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

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Abstract: This article has been retracted.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

Authors: Vo, Thanh | Sharma, Rohit | Kumar, Raghvendra | Son, Le Hoang | Pham, Binh Thai | Tien Bui, Dieu | Priyadarshini, Ishaani | Sarkar, Manash | Le, Tuong

Article Type: Research Article

Abstract: Nowadays, the crime rate increases dramatically in every country. Therefore, it is an urgent need for governments and social associations to produce persistent solutions and disincentive penalties to prevent crime issues. Specifically, social media plays an important role in crime rate detection; thus, reducing crime rates significantly. It would be a good medium for the desired task. In this paper, we analyze Twitter data collected from Twitter accounts for seven different locations (Ghaziabad, Chennai, Bangaluru, Chandigarh, Jammu, Gujarat, and Hyderabad) from January 2014 to November 2018 in a case study of India, which is opted to illustrate the efficiency of …the proposed work. Sentiment analysis has been used to analyze users’ behavior and psychology through the tweets of people to track crime actions. Twitter part-of-speech tagger, which is a Markov Model of first-order entropy, has been used for part-of-speech in online conversational text. Brown clustering is used for a long set of unlabeled tweets. Comparisons are verified with real crime rates from an authorized source of information according to different locations. We also measure the latest crime trends for the highest (Ghaziabad, Uttar Pradesh) and lowest crime cities (Jammu) in India. It has been found that the latest crime trends have been recorded for the time duration of 7 days (23, January 2019 to 30, January 2019). The analyses demonstrate that the obtained results match with the real crime rate data. We believe that these types of studies will help to detect the real-time crime rate for different locations and detect the crime pattern easily. Show more

Keywords: Crime detection, Twitter data, social media, sentiment analysis, Brown clustering

DOI: 10.3233/JIFS-190870

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4287-4299, 2020

Authors: Boulaaras, Salah

Article Type: Research Article

Abstract:  The paper deals with the existence of positive solutions of nonlocalp  (x )-Kirchhoff hyperbolic systems with zero Dirichlet boundary conditions in bounded domain Ω ⊂ R N by using sub-super solutions method combined with a comparison principle. Moreover, the numerical example is presented to illustrate the stationary case.

Keywords: Positive solutions, sub-super solutions method, p (x)-Kirchhoff systems

DOI: 10.3233/JIFS-190884

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4301-4313, 2020

Authors: Milanovic, Marija | Misita, Mirjana | Komatina, Nikola

Article Type: Research Article

Abstract: In this paper, we consider the problem of determining the optimal production quantity under uncertainties. The treated problem has a critical effect on the achievement of business goals, which further propagates to the competitive advantage of manufacturing company. The considered problem is stated as the fuzzy linear programming task. The objective function is defined as the maximization of all profit over the time period. The set of constraints consists of constraints deriving from the enterprise (available capacities) and from the market (demand). The fuzzy rating coefficient values of objective function of each pair of the considered products are described by …linguistic expressions which are modelled by triangular fuzzy numbers. Handling of uncertainties of the stated fuzzy pair-wise comparison matrix is performed by using the extent analysis. The right-hand-side values of the capacity constraints are determined by decision makers and modelled by the triangular fuzzy numbers. The market constraint values are given according to evidence data. Determination of the optimal quantities of considered products for each time period is based on the concept of equal possibilities. The proposed model is illustrated by an example with real-life data. Show more

Keywords: Production plan, fuzzy set, fuzzy analytic hierarchy process, fuzzy linear programming

DOI: 10.3233/JIFS-190913

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4315-4325, 2020

Authors: Khan, Muhammad Jabir | Kumam, Poom | Liu, Peide | Kumam, Wiyada

Article Type: Research Article

Abstract:  As a combination of an interval-valued intuitionistic fuzzy soft set (IVIFSS ) and interval-valued intuitionistic fuzzy set (IVIFS ), the existing notion of the generalized interval-valued intuitionistic fuzzy soft set (GIVIFSS ) is clarified and reformulated. We define two types of containment in GIVIFSS . With this new perspective, the g -union, g -intersection, g ∗ -union, g ∗ -intersection, OR, AND, g-necessity and g-possibility operations are defined for GIVIFSSs . The properties and relations between operations of GIVIFSSs are investigated. An algorithm is proposed for solving MADM problems using GIVIFSS . A descriptive example is presented to see …the applicability of the proposed method. Results indicate that the proposed technique is more effective and generalize over the previous models of interval-valued fuzzy sets. Show more

Keywords: Interval-valued intuitionistic fuzzy set, interval-valued intuitionistic fuzzy soft set, generalized interval-valued intuitionistic fuzzy soft set, multi-attribute decision making

DOI: 10.3233/JIFS-190944

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4327-4341, 2020

Authors: Kang, Xinhui | Porter, Caroline Samantha | Bohemia, Erik

Article Type: Research Article

Abstract: Customer satisfaction is an important indicator of user preferences for products which directly influence customers’ purchase intentions. It is also an essential Kansei factor for enterprises to successfully develop products. Therefore, the objective of this study is to apply the fuzzy weighted association rule mining (FWARM) approach to extract the significant association between customer satisfaction and product form features, thus providing specific parameter guidelines for the enterprise’s business decisions. In previous research, the fuzzy association rule mining (FARM) approach has proved to be a promising way forwards. However, the absence of consideration of the weight of items is always criticized …as creating an uninteresting rule with high frequency and low importance. Therefore, in this study the fuzzy Delphi method (FDM) is used to calculate the weight of each item in the early stage of data mining, and filters out items with a high degree of consensus. Then, the FARM method extracts the fuzzy weighted association rule. Taking the household exercise bike as an example, the authors find that handlebar, LCD screen, rack, main outline and pedestal are vital item features. Subsequently, the method is used to identify 14 rules to inform the development of the exercise bike’s form to achieve high customer satisfaction; valuable knowledge support is provided for manufacturers and designers in the initial stage of new product development, potentially improving customer satisfaction and reducing the risk of product failure. Show more

Keywords: Customer satisfaction, Kansei engineering, Fuzzy Delphi method, Fuzzy weighted association rule mining, Exercise bike

DOI: 10.3233/JIFS-190957

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4343-4357, 2020

Authors: Li, Deng-Feng | Mahmood, Tahir | Ali, Zeeshan | Dong, Yucheng

Article Type: Research Article

Abstract: The interval complex single-valued neutrosophic fuzzy set (ICSVNFS) and hesitant fuzzy set (HFS) are two important different generalizations of the fuzzy set (FS) to cope with unreliable and unpredictable information in the real world. The ICSVNHFS is characterized by interval complex-valued membership, interval complex-valued abstinence, and interval complex-valued non-membership grades, whose ranges are restricted to a unit disc in a complex plane instead of real numbers. In this paper, the notions of interval-valued complex single-valued neutrosophic hesitant fuzzy sets (IVCSVNHFSs) are initiated and also their operational laws are described with examples. Further, based on IVCSVNHFSs, we develop the notions of …the interval-valued complex single-valued neutrosophic fuzzy generalized weighted (IVCSVNHFGW) operator, interval-valued complex single-valued neutrosophic fuzzy generalized ordered weighted (IVCSVNHFGOW) operator, and interval-valued complex single-valued neutrosophic fuzzy generalized hybrid weighted (IVCSVNHFGHW) operator to cope with decision information and also study their properties. Further, a new multi-attribute group decision making (MAGDM) problem is initiated based on the proposed operators. Finally, we provide some numerical examples to illustrate the reliability and superiority of the proposed methods by comparison with other existing methods. Show more

Keywords: Complex neutrosophic set, interval complex neutrosophic set, hesitant fuzzy set, interval-valued complex single-valued neutrosophic hesitant fuzzy set

DOI: 10.3233/JIFS-191005

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4359-4401, 2020

Authors: Han, Shumin | Shen, Derong | Nie, Tiezheng | Kou, Yue | Yu, Ge

Article Type: Research Article

Abstract:  Matching similar records from different databases to prevent duplication in a private manner has attracted plenty of attention, which is referred to as Private Entity Matching (PEM). In spite of various approaches having been proposed to solve this problem, private linking numerical data such as integer (e.g. age), floating point (e.g. body mass index) from multiple databases is an urgent gap, which is commonly required in health domain, statistical departments and more. Hence, this paper targets at solving the problem of linking numerical data from three or more sources in an efficient and secure way. Firstly, we introduce a novel …homomorphic encryption method constrained similar modul, which provides strong privacy to encrypt numerical data in the range of real numbers. Then, to avoid frequent decryptions in the homomorphic encryption schema, we draw an inference about the encryption keys. Finally, an accelerated algorithm is proposed to reduce the complexity of multi-party numerical records matching. Our approach is considered absolute safety that no party learns any sensitive information of the others in the absence of collusion. Experiments on two real-world health information databases of patient records validate our approach with regards to the efficiency improvement and at the same time, at no the sacrifice of linkage quality. Show more

Keywords: Entity matching, privacy, numerical records, multiple databases

DOI: 10.3233/JIFS-191064

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4403-4414, 2020

Authors: Yang, Yongwei | Zhu, Kuanyun | Xin, Xiaolong

Article Type: Research Article

Abstract:  The concepts of fuzzy weak hyper deductive systems and fuzzy sup-weak hyper deductive systems of hyper equality algebras as generalizations of fuzzy deductive systems are initiated in the paper. Moreover, several related properties and characterizations of fuzzy weak hyper deductive systems and fuzzy sup-weak hyper deductive systems are investigated, and the relationships between fuzzy sup-weak hyper deductive systems of homomorphic hyper equality algebras are also presented based on the introduced homomorphism in hyper equality algebras. It is known that a fuzzy weak hyper deductive system is a fuzzy set, while the converse is not true in general. In order to …measure the degree of a fuzzy set becoming a fuzzy weak hyper deductive system, we introduce the notion of fuzzy weak hyper deductive system degrees, and put forward some equivalent characterizations of the fuzzy weak hyper deductive system. Show more

Keywords: Hyper equality algebra, fuzzy weak hyper deductive system, R-implication, fuzzy weak hyper deductive system degree

DOI: 10.3233/JIFS-191112

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4415-4429, 2020

Authors: Wang, Xiao

Article Type: Research Article

Abstract: Uncertain pantograph differential equation is a new type of differential equations in uncertainty theory. In this paper, we first give the concept of uncertain pantograph differential equation. In addition, this paper aims at the stability of this type of differential equations. The concepts of stability in measure and stability in p th moment for uncertain pantograph differential equations are presented and the sufficient conditions for uncertain pantograph differential equations being stable in measure and in p th moment are derived. Finally, this paper also discusses the relationship between these two types of stability.

Keywords: Uncertain pantograph differential equation, stability in measure, stability in pth moment, uncertainty theory

DOI: 10.3233/JIFS-191148

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4431-4439, 2020

Authors: Zhu, Kuanyun | Wang, Jingru | Jiang, Binghua

Article Type: Research Article

Abstract: Recently, Qiao [8 ] investigated the distributive laws of uninorms over overlap and grouping functions when the uninorms were one of the usual classes (e.g., U min , U max , the family of idempotent uninorms, representable uninorms or uninorms continuous on ] 0, 1 [2 ) and obtained some characterizations. As an addendum to [8 ], in this paper, we investigate the distributive laws of overlap and grouping functions over uninorms. In addition, we show some characterizations when the uninorm U belongs to one certain class.

Keywords: Overlap functions, grouping functions, uninorms, distributive laws

DOI: 10.3233/JIFS-191168

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4441-4446, 2020

Authors: Li, Meiqian | Huang, Xianjiu | Zhang, Cailiang

Article Type: Research Article

Abstract:  Intuitionistic fuzzy number, which is an extension of fuzzy number, has been applied in many fields as it considers membership degree and non-membership degree. However, in some circumstances, intuitionistic fuzzy number does not express uncertainty and vagueness well. In order to deal with this problem, a new concept of trapezoidal type-2 intuitionistic fuzzy number(TrT2IFN) is proposed in this paper. Meanwhile, the arithmetical operations of TrT2IFNs are defined. Then, a novel distance measures are proposed by taking advantage of the Hausdorff distance. Additionally, the multi-attributes decision making problem of the TrT2IFN is solved by the grey relational bidirectional projection method. Finally, …the applicability and availability of the proposed method are demonstrated by a numerical example, and the final ranking outcome of alternatives is obtained. This paper provides an effective solution for solving multi-attribute decision making in TrT2IFN environment. Show more

Keywords: Trapezoidal type-2 intuitionistic fuzzy numbers, Arithmetic operations, Score function, Hausdorff distance, Grey relational bidirectional projection method

DOI: 10.3233/JIFS-191174

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4447-4457, 2020

Authors: Dammak, Fatma | Baccour, Leila | Alimi, Adel M.

Article Type: Research Article

Abstract:  In this work we propose an approach of multi-criteria decision making (MCDM) using TOPSIS and VIKOR methods under interval-valued intuitionistic fuzzy (IVIF) sets and possibility theory. We are interested to study positive and negative ideal solutions to propose new formulas. Indeed, these solutions are presented with many formulas in literature [1, 2 ] which could cause ambiguity [3 ]. Due to the importance of possibility theory in resolution of many problems, we propose to use possibility measure for positive ideal solution and necessity measure for negative ideal solution under interval valued intuitionistic fuzzy sets. According to this, TOPSIS and VIKOR …are modified to obtain new approaches. The latter are applied to an example from literature using IVIF data. This example permits to assess the investment projects problem for ranking different projects. The found results showed different solutions from that existing in literature which can give more choice to decision makers with additional information due to use of possibility measures. Show more

Keywords: Interval-valued intuitionistic fuzzy sets, possibility measure, multi-criteria decision making, necessity measure, TOPSIS, VIKOR

DOI: 10.3233/JIFS-191223

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4459-4469, 2020

Authors: Bahrani, Payam | Minaei-Bidgoli, Behrouz | Parvin, Hamid | Mirzarezaee, Mitra | Keshavarz, Ahmad | Alinejad-Rokny, Hamid

Article Type: Research Article

Abstract: Recommender Systems (RS ) are expected to suggest the accurate goods to the consumers. Cold start is the most important challenge for RSs. Recent hybrid RS s combine ConF and ColF . We introduce an ontological hybrid RS where the ontology has been employed in its ConF part while improving the ontology structure by its ColF part. In this paper, a new hybrid approach is proposed based on the combination of demographic similarity and cosine similarity between users in order to solve the cold start problem of new user type. Also, a new approach is proposed based …on the combination of ontological similarity and cosine similarity between items in order to solve the cold start problem of new item type. The main idea of the proposed method is to expand user/item profiles based on different strategies to build higher-performing profiles for users/items. The proposed method has been evaluated on a real dataset and the experimentations indicate the proposed method has the better performance comparing with the state of the art RS methods, especially in the case of the cold start. Show more

Keywords: Recommender system, hybrid recommender system, ontology, profile expansion, KNN

DOI: 10.3233/JIFS-191225

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4471-4483, 2020

Authors: Paramo, L.A. | Garcia, E.C. | Meda, J.A. | de J. Rubio, J. | Escobedo, J.O. | Tapia, R. | Hernandez, J.O. | Lopez, G. | Novoa, J.F. | Aguilar, A.

Article Type: Research Article

Abstract: In this work, state vector estimation by means of the Fuzzy Kalman Filter (FKF) is used to generate a control signal that stabilizes an unmanned quadrotor aircraft. The framework for fuzzy Kalman Filter methodology has been successfully developed, and in this sense, the FKF is implemented and compared with Kalman Filter (KF) and extended Kalman Filter (EKF). It will be proved that the fuzzy version gives some advantages such as a smaller processing time and a smaller Mean Squared Error (MSE). Finally, these results are shown in graphics and tables.

Keywords: Kalman filtering, quadrotor, fuzzy systems stabilization, control systems

DOI: 10.3233/JIFS-191251

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4485-4494, 2020

Authors: Davarpanah, Seyed Hashem

Article Type: Research Article

Abstract: A normal human brain holds a high level of bilateral reflection symmetry. On the sagittal view, the brain can be separated into the left and the right hemispheres with approximately identical anatomical properties, so that symmetrical mirror pixels are almost similar. As a result, the symmetry information can be used to enhance results of brain segmentation methods. In this paper, I introduced a new version of the Fuzzy C-Mean (FCM) segmentation method which is called Genetic Spatial Possibilistic Fuzzy C-Mean (GSPFCM). GSPFCM integrates symmetry information with SPFCM. It is an extension of Possibilistic Fuzzy C-Mean (PFCM) on 3D Magnetic Resonance …(MR) images. GSPFCM uses the spatial information and fuzzy membership values. Spatial and possibilistic information were added in order to solve the noise sensibility defect of FCM. To integrate the symmetry information, I first extracted the Mid-Sagittal Surface using a proposed genetic algorithm. According to this algorithm, inside each axial slice, a Thin-Plate Spline (TPS) surface was constructed and a genetic algorithm was applied to fit this TPS surface to the brain data. Then, the symmetry degree of each symmetry pair voxels was calculated. Finally, the membership values in SPFCM were updated based on the corresponding symmetrical values. The efficiency of GSPFCM, was evaluated using both simulated and real Magnetic Resonance Images (MRI), and was compared to the state-of-the-art methods. My results showed images with different degrees of Intensity Non-Uniformity (INU) and different levels of noise were segmented efficiently by the GSPFCM. Show more

Keywords: 3D brain MR segmentation, Mid-Sagittal Surface, Fuzzy C-Mean, genetic algorithm, fractal dimension, possibilistic information, spatial information

DOI: 10.3233/JIFS-191258

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4495-4510, 2020

Authors: Dai, Lihong | Liu, Jinguo | Ju, Zhaojie | Gao, Yang

Article Type: Research Article

Abstract:  Gaze tracking has wide applications such as in driver fatigue detection, virtual reality, and human-computer interaction. The performance of gaze tracking depends largely on the accuracy of iris center localization. However, most of the existing gaze tracking products are intrusive or require additional equipment with a high cost. Therefore, precise localization methods of iris center in low quality images captured in a non-contact way with visible light need to be investigated. This paper proposes a novel localization method of iris center using energy map synthesis based on image gradient, isophote and midpoint of eye ROI (Region of interest). This method …combines the advantages of higher localization accuracy based on gradient, invariance to the rotation and linear transformation of light based on isophote, and iris center close to the midpoint of eye ROI. Moreover, a post-processing correction method for the closed eyes and for other large deviations of iris center position is adopted to further improve the localization accuracy. The algorithm is verified on the BioID, Talking Face Video and MUCT Face databases, and the results show that the localization accuracy in the paper has outperformed the listed state-of-the-art methods in varying illuminations. Show more

Keywords: Iris center localization, energy map synthesis, gradient, isophote, post-processing correction

DOI: 10.3233/JIFS-191281

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4511-4523, 2020

Authors: Zhaoyan, Hu | Yonglong, Luo | Xiaoyao, Zheng | Yannian, Zhao

Article Type: Research Article

Abstract:  With the popularity of networks and the increasing number of online users, recommender systems have suffered from the privacy leakage of sensitive information. While people enjoy recommender services, their information is exposed to the networks. To protect the privacy of users when using the recommender services, we propose a multi-level combined privacy-preserving model that maintains high accuracy of recommendation with privacy protection and alleviates the data sparsity problem. Our scheme contains two steps of recommendation. First, a multi-level combined random perturbation (MCRP) model is proposed on the client side. Our model dynamically divides multiple disturbance levels and adds noise of …different ranges to the rating matrix according to Gaussian and uniform mixed disturbances. Second, on the server side, we propose a pseudo rating prediction filling (PRPF) algorithm based on the matrix factorization model. Combining the PRPF algorithm with the MCRP method significantly improves the recommender accuracy and effectively increases privacy security. Sensitive analysis and comparison experiments show that the proposed privacy method has certain advantages in security and recommender accuracy by using three publicly available datasets. Show more

Keywords: Recommender system, matrix factorization, privacy protection, random perturbation, sparse data

DOI: 10.3233/JIFS-191287

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4525-4535, 2020

Authors: Lee, Woo-Joo | Jhang, Hyo-Jin | Choi, Seung Hoe

Article Type: Research Article

Abstract:  Soccer recently has become an intellectual game by predicting the outcome of games. In this study, we use path analysis to find the variable that affects the outcome of the Korea National Football Team (KNFT) matches the most, and consequently the odds of victory, defeat, or a draw, as announced by the betting company. We will also investigate the influence of the variables inferred from the path analysis and Korea’s ELO Rating on the difference between scoring and losing points of the KNFT. We will represent the dividend and the difference between scoring and losing points as fuzzy numbers using …the fuzzy decomposition, and then infer the fuzzy regression model for the result of the KNFT’s match. For this purpose, we use data on 113 games of the KNFT from September 2011 to June 2019 and the dividend rate of the KNFT obtained from Wise Toto company. Show more

Keywords: ELO rating, dividend, path analysis, fuzzy partition, regression analysis

DOI: 10.3233/JIFS-191288

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4537-4543, 2020

Authors: Olalekan, Adaraniwon Amos | Bin, Mohd Omar

Article Type: Research Article

Abstract:  In this paper, a deterministic inventory model for deteriorating items with linear deterioration rate is proposed. Demand follows a power pattern. Shortages are permitted and partially backlogged. An optimal solution is derived to minimizes the total average cost. Numerical examples are given, and sensitivity analysis carried out to show how the optimal decisions are affected by changes in different parameters in the model. Graphical representation of the convexity of the total cost against the decision variables shows the efficiency and reliability of the model.

Keywords: Inventory, power demand pattern, deterioration, linearly, shortages

DOI: 10.3233/JIFS-191323

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4545-4557, 2020

Authors: Zhou, Shibing | Liu, Fei

Article Type: Research Article

Abstract: It is critical to determine the optimal number of clusters (NC) in cluster analysis. Many cluster validity indices have been proposed, such as the Silhouette index and In-group proportion index. However, these validity indices have more time complexity. From the viewpoint of sample geometry, a new internal cluster validity index for determining the optimal NC is proposed. The new index can evaluate the clustering quality of a certain clustering algorithm and determine the optimal NC for many kinds of data sets, including synthetic data sets, benchmark data sets, and real data sets. Compared with many well-known validity indices, the proposed …index is more effective and efficient. Theoretical analysis and experimental results show the effectiveness and high efficiency of the new index. Show more

Keywords: Cluster validity index, number of clusters, affinity propagation, hierarchical clustering

DOI: 10.3233/JIFS-191361

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4559-4571, 2020

Authors: Jiang, Tianhua | Zhu, Huiqi | Deng, Guanlong

Article Type: Research Article

Abstract: The conventional production scheduling problem has mainly emphasized the time-related metrics, such as makespan, machine workload and tardiness/earliness, and so on. With the advent of the sustainable manufacturing, the green scheduling problem has been received more and more attention from scholars and researchers. In this paper, we investigate a green flexible job shop scheduling problem (GFJSP) with the consideration of environmental factors. To formulate the GFJSP problem, a mathematical model is first established to minimize the amount of total energy-consumption. To solve the model, a kind of improved African buffalo optimization (IABO) algorithm is proposed based on the characteristics of …the problem. In the proposed IABO, a two-vector solution representation method is first designed, and a population initialization method is adopted to generate the initial solutions with certain quality and diversity. Based on the original ABO, several improvement strategies are introduced to enhance the performance of the algorithm, i.e., the modified individual learning mechanism and the aging-based re-initializaiton mechanism. In addition, in order to adapt our algorithm to the scheduling problem, a discrete individual updating method is developed to ensure the algorithm search directly in a discrete domain. Finally, a number of experiments have been conducted to test the performance of the proposed IABO algorithm. The simulation data demonstrate the effectiveness of the proposed IABO for the considered GFJSP. Show more

Keywords: Green flexible job shop, improved African buffalo optimization, modified individual learning mechanism, aging-based re-initialization mechanism, discrete individual updating method

DOI: 10.3233/JIFS-191370

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4573-4589, 2020

Authors: Ahmad, Shabir | Jamil, Faisal | Khudoyberdiev, Azimbek | Kim, DoHyeun

Article Type: Research Article

Abstract:  Autonomous vehicles technology is an emerging area and has attracted lots of recognition in recent times. Accidents-free driving has always been the focal point of autonomous vehicles. Autonomous vehicles have the potential to eliminate human errors while driving, which has been argued as the predominant cause of traffic accidents. In autonomous vehicles technologies, a variety of efforts have been made to eliminate human drivers. The full elimination of humans is not possible at this moment, but some of the tasks can be automated to facilitate the drivers. In this paper, we investigate the leading causes of accidents based on UK …vehicle safety data of 2017-2018. We analyze the data and investigate the leading factors which cause traffic crashes. Based on the leading features in the dataset, we then run different prediction algorithms to predict the severity of accidents under a given input feature set. The accuracy of the model with Decision Tree classifier, Random Forest, and Logistic Regression are compared, and it has been found that Random Forest performs best among others with 95% accuracy. The trained random forest model is deployed on the Internet of Things server based on Arduino, and a lightweight application is developed to get the vital data from the driver. The data is applied to the trained model to predict the risk index of driving. This application is lightweight but yet provide a significant contribution in terms of safety in autonomous vehicles. Show more

Keywords: Internet of things, cyber-physical systems, real-time systems, virtualisation, virtual object

DOI: 10.3233/JIFS-191375

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4591-4601, 2020

Authors: Riaz, Shah | Khan, Laiq | Karam, Fazal Wahab

Article Type: Research Article

Abstract:  Human-beings in general and pregnant subjects (characterized by a sophisticated dynamic system) in particular are sensitive to whole-body vibrations (WBV) in sitting position while driving. The published literature is thickly populated with the investigation of WBV effects on non-pregnant subjects but sparsely populated with the biodynamic response enhancement of the pregnant subjects in driving conditions. In this paper, the biodynamic response analysis of eleven degrees of freedom (11-DoF) seated pregnant subject at the driver position is carried out while being exposed to vibrations induced by inherent road irregularities. An advanced adaptive NeuroFuzzy (AdaptNeuroFuzzy) control strategy for active suspensions of nineteen …degrees of freedom (19-DoF) integrated vehicle-pregnant subject model is designed to attenuate harmful vibrations and protect the seated pregnant subject and fetus against the risk of damages. Matlab/simulink is employed to carry out simulations. Performance validation of the proposed advanced intelligent control strategy based suspension system is accomplished through comparison with the passive, PID, adaptive PID (AdapPID) and adaptive fuzzy logic control (AdapFLC) via standard performance indices, using an ISO-classified standard random road profile. Show more

Keywords: Adaptive NeuroFuzzy control, integrated vehicle-pregnant subject, seated pregnant subject, vibration attenuation, whole body vibration

DOI: 10.3233/JIFS-191376

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4603-4617, 2020

Authors: Zhou, Jun | Zhou, Xuan | Liang, Guangchuan | Peng, Jinghong

Article Type: Research Article

Abstract:  Underground natural gas storage (UNGS), usually regarded as one of the most important gas storing and peak shaving method today, has been widely used in various parts of the world. The pipeline gathering system plays a key role in UNGS surface engineering. Thus, optimization of the whole system is crucial to lower the total investment. However, we cannot find that any scholars have published related papers on the gathering pipeline network for UNGS at present. This paper focuses on the two-level star gas field gathering pipeline network construction, establishes a mixed integer nonlinear programming (MINLP) model with considering the injection …and withdrawal process of UNGS. Minimizing pipeline network investment is the object of this model. Constraints of connection mode, platform, pipe length, flow rate, node pressure, pipe diameter are also taken into consideration in this model. A special genetic algorithm is proposed to figure out the optimal topological structure, location of platform and central station, pipe diameter, gas velocity along each pipe of this model. Last, two typical real cases are taken to test the applicability of the proposed model and the accuracy of the special GA. The optimal results indicate the mathematical model can lower the total investment and the corresponding GA can solve it efficiently. Show more

Keywords: Layout, pipeline network, GA, underground natural gas storage, MINLP

DOI: 10.3233/JIFS-191383

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4619-4642, 2020

Authors: Qin, Yuchu | Qi, Qunfen | Shi, Peizhi | Scott, Paul J. | Jiang, Xiangqian

Article Type: Research Article

Abstract: Two key steps in multi-criteria decision making (MCDM) are to quantify the considered criteria and to fuse the quantified criterion information to sort all alternatives. One of the most recent and important tools for the first step is linguistic interval-valued intuitionistic fuzzy number (LIVIFN) and one of the most common and effective ways for the second step is aggregation operator (AO). So far, a number of AOs of LIVIFNs have been presented. Each AO can work well under certain conditions. But there is not yet an AO of LIVIFNs that can deal with the situation where the weights of the …considered criteria are unknown and the criteria are in different priority levels and concurrently provide satisfying generality and flexibility in the aggregation of criterion information. To this end, a linguistic interval-valued intuitionistic fuzzy Archimedean prioritised and (LIVIFAPA) operator and a linguistic interval-valued intuitionistic fuzzy Archimedean prioritised or (LIVIFAPO) operator, which have such capabilities, are presented in this paper. The formal definitions and generalised expressions of the two AOs are firstly provided. Then their properties are explored and proved and specific expressions are established. After that, a new method for solving the LIVIFNs based MCDM problems is proposed on the basis of the presented AOs. Finally, the proposed method is illustrated via an example about additive manufacturing machine selection and is evaluated via a comparison with existing methods. The major contribution of the paper is the development of the LIVIFAPA and LIVIFAPO operators for MCDM, which can make up for the above shortcoming of the existing AOs of LIVIFNs. Show more

Keywords: Prioritised and operator, prioritised or operator, linguistic interval-valued intuitionistic fuzzy set, Archimedean t-norm and t-conorm, multi-criteria decision making

DOI: 10.3233/JIFS-191385

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4643-4666, 2020

Authors: He, Peng | Wang, Xue-Ping

Article Type: Research Article

Abstract:  This paper deals with L -fuzzy up-sets by using terminologies of closure operators. It first gives a condition that a family of some subsets of a nonempty set can be represented by L -fuzzy up-sets, and it finally discusses the L -fuzzy sets on quotient sets under closure operators.

Keywords: Complete lattice, L-fuzzy up-set, closure operator, 03B52, 03E72, 06A15

DOI: 10.3233/JIFS-191388

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4667-4673, 2020

Authors: He, Sang-Sang | Wang, Yi-Ting | Wang, Jian-Qiang | Cheng, Peng-Fei | Li, Lin

Article Type: Research Article

Abstract: Failure mode and effect analysis is a powerful risk analysis tool in engineering management. When properly conducted, FMEA can make a huge contribution to reduce costs. The traditional FMEA ranks the failure modes on the basis of Risk Priority Number, which is defined as the multiplication of three risk factors. However, this method has always been criticized for it can’t handle the situation where the information given is uncertain or ambiguous. In order to extend the application of FMEA under the fuzzy environment, in this paper, we proposed a novel risk assessment model known as probabilistic linguistic ELECTRE II method …to rank failure modes based on FMEA. To realize this goal, probabilistic linguistic term sets (PLTSs) that consider both the hesitant information and probabilistic information are introduced to depict decision maker’s cognitive information. To better use the PLTSs in the decision-making process, some important information measures are defined, and a method to obtain the combined weight based on entropy weight of PLTSs is also proposed. Subsequently, we establish a score-deviation based PLTS-ELECTRE II model to study FMEA as a multi-criteria group decision-making problem. Finally, we successfully apply this model in a nuclear reheat valve system and the effectiveness of the proposed method is verified by sensitivity analysis and comparative analysis. Show more

Keywords: Risk management, outranking, probabilistic linguistic term set, combined weight, hybrid distance measure

DOI: 10.3233/JIFS-191398

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4675-4691, 2020

Authors: Ulutaş, Alptekin | Karakuş, Can Bülent | Topal, Ayşe

Article Type: Research Article

Abstract: Logistics centers are home to many and varied facilities, such as storage, transportation of goods, handling, reassembling, clearing, disassembling, quality control, social services and providing accommodation, so on. Providing logistical activities from one location can provide some macro advantages, as well as regional development in developing countries. For the micro level, logistics center selection has an effective role in increasing the operational efficiency and decreasing the costs of the firms. While the wrong location selection for logistics center affects the operations and costs of the companies negatively, the optimal location selection increases the performance, competitiveness, profitability of the firms and …reduces the costs of the firms. Since many different qualitative and quantitative criteria are considered in the selection of the logistics center, this selection problem is an MCDM problem. A new integrated MCDM model is proposed to solve this problem for Sivas province in Turkey. This study presents two contributions to the literature. Firstly, the number of studies related to CoCoSo method is limited in the literature, therefore, the CoCoSo method is proposed in this study. Secondly, a new integrated GIS-based MCDM model comprising fuzzy SWARA and CoCoSo is introduced to literature to address the location selection problem for a logistics center. In this study, the results of CoCoSo method and the resulfts of other MCDM methods (COPRAS, VIKOR, ARAS, MOORA, and MABAC) are compared to test the accuracy of results obtained by CoCoSo. Besides, the criteria weights are changed and the possible changes in the results are tracked. Show more

Keywords: CoCoSo, fuzzy SWARA, GIS, logistics center, location selection, MCDM

DOI: 10.3233/JIFS-191400

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4693-4709, 2020

Authors: Abbaszadeh Sori, Ali | Ebrahimnejad, Ali | Motameni, Homayun

Article Type: Research Article

Abstract: The multi-objective constrained shortest path problem is one of the most significant and well-known problems in the field of network optimization which due to its many applications in routing, telecommunication, transportation, scheduling, etc., has attracted the attention of many researchers. In this paper, the mathematical model of the constrained shortest path problem with three objectives of cost, time and risk is formulated, where the constraint is on the path length. The aim is to find the most desirable path to move commodities from origin to destination based on three factors of cost, time, and risk which the length of path …does not exceed a predetermined value. The approach proposed for solving the problem under investigation is to use fuzzy inference system which finds optimal solution in comparison to linear programming and genetic algorithm approaches in less time. The proposed algorithm is implemented on a network of 27 nodes and 52 arcs. The implementation results of the proposed algorithm show that it is capable of finding the optimal solution. Show more

Keywords: Constrained shortest path problem, multi-objective optimization, fuzzy inference system

DOI: 10.3233/JIFS-191413

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4711-4720, 2020

Authors: Wei, Guiwu | Lu, Jianping | Wei, Cun | Wu, Jiang

Article Type: Research Article

Abstract: In practical multiple attribute group decision making (MAGDM) issues, uncertain and fuzzy cognitive decision information is well-depicted by linguistic term sets (LTSs). These LTSs are easily shifted into probabilistic linguistic sets (PLTSs). In such paper, a grey relational analysis (GRA) method is investigated to tackle probabilistic linguistic MAGDM with completely unknown attribute weights. Firstly, the definition of score function is then employed to objectively obtain the attribute weights based on the CRITIC method. Then, the optimal alternative is chosen through calculating largest relative relational degree from the probabilistic linguistic positive ideal solution (PLPIS) which considers both the largest grey relational …coefficient from the PLPIS and the smallest grey relational coefficient form probabilistic linguistic negative ideal solution (PLNIS). This proposed method extends the applications range of the classical GRA method. Finally, a numerical case for site selection of electric vehicle charging stations (EVCS) is employed to illustrate the proposed method. The effectiveness of the proposed method is also verified by some comparative studies. Show more

Keywords: Multiple attribute group decision making, probabilistic linguistic term sets (PLTSs), GRA method, CRITIC method, site selection, electric vehicle charging stations

DOI: 10.3233/JIFS-191416

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4721-4732, 2020

Authors: Wang, Zhihua

Article Type: Research Article

Abstract: Let k  ≥ 2 be an integer. The purpose of this paper is first to introduce the notation of Felbin’s type fuzzy normed linear spaces, and then by virtue of this notation to study some stability results concerning the more general cubic functional equation of the form f ( x + ky ) + f ( x - ky ) + f ( kx ) = k 2 f ( x + y ) + k 2 f ( x - y ) + ( k 3 - 2 k 2 + …2 ) f ( x ) in the setting of Felbin’s type fuzzy normed linear spaces by employing the direct and fixed point methods. Then some applications of our results for the stability of the cubic functional equation from a real normed space to a Banach space will be demonstrated. Furthermore, the interdisciplinary relation between the theory of Felbin’s type fuzzy spaces and the theory of functional equations are also presented in this paper. Show more

Keywords: Fixed point method, Felbin’s type fuzzy normed linear space, fuzzy real number, generalized Hyers-Ulam stability, 39B82, 39B72, 46B03, 47H10

DOI: 10.3233/JIFS-191418

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4733-4742, 2020

Authors: Khan, Zahid | Gulistan, Muhammad | Hashim, Rabiya | Yaqoob, Naveed | Chammam, Wathek

Article Type: Research Article

Abstract: The Shewart S -control chart is commonly used as one of the statistical tools for monitoring the process variability. The existing design of S -control is based on the assumption that inspected quality of the observed process is, an exact and clearly specified quantitative quantity. If however, measured data involve some vague and imprecise observations, the conventional approach of the S -control, cannot be practiced. Designing of a generalized neutrosophic S -control chart which could support the indeterminate values in the processing data is originally developed in this article. The associated properties of proposed design under neutrosophic environment have been …established in this study. The proposed chart represents a general design of the existence structure of the S-chart. Using neutrosophic average run length (ARL n ) as a performance measure, a comparative study of the proposed chart with the conventional approach of S -control under vague parameter values is evaluated. Findings both from analytical and simulation studies indicate that proposed design of S -control leads to efficient and more flexible approach over the traditional S -control. A real data example has been provided for demonstrating the implementation procedure of the proposed design. Show more

Keywords: Quality control, fuzzy charts, neutrosophic measures, S-control chart

DOI: 10.3233/JIFS-191439

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4743-4751, 2020

Authors: Ahmad, Jawad | Tahir, Ahsen | Larijani, Hadi | Ahmed, Fawad | Aziz Shah, Syed | Hall, Adam James | Buchanan, William J.

Article Type: Research Article

Abstract:  Energy uncertainty and ecological pressures have contributed to a high volatility in energy demand and consumption. The building sector accounts for 30 to 40% of the total global energy consumption. There is a high demand for novel techniques and viable energy strategies for reducing energy consumption in this domain. Energy prediction models have the potential to play a pivotal role in optimising energy consumption. The proposed work presents a new and accurate Energy Demand Prediction (EDP) model for large buildings. This approach leverages the Random Neural Network (RNN) prediction methodology. The proposed RNN-based EDP is compared with traditional Artificial Neural …Network (ANN), Support Vector Machine (SVM) and linear regression models. A large building is modelled and simulated for one year in the Integrated Environment Solutions Virtual Environment (IES-VE). Several data inputs such as air temperature, internal gain and the number of people (occupancy) are calculated from IES-VE model and provided to traditional ANN and the proposed RNN predictor. A number of test parameters such as Root Mean Square (RMSE), Normalized Root Mean Square (N-RMSE), Mean Absolute Percentage Error (MAPE) and R provide the proposed RNN model with higher accuracy over the traditional ANN, SVM and linear regression. The proposed RNN predictor provides approximately half of the error of the ANN model. The traditional ANN model gives higher error values of 2.07×, 1.83× and 2.35× for RMSE, NRMSE and MAPE, respectively as compared to the proposed RNN model. Furthermore, the error values of SVM and linear regression were also higher than the proposed EDP scheme. Show more

Keywords: Buildings, energy demand, random neural network, prediction

DOI: 10.3233/JIFS-191458

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4753-4765, 2020

Authors: Madadi, Masoumeh | Saadati, Reza

Article Type: Research Article

Abstract:  We attempt to solve some bi-additive θ -random operator inequalities and use the fixed point technique to prove the fuzzy version of Hyers-Ulam-Rassias stability of them.

Keywords: Hyers-Ulam-Rassias stability, bi-additive θ-random operator inequality, fuzzy sets, 54H20, 46L05, 39B62, 43A22

DOI: 10.3233/JIFS-191482

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4767-4777, 2020

Authors: Wang, Rui | Li, Dengfeng | Yu, Gaofeng

Article Type: Research Article

Abstract: One of the critical activities for bilateral matching decision is matching accuracy, which may be regarded as a type of bilateral matching decision problem with heterogeneous information and attribute association. This paper aims to develop a new fuzzy linear programming method to address such problems. In the proposed method, the multiple attributes are expressed as exact numbers, interval numbers, triangular fuzzy numbers, intuitionistic fuzzy numbers, linguistic terms, and neutrosophic numbers. Firstly, the distance of heterogeneous data and fuzzy measures are introduced; meanwhile, heterogeneous information attribute weights are calculated based on the Choquet integral. Then based on the psychological characteristics of …matching participants’ loss avoidance and superiority maximization, the lexicographical method is used to solve the multi-objective linear programming model to obtain the optimal bilateral transaction matching pair. Finally, an example of second-hand housing online rental-sales matching problems is analyzed to demonstrate the implementation process and applicability of the method proposed in this paper. Show more

Keywords: Bilateral matching decision, attribute association, heterogeneous information, prospect theory

DOI: 10.3233/JIFS-191495

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4779-4792, 2020

Authors: Jing, Xin | Li, Shihao | Cheng, Jing | Guo, Junjun

Article Type: Research Article

Abstract: In order to solve the problem of resource waste in colleges and universities, this paper proposes a multidimensional situational information fusion method, which can be used to normalize, analyze and predict the multi-source data such as natural, humanistic and spatio-temporal data on campus so as to meet the application requirements for high-level decision-making. With this method, firstly, the event object model is used to normalize multi-source data. Then, the multidimensional situational information fusion mechanism of twice reasoning is used to obtain the real-time situation and equipment control scheme of the campus so that real-time intelligent semantic understanding is realized. In …the process of reasoning, the improved KNN prediction model is used to predict situational trends, and the prediction information is used to continue deep reasoning and mining. Finally, the real-time energy-saving regulation is carried out through control instructions. In addition, through simulation verification, experimental results show that this method can quickly identify, integrate, and predict the current real-time situation and generate reasoning results, and finally achieve the goal of intelligent control for energy saving on campus. Show more

Keywords: Smart campus, energy saving, situational information fusion, intelligent control

DOI: 10.3233/JIFS-191513

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4793-4807, 2020

Authors: Mohanta, Kartick | Dey, Arindam | Pal, Anita | Long, Hoang Viet | Son, Le Hoang

Article Type: Research Article

Abstract:  Information in many real life problems collect from multi agents, i.e., "multipolar information" exists. This multipolar information cannot be properly modeled by m - polar fuzzy graph or intutionistic fuzzy graph. An m -polar neutrosophic model is very much efficient for such real word problems which can construct more precise, flexible, and comparable system as compared to the classical, fuzzy and neutrosophic graph models. In this paper, we present the definition of m -polar neutrosophic graph model. Some new operations, such as union, join, composition and ring sum of two m -polar neutrosophic graph are defined here. We define six …new products on m -polar neutrosophic graphs namely strong product, semi strong product, complete product, direct product, cartesian product and lexicographic product. Some idea of complement, isomorphism, weak and co weak isomorphism on m -polar neutrosophic graph are introduced here. We also present several associated properties and theorems of m -polar neutrosophic graph. We introduce a model of m -polar neutrosophic graph, which is applied in evaluating the teacher’s performance of a college. The performances of the teachers are computed based on the response score (feedback) of the students of the college. We also present a numerical example to illustrate our proposed model. Show more

Keywords: Multipolar information, neutrosophic set, m-polar neutrosophic graph, union, direct product

DOI: 10.3233/JIFS-191520

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4809-4828, 2020

Authors: Fezai, Radhia | Mansouri, Majdi | Abodayeh, Kamaleldin | Nounou, Hazem | Nounou, Mohamed | Messaoud, Hassani

Article Type: Research Article

Abstract:  In this paper, a novel fault detection and isolation (FDI) framework based on kernel PCA (KPCA) and generalized likelihood ratio test (GLRT) that is capable of detecting and identifying faults is developed. Specifically, three main objectives are addressed. First, system model identification and residuals generation are addressed using KPCA model. Second, KPCA-based GLRT method is proposed to detect different types of faults in the systems. Third, partial KPCA (PKPCA)-based GLRT is developed for fault isolation. The proposed approach aims to apply a structured PKPCA -based GLRT to a set of sub-models. The fault detection and isolation performances using PKPCA-based GLRT …are illustrated through two examples: a simulated continuous stirred tank reactor (CSTR) data and an air quality monitoring network data. The obtained results demonstrate the effectiveness of the partial KPCA-based GLRT method over the partial PCA-based GLRT method. Show more

Keywords: Partial kernel principal component analysis (PKPCA), fault detection and isolation (FDI), generalized likelihood ratio test (GLRT), continuous stirred tank reactor (CSTR), air quality monitoring networks (AQMN)

DOI: 10.3233/JIFS-191525

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4829-4843, 2020

Authors: Liu, Yang | Lio, Waichon

Article Type: Research Article

Abstract: Nowadays, uncertainty theory has become a branch of axiomatic mathematics and has been studied by many researchers. In particular, uncertainty distribution is one of the most important tools to deal with indeterminate quantity in uncertainty theory. Peng and Iwamura (2010) presented a sufficient and necessary condition of a function being an uncertainty distribution. This paper gives a counterexample to illustrate this condition is not appropriate. A revision of the sufficient and necessary condition is also provided in this paper.

Keywords: Uncertainty theory, uncertain variable, uncertainty distribution

DOI: 10.3233/JIFS-191535

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4845-4854, 2020

Authors: Zhang, Peiwen | Tao, Zhifu | Liu, Jinpei | Jin, Feifei | Zhang, Junting

Article Type: Research Article

Abstract: The aim of this paper is to develop a multi-attribute group decision making (MAGDM) with picture fuzzy sets based on ELECTRE TRI method, i.e., an ELECTRE TRI based group decision making with picture fuzzy information is given. The MAGDM with picture fuzzy information based on picture fuzzy ELECTRE TRI outranking method is divided into three stages, i.e., the group decision information aggregation stage, determination of parameters and ELECTRE TRI outranking based outranking stage. A novel comparison law for picture fuzzy sets is introduced. In the group decision information aggregation stage, the concept of picture fuzzy normalized weighted Bonferroni mean (PFNWBM …) is developed. The developed decision procedure is further applied to the assessment of energy security. The numerical example shows that the developed group decision procedure is feasible and valid. Show more

Keywords: Multi-attribute group decision making, picture fuzzy sets, ELECTRE TRI, supplier selection, picture fuzzy normalized Bonferroni mean

DOI: 10.3233/JIFS-191540

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4855-4868, 2020

Authors: Liu, Yan | Wang, Xiao-Kang | Wang, Jian-Qiang | Li, Lin | Cheng, Peng-Fei

Article Type: Research Article

Abstract: This paper proposes a cloud model-based Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) method with 2D uncertain linguistic variables (2DULVs). 2DULVs are adopted by decision makers (DMs) to evaluate each alternative under the criteria because they can provide extra evaluation information. Cloud model is adopted to depict randomness and fuzziness. The possibility degree and possibility degree index are defined to develop an improved PROMETHEE II method for sorting alternatives. Entropy weight method is used to calculate the weight of each criterion. A renewable energy performance sample is used to illustrate the applicability of the proposed method. Sensitivity analysis and …four comparative experiments demonstrate the stability and accuracy of the proposed approach. Show more

Keywords: 2D uncertain linguistic variable, cloud, possibility degree, possibility degree index, improved PROMETHEE

DOI: 10.3233/JIFS-191546

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4869-4887, 2020

Authors: Qin, Xuezhi | Lin, Xianwei | Shang, Qin

Article Type: Research Article

Abstract:  In order to introduce the long memory property of financial markets into the study of binary option pricing under fuzzy environment, the fractional Brownian motion is used to describe the dynamics of the stock price. This paper develops a new framework for pricing the binary option by using fuzzy set theory based on the long memory property of financial markets. The fuzzy price of the binary option is obtained by using a risk-neutral pricing principle and quasi-conditional expectation. To better understand the pricing model, some Greeks of this pricing model are given. In addition, the influence of the Hurst parameter …H , a measure of long memory in the financial market, on binary option pricing is analyzed. Finally, the study provides an example that study binary option by fuzzifying the maturity value of the stock price using the triangular fuzzy number. The numerical experiment demonstrates the fuzzy pricing model proposed is rational and practicable. Show more

Keywords: Binary option, fuzzy option pricing, fractional brownian motion, asset-or-nothing option

DOI: 10.3233/JIFS-191551

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4889-4900, 2020

Authors: Yin, Shizhuang | Wang, Tao

Article Type: Research Article

Abstract: In order to solve the clustering problem of unknown binary protocols, an improved k -means unknown binary protocol clustering method is proposed, which determines the initial clustering center and improves the clustering distance. Firstly, the k value is determined and the clustering center is extracted by using DCBP (Determine the initial clustering center of binary Protocol) algorithm and the change rate of error square, and then the data are clustered by improving the k -means algorithm of distance function. The unknown binary protocol bit stream is divided into different subsets of binary protocols. By improving the k -means algorithm, …the Pearson distance improves the accuracy of binary protocol clustering from 96% to 98.9%. The DCBP algorithm helps us to determine the k value accurately. The k value determined in this paper is 5, and the clustering accuracy is 98.9%. The clustering accuracy is 80% when k is 4 and 92.2% when k is 6. And the operation speed of the improved k -means algorithm is better than that of the AGNES algorithm. The algorithm is better adapted to the clustering of unknown binary protocols, and improves the accuracy of clustering and the speed of operation. Show more

Keywords: Protocol identification, unknown binary protocol, Pearson distance, determine cluster center

DOI: 10.3233/JIFS-191561

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4901-4913, 2020

Authors: Gao, Yi | Sun, Xia | Wang, Xin | Guo, Shouxi | Feng, Jun

Article Type: Research Article

Abstract:  Forum posts in Massive Open Online Courses (MOOCs) support an important way for online learners to interact with each other and with instructors. Instructors explore the sentiment from posts in MOOCs to detect learners’ trending opinions towards the course so that they can improve MOOCs. However, it is unrealistic to expect instructors to adequately track learners’ sentiment under the large number of messages exchanged on the forums. Fortunately, sentiment classification can automatically analyze learners’ emotion on the course of MOOCs from posts. Traditional classifiers based on machine learning algorithm, which often depend on human-designed features and have data sparsity problem. …In contrast to traditional approaches, we develop a novel neural network model called parallel neural network (PNNs) for sentiment classification of MOOCs discussion forum to alleviate the aforementioned problems. In our model, we design a parallel neural network structure to replace the popular serial neural network structure so that PNNs can preserve the validity of features as far as possible when neural network model training. Meanwhile, we also introduce Self-attention mechanism that automatically identifies which features play key roles in sentiment classification to obtain the important components in posts. We experiment on a public MOOCs dataset and two common sentiment classification datasets, and achieve a good performance. That means PNNs is a substantially reliable classification model for identifying the sentiment polarity of posts. The study has great potential application value on the platform of large scale courses, which can help instructors to gain the emotional tendency of learners for the course content in real time, so that timely intervention to support learning and may reduce the dropout rates. Show more

Keywords: Parallel neural network, sentiment classification, MOOCs, learners’ sentiment

DOI: 10.3233/JIFS-191572

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4915-4927, 2020

Authors: Alos, Ahmad | Dahrouj, Z.

Article Type: Research Article

Abstract: The importance of detecting faults in Unmanned Aerial Vehicles motivated researchers to work in this area over recent years. Complex relationships among UAV attributes (Sensor readings, and Commands) make the task a bit challenging. Many known algorithms consider detecting the faults by spotting data anomalies in the values of each attribute without concern for their context, which leaves an opportunity for potential improvement. The contextual faults occur when a defected sensor shows an invalid value concerning other attributes. Our contribution is a novel matrix platform for detecting the potential contextual faults. This platform consists of multiple small Decision Trees, instead …of using one huge single Decision Tree, which could be difficult and time-consuming to produce, particularly in the case of a large dataset with too many attributes. We propose to use the C4.5 decision tree algorithm to build each decision tree. The Decision Tree is a machine learning technique, which is an effective supervised method used for classification. It is computationally inexpensive and capable of dealing with noisy data. Besides, our approach uses a sliding window technique during training and testing phases, which brings into consideration the effect of the previous state of the system on the process of detecting the contextual faults. The algorithm starts by collecting the attributes of the UAV into a table of pairs, where each pair consists of two attributes; then, it defines the Decision Tree matrix by assigning one Decision Tree for each pair of attributes. The Training step includes constructing training sub-datasets using the values of sliding windows. The C4.5 algorithm uses each constructed training sub-dataset to induce one Decision Tree in the matrix. Finally, the testing step is responsible for reading the values of the sliding windows and using the concerned Decision Tree to detect the contextual faults. We evaluated our approach using Detection Rate, False Alarm Rate, Precision, and F1-score indicators. Moreover, we made a comparison with other broadly used algorithms, such as K-Means and One-Class SVM. Our approach showed superior results in detecting different types of faults (sensor-offset, sensor-stuck, sensor-drift, and sensor-cut). The DT-Matrix performance was neither affected by the small values of the outliers, nor by the number of the outliers, and this caused the DT-Matrix to work better in most of the experiments compared to the other algorithms. Show more

Keywords: UAV, decision tree, anomaly detection, abnormal, classification, system failure, sensor faults, contextual faults, supervised algorithm

DOI: 10.3233/JIFS-191575

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4929-4939, 2020

Authors: Xue, Wei | Wang, Qi | Liu, Xiaona

Article Type: Research Article

Abstract: Although the Takagi-Sugeno-Kang (TSK) fuzzy classifier has achieved great success, how to further improve its classification performance and enhance its interpretability is still one of the most difficult challenges. Involved with the fusion of existing decision information and pre-known classification task, a newly proposed deep/hierarchical TSK fuzzy classifier (EDIPK-TSK) with interpretable fuzzy rules makes full use of the classification advantages of each base classifier to construct a multi-layer deep learning structure. This study first considers that the existing decision information of each training sub-block is sequentially projected into the subsequent sub-blocks for training. Undoubtedly, the existing decision information has played …a guiding role in the current learning process to some extent. Simultaneously, the pre-known classification task is fused into the decision information for fine-tuning of it, which can significantly improve the efficiency of guidance and accelerate the fitting speed of the model. In each layer, the use of interpretable integration input space guarantees that EDIPK-TSK is not a black box. The proposed deep classifier can realize learning by using short fuzzy rules, which ensures the satisfactory interpretability of the classifier. The final experimental results also verify that EDIPK-TSK has strong classification advantages and interpretability. Show more

Keywords: Fuzzy classifier, deep learning structure, existing decision information, interpretability, classification performance

DOI: 10.3233/JIFS-191579

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4941-4957, 2020

Authors: Chang, Wen-Jer | Chang, Chih-Ming | Lin, Yann-Hong

Article Type: Research Article

Abstract: A novel robust fuzzy controller design problem subject to multi-variance constraints and pole location constraints for nonlinear discrete-time systems with internal and external noises is studied in this paper. Based on the Takagi-Sugeno fuzzy model, the nonlinear discrete-time systems are represented by blending many linear subsystems. The control performances considered in this paper include stability requirement, pole location constraint, individual state variance constraint, and minimum output variance. Applying the Lyapunov theory, a discrete-time robust fuzzy controller is designed based on parallel distributed compensation technology and the relevant conditions are deduced in the form of linear matrix inequalities. By solving these …conditions, a discrete-time robust fuzzy controller can be obtained to satisfy the above performance constraints. At last, some simulations for controlling a nonlinear inverted pendulum system and a nonlinear ship steering system are provided to show the feasibility and applicability of the proposed robust fuzzy control method. Show more

Keywords: Robust fuzzy control, discrete-time Takagi-Sugeno fuzzy model, variance constraints and pole location constraints

DOI: 10.3233/JIFS-191600

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4959-4975, 2020

Authors: Shahzadi, Gulfam | Akram, Muhammad | Davvaz, Bijan

Article Type: Research Article

Abstract:  A Pythagorean fuzzy soft set, an extension of intuitionistic fuzzy soft set, plays an essential role to handle the vagueness in many real-life problems. We apply this concept to graph theory, and present certain new notions including, perfectly regular Pythagorean fuzzy soft graphs (PFSGs), perfectly edge-regular PFSGs and explore some of their properties. We formulate the notion of perfectly irregular PFSGs, perfectly edge-irregular PFSGs and open neighborhood degree sum ( O ˆ NDS ) and closed neighborhood degree sum ( C ˆ NDS ) of PFSGs. …Finally, we discuss some decision-making problems of PFSGs. Show more

Keywords: Pythagorean fuzzy soft graphs, perfectly edge-regular, perfectly edge-irregular

DOI: 10.3233/JIFS-191610

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4977-4991, 2020

Authors: Taimoor, Muhammad | Aijun, Li

Article Type: Research Article

Abstract: An online fault detection, isolation, and reconstruction strategy is proposed for actuators and sensors fault detection of an aircraft. For increasing the fault detection capabilities, the Extended Kalman Filter (EKF) is used for the weight updating parameters of multi-layer perceptron (MLP) neural network. The main purpose of using the EKF is to make the weight updating parameters of MLP adaptive in order to increase the fault detection, isolation and reconstruction preciseness, efficiency and rapidness compared to the conventional MLP where the fixed learning rate due to which it has slow response to faults occurrence. Because of the online adaptation of …weighting parameters of MLP, the preciseness of the faults detection is increased. For testing and validation of the proposed strategy, the nonlinear dynamics of Boeing 747 100/200 are used. Results demonstrate that the proposed strategy has better accuracy and rapid response to fault detection compared to convention multi-layer perceptron neural network based faults detection schemes. Show more

Keywords: Actuators, sensors, fault detection and isolation, aircraft, neural networks, nonlinear systems

DOI: 10.3233/JIFS-191627

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4993-5012, 2020

Authors: Xue, Zhan’ao | Zhao, Li-Ping | Zhang, Min | Sun, Bing-Xin

Article Type: Research Article

Abstract: Three-way decisions have become a representative of the models dealing with decision-making problems with uncertainty and fuzziness. However, most of the current models are single granular structures that cannot meet the needs of complex fuzzy environmental decision-making. Multi-granulation rough sets can better deal with fuzzy problems of multiple granularity structures. Therefore, three-way decisions will be a more reasonable decision-making model to address uncertain decision problems in the context of multiple granularity structures. In this paper, firstly we propose the four different conditional probabilities based on support intuitionistic fuzzy sets, which are referred to as support intuitionistic fuzzy probability. Then, a …multi-granulation support intuitionistic fuzzy probabilistic approximation space is defined. Secondly, we calculate the thresholds α and β by the Bayesian theory, and construct four different types of multi-granulation support intuitionistic fuzzy probabilistic rough sets models in multi-granulation support intuitionistic fuzzy probabilistic approximation space. Moreover, some properties of lower and upper approximation operators of these models are discussed. Thirdly, by combining these proposed models with three-way decision theory, the corresponding three-way decision models are constructed and three-way decision rules are derived. Finally, an example of person-job fit procedure is given to prove and compare the validity of these proposed models. Show more

Keywords: Support intuitionistic fuzzy sets, rough sets, support intuitionistic fuzzy probabilistic, multi-granulation rough sets, three-way decisions

DOI: 10.3233/JIFS-191657

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 5013-5031, 2020

Authors: Ghasemzadeh, Mehdi | Hadidi, Khayrollah

Article Type: Research Article

Abstract: In this paper, the design and simulation results of a general-purpose fuzzy logic controller (FLC) with mixed-signal (analog and digital) inputs and digital outputs are presented. Based on a new strategy, it provides simplicity and high speed from the analog prospective and a total digital system advantages with unchanged digital system properties. A novel and reliable structure with respect to other topologies for the fuzzifier section is designed which enhances the accuracy and the velocity. In order to detect minimum and maximum of the input currents at the same time, an inference engine consisting of a min & max circuit …is an addition. The benchmark for the defuzzifier in the proposed design is simplicity and through a simple approach, the center of area (COA) is attributed to the defuzzifier. The proposed controller circuit consists of two inputs, sixteen rules and one output designed in 0.35μ m CMOS standard technology and simulated with MATLAB systematically. The total controller circuit is simulated with HSPICE simulator (BSIM3v3 parameters) and the layouts were extracted with Cadence Virtuoso v 5.1. The inference speed of the controller is about 41.3 MFLIPS (fuzzy logic inference per second) and power consumption is 3.2 mW. Show more

Keywords: A/D converter, CMOS fuzzy controller, current mode circuits, defuzzifier, fuzzifier, fuzzy logic

DOI: 10.3233/JIFS-191672

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 5033-5044, 2020

Authors: Rajendra Thilahar, C. | Sivaramakrishnan, R.

Article Type: Research Article

Abstract: A tele-operated robot stereo vision system is used for stretching out the operator’s eye-hand motion and its distance based co-ordination with experts. The major challenge is the reduction of communication delay by using effective decisions to avoid tele-operation instability. This problem can be handled effectively by using the principles of Augmented Reality which provides facilities for superimposing virtual objects onto the real video images of the workspace to create a simulation plan in the client system. In this paper, we propose a new feature selection algorithm called Fuzzy Rules and Information Gain Ratio based Feature Selection Algorithm for selecting the …optimal number of features from the full set of available features. Also, a new Fuzzy Rule based Neuro-Genetic Classification Algorithm is proposed in this paper for classifying the augmented images more accurately. The main advantages of the proposed model are reduction in classification and communication time and increase in decision accuracy. Show more

Keywords: Image classification, augmented reality, virtual reality, fuzzy systems, robot motion

DOI: 10.3233/JIFS-191674

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 5045-5054, 2020

Authors: Qiao, Sha | Zhu, Ping

Article Type: Research Article

Abstract:  A large number of studies have investigated the systems of fuzzy relation equations and inequalities, which have a much wider field of application. In this paper, we study several types of systems of fuzzy relation equations and inequalities consisting of a given family of k -ary fuzzy relations, where natural number k  ≥ 2, and three unknown fuzzy relations over complete residuated lattices and meet-continuous lattices. Their solutions are triples of fuzzy relations. For the systems of fuzzy relation inequalities, we give the greatest solutions contained in a given triple of fuzzy relations, the least solutions containing a given triple of …fuzzy relations, or give maximal solutions contained in or containing a given triple of fuzzy relations, or belonging to a given interval of triples of fuzzy relations over complete residuated lattices and complete meet-continuous lattices. For the systems of fuzzy relation equations, we present a method of computing maximal solutions contained in a given triple of fuzzy relations and a method of computing minimal solutions containing a given triple of fuzzy relations. Furthermore, we provide some conditions under which there exist the greatest solutions contained in and the least solutions containing a given triple of fuzzy relations. Show more

Keywords: Fuzzy relation, fuzzy relation equation, fuzzy relation inequality, complete residuated lattice, complete meet-continuous lattice

DOI: 10.3233/JIFS-191695

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 5055-5076, 2020

Authors: Lin, Chih-Hong

Article Type: Research Article

Abstract: A six phase copper rotor induction motor (SPCRIM) drive system still exists in lots of nonlinear characteristics such as the added load torque, the Stribeck effect torque, the the cogging torque, the coulomb friction torque and the parameters variations. Due to some uncertainties effects, the using linear controller can not achieve better control performance for the SPCRIM drive system. To obtain better performance, a clever backstepping control system using two adaptive laws and a hitting function is proposed for controlling the SPCRIM drive system. To improve larger chattering phenomenon under uncertainties affects for aforementioned control system, the clever backstepping control …system using two adaptive laws, a revised recurrent fuzzy neural network (RRFNN) and a compensated controller is proposed to estimate the required lumped uncertainty and to compensate the minimum reconstructed error of the estimated law. Furthermore, the corrected particle swarm optimization (CPSO) algorithm by using variable dynamic inertia weight and variable dynamic constriction factor with segment regulation mechanics that is the innovativeness for using the CPSO algorithm is adopted to regulate four variable learning rates of the weights in the RRFNN to speed-up parameter’s convergence. Finally, comparative performances through some experimental results are verified that the clever backstepping control system using two adaptive laws, a RRFNN and a compensated controller has better control performances than those of the proposed methods for the SPCRIM drive system. Show more

Keywords: Backstepping control, copper rotor induction motor, Lyapunov stability, particle swarm optimization, recurrent fuzzy neural network

DOI: 10.3233/JIFS-191712

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 5077-5093, 2020

Authors: Imran, Muhammad | Siddiqui, Muhammad Kamran | Baig, Abdul Qudair | Shaker, Hani

Article Type: Research Article

Abstract:  The topological descriptor are numerical parameters of a graph which characterize its topology and are usually graph invariants. Nowadays, Biological science is an energizing and quickly creating branch of knowledge together with the topological descriptor. In recent years, the investigation of living things has experienced huge extension. All the living things are composed of a fundamental unit of life called cells. The microbiology is a science that deals with the living creatures that can not be seen by naked eyes like bacteria, viruses. In recent years, eccentricity based topological indices gain a lot of importance in many disciplines like chemistry, …computer science, integrated circuits, electric circuits, communication networks, biological networks. In a connected graph G , the vertex set V  (G ) shows the bacteria and the edge set E  (G ) shows the relationship between two bacterium. Mostly, the reproduction of bacteria and other microorganisms occur by binary fission process. The topological indices play a vital and useful role in indicating and analyzing physical, chemical and biological properties of any molecular graph. In this paper, we have computed eccentric polynomial and eccentric atom bond connectivity index of hyper binary trees networks (k -level) and relate these networks to biological networks. Also discuss how biological activities of these networks work in daily life. Show more

Keywords: Biological networks, molecular structure descriptor, eccentric atom bond connectivity, eccentric connectivity polynomial, binary tree, hyper binary trees networks

DOI: 10.3233/JIFS-191714

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 5095-5105, 2020

Authors: Akram, Muhammad | Dudek, Wieslaw A. | Habib, Amna | Al-Kenani, Ahmad N.

Article Type: Research Article

Abstract: The imperfect competition models are equipped by fuzzy set theory with direct assessments of uncertainty. An appropriate point of departure for origination of a system with potentially broader coverage can be provided in view of fuzzy sets. In this way, several extensions of fuzzy set have been introduced to deal with uncertain and ambiguous information including relationships between objects. The q-rung picture fuzzy (q-RPF) model, which inherits the virtues of q-rung orthopair fuzzy set and picture fuzzy set, is one of the convenient way to represent such information. In order to exhibit interactions in various economic structures the conception of …q-RPF economic competition graphs can be employed. Thus the intention of present study is to deal with q-rung picture fuzzy competition graphs (q-RPFCGs) and in particular, q-rung picture fuzzy economic competition graphs (q-RPFECGs) with its generalizations: q-RPF k -economic competition graphs; p -economic competition q-RPFGs; and m -step q-RPFECGs through several important results. Furthermore, this paper offers a brief review for perfect and imperfect competition in competitive market structures and sketch q-RPFECGs to represent duopoly, oligopoly, and monopolistic competitions in graph theoretic approach. Also, it designs an algorithm to calculate the strength of economic competition among buyers and sellers in imperfect competitive markets with q-RPF information. Show more

Keywords: q-Rung picture fuzzy competition graphs, q-rung picture fuzzy economic competition graphs, imperfect competition models, duopoly, oligopoly, monopolistic competition

DOI: 10.3233/JIFS-191726

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 5107-5126, 2020

Authors: Vinh An, Truong | Van Hoa, Ngo

Article Type: Research Article

Abstract: In this work, a new class of generalized fractional integral equations involving the kernel ψ -function in the fuzzy setting is introduced. With this problem, we can recover a wide class of fractional fuzzy integral equations by choosing the kernel ψ -function. In this sense, we provide sufficient conditions for the existence, uniqueness of solutions and δ -Ulam-Hyers-Rassias stability of the given problems. Some examples are given to illustrate our main results.

Keywords: δ-Ulam-Hyers-Rassias, kernel ψ-functions, Fuzzy fractional integral equations

DOI: 10.3233/JIFS-191743

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 5127-5141, 2020

Authors: Vinh An, Truong | Van Hoa, Ngo

Article Type: Research Article

Abstract:  In this work, we consider a new form of fuzzy fractional Volterra integral equations (FFVIEs) involving the generalized kernel functions. By using the monotone iterative technique (MIT) combined with the method of lower and upper solutions, the existence of extremal solutions of FFVIEs is established. Some examples are given to illustrate our main results.

Keywords: Fuzzy fractional integral equations, generalized kernel functions, extremal solutions, monotone iterative technique

DOI: 10.3233/JIFS-191746

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 5143-5155, 2020

Authors: Akbari, Reza | Dabbagh, Rahim | Ghoushchi, Saeid Jafarzadeh

Article Type: Research Article

Abstract: One of the most crucial components in risk management in an organization is detection of risk modes in a system, prioritization of them and making plans in order to enact corrective actions. And one of the common methods for prioritization of risks is the conventional Failure Mode Effects Analysis (FMEA). Although this approach is widely used in different industries, it suffers from some shortcomings, which can lead to failures in reaching reality-based results. This research study, therefore, proposed an approach in three phases for the compensation of the shortcomings of the FMEA method. In the first phase, the FMEA method …was used to detect different risk modes and then assign values to the Risk Priority Number (RPN) determinant factors. In the second phase, the weights of the triple factors were calculated by means of Fuzzy Best-Worst Method (FBWM) and experts’ opinions. And finally, with respect to the outputs of previous phases, the risks were ranked by means of the proposed Z-WASPAS method. In addition to the assignment of different weights to the triple factors and considering the feature of uncertainty in these factors, the proposed approach paid attention to reliability in the risk modes via the Z-Numbers theory. The proposed approach was applied in the operation processes of Mes-e Sarcheshmeh molybdenum factory in Iran and the results indicated a full ranking of risks compared to other conventional methods such as FMEA and fuzzy WASPAS. Show more

Keywords: Failure mode effects analysis, HSE, Z-Numbers, fuzzy BWM, WASPAS

DOI: 10.3233/JIFS-191749

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 5157-5173, 2020

Authors: Shukla, Shilpi | Jain, Madhu

Article Type: Research Article

Abstract: Deep learning is far and wide considered to be the most powerful method in computer vision fields, which has a lot of applications such as image recognition, robot navigation systems, and self-driving cars. Recent developments in neural networks have led to an efficient end-to-end architecture to human activity representation and classification. In light of these recent events in deep learning, there is now much considerable concern about developing less expensive computation and memory-wise methods. This paper presents an optimized end-to-end approach named stochastic deep conviction network (SDCN) formulated using the deep learning method. It comprises of deep learning method namely …deep belief network (DBN), two supervised machine learning algorithm support vector machine (SVM) and decision tree (DT) with optimization capability for speech emotion identification. In the beginning, pre-processing is performed and the features are automatically extracted from the input speech signal by the DBN. Since speech signal features loses most of the information and the performance cannot be guaranteed because dynamic interactions can generate uncountable emotion-specific experiences that have the same core feeling state but different perceptual inclinations so DBN provides more robust features. The next step is to classify the emotions in the training phase; here the SVM classifier is chosen which performs dual classification. In order to enhance this classification process, defects must be reduced and the best discrimination of the extracted features should be obtained hence particle swarm optimization (PSO) technique is being added along with SVM classifier in the training phase. To reduce the over fitting problem and risks of a single classifier a DT is being used in the testing phase for the exact identification of emotions (anger, disgust, fear, happiness, neutral and sadness) and therefore it obtains better performance than a single classifier. The complication of the decision tool is that it can increase the computation time. Thus to eliminate this defect whale optimization (WO) technique is being added to the decision tree to reduce the complexity of the system, which in turn lessens the time taken for recognizing the emotion of the speech signal. This formulated proposed SDCN system improves the recognition rate accurately. In this work, theMATLAB environment is being preferred to perform speech emotion recognition. Using the proposed technique the achieved accuracy of emotion detection is above 95% and the identification of various emotions exceeds 98% recognition rate with a computation time of 23 seconds, which has not been achieved so far by any other existing techniques. Show more

Keywords: Stochastic deep conviction network, restricted Boltzmann machine, particle swarm optimization, support vector machine, whale optimization

DOI: 10.3233/JIFS-191753

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 5175-5190, 2020