properties of each type of HFβ CRSs. Particularly, the relationships of each type of rough approximation operators w.r.t. two different hesitant fuzzy β coverings are groped. Moreover, the relationships between our proposed models and some …other existing related models are established. Finally, we give an application model, an algorithm, and an illustrative example to elaborate the applications of HFβ CRSs in multi-attribute decision making (MADM) problems. By making comparative analysis, the HFβ CRSs models proposed by us are more general than the existing models of Ma and Yang and are more applicable than the existing models of Ma and Yang when handling hesitant fuzzy information. Show more

Keywords: Hesitant fuzzy β covering, hesitant fuzzy β neighborhoods, hesitant fuzzy complement β neighborhoods, HFβCRSs, MADM

DOI: 10.3233/JIFS-190959

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2387-2402, 2021

Authors: Wang, Chuanxu | Song, Changqun | Xu, Lang

Article Type: Research Article

Abstract: Based on an unqualified product recalling process in a supply chain, this paper establishes an evolutionary game model between consumer federation and manufacturer, as well as analyzes the effects of manufacturer’s pricing strategy and consumer federation’s supervision on the decision-making and dynamic tendency. Under this structure, the manufacturers’ pricing strategies on recalls mechanism have two scenarios: the high penalty and low penalty from consumer federation. Results shows that, when the consumer federation adopts high penalty measures, there will be an ESS for consumer federation that can both minimize the cost and protect consumers’ rights. Further, the probability of manufacturer adopting …“recall” strategy is positively correlated with the change in the product price, and both the probability of consumer federation adopting “regulate” strategy and manufacturer adopting “recall” strategy are positively correlated with the penalty coefficient. Show more

Keywords: Recall mechanism, evolutionary game, behavior strategy, consumer federation

DOI: 10.3233/JIFS-200086

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2403-2415, 2021

Authors: Gosain, Anjana | Dahiya, Sonika

Article Type: Research Article

Abstract: DKIFCM (Density Based Kernelized Intuitionistic Fuzzy C Means) is the new proposed clustering algorithm that is based on outlier identification, kernel functions, and intuitionist fuzzy approach. DKIFCM is an inspiration from Kernelized Intuitionistic Fuzzy C Means (KIFCM) algorithm and it addresses the performance issue in the presence of outliers. It first identifies outliers based on density of data and then clusters are computed accurately by mapping the data to high dimensional feature space. Performance and effectiveness of various algorithms are evaluated on synthetic 2D data sets such as Diamond data set (D10, D12, and D15), and noisy Dunn data set …as well as on high dimension real-world data set such as Fisher-Iris, Wine, and Wisconsin Breast Cancer Data-set. Results of DKIFCM are compared with results of other algorithms such as Fuzzy-C-Means (FCM), Intuitionistic FCM (IFCM), Kernel-Intuitionistic FCM (KIFCM), and density-oriented FCM (DOFCM), and the performance of proposed algorithm is found to be superior even in the presence of outliers and noise. Key advantages of DKIFCM are outlier identification, robustness to noise, and accurate centroid computation. Show more

Keywords: Fuzzy clustering, identification of outlier, FCM, IFCM, DOFCM, KIFCM, kernel functions

DOI: 10.3233/JIFS-201858

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2417-2428, 2021