Journal of Intelligent & Fuzzy Systems - Volume 38, issue 4

Authors: Zhou, Jingyong | Guo, Yuan | Sun, Yu | Wu, Kai

Article Type: Research Article

Abstract: With the rapid development of database and Internet technologies, data collection and storage is possible. It is often impossible to correctly analyze the valuable information contained in the data, and it becomes more difficult to obtain valuable information. Therefore, it faces the status of “rich data and scarce knowledge”. Traditional information processing technology can no longer meet the needs of reality. There is an urgent need for more capable and effective information processing skills to help us analyze the information we need from big data and guide us to make the right decisions. Data mining technology is born in the …background. Data mining technology is one of the effective methods to solve rich data and improve lack of knowledge. It is also one of the main research topics in the field of information science. Related research and applications have greatly improved people’s decision-making ability. It has been recognized as one of the extremes of data research and has a very broad application prospect. Large databases often contain redundant and unnecessary attributes for many search rules, so the ability to remove duplicate attributes can greatly improve the clarity of potential system knowledge and reduce the time complexity of finding rules. At the same time, it enables efficient operation and improved adaptability. Because the structure of the neural network is variable, it has strong self-organization, self-learning, nonlinearity and high fault tolerance, but the ability to express and interpret knowledge is very poor. The network parameters lack physical meaning and learning. Therefore, it has become an inevitable trend to form a fuzzy neural network combining the characteristics of the two. Therefore, exploring the organic combination between rough sets and fuzzy neural networks is undoubtedly of great significance for data mining technology research. This paper proposes a data mining method based on the combination of rough set and fuzzy neural network technology. Using the approximate set to discover the rules of the database rules, the initial structure of the fuzzy neural network is determined, and the training data is used to train the network. Since the fuzzy neural network thus constructed has a good topology of data distribution features from the beginning, the network scale can be greatly reduced and the network training speed can be improved. Show more

Keywords: Data mining, rough set, fuzzy logic, fuzzy neural network

DOI: 10.3233/JIFS-179594

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3717-3725, 2020

Authors: Zhao, Wei | Luo, Zeju

Article Type: Research Article

Abstract: With the advent of Web 3.0 era, the number and complexity of Web pages in Bayesian networks have shown an explosive growth trend. Accompanying this is the geometric growth of information contained in Web pages. Web text data in Bayesian networks usually hide rich knowledge and rules of user value. However, due to the semi-structured, real-time and discrete characteristics of Web text data, it is difficult for users to obtain the knowledge they need directly from such complex data sets. The emergence of fuzzy mathematics provides a good research idea and method for solving such problems. It can use the …idea of fuzzy mathematics to analyze the practical problems in text data. Therefore, how to effectively mine the Web text data information and knowledge that users really care about from Bayesian network, and present it in a way that users can understand, it is a very popular research topic at present. In this paper, we select the text of Bayesian network: microblog data for experiments. User data model of microblog is established by using relevant knowledge of fuzzy theory. The concept of fuzzy measure is introduced to calculate the non-additive measure value under the interaction relationship between the detection indicators. Determine the membership function relationship between the detection user and the text data, calculate the integral values of Choquet integral, Sugeno integral and Wang integral of the membership function under the non-additive measure, the final valuable web text data is judged by integral value. On the basis of the above research contents, the research results of Web text mining technology and fuzzy arithmetic mathematics are combined, design and implement information acquisition and analysis for Bayesian network community. The recall rate obtained by the experimental method in this paper is as low as 4%, and tends to be more stable. Show more

Keywords: Fuzzy algorithm, Bayesian network, data mining, web text data mining

DOI: 10.3233/JIFS-179595

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3727-3735, 2020

Authors: Man, Na | Wang, Kechao | Liu, Lin

Article Type: Research Article

Abstract: A fuzzy system is a system that defines input, output, and state variables on a fuzzy set and is a generalization of a deterministic system. The fuzzy system begins at the macro level and covers the fuzzy features of human brain thinking. It has advantages in describing advanced knowledge. Fuzzy sets can mimic the comprehensive conclusions of people solving fuzzy information problems, which are difficult to solve by conventional mathematical methods, so computer applications can be extended to humanities, social sciences and complex systems. In this way, it can better solve nonlinear problems and is widely used in automatic control, …decision analysis, time series signal processing, economic information systems, medical diagnostic systems, and earthquake prediction systems. This paper aims to study the data mining algorithm of fuzzy systems based on fuzzy sets. By using the powerful fuzzy data modeling function of fuzzy theory, it combines with other intelligent processing methods, and makes practical use in industrial life. By comparing the application of fuzzy set data mining and algorithm, it is found that in the application state, the economic benefits of the factory are improved by 36% and the production efficiency is improved by 25% under the application of data mining and algorithm. The research data shows that the data mining and recommendation algorithms of fuzzy sets are beneficial to the development and operation of the factory. The research results show that compared with the conventional production and processing plan, the technology uses fuzzy set theory to transform the fuzzy attributes, which is more advantageous in scientific and technical systems and algorithms with its scientificity, accuracy, innovation and extensiveness. Show more

Keywords: Data mining, recommendation algorithm, fuzzy system, fuzzy set

DOI: 10.3233/JIFS-179596

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3737-3745, 2020

Authors: Zhang, Jing

Article Type: Research Article

Abstract: In recent years, with the rapid increase in the number of electric vehicles in China, the accidents caused by electrical system failures are also increasing year by year. Therefore, it is necessary to carry out reliability analysis on the electrical systems of electric vehicles. Traditional reliability analysis cannot be quantitatively evaluated and it is not possible to accurately diagnose multiple fault conditions of the system. Aiming at this problem, this paper combines fuzzy mathematics theory with fault tree analysis, and establishes a multi-state fuzzy fault tree to analyze the reliability of pure electric bus high-voltage electrical system, including qualitative analysis …and quantitative analysis. The results show that the multi-state fuzzy fault tree reliability analysis method can accurately describe the various fault states of the high-voltage electric system of pure electric passenger car, and can quantitatively evaluate the reliability, which has great practical significance. Show more

Keywords: Polymorphic fuzzy fault tree, eliability analysis

DOI: 10.3233/JIFS-179597

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3747-3754, 2020

Authors: Chen, Ying | Qi, Pengyuan | Liu, Songqing

Article Type: Research Article

Abstract: In order to effectively avoid the violent vibration in the process of mechanical processing and to achieve high efficiency and high precision machining of mechanical parts, the improved algorithm of adaptive neuro-fuzzy inference system is used to study the optimization of parameters in the process of side milling of mechanical parts, and an adaptive network structure is formed. It has the learning ability of artificial neural network and the expression ability of “if-then” of fuzzy reasoning system, which is a new prediction and control method. The results validate the applicability of the stability. The machined surface topography is measured and …the effect of flutter on the surface topography is analyzed. The three-dimensional stability of milling provides a theoretical basis for the rational selection of milling parameters of mechanical parts, the realization of stable milling and the improvement of processing efficiency. Thus, the relationship between the radial depth of cut, the axial depth of cut and the spindle speed is established, and the contour of material removal rate is obtained. The corresponding spindle speed and radial shear depth are obtained when the material removal rate is maximum. The reasonable selection of machining parameters is carried out in the region near the maximum spindle speed with stability. Show more

Keywords: Adaptive neuro-fuzzy reasoning system, machining, parameter optimization, machining error

DOI: 10.3233/JIFS-179598

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3755-3764, 2020

Authors: Wang, Hanxu | Yao, Yubing

Article Type: Research Article

Abstract:  With the development and popularization of electronic computers and the Internet, the problem of language barriers has once again become prominent in the new era, and people are more in need of machine translation. However, there is currently no suitable method for effective semantic ordering of English machine translation. In order to better perform semantic ordering on English machine translation, the article combines fuzzy theory to construct an algorithm model, and analyzes the experimental results through evaluation indicators. The results show that with the increase of training concentration training examples, the semantic parser can learn more natural language sentence analysis …methods from the training examples, and the natural language sentences that can be correctly parsed gradually increase, so with the training examples increased recall rate and F value gradually increased. The experimental results also show that the use of higher precision syntax analyzers can effectively improve the performance of statistical machine translation systems, whether in phrase-based or machine-based translation methods. Show more

Keywords: Semantic ordering, machine translation, fuzzy theory, semantic parser, system performance

DOI: 10.3233/JIFS-179599

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3765-3772, 2020

Authors: Gao, Huanbing | Lu, Shouyin | Wang, Tao

Article Type: Research Article

Abstract: The fuzzy control algorithm is used to establish the relation between the functions of motion of 6-DOF (degree of freedom) industrial robots on the rotation angle of each joint. The algorithm optimizes the motion path of the robot, thereby accelerating the increase in industrial productivity and promoting the development of industrial production. During the motions, the 6-DOF industrial robots have weak avoidance ability toward the encountered obstacles, which is not conducive to the safe production and will reduce industrial efficiency. Therefore, by analyzing and summarizing the previous researches, the fuzzy control algorithm is used to construct and optimize the kinematics …model, thereby proposing a method of robot motion path planning. Also, based on the unstructured operating environment, a multi-functional motion navigation system for 6-DOF industrial robots is proposed. The experimental results show that the fuzzy control algorithm can optimize the robot motion path, shorten the time of motion, and make the robots reach the destinations smoothly. The algorithm can avoid safety accidents in industrial production effectively, reduce casualties, improve industrial productivity, and promote the optimized allocation of human resources. The motion system of 6-DOF industrial robot based on fuzzy control algorithm has excellent practicability, which can promote the development of industrial production effectively and be widely applied to industrial production. Show more

Keywords: Fuzzy control, motion path, robot

DOI: 10.3233/JIFS-179600

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3773-3782, 2020

Authors: Xu, Huanchun | Hou, Rui | Fan, Jinfeng | Zhou, Liang | Yue, Hongxuan | Wang, Liusheng | Liu, Jiayue

Article Type: Research Article

Abstract: The data of time series are massive in quantity and not conducive to subsequent processing. Therefore, the unordered time series fuzzy clustering algorithm of adaptive incremental learning has been utilized to explore the segmentation of time series in further. The research results show that the emergence of incremental learning technology can solve such problems. Also, it can continuously accumulate and increase the data, as well as improving the learning accuracy. Incremental learning technology correctly processes, retains, and utilizes the historical results, thereby reducing the training time of new samples by using historical results. Therefore, the clustering algorithm mostly clusters the …cluster-liked shape of discrete datasets and uses the hierarchical clustering algorithm, which is more suitable for measuring the similarity of time series, to replace the Euclidean distance for distance metric and hierarchical clustering. The distance matrix update method is improved to reduce the computational complexity, which proves that the algorithm has higher clustering validity and reduces the operating time of the algorithm. Show more

Keywords: Time series, incremental learning, fuzzy clustering

DOI: 10.3233/JIFS-179601

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3783-3791, 2020

Authors: Liu, Yunpeng | Dong, Xinling

Article Type: Research Article

Abstract: Network virtualization technology releases human resources to some extent through network and cloud computing technology, reducing the workload of staff. The application of network virtualization technology in cloud computing data centers is based on this condition to improve work quality and efficiency. The purpose of this paper is to use the fuzzy algorithm to realize network virtualization of cloud computing data center. In this paper, we study the adaptive fuzzy control in depth, and conduct the practical application based on the basic knowledge of adaptive fuzzy control we learned, achieved “learn to use”. Apply the design of adaptive fuzzy control …to the load balancing algorithm of the network virtual cloud computing data center, realized the load balancing algorithm of the network virtual cloud computing data center based on adaptive fuzzy control. According to the load balancing algorithm based on adaptive fuzzy control to achieve this algorithm by using Internet knowledge, and designed the load balancing system of the whole network virtual cloud computing data center. Test the whole load balancing system which has been achieved, and obtained the performance variance curve of the system under different algorithms. Then obtained advantages and disadvantages of the algorithm by analyzing the experimental data. The experimental results show that the proposed method can effectively improve the execution performance of communication-intensive applications and ensure the stable execution of the application. At the same time, the algorithm inherits the advantages of the general fuzzy control load balancing algorithm. The stability is strong and the variance curve does not appear pulsed fluctuation. There is also no divergence phenomenon with time increased. Show more

Keywords: Fuzzy Algorithm, Network Virtualization, Cloud Computing, Network Load

DOI: 10.3233/JIFS-179602

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3793-3801, 2020

Authors: Chen, Guobin | Chen, Zhongsheng

Article Type: Research Article

Abstract: With the rapid development of the economy, the demand for urban land resources is also growing. How to make more rational use of land resources and make more rational planning of cities become a major problem in current economic development. At present, the use of remote sensing images to classify urban land use areas has become a research hot spot. However, the traditional classification accuracy rate using the maximum likelihood classification method needs to be improved. How to improve the classification accuracy rate of urban land use area of remote sensing image has become the focus and key of the …research. Both rough sets and fuzzy sets are mathematical methods for dealing with uncertain problems. The rough fuzzy sets generated by the combination of the two can solve the problem of information loss due to the rough set discretization process. Based on the advantages of fuzzy rough sets, this paper applies fuzzy rough sets to the study of urban land use area classification of remote sensing images, so as to improve the accuracy of urban land use area classification of remote sensing images. Firstly, the spectral features and texture features of the remote sensing image are extracted after preprocessing the remote sensing image. Secondly, using the domain relationship fuzzy rough set reduces the extracted features. Finally, the support vector machine is used to classify the reduced feature set, and the classification of urban land use area is realized. In the simulation experiment, the classification accuracy is evaluated by the overall classification accuracy, Kappa coefficient, and single class classification success index. The evaluation data shows that the fuzzy rough set is applied to the remote sensing image urban land use area classification, which has a good application effect. Show more

Keywords: Urban land use area, remote sensing image, fuzzy rough set, support vector machine

DOI: 10.3233/JIFS-179603

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3803-3812, 2020

Authors: Wang, Zhe | Zhu, Hong

Article Type: Research Article

Abstract: In order to achieve the improvement and optimization of e-commerce logistics, a fuzzy algorithm was built to optimize the e-commerce logistics in the marine economy. Through the construction of the fuzzy algorithm, a comparative analysis was made on the effects of logistics before the optimization of marine e-commerce, such as customer satisfaction, product arrival rate on time, logistics cost, number of routes, safety rate, work efficiency, and other aspects. The research shows that the fuzzy algorithm has a better performance in logistics cost than before. After optimization, the monthly mileage of logistics has also been significantly reduced, and the economic …benefit is very significant. The algorithm in this paper has an excellent effect in customer satisfaction and product arrival rate on time, which has obvious improvement effect on the logistics network. The improvement of the customer satisfaction is of great value and significance to the follow-up development of marine e-commerce. The improvement and optimization of logistics path based on fuzzy algorithm has reference value for the development of logistics in other fields. Based on the fuzzy algorithm, the function of the algorithm is studied by analyzing the logistics optimization of marine e-commerce. The fuzzy algorithm used shows a comprehensive and excellent result in improving the marine logistics situation, and shows that the optimization and improvement of the algorithm is a process that needs to be improved in many ways. The research greatly improves the understanding of the fuzzy algorithm and the e-commerce logistics in the marine economy environment. Show more

Keywords: Fuzzy algorithm, marine economy, e-commerce logistics optimization

DOI: 10.3233/JIFS-179604

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3813-3821, 2020

Authors: Lyu, Yuhong | Li, Xiaoqian | Wang, Xuemei | Zhao, Wen | Cui, Xiangyi | Yue, Changwu

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219321 .

DOI: 10.3233/JIFS-179605

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3823-3829, 2020

Authors: Zhu, Wenjun | Shi, Huisheng

Article Type: Research Article

Abstract: With the wide application of fuzzy mathematics in various fields of life, fuzzy mathematics has shown its strong vitality in many disciplines, such as civil architecture, environmental protection, medical science, operations management, chemical industry and so on. With the increasing depth of research issues, various disciplines are also constantly intersecting. Fuzzy mathematics is gradually being combined with other analytical methods. The purpose of this paper is to discuss the application of fuzzy mathematics in cartography. Through the quota selection model and the structure selection model, the membership function, the equal ratio sequence method, and other fuzzy mathematics methods are applied …to the river map making. Through the above methods, it is concluded that they can solve many kinds of problems, including the study of the geographical distribution of cartographic objects, cartographic selection, mutual relations, and evaluation and prediction models. Therefore, it can be concluded that the concept of fuzzy mathematics is applied to cartographic generalization. Fuzzy mathematics can deal with this kind of fuzziness better, which makes cartographic generalization possible to use more map information. It also provides a new means for the study of cartographic generalization. At the same time, it also provides a new research way for map database and automatic mapping. The accuracy of the experimental method in this paper is 5% higher than that of traditional mathematical cartography; it tends to restore the truth. Show more

Keywords: Fuzzy mathematics, mapping, equal ratio series, comprehensive evaluation

DOI: 10.3233/JIFS-179606

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3831-3840, 2020

Authors: Feng, Yanping

Article Type: Research Article

Abstract: It is a common disease of abnormal early pregnancy in obstetrics and gynecology. Inside them, abortion and hydatidiform mole are two common types, which seriously threaten the life safety of pregnant women. To evaluate the diagnostic value of transvaginal ultrasound and serum-HCG in hydatidiform mole and missed abortion. This article introduces the application of transvaginal ultrasound in disease diagnosis. An edge detection algorithm based on multiplicative gradient was proposed: normal pregnant women and abnormal pregnant women were selected as the research objects. According to the pathological examination results of abnormal pregnancy, all pregnant women were divided into two groups: hydatidiform …mole group and abortion group. The level of serum β -HCG was measured and its diagnostic value was analyzed. Transvaginal ultrasound combined with serum beta-hcg confirmed the diagnosis of β -HCG. Show more

Keywords: Vaginal ultrasound, hydatidiform mole, missed abortion, edge detection algorithm, multiplicative gradient

DOI: 10.3233/JIFS-179607

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3841-3847, 2020

Authors: Xu, Hui | Li, Yuanhai | Zhang, Jingjun | Cheng, Liang | Pan, Song

Article Type: Research Article

Abstract: The traditional epidural anesthesia often relies on blind touch method, which has high technical requirements and low efficiency for doctors. But the ultrasonic image localization method also affects the doctor’s use because of the insufficient definition, the big spot noise and so on. Based on this, a new lumbar ultrasound processing system is proposed. Anisotropic diffusion filter can enhance contrast and make image smoother. The new adaptive threshold binarization algorithm can not only remove speckle noise, but also get clear ultrasound image. The template matching algorithm is used to locate the epidural anesthesia automatically. Through several ultrasound images were examined, …The success rate was 98.12%. This technology was used in the control experiment of obese patients, which showed that this technology can better solve the problem of low epidural anesthesia operation. Show more

Keywords: Ultrasound image, epidural anesthesia, ultrasound location guidance

DOI: 10.3233/JIFS-179608

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3849-3857, 2020

Authors: Zhang, Yihong

Article Type: Research Article

Abstract: Pathological diagnosis is the most common and reliable method of cancer diagnosis, but the technology of pathological diagnosis is relatively backward. It is an urgent problem to identify and classify the pathological pictures of cancer cells. Based on this, the digital image processing and recognition technology are analyzed for the classification and recognition of hydrothorax cancer cells. There is a big difference in the morphology of pleural effusion cancer cells, and uncertainty, so the edge detection algorithm is improved, with the simulated edge detection method used to extract information. After image segmentation, feature extraction is of vital importance for cell …image classification. A method of block statistics based on Gabor coefficient is proposed. Firstly, the cell image is filtered by multi-scale and multi-directional filtering, then the average and variance are calculated, and the image is divided into several blocks to solve the problem of large amount of data. Finally, BP neural network is established to input the morphological characteristics of hydrothorax cells, and the results are classified directly. After the experiment, the proposed classification method can improve the classification effectiveness; the design model can accurately identify the breast water cancer cells, and can be effectively applied to the early diagnosis of breast water cancer cells. Show more

Keywords: Hydrothorax cancer cell, image segmentation, image recognition, feature extraction

DOI: 10.3233/JIFS-179609

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3859-3866, 2020

Authors: Liu, Xiaopei | Teng, Jianfu

Article Type: Research Article

Abstract: In China, moyamoya disease is one of the main causes of stroke events in children and adults. Early diagnosis and early treatment can prevent irreversible damage to nerve function and greatly improve the quality of life of patients. Based on the improved sparse Bayesian low-dose CT image blind restoration algorithm, moyamoya disease diagnosis and treatment has important application value guidance and evaluation Cheng, through the retrospective analysis of the clinical characteristics, imaging characteristics and treatment of 105 patients with moyamoya disease diagnosed by Blind image restoration algorithm based on improved sparse Bayesian low dose CT from April 2012 to November …2015 in a hospital in Hefei City, we think that CT angiography has important application value in the diagnosis and treatment of moyamoya disease, and we can draw a conclusion. Among these 105 patients, women are the majority. The ratio of male to female was 1:1.188, and the peak period was 30– 40 years old. There were 41 cases of ischemic stroke and 64 cases of hemorrhagic stroke. 29 patients underwent STA-MCA bypass, temporal muscle compression and dural reversal. Blind image restoration algorithm based on improved sparse Bayesian low dose CT was reexamined. The results of Blind image restoration algorithm based on improved sparse Bayesian low dose CT and DSA were the same. It can be seen that Blind image restoration algorithm based on improved sparse Bayesian low dose CT is a reliable method to diagnose moyamoya disease, which can be used as a preoperative guidance and postoperative evaluation of bypass vessel patency and collateral circulation formation. Show more

Keywords: CT angiography, moyamoya disease, artery bypass

DOI: 10.3233/JIFS-179610

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3867-3875, 2020

Authors: Liu, Na

Article Type: Research Article

Abstract: Medical image segmentation is an important step of medical image processing, which divides medical image into thousands of regions and extracts the regions of tissues and organs of interest. The accuracy of segmentation is very important for the follow-up processing of medical image and doctors’ judgment of the real situation of diseases. Medical image segmentation is a classic problem in the field of image segmentation. 3D image reconstruction technology is to obtain 3D structure information from 2D images of objects, to provide users with realistic 3D graphics, and to restore the prototype of objects, so that users can observe and …analyze from multiple perspectives, which greatly improves the accuracy of measurement and the scientific accuracy of medical diagnosis, and plays a very important role in assisting doctors in clinical diagnosis. Based on the three-dimensional image model of MRI, the load variation of the internal oblique muscle can be applied to the finite element analysis of the near end of the patellar tendon. Show more

Keywords: Three-dimensional reconstruction technology, medical image, MRI

DOI: 10.3233/JIFS-179611

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3877-3883, 2020

Authors: Nong, Zhonghai

Article Type: Research Article

Abstract: Image recognitionan is an important part of pattern recognition, It has been widely used in modern production and life. Using computer technology and modern information processing technology to complete human visual cognition and understanding. Thus, It have become a hot topic about how to study image recognition based on medical color feature extraction. This study firstly studied the existing literature, explored and analyzed relevant theories and technologies, and studied several algorithms related to current image recognition. Then the image recognition algorithm based on wavelet moment and support vector machine is combined with the artificial intelligence technology based on image feature …extraction theory to establish the color medical image recognition algorithm based on wavelet moment and support vector machine. In order to verify the feasibility and advancedness of the new algorithm, practical experiments are carried out, and the experimental results are compared and analyzed by statistical method. The concrete chart proves the correctness of the conclusion. The final of the new algorithm is proved to be successful. Show more

Keywords: Feature extraction, image recognition, color characteristics, medical image database

DOI: 10.3233/JIFS-179612

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3885-3891, 2020

Authors: Wang, Kailing | Yu, Qinglian

Article Type: Research Article

Abstract: Three-dimensional reconstruction technology can display the three-dimensional graphical data of medical images to diagnostic personnel, so as to facilitate multi-dimensional and multi-level observation of patient data, and assist doctors in qualitative and quantitative analysis of pathological tissue. A surface reconstruction algorithm for three-dimensional medical images based on segmentation is proposed. It combines image segmentation with MC (marching cubes) algorithm organically, which can be based on the characteristics of different medical images. An appropriate segmentation method is used to segment different tissues accurately, and the result of segmentation is used to extract the isosurface accurately, which avoids the limitation that MC …is only suitable for threshold segmentation. After the medical image is segmented by combining threshold and region growth, the segmentation results are input as the reconstructed data, and the improved algorithm of three-dimensional reconstruction is realized. The medical image is rendered on three-dimensional surface, and the debugging results of the software are displayed. At the same time, a cube detection method based on region growing is adopted to improve the efficiency of surface tracking. Experiments show that this algorithm can improve the reconstruction speed and display effect. Show more

Keywords: Three dimensional reconstruction, image processing, segmentation

DOI: 10.3233/JIFS-179613

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3893-3902, 2020

Authors: Guo, Rui | Shen, Xuanjing | Kang, Hui

Article Type: Research Article

Abstract: Image segmentation is very important for various fields. With the development of computer technology, computer technology has become more and more effective for image segmentation, and it is studied on the basis of partial differential equations. The curve representation method in plane differential geometry is expounded, with the SegNet-v2 segmentation model analyzed and tested in medical image segmentation. The test results show that the partial differential equation image segmentation algorithm can achieve more accurate segmentation, especially in medical image segmentation, which can achieve good results, and it is worth in practice to further promote.

Keywords: Partial differential equation, image segmentation, algorithm analysis

DOI: 10.3233/JIFS-179614

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3903-3909, 2020

Authors: Kung, Wang

Article Type: Research Article

Abstract: In order to improve the signal processing technology of contact image sensor, the optimization of image processing technology is carried out from the aspects of feature extraction, fine-grained semantic feature generation and semantic analysis matching optimization. To solve the problem of inaccurate feature extraction, a multi-scale feature representation algorithm for food images is proposed. By extracting the features of multi-scale convolution layer, and according to the food image, the feature extraction process is dispersed into each convolution process. By comparing the features of the layers with the image library, the most accurate features are selected for transmission. To solve the …problem of conservative vocabulary and poor generalization performance of generated sentences, a fine-grained image semantic analysis algorithm based on subword segmentation is proposed. The results show that compared with the mainstream methods, the proposed method has improved in varying degrees on the four evaluation indicators. The research provides a reference for the optimization of image sensor signal processing technology and the wide application of BP neural network algorithm. Show more

Keywords: BP neural network algorithms, contact image, sensors, signal processing technology

DOI: 10.3233/JIFS-179616

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3911-3919, 2020

Authors: Yi, Bo | Cao, Yuan Ping | Song, Ying

Article Type: Research Article

Abstract: With the rapid development of information science and technology, network security has occupied a very important position in people’s lives. Since the network security situation problem does not form a unified optimal solution in the model and algorithm, it is still necessary for researchers to continue to explore. In order to better evaluate the network security risk, based on fuzzy theory, particle swarm optimization and RBF neural network, this paper proposes a network security risk assessment model based on fuzzy theory. By mining the rules in the historical data of the network security situation and combining with the current network …status, the assessment of the current network security situation is realized, and the objectivity and comprehensibility of the evaluation results are improved. The experimental comparison shows that the fuzzy theory prediction model with PSO-RBF neural network has more rapid and effective evaluation and prediction results than the fuzzy theory prediction model with RBF neural network only. Show more

Keywords: Cyber security risk assessment, fuzzy theory

DOI: 10.3233/JIFS-179617

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3921-3928, 2020

Authors: Wang, Jing

Article Type: Research Article

Abstract: In recent years, technology of face recognition has developed rapidly, more and more face recognition technologies have been integrated into our work and life. In practical applications, due to influence of various factors, the resolution of the face image is low, the noise interference is large, and the illumination changes sharply during the imaging process, which brings difficulties to the face recognition, which seriously affects the accuracy of the face recognition method. This paper aims to introduce two-type fuzzy theory into face recognition and study its extraction and recognition methods of face feature. Firstly, it introduce the face recognition technology …simply. Face recognition is a technique that uses a computer to analyze a face image and extract valid identification information to identify the identity. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two methods for extracting features from face recognition. Principal Component Analysis (PCA) is a data analysis method that uses a small number of characterizations to reduce the number of dimensions, which reduces computational complexity greatly. The purpose of linear discriminant analysis is to extract data from high-dimensional feature spaces. Extracted the low-dimensional features with recognition ability, and studied the two-type fuzzy system based on fuzzy sets deeply. Obtained the output function of the two-type fuzzy system by studying the structure of each layer of the two-type fuzzy system. Introduce two types of fuzzy ideas into linear discriminant analysis. Discussed the construction of fuzzy membership functions, the selection of kernel functions and the determination of clustering rules. Finally, the ORL face database of the trained fuzzy face recognition model. As a result, the face recognition method based on the type 2 fuzzy has certain feasibility. The experimental results show that face recognition based on interval two-type fuzzy neural network has good recognition rate and anti-noise ability. Show more

Keywords: Type 2 fuzzy rules, linear identification, face recognition, feature extraction

DOI: 10.3233/JIFS-179618

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3929-3938, 2020

Authors: Lyu, Yi | Jiang, YiJie

Article Type: Research Article

Abstract: The purpose of this paper is to accurately locate the fault prediction and diagnosis technology, to have a high degree of automation, and to handle it quickly, for the large aircraft avionics system failure presents the feature of multiple coupling, complex impact and rapid spread. At the same time, the fault prediction diagnosis technology is one of the most important contents of the avionics system equipment prediction, so how to quickly and effectively predict the failure of key system parts of avionics is the core essential to ensure the complete operation of the whole system. This paper through establishing the …gray neural network model, combining the advantages of gray model to deal with poor information and the characteristics of artificial neural network processing nonlinear data, to realize the fault prediction of avionics system, At the same time, At the same time, through the fuzzy recognition method based on the deterioration degree, established the bridge between the two, in turn, to achieve the health prediction management of system. The method mainly includes: Firstly, by combining gray theory and artificial neural network algorithm with fuzzy recognition to establish a network model that contains gray neural network models and can reflect the excellent characteristics of fuzzy recognition and conduct experimental analysis; Second, on this basis, improve the weight update strategy of the gray neural network by using additional learning rate method which based on momentum and improve the accuracy of the algorithm. Therefore, it can be concluded that the predictions presented in this paper should not be directly imitated when the system disturbance factor is too large or the system is abnormally caused by a serious disturbance suddenly appearing at a certain point in time, but should properly processed the data firstly according to the actual situation. According to the time series of the actual situation, several models are established, and the data correction is explained from the model prediction effect, and the gray model and description are improved. The improved combination of gray neural network and gray neural network can not only improve the prediction accuracy, but also provide a feasible method for such time series prediction, which provides a practical and effective technical method for avionics system fault prediction. Show more

Keywords: Ashy neural network, avionics system, fuzzy recognition, fault prediction, combined forecast

DOI: 10.3233/JIFS-179619

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3939-3947, 2020

Authors: Liu, Yanbing | Dhakal, Sanjev | Hao, Binyao

Article Type: Research Article

Abstract: The coal-rock interface identification function enables the shearer to automatically identify the coal-rock interface and demonstrates outstanding advantages in improving economic efficiency and safe operation. It can improve the recovery rate of coal seam, reduce the content of rock, ash and sulfur in coal, improve the efficiency of coal mining operation and reduce equipment wear. It is one of the key equipments to realize coal mining automation. At present, there are more and more researchers on the research of coal rock interface identification technology. A common method is to use a single sensor to establish a coal rock identification system, …and use the neural network algorithm as the core algorithm of the system. Therefore, this paper proposes a recognition system based on wavelet packet decomposition and fuzzy neural network. A variety of sensors are used to collect the response signal of the shearer, and then the multi-signal feature extraction and data fusion of the coal-rock interface identification method are realized, thereby improving the recognition rate. On the basis of the physical simulation system of coal and rock interface, a large number of tests were carried out, and a large amount of test data was collected through experiments. In view of the many advantages of wavelet analysis, this paper uses wavelet packet technology to extract signal features. An energy allocation method based on wavelet packet decomposition can determine the sensitive frequency band of each sensor signal and extract each feature value. The wavelet packet energy method is used for feature extraction, which completes the conversion from mode space to feature space, and provides reliable and accurate feature level data for data fusion. The results show that neural networks and genetic neural networks can be trained and simulated using experimental data. Data fusion based on genetic neural network can perform state recognition and has high recognition accuracy. Multi-sensor data fusion technology based on genetic neural network is feasible in coal-rock interface identification. Show more

Keywords: Multi-sensor, coal-rock interface identification, fuzzy neural network, wavelet packet decomposition

DOI: 10.3233/JIFS-179620

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3949-3959, 2020

Authors: Fan, Linyuan

Article Type: Research Article

Abstract: Multiple sensor information fusion technology originated from the military. It has developed into a very active and popular field of defense research. It is also a high-level universal key technology, which has attracted attention in many disciplines and fields. Many countries, including China, have listed them on the research list. The purpose of this paper is to study multiple sensor data fusion through fuzzy sets and statistical theory. According to the scholars’ research results at home and abroad, proposed a new evidence synthesis algorithm. The algorithm combines the advantages of modifying the original evidence and modifying the comprehensive rules. By …comparing the accuracy of 100 sensor data in multiple sensors and single sensors, the consistency information and conflict information between the evidences are, mined and comprehensively considered the consistency information and conflict information between the evidences,, and analyzed the evidence empirically. Considered ENCE in the weight distribution of conflict evidence fully. By comparing the results of multiple sets of experiments, established multiple sensor data fusion algorithm based on fuzzy sets and statistical theory. Record experimental data and analyze the experimental results. The experimental results show that compared with other methods, the method can reflect the credibility of the evidence more objectively, the convergence speed is faster, and the fusion result is more in line with the actual situation. The experimental results show that the fusion algorithm based on the best fusion set and the new integrated method of conflict evidence can be used as the core algorithm of local fusion center and global fusion center in the fusion system respectively, and can also be used as the secondary fusion model of the fusion system. Show more

Keywords: Multiple sensor data fusion, weight distribution, fusion precision, convergence speed

DOI: 10.3233/JIFS-179621

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3961-3970, 2020

Authors: Yuan, Yana | Chai, Huaqi

Article Type: Research Article

Abstract: The discussion of knowledge management and technological innovation has never stopped, and the discussion of the relationship between the two has not only important practical significance but also profound theoretical significance. The purpose of this paper is to study a new method of knowledge fusion from the perspective of science and technology philosophy. From a new perspective, this paper analyzes defects of subjective tendencies, decision-dependent partial attributes of knowledge source existed in practical applications of management field and proposed a knowledge fusion method based on fuzzy set theory. This paper firstly explains the characteristics of knowledge sources, and transforms knowledge …into a new knowledge layer through combining multi-source knowledge, then improves the connotation, level and self-confidence of knowledge, finally improves the ability of the system to accomplish tasks and goals. Then combine the fuzzy set theory with the knowledge fusion algorithm reasonably and effectively, and obtain the results of knowledge fusion by using evidence synthesis and decision rules, so as to make up for the lack and defects in the knowledge fusion process and solve the uncertainty problem in knowledge reasoning. Finally, through the practical example, merged the fuzzy set theory proposed in this paper into knowledge fusion to deal it, obtain a kind of processing of fuzzy set theory, forming a knowledge fusion method based on fuzzy set theory. Based on fuzzy set theory, obtain the observation results of knowledge fusion algorithm combined with the various warning models, then to discuss and analyze the enterprise warning problem deeply. Therefore, the examples and simulation results show that the advantages in practicality and versatility of knowledge fusion method proposed through fuzzy set theory is higher than the common knowledge fusion method. The method used in the production of manufacturing products can help manufacturing companies improve development quality of product and shorten development cycle of product. Moreover, in the product design industry, it has verified that knowledge fusion can promote the dissemination of knowledge in the field of knowledge management, which helps to share and reuse design knowledge, reduce difficulty of development and improve efficiency of development. Show more

Keywords: Knowledge management, information fusion, fuzzy set theory, fuzzy petri net, knowledge update

DOI: 10.3233/JIFS-179622

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3971-3979, 2020

Authors: Wang, Renqiang | Miao, Keyin | Sun, Jianming

Article Type: Research Article

Abstract: In view of the objectively ambiguous feature of infrared image of unmanned autonomous ship, this paper presents a quantitative method to deal with the ambiguity problem in infrared image by using the fuzzy mathematical model to realize the purpose of intelligent recognition of infrared imaging target. In order to simplify the computation of target recognition and improve the response time and accuracy in the selection of target features in infrared images, three features of target location, radiation distribution and shape are selected for analysis in this paper. The membership functions of these three features are weighted to calculate the confidence, …and the classification and recognition are realized according to the confidence. Finally, the simulation results show that the recognition method proposed in this paper can effectively identify the target, and the recognition rate is very high. Compared with the recognition methods based on neural network and SVM, the recognition distance of this method is longer than that of the latter two methods. Show more

Keywords: Fuzzy mathematical model, unmanned autonomous ship, infrared imaging, intelligent target recognition

DOI: 10.3233/JIFS-179623

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3981-3989, 2020

Authors: Wang, Wei | Hu, Xiaohui | Wang, Mingye

Article Type: Research Article

Abstract: With the development of Internet technology, the growth of network services is accelerating. For more and more network service requests, how to ensure the response speed and query accuracy required by users is a huge challenge. In order to realize fast clustering of large data business request data and improve the accuracy of clustering. This paper presents a data fuzzy clustering algorithm based on Adaptive Incremental learning time series. The algorithm defines large data clustering in time series, and the incremental time series clustering method is used. Firstly, the complexity of network data is reduced by data compression, and then …time series data clustering based on service time similarity is carried out. In this paper, the time series fuzzy clustering algorithm based on Adaptive Incremental Learning inherits the clustering structure information obtained by previous clustering. Initialize the current clustering process, and then search the outlier samples in the current data block adaptively without setting parameters. Automatically create new clusters from outlier samples, and finally check empty cluster recognition. Identification determines whether certain clusters need to be deleted to ensure the efficiency of subsequent cluster processes. The experimental results show that the algorithm has good clustering accuracy and efficiency for isochronous and unequal time series. Show more

Keywords: Network data, adaptive incremental learning, time series, fuzzy clustering algorithm

DOI: 10.3233/JIFS-179624

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3991-3998, 2020

Article Type: Other

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3999-3999, 2020

Authors: Liu, Peide | Li, Ying

Article Type: Research Article

Abstract: The intuitionistic linguistic (IL) variable (ILV) can express the vague and uncertain information in a better way, and the partitioned Heronian mean (PHM) operator can group attributes that have relationships with each other into one zone and the independent attributes are in different zones, so in this paper, we will propose some new PHM operators for IL information (ILI) and then apply them to multiple attribute group decision-making (MAGDM). Firstly, the some improved operational rules for ILVs are developed, which can provide a more accurate result and avoid the loss of information, then we extend the PHM operator to the …IL environment and propose the intuitionistic linguistic partitioned Heronian mean (ILPHM) operator and the intuitionistic linguistic weighted partitioned Heronian mean (ILWPHM) operator, which can fully consider the advantages of the ILI and the PHM operator. Meanwhile, we discuss some desirable properties and special cases of the two operators. Further, we develop the MAGDM approach with ILI based on the developed operators. Lastly, a numerical instance is given to verify the feasibility and the superiority of the proposed method. Show more

Keywords: Intuitionistic linguistic information, PHM, MAGDM

DOI: 10.3233/JIFS-181175

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4001-4029, 2020

Authors: Lu, Yanling | Xu, Yejun | Qu, Shaojian | Xu, Zeshui | Ma, Gang | Li, Ziwei

Article Type: Research Article

Abstract: In multiattribute decision making problem, it is usually that the opinions of agent may be influenced by close friends or people with similar interests among his social network. This situation could act on the evaluation process of agent toward candidates, thereby affecting the overall two-sided matching results. This paper proposes a framework based on social network analysis. It can effectively achieve multiattribute social network matching that the relative weights of influencers are unknown before. In the proposed framework, the complete trust assessment matrix can be obtained through depicting the trust propagation relationship between agent and influencers. According to the trust …assessment result, a nonlinear optimization model is developed to calculate the unknown weights of influencers, and then the obtained weights of influencers are integrated into the two-sided evaluation process. Furthermore, two multi-objective optimization models are constructed to manage the rational agents and conservative agents. Based on the multi-objective optimization models, we generate the optimal matching pairs. Finally, detailed comparison analysis and discussion are presented to check the effectiveness of the proposed social network matching method. Show more

Keywords: Multiattribute matching decision making, social network analysis, optimization model, risk preference

DOI: 10.3233/JIFS-182535

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4031-4048, 2020

Authors: Ramadan, A.A. | Kim, Y.C. | Elkordy, E.H.

Article Type: Research Article

Abstract: In this paper, we investigate the relations between the L -fuzzy pre-proximities, L -fuzzy interior operators and L -fuzzy topological spaces in complete residuated lattices. In addition, degrees of L -fuzzy continuity, L -fuzzy proximity and L -fuzzy interior mappings are proposed and their connections are studied. Also, we show that there is a Galois correspondence between the category of separated L -fuzzy interior spaces and that of separated L -fuzzy pre-proximity spaces. Finally, we give their examples.

Keywords: Complete residuated lattice, L-fuzzy pre-proximity, L-fuzzy interior operators, Galois correspondence

DOI: 10.3233/JIFS-182652

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4049-4060, 2020

Authors: Yanmaz, Ozgur | Turgut, Yakup | Can, Emine Nisa | Kahraman, Cengiz

Article Type: Research Article

Abstract: Classical multi-criteria decision making (MCDM) methods have been extended to their fuzzy versions under uncertainty in the literature. Besides, these ordinary fuzzy MCDM methods have been further extended to their new versions through the recently developed types of ordinary fuzzy sets. This study extends the evaluation based on distance from the average solution (EDAS) method by using interval-valued Pythagorean fuzzy numbers to solve fuzzy multi-criteria group decision- making problems with a larger membership domain providing more flexibility. An illustrative example of the car selection problem is given to show the effectiveness and applicability of the proposed model and results are …compared with intuitionistic interval-valued fuzzy EDAS method. A sensitivity analysis is also performed to reveal the effect of the weights on alternative rankings. Show more

Keywords: EDAS, pythagorean, fuzzy, car selection

DOI: 10.3233/JIFS-182667

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4061-4077, 2020

Authors: Sánchez, Daniel Eduardo | Esmi, Estevão | de Barros, Laécio Carvalho | Miebach, Alessandro Donadio | Cecconello, Moiseis dos Santos

Article Type: Research Article

Abstract: In this manuscript, we present numerical solutions of a p -fuzzy system that models the Goodwin economic cycle. This model describes the dynamic interaction between labour share and the employment rate in an economy. One can use p -fuzzy systems to obtain solutions for differential equations whose the vector field are uncertain and partially known. The proposed p -fuzzy system is based on a fuzzy rule-based systems whose fuzzy rules represent economic premises described in the Goodwin model. We use two approaches to adjust the fuzzy terms of the corresponding fuzzy rule base from the economical data of a given …country. The first approach is based on statical measures and the second one uses fuzzy c-means clustering method. Finally, we test our proposal to estimate the growth cycles of the labour share and the employment rate of Germany and we compare the obtained results with the historical data. Show more

Keywords: P-fuzzy systems, Goodwin model, fuzzy rules, fuzzy numbers, fuzzy c-means

DOI: 10.3233/JIFS-182762

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4079-4090, 2020

Authors: Li, Jingmei | Wu, Weifei | Xue, Di

Article Type: Research Article

Abstract:  Transfer learning is a new machine learning algorithm. It solves problems in different but related target domains by utilizing the knowledge in existing data. Based on the classical SVM algorithm and transfer learning, a selective transfer learning support vector machine (STL-SVM) algorithm is proposed in this paper. First, STL-SVM uses the maximum mean discrepancy to measure the weight vector of the source domain samples relative to the target domain, and selects samples from the source domain according to each weight to avoid negative transfer. Then, the knowledge in the source domain is learned by the approximate extreme point support vector …at the minimum training data cost. Finally, the object function is constructed by the obtained knowledge and the soft-margin SVM. In the constraint conditions of the objective function, the learned knowledge that is highly correlated with the target domain is selected, and further, the phenomenon of negative transfer is avoided in principle. STL-SVM solves the problem of negative transfer, and has considerable advantages in training time efficiency compared with the existing algorithms. The experimental results on artificial and real datasets show the effectiveness of the proposed algorithm. Show more

Keywords: Machine learning, support vector machine, transfer learning, classification

DOI: 10.3233/JIFS-190055

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4091-4106, 2020

Authors: Sae-Ueng, Pannipa | Skrbic, Srdjan

Article Type: Research Article

Abstract: In recent years, there has been an increased interest in processing fuzzy queries over XML data that is also possibly fuzzy. Attention has been paid to various extensions of XML query languages that introduce concepts of fuzzy theory. We propose an extension of the XQuery query language in an attempt to handle flexible queries that provide priority, threshold, and fuzzy expression as well as fuzzy linguistic labels allowing users great flexibility in customizing query constraints. We give a detailed description of an advanced query processing software system developed using GPFCSP (Generalized Prioritized Fuzzy Constraint Satisfaction Problem) as the theoretical background. …The software, called FXI (Fuzzy XQuery Interpreter), was developed as a web application using Java, AngularJS, and eXist-db — an open source native XML database and it incorporates various advanced features such as fuzzy ordering operations and fuzzy compatibility calculations that includes priorities. The paper presents its design, the most important considerations related to implementation, as well as testing using realistic scenarios. Show more

Keywords: Fuzzy XQuery, XQuery Interpreter, XQuery, XML Database

DOI: 10.3233/JIFS-190202

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4107-4118, 2020

Authors: Yang, Xiangfeng | Ni, Yaodong

Article Type: Research Article

Abstract:  Along with increasing and extension of uncertain set theory in decision making, ordering and ranking of uncertain sets have significantly become relevant, that is, an uncertain set needs to be evaluated and compared with the others. This paper introduces the size relation between two uncertain sets. It derives a formula to calculate the uncertain measure that an uncertain set is less than or equal to another one. Moreover, some examples are given to illustrate the size relation of uncertain sets. Finally, clustering is proposed as an application.

Keywords: Uncertainty theory, uncertain set, membership function, size relation, clustering

DOI: 10.3233/JIFS-190342

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4119-4125, 2020

Authors: Murillo, Javier | Guillaume, Serge | Sari, Tewfik | Bulacio, Pilar

Article Type: Research Article

Abstract:  Fuzzy measures are used for modeling interactions between a set of elements. Simplified fuzzy measures, as k -maxitive measures, were proposed in the literature for complexity and semantic considerations. In order to analyze the importance of a coalition in the fuzzy measure, the use of indices is required. This work focuses on the generalized interaction index, gindex . Its computation requires many resources in both time and space. Following the efforts to reduce the complexity of fuzzy measure identification, this work presents two algorithms to compute the gindex for k -maxitive measures. The structure of k -maxitive measures makes …possible to compute the gindex considering the coalitions at level k and, for each of them, the number of coalitions sharing the same coefficient (called inheritors). The first algorithm deals with the space complexity and the second one also optimizes the runtime by not generating, but only counting, the number of inheritors. While counting the number of descendants is easy, this is not the case for the number of inheritors due to all the inheritors of previous considered coalitions have to be taken into account. The two proposed algorithms are tested with synthetic k -maxitive measures showing that the second algorithm is around 4 times faster than the first one. Show more

Keywords: Fuzzy measures, Shapley index, interaction index, k-maxitive measures

DOI: 10.3233/JIFS-190403

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4127-4137, 2020

Authors: Temucin, Tolga | Tuzkaya, Gulfem

Article Type: Research Article

Abstract: Reverse logistics has become one of the emerging research and application areas since the early 1990s due to several driving forces such as economic benefits, governmental regulations, increasing levels of environmental problems, etc. As an essential part of reverse logistics (RL) literature, Reverse Logistics Network Design (RLND) efforts can provide economic and ecological advantages to the supply chains. In this study, we consider a reverse distribution channel and try to determine the locations of the collection centers (CCs) and repair centers (RCs) and the amount of product flow between the customers-CCs and CCs-RCs. Considering the multi-objective nature of the RLND …problems, total cost and total delay minimization and total average capacity utilization maximization objectives are taken into account. This problem is formulated as a multi-objective mixed integer nonlinear programming model and is solved by two approaches: a heuristic and a tabu search based meta-heuristic in which both of the solution approaches are integrated with weighted max-min approach. Numerical analyses have been carried out to show the effectiveness of the proposed approaches and the solutions obtained from each approach have been compared. Show more

Keywords: Reverse logistics, network design, tabu search, weighted max-min method

DOI: 10.3233/JIFS-190431

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4139-4157, 2020

Authors: Liu, Donghai | Peng, Dan | Liu, Zaiming

Article Type: Research Article

Abstract:  An important feature of hesitant interval-valued fuzzy set(HIVFS) is that it can describe the hesitant situations flexibly. In this paper, we first introduce the hesitance degree of HIVFS, then some distance measures between hesitant interval-valued fuzzy sets(HIVFSs) based on the hesitancy degree and least common multiple principle are proposed, which can not only describe the degree of the decision makers’ hesitance in decision making process but also measure the relationship between HIVFS more objectively. Furthermore, we develop the TOPSIS method to the proposed distance measures with different preferences for the hesitancy degree and membership values. Finally, a numerical example is …given to illustrate the feasibility of the proposed method, and the effectiveness of the method is verified by some comparative analysis. In order to understand the effect of the preference parameter α and the distance parameter τ in decision making process, we make the sensitivity analysis of parameters by evaluating the example about commercialization of possible emerging technology enterprises. Show more

Keywords: Hesitance degree, hesitant interval-valued fuzzy set, the least common multiple principle, TOPSIS

DOI: 10.3233/JIFS-190445

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4159-4172, 2020

Authors: Gong, Jianye | Jiang, Bin | Shen, Qikun

Article Type: Research Article

Abstract:  This paper studies the distributed adaptive output-feedback fault tolerant control problem for leader-following multiagent systems with sensor faults. By using the approximation theory of neural networks, an unknown continuous function is approximated, and the problem that the neural network output deviates from the true approximation of the unknown function due to the faults of neural network input is solved. A filter observer is adopted to estimate the unmeasurable states. Based on the adaptive backstepping technique and fault tolerant control technique, a distributed adaptive neural output-feedback control scheme is proposed to guarantee the output consensus of all nodes under directed communication …graphs. Based on graph theory and Lyapunov stability theory, it is proved that the proposed adaptive neural control scheme guarantees the uniformly ultimate boundness of the closed-loop systems, and the tracking errors converge to a small adjustable neighborhood of the origin. The simulation results demonstrate the effectiveness of the control approach in this paper. Show more

Keywords: Fault tolerant control, adaptive control, multiagent systems

DOI: 10.3233/JIFS-190531

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4173-4190, 2020

Authors: Wan, Qiang | Zhu, Mei | Liu, Yan

Article Type: Research Article

Abstract: The traditional fixed sampling interval (FSI) synthetic X ¯ control chart is sensitive to large and moderate process shifts, but insensitive to small process shifts. This paper proposes an improved variable sampling interval (VSI) synthetic X ¯ control chart with two sampling interval lengths for monitoring the process mean. A Markov chain method is used to compute the adjusted average time to signal (AATS) of the proposed chart. The statistical design of the VSI chart is then formulated and solved with a specific genetic algorithm. Comparisons are made …to show the capability of the VSI chart in yielding average performance boost of 16.4%, in the AATS of the VSI chart scheme compared to the situation in which the corresponding FSI chart is used. Show more

Keywords: Statistical process control, synthetic X control chart, variable sampling interval, adjusted average time to signal

DOI: 10.3233/JIFS-190600

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4191-4203, 2020

Authors: Dehghan, Motahareh | Sadeghiyan, Babak | Khosravian, Erfan

Article Type: Research Article

Abstract:  In this paper, we present a protocol for secure resolution of collision between aircraft, while their corresponding trajectories are kept private. For privacy reasons, we assume that each aircraft’s position, heading and velocity are not available to all aircraft involved in the protocol. Our new secure protocol is based on an algorithm, which is for collision resolution of aircraft, without considering the privacy requirement. Our approach is Secure Multi-party Computation. To present our Secure Multi-party Collision Resolution Protocol, we propose a new Secure Sorting Protocol and a Modified Distributed Oblivious Transfer Protocol. We also take advantage of employing a …Secure Multi-party Collision Detection Protocol, a Homomorphic Encryption, a Secure Comparison Protocol, and a Secure Maximum Finding Protocol. To prove the security of our proposed protocol and its sub-protocols, we use the Ideal/ Real Simulation Paradigm. The communication complexity of our proposed protocol is in O  (n 2 ), and its computation complexity is in O(n) . We have also done a simulation for the execution of our proposed protocol for multiple moving aircraft, to show its practicality. Show more

Keywords: Privacy, collision detection and resolution, line segment intersection algorithm, exact and inexact collision

DOI: 10.3233/JIFS-190675

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4205-4221, 2020

Authors: Van Can, Nguyen | Tu, Doan Ngoc | Tuan, Tong Anh | Long, Hoang Viet | Son, Le Hoang | Son, Nguyen Thi Kim

Article Type: Research Article

Abstract: In Botnet Detection, Domain generation algorithms are the most effective method to intercept and analyze captured package. In this article, we propose a new method to classify harmful domain names using Neutrosophic Sets. Data of domain name, after being selected featured and fuzzed into Neutrosophic Sets will be used to classify benign domain names, malicious domain names and indeterminacy domain names, minimizing false detection of benign domain names. The proposed model is going to be tested and evaluated with other malicious domain detection models in the aspects of accuracy points, Accuracy, Revocation, and F1, all of which show that our …proposed model has good results. Show more

Keywords: DGA domain detection, neutrosophic clustering, classifying

DOI: 10.3233/JIFS-190681

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4223-4236, 2020

Authors: Liu, Mingtang | Wang, Li | Qin, Zening | Liu, Jiaqi | Chen, Jian | Liu, Xuemei

Article Type: Research Article

Abstract: In order to make the feature data more linearly separable for support vector machine(SVM) classifier, a set of different scale and direction parameter was proposed for improving the recognition effect of cement slope damage in high fill channels. The Gabor wavelet was used to extract the multi-scale and multi-directional features of the high-fill channel’s abrupt features. Then SVM algorithm was utilized to perform damage classification and level recognition. To compare the recognition effect of the Gabor-SVM method, histogram-SVM, grayscale symbiotic matrix -SVM, canny-SVM algorithm were adopted to identify the damage degree of cement surface in the same environment, and these …damage recognition rates are compared with Gabor-SVM’s. The experimental results show that the damage recognition model, based on Gabor-SVM, tends to better stable value when the wavelet takes 6 scale and 12 directions. The recognition rate of the normal slope is 0.98, while the recognition rate of the crack, hole, and broken slope are 0.63, 0.88 and 0.90, respectively. Overall, the damage recognition model, based on Gabor-SVM, has better recognition effect, and it will provide technical support for finding potential leakage hazards in the high fill channel of South-to-North Water Diversion Project. Show more

Keywords: Key words: Middle route of South-to-North Water Diversion Project, Feature extraction of multiple scales and directions, damage recognition, Gabor wavelet, support vector machine

DOI: 10.3233/JIFS-190767

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4237-4246, 2020

Authors: Gao, Rong | Zhang, Zanmei

Article Type: Research Article

Abstract:  With resource exhaustion and environmental problems becoming more and more serious, green supply chain management attracts the attention of enterprises and scholars. For a new type of environmental-friendly product in this supply chain, we have no enough historical data to refer the demand. Hence, uncertainty theory is introduced as a tool to characterize the uncertainty of demand. Then this paper investigates the pricing, green degree and sales effort decisions in a green supply chain with a manufacturer and a retailer, where the demand of the product is described by uncertain variable. Considering the different power structures of supply chain, we …build three expected value models and obtain equilibrium solutions, respectively. Then we analyze the influences of uncertain parameters and different power structures on decisions and profits. The results show that the green degree coefficient and sales effort coefficient have positive influences on the pricing, green degree and sales effort. Moreover, the results also show that decision leaders do not necessarily make more profits, which is up to the weights of green degree and sales effort. In addition, numerical analyses are given to show the correctness of results. Show more

Keywords: Green supply chain management, pricing decision, uncertain demand, uncertainty theory

DOI: 10.3233/JIFS-190783

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4247-4264, 2020

Authors: Shokri, Javad | Park, Choonkil

Article Type: Research Article

Abstract:  We establish some stability results concerning the 2-dimensional vector variable quadratic functional equation f ( x + y , z + w ) + f ( x - y , z - w ) = 2 f ( x , z ) + 2 f ( y , w ) in non-Archimedean L * -fuzzy normed spaces.

Keywords: ℒ*-fuzzy metric and normed space, Hyers-Ulam stability, quadratic functional equation, non-Archimedean ℒ*-fuzzy normed space

DOI: 10.3233/JIFS-190802

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4265-4272, 2020

Authors: Madura Meenakshi, R. | Padmapriya, N. | Venkateswaran, N.

Article Type: Research Article

Abstract: This paper proposes a noise removal algorithm to improve the quality of images captured by IR thermal cameras. Due to atmospheric disturbances and the temperature reflected from the surrounding objects, IR images are generally noisy. For removal of noise in IR thermal images the proposed algorithm has two stages. In the first stage, a support vector machine incorporated with fuzzy logic (FSVM) is used to obtain noise pixels from the thermal image and in the second stage an adaptive directional weighted median filter (ADWMF) is applied to restore noisy pixels. Our proposed algorithm is compared with another fuzzy- logic based …noise classification technique, namely the adaptive fuzzy inference based directional median filter (AFIDMF). Simulation results indicate that the performance of the proposed filter is better, compared to AFIDMF based on the measures such as peak signal to noise ratio (PSNR) and structural similarity index (SSIM). Show more

Keywords: IR thermal images, fuzzy membership, fuzzy inference system, support vector machine, directional weighted median filter

DOI: 10.3233/JIFS-190860

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4273-4286, 2020