Journal of Intelligent & Fuzzy Systems - Volume 39, issue 1

Authors: Singla, Nikita | Sadawarti, Harsh | Singla, Jimmy | Kaur, Balwinder

Article Type: Research Article

Abstract: In this research work, a new multilayer fuzzy inference system is proposed for diagnosis of renal cancer. This proposed automated diagnosis of renal cancer using multilayer Mamdani fuzzy inference system can help to classify the different stages of renal cancer such as no cancer, stage 1, stage 2, stage 3 or stage 4 cancer. This expert system has four input variables at layer 1 and similarly seven input variables at layer 2. At layer 1, the input variables are smoking, dialysis, occupational exposure and genetic or hereditary that recognize the output conditions of renal or kidney to be normal or …to have renal cancer. The further input variables for layer 2 are haematuria (blood in urine), red blood cell count, flank pain, tumor size, Von Hippel-Lindau gene, high blood pressure and trichloroethylene exposure that reveal the output condition of kidney such as stage 1 cancer, stage 2 cancer, stage 3 cancer or stage 4 cancer. The novelty in this research work is development of multilayer fuzzy inference system that deals with fuzzy values, uncertain and ambiguous data to detect the stage of renal cancer by using two layers. This paper presents an analysis of results accurately using the proposed expert system to model the renal cancer process with medical expert advice. The confidence indicator for this proposed expert system is 95%. Show more

Keywords: Artificial intelligence, fuzzy inference system, renal cancer

DOI: 10.3233/JIFS-191785

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 885-898, 2020

Authors: Sun, Baofeng | Zhang, Xinkang | Qiao, Hai | Li, Gendao | Chen, Yifei

Article Type: Research Article

Abstract: The efficient operation of Intelligent Warehousing System does not rely on individual resource scheduling in stages but multi-type resources collaborative scheduling. In this paper, a collaborative scheduling model for stackers, automated guided vehicles and picking workstations in outbound process is abstracted into a hybrid flow-shop scheduling problem within an automated warehouse scene. Considering the impacts of uncertain factors related to scheduling, the objective function of this model is minimizing the makespan based on the triangular fuzzy processing time. A genetic algorithm is designed to obtain feasible solution of this model with the form of vector coding and the approach of …ranking fuzzy numbers. Example analysis shows that the validity of the model and algorithm is verified. Within different resource allocation schemes, their evaluating indexes are significantly different, which are the likely completion time of system operation, the capability coordination degree and the initial investment. Furthermore, the increase of picking workstations is contributed much more to reducing the likely completion time and to improving the capability coordination degree than that of automated guided vehicles. Show more

Keywords: Automated warehouse, fuzzy processing time, collaborative scheduling, genetic algorithm, capability coordination degree

DOI: 10.3233/JIFS-191827

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 899-910, 2020

Authors: Liu, Liming | Chu, Maoxiang | Gong, Rongfen | Qi, Xinyu

Article Type: Research Article

Abstract: In this paper, we propose a nonparallel support vector machine with pinball loss (Pin-NPSVM) that deals with the noise sensitivity and resampling instability of NPSVM. More specifically, we redefine a pinball loss funtion and build a pair of quantile hyper-planes. Each quantile hyper-plane is constructed by using the new pinball loss instead of ɛ -insensitive loss, which makes the new classification model be insensitive to noise samples, especially for feature noise samples around the decision boundary. Moreover, instead of hinge loss, Pin-NPSVM also builds a pair of decision boundaries based on traditional pinball loss, which further improves the anti-nosie ability …of the classification model. In a word, Pin-NPSVM not only inherits the characteristics of the nonparallel optimal hyper-planes, but also has a consistent model with Pin-SVM, which can process noise data well. Finally, numerical experimental results show that the Pin-NPSVM has more obvious advantages than other models in classification performance, especially for noise datasets. Show more

Keywords: Pattern classification, nonparallel support vector machine, pinball loss, anti-noise

DOI: 10.3233/JIFS-191845

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 911-923, 2020

Authors: Kim, Kwang Baek | Kim, Gwang Ha | Song, Doo Heon | Park, Hyun Jun | Kim, Chang Won

Article Type: Research Article

Abstract: Background: Hepatorenal index (HRI) has been an efficient and simple quantified measure in distinction between normal and abnormalities of diagnosing fatty liver. However, considering the clinical significance, the diagnosis of severity stage is more important and single HRI cutoff may not be enough. Also, the segmentation of Liver/Kidney area should be automatic to get rid of operator subjectivity from ultrasonography analysis. Method: Double-layered Fuzzy C-Means (DFCM) pixel clustering method is proposed to extract the target area of analysis automatically. HRI and other shape related variables of Liver intensity distribution such as the skewness, the kurtosis, and the coefficient …of variance (CV) are automatically computed for the fatty liver severity stage classification. Result: From fifty ultrasound images obtained from regular health checkup with 24 normal, 12 mild, 11 moderate, 3 severe stage determined by three different radiologists, the proposed DFCM automatically extracts the region of interests(ROI) and generates a set of statistically significant variables including HRI, the skewness, the kurtosis, the coefficient of variance of liver intensity distribution as well as liver echogenicity. In severity stage classification, the echogenicity of the liver and distribution shape variables such as the skewness and the kurtosis are better predictors than HRI based on our simple decision tree learning analysis. Conclusion: For better diagnosis of fatty liver severity stages, we need better set of features than the single HRI cutoff. Better machine learning structures are necessary in this severity stage classification problem with automatic segmentation method proposed in this paper. Show more

Keywords: Fatty liver severity classification, Fuzzy c-means, Self-organizing map, Hepatorenal index, Decision tree

DOI: 10.3233/JIFS-191850

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 925-936, 2020

Authors: Zhao, Tao | Xiang, Yunfang | Dian, Songyi | Guo, Rui | Li, Shengchuan

Article Type: Research Article

Abstract: This paper focuses on the path planning of mobile robot. Fuzzy logic is employed to deal with the uncertainty in the process of path planning. The hierarchical interval type-2 fuzzy method is obtained by combining the hierarchical fuzzy and interval type-2 fuzzy method, which is used in the path planning of mobile robot. Hierarchical fuzzy structure can simplify complex system and get fuzzy rules more easily. For multi input system, it can also solve the problem of rule explosion. Compared with type-1 fuzzy, interval type-2 fuzzy can better deal with the uncertainty in the process of path planning. Finally, in …order to get a better path, genetic algorithm is used to optimize the membership function in the fuzzy path planner. Through the simulation experiment, the proposed hierarchical type-2 fuzzy planning method can effectively solve the path planning problem. Compared with the type-1 fuzzy method, the interval type-2 fuzzy method and the hierarchical type-1 fuzzy method, the proposed method obtains better results. Show more

Keywords: Mobile robot, path planning, interval type-2 fuzzy, hierarchical fuzzy, genetic optimization

DOI: 10.3233/JIFS-191864

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 937-948, 2020

Authors: Luo, Dandan | Zeng, Shouzhen | Yu, Guansheng

Article Type: Research Article

Abstract: The power average (PA) operator can reduce the influence of unreasonable information given by biased decision makers effectively, while Heronian mean (HM) operator can take into account the correlation information between attribute variables in multiple attribute decision making (MADM). Pythagorean fuzzy set (PFS) is a useful tool to handle uncertain information, which has been widely applied in kinds of areas. In order to better infuse the Pythagorean fuzzy evaluation, in this paper we unify the advantages of the PA operator and HM operator, and present the Pythagorean fuzzy power Heronian mean (PFPHM) operator and the Pythagorean fuzzy weighted power Heronian …mean (PFWPHM) operator. Some merits of the developed operators are further explored. Furthermore, on the basis of the PFWPHM operator, an approach for MADM under PFS situation is presented. Finally, a numerical case concerning investment company selection is illustrated to demonstrate the availability and feasibility of the developed approach. Show more

Keywords: Pythagorean fuzzy set, HM operator, PA operator, multiple attribute decision making, investment selection

DOI: 10.3233/JIFS-191905

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 949-959, 2020

Authors: Berlin, S. Jeba | John, Mala

Article Type: Research Article

Abstract: Though deep learning networks have proven ability to perform video analytics in complex environments, there is an increased attention towards the development of compact networks which would facilitate edge processing and the result of which have yielded high performance compressed deep learning networks such as, MobileNet, PWCNet and BindsNet. In the work proposed herein, a dual network configuration is used for human action recognition, wherein, the MobileNet captures the spatial appearance of the action sequences and the PWCNet is used to extract the motion vectors. A novel Spiking Neural Network (SNN) based configuration is used as the classifier and the …SNN implementation is based on BindsNet. The proposed configuration is experimentally validated on challenging datasets, viz., HMDB51 and UCF101. The experimental results demonstrate that the proposed work is superior to the state-of-the-art techniques and comparable in few cases. Show more

Keywords: MobileNet, PWCNet, BindsNet, diehl and cook nodes, spiking neural network

DOI: 10.3233/JIFS-191914

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 961-973, 2020

Authors: Lian, Jing | Wang, Zhenghao | Li, Linhui | Zhou, Yafu | Yin, Yuhang | Li, Lei

Article Type: Research Article

Abstract: Object detection and tracking are critical and challenging problems in vehicle environment perception systems, and have received broad attention in recent years. A novel detection and tracking algorithm taking both accuracy and real-time performance into account is proposed in this paper. First, we employ a fusion algorithm based on stereo vision and deep learning in object detection, which achieves high accuracy using two complementary algorithms. Then, a prediction-association algorithm which uses a Kalman filter and Hungarian assignment for multiple object tracking is employed for object tracking. In addition, a detection and tracking framework based on stereo vision improves the robustness …of environmental perception system. Experimental results demonstrate that the proposed algorithm has high accuracy and can meet the real-time performance requirement. Show more

Keywords: Environmental perception, stereo vision, deep network, multiple object tracking

DOI: 10.3233/JIFS-191917

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 975-986, 2020

Authors: Rached, Taciana Saad | Vieira, Maria de Fátima Queiroz | Santos, Danilo | Perkusich, Angelo | Almeida, Hyggo

Article Type: Research Article

Abstract: In this article, we propose a method to recognize human emotions based on user context and brain signals. We evaluated the method through an experiment during which individuals performed tasks using a simulator for electrical power systems operator training. We collected user context through log data retrieval and brain signals using an Electroencephalography (EEG) portable monitor. The experimental results demonstrated that the method could be successfully applied to recognize the emotional states based on EEG signals and user context.

Keywords: Emotion recognition, electroencephalography, signal processing, context-awareness

DOI: 10.3233/JIFS-191923

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 987-1003, 2020

Authors: Thao, Nguyen Xuan | Smarandache, Florentin

Article Type: Research Article

Abstract: The single-valued neutrosophic set (SVNS) is an extension of the fuzzy set and intuitionistic fuzzy set. This is a useful tool to deal with uncertain and inconsistent information. In the information theory, the distance measure, entropy measure and similarity measures have an important role. Several entropy measures of SVNSs have been proposed and applied in many real problems. But they have some restriction in practice and in the academic study. The similarity measures induced from entropy were studied and gave interesting results. In this paper, we introduce a new entropy measure concept based on the SVNS, which overcomes the restriction …of existing entropy measures. At the same time, we also investigate some similarity measures which are induced from new entropy measures and apply them to propose the multi-criteria decision making (MCDM) model in selecting the supplier. Show more

Keywords: Entropy of SVNS, similarity measure of SVNS, MCDM

DOI: 10.3233/JIFS-191929

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 1, pp. 1005-1019, 2020